From fbd70f7a1ee160f7c49b29132be247387550c836 Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 26 Aug 2018 20:35:29 -0400 Subject: [PATCH 01/10] Changes for 2018 start of class --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 33951d7..d224b38 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,8 @@ # big-data-python-class Source code for the big data python class -This project has been created to support the Fall 2015 "Big Data using Python" topics class for the Computing Sciences Graduate Program of Villanova University. +This project has been created to support the Fall 2015 "Big Data using Python" topics class for the Computing Sciences Graduate Program of Villanova University. It has been updated for the 2017 class and is in the process of being used for the 2018 class. +Many of the assignments and lectures are expected to change. The intent is to support education in the use of python to address data analysis and big data problems. From 6768a77c5b437727ebc7aa880fcd27147ff6f55e Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Thu, 6 Sep 2018 22:17:55 -0400 Subject: [PATCH 02/10] Lecture2 updates with new links --- ...roduction-to-Python-Programming 2017.ipynb | 124 ++++++++++++------ tutorials/Python_Basics.ipynb | 20 +-- 2 files changed, 87 insertions(+), 57 deletions(-) diff --git a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb b/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb index c4770b6..91142bb 100644 --- a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb +++ b/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb @@ -15,7 +15,9 @@ "\n", "[http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", "and\n", - "Chapter 2 of the Datascience from scratch: First principles with python\n" + "Chapter 2 of the Datascience from scratch: First principles with python\n", + "Code from https://github.com/joelgrus/data-science-from-scratch\n", + "An official tutorial on ipyhthon: http://ipython.org/ipython-doc/2/interactive/tutorial.html" ] }, { @@ -69,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -79,12 +81,12 @@ " Volume in drive C is OS\n", " Volume Serial Number is 7417-6934\n", "\n", - " Directory of c:\\Users\\PS\\git\\big-data-python-class\\Scripts\n", + " Directory of C:\\Users\\PS\\git\\big-data-python-class-2018\\Scripts\n", "\n", - "09/07/2017 08:34 PM 19 hello-world.py\n", - "09/07/2017 08:51 PM 69 hello-world-in-swedish.py\n", - " 2 File(s) 88 bytes\n", - " 0 Dir(s) 382,551,269,376 bytes free\n" + "09/05/2018 10:46 PM 19 hello-world.py\n", + "09/05/2018 10:46 PM 72 hello-world-in-swedish.py\n", + " 2 File(s) 91 bytes\n", + " 0 Dir(s) 321,133,596,672 bytes free\n" ] } ], @@ -104,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -164,9 +166,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "#!/usr/bin/env python\n", - "# -*- coding: UTF-8 -*-\n", - "\n", + "#!/usr/bin/env python\r\n", + "# -*- coding: UTF-8 -*-\r\n", + "\r\n", "print(\"Hej världen!\")" ] } @@ -248,9 +250,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import math" @@ -470,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -509,7 +509,7 @@ "108" ] }, - "execution_count": 4, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -531,10 +531,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "list_of_lists = [[1,2,3],[4,5,6],[7,8,9]]\n", @@ -592,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -601,7 +599,7 @@ "2" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -628,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -637,7 +635,7 @@ "5" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -648,10 +646,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [], "source": [ "another_double = lambda x: 2 * x\n", @@ -678,10 +674,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "# variable assignments\n", @@ -698,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -707,7 +701,7 @@ "float" ] }, - "execution_count": 17, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -725,10 +719,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "x = 1" @@ -736,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -745,7 +737,7 @@ "int" ] }, - "execution_count": 19, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1833,6 +1825,7 @@ "metadata": {}, "source": [ "Lists are very similar to strings, except that each element can be of any type.\n", + "Other languages call these arrays, simply an ordered collection of things.\n", "\n", "The syntax for creating lists in Python is `[...]`:" ] @@ -4096,8 +4089,53 @@ "* http://www.python.org - The official web page of the Python programming language.\n", "* http://www.python.org/dev/peps/pep-0008 - Style guide for Python programming. Highly recommended. \n", "* http://www.greenteapress.com/thinkpython/ - A free book on Python programming.\n", - "* [Python Essential Reference](http://www.amazon.com/Python-Essential-Reference-4th-Edition/dp/0672329786) - A good reference book on Python programming." + "* [Python Essential Reference](http://www.amazon.com/Python-Essential-Reference-4th-Edition/dp/0672329786) - A good reference book on Python programming.\n", + "* [ZEN of Python](https://legacy.python.org/dev/peps/pep-0020)" ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -4116,7 +4154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.15" } }, "nbformat": 4, diff --git a/tutorials/Python_Basics.ipynb b/tutorials/Python_Basics.ipynb index 40255ef..ed54850 100644 --- a/tutorials/Python_Basics.ipynb +++ b/tutorials/Python_Basics.ipynb @@ -56,9 +56,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -148,9 +146,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "SyntaxError", @@ -179,9 +175,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -220,9 +214,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -693,9 +685,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.15" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From dda908fdddd9035f7a8e7b1cb2c60344215d638d Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Thu, 6 Sep 2018 23:20:17 -0400 Subject: [PATCH 03/10] Updating Lecture 2 --- .../Homework 2/Homework2-python_tutorials | 6 +- .../Lecture 2 continued.ipynb | 368 ++++++++++++++++++ ...-Introduction-to-Python-Programming.ipynb} | 74 +--- 3 files changed, 375 insertions(+), 73 deletions(-) create mode 100644 Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb rename Lectures/Lecture2-Jupyter_and_python/{Lecture-2-Introduction-to-Python-Programming 2017.ipynb => Lecture-2-Introduction-to-Python-Programming.ipynb} (98%) diff --git a/Homeworks/Homework 2/Homework2-python_tutorials b/Homeworks/Homework 2/Homework2-python_tutorials index b0c929d..60ecc96 100644 --- a/Homeworks/Homework 2/Homework2-python_tutorials +++ b/Homeworks/Homework 2/Homework2-python_tutorials @@ -25,8 +25,10 @@ B. Use of dictionaries For more than 3 points you can add meaningful sections to the two pages and create more pages on other interesting aspects of basic python, for example random functions, regexp, map/reduce, file manipulations... Be sure to name your file -LASTNAME_HOMEWORK2_LIST_AND_TUPLES_TUTORIAL_Fall_2017.ipynb -LASTNAME_HOMEWORK2_DICTIONARY_TUTORIAL_Fall_2017.ipynb +LASTNAME_HOMEWORK2_LIST_AND_TUPLES_TUTORIAL_Fall_2018.ipynb +LASTNAME_HOMEWORK2_DICTIONARY_TUTORIAL_Fall_2018.ipynb +LASTNAME_HOMEWORK2_OTHER_TUTORIAL_Fall_2018.ipynb + You can upload the files or the zip of all the files. Or put them in your homework directory in your big-data-python-class github repo and send me the url in the submissions or both. diff --git a/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb b/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb new file mode 100644 index 0000000..914a480 --- /dev/null +++ b/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 2 continued" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### regular expressions\n", + "Provides a way to search text.\n", + "Looking for matching patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "import re\n", + "print all([\n", + " not re.match(\"a\",\"cat\"),\n", + " re.search(\"a\",\"cat\"),\n", + " not re.search(\"c\",\"dog\"),\n", + " 3 == len(re.split(\"[ab]\",\"carbs\")),\n", + " \"R-D-\" == re.sub(\"[0-9]\",\"-\",\"R2D2\")\n", + "]) # prints true if all are true" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Object-oriented programming\n", + "Creating classes of objects with data and methods(functions) that operate on the data." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "class Set:\n", + " def __init__(self, values=None):\n", + " \"\"\"This is the constructor\"\"\"\n", + " self.dict={}\n", + " if values is not None:\n", + " for value in values:\n", + " self.add(value)\n", + " def __repr__(self):\n", + " \"\"\"Sting to represent object in class like a to_string function\"\"\"\n", + " return \"set:\"+str(self.dict.keys())\n", + " def add(self, value):\n", + " self.dict[value] = True\n", + " def contains(self, value):\n", + " return value in self.dict \n", + " \n", + " def remove(self,value):\n", + " del self.dict[value]\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "#using the class\n", + "s = Set([1,2,3])\n", + "s.add(4)\n", + "s.remove(3)\n", + "print s.contains(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set:[1, 2, 4]\n" + ] + } + ], + "source": [ + "print s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functional Tools\n", + "sometimes you want to change behavior based on the passed value types" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "def exp(base,power):\n", + " return base**power\n", + "\n", + "#exp(2,power)\n", + "def two_to_the(power):\n", + " return exp(2,power)\n", + "\n", + "print( two_to_the(3) )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 10]\n" + ] + } + ], + "source": [ + "def multiply(x,y): return x*y\n", + "\n", + "products = map(multiply,[1,2],[4,5])\n", + "print products" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### enumerate\n", + "enumerates creates a tuple (index,element)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index:0 Item:a\n", + "index:1 Item:b\n" + ] + } + ], + "source": [ + "#example use\n", + "documents = [\"a\",\"b\"]\n", + "def do_something(index,item):\n", + " print \"index:\"+str(index)+\" Item:\"+item\n", + " \n", + "for i, document in enumerate(documents):\n", + " do_something(i,document)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index:0\n", + "index:1\n" + ] + } + ], + "source": [ + "def do_something(index ):\n", + " print \"index:\"+str(index) \n", + " \n", + "for i, _ in enumerate(documents): do_something(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zip and argument unpacking\n", + "zip forms two more more lists for other lists" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('a', 1), ('b', 2), ('c', 3)]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list1 = ['a','b','c']\n", + "list2 = [1,2,3]\n", + "zip(list1,list2)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 'b', 'c')\n", + "(1, 2, 3)\n" + ] + } + ], + "source": [ + "pairs = [('a', 1), ('b', 2), ('c', 3)]\n", + "letters, numbers = zip(*pairs) # * performs argument unpacking\n", + "print letters\n", + "print numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### args and kwargs\n", + "higher order function support - functions on functions" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "def doubler(f):\n", + " def g(x):\n", + " return 2 * f(x)\n", + " return g\n", + "\n", + "def f1(x):\n", + " return x+1\n", + "\n", + "g= doubler(f1)\n", + "\n", + "print g(3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unamed args (1, 2)\n", + "Key word args {'key2': 'word2', 'key1': 'word1'}\n" + ] + } + ], + "source": [ + "def magic(*args, **kwargs):\n", + " print \"unamed args\", args\n", + " print \"Key word args\", kwargs\n", + " \n", + "magic(1,2,key1=\"word1\",key2=\"word2\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb b/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb similarity index 98% rename from Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb rename to Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb index 91142bb..ec59145 100644 --- a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming 2017.ipynb +++ b/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb @@ -4001,80 +4001,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### regular expressions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### Object-oriented programming" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### Functional Tools" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### enumerate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### Zip and argument unpacking" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "### args and kwargs" + "* [Lecuture 2 continued](./Lecture%202%20continued.ipynb)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, From a428045c8dad8f6cc9eae98e13d8bdedcc2b309f Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 23 Sep 2018 14:03:39 -0400 Subject: [PATCH 04/10] Lecture4 update --- .../Data_Visualization.ipynb | 472 ++++++++++-------- Lectures/Lecture4-Matplotlib/normalvars.png | Bin 41517 -> 39074 bytes 2 files changed, 274 insertions(+), 198 deletions(-) diff --git a/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb b/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb index a10a4e2..531b8ca 100644 --- a/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb +++ b/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -34,7 +34,7 @@ "" ] }, - "execution_count": 3, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -92,10 +92,8 @@ }, { "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -119,24 +117,24 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 98, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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XF38fNVp9cbE42TXR8u37MtHDrKwUg+lGjSPYtAmuvBLe975079k83pdpBk66RHSrZLl0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\nMcbjCbYnqas2by6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\n", 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3AX+UdGh+/Ie2RmZmo14zOqVXrqS5vdXTLQxz9GKzzuQ+U61WfIHfBRS7Jzwz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\nASxx98/SlievEls1/EQMgGxw92JMqQwR6lCJiIi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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -207,21 +205,21 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']\n" + "['seaborn-darkgrid', 'Solarize_Light2', 'seaborn-notebook', 'classic', 'seaborn-ticks', 'grayscale', 'bmh', 'seaborn-talk', 'dark_background', 'ggplot', 'fivethirtyeight', '_classic_test', 'seaborn-colorblind', 'seaborn-deep', 'seaborn-whitegrid', 'seaborn-bright', 'seaborn-poster', 'seaborn-muted', 'seaborn-paper', 'seaborn-white', 'fast', 'seaborn-pastel', 'seaborn-dark', 'tableau-colorblind10', 'seaborn', 'seaborn-dark-palette']\n" ] }, { "data": { - "image/png": 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NHMq92drwK/ah2UaTisKx2OX1Kz6QqpL0hRCDJe8XGwVvGv9Tp8ik2Fxy2+fsamysEypP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\nMJBlGaIogu/7AN7kXriuCyklACCOY/i+v3XSiWVZ+eepBtp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\n", 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H7ocYNyxfW24CKESIuO5gdSO1x1uB5Lxwqy88Hg8fWapR2G4KBAJqqpSzKESI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\nCdinP1AqLAEAAAAASUVORK5CYII=\n", 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\n", 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B5Pf7ef3zHQDExni4YYTOk4gUKgS1qEubxjz5vYHUi4vC6/Pzu9fWsHBtttOx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\nCERERAQVAhEREUGFQERERID/D4zaLrTRw/ZeAAAAAElFTkSuQmCC\n", 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\n", 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iQn3kikETtTm5ZC9aQtGan9DMtqmodUYjyqN/JeT8oU6urv1ISBBCiC5iR3ox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\nQlxCKisrAeJ6K9fnkKAoyv8DftxLsfGqquZ12ycB+AR4U1XVF08rq+v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\n", 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" ] }, "metadata": {}, @@ -377,26 +375,27 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([,\n", - " ,\n", - " ], dtype=object)" + "array([,\n", + " ,\n", + " ],\n", + " dtype=object)" ] }, - "execution_count": 103, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Guq4PB/6F7VbFXcDzhmFsdcSxlVIcPHiQ3377zVz+/vtvRxy6WF5eXgQEBBAe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\nlPLQxEAppZRSHpoYKKWUUsrj/wPdUHBVvXsomgAAAABJRU5ErkJggg==\n", 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\n", 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mlLJ6Uxprt6TVm/NRl6+3OyP6xnL1pfHER7rGHJDzyalZMWNwMxDo5d8k98jf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\n", 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" ] }, "metadata": {}, @@ -453,24 +452,24 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'cumulative sum')" ] }, - "execution_count": 105, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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0k9sno3PzywEyu7D4mzEmE4hcZH2iMWYuUGytzTXGPAjcbozZBewD7gUOAi8A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vnPUsqbO385nw2bktkyH7jgOBobvC4sKX9mzV8OwAH7tTChfXTK0UjKEjsgVU3r6BGs3NK1w1T2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\nG3NJkiRJklL0/1i/zHszcNoNAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -497,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -510,12 +509,12 @@ "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", - "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", - "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", - "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135 \n", - "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", + "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", + "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", + "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", "\n", " home.dest \n", "0 St Louis, MO \n", @@ -525,7 +524,7 @@ "4 Montreal, PQ / Chesterville, ON " ] }, - "execution_count": 106, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -537,24 +536,24 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 107, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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idiz7jwR+CXwAeD1wAfDZiPjTTdT8KeDPgJOBHmAmcENEHD7Mz/68MvDtAqzq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\nL/IN/do28sK/1Lela1EzlM8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+ "image/png": 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\n", 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rFi6CPfquKLoMSZ2I4UCqkpPPK7qC9Zszp+gKJHUmHlaQqqBnz55Fl7BBnb0+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\nHgXOSSldXRp/GTABGJpSerm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\n", 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SJJXMexFKkiSVrJaA1fNehD+sPPZehJIkSXgvQkmSpNJ5L0JJkqSSeS9CSZKk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\nAAAASUVORK5CYII=\n", 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\n", 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\n", 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8efNnfZ+5fK6jeZ/J/Wi3ac2bP+192vv3sXTJMAMDA42ea1rHbrfZt28f8+fP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\n", 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\n", 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\n", 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JKordAYAQwuXAtUAr8DhwZYzxoVHanw3cAiwHNgKfizHePqTNRcBngWOAZ4Hr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\ngE172AAAAABJRU5ErkJggg==\n", 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\n", 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JkpT74acJQA1Q12L5CrLDQa2Z1Er7OmBURAwpfJ7cgT4fBs4ETiCbvZkO3B8R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\ngIioBo4FHiqx2UOF9cWOAx5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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -875,7 +874,7 @@ } ], "source": [ - "titanic.fare.hist(bins=doanes(titanic.fare.dropna()), normed=True, color='lightseagreen')\n", + "titanic.fare.hist(bins=doanes(titanic.fare.dropna()), density=True, color='lightseagreen')\n", "titanic.fare.dropna().plot(kind='kde', xlim=(0,600), style='r--')" ] }, @@ -904,24 +903,24 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 118, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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hM1dFxHnAaRFxLyVwnAk8DPywKdMRERcB50bEQspyvecDN2TmzXVOSZIktUuPeywy8xbg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\nS+4YsLrHbziai04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\n", 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" ] }, "metadata": {}, @@ -948,14 +947,14 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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3H8arVlZTXqJd+UTSkeY0iMiYdR7pY+OWZjZuaabziLZmEUlXChpEREQkFA1P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\nNMjfRS92lUXcRxYAAAAASUVORK5CYII=\n", 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\n", 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\n", 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+ "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1101,9 +1100,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -1118,30 +1115,30 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - " id player year stint team lg g ab r h ... rbi sb cs \\\n", - "0 88641 womacto01 2006 2 CHN NL 19 50 6 14 ... 2 1 1 \n", - "1 88643 schilcu01 2006 1 BOS AL 31 2 0 1 ... 0 0 0 \n", - "2 88645 myersmi01 2006 1 NYA AL 62 0 0 0 ... 0 0 0 \n", - "3 88649 helliri01 2006 1 MIL NL 20 3 0 0 ... 0 0 0 \n", - "4 88650 johnsra05 2006 1 NYA AL 33 6 0 1 ... 0 0 0 \n", + " id player year stint team lg g ab r h ... rbi sb cs \\\n", + "0 88641 womacto01 2006 2 CHN NL 19 50 6 14 ... 2.0 1.0 1.0 \n", + "1 88643 schilcu01 2006 1 BOS AL 31 2 0 1 ... 0.0 0.0 0.0 \n", + "2 88645 myersmi01 2006 1 NYA AL 62 0 0 0 ... 0.0 0.0 0.0 \n", + "3 88649 helliri01 2006 1 MIL NL 20 3 0 0 ... 0.0 0.0 0.0 \n", + "4 88650 johnsra05 2006 1 NYA AL 33 6 0 1 ... 0.0 0.0 0.0 \n", "\n", - " bb so ibb hbp sh sf gidp \n", - "0 4 4 0 0 3 0 0 \n", - "1 0 1 0 0 0 0 0 \n", - "2 0 0 0 0 0 0 0 \n", - "3 0 2 0 0 0 0 0 \n", - "4 0 4 0 0 0 0 0 \n", + " bb so ibb hbp sh sf gidp \n", + "0 4 4.0 0.0 0.0 3.0 0.0 0.0 \n", + "1 0 1.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0 2.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0 4.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 23 columns]" ] }, - "execution_count": 123, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1160,7 +1157,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1169,15 +1166,15 @@ "(0, 200)" ] }, - "execution_count": 124, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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AbCGfP5W/+7u75/gOJDUi50xIbaa+O1NuB7YCq4Dn8d73foUTTzzFpalSk7NnQtKMssy3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\n+T4TMcYCcD9wUel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\n", 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" ] }, "metadata": {}, @@ -1198,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1207,15 +1204,15 @@ "(0, 200)" ] }, - "execution_count": 125, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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M4HYvIxjcyN/8zT6CweBob12mAYUJEZFJoLV19DaGYbBs2SYWLWokFjuK43wwwbG9vZfe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\nN7N7CLog1xHBGISeB15291eBWEp5lOJxmZm9Z2Z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\n", 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" ] }, "metadata": {}, @@ -1229,14 +1226,14 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+x1/Nm7O+WjX+rFmTTdddR6ePP6b7Aw9ku2/Fimexb19KjsdPTc06iASXd79B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\nDjp17kynzp2LuykiIpKDQn0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\n", 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" ] }, "metadata": {}, @@ -1257,14 +1254,14 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+X8+/rb2HqkPnqDp0jp89d4gZ09JZuXgmty0rYnFpLglxMjzevy2K/3agNm3c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\nz+d7Ux4Ob9L2yGNPKjcm4V1XLKB0+hQAfvQ/e+ju7Xc5InFSLE0Ro22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YG+0fz5lZyJfv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\nAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": {}, @@ -1272,7 +1269,7 @@ } ], "source": [ - "_ = pd.scatter_matrix(baseball.loc[:,'r':'sb'], figsize=(12,8), diagonal='kde')" + "_ = pd.plotting.scatter_matrix(baseball.loc[:,'r':'sb'], figsize=(12,8), diagonal='kde')" ] }, { @@ -1283,7 +1280,13 @@ "\n", "One of the enduring strengths of carrying out statistical analyses in the R language is the quality of its graphics. In particular, the addition of [Hadley Wickham's ggplot2 package](http://ggplot2.org) allows for flexible yet user-friendly generation of publication-quality plots. Its srength is based on its implementation of a powerful model of graphics, called the [Grammar of Graphics](http://vita.had.co.nz/papers/layered-grammar.pdf) (GofG). The GofG is essentially a theory of scientific graphics that allows the components of a graphic to be completely described. ggplot2 uses this description to build the graphic component-wise, by adding various layers.\n", "\n", - "Pandas recently added functions for generating graphics using a GofG approach. Chiefly, this allows for the easy creation of **trellis plots**, which are a faceted graphic that shows relationships between two variables, conditioned on particular values of other variables. This allows for the representation of more than two dimensions of information without having to resort to 3-D graphics, etc.\n" + "Pandas recently deprecated some really good graphing functions such as Trellis plots, and suggested that we use the seaborn package for such functions. I found https://seaborn.pydata.org/generated/seaborn.FacetGrid.html that demonstrates this setup and have integrated in this lecture:\n", + "\n", + "Chiefly, this allows for the easy creation of **trellis plots**, which are a faceted graphic that shows relationships between two variables, conditioned on particular values of other variables. This allows for the representation of more than two dimensions of information without having to resort to 3-D graphics, etc.\n", + "\n", + "For more help with seaborn\n", + "https://www.tutorialspoint.com/seaborn\n", + "https://seaborn.pydata.org/examples/index.html\n" ] }, { @@ -1292,7 +1295,7 @@ "source": [ "Let's use the `titanic` dataset to create a trellis plot that represents 4 variables at a time. This consists of 4 steps:\n", "\n", - "1. Create a `RPlot` object that merely relates two variables in the dataset\n", + "1. Setup the Seaborn package\n", "2. Add a grid that will be used to condition the variables by both passenger class and sex\n", "3. Add the actual plot that will be used to visualize each comparison\n", "4. Draw the visualization" @@ -1300,14 +1303,14 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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haiXzQgHbD55h56EUEpOz7M50tvbxICzo0oFFh3YtG93swKBewXQLb8O7qw7y\nU/xZTqblMv1v23j6wd7c1rPyo7rEteDIvzjVJY1G2Tmn2qTRylkcFpWTRutSb5N0PqeQj9bGse3g\nGeuxVi3duatvKDF9QmjfxqsBW+cYk8lEVGhrokJbM3HEjWzZn8z63Sc4lZZLQVEp63edYP2uE9zQ\nqTV39wulf/dAWeshRCXFJWVsP3iGTftOcfjY5WvsvVo048bwNkR2bEXnDq3oHOyLn49HA7S0frT2\n8WDmxL6s236Mj9cdpqColDeW/MiI28MZd283mrk51+zM9c6pvuLaSxoVNdv5cwrvrjpInuWeq693\nc0bf2YWh/UOvaDW4M/Bq0Yx7B4Yz7LYw4k+cZ8PuE+w8lEJJaTnxJ84Tf+I8C788xE0qgIE9g7gp\nqp1kfIgm7WJBCeu2JfHtruNkX7z0o1SF+tHvxkB6RrYlPLjVdZ/UaTKZGHF7ZyI7+jFv6Y+cyy7k\nm23HOHIyixmP3Uob3xYN3cQmozEkjdal3iajpLScD9b+woZdJwAwmWBov048OuwGvD2vr390TSYT\nXcPa0DWsDZNG3Mjmn5L59+4TpGTmUVJazt7Daew9nIbJBJ07tKJ3F3+6hrWhS4ifDEBEk1BYXMra\nbUms2ZJk/fIBEOzvzeBbOzKoV3Cjmum8mm4Ia83fp0fz9rL9HEzMIOFkFs+8s5UXHr2FGzvLLpZr\nwemTRutYb5OQm1/MXz7dR1ySMVXaxteDZx++me5NoPP4ertzf3QEI3/TmcTkC+w4lMLOQ2dIzyrA\nbIajyRc4mnwBSAQgsI0XXUL8iOjoS1iQL+HBvrS8zgZkoukym83s+jmVj9bFkZFVYD3eq4s/9/8m\ngl5d/HG5zmcyasPX253Zj/dnxcYEVm46woWLRfx58S4mjOjGfQPDr/nOmqamUSSN1lRvU3T2fD6z\n3t/FmQzjj+gmFcD0h27C17v2aX3XA5PJRJcQP7qE+DF+eFdOpOZwQGdwQKdz+Pg5SixhY6nnjLyR\nrQdOW8/192tBeFDFAMSHsCBf2rX2lA8d0ahk5RSy8MtD7D2cZj3WvXNbHrkniq5hbRqwZc7J1cXE\nI0NvoEtHP+Yv309+YSkffB1H4qkLTB3Ts9HfgnZmjSJptBb1NimpmXm8vHin9ZvM8IFhTBpx4zXd\nyuqMTCYTYZYBxKg7IigpLedEajZHThlBZ0dOZXEm46J1i3BGlpGkavtB7eXhRliwr3Ug0rmDL6Ht\nfeTboXBUKN0lAAAgAElEQVRKOw+lsPDLQ+TmG+s0AvxaMHHEjfTvHigD5xr06daed575Da9/so/k\ns7ls+e9pTqbl8NK4Pk32tlN9c4ak0eEYA4mbgFBgmtb677YFlFKzgZk2h/oCnwFdHW51I5eScZGX\nF+0kM7sQgAn3deP+6IgGbpVzaubmQmRHPyI7+nHvbUbIWGFRKSfSjIc/HUsxfj+emkNxiZGcmFdY\nSlzSOettKgAfr+bcpALorQLorfzxa3n9rOIXjVNhUSnvfXWI/+z/vxm7+waF8+iwG+QbugOC/b2Z\n//Tt/H3lAXYeSuF4Sg7TFmzluUdu5uaoyssGxZVyhqTRFsBRYBXGTIi99Jk4y/kVHMvivg6kZFzk\nxfd2WB+U9vjI7tw3KLyBW9W4eLi7WbfbVigrN5OScZHjKdkcO2P5lZJtXd2fk1fMlv+eZst/jQ/3\nGzq15vbewdzWM0gGH+KaS8m8yF8//ZETqTmAcWvw6Qd70zPSv4Fb1ji1cHfjhf+5hTUdj/LZt79y\nsaCEOR/u4eGhUYy5s4vMbl5FzpA0+hPwk+V6b1RTd5nWOt3B9l43zucU8sr7u62DjSkP9LBGg4sr\n4+piomO7lnRs15Lbe3cAjEV4WblFxJ84zwGdzv6EdDIvGLewKrbifvD1L/RWAdzTvxO33NCuyd/S\nEvVv3+E03lm+n7xC4/vWbT2DeGpML7xaSAbNlTCZTIy6I5LOwa2Yt/QncvOLWbYhgcRTF5g29ib5\n871KnDFp1J5IpdQZoBBj98uLWuvkOlyn0ckrKGH2B7tJP58PwORRMtiobyaTidY+HtzWI4jbegRh\nNptJPpvLvl/Psv3AGY6lZFNuhv0JxmCkja8HQ/t34t7bwmT3i6gXX29N4qNvjMQAFxcT44d35be3\nd5a1GldRzy7+/G3ab/jrZ/s4ejqbvYfTePbvW3lpXB9C5AnWV8yRr2TVJX5WztqoUG3SqAN17wEe\nA+4GpgBhwHallLcD12iUSkrL+Mun+zieYkyfPhjTxboeQVw7JpOJkPY+jL4zkr8/G817f7qT3w3p\nQmsf46/xuexCPv93AhNe3chH38RxLrughisKUTvl5WY++ibOOtho5e3Oa08MYORvImSwUQ8CWnsy\n74+DGHJrCABnMvJ4LnYb22x2uIm6aRSri7TW/7Z5GaeU2gucxEgl/bhhWlX/zGYzsasO8vPRTABi\n+oTw8N2S5u4MOrZryf/ccwNj71LsO5zGtzuP8/PRTAqLy/h6axL/2nGMO28J4fcxCn8/STIUdVNS\nWs7fvvgv2w4YjysIauvFnMf7yy6Keta8mSv/+2AvuoS04v2vf6GgqIy3lu3nvzqdx0d2l8co1JHT\nJI06QmudrZQ6AnSu6zUagy83J7LFsgr91q7tmDq6p3yjcTJuri4M6BHEgB5BJCZn8eXmRHb/kkpp\nmZmNe0/yn/3JjBgUzug7I6+75FdRv/ILS/jLp/s4lGh84VAhfrwysW+Ty9ppKCaTiXsGhBEW7Mtb\nS38iPauAH35M5tfj53n+kZuJ7OjX0E1sdGp9S8XybJOKxE/gksTP3XZO221539ZlSaOOstxKieQ6\njjbfE5fK0g3xAHQK9OH5R26RRYlOLrKjHy8+1of3/nQnQ24NwcVkfEP96j9H+cNfvmfNlqOUlJY1\ndDNFI3A+p5AZC3dYBxu33NCO1yYPkMFGA4gKbc3fn72D23sFA0YO0vOx2/lqcyLl9h65K6rkDEmj\nzYBulpfuQAelVC/gotb6qKXM2xix5qeAIIxk0mJghYPtbxSOp2Qz//P9mM3GQ9hemdCXFk74KHlR\ntQ4BLXn69725P7ozS9bHs/dwGhcLSvh43WG+23OCJ+7vQW8V0NDNFE7qdHous97fTbol2C+mTwhT\nR/eULxwNyLtFM5575GZ6qwD+seZnCovL+PTbXzl4JIP/fbC33DatJYf+BmutVwHPYQR1HQB6UEPS\nKMa22RjgIMZ22MpJo8HAfy2/2lmu/1/g/UplVgAJwEogA+intb78ecuNXPbFIl77eC+FxWW4ubrw\n0rg+BLT2bOhmiToIae/Dnyf05Y2pA4kKNaZfz2TkMfP93cxb8qMsLBWXSThxnj+9u9062Ph9jOKp\n3/WSwYYTMJlMDOkTwt+mR9O5gy8ABxMz+OPbm9m09yRms8x21ORK/hZXmTSqtb6zUrmakka9gDUY\ni0BNGEmjLrbX0VqPBf6CsSakLRBu+f26UlJazl8+3Wf9sJk6uqc8C+E60C28DW8+NYhpY2+ilWVK\nfMehFKbM+4FvtifJtKwAjIyNlxfvIje/BBcTPDm6Jw8PjZJ1W04m2N+bt566nQfuiMDFBPmFpcSu\nOsicD/fIl4gaODTgsEkanQX0Bg5hJI1WGXFnkzT6A9AT+BtG0uhdNsUqkkZnYAwoLvv0dbTexshs\nNrN49c/8evw8APdHRzCkT0gDt0pcLSaTiTtv6ciiGYO597YwXExQUFTGB1/H8dKinaRm5tV8EXHd\n+m7PSV7/ZC/FJWU0d3PhxXF9uKd/p4ZulrCjmZsL44Z3Y95Tgwj2N9IZ9iekM/XNzfzw4ymZ7bDD\n0RkOa9Ko1joBI0k0HyNptCrWpFFtWIjxkLZpFQW01j9prV/QWq8E7O1ccbTeRudfO46zce9JwFgg\n9ti9Te4xMU2Cd4tmTB7Vg/lP/4bwIGNa9vCxczw1/z+s235MZjuaGLPZzIrvEvh//zxIuRlaejbj\ntcm30e/GwIZumqgFY0FpNCN/0xmTyXgW09++OMDcj/ZagxrF/6n1gMMmadSaHKq1NlteO5o0aq/8\n1aq3UTl4JJ0PLaE+HQK8ee7hm3GV/P7rWkTHVsx/5nYeukvh6mKiqLiM97/+hT8v3mV9CrC4vpWW\nlfPuqoMs36gB45ko8/44iBvCWtdwpnAm7s1cmTjiRt6YOpDAtkY+yk/xZ5n61ma+3ppEWVl5A7fQ\neTSGpNG61NtopGRcZN6SnygvN+PVohmvTOgruf1NhJurC2PvjmLBtP+b7fglKZP/nf8fdh5KaeDW\nifqUX2g8IGzTvlOAsfX9racG0bFdywZumairrmFtiH02mvujI3BxMVFYXMZH38TxXOw2kk5faOjm\nOQVZ+tyA8gpKeO2TvVwsMBaJvfA/txDkf92ntYtKwoJ8mf/M7fxuSBdMJrhYUMIbS37k3VUHKSxq\ncg9Fvu5lXijghf+3g4NHjM19vbr4M++PA2njK1srGzuP5m5MuK8b7zx9OxGWnSxHT2cz/e/b+Oib\nuCbfnx0ZcDRU0mhd6nV6ZeVm3v58P8lnLwIwccSNks3QhLm5uvA/99zA61Nuo62v8cj7jXtP8syC\nLRyVb0fXjWNnsnn279usj5aP6RPCrEn9JCr7OtO5Qyve/t/bmfTbG/Fo7kp5uZmvtyYx9a3N7Ps1\nraGb12CcPmm0jvU6NbPZzD/W/MxP8cZdopg+Idw3KLyBWyWcQffObYl97g5u6xEEGLkdz8duY/V/\njsqC0kZux6EzvPD/tnM+pxCAR4ZG8dTveuEmGRvXJVdXF357e2cWPn8nt9xgfF9Ozyrg1Y/2Mvej\nPU1yZ1qjSBqtqd7G5qv/HGXDrhOAkdEw5YEestdeWLX0bM4Lj97Cpn2neP/rXygqLuOTfx3mgE5n\n2kM30drHo6GbKBxQVm5m6fpf+eo/xseZm6uJ/32wN3fc3LGGM8X1IKC1JzMn9mXHoRQ+XBvH+ZxC\nfvz1LAePZDDqjghG3xmJR/OmkSTdKJJGa1Fvo7FlfzKfffsrYDxx9M/j+9DMzbWBWyWcjclk4q6+\nofxt2m8uSTV86u3/NOkp2cYm+2IRsz/YbR1stPbx4K9TB8pgo4kxmUwM6hXMohfuZFR0BK4uJkpK\ny1m56QhT39zM7l9SmkR2x5UMq6pMGq2iXLVJo1rrE5Zgr1eBUCAReEFrvaGijFJqNjDT5rS+wGdA\nrcIqysvLTTk5Ofz444+1KV5v4k+c4+NvDlNWbsbHqxm/HXYT8YcPNWibhPN7sJ8H63ensWX/aS4A\nz75+mIE9g7hvUHijGqzm5ORQXl5e66k8Z+m3dZVw4jzLNyZwMb8EgPBgXx69/QZy05P4MT2pgVsn\nGsqN7cF/sA9rtiRxJDmLC2nwp/ifUSF+jPhNOIFtnG/jgKN91x6TI6Mqy8DgM+AJYC/GjMUYQFU1\n22BJGo0D3gM+xFiH8TfgXq31RkuZAcBWjKTRfwEPAy8AN2mtD1vKzAZGYbOOAyjVWp+vTbtDQkLO\nPP7440ERERG1/lmFEFfX0aNHef/991NOnToVXJvy0m+FcA6O9l17HJ3hsCZ+AiilJmPcMpkAzKui\nvDVp1PJaK6UGYgxUNlqOPQ1s0FrPt7yeqZSKAf4ITLG5VpnWOt3B9lpFRERw44031vV0IUQDkH4r\nxPXDGZJG+1VR5rsqrhmplDqjlEpSSi1TSslN0EYiLi6O0aNHk58vUb9CNBbSb8XV5sgMR3WJn1F2\nzqk2adSSxdHezjVt8zv2AI8BGgjCeIjbdqXUjVrriw78DEI4JD09nX/+85/ExcVx4cIFWrduze23\n384DDzyAm1vTWFkuRGMj/dY5NYo/ea31v21eximl9mI8zv53wMcN0yrRFJw5cwaAyZMnExgYyMmT\nJ1m8eDGFhYU89thjNZwthGgI0m+dkyMDjvpKGk1z8JporbOVUkeAzrVot7hCM2fOJCQkBLPZzLZt\n23Bzc+Ouu+5i7Nix1jIlJSV88cUX7Nixg+zsbNq0acOoUaMYPLhy7hvk5ubywQcfEB8fT15eHu3a\nteOBBx5g4MCB1jK7d+9m1apVpKWl4e7uTlhYGDNmzMDd3Z24uDiWLl3K6dOncXV1pWPHjjzzzDP4\n+/tf9Z+9d+/e9O7d2/o6ICCAlJQUvvvuO/ngEk5N+q30W2dT6wGH1rpYKVWR+PkNXJL4GWvntN3Y\nhHxZVE4a3W25ZmylMnZTRJVS3kAksKS27RdXZsuWLQwePJh58+aRlJTE4sWL8ff3Z8gQY+NQbGws\nR44cYeLEiXTq1ImMjAwuXKg6krukpISIiAhGjRqFp6cnP/30E7GxsbRv356IiAiysrJYsGABjz76\nKH379qWgoID4+HjMZjNlZWXMmzePu+66i+nTp1NaWkpiYmK1wWlPP/00mZmZdt/v2rUrL7/8cq3/\nLPLy8mjZUh6yJZyf9Nv/I/224TV40ijwd2CrUmo6sB74Pcbi1EkVBZRSb2MMck5hrOGYAxQDKxxs\nv6ijtm3bMn68EbMSFBTEyZMnWbduHUOGDCElJYXdu3cza9YsunfvDhjfKOxp3bo1I0aMsL4eNmwY\nBw8eZOfOndYPrvLycvr27Wv99hMSEgIY37IKCgq46aabaNfOmBgLDq5+p9Yrr7xCaan9hya5u9f2\nwcWQmprKhg0b5FuSaBSk3xqk3zoHhwYcWutVSil/jMTP9hipn9UmjSql7gUWYGx/TaZS0qjWerdS\n6iHgNeAvwBFgpNb6V5uqgzEGF22ADGA70E9rfc6R9ou669Kly2Wv161bh9ls5vjx47i4uNC1a61y\n2CgrK2P16tXs2rWL8+fPU1paSmlpKR4eRmR3p06d6N69O9OnT6dnz5706tWL/v374+XlRcuWLbnj\njjt47bXX6NGjBz169GDAgAH4+fnZra9t27Z1/8FtnDt3jtdee40BAwZYvyEK4cyk30q/dSYOLxrV\nWi8EFtp577KkUa31VowZi+qu+SXwZTXvj7X3nrg2qguIa968uUPXWrt2LevXr2fChAmEhITg7u7O\nxx9/bP024+LiwqxZs0hISODQoUOsX7+e5cuX88YbbxAQEMDUqVMZNmwYBw4cYOfOnaxYsYKZM2de\n9uFa4WpMzZ4/f55Zs2YRFRXFlClTqi0rhLOQfiv91pk4POBQSk0FnsdY2HkIeEprbTd7WCkVjXEr\npivGDMdrFcFhNmXGUE20eV3qFVdXYmLiJa+PHDlCYGAgJpOJ0NBQzGYzhw8fpkePHjVeKyEhgT59\n+jBo0CAAysvLSUlJsU6/VoiKiiIqKooxY8YwefJk9u3bx/DhwwEICwsjLCyMUaNG8dJLL7F9+3a7\nH1xXOjV77tw5Zs2aRUREBH/84x9r/PmEcBbSb6XfOhOHBhyWaPP5XBpt/p1Sqrpo828xos3HYiwO\n/VAplVop2nw5l0abf62Uso02d6hecfVlZmby6aefEhMTw7Fjx9iwYQPjxo0DjPu+0dHRLFy4kIkT\nJxIaGkpGRgY5OTkMGDDgsmsFBQWxe/dutNZ4eXmxbt06srOzre8fOXKEX375hV69euHj40NiYiI5\nOTkEBweTnp7Oxo0b6dOnD61atSIlJYXU1FSio6Pttv1KpmbPnTvHzJkzCQgI4NFHH71kQV1108FC\nOAPpt9JvnUljiTZ3tF5xlUVHR1NcXMyMGTNwdXVl+PDhxMTEWN9//PHH+fzzz/nggw/Izc3F39+f\nUaNGWd+3XY0+evRozp49y6uvvoq7uzsxMTH06dOHgoICADw9PYmPj+fbb7+loKAAf39/xo0bR+/e\nvcnOziYlJYW33nqL3Nxc/Pz8uOeeey5py9V06NAhzp49S3p6Oo8//vglP88///nPeqlTiKtF+q30\nW2dS64e3WaLN84AHtNbf2Bz/FGiltR5ZxTnbgJ+01tNtjo0HFmitW1lenwTma61jbcrMxlg42qsu\n9VYWEhJy5s033wySZzLUzcyZMwkLC7OudheiLuLi4vjTn/7k0MPbpN/WnfRbcbU42nftqfWzVKg+\n2rxyuFeFaqPNLa9rijavS72VtallOSFE/XKkL0q/FcJ5XHF/dGTA0ZiVNHQDhBCAY31R+q0QzuOK\n+2NjiDavS72XSE5OHrZly5ZtSUlJtSkuKrn55psBY1ucEHWVnJxMcnJy5eTh6spLv70C0m/F1eJo\n37XH6aPN61jvZTp06EDnzvLoFSEaSm3Xi9mSfitEw6tL361Ko4g2r6neWiiMiIhAFp81XmazmcWL\nF7Nnzx7y8vJ4++236dSpU4O0JT09nSeffLJB29CIFTpSVvqtcNS7775Lfn4+L7zwQkM35XrjSN+t\nkkNrOLTWq4DnMKLNDwA9qCHaHGP7agxwEGM77GXR5sBDwOOWMqOoFG1ei3rFde7AgQNs2bKFl19+\nmY8++oiOHTvWfJIQoskxmUzVPhRONJxGEW1eU73i+peWloafn5/dVEIhhABjNvRq3QIQV5fDAw5x\nfdi9ezerVq0iLS0Nd3d3wsLCmDFjhjUu+Pvvv+ebb74hIyMDf39/hg0bxtChQwFYuHAhSUlJzJs3\nj2bNmlFSUsKLL75IaGgoTz311FVv67vvvsvWrVsBI3zI39+fRYsWUV5eztdff82mTZu4cOECQUFB\njB49mv79+wPG3vHZs2fz5z//mWXLlnHmzBmioqKYNm0aR44cYcmSJWRlZXHzzTczZcoU689+4MAB\nvvzyS5KTk3FxcaFLly5MmDCB9u3t78I+deoUS5YsIT4+Hg8PD3r27Mn48ePlcdjimmlMfRqMnJDQ\n0FBMJhNbt27Fzc2Nhx56iAEDBvDhhx+yd+9efH19mTRpEr179waMOPVFixYRFxfHhQsXaNu2LUOH\nDuXee++1W4/ZbGbNmjV2PyfEtSMDjiYoKyuLBQsW8Oijj9K3b18KCgqIj4+3fivYtm0bK1euZNKk\nSYSFhXH8+HEWLVqEh4cH0dHRTJw4kWeffZZly5Yxfvx4li9fTn5+PpMmTbJb51dffcXq1aurbVds\nbCxt2ly+1XvixIkEBgayadMm3nzzTVxcjDuBq1evZvv27UyePJnAwEAOHz5MbGwsPj4+dOvWzXr+\nqlWr+MMf/kDz5s2ZP38+b7/9Nm5ubkyfPp2CggLefPNNNmzYwMiRRoZcUVERI0aMIDQ0lMLCQr74\n4gvefPNN5s+fX+VUbV5eHrNmzSImJoYJEyZQVFTE0qVLmT9/PrNnz67x/4cQV6qx9ekKW7ZsYeTI\nkcybN48dO3bwj3/8g127dtG/f3/GjBnDunXriI2NZfHixbi7u2M2m2nbti3PP/883t7eaK1ZvHgx\nfn5+VcaxV7SzNp8Tov7JgKMJysrKory8nL59++Lv7w9wyQOYVq5cybhx4+jbty9gPHMhOTmZjRs3\nEh0djYeHB08//TSvvPIKHh4erF+/njlz5tCiRQu7dd59993cdttt1barVatWVR739PTEw8MDFxcX\nfH19ASgpKWHNmjXMmjXLepslICCA+Ph4Nm3adMkHydixY1FKATB48GA+//xz3nvvPQICAgDo168f\ncXFx1gFHv379Lqn/ySefZMKECZw+fbrKtSMbNmwgPDychx56yHps6tSpPPHEE6SmphIYGFjtzy3E\nlWpsfbpCp06deOCBBwAYNWoUa9asoVWrVtbHyI8ZM4bvvvuOU6dOERkZiaurKw8++KD1/ICAABIS\nEti1a1eVAw5HPidE/ZMBRxPUqVMnunfvzvTp0+nZsye9evWif//+eHl5UVhYyNmzZ1m4cCHvvfee\n9Zzy8nI8PT2tr7t06cKIESP46quvuP/++4mKiqq2Tm9vb7y9va/az5CamkpRURFz5sy55HhpaSnh\n4eGXHLPdSeLr64u7u7t1sFFx7OjRo9bXKSkpfPHFFxw9epTc3FzKy8sB40FYVQ04Tpw4QVxcHA8/\n/PAlx00mE2lpaTLgEPWusfbp0NBQ63+7uLjQsmXLS45VfMGwfUjchg0b2Lx5M5mZmRQXF1NaWkpY\nWFiV13fkc0LUPxlwNEEuLi7MmjWLhIQEDh06xPr161m+fDlvvPEGzZs3B2DKlClERkZedl6F8vJy\nEhIScHV1JTW15vy1qzH9aquw0Nih9fLLL9O6detL3mvWrNklr11dXat9bTKZLllk9te//pV27dox\nZcoUWrduTXl5OdOmTbP7qOzCwkJuvfVWHnnkkcvekydTimuhsfbpqvqi7bGKW5gV/XPHjh0sWbKE\ncePGoZTCw8ODtWvXkpiYWOX1HfmcEPVPBhxNWFRUFFFRUYwZM4bJkyezb98+hg8fjp+fH2fPnmXQ\noEF2z127di0pKSnMnTuXuXPnsnnzZu6880675a/G9Kutjh070qxZMzIyMujatWutz6tJbm4uqamp\nTJ061foNLz4+vtpzwsPD2bNnD/7+/pd9gApxLTXmPl0bCQkJKKW4++67rceqGxzV1+eEqBsZcDRB\niYmJ/Pzzz/Tq1QsfHx8SExPJyckhONh4EOCDDz7Ixx9/jKenJ7169aKkpISkpCTy8vK47777OHbs\nGCtXruT5559HKcW4ceP45JNP6NatG+3aVU6gN1ztWyotWrRgxIgRfPrpp5jNZqKiosjPzychIQFP\nT0+io6PrdF0vLy9atmzJxo0b8fX1JTMzk2XLllV7zj333MP333/PggULGDlyJN7e3qSmprJr1y6m\nTJlyybdIIerD9dCnayMoKIitW7dy8OBBAgIC2Lp1K0lJSXbbWF+fE6JuHB5wKKWmAs9jPMvkEPCU\n1vrHaspHYySFdgWSgde01p/ZKft7YDmwVmt9v83x2cDMSsUTtNYyZK2DFi1aEB8fz7fffktBQQH+\n/v6MGzfOuvVsyJAhuLu7s3btWpYsWYK7uzuhoaEMHz6ckpISYmNjueOOO6zPaoiJiWH//v28++67\nzJ0795r9Azt27Fh8fHxYvXo1Z8+excvLi/DwcOsiNKDKXSWVj9m+dnFxYdq0aXz00UdMnz6d4OBg\nxo8fz6xZs+ye4+fnx+uvv87SpUt59dVXKSkpwd/fn969e8tgQ1wT10ufrklMTAzHjx/nnXfewWQy\nMXDgQIYOHcqBAwesZSoHf9Xmc0JcGyZHAlKUUg8CnwFPAHsxkkPHAKqq1E+lVBgQB7wHfIjxPJS/\nAfdqrTdWKtsJ2A4cA85prUfZvDcbI4F0iM0ppVrr87Vpt8lkunXFihX7JCJZiIYTFxfH2LFj+5jN\nZrtfUGxJvxXCOTjad+1xdIZjOvB+xQyFUmoyRnT5BGBeFeUnA0la6+ctr7VSaiDGQMU64FBKuQKf\nY8xi3A5UdeOvTGud7mB7hRBCCOEEaj1PppRqjhFR/n3FMa212fLaXmRbf9vyFhurKD8TSNNafwLY\nC8GPVEqdUUolKaWWKaXkYRpCCCFEI+HIjbm2gCtwttLxdIyHtlWlXRXlzwI+Sil3AMuMxwTgD5b3\nzZZftvYAjwF3A1OAMGC7UurarlgSQgghRJ006C4VpVRLYCnwB5v1GCYqzXJorf9t8zJOKbUXOAn8\nDvi4pnoiIyM3z5kzxxoiI4S49rKzs4mMjNwM1OoBM9JvhXAOjvZdexwZcGQCZRizFrbaAfY2Qqdx\n+exHOyBHa12klLoBCAXWVURPY5l1UUqVAF201scrX1Rrna2UOgJ0rmXb3QDc3GQXsBANzJFOKP1W\nCOdxxR2x1hfQWhcrpfZj7BT5BkAp5QIMBmLtnLYbGFbpWAywy/Lf8YDtEnQT8BrgDTwNnK7qopZb\nKZHAktq03WQypQYFBYX98MMPtSkuhKgHgwcP5vTp0zVHWFpIvxXCOTjad+1xdMTyDvCZUuon4Efg\nGaAF8AmAUuqvQJDW+jFL+cXAH5VS8yxl7sTYRjsMQGtdBPxqW4FSKtvy3q82x97GGOScAoKAOUAx\nsMLB9gshhBCiATiU5qK1XgU8B8wFDgA9gKE2GRztgY425U9gbJuNAQ5ibIedqLXeVE01VS0aDcYY\nXCQAK4EMoJ/W+pwj7RdCCCFEw7iS+Dgzly/uHK+1rhy+X3mbq71trxVJo49Vcd2xwF8w1oS0BcIt\nvwshhBCiEXBowGFJGp0PzAJ6Y0Sbf6eU8rdTPgz4FvgB6ImRMvqhUuquKsp2At7CSBs1V3rPoXqF\nEEII4VwcneGwJo1qrRMwkkTzMXI0qmJNGtWGhcCXGLdWrColjR7j8lkQR+sVQgghhBNx+qTROtYr\nhPsxLxAAACAASURBVBBCCCfSGJJG61KvEEIIIZxIgz5zuLZJo0IIIYRo3Jw+aRQ4U4d6hRBCCOFE\naj3DobUuBiqSRoFLkkZ32zltt+V9W1Uljfa0/OqFEfC12fL6dB3rFUIIIYQTaRRJozXVK4QQQgjn\n1iiSRmtRrxBCCCGc2JU8/a3KpNEqylWbNKqUGgW8hPHk12ZAIkbIl22Z2RhbZyv0BT4Dutah3UII\nIYS4xpwhafQc8CrQD+iOcZvkE6XU3ZUuF4cxg1Lxa6AjbRdCCCFEw3F0hsOa+AmglJqMcctkAjCv\nivLWpFHLa23J3ZiGEQCG1nprpXNilVKPAQOA72yOl2mt0x1srxBCCCGcQK0HHDaJn69XHNNam5VS\ndUkaXWCnDhPGwtJIjJ0qtiKVUmeAQozdKS9qrZNr234hhBBCNJwGTxoFUEr5KqUuAkXAemBapZmP\nPRhPkb0bmAKEAduVUt4OtF8IIYQQDeRKFo1eTTkYO0+8MfI2YpVSqVrr9QBa63/blI1TSu0FTgK/\nAz6+1o0VQgghhGMaNGm04oDlYWzHLC9/tiSQTsOY7biM1jpbKXUEY2eLEEIIIZxcrQccWutipVRF\n4uc3cEniZ6yd03ZjCfmyYZs0ao8r1dzusdxKiQSW1NxyUR2z2UzCiSx+jE/j1+PnybhQQEFhKS09\nm9G+jRc9ItrS98b2dAho2dBNFUJUwWw2k5qZx5mMi2RmF/5/9u48Lupq/+P4a4Z9E1BAQQVR9OC+\n72aWmaZmZlppm1umWb/S23orl3ZtvXpttVwytxZTS1PL3HHHBYXDIriBC8omss/8/vgOXERQBpGZ\ngfN8PHjYzHznez6U3+nM95zzPuh1OjxcHQis50F9X3d0OrU1lWIdLJo0anrP66ZznQCcTK89Dkwo\ndszHaJ2cU0AAMBPIBZaZWb9iYjQa2XU0iRWbJPGJ6de9nnE1l8TkTA7KCyz84zgdQv0Yda9ABNW2\nQLWKopR0OT2bNdvi2HU0iaTkzFKPqV3Lia6t/BnUI5gg/1pVXKGiXMusDoeUcqUpc+NttKGScG6S\nNCqEGIS2KuUF4DTXJ426Al8ADYAstP1VHpNS/lTsmPponYs6wEVgO9BNSnnJnPoVzblLmcxdeYgj\nsclFzzk62NG8kTcN/TxwcbYnPTOX+MQ0Yk6nYjTCwagLHIy6QL8ugYwb0go3FwcL/gaKUnNdzc5j\n6QbJ+l3x5OYbbnjs5fQc1u9KYP2uBDo1r8u4IS3V3UrFYiyeNIrWabkPbYWKE+Bm+rP4eUcKISYD\nL6PNAWmMtmomvqLF11Q7Dp9l7spDXM3OB8DP24XhdzelT8eGuDhd/9chJT2bdbsSWLs9jszsfDbt\nPcXRuGRefbIzIQ28qrp8RanRIuKS+Wx5OBcuXwVAp4OOoXXp0dqf0Ea18fVywQikZuQgT6VwIPI8\nOw4nkl9gYH/kecLlBYbdFcKo/qHY25mV+6got8ysDkexpNFngD1oEzs3CCFEafuaFEsa/QIYiTb/\nY75pBcpG02GFSaNRaMMk96MljV6UUm6oSLvK9YxGIz/9HcMP6yMB0Ot1PNy3GcP7NsXJwa7M93nX\ncuaxAaEM7NmI+asj2BZ+lnOXrvLq3O289Hgnurf2r6pfQVFqtLXbTzB/9VEMpp2murf25/EBoQTW\nu36oxMXJHn8fN/p0aMD4B1rx29Y4ftsaR36BgZ/+juFQ9EVefrwT/j5uVfxbKDWZrSSNmtuuUozR\naOTb1RGs3a4tBKpdy5lXn+xEi+A65T6Ht4czLz/eifbN/Pjy1yPk5hXw4aK9PDu8Hf27Bd2u0hWl\nxjMYjCz4/Ri/bY0DwN3FgUkPtaF3+wbler+nuxNPDWpB/25BRUOpMadTmfr5Vl4f3Zk2IaXuTKEo\nla7c99SKJY0WJYealrNWJGm01OOFEDohRF+KJY1WsF3FxGg0suiP40WdjaB6HnzyQm+zOhvF3dMl\nkPcm9cDD1RGDEeb9fIjN+1Xgq6LcDkajkW9+O1rU2fD3ceOTF3uXu7NRXL06brz9TA+eHNgcvQ6u\nZOUx7esw/tp7qrLLVpRS2ULSaEXaVUxW/hXNL//EAtA4wJMPJ/fCx8vlls4ZGlSbWc/1wsvDCaMR\n/rP8IDsPJ1ZGuYqimBR+WfhjpzZVrWlDLz56/g4CfCoesGyn1zGibzOmje+Gi5M9BQYj/1kRzrpd\najqccvtZy6yhwqTRTsDraMMqJfM7FDNt2nOSJX9GAdDAz523n+mOu6tjpZy7YV0P3n2mBx6uDhiM\n8NGS/RyOVtNpFKWyrNsZf82XhbcndMfT3ekm7yqfjqF1+ej5O6hdSzvfl78cYc22uEo5t6KUxZwO\nx21NGpVSnpBSHpFSfgosRZvnUdF2a7zj8Zf44pfDgLYS5d2JPSrtw6pQkH8t3p7QA1dn7ZvSB4v3\ncfbilUptQ1FqokPRF/hmdQQA9X3dKvXLQqEg/1p88GwvfDydAfh2dQS/bY2t1DYUpbhydziklLlA\nYdIocE3SaFgZbwszvV6cWUmjFWy3RruQcpUPFu4jv8CIi5Md08Z1o47nrQ2jlCWkoRevPtkZvV5H\nZlYeb8/fzZWrubelLUWpCc5dyuTDxfsxGIy4uTjw1rhulf5loVCArzsfTO6Fn7f2+fDdmmNs2nPy\ntrSlKDaRNHqzdpX/ycsv4IOFe0m9ot1Amjqq421PGOwg/Hj6gVZ8veooicmZzPphPzOe7o6dXkUq\nK4o5CgoMfPLjATKz8tDr4NUnOlHf9/Zuil2vjhsfPNuLV/67nUtp2fz3p0O4uzqqJe9KpTNrDoeU\nciXwElrSaDjavIsbJo2iLV/tBxxCGyYpK2k0AtgBPIiWNLrQjHYVk4W/Hyf2TBoAjw8IpVurqvnQ\nGNQzmPt6NALgUPRFVmySVdKuolQnyzZJok6mADCyfyjthV+VtOtX25W3J3S/Zk7W0WJJxIpSGW5l\n0mipSaNSyrtLHHezpNFTaCtQsk2vZQEJxQ8QQswA5gKBgCPQFVhU8dKrpz0RSawxLX/tIPwY0bdZ\nlbWt0+l4+oHWiCBvAJZvkhyUF6qsfUWxdRFxyfz0VzQALRvXqdLrFyCwXi2mj++Gs6MdefkG3luw\nh9PnM6q0BqV6M6vDUSzxczrQHjiMlvhZanJMsaTRv4G2wOdoSaP3FjvsTuBHoA9arsZpYKMQIqDE\n6SLQ7qAU/vQyp/bq7mJKFp8vDwe0DZumjOyAvoqHNBzs9bz6RGc8XB0xGuGTHw+QnJpVpTUoii26\nkpXHJz8ewGAENxcHpo7qYJEhSRFUm9ef6qLNycrOZ8b83aRkZFd5HUr1ZO4djqLETyllFFqS6FW0\nxM/SFCWNSs084Gf+twIFKeXjUsqvTCtUJDDeVFfJOyUFUsoLxX4um1l7tWU0GpmzIpwrWXnodNq8\nDS+P2zPJ7GZ8vV3412Md0OkgPTOX2T/sJ7/gxhtMKUpNt/D3YySnaf9jf35EO/y8XS1WS4dQP559\nqA0AFy5f5b3v95KTV2CxepTqw6qSRk3cAAegZIeiqRDirBAiTgixRAjRsJT31kjrwxI4FKNNZxnW\nJ4S2TS0bVdwxtC4Pm24HRyZcZolp/xZFUa53NC6ZDbu1lSG929enZ9uSN3erXv9ujXjorhAA5KkU\nPlt6EEPhJi6KUkFWkTRawizgLNd2VHYDTwH9gUlAMLBdCHF7p2/bgKTkTL5fewzQYssfGxBq4Yo0\nI/uH0ibEB4Bf/ollf2TJvwaKouTmFfDflYcA8HB14OkHWlu4ov95cmALerbROj87jySyeN1xC1ek\n2DprSRoFQAjxGvAw8KApfwMAKeWfUspfpJQRpl1mBwJepmNrrAKDkc+XHyQntwA7vY4pIzvgYF/2\nzq9VyU6v41+PdcTTXQsr+nTpQTWfQ1FKWL5JkpicCcD4B1pZbCi0NHq9jimjOiACtYngv/wTy59h\nCRatSbFtVpE0CiCEeAl4FbhXShlxo0KklGlANNCkfKVXT2u2xXE8Xht5evReQZMGXhau6Fq1azkz\ndVRHdDrIuJrLxz8eoEDN51AUAE6dS+dXU3R5u2a+3NXR+kaJnRzseHNsV/xqa3NKvvz1iFp9plSY\nVSSNCiFeAd4E+kspD96sFtNQSlNqcLT5qXPp/GCaGxHS0Ivhdze1cEWl6yD8imo7duISSzeqfA5F\nMRqNzF8dQYHBiKO9nsnD26LTWWdQnpeHEzPGd8PN2R6DwciHi/YRn5hm6bIUG2QNSaOvAjOBUcAp\nIUThHZEMKWWm6ZiPgTVomR0BpuNzgWVm1l8t5BcY+Gx5OHn5Bhzs9Ux5tD32dlY1OnaNx/qHcuzE\nJY7HX+anv6Np1bhOlQUaKYo12hd5nnDTZocP3hVCvTpuFq7oxhrW9eDfY7ow/ZswsnLymTl/Nx//\nX+9b3nlaqVmsIWl0ItqqlJ+BxGI//yp2TH20zkUUsAK4CHSTUl4yp/7q4ufNMcSeTgXgifuaE1jv\n9kaX3yo7Oz0vP96pKJ/j06UHuZyu1vYrNVNevoHvTBuz1fF0Zvhd1nl3sqQ2Ib783yPtAbiUls3M\n+bvJzMqzcFWKLbGGpNH30YZYUk0/fwNdpZRvFzvvSNNx59BWyzQ2/VnjxCemsdw0LNG8UW2G9LaN\naSw+Xi5MGal9WKVeyeGTHw9QoJbZKTXQ7ztOFE0UHT2oBc5O5t5otpy7Ojbk8fu0lXAJSel8uGif\nytlRys0mkkbNbbe6yss38PmycG3c18GOF0e2t6kN0jq3qMeDfbS1/Udik1lpinFWlJoiNSOH5aZ9\nhkSQN3d2aGDhisz3cN9m3Ns1CIBDMRf570+HMBrVlwfl5mwladTcdquln/6O5oRpstboQS0I8LG9\nGJInBzb/334rG6PUBlFKjbLkz0iuZucDMGFoa6udKHojOp2OSQ+1oUOoNg/r732ni+66KsqNWH3S\naAXbrXZiz6QW3RFo1aQOg3oGW7iiirE3zedwc9F2pfz4x/2kZuTc/I2KYuNOnE1j4x4tUfTuTg1p\nZsq3sEX2dnpefaITjQM8AVi6URb9bopSFltIGq1Iu9VKXn4B/1muDaU4O9rxwiPtq3xjtspUt7Yr\nL5gmn11Oz+GzZSo2WanejEYj3/x2FKMRnB3teHJgc0uXdMtcnR2YNr5r0UqVeT8dYvuhsxauSrFm\nVrWWsqyk0Zpu+aZoEpLSARg9uKXVL6Erj+6t/bn/jsYAHJQXWKZuySrV2M4jiRw7oS2qG9G3GXU8\nq8dy0jqeLrw9oTu13BwxmHaIVtsYKGWxhaTRirRbbUSfSuHnzTEAtAnx4b7ujSxbUCUaM7gFIQ21\ndNTlmyRbD56xcEWKUvly8gpYYNrvyK+2K0PvtI2VZeXVsK4HMyd0x9XZngKDkQ8W7lVzs5RSWX3S\naAXbrRauZufx8Y8HMBiMuDjZ8X82PpRSkoO9HW+O6ULtWs4A/GdFOFEnS24SrCi27bctsVxI0fYR\nGnt/SxwdrGO/o8oU0sCL6eO74eRoR26+gXe+301UgrqWlWuZO6TyKfC0EOJJIURz4EtKJI0KIRYV\nO/4roLEQYpYQIlQI8Sxa0uhnhQeYkkbfRltxckoIUc/0U3zc4IbtVldfrzpKkmm9/oShbahr2s+g\nOqnj6cJb47ri5GhHXr6B977fy4XLVy1dlqJUiktpWfxkukPZuokPPVr7W7ii26dFcB3eGN0Fezs9\nWTkFTPtmV9EwkqKAjSSNlqPdamfrwTNs3n8agN7t6tO3s/Vt7FRZQhp48a9RHQAtFGzG/DDSrqiV\nK4rtW/jHcXJyC9Dr4OmhrWxyGaw52gs/Xn+qc1GnY/q3YRyOrrYf04qZzI64M2VpzCvjtTGlPLcV\nbVlrWecr1/rOG7Vb3Zy7lMkXvxwGtDHfZ614Y6fK0r11AKMHtWDhH8c5ff4KM74N492JPXFzcbB0\naYpSIVEnL7PlgDYv6d5ujQg2LSGt7rq0rMebY7vw/oK95OQWMPO73fx7dBc6NS85DU+pacxepSKE\nmCyESBBCZAkhdgshOt/k+D5CiINCiGwhRIwQ4qkSr7cUQvwihIgXQhiEEC+Uco4ZpteK/xw3t3Zb\nkJdfwEdL9nM1Ox+9XsfLj3WsMf/THXZXSNGEutgzabzz/R6yc/ItXJWimM9gMPLtb0cBcHO25/EB\noRauqGp1DK3LtHHd/jdUumAPOw8nWrosxcKsIdrcBYgFXkNb1VJWIEME2pBN4U8vc2q3BUajkS9/\nOUL0KW1jtlH3CkIb1bZwVVVHp9Mx9v6W9O+mxSYfO3GJad+EcUVtEKXYmM37TxVdx4/eK/B0Lyt2\nqPpq28yXmU93x8XJjvwCI7N+2MfqbXGWLkuxIGuINt8vpXxVSrkCuNHAfYGU8kKxn2o3BfrP3SfZ\ntPcUAJ1b1GVE32YWrqjqabHJbelj2mMiMuEyb3y5U83pUGzGlaw8Fv6h3YBtWNedwb0aW7giy2nZ\nuA7vTuyJp7u2U/T81RHMXx2hgv5qKGuMNi9LUyHEWSFEnBBiiRCiWs2iPBqXzDerjgAQ4OPG1FEd\nq9USWHPY6XW8OLJD0Z2OE2fTeGXuds5evGLhyhTl5pZtiCLtipZbOGFoa+ztrCpfsco1C/Tmo+d7\nE+CjLTxcvS2O2Uv2k5NXYOHKlKpmjdHmpdkNPAX0ByYBwcB2IYTt7V5WioSkdN77fg/5BVrexhtj\nuuBeQ+ZtlMVOr2Py8LZFczoSkzP513+2qRnvilVLSErn953xAPRsE0C7Zn4Wrsg6+Pu4Mfv5Owg1\nbdy483Air/13OxdS1BL4msQmut5Syj+llL9IKSOklBuBgYAXWgy6TbuQcpXp34SRmZ2PnV7Hq092\nJrBeLUuXZRUK53Q882Br9DrIzMpj2rdh/Lw5Rt2SVayO0Wjk61VHMBiMODrYMXZIS0uXZFU83Z14\nd1JPupuySGLPpDHls60ciVVfImoKq4k2N4eUMg2IBmw6Izg5NYtpX+/icno2AP/3SDs6hqqlY8Xp\ndDoG92rM9Ke74+Zsj8FgZNEfx5n+bRgppn9vimINth86S0ScFnT18D1N8fOufkF9t8rJwY7XnuzM\n4wNC0ekgPTOXt74O47etcRiN6ktEdWcV0ebmMg2lNMWG91I5dymTV+ft4OxFLUn0qUEtuLtToIWr\nsl4dhB+fTrmTkAZalsGh6ItM/ugfNu8/rT6oFIvLzMrjuzXafin+ddx48M4QC1dkvfR6HY/0E0wb\n163oS8R3ayJ4b8FeNTm8mjM3+OtTYJEQYj+wD3iREtHmQICUsjBr4yvgOSHELNMxd6NFmw8sPKEQ\nwgEovPfoBDQQQrQDrkgpY03HfAysAU4BAcBMIBdYZmb9ViE+MY2Z83dzKU37hv7kwOYMv7uphauy\nfgE+7sx+vjeL1x3nt61xZFzN5bNlB9ly4DQTHmxNAz8PS5eo1FALfj9WdKfy6aGtquV+KZWtU/O6\nfDrlTt5fsJeT5zLYc+wc0R//wwuPtld3eqspa4g2rw8cNP3UNZ3/IPBNiWOWAVHACuAi0E1KaXNB\n/dvCz/DSnO1FnY2nH2hVI5e/VpSDvZ5xQ1rx3qQe+JtmvYdHX+S5j/7h61VHSM/MtXCFSk1zOOYi\nG3afBKB3+/p0blHWHHqlpAAfdz5+oTcDezQCICUjhxnf7uab346SnatC/6qbW5k0agSuWbcppRwj\npby7xHEl13aWfOwGrAJOml6bIqXUFz+PlHIk8D7anBAfoLHpT5uRnZPPN78d5aMlB8jNK8DBXs+U\nke0Z0tump6FYTJsQX+a+dBcj+jbF3k5PgcHI7zvimfD+Jlb+FU2mCgtTqkB2Tj7//ekQALXcHJkw\ntLWFK7I9zo72THqoLW+N64qnuyMAa7ef4LmP/uFg1AULV6dUJptIGjW3XWtzUF5g8sf/sHb7CQB8\nPJ35cHIvNWfjFjk52PHkwBZ8+erd3NGuPgCZ2fn8sD6Sce9uZMmfkeqOh3JbLfkzinOXtKWdE4a2\nrpGJopWlS4t6zH3pLjq30IZTzl++yvRvw/hoyX4upWVZuDqlMpg7h6MoaRRACDERbchkLDCrlOOL\nkkZNj6UQohfa0MpG0JJGgf2m831YSe1ahePxl1i+URJeLDuic4u6PP9wO7w9nC1YWfVSr44brzzR\niSG9G7P4j0iOxiWTmZ3Pik3RrN4ax4DujRjUM5h6ddwsXapSjRyOvsia7VpUd5cW9ejdvr6FK7J9\n3h7OvDW2KzsOJfLN6qOkZuSwLfwsuyPOMaxPCMPuCsHFyew9RxUrUe7/csWSRt8rfE5KaRRCVCRp\n9LPb3K7FXM3OY8fhRP7ae4rIhP+lr3u5OzHhwdb0ahtQ7Xd+tZTQoNq8/2xPjsdfYuVf0RyIukB2\nbgG/bY1j9bY4urSox5DejWndxEf9N1BuSdqVHD5ddgCjETxcHXl2eBv1d6qS6HQ67mhfn/bCl0Xr\nItm4O4HcvAKWb5Js2J3AQ3c3ZUD3Rjipibk2x5yu4o2SRsvaCvGGSaPlzOKoSLtVJi+/gJNJGUSc\nSOZg1AWOnbhEbr6h6HU3FwceuKMx9/duUuPTQ6tKi+A6zHi6O7GnU/l5cwxhRxMxGGHPsXPsOXaO\noHoe3H9HY+7s0ABnR/VtSTGPwWDk8+XhXE7XPr5efLQ9dTxdLFxV9ePu6sjk4W0Z2KMR3685xqGY\ni6Rk5DB/dQQ/b47hwTtDuLdbkPpctSHq09YMefkFJCSlE3smjbgzqcSeSeVkUjr5BdfnQDSs607f\nToEM6N6oxmwvb21CGnrx2lOduZBylXU749mw+yRXsvI4eS6D//50mAW/H6dv54bc172RWlKrlNvy\nTZL9kdr3n0E9g+nSUq1KuZ2CAzx5+5nuHJQXWLohiuhTqaRm5LDg92Ms3RhFnw4NGNQzmOAAT0uX\nqtyEOR0OSyWNVqTdSnHh8lUOxVwk+lTKDTsXhZo08KSD8KNbK3+aNvRSt1ithJ+3K6MHt+TRewVb\nD55hzfYTnDqXQWZWHmu2nWDNthO0berDfT2C6dqyXo3fbEspW9jRJJZtlAA0C/Ri7P0qvrwq6HQ6\nOobWpYPwI1xeZPkmSWTCZXJyC9iw+yQbdp+keaPa9OnYgJ5tAtTkXStV7g6HlDJXCFGYNLoGrkka\nnVPG28IoFvJlYlbSaAXbrZACg5FjJ5LZdSSJcHmBxOTMMo/19XYhpIEXTRp4EtLAi5AGXuovuZVz\ndrSnf7dG3Ns1iKNxyazblcDuo0kUGIwcjknmcEwytWs5cW/XRvTvFoSPl7pNrvxPVMJlPv7xAABe\nHk78e3QXFfBVxXQ6HR1C/egQ6kfM6RT+2BnPtvCz5OUbiEy4TGTCZb5ZdZR2zXy5o119OjWvqz6X\nrYhNJI3erN1bdf6ydst9y8EzRWmBxfl4udC0oepcVBc6nY42Ib60CfHlcno2G/ecZENYAslp2VxO\nz2H5JsnKv6Pp2rIe93VvRNumvuj16m5VTXbyXDoz5+8mN68AR3s9/36qi5q3YWFNG3rz4qPejBnc\nkr/3neKfA2dISEqnwGDkQNQFDkRdQKeDZoHedG5elw6hfjSu74WdupYtxqwOh5RypSn74m20oZJw\nbpI0KoQYhLYq5QXgNGUnjYKWwfGS6WcLWgelPO2azWg0cjz+Mmu2x7H7aBLFNx91cbKnvfClfTM/\n2jXzVcspq7HatZx5tJ9gxN1N2Rd5nvW7EjgoL2AwGAk7mkTY0ST8fdy4r3sj+nYOpJabo6VLVqpY\nfGIa074J40pWHnq9jlee6ETz4NqWLksx8XR3YthdTRl2V1NOJqWzNfwM28LPcv7yVYxGkCdTkCdT\nWPJnFG7O9rRoXIfWTXxoHeJDcICn6oBUoVuZNFpq0mgpx90wadTUKXkEeAcIAmKAV6WU6wuPEULM\nAKYVe1tXYBHQojyFGgwGXXp6Ovv27QMg/mwa68ISiDuT+r+idNCycR06hdaleXBtHOx1wEVOn7jI\n6RPlaUWxdXbA4Pb2dAv2JuxoInuOnedqdh6p5yAy4hBzvtfTrpkvPdv4E1ivlpqjY6b09HQMBkO5\n/6WVvG4tIe5sKt+tjiA7twCdDh65R6DPOsO+fWcsVpNyY819IbSfJ+cu2XM8XhtmiU9Mx2g0kgqc\nTYBNm7VjnRz0NKhbi6B6HgTW0/5Ud6+vZ+61WxadOTttmjoGi4BngD1oAV4jAFHa3QZT0mgE8AUw\nH20exufAICnlRtMxPYCtaEmjvwOPAa8CHaSUx0zHzACGUWynWiBfSnmZcggMDDw7YcKEgJAQtYOj\nolhKbGws33zzTeKpU6fKlZClrltFsQ7mXrtlsXjSKNpQy3op5Semx9OEEP2A54BJxc5VIKWscLB+\nSEgIrVq1qujblQqKiIhgxowZLF68GFdXV0uXo9gYdd1ajrp2lcpmDUmj3dD2SSluAzC0xHNNhRBn\ngWy01S+vSylPl7d+RamIDz74gISEBNLT03Fzc6NNmzY88cQTeHt7W7o0RVFuQF271secwIEbJX6W\nlXxzw6RR0+N65TjnbuApoD/aXY9gYLsQwt2M+hXFbK1bt+all15i7ty5vPzyy5w7d47Zs2dbuixF\nUW5CXbvWxyaSRqWUfxZ7GCGE2IO2nf3DwPeWqapmmDZtGoGBgRiNRrZt24a9vT333nsvI0eOLDom\nLy+P5cuXs2PHDtLS0qhTpw7Dhg2jb9++150vIyODb7/9lsjISDIzM6lbty4PPfQQvXr1KjomLCyM\nlStXcu7cOZycnAgODua1117DycmJiIgIfvjhB86cOYOdnR0NGzbkxRdfxNf39mwcPHjw4KJ/9vHx\n4cEHH2T27NkUFBRgZ6cyGBTrpa5dde1aG2tIGj1n5jmRUqYJIaKBJuWoW7lFW7ZsoW/fvsyaNYu4\nuDi++uorfH19uecebQ7vnDlziI6OZty4cTRq1IiLFy+Smppa6rny8vIICQlh2LBhuLq6sn//1PUS\nvwAAIABJREFUfubMmUO9evUICQkhJSWFzz77jCeffJKuXbuSlZVFZGQkRqORgoICZs2axb333svU\nqVPJz88nJibmhqtFXnjhBZKTk8t8vUWLFrzxxhvl+veQkZHBtm3baNmypfrAUmyCunY16tq1DtaQ\nNBpmOuecEseElVWLaSilKbC4vPUrFefj48OYMdqK54CAAE6ePMnatWu55557SExMJCwsjOnTp9O6\ndWsA/Pz8yjxX7dq1GTJkSNHjgQMHcujQIXbu3Fn0oWUwGOjatWvRN5/AwEBA+9DIysqiQ4cO1K2r\n9VHr17/xpOm33nqL/Pz8Ml93crr5ErgffviBP//8k5ycHJo0acKbb7550/coijVQ1666dq2JxZNG\ngf8AW4UQU4F1wKNok1PHFx4ghPgYrZNzCggAZgK5wDIz61cqoFmzZtc9Xrt2LUajkfj4ePR6PS1a\nlCsShYKCAn799Vd27drF5cuXyc/PJz8/H2dnZwAaNWpE69atmTp1Km3btqVdu3Z0794dNzc3PDw8\nuOuuu3j33Xdp06YNbdq0oUePHjecBObj41PxX9xk6NCh3HPPPVy4cIGffvqJWbNm8c4776gcDsXq\nqWtXXbvWxOJJo1LKMCHEKOBd4H0gGhgqpTxerOn6aJ2LOsBFYDvQTUp5yZz6lYq5UVaLo6N5yZur\nV69m3bp1jB07lsDAQJycnPj++++Lvsno9XqmT59OVFQUhw8fZt26dSxdupQPP/wQPz8/Jk+ezMCB\nAwkPD2fnzp0sW7aMadOmXffBWqgybst6eHjg4eGBv78/DRo04JlnnkFKSWhoqFm/u6JUNXXtqmvX\nmlg8abTEc8bSjpFSjhRCTAZeRpvf0Rht1Uy8mTUrFRATE3PN4+joaPz9/dHpdAQFBWE0Gjl27Bht\n2rS56bmioqLo0qULd9xxBwAGg4HExMSiW6+FQkNDCQ0NZcSIEUycOJG9e/cWTQILDg4mODiYYcOG\n8e9//5vt27eX+aFVGbdlizMYDNf8qSjWTF27/6OuXcszq8NhShr9hGuTRjcIIW6UNPoHWtLoSLS5\nGvOFEEklkkaXcm3S6G9CiOJJo2a1q1Su5ORkFi5cSL9+/Thx4gTr169n9OjRgDbm26dPH+bNm8e4\nceMICgri4sWLpKen06NHj+vOFRAQQFhYGFJK3NzcWLt2LWlpaUWvR0dHc/ToUdq1a0etWrWIiYkh\nPT2d+vXrc+HCBTZu3EiXLl3w8vIiMTGRpKQk+vTpU2btt3JbNiYmhpiYGJo3b46bmxvnz59n2bJl\n+Pv7I4So8HkVpaqoa1ddu9bEVpJGzW1XqUR9+vQhNzeX1157DTs7OwYPHky/fv2KXp8wYQI//vgj\n3377LRkZGfj6+jJs2LCi14uPlw4fPpzz58/zzjvv4OTkRL9+/ejSpQtZWVkAuLq6EhkZyR9//EFW\nVha+vr6MHj2a9u3bk5aWRmJiIh999BEZGRl4e3tz3333XVNLZXJycmLv3r2sXLmSnJwcvLy86NCh\nA8OHD1cz3RWboK5dde1ak3LvpWJKGs0EHpJSrin2/ELAS0pZMhkUIcQ2YL+Ucmqx58YAn0kpvUyP\nTwKfSCnnFDtmBto8jnYVabekwMDAs7Nnzw5QEcnmmzZtGsHBwUUz3RWloiIiInjllVfM2ktFXbcV\np65dpbKYe+2WxRaSRivSrqIoiqIoVsScDoctq2PpAhRFAcy7FtV1qyjW45avR1tIGq1Iu9cwGAx5\nW7ZscYqLiyvP4UoxHTt2BLQlcYpyK06fPo3BYMgr7/Hqur016tpVKou5125ZrD5ptILtXuPs2bMD\nGzRosK1JE5WEriiWYjQaOXv2bMnPgzKp61ZRrIO5125ZbCJp9GbtlkN2SEgIavKZolhctjnHqutW\nUayGOdduqcyawyGlXAm8hJY0Gg604SZJo2jLV/sBh9CWw16XNAqMAiaYjhlGiaTRcrSrVHNGo5Ev\nv/ySp556iuHDh5OQkGCxWi5cuGDxGhRFKd3cuXOZNUulJVgjs5NGpZTzgHllvHbd+isp5Va0OxY3\nOufPwM8VbVep/sLDw9myZQvvvPMOdevWxd3d3dIlKYpihXQ6ndorxUrdSrS5olSZc+fO4e3tXWYM\nsqIoCmh3Q8ubL6VULdXhqKHCwsJYuXIl586dw8nJieDgYF577bWi/Qn++usv1qxZw8WLF/H19WXg\nwIEMGDAAgHnz5hEXF8esWbNwcHAgLy+P119/naCgIJ5//vlKr3Xu3Lls3boV0NIOfX19+fLLLzEY\nDPz2229s2rSJ1NRUAgICGD58ON27dwe0sJoZM2bw5ptvsmTJEs6ePUtoaChTpkwhOjqaxYsXk5KS\nQseOHZk0aVLR7x4eHs7PP//M6dOn0ev1NGvWjLFjx1KvXtmxL6dOnWLx4sVERkbi7OxM27ZtGTNm\nDB4eHpX+70NRSmNL1zRowWRBQUHodDq2bt2Kvb09o0aNokePHsyfP589e/bg6enJ+PHjad++PaDt\ng/Lll18SERFBamoqPj4+DBgwgEGDBpXZjtFoZNWqVWV+TihVR3U4aqCUlBQ+++wznnzySbp27UpW\nVhaRkZFF3wq2bdvGihUrGD9+PMHBwcTHx/Pll1/i7OxMnz59GDduHP/6179YsmQJY8aMYenSpVy9\nepXx48eX2eYvv/zCr7/+esO65syZQ5061y/1HjduHP7+/mzatInZs2ej12tTj3799Ve2b9/OxIkT\n8ff359ixY8yZM4datWrRsmXLovevXLmSp59+GkdHRz755BM+/vhj7O3tmTp1KllZWcyePZv169cz\ndKgWWpuTk8OQIUMICgoiOzub5cuXM3v2bD755JNSb9VmZmYyffp0+vXrx9ixY8nJyeGHH37gk08+\nYcaMGTf976Eot8rWrulCW7ZsYejQocyaNYsdO3bw9ddfs2vXLrp3786IESNYu3Ytc+bM4auvvsLJ\nyQmj0YiPjw8vv/wy7u7uSCn56quv8Pb2LnX/l8I6y/M5odx+qsNRA6WkpGAwGOjatSu+vr4A1+z4\nuGLFCkaPHk3Xrl0BbZOn06dPs3HjRvr06YOzszMvvPACb731Fs7Ozqxbt46ZM2fi4uJSZpv9+/en\nZ8+eN6zLy8ur1OddXV1xdnZGr9fj6ekJQF5eHqtWrWL69OlFwyx+fn5ERkayadOmaz5IRo4cWbRh\nU9++ffnxxx/54osv8PPzA6Bbt25EREQUdTi6det2TfvPPvssY8eO5cyZMzRs2JCS1q9fT+PGjRk1\nalTRc5MnT+aZZ54hKSkJf3//G/7einKrbO2aLtSoUSMeeughAIYNG8aqVavw8vLinnvuAWDEiBFs\n2LCBU6dO0bRpU+zs7HjkkUeK3u/n50dUVBS7du0qtcNhzueEcvupDkcN1KhRI1q3bs3UqVNp27Yt\n7dq1o3v37ri5uZGdnc358+eZN28eX3zxRdF7DAYDrq6uRY+bNWvGkCFD+OWXX3jwwQcJDQ29YZvu\n7u6VOtEzKSmJnJwcZs6cec3z+fn5NG7c+JrnGjVqVPTPnp6eODk5FXU2Cp+LjY0tepyYmMjy5cuJ\njY0lIyOjaDvr5OTkUjscCQkJRERE8Nhjj13zvE6n49y5c6rDodx2tnpNBwUFFf2zXq/Hw8PjmucK\nv2AU35V2/fr1bN68meTkZHJzc8nPzyc4OLjU85vzOaHcfqrDUQPp9XqmT59OVFQUhw8fZt26dSxd\nupQPP/wQR0dHACZNmkTTpk2ve18hg8FAVFQUdnZ2JCXdPPC1Mm6/FpedrS0Jf+ONN6hdu/Y1rzk4\nOFzzuOTukCUf63S6ayaZffDBB9StW5dJkyZRu3ZtDAYDU6ZMIT8/v8xaOnfuzOOPP37da97e3uX6\nfRTlVtjqNV3atVj8ucIhzMLrc8eOHSxevJjRo0cjhMDZ2ZnVq1cTExNT6vnN+ZxQbj/V4ajBQkND\nCQ0NZcSIEUycOJG9e/cyePBgvL29OX/+PHfccUeZ7129ejWJiYm8/fbbvP3222zevJm77767zOMr\n4/ZrcQ0bNsTBwYGLFy/SokWLcr/vZjIyMkhKSmLy5MlF3/AiIyNv+J7GjRuze/dufH191dbXikXZ\n8jVdHlFRUQgh6N+/f9FzN+oc3a7PCaVizO5wCCEmAy+j7WVyGHheSrnvBsf3QUsKbQGcBt6VUi4q\n49hHgaXAainlg8WenwFMK3F4lJRS/Q2qgJiYGI4cOUK7du2oVasWMTExpKenU7++tvPwI488wvff\nf4+rqyvt2rUjLy+PuLg4MjMzuf/++zlx4gQrVqzg5ZdfRgjB6NGjWbBgAS1btqRu3ZJb3mgqe0jF\nxcWFIUOGsHDhQoxGI6GhoVy9epWoqChcXV3p06dPhc7r5uaGh4cHGzduxNPTk+TkZJYsWXLD99x3\n33389ddffPbZZwwdOhR3d3eSkpLYtWsXkyZNuuZbpKLcDtXhmi6PgIAAtm7dyqFDh/Dz82Pr1q3E\nxcWVWePt+pxQKsasDocQ4hHgE+AZYA9acugGIYQoLfVTCBEM/AF8AYxE2w9lvhAiSUq5scSxjYCP\ngO1AaYuoI0zvL1T6/W3lplxcXIiMjOSPP/4gKysLX19fRo8eXbT07J577sHJyYnVq1ezePFinJyc\nCAoKYvDgweTl5TFnzhzuuuuuos2h+vXrx4EDB5g7dy5vv/12lf0PduTIkdSqVYtff/2V8+fP4+bm\nRuPGjYsmoQGlriop+Vzxx3q9nilTpvDdd98xdepU6tevz5gxY5g+fXqZ7/H29ua9997jhx9+4J13\n3iEvLw9fX1/at2+vOhtKlagu1/TN9OvXj/j4eD799FN0Oh29evViwIABhIeHFx1TMvirPJ8TStXQ\nmROQIoTYA+yRUv6f6bEO7a7FXCnldVmypj1U7pNStin23DLAS0p5X7Hn7IBtwHygt+n1knc4HpBS\ntjfv19PodLrOy5Yt26v2ZFAUy4mIiGDkyJFdjEZjmXdEi1PXraJYB3Ov3bKUu9sqhHBEiyj/q/A5\nKaXR9LisBJXuxY832VjK8dOAc1LKBUBZmbRNhRBnhRBxQoglQojrlwsoiqIoimKVzLlP5gPYAedL\nPH8BbdO20tQt5fjzQC0hhBOAEKIXMBZ42vS6keuHVHYDTwH9gUlAMLBdCKE21FAURVEUG2DRVSpC\nCA/gB+BpKeVl09M6StzlkFL+WexhhGlo5yTwMPD9zdpp2rTp5pkzZxat6VYUpeqlpaXRtGnTzUC5\n8t7Vdaso1sHca7cs5nQ4koECtLsWxdUFylqXdI7r737UBdKllDlCiOZAELC2MAkS010XIUQe0ExK\nGV/ypFLKNCFENNCknLXbA9jbq1XAimJh5lyE6rpVFOtxyxdiuU8gpcwVQhxAWymyBkAIoQf6AnPK\neFsYMLDEc/2AXaZ/jgSKzwjTAe8C7sALwJnSTmoaSmkKLC5P7TqdLikgICD477//Ls/hiqLcBn37\n9uXMmTM3T5QyUdetolgHc6/dspjbY/kUWCSE2A/sA14EXIAFAEKID4AAKeVTpuO/Ap4zrVZZANwN\njMDUCZFS5gDHizcghEgzvXa82HMfo3VyTgEBwEwgF1hmZv2KoiiKoliAWYurpZQrgZeAt4FwoA0w\noFgGRz2gYbHjE4BBaHc1DqHldoyTUm66QTOlTRqtj9a5iAJWABeBblLKS+bUryiKoiiKZdxKmouR\n6yd3jpFSlszCLbnMtaxlr4VJo0+Vct6RwPtoc0J8gMamPxVFURRFsQFmdTiKJY1OB9qjRZtvEEL4\nlnF8YdLo30Bb4HO0pNF7Szm2EWUkjZrbrqIoii1LSs7kt61xfPnLYb5dfZSNe06SkpFt6bIU5ZaY\nO4djKvBN4V4oQoiJaEMmY4HrkkaBiUCclPJl02Npyt2YghYAhuk8dsCPaAFgvYGSO/6Y266iKIrN\nSbuSw3drIvjnwPXz5e3t9NzTJZAn7mtOLTdHC1SnKLfG6pNGK9iuoiiKTTl5Lp2pn28t6mzodFC3\ntiu1azkBkF9g4M+wBCbP3syxE2r6mmJ7zLnDcaOk0dAy3nPDpFFTFkdh0mhb0+slJ41WpF1FURSb\nkXjxCm9+tYvUjBwA+nUJZFT/UHy8XACtM/Lz5hi2HDhD6pUc3vxqF8+NaEvfzoGWLFtRzGITSaOK\noijVVVZOPu8u2ENqRg46HTw3oh33dg265pigerX416iO9GwTwCc/HiA7t4DPl4eTk1fAwB7BFqpc\nUcxjzqTRSk8aRUsKLUwazTOliz4BDDE9Dq5gu4qiKDbhq1+PcPr8FQDGD2l1XWejuG6t/Jn9/B34\neDoXvXfrwVLzERXF6pS7wyGlzAUKk0aBa5JGw8p4W5jp9eJKSxpta/pphxbwtdn0+EwF21UURbF6\nB6LOs3n/aQB6t6vP/Xc0vul7ggM8eXdST7zcnTAa4bNlBzkUfeF2l6oot8wmkkZv1q6iKIqtyS8w\n8M2qowB4eTgx8aE26HTlG02u7+vOzAnd+fcXO8jMzmf2D/v59MU7qVfH7XaWrCi3xCaSRsvRrqIo\nik35e98pEpMzARgzuCUeruYtdW1c35NXnuiMXgcZV/N4f+FesnPzb0epilIpLJ40KoQYJoTYL4RI\nEUJcQRtW+bXEMTOAuUAg4Ah0BRbdQu2KoigWk5NXwLKNEoBG/rW4s0ODCp2nQ6gfTwxsAUB8YnrR\nHRNFsUbWkDR6CXgH6Aa0RhsmWSCE6F/idBFod1AKf3qZU7uiKIq1+GNHPJfStOTQJ+5rjp2+4gvz\nHrorhB5t/AHYtPcUO48kVkqNilLZLJ40KqXcWuI9c4QQTwE9gA3Fni+QUqqZUYqi2LS8/AJWbYkF\nIDTIm84tSi7AM49Op+P5Ee2IPplCclo2/115CBHoXZThoSjWwlqSRgvb0Akh+gJN0VaqFNdUCHFW\nCBEnhFgihGhYyikURVGs2rbws6Re0QK+Huknyj1R9EbcXR2Z+lhHdDq4kpXHZ8sOUmAouem2oliW\nOUMqN0r8LJm1UeiGSaOFTwghPE3zN3KAdcCUEnc+dqPtItsfmAQEA9uFEO5m1F/tpWbksD38LMs2\nRLFg7TFWbYnloLxAXr7B0qUpigIYjUbWbD8BaCtNOgi/Sjt36yY+DL+7KQBHYpNZvTW20s6tKJXB\nokmjxaSjrTxxR8vbmCOESJJSrgOQUv5Z7NgIIcQe4CTwMPB9VRdrbU6fz2D5Rsmuo4nkF1z/rcbN\n2Z67Owcyom9TvD2cLVChoigAx+Mvc+JsGgD339EY/S3M3SjNqP6hhEdfJPZ0Kkv+jKJzi3o0rOtR\nqW0oSkVZOmkU0IZmpJQnpJRHpJSfAkvR5nmUSkqZBkSjJZXWWAaDkVVbYnnh0y1sO3S2qLPh6GCH\nt4dT0YdZZnY+a7efYML7f7FqSywGdatVUSxizfY4wPQloFPljwrb2+mZ8mh77O305OUbmLMiXA2t\nKFaj3Hc4pJS5QojCxM81cE3i55wy3haGKeSrmOJJo2Wx4wadIdNQSlNg8c0rr57y8g18vvwg28LP\nAtoHzYDuQfTv1ojAuh7o9Try8g0cjUtm056T7DicSHZuAd+vPcb+yPO89HhHdbdDUapQSno2uyPO\nAdCvaxAuTrfnBnNgvVqM6i9YvC6SqJMp/L7jBA/0rtHfzRQrYdGkUdN7Xjed6wTgZHrtcWBCsWM+\nRuvknAICgJlALrDMzPqrhbz8At5fuI/9kdr0mCYNPJk6sgOB9Wpdc5yDvZ4Owo8Owo/hZ1KZ+9Mh\n4s6kcSQ2mZf+s43p47td9x5FUW6Pfw6cKbq72L9b2fulVIYH+4Sw80gicWfSWLwuks4t6hLgo6a8\nKZZlDUmjrsAXaDkbO4AHgceklAuLHVMfrXMRBawALgLdpJSXzKm/OjAajcxZeaios9Eh1I8Pn+11\n045DkwZefPR8b4beqX3TuZCSxStztyNPXr7h+xRFuXVGo5G/958CtKWwDfxu77wKezs9LzzSHns7\nHbl5BcxZcUgNpSoWZ/Y9PSnlPGBeGa+NKeW5rWjLacs631vAWzdpc6SZZVZbyzdFs+WAtjtk5xZ1\nef2pLjjYl6/f6GCvZ9yQVgTW9WDez4fJzM5n2jdhvPNMD5oFet/OshWlRos5ncqpcxkA3NMlsEra\nDA7w5OG+zVi6UXLsxCXW74pnUK+bbw6nKLeL2R0OIcRk4GW0yZ+HgeellPtucHwftKGYFsBp4N3C\n4DDT68OAf6NNAHUAYoBPpJRLbqXd6uigvMDSDVGAto/Cy493Kndno7h+XYPwcHPkw0X7uGrqdLz7\nTA9CGnpVdsmKogB/7dPubjg62NGrbf0qa3d432bsOppEQlI6i9Ydp3OLevjVdq2y9hWlOJuINje3\n3eooNSOHz5YdBKCWmyPTxnW9pUln3Vr588oTndDrdWRm5THtmzASk69UVrmKopjk5RcUTe7u0dof\nNxeHKmvbwV7PC4+2R6/XkZVTwLyfD2M0qqEVxTLM/XpcFG0upYxCiy6/ihZtXpqiaHOpmQf8TLEl\nr1LKrVLK1abX46WUc4AjaNHmFW23WjEajfz3p0OkZmgriV98tD11PG89trhHmwBefryjabfJXGZ8\nu5u0Kzk3f6OiKOV2IOoCmVl5ALdlKezNhDTw4kHT3K2D8gKb95+u8hoUBWwg2ryC7VYru44mseeY\ntpxucK9gOrcoK9jVfL3a1ufpoa0BSErO5L0Fe8nNK6i08ytKTbf9kHZ3w9PdkTYhPhapYWT/UOr7\nugEwf3UEKenZFqlDqdlsIdq8Iu1WG5lZeXyz6ggAft4uPGXairoyDe7VuGidfmTCZeauPKRuuypK\nJcjOzWev6ctCzzYB2NmZP+eqMjg52PH8w+2L9lr58tcjFqlDqdks87f/eoXR5p2A19GizUsGhtVI\nS9ZHcjldG+aYOKwNzrcpLGjM/S3p3lrb4nrLwTOsNe33oChKxe2PPE92rnbH8I52VTdZtDQtG9dh\nUI9gAMKOJqlt7JUqZwvR5hVpt1qIPpXCH7viAe3bUWUOpZRkp9cxZWQHAutp+QDfrT3G0djk29ae\notQEhcMptWs50yK4joWrgScGNsfXW5v/9dWvR8i4mmvhipSapNwdDillLlAYbQ5cE20eVsbbwkyv\nF2dWtHkF27V5RqORb387itEILk72PD201W1v08XJnjdGd8HN2R6DwcisH/ZxMSXrtrerKNXR1ew8\n9h/XRoJ7tQuo9I3aKsLV2YHnRrQDtJVv81dHWLgipSaxiWjzm7VbHe06kkTUyRQAHu3XrFJWpZRH\ngK87Ux/ryDvf7SHtSi4fLNrLh5N74ehgVyXtK0p1sff4eXLzDYDlh1OK6yD86Nu5IX/vO83m/ae5\no119OjUveQNZUSqfTUSbl6PdaiUv38DCP44B4FfblcFVnA7YpUU9RvUPBbSExG/VtyBFMdt2U/aG\nn7cLwsqSfMcPaYW3hzZvf97Ph7manWfhipSa4FYmjRqBa+4RSinHSCnvLnFcyfuIJR+fQluBkm16\nLQtIKH6AEGIGMBcIBByBrsAiqql1u+I5d+kqAE8NbG6RuwuP3NOMzi20bz1/hiWotfuKYoYrWXkc\nlNpwyh3t6qPTWX44pTh3V0cmDmsDQHJqFov+OG7hipSawBqSRu8EfgT6oOVqnAY2CiECSpwuAu0O\nSuFPL3NqtxUZV3NZvlEC0CzQy2K3YvV6HVNHdiiKQZ7382FOJqVbpBZFsTW7jyaRX6AtLe9lRcMp\nxfVoE0DPNtrH7LpdCUTEqUniyu1lDUmjj0spvzKtUJHAeFNdJe+UFEgpLxT7qZbbnK78K5orplTC\nsfe3sug3I3dXR15/sjP2dnpy8wr4YNFedetVUcqhcHWKv48bTep7Wriasj0zrDUerlrU+tyVh8hR\noX/KbWRVSaMmbmibuJXsUDQVQpwVQsQJIZYIIao+I/g2O3cpk993aPkX3Vv707Kx5ZfRhTT04pkH\ntSTSsxczmbNChYIpyo2kXcnhUIw2vay3FQ6nFOft4cz4B7TrOzE5k2WmzSEV5XawiqTREmYBZ7m2\no7IbeAroD0wCgoHtQgj3cldvAxb9cZz8AiN2eh2jB1V+omhF9e8WxF0dGwCw80gia1QomKKUKexo\nEgaD1im3ptUpZbmrYwM6hPoBsGpLLDGnUyxckVJdWUvSKABCiNeAh4EHTfkbAEgp/5RS/iKljJBS\nbkRbOutlOrZaiEq4zI7DWvLfoJ7BBPhaT19Kp9Px7PC2BJlCwRasPcbx+EsWrkpRrFPhcErDuh4E\n+deycDU3p9PpmDy8LS5OdhiMMGfFIfJMy3kVpTJZRdIogBDiJeBV4F4p5Q3XYUop04BooEn5Srdu\nRqOR+Wu0X9nNxYFH+gkLV3Q9Z0d7Xh/dBRcnewoMRmYt3l+0e62iKJqU9OyiyZe921v/3Y1Cft6u\njB7cEoCEpHR++SfGwhUp1ZFVJI0KIV4B3gT6SykP3qwW01BKU6pJtPnOI4lIU8jXI/c0o5abo4Ur\nKl19X3deeKQ9AJfTs/n4x/0UGNR8DkUptP3wWQovCVsYTiluQLdGRfPGVmySJKhVaUolM3dI5VPg\naSHEk0KI5sCXlEgaFUIUz8f4CmgshJglhAgVQjyLljT6WeEBQohX0QK9xgKnhBD1TD9uxY75WAjR\nWwjRSAjRA1gF5ALLzP6NrUxefgELf9fWwNet7crgXsEWrujGerYNKNpZ9nBMsppkpijFbDuoDaeE\nNPCkvhUNi5aHXq/j/x5uh6O9nvwCI5/8eIC8fLVqRak81pA0OhFtVcrPQGKxn38VO6Y+WuciClgB\nXAS6SSltfiLB7zviOX/ZFPI1qAUO9tYfIT56cAuaN6oNwIq/otkfWXJesKLUPEnJmchT2p3KOzs0\nsHA1FRPg637N0MoP69UXCqXyWEPS6PtoQyyppp+/ga5SyreLnXek6bhzaKtlGpv+tGlBj4H4AAAe\nuklEQVTpmbms+CsaABHkTa+2JbPOrJO9nZ5Xn+yEp7s29PPJjweKOk2KUlNtDT8DgE5ne8MpxQ3q\nGUwHoa1a+W1rLIdjquUOEooF2ETSqLnt2oqlG6LINIV8jbNwyJe56ni68PJjndDrtBjnDxfvU7df\nlRrLaDSy5YDW4WjdxKfKNlu8HfR6Hf/3SDs8XB0xGuHzZQe5oraxVyqBrSSNmtuu1Ys5ncK6XfEA\n9GobQPPg2hauyHxtm/kyaoC2yVvs6VS+/U1t8qbUTCfOpnH24hXAdodTiqvj6cJzI9oCkJyWzefL\nw1Xgn3LLrD5ptILtWrUCg5Evfj6M0QguTnaMf6CVpUuqsBF3Nyva2np9WAL/HFCbvCk1z5aD2t0N\nezs9PdrYxtDozfRoE0D/bkEA7Dl2jl//ibVwRYqts4Wk0Yq0a9X+3BVP7Jk0AB4b0Nzmb79OHdUB\nP2/td/jvT2qTN6VmKTAY2Wbair5zi7q4uzhYuKLK8/TQ1jQO0PaCWbzuOEdj1QZvSsXZRNJodXIp\nLYvF6yMBaBzgyeCe1r0Mtjw8XB157Sm1yZtSMx07kczl9GwA7mxv+8MpxTk52PH66M64OdtjMMLs\nJfuLfldFMZctJI1WpF2rZDAY+c/ycK5m56PTwbPD22BnZ1V9vgpr2tCbCUO1oaGzFzP5+McDFBSo\neGSl+vt7nzaM6OpsT6cWJT+mbF+9Om5MGdkBgNSMHN5fsFftKqtUiNUnjVawXau0blc84dHaErOh\nd4YggmxvouiNDOjeiLs7aTEs+46f5+vfjqqJZkq1diUrr2gPpDvbN8DJwfpzdCqiayt/RvRtCoA8\nlcJnyw4WbVCnKOVlb+bxnwKLhBD7gX3Ai5RIGgUCpJRPmY7/CnhOCDHLdMzdaEmjAwtPaEoanQmM\nwpQ0anopQ0qZWZ52bUHs6VQWrD0GQCP/WjxxX6iFK6p8Op2O50a05ULKVSLiLrF+VwK+Xi6M6NvM\n0qUpym2x9cBpck3f9u/tGmTham6vxwc05+zFK+w6ksTOw4ks8j7OmPtbWrosxYbYRNJoOdq1aqkZ\nOby3cC+5+QYcHez412MdbSJRtCIc7O14Y0xXAk07yy5eF8mG3SctXJWiVD6j0ciGPdrf7cb1PQlp\n6GXhim4vvV7HlJEdaBao/Z6/bollpSm4UFHKwxqSRgcDvwGF/1eaIqXUF08aFULMAOYCgYAj0BVY\nhA3Izsnn/YV7SU7NAuD5h9vRyAa2rL4V7i4OzBjfnTqezgDM+/kQf+87ZeGqFKVyxZ5JJT5RW5FV\n3e9uFHJ2tOetsd2K9on5YX0kq7fFWbgqxVZYQ9KoCxALvIY2ybSsgcEItDsohT+9zKndEnLzCnhv\nwV4iEy4DMPTOJvSpBqFA5eHr7cI7z/TAy8MJoxH+syK8KKtAUaqDdTsTAHB0sKsx1zWAl4cT707s\ngV9tVwDmr45g2Uap5mspN2XuHI6ixE8AIcREtCGTsWj5GSUVJY2aHkshRC+0oZWNAFLK/cB+0/k+\nvEHbBVLKC2bWazEZV3P5YOE+jsZp69bvbN+gaFOkmqJhXQ/efaYHr3+xk4yruXy69ABZ2Xnc18P2\nlwIrNVtKenZRB7pPhwa4VaPsjfLw8XLhvYk9eOOrXVy4fJWlG6JIz8zh6Qdao9fbzhYNStWyxqTR\nsjQVQpwVQsQJIZYIIRre/C2WEXsmlZfnbCvqbHRv7c+LI9tjVwMvxCD/Wrw7sQe13LR9Gb745QjL\nNkSpb0OKTftjZzz5pmXfQ+9sYuFqLKNeHTdmP9eL/2/vzuOjKs8Fjv9mJvtK2BJ2CJKXPeyLgLiA\nINYWvXUBr0LrVaQuiNYiXuutW3tpC7hhrV1Ea1Gw3lKrgGwCAiEsAVnzsmYhCTtkI3vm/nHOhCEJ\nZJuTyfJ8P598YA5nzvOeJM/wnve853m7mPO1vtp8gvlLdskjs+KaGmKl0cpsA6YBE4CZQDfgO6VU\nSA2OYbnMnAL++u8DPPfWJtLOGg/Y/GBUN+Y8NASfJlJvozaiO4Qz78nRZdVIl6zWvLNsjyz2Jhql\n/MJiVmxNAmBIr0g6RYZ6t0Fe1Co8kN88MZqeXSIA2LQ7jbmLNnM+M8/LLRMNUU1vqXiF1nqV28v9\nSql4jEmm9wF/9U6rjFnql3IKSEy6yPYDp9i0J63sEbkAPweP/LAvE0d29VbzGpSObUP57VNjePmD\nOFJOZbNmewopp7OZO21ooy7tLpqf9TtTyTZXT22uoxvuQoP8eG3Gjcxfsott+09xJPUSsxduZO60\nYY1yUUphnZp0OCytNFoTWutMpdRhwGPZ7nQ6STmVTWLyBZLSs7iQnU/O5SJy8oooKSmlpNRZ9lVq\nvi4sKiE3v7jCsYb1juKxu/sRaU6qEoZW4YHMe3IM8/++i52HTqOTLzJ74UaemzqY2JhK5x0L0aAU\nFJXwufkoaHT7cPrf0NrLLWoYAvx9mDttGJ+t0Xy6WnMxu4AX/7CZRyf3446RXbHZmt/tZFFRtTsc\nWutCpZSr4ueXcFXFz7ev8bY43Ip8mSpUGq0p81ZKD+DjuhwHjBoZX285wYaEVE6dv1zr44QF+zG8\nTxR3jYmmm7nYkagoJNCXl346nCXfJLJs7WEuZhfw0h+3Mnlsdx6e1KvJ1icRTcPXm09wLtNYS2Tq\nBCX/kbqx221MndCTbu3DWLAkgfzCEv7wxV508kV+9uPYJluFVVRfQ6g06gu4Ht/wBzoqpQYAOVrr\no+Y+v8fo5KQA7TEqkxYCn9aw/WXyCor5bLXmq83HKSy+es2PlmEBRLYMIjTIj+BAH3x9HDjsNhx2\nG3aHDYfdbrx22IhqGUSXdmFEd2jRLCeF1obDbuOhO3rRo1ML3l66h+zLhSzfeIwEfYaf/UcsfaJb\nebuJQlSQk1fE5+uM0Y1eXVsyrE+jXKzaciP7tWf+rBB+vXg7aWdzWb8zlaT0LOZOH0pUq2BvN094\nUY06HFrrZWbNjVcxbpXspopKo0qpO4GFwCwglYqVRjsArvVTnBgVRX8ObMDooLj2+RRoBZwFvgNG\naK3P16T9LgmJZ3h72W7OZ15Z9XBgTBtuGtiRwT3bEhEWUJvDihoa0bcdMZ0jeOuz3SToM6ScyuaF\nRZsZN7Qz03/Qm/CQmswrFsJa/1h3mJw8YxXkaXf2ltGN6+gcFcaCZ8by5me7iduXwfH0TOP26YOD\nGdKr6S1wJ6qnLpNGK600Wsl+VVUaDQb+ifHIbReMSqNvlTvuFKXUE8DzGHNAojGemjlRkwaXlDr5\n+6pDfL7uSNm2Yb2jeHhSL7o08eqfDVXLsAB+9egIVscns/irg+TkFbF2Rwrb9mcw5XbFHTd2ldss\nwuuOnbzE8o1GRc0hvSJlFK4aggJ8mTttKF98e5S/rTBy+9W/bGPK7T25f1yM1OtohhpFpdGaxq2M\nE5j38Y6yzkZYsB///ZNh/PKR4dLZ8DKbzcaEEV15/4XbGDe0M2AMX//pX/t5fN561u9MpURWphRe\nUlRcyltLd1NS6sTfz8GMu/t5u0mNhs1m48e39uCVx0aW1eJZ8k0ir38YXzZaJJqPmhaHKKs0qrVO\nxKgkehmj0mhlyiqNasMijEXaZrt20Frv1FrP0VovBa715EpN41ZwKSufuH3GwzS9urbk7eduZkTf\ndtV9u6gH4SH+zHpgIP/7xGh6mAthnblwmYWfJjBr/rds2n1SOh6i3i1be7hszZTpd/aWeQi1MCCm\nLQtnjy1b4G7HwdM8u3AjSRlZXm6ZqE8NvtJoLeNW4JoYOqJvFK8/fqPUfmjA+kS3Yv6sm3hh2lA6\ntDE+3JNPZfO7T3Yxc946VscnU1Ruoq8QVtiVeJqlazVg/F5OkrL8tdY2Ioh5T4xmwghjobuM87n8\n4p1NxO+/VlUF0dQ0hkqjtYlbqduHd+GFh4fiJ49nNXg2m41R/duz6PlbefLeAUS1MmqaZJzL5Z1l\ne3js12v48rtj5BdWrIMihCccTb3EvI934nQaxa1mTxkk8w7qyM/XwZP3DuDJewfg47CRV1DCG4u3\n8/m6w7LcQTPQbOptR4T68+S9sTiacYnxxsjhsDNhRBfen3Mbz00dRGdz3YZzmfn8afl+/uuNNSxd\nq8kxKz8K4Qk6+QK//ONW8gqK8XHYmPPwECnk50ETRnTh9cdHER5izOv4eMUhFixJKKvULJqmmvzv\n661Ko7WJW4Gfr0MeY2vEHA47Nw/uxDvP3cKL04eVzfHIzCnkk5WJ/PT1NXz47wNcyMqv4khCXJvT\n6WR1fDIvvreFnLwi7HYbzz04mNgeUgnX0/pEt2LBrLF0NSftb0g4ydz3NksON2HV7nBorQsBV6VR\n4KpKo3HXeFuc+e/ualRptJZxRRNlt9sY2a8d82fdxGszRhLbwygtnVdQzP9tOMojr6/h3c/3kHEu\n18stFY1NUkYWL/5hC+8s20NhcSl+PnZeeHgoo2M7eLtpTVbblkH89qkxjOhrXJceTrnEs29u5Gjq\nJS+3TFihUVQarSquaH5sNhsDYtoyIKYth1Mu8o/1R4jbl0FxSSnfbEtmTXwyo2M7cP/4GDpHyWPP\nonIXs/LZffgs63aksPfoubLt7VsH8/xDQ7ihYwsvtq55CDTXYfm7udzB+cx85izazDP3D2TMQOns\nNSWNotJoNeKKZiymcwQvTh9G6uls/rH+CBsTjMdnN+1JY/P3adw6pDNTJ/SkTYQ8mdScFJeUkplT\nQGZOIZdyCsy/F3Apu4D0c7mknMoi7ezVI2F+PnbuGxfDPbfcIAXn6pHdXO6gc2Qoby/dTWFRCb/9\nZCfJp7OYentPmazbRHi90qjZKbkfeA2j0ugRYI7WeqVrH6XUr4CX3d42HPgI6F3bxoump1NkKLOn\nDOLBiT1ZvvEYq+KSKCouZe2OFDbuPsmdo7px720xhAX7ebupwoPOXLiMTrlIckYWyWYn4lJ2QdkS\n8tXRNiKQCSO6Mn5YZ1nawIvGDupIu9bBvPFhPBeyCli65jBJ6Vk8ff9AydsmoEYdDreKnzOAeIwC\nXt8opVRlow1ulUbfA6ZgzMP4s1IqQ2u92tznRmAJRqXRr4AHgeVKqUFa6wNuh9uP2zwOQJ6HFJVq\nGxHEY5P7cc/NN7Dkm0TW7UihqLiU5RuPsWZ7Cg9N7MnEG7vJYnuNlNPp5ER6Ft/uSmXHwVMVRimu\nx26DsGB/IlsG0TEyhO4dWhDbozWdIkNlUnkDEdM5ggXPjOWND7dzJPUS8QdO8dTvv+XZKYOIjZHJ\nu41ZTUc4yip+AiilHgfuxKj4Oa+S/csqjZqvtVJqNEZHZbW5bRawUms933z9slJqPPAkMNPtWCVa\n6zM1bK9oxlq3COTp+wdy98038LeVh4jbl0FuXhHv/3Mfq+NTmHFPP3p3kzUxGoviklI270njnxuP\ncTwts8K/B/g56BIVRsfIEFqGBdAi1J+IkADCQ/1oEeJPeIg/IUF+0tFsBFqFB/KbJ0bzp+X7+GZb\nMhey8nnpj1uZPLY7D07oSYB/XQbnhbdU+6fmVvHzDdc2rbVTKVWbSqML3V6PwBg1cfcNMLncth5K\nqTQgH+PplLla69Tqtl80X50iQ3lx+jAOnbjA+//cy/G0TI6nZzLn3c3cOqQT03/Qm4hQGUZvqJxO\nJ5v3pPPRioOcvnC5bLuPw87gnm0Z0iuS/je0JqpVsNzrb0L8zSJhg3tG8s6yPWRfLmT5xmNs2ZvO\noz/qx4i+UTIq1cjUpJt4vYqfPa/xnutWGjVrcURd45ju9Tu2AdMADbTHWMTtO6VUX611Tg3OQTRj\nvbq1ZMEzY1kVl8TfVh4iN6+I9TtTid+fwUOTejNxZFe5+m1giopLmfPuZg4lXSjb1q51MD8aE83Y\nQR0JCZL7+k3dyH7tiOncgnc//56dh05z9mIev168nSG9Ipl2Z++yOh6i4WsU41Ja61VuL/crpeKB\nZOA+4K/eaZVojBx2G3eO6sbo2PZ8vOIQq+OTyc0v5v3/28va7cn87Mex9OgU4e1mCtOFrPyyzkZk\nyyCmTerNjbHtpWPYzLQKD+TlR4azbX8GHyzfz7lLeew8dJpdiacZE9uBKRMUHduGeruZogo16XBY\nVWn0VA2PidY6Uyl1GOhejXYLUUF4iD9P3TeA8cM7894/vudEehZHT2by3FubuGNkVx6a1JuQQF9v\nN1Ng1Gl4YHwMd42JlkdVmzGbzcbIfu0ZGNOWZesO869NxyksKil7/H1433b8cEw0faJbya2WBqoh\nVBqN4+qnT1z7XLOKqFIqBOhBDUqbC1GZnl1asvCZsTz6o74E+vvgdMKKrUnM/N91fLsrVRaU8rLW\nLQJZ/PLt3HNLD+lsCAAC/H14eFJv/vziOO4aE42Pw06pE+L2ZTD3vS08s3Aj63emyLosDZDXK40C\nbwEblVLPAiuABzAmp/6Xawel1O+BL4EUjDkcrwCFwKc1bL8QFTgcdn54U3dGxbbnL18e4Ls9aVzK\nKWDBkgRWxyfz2OR+dGsf7u1mNksOu42gABlpEhVFhAWUPf7+1ebjrNqWTG5eEcfTMln46W4+WL6f\nsQM7MH5YF7p3DJdRjwagRkunaq2XYVQBfRWj2md/qqg0ivHY7HhgD8bjsFdVGtVaxwFTgcfMfe4B\nJmutD7qF7oDRuUgElgJngRFa6/M1ab8Q19MqPJBfPDSEVx4bSbvWwQDsP3aep+dv4Hef7CT9rMxP\nFqKhad0ikOk/6MPiX97OzP/oT4c2Ru7m5hWxYmsSs9/cyNPzN/DF+iOyxpKXeb3SaLltzsr20VpP\nUUo9ATyPMb8jGuOpmRM1bLMQVRqk2vLuz2/hi2+P8sW3RygoLGHT7jQ2f5/OuKGdmTy2O50iZYKa\nEA1JgL8Pk27sxsQRXdl39Bxrd6SwdW86hcWlJGVksfjrgyz++iDRHcIZ1b89w/tE0TlKCr7Vp0ZR\nabSmcYWoKz9fB1NuV0wc0YVlaw+zalsSxSXG0uWr45MZ1LMtd4zsyuCebWVugRANiN1uIzamDbEx\nbZhxT3++25PGtztTy552Op6WyfG0TP628hARof7ExrShb3RrenRqQeeoUHwcNRr4FzXQWCqN1jSu\nEB4RERbAjHv6M/nmG/hstWZDwkmKS0pJSDxDQuIZggN9ubFfu7LiU1IXQoiGIyTQlztGduWOkV05\nn5nH1r0ZbNmbzsET53E64WJ2ARt2nWTDrpMA+PrY6RIVSrvWIUS1CiKyZTAtQvwIC/YnLMSP0CA/\nAvwc+PrYZWSkFhp8pdFaxhXCoyJbBjHrgYE8fGcvVm1NYmVcEhezC8jNK2LN9hTWbE/BZoNu7cLp\n1iGMbu3Dad86mNYtAmkVHkhQgE+1r5ycTifFJU5KSkopLjX/LCmluMRJcKCvPK4rRC20Cg/krjHR\n3DUmmkvZBXx/5GzZ15mLeYBRaO7oyUyOnqxYOt+dzWaMgvr5OPD3c+Dvay977eNjx9f9y2F0UHx9\nXa/t+Po4rt7Hbd8K7/ex47DbcfVvbDabMefAZsw9sNls2GxXb7fbbPj62PH388Hf14jVEDSGSqO1\niSuEJSJCA5gyoSf3jYth37FzbExII/5ABtmXi3A64Xi6UTYdKlbd9/WxE+Dng6+PHafTidMJTpyU\nlkJJaWlZJ6Ok9NqP4vo47Lw6YyT9ure28CyFaNpahPozdlBHxg7qCMDFrHyOnrzE0dRLJJ/O5tT5\nXE6dyyU3v/I1Qp1OKCgsoaCwhOzLle7SYNjtNqZOUNw/Tnm7KY2j0qgHtMvIyOC228qXBBHCM4pK\nSiksKqGouJTi4ut3Gurq8Th/Avwa37yRjIwMgHY1eIvkrfAqpxNKnU5KS51lf7q2O51OnHDl4sEJ\n4LqQMHZy4tp+ZX/MC436LPEzb6ODD37jX+v31yJ3K9UYKo3WJm55BSUlJZw8eVIKhYlG71wDv6K6\njnZAQZV7XSF5K4QHFAG5179LVJWa5m6lqt3h0FoXKqVclUa/hKsqjb59jbfFcXWRL7h2pdG3y+0T\nV4e45dveojr7CSEaDslbIZqWRlFptKq4QgghhGjYGkWl0WrEFUIIIUQDZpPFqYQQQghhtYbxcK4Q\nQgghmjTpcAghhBDCctLhEEIIIYTlpMMhhBBCCMtJh0MIIYQQlpMOhxBCCCEs1+TXUlFKPQE8j1EK\n/XvgKa31Dg8c9ybzuIMwyr7erbX+V7l9XsUoYNYC2ALM1FofrUPMuRh1ShSQh1GxdY7W+rAVcZVS\nM4HHga7mpgPAq1rrVZ6OdY34LwC/Bt7SWs+2IqZS6lfAy+U2J2qte1sRzzxeB2AeMBEIAo4CP9Fa\n7/J0TKVUEtC5kn96T2v9pFLKBrziiVhuMX2A1zCK+EUC6cBirfXr5fa75jlalbfmses1dyVvPR+z\nqeeteawk6jF3PZG3VWnSIxxKqfuB+cD/AAMxPri+UUq18cDhgzCKkD1hvr6qoIlSag7wFDADGA7k\nmrFrv4IO3AS8Yx5vPOALrFZKBVkUNxWYg/HBPBhYD3yplOpjQayrKKWGYhSD24vb99aimPsxita5\nvkZbFU8pFYGRpAUYH1y9gGeBixbFHMzV5zbe3L7M/PMXHozl8iLGB9LPMFZ0ngP8Qin1lGuH652j\nxXkL9Z+7kreSt7VR37lbp7ytToCmPsLxLPCB1vojAKXU4xiVT3+K0VOtNfNqYZV53Kv+zex5PgO8\nprX+t7ntYeA0MBlYWsuYd5SLMx04g/HBstnTcbXWX5Xb9JJ59TRMKXXQk7HKnVcI8AnGL/8v3bZb\n8n0FSrTWZypphxXx5gDJWutH3LYlWxVTa33e/bVS6i7gqNZ6k4Xfz6HAcq31SvN1ilJqqrm9Oudo\nWd5C/eeu5K3kbW1ieiF365q3VcZssiMcSik/jIRe69qmtXaar0daHL4bxpCUe+wsIN7DsV2LW12w\nOq5SyqGUegDwB76zMhawCPhKa70esLlttypmD6VUmlLqmFLqE6WUqzy/FfF+COxSSn2ulDqtlEpQ\nSrmvG2Tlz9AP+E/grxbHWgmMU0r1MOPGAqPM7VXFHYX38raqtnkqvuSt5G2N1FPu1iVvqxWzyXY4\ngNaAA6P35e4MxvCUlVzHLx/7tKdiK2PF3DeBzW7rzng8rlKqn1IqB8gHPgDuM+/XWXKO5ofjAGCu\nucl9uNuKmNuAacAEYCZGUn1nXq1ZES/ajKOB24E/AG+bVwpYFNNlMhAOLLYyltb6PYyrHa2UKgQS\ngIVa60+rEbcz3stbsDh3JW89FrM55S3UQ+7WMW+rFbOp31JpaGxAqYeOtQjojdt9S4viJmIslheO\nsdLvZ0qpm62IZV6hvAWM01oXuh3Pdu131S2m+0Q6YL9SKh5jqPQ+jHP3aDyMTv52rfVL5uvvlVJ9\nMSb5fXyd93nid+cRYIXW+lQV+9UpllLqaYz/DB7AmLA4EHhTKZWhta7qHBsqT+Wu5K0HYjazvIV6\nyN065m21YjblEY5zQAnGEJC7SCDD4tiuX4rKYlf1C1MlpdS7wCTgFq11upVxtdZFWuvjWuvdWusX\nMYbPZnLle+jJcxwMtAESlFJFSqkijAl3T5s9bku/rwBa60zgMNAda84xHThYblsiV2ajW3KOSqku\nwG3An902W/X9/G+M+7zLtNYHtNafAAu5cvV7vbjJeC9vwcLfMclbydvaqMfcrUveVitmk+1wmD3t\nXcA41zZzOPM2IM7i8CcwfgDuscOAYXWJrZSymR9aPwJu1Vonl9vFkrjlOAC71tqKWGuBvkCs+TUA\n2IkxEW0A9XB+5pBsDyDDonPcgjED3F0MkGT+3apz/AnG0OfXbtusimXD6DS4K+XKFe/14m7Be3lb\nVdtqFV/yVvK2ljFd6it365K31YrZ1G+pLAA+UkrtBHZgzLANBD6s64GVUsEYv+Au0UqpAcB5rXWq\nUupNjNnhRzB+KV8D0oDldQi7CJiC8cGVq5Ry3Te7pLXO11o7PRlXKfUbYAXGY3ahwFSMKxfXc9ke\nPUetdQ7lriKUUpeBC6773Z7+viqlfg98CaQA7TGeay8EXPctPf1zXAhsVUZths8xkvVR8wtP/wyh\n7D/snwAfaa3Lhj6tiGVabh4zFePnORCYDfylBnEtyVvwSu5K3kre1ko9564n8va6muwIB4DWehnw\nc+BVjOfu+wMTtdZnPXD4oRiTahIwJkgtMP/+ihn7txjP3n8AbMd49n+i2z3O2ngcCAM2YAzxub7u\nc+3g4bhtMO5PJmJcxQwGJpiz0K06x/KcuE1AsyBmB4wPqUSMCVNngRGuR9I8HU9rvRO4G+M/oH0Y\nw5iz3CZmWXGO44COXJnh7t4eK36GszG+l4swPrh+B7yP26OS14trcd5C/eeu5K3kbW3VZ+7WKW+r\nE8DmdDqr3ksIIYQQog6a9AiHEEIIIRoG6XAIIYQQwnLS4RBCCCGE5aTDIYQQQgjLSYdDCCGEEJaT\nDocQQgghLCcdDiGEEEJYTjocQgghhLCcdDiEEEIIYTnpcAghhBDCctLhEEIIIYTlpMMhhBBCCMv9\nPxhMecXr8r7bAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": {}, @@ -1315,26 +1318,43 @@ } ], "source": [ - "from pandas.tools.rplot import *\n", - "\n", - "titanic = titanic[titanic.age.notnull() & titanic.fare.notnull()]\n", - "\n", - "tp = RPlot(titanic, x='age')\n", - "tp.add(TrellisGrid(['pclass', 'sex']))\n", - "tp.add(GeomDensity())\n", - "_ = tp.render(plt.gcf())" + "import seaborn as sns; \n", + "sns.set(style=\"ticks\", color_codes=True)\n", + "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")\n", + "g = g.map(plt.hist, \"age\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Using the cervical dystonia dataset, we can simultaneously examine the relationship between age and the primary outcome variable as a function of both the treatment received and the week of the treatment by creating a scatterplot of the data, and fitting a polynomial relationship between `age` and `twstrs`:" + "Using the cervical dystonia dataset, we can simultaneously examine the relationship between age and the primary outcome variable as a function of both the treatment received and the week of the treatment by creating a scatterplot of the data, and fitting a polynomial relationship between `age` and `twstrs` (http://nevro.legehandboka.no/imagevault/publishedmedia/jipv0xim2ibb749yalju/6454-2-twstrs.pdf) :" ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1348,7 +1368,7 @@ "4 1 5 12 1 1 5000U 65 F 39" ] }, - "execution_count": 129, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1360,14 +1380,33 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { - "image/png": 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TNJ2GYTwOTDcMYyeVhe7ZwEHg7UAmWsJHKASUUO2mY8UbiEhT+BMvGmodDvZQ\nmvo0ZxfFQMQxX85R13uzHz/C03+cyeo1nwOKSyLiP3/zOc0xjNIXVr5Pie8aXbg2TXODYRhXAo8A\nDwB7gNtN03zN45g5hmEkAwuAdGAVMNY0TfXzlVpZPaCEequ61W4gIk1h9Xjhq+bqYRKIOObrOep7\nbykpKSH3txIRa1I+R6zGl5ZrTNN8D3ivgWNmADOakiiR5lJfS0wotKqLSP2sPoFNsHuYBCKO+XsO\n9Z4REWmY1e9T4htfJjQTCbiWnHirockjgjnZkJUncLNy2iSyhdsENi7BmCTSJRBxrCnnCOZ7E5GG\n6Z5ufaFwn5LG86nlWiTQWmrirZZslbZyV3Mrp00knCawEREJNt3TQ4fuU+FDhWtpULAn9GqJgNKY\nsZvB6KZj5a7mjU1bbf8PoTrpm7Q8X/93wmUCm+biimMduvdk28a1QOWSWL7EMXVZFAk9Vs5vNFaw\n8hZWzbNE6n0q3KhbuNSrudbds2LXwWB007HyuraNSVtt/w/zFizU2oziF3/jixXjhVXFxcXRr1tn\nljwxh7Q2GaS1yWDJE3Po161zoz87dVkUCT1Wzm80RrDyn1pPWoJNLdctpLy8nH379gHQoUMHEhMT\nWzhFNTWlJTNY6QnUdRrbEqNuOt+r6//h5Ucf4qe3/47Yqs8l1GrGpWWEQ6tKKLDb7Wzdd4Br757u\n3tbt7uksWzwfu93e6M+5rlho1Rag5hLp718kGAJ9f3B9Tx3lZby3Yg1jrr8lIOcVqU2jW64Nw5hp\nGEZFtcfWasfMMgzjkGEYRYZhfGQYRq/AJzk8fPHFF7z00ku89NJLzJkzhyeeeIKlS5eyatUqdu7c\nSUFBQUsn0e+WzGDUAAb6Or60xASylawlJ3BrSENpq+v/YcS4/3N3N3UJlZpxaTmh3qoSKpryOVef\nCKl6LIz0FqBIf/9ibVbObzQkkPcHz+/pLkc8J4pK2PG/jU0+r0hdfG253gyM9vi9zPWDYRj3ALcB\nk4G9wGzgA8Mw+pmmWdrEdIa9vLw88vLy2LZtm3tbcnIyWVlZ7kdmZiZt27YlKsoavfmbq+UpWNdp\niVbplprALdTTJiLNq6GJkCK950Gkv3+xPt3T6/ieGn15e+HT9Oh3prvHnUgg+VpKKzdN84jH4ziA\nYRg24A5gtmma75qm+Q2VheyOwITAJjk8DBs2jJ/85CcMHjyYLl26EBsbW+OYwsJCdu/ezerVq3nj\njTeYN2/qH0gTAAAgAElEQVQeDz/8MAsWLOCdd97h888/Z8+ePRQUFOB0Ov1KR31LNPjbkhnoGsDG\nXsef5SZaYuzmiOHDmDvjPvrGl9E3voy5M+6zzMyd9aWtrv+HVf96g77nDPHaFgo149KyQrlVJZT4\n8zl7Zki79u5D1959+OHkqSz5z4fu2BrpPQ8i/f1LaLByfqM+rrjlsNv5+vNVfP35Khx2u8/3h7q+\npz+4YJRXj7vmuu8017JoWn6tZfnacn26YRgHgRLgc+Be0zS/BXoAmcDHrgNN0zxpGMZaYCiwJEDp\nDRs2m41+/frRr18/Vq1ew5e799Nj4CDKi07x3e7tdGrXlpLiYoqLi71eV15ezuHDhzl8+LDX9qSk\nJNq3b0+7du1o166d++ekpKQ609BQy0Qo1XqG2nITVp4Rsq601fX/MHrw2ax6/QXL/4+ItYRSfAll\n/nzOjVlNQURCg5XzG3XxnIhxxLj/A2DJE3MYPfjsgNwfnM4Kjh48wP4d25vtvtNc+dRQyw+HI18K\n118AvwBMKlukZwCrDMM4A8iqOia32mtyPfaJh/omV+h13oUsWzyfvz5wLyUlJeTk5JCbm+t+HD9+\nvEZLdVFREXv37mXv3r1e25OTk2nXrh1t27alXbt2ZGRkkJGRQUJCQqO6tNXXdbq5lmdp6DrB7p6n\nCWu+15hJjSJ50jfxjSYM9I+vy+EF43OO9OW5Iv39iwRTUyZi9IyFw4cN5d1H/lLL93Qllw4fQmxs\nWbPcdwKZT60v1mu4ijU0unBtmuZ/PX7dXNUqvQ+4Gthex8tsQIX/yQtPXrVKzmj35Aq9B57jPqbX\neRfy6YqVXHLxGNLS0jAMw73P4XBw5MiRGo/aJkErLCyksLCwRqE7KiqKzLQU9q/+mPjUdOJapRHf\nKo3Tzh1Wo2XC15bMQNcANnSdDz78yO9WloYKzqoBrKm2/4dQrBkXa9D/jm9qi0n9unVm674D9cYp\nXz7nxhQcI73nQaS/f5Fg8rf3TI34+Mhf6NetM8uqfU+vufySZs3HBao3UEN5UvU6sga/l+IyTTPf\nMIwdwGnA8qrNmXi3XmcCX/qfvPATiMkVYmNj6dSpE506dfLaXlRUxNGjRzl69ChHjhzh2LFjHDly\nhMLCwhrnqKiogOJC8r/dU2PfhpgYDny7nzZt2tC6dWuv56SkJGw2m/vY5mp5CsZ1NGGPiISS2mJS\nh+49WfLEHK8WnqbGqcYWHCO950Gkv38RK6krz7Zs8Xz+dO9vWb3mcyB0v6ctmSd1Op2UlJRQUFBA\nYWGh17PrZ9fvZWVlDB8+nGHDIrcRyu/CtWEYKcDpwGLTNLMNw8ihcibxr6v2pwKDgacDkdBw0dDk\nCmcNHQH417UsKSmJbt260a1bN6/txcXFHDt2jGPHjnH06FH3z8ePH/cqKLuUlZVx4MABDhw4UGNf\nXFwcrVu3Jj093f1o3bo1AwecRVpaWlC/3HW1vPjTPa8xQUo1gCLiK6fTidPppKKiwuvh2u75cKke\nh6OiorDZbNhsNvfPUVFRfLJsOacNucDr2G0b17rHJHpqapxqbMExGD0PQmkojnpeiASOe8ikw8HO\nzz/zKV9XX55t9ZrPW/R7GohhJI3Jk/pyHafTSXFxcY3Ccm3PhYWFlJeXN/r9btiwQYXrxjAM4y/A\nv4D9VI65fhCwA69VHfI4MN0wjJ18vxTXQeDtAKY3bAVzcoXExES6dOlCly5dvLavWPUZb/73Yzr3\nOYOK0iJO7N9Dh4w2VJSXc/LkyVrPZbfb3WO/axMfH09aWhrp6emkpqaSlpbm9Zyamkp0dHRA3peL\nJuwRkYY4nU7KysooLS2t8bDb7e6Hw+Hw+rmsrKzGs+tRXl5OeXm5+2dXQTrYvvnfZ9iiorFFRUF5\nOacO7cbcuQlbdDS2qGiioqMpLS1le4yNUyfziY2NJSYmhtjYWOLi4rwe8fHx7mfPR0xMTIsUHDUU\nRyQyeX/3Yzh25AjvPvMYA354KRDawy6aaxhJTEwMV466kH+9NJ/Ofc/C6bBzdPd2zjm9B//5z39q\nFJgDeb+y2WwkJSWRmprKiBEjAnbeUORLy3UnKgvSbYGjwCrgPNM0vwMwTXOOYRjJwAIgvWr/WNM0\nNQe8h7prlZpncoXqLQJDhwyu+j2NiyZf476uw+Hg+PHjnDhxwv3s+jk/P7/OL2Rpaal7DHhdXF++\n1NRUWrVqRatWrUhJSfF6Tk5O9mk9b03Y479QaiUScTqdnDp1imXLP6WszEHfPn1wOBwUV62uUFRU\nRElJSa0PX2reLc3pxFlehrO8cmITZ0U5dkfNW20ekHfihF+XiIqKIiEhwf1ITEz0+jkxMZGkpCSS\nkpK8fk5ISKi1R1RjaCiOiDd/7s/NdU8P5HVq/e7f/QAfvTCPXjElxEbHNJivs3qezd98allZGYWF\nhfQxevPBsy+QEmOjvLQYR0kxZSXFHM3eSVxmex7d9D+KiooAaBNro2jXNwCkxNjYv28v+/f5nmZX\ngTklJYWUlBSSk5NJTk52/+zanpKSQmJiok/59nDmy4RmExtxzAwqZxGXOtRVe9UckyvU1SJQW8tE\nbGwsmZmZZGZm1thXUVHBqVOnyMvL48SJE+Tl5ZGfn+9+rq/wDZVjw4uKisjJyak3va4vtOsLnJSU\n5P5iu372zNBpwh7fqZVIrMDpdFJUVMSpU6fqHMvl+fDsUr1l8+ZmTWtUVBQxMTFER0cTHR3t9XN0\ndDRRUVFERUURHR1N/smT5B77jqT0tmCDwhPf0Tkzk4yMtjUKoJ7vybMbuetnV6t4Xl4+3+XlEZ+U\nDE4njpIiYqOjKSsvJyo6GgLUElFRUeGO1b5+Pp6x2hWnPTNhrkrU6pkx9SgS+Z4/9+dQXe6pru++\nMfyHxEaXNeq7Hwp5tri4OC4eM5rS0lJOnTrldV+r7X5XUFBAaWmp+/VpsTYOrlvhfc4oG0ePHm10\nGmw2W40Csmeh2XO7rwVmNdZU8nvMtfivJSZBCWSLQFRUFGlpaaSlpdUY3w2VmbKCggJOnjxJfn6+\n1/OpU6c4efIkBQUFDXZH8SVjZ7PZ3K0png/PFpf4+Hh3y0t8fDw/Gnk+/148n56DzwdbFLvXroyo\nCXvq+58YfO457mNKS0spKSlxP3s+iouL3c8Oh4MzzjiDwYMHt9RbkhBQUVHBhg0byM7O5tSpU+4C\ndbC6U3u2vrpiQPWu0NW7SsfFxREbG+v1cHWrbmxGw263c9eDD3t9vwCWLZ7P3JtubHIrT31LcV14\nwQiioqJwOBy1Pqp3hXf97vldr/59byxX/K9t9YrqbDYbKSkp7l5MJ07kURKdwInYKGKTUohNSiY2\nMdm/D0kkhPmTZ2uunh9W7mHSUnm28vJyioqKai0ou7b7O365MaKjo70KyfUVnhMTE/3uXVQfNdZ8\nz1Z9veSWZBjGns6dO/f45JNPWjopYeeDDz9iW2lMjZba/Tu20ze+cbWCgVRRUUFhYaFX5trz2RWI\nTp48WWNN72Cw2WzujHb1cYnVM9eunz0ftbVgebZiuR6eExR5PmpTfeKj2iZJcj1cYz+rjwH1HB9a\nfdzot98e4GRZBXHx8VSUOagoK6OizEFZaSlU+Bf4o6KiuPfee4mJCf96u1GjRnHgwIFs0zR7+vra\nSI51e/fu5cUXX/T5da7vVFl0LEmp6UQnJBITn0B0XAIn8/PonBDFeUMGu3uyJCYmEh8fH5RMRGNY\nLeY2hdPppLS01N31vqSkhKKiInc3fM+fPTOTgcpAVjiddMjKIj093T2nR3JyMjt37SY+Pp4xo0cR\nHx8fkGtJTZEY61q6Bc6f+NFcMScY16m3MrIFGjVKS0v5+JNlOBwOzjyjP6WlpV4tytUL0r5UQDZW\nfHy8V6HYszdn9UJzS97rwHp/P381JdZ5Cv8csFhSVFSUe7x1bVw1YKcNuQAqytm7fjWjhw2iV8+e\nXkHNc5yl62G3+z7M37XMQDACpNWVBeg8NpuNc845JyIK1uK/tm3bkpiYSHFxMQDJyck15l2oPp7L\nlXn48KOPa83Ule7YTkZ8GaeddlpLvKWwZ7PZ3K3/rVu3btRrnE4nn65cydsfLqfLGQNxOuzk7tzK\nGb160qZ1ursi1VWZWp8om63eiTQ3rF9HSqtWdO7Uyb2ahWv5yPT0dMUk8Yla4Jpfc3TpttvttQ4z\n8sxTFhYWciIvj9KSEndhddP/vgrI9T3HL9c2bKZ6QTo2NjYg120OGtLjTXecCGH1iR481dblqFvf\nM/hg8XwuGTOmwUBbXl7u1W3Z1dJSvWtz9e6Q1btKRhSbjaiYWKJiYikpKqBLx47u7rOeD8/utdW7\n3rd0zamEhlatWvGb3/yGwsJCkpOTfVo9IJTiWCilNRgcDgdvfLicEddcz7aNawEYdPX1rHr9hRot\nGeXl5V6F7ePHj7Nly1bs9lISEhIa7MVks9koLCjANM1a96elpdGmTRuvR0ZGBq1btw746hUS2qzS\n5dmf+NFcMSdY1/G1S7fD4ahRMK7ee8Zzu8PhaHRaGpuXiYuLq7eg7Gt37JbuMSGBocJ1hLDaRA/1\nBZD6asD++vgT/GDgwHqDTnR0tDug+cu1bI9rOR7Xo7bleKovy+P5aMxat3WNNa2t63ht3cxr64pe\nvdu661F9DOnGr/7Hmx8tp9d5lWvnuv4nVEMvwRQdHU1qaqrPr7NaHKtPKKU1GJZ/uoK4Nu15b/Gz\n/OCCUQC8t/hZMrI61mjJiI6Ods/j4TJ8+HCv87nGcn/w4UdkFzpIbdUKe2EBjsJT2IsKsBechDoK\n366JNrOzs72222w2WrduTdu2bWnbti0ZGRlkZGTQrl07kpKSAvVRKMMcRIH+bK3SAudP/GiumBOs\n67hWfTjzjP4UFRWxbdu2OgvMJ0+eDMo8HTabDWJiiU9pRUx85dCjmIRECk4V0DExmvOGDHYXomNj\nY/ls9RrA//89z//f2Li4qvxY6PWYiPTK5OpUuI4gVpmcy98uV05nBQcc0SSWxgQ96NhsNnchNJz9\ncOSFnD9saIv/T4g0llXiWGOEUloDzeFwcGhfNlffepd7W9fefVj69Fwcfbr7fL6oqCj3Eo5x8TG0\nr5aJ22du4zRbEWedeYZ76UjX8pHfffed14y7Lk6nk+PHj3P8+HF27tzptS8xMZF27dq5C9vt27en\nXbt2pKSk+NRDR12MgyfcP1t/4kdzxZzGXMdut9coGNf3HIweg67Jbqu3LtfV2vzpipVst8fWOp68\nfXwZp59+OhCY/73q51i5dCmDx1zqvrZVJolrjEivTK7O78K1YRi/Bx4GnjBN806P7bOAm6hc63o1\n8CvTNHc1NaESGL4sVxUMjelyVVcN2FerlnP55F8SGxdXa9BpqdaBUG+VaOn/CRFfhdL/rD9pDfWY\nAkCUjSGjx9bYPGT0WIjyPxNd1/1h99qV3FqVwe/evbvXvtLSUn476xGGXH4lpafysZ/Kp/RUHsf2\n7iYpPq7W7qLFxcXs37+f/fv3e213FbrbtWvnXq6yffv2JCQk1DiHVboYh6NgfbZWa4HzJ34EIz66\nJjWsXiBOSkygqKiI9957r0aBuawsUDO6eIuOiycmIZHo+ARi4hPJ3beH0RecT2pqao2Cs69LSf3w\nopH8u4G/fyD+92o7x7V3T+fthU/To9+ZxFado9d5F/LhRx+7G3qsfD+I5Mrk6vwqXBuGMQiYAnwN\nOD223wPcBkwG9gKzgQ8Mw+hnmmbNamOJOI3pclWjBszp5IuP32fg8AvdAaf6a1qqBjvca85FpHmF\nS0yJjY7B5qjZwmvDRmy0/53m/Gkh+XTFSk4770KS2rYnqW3773dkdKZPnINhQ8/j2LFjNR6nTp2q\nca66Ct1paWnugnZWVhZZWVms37DREl2Mw1Gwum9HSguc5zr2rgJx9Z+r/x6s5RJdLcuugnH1Z9fP\n69ZvYFdFIt2Mvl6vt7XfTnlFWUCWAW3M3z8Q/3t1neMHF4xi28a1nDV0BAD7d2zjq/17OHv05YD1\n7wehVPEdTD7f4QzDSAFeprJ1+n6P7TbgDmC2aZrvVm2bDOQCE4AlgUiwRIYRw4dx9oCzeGre39mz\nJ5uBF11O74E/qPXYptQiNqWFKFxbJcKi1UwkBIVTTHG3AFbLCO9au7LJLYCBbCGx2Wzu7uY9e3qv\nvlJSUsKxY8c4cuQIR48e5ejRoxw5cqTWQrdrXPeOHTvc26KiorAlpnDw5DES09uS0CaDhLQ2fqVT\nmk+otcA5nc4aXbDrerj2B2tlFNeM2J4FZM/fqxeek5KSGt2yHBcXh600+JOmWuXv77Db2bttC9fe\nPd29LVTvB5HGn+rjp4F/m6a5zDCMBzy29wAygY9dG0zTPGkYxlpgKCpcC43vcuXZetO/77msfOef\ngJPeA8+p8Rp/axGb2kJklYlPAilcWs1EQlE4xRRfWgD9qdDzpYXE366+CQkJdO7cmc6dO3ttLykp\n4ciRI+Tm5rqfc3Nza4wZraiogMKTHN+19fuNNhuO8gqKzx7I+vXr6dChA1lZWVouzEfB7r7dki1w\nZWVl9RaMPZcfdW0LVquya4LYxMRESkpKiImJpUeP7rRq1arWlubGzIjtr+bssl/f3z8Q6ajrHCvf\n+SfDLhvP/h3bWfmPl7jgqmtrvDYU7weRxqdobhjGNcBAYFDVJs+pObOqnqsvRJnrsU8iXGMyXHWN\nRXn50YeIiY0jJja2yd20wqmFKFD0mYhIIDWmBag5KvQC3dU3ISGBrl270rVrV/c2p9NJfn4+ubm5\n5OTkuB95eXneL3Y6iY2ysWnTJjZt2gRUtnC3b9+ejh070rFjRzp16kS7du20TFg9QqX7dnl5uVdh\nuPrDtc+zxdmXJaN8FR8fX6NFuXpLsufPcXFxfLbmc/d3tAx4f03l53zuuecGLZ21scrfPBDpqOsc\n035+NY5SO1BGr4k/YVeZljcNRY0uXBuG0QV4AhhtmqaretZW9aiPDQhOlZr4zApdfhvKcNXVenPB\n+Kso3raBMwcO9HqNP7WIgWghstrEJ00VTq1mIg2xQiysLtxiCtTfAtTYCr1A/K2C3dXTZrORnp5O\neno6hmG4t5eUlHDgwAE+W72awoJCwMl3333ntWZ3RUWFuzD+5ZdfAhATE+MuaHfq1InOnTuTmpoa\ntFbBUNTc3XfrKihX3+b5e22z1AdKdHR0jUJyXQVm18PXChurVbpbpct2INLR0Dnsdjv/DbP7QaTw\npeX6HKAd8KXHjSMaGGEYxq2A66+fiXfrdSbwZRPTKQFgpS6//na5+sHAgTVe11K1mVapRRUR31gp\nFnqKtJjSmAq9QP6tWqKrb0JCAr169aJXr17ubQ6Hg5ycHA4fPszhw4c5ePAgR48e9XpdWVlZjYnT\nUlJSGDZsGEOHDm229Fudv39Tz67XtRWOa9sWzIKya8mo+grL1QvOsbGxQa9ssWKlu1UmzQpEOuo7\nR6TdD8KJL4Xrj4EzPH63AS8A24A/A9lADjCaylnEMQwjFRhM5ThtaUFWq32sjz+tN77WIgaqhcgq\ntaiBEI6tZiLVWT0WhlNMaSrX32rENdezbeNaAEZccz1LXn/BEn8rf8XGxtKlSxe6dOni3ma32zl8\n+DCHDh3i0KFDHDhwoEaX8oKCAj766CMGDx6sLuPVnDx5koKCgloLzNULy8XFxUHteg14FZTrKjR7\nzpKdkJDgd0HZir1wJDB0PwhNjS5cm6ZZAGz13GYYRhFw3DTNrVW/Pw5MNwxjJ98vxXUQeDtQCRb/\nWLH2sS7+1tb5UosYyBpBq9SiNpVqSSUShEIsDJeY0pCGKvSWf7qCuDbteW/xs/zgglEAvLf4WTKy\nOlrmbxUocXFxdOvWjW7durm3FRYWcvDgQQ4cOMC3337LoUOHaNO2LeXl5Spce/jvf//L2rVrg3b+\n+grKtRWcExISfFpbuSlWrV7D6+99wOlVMe1fM//INZdfErBeOKp0b3mRcj8IJ02dntKJx6RmpmnO\nMQwjGVgApAOrgLEeY7RFGqU5autUI1iTPhMRaS4NVeg5HA4O7cvm6lvvcr+ma+8+LH16Lo4+3Vso\n1c0nOTmZ3r17k3v0GOt3ZNPrvAspBu6yyDAGq9izZ0+jjouKinIXhqs/17WtOQvKvrLb7Tz/z7f5\n0c13urd1Nfry/DOPBaxnhyrdRXzXpMK1aZoX1bJtBjCjKeeVwAvF2sfmqK2zUo2gVbp2WekzEQm0\nUIyFgWCV+FJdvRV6UTaGjB5b4zVDRo+FqMios7f6MAYrGDduHF999RVRUVH1Fpjj4+PDakK4Dz/5\nhLMuuqTG9rMuuoQPP/mEKy69NCDXUaW7iG+0sGKEUO2jtVl1giWRcBOJsdDq8aWuCr3Y6BhsjpqF\nIRs2YqMjI/sSCsMYWlpta5FHgm++2UxCn3Nq2WPjm282B6xwDap0F/FFZNydBAhe7aNVW0RCRXMu\nRyMikdUSE8otn+5eBkZfr+271q4M614GInXxzAf06WOw5OP36Vbt+7H24/e59pIaHUsDdl3lP0Tq\nZ82BJBI0rtrHSy4eE5DguGr1Gu568GG2lcawrTSGux58mFWr1wQgpZGjoZYJaNnP2W6388GHH/HB\nhx9ht0dGV0wJf4GOhS7+fF+C+R1rTHyxKlcvg2WL57N/x3b279jOssXzw7qXQXUXjbyQXV/U/Dvt\n+mIFF42s+XeV8FU9H/DhmvXk5+bw9sKn3d+Ptxc+TfGJY1w8ZnTQrmu1fJ7yKGI1arkWv4Vyi0go\nacnP2erdSUWsxJ/vi75j9YukXga1icRhDFJTbfmADt178uyDv6e8vJydX38FQHl5OQnxCUG9rpXy\neYqfYkVquRa/hXKLiJU01DLRUp+z5021a+8+dO3dhx9OnsqS/3yo2mGRavz5vjTHd8yKLZ++tjQF\nq5dBqBgxfBhzZ9xH3/gy+saXMXfGfSo8RJja8gHbNq7l8sm/ZNz1N9OuU2faderMuOtvZuj4qwOW\nN7ByPk95FLGqRrdcG4bxK+BmoHvVpi3ALNM0/+txzCzgJiqX4VoN/Mo0zV0BS61IGLJqy4Qm0hFp\nPH++L83xHbNafFFLk380oZTUJTYujrOGjmjpZDQ75VHEqnxpuf4WuAf4AXAOsAz4l2EY/QEMw7gH\nuA2YCgwBCoEPDMOID2iKxTKs2CISCC0xfqe+lolw/ZxFpHlYpeVTLU0i/qktH9D3nCGs+tcbNY4N\nZN5A+Q8R3zW6cG2a5r9N0/yvaZq7TdPcZZrmdOAUMNgwDBtwBzDbNM13TdP8BpgMdAQmBCXl0uLC\ncbKZlpy4o66ujy31OeumKtJ4/nxfmvM7ZoWu1VbuYipiZa58wEcvzOOTf77GJ/98jU9fWcjowWcH\nNW9g5XxeS+dRNJGa1MWvCc0Mw4gGfgLEA6uAHkAm8LHrGNM0TxqGsRYYCixpelLFisJpshkrT9zR\nEp+z1bqTiliZP98XfcdExBdRUVGcfuZAAHZ+sYIz+/fjpusmBzVvYNV8XkvGTw1vkfr4VLg2DONM\n4HMqC9XFwNWmae4yDMP135Rb7SW5QFaTUymWFi5jwaw+fqclPmer3lQldIXzeqn+fF8i6TvmXre6\ndx+v7bu+WKF1q0Xq4ar8H/WLm93buhp93ZX/wc4bWDWf1xLx08oNMWINvrZcbwfOAtKobLl+3TCM\nkfUcbwMq/EuaiFiBVW+qEnoiobbfn+9LpHzH1FIv4h+rV/63pOaOn/pbSEN8KlybpukA9lT9+pVh\nGIOAXwEPV23LxLv1OhP4sqmJFGkOalURCZ5wqO0P51b35hJJLfUiIhJ5mrrOdTQQZZpmNpADjHbt\nMAwjFRhMZTdyEcuz8sQdIqEu1CezasnJDsONFSZXEwklmrzLOlr6byHW58s6148A/6FySa5WwM+A\nC4CHqg55HJhuGMZOYC8wGzgIvB3A9IoElVpVRKS6cGh1F5HQpcm7rEPDW6QhvnQLbwcsBjoA+cAm\n4BLTNJcBmKY5xzCMZGABkE7lLOJjTdOM7CouCTmRMv5RpDmF8rALjbETkZamybsapzmG76ghRurT\n6MK1aZo3NeKYGcCMJqVIRETCjmr7RUSaRpN31a85W9nVECN1aeqYaxERkUYZMXwYc2fcR9/4MvrG\nlzF3xn0h0bVQY+xERKzNs5W9a+8+dO3dhx9OnsqS/3wY8ePEpXmpcC1iEZowRCJBKE5mpckORSQS\nhVLFYqhPminhw9d1rkUkCDRhiIi1aYydiEQaDecR8Z0K1yItLBQnDBGJRBpjJyKRJlQqFkN50kwJ\nLypci7SwUJswRERERCJHKFQsqpVdrEKFaxERERERCWmh0sou4a3RhWvDMO4FfgwYQDGwBrjHNM0d\n1Y6bBdxE5VrXq4Ffmaa5K2ApFgkz6sokIiIi0nSh0Mou4c2X2cIvAJ4ChgBjgFjgQ8MwklwHGIZx\nD3AbMLXquELgA8Mw4gOWYpEwo5mIRURERERCX6Nbrk3TvNTzd8MwrgOOAD8APjMMwwbcAcw2TfPd\nqmMmA7nABGBJgNIsEnbUlUlEREREJLQ1Zcx1etXz8arnHkAm8LHrANM0TxqGsRYYigrXIvVSVyYR\nERERkdDlS7dwN8MwooDHgc9M09xatTmr6jm32uG5HvtEREREREREwo6/LddPA/2A8xtxrA2o8PM6\nIiIiIiIiIpbnc8u1YRh/Ay4DLjJN85DHrpyq58xqL8n02CciIiIiIiISdnxZistG5Wzh44GRpmnu\nq3ZINpWF6NHA11WvSQUGU9nSLSIiIiIiIhKWfOkW/jQwkcrCdaFhGK5x1HmmaZaYpuk0DONxYLph\nGDuBvcBs4CDwdgDTLCIiIiIiImIpvnQLvxlIBT4FDnk8rnYdYJrmHCpbtxcA64AkYKxpmvYApVdE\nRERERETEcnxZ57pRBXHTNGcAM/xOkYiIiIiIiEiI8WspLhERERERERH5ngrXIiIiIiIiIk2kwrWI\niAnVgNkAACAASURBVIiIiIhIE6lwLSIiIiIiItJEKlyLiIiIiIiINJEK1yIiIiIiIiJN1OiluAAM\nw7gAuBv4AdABuNI0zXeqHTMLuAlIB1YDvzJNc1dgkisiIiIiIiJiPb62XCcBXwG3Vv3u9NxpGMY9\nwG3AVGAIUAh8YBhGfBPTKSIiIiIiImJZPrVcm6b5X+C/AIZheO0zDMMG3AHMNk3z3aptk4FcYAKw\nJADpFREREREREbGcQI657gFkAh+7NpimeRJYCwwN4HVERERERERELCWQheusqufcattzPfaJiIiI\niIiIhJ3mmC3cRrWx2SIiIiIiIiLhJJCF65yq58xq2zM99omIiIiIiIiEnUAWrrOpLESPdm0wDCMV\nGAx8HsDriIiIiIiIiFiKr+tcJwOne2zqaRjGQOA70zS/NQzjcWC6YRg7gb3AbOAg8HaA0isiIiIi\nIiJiOb62XA8Cvqx6OIG5VT8/CGCa5hzgKWABsI7KdbHHmqZpD1SCRURERERERKzG13WuP6WBArlp\nmjOAGU1Ik4iIiIiIiEhIaY7ZwkVERERERETCmgrXIiIiIiIiIk2kwrWIiIiIiIhIE/k05jrYKioq\nbCdPnmT9+vUtnRQRkXqdPHmSiooKmz+vVawTkVChWCcikaApsc6TpQrXpaWlcePHj2f37t0tnRQR\nkXqNHz+eBQsWxPnzWsU6EQkVinUiEgmaEus8BaVwbRjGrcDdQCawCbjNNM1GVVv26tWLM844IxjJ\nEhGxDMU6EYkEinUiEkkCPubaMIyfAn+lcjmus6ksXH9gGEa7QF9LpD7Lli1j8uTJLZ0MEZGgUqwT\nkUigWCehIBgt13cBC0zTfBHAMIybgcuBG4A/B+F6IpZQXl7Oq6++ypdffsmRI0dISkrirLPO4tpr\nr6V169YtnTwRkYBQrBORSKBYJ/4IaMu1YRhxwA+Aj13bTNN0Vv0+NJDXErGakpISsrOzufrqq3n0\n0Ue5++67OXjwII888khLJ01EJGAU60QkEijWiT8C3XKdAUQDudW2HwH6BPhaEkI2bNjAk08+yYsv\nvojNZiM7O5u7776bCRMmcO211wIwb948HA4Ht99+OwDbtm3jlVdeYffu3aSmpjJ48GCuvfZa4uPj\nAXA4HLz66qt89tlnFBUV0aVLF37+85/Tv3//WtOQn5/PH//4RzIyMrjzzjuJjY0N6HtMTk7mgQce\n8Np200038fvf/57vvvuOtm3bBvR6ImI9inWKdSKRQLFOsU5qZ6nZwgH9l4apvn37UlxcTHZ2Nj17\n9mTLli20atWKLVu2uI/ZunUrV155JQA5OTk89NBD/OxnP2PatGnk5+ezcOFCFi5cyK233grAwoUL\nOXjwIL/5zW9o3bo1a9eu5aGHHmLu3Ll06NDB6/rHjh3jwQcfpE+fPtxyyy3YbLXPtD9//nxWrlxZ\n73t55ZVXGv2+CwsLsdlsJCUlNfo1ElL8jVmKdWFKsU6xLkwp1okXxTrFujDV5JgV6ML1MaCcylnC\nPWUChxvxegcQH+A0iQUkJyfTo0cPNm/eTM+ePdm6dSs/+tGPWLp0KaWlpRQUFJCTk+OunXzzzTe5\n4IILuPzyywHIysrihhtu4IEHHmDKlCnk5eWxfPly5s+f7x73Mm7cOL766iuWLVvGpEmT3Nc+ePAg\ns2bN4rzzzuP666+vN53XXHMN48ePD8h7ttvtvPzyy5x//vkkJiYG5JxiOY4mvE6xLgwp1inWhSnF\nOvGiWKdYF6b8jXVuAS1cm6ZpNwxjIzAa+BeAYRhRwCjgyYZe/+233/4QWBfINIl19OvXj82bNzNu\n3Di2bdvGpEmTWLNmDVu3bqWgoIDWrVuTlZUFwN69e9m/f3+ttY1HjhwhJyeHiooKpk2b5rWvrKyM\nVq1auX+32+3cf//9jBgxosEADJCWlkZaWloT32llOv76179is9mYMmVKk88n1lQVs/x9nWJdmFKs\nk3CjWCe1UayTcONvrPMUjG7hc4EXDcPYAKwH7gASgReCcC0JIf3792fZsmXs3buX6OhoOnXqRP/+\n/dmyZQuFhYVe62CWlpZy8cUXc9lll9U4T0ZGBnv37iUqKopHH32UqCjvefk8axNjY2MZMGAAGzZs\nYPz48bRp06beNDbUfchms/Hyyy/Xew5XAP7uu++YOXOmajdFIoxinYhEAsU6kZoCXrg2TXNp1ZrW\ns4As4CtgrGmaRwN9Lfl/9u48Pqry7P/4Z7KHrEDIwr4oB8GKVEGBolAWafVRtLRK6YNVLFh3/Vlp\n1RoWH/pU+7hA3ShYRKtiK2qptYrigqAgqCiLhy3sSQgSAgnZM78/kjnOZCMzmX2+79frvCY558zM\nPZPMNec+93WuO7Q4rs9ZuXKllSY0aNAgVqxYQVlZmUvaTt++fTlw4IB1xrOxPn36UFdXR0lJCWed\ndVaLz2mz2bjtttt49NFHyc3NZe7cua1On9De9CFHAC4oKGDu3LkkJyd7/FgiEpoU60QkEijWiTTl\n1am4HEzTfMI0zd6maSaYpjncNM3PfPE8ElqSk5Pp1asXa9assYLwwIEDycvLIz8/n4EDB1r7Tpo0\nCdM0Wbx4MXl5eRw+fJgNGzawePFiALp27cqoUaNYsGAB69evp7CwkJ07d7JixQo2bdrk8rw2m43b\nb7+dXr16kZuby/Hjx1tsY1paGtnZ2a0uLamtreVPf/oTe/bs4fbbb6empobi4mKKi4upqalpz1sn\nIiFEsU5EIoFinUhTwVYtXMLcoEGD2Ldvn5UqlJycTI8ePSgpKaFr167Wfr169WLu3Lm8+OKL/P73\nv8dut5Odnc3IkSOtfW655Rb+8Y9/sHTpUo4dO0Zqair9+/fn/PPPt/ZxVI+Mjo7mzjvv5JFHHmH2\n7NnMnTuX1NRUr762b7/9lo0bN2Kz2bj77rtd2jB79uwWp5IQkfCjWCcikUCxTsSVzW63B7oNFpvN\nNvSll17a4HyNhohIMNqyZQtTpkwZZrfb3c7MUawTkVChWCcikaA9sc6ZT9LCRURERERERCKJOtci\nIiIiIiIi7RRs11wnfPDBB+zevTvQ7RARadWBAwcAEjy8u2KdiIQExToRiQTtjHWWYOtc0717d/r1\n6xfoZoiItKq99SoU60QkFCjWiUgk8FYdsmDrXFecccYZqPCFiISICk/vp1gnIiFEsU5CzurVq1m6\ndCnLli3z23M+8MAD9OnTh+uuu85vzyle5WmsswRb51qCgL8Dw8KFCzl16hSzZs3y6fNs3bqVN954\ng7y8PIqLi7nnnnsYNmxYk/1eeukl3nvvPcrKyhgwYAAzZswgJyfH2l5VVcVzzz3H2rVrqa6u5txz\nz2XGjBmkpaVZ+5w8eZIlS5awadMmbDYbF154Iddffz0JCd9lmxQVFbFo0SK2bt1KQkICo0ePZurU\nqURHRwOtfylMnjyZWbNmMXToUG++RSIRL1zj3/Lly/n73//usq5bt248/vjjLuv8Ef+2bNnC7Nmz\nWbZsGR06dHB5/htvvJHLLruMyy67zNtvgUjECte4tnDhQj788EOgfmqujIwMRo8ezVVXXWUdS4n4\nmwqaiUfsdju1tbWBboZbqqqq6NOnDzfccAPw3VyJzl577TXeeustZs6cyf/+7/8SHx/PvHnzqK6u\ntvb561//ysaNG7n77ruZN28excXFPPTQQy6P8/jjj3Pw4EFyc3O599572bZtG08//bS1vba2lvnz\n51u3t956K++//z4vv/yyj169iHhLKMY/gB49erBkyRJrefDBB122+yv+tcZmszUbm0XEt0Ixrtls\nNoYMGcKSJUt44oknuOKKK3jllVf45z//GeimSQTTyLW4WLhwIdu2bWPbtm28+eab2Gw2nnzySQoL\nC5k9ezb33XcfL774Ivv37+eBBx5g4MCBvPbaa6xatYrjx4/TtWtXJk+ezPDhwwGoq6vjqaeeYsuW\nLRw/fpyMjAwmTpzIpZdeCtSPpjjOOk6ePBmAOXPmMGjQIK+/tiFDhjBkyJAWt9vtdv71r38xefJk\na0T4tttuY/r06WzYsIGRI0dSVlbG6tWrufPOO600t5tvvpnbb7+dHTt20L9/fw4ePMiXX37JQw89\nRN++fQGYPn068+fP59prr6Vjx45s3ryZgwcPMnv2bNLS0ujduzdTpkzh+eef55prrtEZV5EACOf4\nB/UjO84jzM78Gf9ExH/COa7Z7XZiYmKsuDZhwgTWr1/PZ599xpVXXtlk/4KCApYuXcrOnTupqKig\ne/fuTJ06lXPOOcfap7q6mpdffpmPP/6YkpISOnfuzFVXXcXYsWMB2L9/P8uWLWP79u0kJCQwePBg\nrrvuOlJSUqzHqK2t5S9/+QsfffQRMTExTJgwgSlTpljbS0tLefbZZ9m0aRPV1dUMHDiQ6dOnu2QJ\nSehS51pcTJ8+nfz8fHr16sU111wDQEpKCoWFhQD87W9/Y9q0aWRlZZGUlMSrr77KmjVruPHGG8nJ\nyWHr1q0sWLCA1NRUBg0ahN1uJyMjg9/85jckJydjmiZPP/00HTt2ZMSIEVxxxRUcOnSI8vJybrnl\nFgCSkpKabdurr77KihUrWm3/ggUL6Ny5s0evvbCwkJKSEpcg26FDB84880xM02TkyJHs2bOH2tpa\nl326detGRkaGdXBpmiZJSUnWgSXAOeecg81mY+fOnQwbNgzTNOnVq5fLge7gwYNZtGgRBw4coHfv\n3h69BhHxXLjHv/z8fH71q18RGxuLYRhMnTqVjIwMwL/xT0T8J9zjWuNMl9jYWE6ePNnsvhUVFZx3\n3nlMnTqV2NhY3n//ff7whz+wcOFCKxYuWLCAHTt2MH36dHr37k1RURHHjx8HoKysjNzcXMaPH8/1\n119PZWUlzz//PP/3f//H7Nmzref54IMPGDt2LH/84x/ZvXs3Tz/9NF26dGHcuHEA/PnPf6agoIDf\n/e53JCQk8MILL/A///M/PP744xpcCQPqXIuLDh06EBMTQ1xcXLMjHFdffbV1YFVdXc1rr71Gbm4u\n/fv3ByAzM5Pt27ezatUqBg0aRHR0NFdffbV1/8zMTL755hvWrVvHiBEjSEhIIDY2lurq6hZHVBwu\nueQSRo4c2eo+6enp7r5kiyN4Nn6MtLQ0a9vx48eJiYlpcp1genq6yz6pqaku26Ojo0lOTnbZp/Hz\nOH4vLi5W51okAMI5/vXv359bbrmFbt26cezYMV555RXuv/9+Hn30URITE/0a/0TEf8I5rsF3FZ7t\ndjtfffUVmzdv5sc//nGz+/bu3dvl+GrKlCls2LCBzz77jB/96EccPnyYTz75hNzcXL73ve9Zr8/h\nrbfeom/fvvz85z+31t18883MnDmT/Px8a+Q5IyPDur69a9eu7Nu3j5UrVzJu3DgOHz7Mxo0bmT9/\nvvUe33777cycOZMNGzZYGQISutS5FrecccYZ1s/5+flUVlYyZ84cl31qampcRi3eeustVq9ezdGj\nR6mqqqKmpoY+ffq4/dzJyckkJyd73ngP2e12n1wD6K2S/yLiH6Ec/5wvienZsydnnnkmN954I+vW\nrbPSHZvjq/gnIsEhlOMawKZNm5g6dSq1tbXY7XZGjRrFz372s2b3LS8v55VXXuHzzz+nuLiY2tpa\nqqqqOHr0KAB5eXlERUUxcODAZu+/d+9etmzZwtSpU13W22w2CgoKrM61o9Ps0L9/f1auXIndbufQ\noUNER0dz5plnWttTUlLo2rUrhw4d8vh9kOChzrW4JT4+3vq5oqK+Wv19991Hp06dXPaLjY0F4OOP\nP2bZsmX88pe/xDAMEhISeOONN9i5c6fL/m05ePN1Wrjj7GjjUeWSkhLrSyU9PZ2amhpOnTrlMnrj\nfJ/09HROnDjh8ti1tbWUlpa67LN7926XfRyjOo5rEjt06EBlZWWTdpaVlVnbRcR/win+JSUl0bVr\nVwoKCgD/xj/HfRs/DtTHN8U2Ef8J9bh29tlnM2PGDGJjY+nYsSNRUS3Xal62bBlfffUV1157LdnZ\n2cTFxfGnP/2JmpoaAOLi4lptS0VFBUOHDuUXv/hFk23O9SQ0eBLZ1LmWJmJiYqirqzvtfj169CA2\nNpaioqIWz/J98803GIbBJZdcYq3Lz89v8nxtqVDp67TwrKws0tPT+eqrr6y0oVOnTrFr1y4mTpwI\nQN++fYmOjuarr77iwgsvBODQoUMcPXoUwzAAMAyDsrIy9uzZYx2Ufv3119jtdutM5YABA1ixYgUl\nJSVW2tTmzZvp0KED3bt3B+pTiWpra10eB2DPnj3WdhHxrkiJf+Xl5eTn53PxxRcD/o1/OTk52Gw2\ndu/ebV3nCPXFhk6dOqXYJuJl4RzX4uPjyc7OPu1zQX3bx4wZY9V+KC8vp7Cw0Cq21qtXL+x2O1u3\nbnWpLeHQt29fPv30U7p06dLqtdGNTzTs2LHDinvdunWjtraWHTt2WHHz5MmTHD582Dr+k9CmzrU0\nkZmZyc6dOzly5AgJCQkuFRCdJSYmcvnll7N06VLsdjsDBgzg1KlTfPPNN3To0IHRo0fTtWtXPvzw\nQ7788ksyMzP58MMP2b17N1lZWdbjZGVlsXnzZg4fPkxycjJJSUnNBq32pg9VVFS4fAEUFhaSl5dH\nSkoKGRkZ2Gw2LrvsMl599VVycnLIzMzkpZdeolOnTlYgTkpKYuzYsSxdupTk5GQSExNZsmQJhmFY\nB47du3fn3HPP5amnnmLmzJnU1NSwePFifvCDH1hnNgcPHkz37t1ZsGAB//3f/01xcTEvv/wyEydO\nJCam/mPZs2dPBg8ezJNPPsm1115LZmYmhw8f5tlnn2XkyJGquiviA+Ea/5577jnOP/98unTpwrFj\nx1i+fDkxMTGMGjUKwK/xLzExkXHjxrF06VKioqLo2bMnR48e5YUXXqB///7WAaeIeEe4xjV35eTk\n8Omnn3L++ecD8NJLL7lsz8zMZPTo0TzxxBNMnz6dXr16UVRUxIkTJxgxYgQ/+tGPePfdd3n00UeZ\nNGkSycnJ5Ofns27dOm666SZrtP7o0aMsXbqU8ePHs2fPHt566y1++ctfAvUDI0OHDuWpp57ixhtv\ntAqade7c2ZqpQUJbmzvXhmHEAPOAa4As4DCw1DTNBxvtNxe4AUgH1gK/Nk1zl9daLD53+eWX8+c/\n/5k77riD6upqnnzySaD5FJ8pU6aQmprKihUrKCwstKrE/uQnPwFg/Pjx5OXl8cgjj2Cz2fjBD37A\nxIkT+eKLL6zHGDduHFu2bOGee+6hsrKS2bNn+2TKhl27dlnVHG02G0uXLgVgzJgx3HzzzQBMmjSJ\niooKnn76aU6dOsVZZ53F/fffb6VDAVx33XXYbDYefvhhampqOPfcc5kxY4bLc91xxx0sXryY2bNn\nY7PZGD58ONOnT7e2R0VFce+997Jo0SLuvfde4uPjGTNmjFXJ0+Guu+5i+fLlPP300xQXF9O5c2cu\nuOACfvrTn3r9/RGR8I1/3377LY899hgnT54kNTWVs846iz/84Q8uB9n+in8A119/Pa+99hovvPAC\nRUVFpKenM3jwYJdCQSLiHeEa12w222nTz523//KXv+TJJ5/k3nvvJTU1lUmTJlFeXu6y/4wZM/jb\n3/7GX/7yF06ePEmXLl246qqrgPrU7//5n//h+eefZ968eVRXV9OlSxeGDBni8jyjR4+mqqqK3/72\nt0RHR3PZZZcxfvx4a/stt9zCs88+y/z586mpqWHgwIHcd999qhQeJmxtvS7AMIwHgFuBacBWYCjw\nV+A+0zQXNuwzC/htwz57qe+Mfw8YaJpm04tHGzfGZhv60ksvbXDMnykiEqy2bNnClClThtnt9s/c\nva9inYiECsU6EYkE7Yl1ztxJCx8KvG6a5lsNv+83DOPnDesxDMMG3AHMM01zZcO6aUAhMAlY3p6G\nioiIiIiIiASrlkvqNfUWMM4wjDMBDMMYDIxsWA/Qh/p08XcddzBN8wSwHtCkbSIiIiIiIhK22ty5\nNk3zSepHn03DMKqAz4FHTdN0VANwlOorbHTXQqdtIiIiIiIiImHHnWuubwN+B9xO/TXXQ4DHgLtM\n01xmGMYI4GMgxzTNQqf7vQLUmqY55XTP0b9//5PR0dHJjqmJRESCVUlJCbW1taU7duxovuxqKxTr\nRCRUKNaJSCRoT6xz5s411/cBc0zTfKXh962GYfSivsO9DChoWJ+F6+h1FvWj3G1uj2MqIhGRIOdp\nsFKsE5FQolgnIpGg3cHKnQewAY1nhK9rWA+QR30HexzwFYBhGKnAMOCJNj2BzZbftWvXPu+9954b\nzRIR8b+xY8dy8ODB/NPv2ZRinYiECsU6EYkE7Yl1ztzpXL8O3G8YxgFgG/Vp4XcCSwBM07QbhvFY\nwz47+W4qrkMN9xUREREREREJS+50ru8ETlA/Cp0FHAaeBuY6djBN8yHDMJKARUA6sAaYaJpmldda\nLCIiIiIiIhJk2ty5Nk2zDLi7YWltv1wgt53tEhEREREREQkZ7sxzLSIiIiIiIiLNUOdaRERERERE\npJ3UuRYRERERERFpJ3WuRURERERERNpJnWsRERERERGRdmpztXDDMPYCPZvZ9KRpmrcYhmED5gA3\nUD8N11rg16Zp7vJCO0VERERERESCljsj1+cB2U7L+Ib1rzTc3gPcCswELgDKgLcNw4j3TlNFRERE\nREREgpM781x/6/y7YRj/BewyTfOjhlHrO4B5pmmubNg+DSgEJgHLvddkERERERERkeDi0TXXhmHE\nAb8Anm1Y1QfIAt517GOa5glgPTC8nW0UERERERERCWqeFjSbBKQBSxt+z264LWy0X6HTNhERERER\nEZGw5Gnnejrwb9M0C06znw2we/gcIiIiIiIiIiHB7c61YRi9gLHAYqfVjk52VqPds5y2iYiIiIiI\niIQlT0aur6M+3ftNp3V51HeixzlWGIaRCgwDPmlPA0VERERERESCXZurhQMYhhFFfef6OdM06xzr\nTdO0G4bxGHC/YRg7gb3APOAQ8Lr3misiIiIiIiISfNwduR4HdOe7KuEW0zQfAhYCi4ANQAdgomma\nVe1tpIiIiIiIiEgwc2vk2jTNd4DoVrbnArntbZSIiIiIiIhIKPG0WriIiIiIiIiINHBr5FpERETq\nVVVV8f4HHwIwZvTFxMXFBbhFIiIiEkgauRYREXHTmrXruGvOfLZXxrC9Moa75sxnzdp1gW6WiIiI\nBJBGrkVEJOK0Z9S5qqqK5f9+hx9Om2mt69l/AMuXPcMFQ8/XCLaIiEiE0si1iIhElPaOOr//wYec\nceHFTdafceHFVoddREREIo9GrkVEJGJo1FlERER8xa2Ra8MwuhmG8YJhGEcNwzhlGMZXhmGc12if\nuYZhHG7YvsowjDO822QRERHPeGPUeczoi9n1adN9d336IWNGN31sEREJHlVVVbz9zirefmcVVVVV\ngW6OhJk2d64Nw+gIrAUqgYnAWcBdQLHTPrOAW4GZwAVAGfC2YRjxXmyziIhIwMTFxXH1jyewetkz\n7N/xDft3fMPqZc9w9Y8naORbRCSIqRil+Jo7aeGzgH2maU53WrfP8YNhGDbgDmCeaZorG9ZNAwqB\nScDy9jdXgoGmnxGRUDVm9MWsnDOfnv0HuKzf9emH3JR7b5sfZ9TIEVww9HwrFt6Ue69ioYhIENNl\nQeIP7qSFXw5sMgzj74ZhFBqG8blhGDc4be8DZAHvOlaYpnkCWA8M90prJeB0xk9EQpk3R53j4uK4\nZMJ4LpkwXgdlIiJBTsUoxR/cGbnuC/wa+D/gQWAYsMAwjCrTNJcB2Q37FTa6X6HTNglhOuMnIuFA\no84iIiLiC+6MXEcBm0zTvN80zc2maf4F+Atw42nuZwPsnjZQgkewnfFTQQoR8ZRGnUVEIouKUYo/\nuNO5Pgxsa7TuG6Bnw88FDbdZjfbJctom4hVKTxcRERGRtlIxSvEHdzrXa4EBjdb1B/Y2/JxHfSd6\nnGOjYRip1KePf+J5EyVYBMsZP+f09J79B9Cz/wB+OG0my//9jkawRURERCKEu1mMo0aO4JHcezkr\nvoaz4mt4JPdeRo0c4YeWSqRw55rrR4F1hmH8Dvg79Z3mXzUsmKZpNwzjMeB+wzB2Ut/pngccAl73\nZqMlMBxn/JYve8ZKD9/16Yd+P+N3uvT0SyaM91tbRERERMT/1qxdx/J/v2MdE66cM5+rfzzhtJ1l\nx2VBIr7Q5s61aZobDcO4EvgD8ACwB7jdNM2XnPZ5yDCMJGARkA6sASaapqnhxBDWeOotFQISERER\nEV9qbepXFdmVYOXOyDWmab4JvHmafXKB3PY0SoJHS2cFA3nGz1vz1IqIRIrWDlJFRILN6Ual3cli\nVPwTf3Krcy2RJVjPCgZLerqISCjwNHVSRCQQvHn8qfgn/uZOQTOJMME29ZYzFaQQETk9bxaA1PSH\nIuIPbTn+bEuRXRXAlUBQ51pCluapFRFpnbdOkmr6QxEJJm2ZViuYB4kkfKlzLS0Klqm3RETCWbCP\nCHt79CfYX69IOAnFz1tbjz9DLYsxFP8W4j51rqVFbTkrKOFHwV/Ef3w9IuyNk6TeHP3RCLiI/4Tq\n582d48/Wshhbin/m2tVU19b49TgnVP8W4j4VNJNWjRo5QlNvRRAV/hDxH38UjQymApDBWiRTJByF\n+ufNG8efzcW/T954hQ4pKeyqSYAa/xznhPrfQtzT5s61YRizqZ/f2tk3pmkOdNpnLnAD9XNcrwV+\nbZrmLi+0U9rAV1MNOM4KSnhT8BfxL3emkmmP9h6kemv6Q3+9XhEJj8+bN44/neNfdXU1OzMzGXvt\njdZ2fxznhMPfQtrO3bTwLUC20/IDxwbDMGYBtwIzgQuAMuBtwzDivdNUaY3STaS9VPhDJHy1pwCk\nLhESkVDmiH+xsbGcOXx0k+06zhFvcrdzXWua5hGn5RiAYRg24A5gnmmaK03T/BqYBnQFJnm3EHlG\nJAAAIABJREFUyeGpPde5aqqBwNI1yiLiiWAvGukc2y4Yen67CwcF++sVCSeR+nkLxmOySP1bRCp3\nO9dnGoZxyDCM3YZhvGAYRo+G9X2ALOBdx46maZ4A1gPDvdPU8NXeUWeNOAZOOGUMKPiL+Fcwjwg3\nF9vWf7axXdMfBvPrFQk3kfh5O90xWaCOcyLxbxHJ3Clo9ilwLWBSPyKdC6wxDONs6lPEAQob3afQ\naZs0Q9e5hq5w+9sFU+EjkUgRjEUjfRnbgvH1ioSrSPq8tSVuBfI4J5L+FpGuzZ1r0zT/4/TrFsMw\n1gP7gJ8B37RwNxtQ53nzwp83ihx4q9iMuCccC1Qo+Eso8FXxxkAJtqKR3oxtzf2tgu31SugKt1jg\nC776vAXbe9/WuBXI4xzFvsjg8TzXpmmWADuAfkB+w+qsRrtlAQWePoe0jdJNxJvaU/hIxNfC6VKM\ncKe/lfiS/r8CJ9Tfex3niC953Lk2DCMZOBPIN00zj/pO9Din7anAMOCT9jYynHnr+o9RI0e0u9iM\nuCdcrlEOxuIfIs1R8Ub/8EZs099KfEn/X4ETrO99uByTnY6O2YKfO/Nc/wn4J7Cf+muu5wBVwEsN\nuzwG3G8Yxk5gLzAPOAS87sX2hh1vXv8RqHSTYEsN8pdwuEZ5zdp1LP/3O1b7V86Zz9U/nqATMxKU\n2pOuHKlxyhPeiG3heNmMBA/9fwVOsL73/j4mC8R3SrAfs+l7tp47Bc26Ud+R7gwUAWuAC03T/BbA\nNM2HDMNIAhYB6Q3bJ5qmqdMqpxHK17kG+wfd10L5bxduBdlEWhLpccoToRzbRCQy+StuBeI7JdiP\n2fQ9+x13CppNacM+udRXERc3hWKRg2D/oPtLKP7tIHjPPktkcedMtyfFGyMlTvlixKA9sU2FNsWX\n9P8VOL58770Rx3x9TBao75RgPmaLlO/ZtvL4mmsRf8+vHajrTHR9i4hvuFsUx5H2995zT7Pf3M5+\nczvvPfd0q2l//o5TgRCMxYUipdCmvh8CI1L+v4KRr977YIxjzYmE7xR36T1x5U5auEjABCrdJJzT\nXHTmXwKpPWe66+rq2Pn1l9bPkSyYRwzCPbU8nL8fQkG4/38FM2+/98Ecx4KFjtlCh0auxWP+qszY\n1sqU3h5BCNaKmN6iM/8SSJ6c6XZ8JsdfdxNjJ09h7OQpjL/uplY/k+FeQTbYRwzCdcqbcP9+CBXh\n+v8VCrz53gd7HHMWqO+UYD5mC/fvWXdp5Fo85q/KjG25zsQXIwjBfH2Lt+jMv4QSTz6T4VDVX4JP\nJHw/iEhTgfxOCdZjNn3PulLnWtrF0w+6N4vvKJ2ofUK1IJuEJsdnv7q6mp2ffOyXFLdgPSDxBqUK\nikioaymOmWtXc8bFI3j7nVVBNbVTIL9TgvWYLZy/Z93lcVq4YRi/NQyjzjCMRxutn2sYxmHDME4Z\nhrHKMIwz2t9MCWbupga5W7TidOkm7Uknai2VXGkuIt7l+OxvKa5gW9EJKk+WsGrxAvZ8vp68Lzfy\nwbJnuHLsxdhsNux2e5P7t+czGa7po8GcKthWoVgUTN8PIt7TXBxb/vBcKior2VWTEJQFzsL1O6U9\n9J7U82jk2jCMocAM4CvA7rR+FnArMA3YC8wD3jYMY6BpmpXtbq2EPE9GmX2VbnK6VHKluYh4j+Oz\nP/zyyez6z6sA9MjoCEDZjs0AdI6z8fGaj/h4zUfYbDbi4uKIj4+3buPj4xnYPYsNf3uGtJzu2KJj\n+PbAXi4692z27NlDQkKCyxIfH4/NZgvYa/aXUB4xCNWiYPp+EPEu5zhWXV3NzsxMxl57o7VdGYme\nqauro6amxlqqq6tdfm/LUltb2+ZtUVFRjBgxgiFDhgT6pQeM251rwzCSgReAG4DfO623AXcA80zT\nXNmwbhpQCEwClnujwRLaPL1OrbWDR1/OfRvKB62+4ov5dCX8tfTZb4ndbqeyspLKyqbnZROjbVQd\nOQRAcoyN7du2sn3b1mYfJyEhgcTERBITE11+diwdOnSgQ4cOLj+HYqc8WFMFWxPql/To+yF86Xsu\nMBxx7O13VnHm8NFNtodqTQO73U5dXZ1Lx7YtP9fU1FBRUcGePXnU2evIyc526Sy31FF2Xt9cFpiv\nffTRR+pcu+kJ4F+maa42DOMBp/V9gCzgXccK0zRPGIaxHhiOOtcuSktL+fjjj6mrqyMzM9NaEhIS\nAt20oNXSwaMnIwjudPJD8aDVV0J1lEmCR2J6Z8645CpOHS2krqaa4sICOtqq6ZqTQ1VVlbU4OtaO\nn6urqz16voqKCioqKiguLm7zfaKioqyOtmNJSkpqcQnFzngwCIeiYPp+CD/6ngt/drvdpRNaXV3t\n8nNbt7W0v686uQX5+V549d5ns9mIiYkhJiaG+Ph4Ro0aFegmBZRbnWvDMK4BzgWGNqxy/k/Jbrgt\nbHS3Qqdt0mDdunWsX7++yfqUlBQyMzPJyMigS5cu1pKYmBiAVnqfr4rvaATB90J9lEkCy/mzn9gx\ng8SOGQB8/dkz3NWGz2tdXZ3V0a6oqLA6347fGy/l5eVNbtuqrq6O0tJSSktL27R/dHQ0SUlJJCcn\nk5yc7PJzcnIyKSkp1m1MjOqIigSrYPuei6QRdLvdTm1tLdXV1Zx/3vf59yML6dKlC3W1Ndhra6ir\nqeHAhg8ZftmPeG7Z89jr6ujVq6c1Ity4U9u4U9y48xtuYmNjrQ5u46XxtujoaGJjY61bx7qW7t94\naXy/qKgonWB20uZvecMwegCPA+NM03RUHLE1LK2xAXWeNS989e7dm40bNzYZjTl58iQnT55k9+7d\nLuuTkpLo0qULnTt3JiMjw1rS0tLa9Q/t78Dty+vU3BlBUIVd94XDKJMETns/+1FRUda11GlpaW4/\nvyPNvLy8nPLyck6dOtXirWMpKyujtrb2tI9dW1vLiRMnOHHixGn3TUhIICUlpdklNTWV1NRUkpKS\niIryuN5os4LxIF1xWIJNMH3PBdMIuiMVubq6mqqqKpfOqzcXZ53ibOx6+1WXdakxNv7zn/9Yv+/b\nt9cfL98tzXVoHT83t855iYqKYk9eHlG2KOx2O4UkkNmjJ7boGKKio7FFx1BwYB9nJtTxwzFjrMdQ\n5za4uHMK/TygC/C5YRiOddHAKMMwbgYc345ZuI5eZwGft7OdYad///7ceeedFBYWcuTIEY4cOUJR\nURFHjhyhoqKiyf5lZWWUlZWxd+9el/UxMTF06tSJzp07u9x26tSJ5OTkVj9sgQrcwTDKrGI0Iv4X\nyM++zWazOucdO3Zs033sdjvV1dWUlZVZne2WltLSUsrKyk77mI6R9aKiolbb6uhsO27T0tJcbpOT\nk9vcAQ+mg3RnisMizXN3BN0x4uvc8W3Lz42X5rZVVVW16SRjsGrcuW3c0XVe586+LXWUPe3kNo7T\nX7z7Jp179qXfBT9w2S+6qIj4+BqSk5Pb/d6Ib7jTuX4XONvpdxvwV2A78EcgDygAxlFfRRzDMFKB\nYdRfpy2NJCYm0rt3b3r37m2ts9vtlJaWUlRUZC1Hjx7l6NGjzR641dTUWJ3zxmJjY62OdseOHV2W\nxMTEgKY+BcN1asHQyQ8lGmUSbwiGz35bOSqWx8XFtalDXldXx6lTp6yU8tLSUk6ePNnk95MnT7aa\nlmi32087Eh4VFUVKSgrp6emkpaW5LI51sbGxQZfm2pjisAQTX33POa7xdXRem7t1/tk0d9Cte3cO\nffYRdTU1DUs12R3TePzxBSQkxLvcv64utBJEbTab1VFtbXHu0O7dt4+jdbF0yu5aP4obE0NUdAxH\n8w9TevIEZ557HlHRMXzy+nLm/uYOkpKS2tXZ9aeW4vQLDz/IBRMuJdYpJuqYK/i1uXNtmmYpsM15\nnWEYp4Bjpmlua/j9MeB+wzB28t1UXIeA173V4HDnGLFISUmhb9++LtvKy8utjvbRo0f59ttv+fbb\nbzl27FizgbW6uprCwkIKCxtfBl8vMzmBPe+tJDYphbikZOKSU+k9aDBv/edtLrv0x0RHR/vkNQaT\nUDrQDzSNMom0LioqyrrOujV2u52Kigqro+1YTpw4Yd2eOHGi1ZHwuro6SkpKKCkpaXGfpKQkbDYb\nXbMyKdi8viHWpxCXnEq/YaOC5nIOxWEJFjExMfxkwg95bdkz9Dp3KNTVcfDrTYwb9n127drVpDPc\neES4cSfZ+WdPHGs6bsIp4NSp02fJeMpxPa1jqaqqovjESRLTOkJUNKXHjtKnRzd6dO/epg5yc4sn\nx5dvv7OKk5UxdD7T9cTH8bJyqK6x6nj0vfBi1m/4LKRiSkuXI1x0+U9Y/vAcLvrpfwM65goV7a2s\nYsepqJlpmg8ZhpEELALSgTXARKdrtKUdEhMT6dGjBz169HBZ7zjIcu5sFxcX8+2333L8+PEWz2ja\nqyopK8qHItfqg18CX37xOcnJyaSnp1vXATqWtLQ0q0BPJHTA5TsaZRJpP5vNZk0FlpmZ2eJ+NTU1\nLp1tR2fa+ffWCrV91zkvpai4aRr653Fx5B8+RMeOHUlPT7eynDp16kRiYmJIjPiIABw9epTy8nKX\nDm5zS+NOcOP1joySznE2SrdtBCA91sbmL79g85dfBPIlWhyd37i4OJfbxj+7s835d+fLTaqqqrhr\nznyXEVWA1cue4ZfXXuvX7/+Wsgo+/+g9Lp32K7+1w69sNq6f8lNio+v/L3XMFRra1bk2TXNMM+ty\ngdz2PK64Jyoqykr3PuOMM1y2OTrexcXFLsuxY8c4nJ9PVCsHT22plpuUlGRdE9i4Mq7zogq54aO5\nUaaqqipWv/8BNpstaIoliQSD9hQSi4mJsWJ7a49fUlLC8ePHrc634+fjx49z8uTJVu+7b98+9u3b\n12RbfHy81dF2Xjp37myNiIsEg5UrV/L558FV2seRzty4A+y4zKRxh7a5223fmLz7yQZ6Dh6GLSqK\nvM8/ZfLEcVw86genb4CXBFOBt+ay5z564x8MG/+jkE+bPt3lCDqmCi3q8YQ55453Y2vWruOVf79D\n78HnU1dZQeGOLQzs25uU5CTrIO10nWtHMZ+CgoJW94uLi7OmqElKSnKZO9bxc2JionWreWMDy3F9\nWOPqyc5VlB23RUePUlpaSlRUFLGdMln54dqgKJYkEmj+KCQWFxdnTdnYnJqaGt57/33+8+Facvr1\np66ygpLD++nSMY3amhpOnTrV7P0qKyspKChoNrbHxsbSuXNnl0KajiVcpo2U0HHo0CGP7+vc4W2u\nA+zpNm9U++/Xrx+XjB9nnZy7RZ2sJtlz5/ziZ6xY9T4JHZKA0E2b1mV34UWd6wjmGqTiGXN108Dt\nnJbonI7Y+FpBu93e/JM0qKqq4tixYxw7dqxNbXOkTdbV2YmJiSYzM5MOHTpY1X7j4+OtW+clLi7O\num1LIYu2jCoF4xQ2p+MonOJYnOcEdp4nuLm5gR2LO9VBo2w2sNupKS5i9NTpLP/bkqAoliQSKMFS\nSCwmJoZLxo9nzMUXfxfHrv+F9fyVlZUuGU3OP5eUlDQb26urq1vseCcmJlpTRjpPHdmxY0evXkYU\ninFZfGPSpEls3rwZu93u0uE93RIbGxv0J/EDXY8gGAuZNn5PfjBieFhcqqbL7sKHOtcR7nSBuy1p\niY4KuY6quI2r4zpPV9PcNGPNsdvtLiMqpxtBb46jGmXjdCtHutbxkhIO5BeSkpmDLSqK1R+v5ax+\nfenTuxfR0dFER0ezd98+Nm39hsy+BthsvDNvPsOHnMPAAQOseQUdi+M5HbeOg1LnW8dSV1dn3Tov\ntbW11m1NTQ21tbXWz80tzU2dUVVV5d/KoVHRxCV2ICahA536DSAqOkZzX0vEC6Z0Smg51sfHx5Od\nnU12dnaTbbW1tRw/ftw6Meq8FBcXN9vxLi8v5+DBgxw8eNBlvSOLqkuXLnTu3JkuXbpYHe/4+Hi3\nXkuwTi0mgdHS/6+0XyiMqAb6BIQ3hdNriWTqXEu7tbVCLtQfrDmnFDunGTtGTMvKyti5J48OySnU\nVldSV13tUbvsdrs1ctuShGgb1d/Wj750iLaxb28e+/bmueyTEmOjfP8OAFJjbGz9+mu2fv21R20K\ndjExMVZqvmNJSEhwSd93Tulf98mn7KiJb3JWW0RCX3R0tJXu3Zij4+0opOkopnn06NFmr/Ouq6uz\n9mssNTXVSm13XhISEprsGywZASKBUFpaysInnwLg1pt+7Ze5jjWiKuIeda7Fr6Kjo62q4y15+51V\nFHfqaXXY7HV11FZXcWDHdnpHVTLk3MFNUpsdqc+NU6GdR3Orq6tbnVs2VDWeNsM5Nd55aS6N3pFm\n77jOvbnCc470yxMnSznvvPNcvlTHjf0h/w6ylDGRYNCedMpQSHlureNdWVlpdaQd00Y6bpuLwY7L\njXbv3u2yPiUlhS5dupCZmWktm7/62uOMgFB4X0Va8uSixby74QtGXf4TAKbdMYtxw4Zw04wbfP7c\nGlEVaTt1riXo2aKiiIlPIDo+keT42Cbzf7vDUajr7XdWsaMiipxevamrrcFeW4u9ro7CfXl0j6lh\n8Dnf4/MvvuRQlY1OWdlQV4fdXp/KXVxYQGZ0LWeeeYaV3u14bOdboEm6eFRUFHv37+erb3aS0asf\n2GwU7d3F+WcPxOh/JlFRUURFRRETE2Olpjt+jomJsVLanX/2RuGUlpwu/TIUUsZEAsHTz0Y4pDzH\nx8fTtWtXunbt6rLeMXtFUVERR48etZaioqJmLxly1PTYs2ePy3pbXDx7C/aSkN6JhLROJKR3wm5v\n/VKYcHhfJXKVlpby7oYv+MVv7rfW9frN/bzw8INM+3mpX0awRaRt2ty5Ngzj18CNQO+GVVuBuaZp\n/sdpn7nADdTPcb0W+LVpmru81lqJCL4soOG4DnvC+HH8Z858ep892GX73rfe4LaGlKd+/fpx15z5\nnDHcdZTkq0/XcqeHaVFVVVUs/897LimNZ/5gLKuXPcNVV05q9TH9PerS1vRLpYyJNM/dz0a4pzw7\nz17Rv39/a73dbqe0tJSioiKX5ciRI812uu1VlZw8vJ+Th/e7PMaerCxee62UrKwssrKyyM7OJikp\nKezfVwl/C598yhqxdjbq8p+w8Mmn+N09vwlAq8TblF0THtwZuT4AzAJ2Ajbgl8A/DcMYYprmVsMw\nZgG3AtOAvcA84G3DMAaaplnp1VZLWPPHaGhbnsMX7fC0yFEgRl3caatSxkSa585nI9iKoPmLzWYj\nJSWFlJQUl8wk5073kSNHOHLkCEVFReTn5zeZzcBms1n7OEtOTiY6OoZu3XtwfN8uEtI7E5+Shi0q\nKuzfVxEJHcquCR9t7lybpvmvRqvubxjNHmYYxjbgDmCeaZorAQzDmAYUApOA5V5qr0QIT0ZD3T3j\n15bnCIZRWY26iEiw8mSkpa33aa3TXVRUxHur36f81CmSk5OsFPPGFcy/m2niOAeO1Fcwt0VHk5DW\nidqoaApSOnDw4EGysrKIjY119+WL+MWtN/2aaXfMopdTWjjAmn++yrLH/higVkl7OWJhdW0Nb364\njvHX3WRt03Fe6PLommvDMKKBnwLxwBqgD5AFvOvYxzTNE4ZhrAeGo861eMCdER9Pz/i15Tm8OSrb\nWsr7Db+7m7ffWWXt5wimgRrNCsb5LUXCWah95jyJu94YnbHZbGRmZjLlmqtd1tfU1FBUVERhYSEF\nBQUcOXKEgoICysvLXfaz19ZSfqwIgL1HYcmSPdhsNjIyMsjJySE7O9u6ba5iuYi/JScnM27YEF54\n+EErPXzNP19l3LAhAPzhoYcB/1UQl/ZziYX2aIpPVbDjy030P/c8ax9l14QmtzrXhmF8D/iE+k51\nOfAz0zR3GYbh+FYsbHSXQkCTD4pPhdLIbkup5gN7dee3f/hTUKUDqViZiH+F0mfOk7jr61gdExND\nTk4OOTk51jq73c7Jkyd57/33+WTjF6RndKG2vIy6ykYd7obR8KKiIr766itrfadOnejatav1uDk5\nOepwS0DcNOMGpv38u6m4lj32R5a9+DLT7pgVkAri4rlmY6FxFq8vfoI+A79HbJDFe3GPuyPX3wDn\nAGnUj1y/bBjG6Fb2twGtl/AUaadQu06xcar5Db+7m9/+4U8tHnD6ezSrccpmoNPiRSJJMFyK0hae\nxN1AxGqbzUZqaipXXnEFl/7oR9b7OmL4hRQXF1NQUEB+fr410l1X53rIcuzYMY4dO8aWLVusdY4O\nt2PJyckJyr+RhJ/k5GSreJkqiIeulmLh9y8ay/ZN6zln+CggeLOWpHVuda5N06wGHHNifGEYxlDg\n18D8hnVZuI5eZwGft7eRIuHGOdX87XdWnfaA01+jWS2lbAbbCQqRcKYCgb7R+H1NSUmhZ8+e1u+O\ntPL8/HxrKSgoaFI8rbkOd5cuXejWrRtdu3alW7duZGVlER0d7fsXJRFLFcTDj91eR9Ghg+zf8U3Q\nZi3J6bV3nutoIMo0zTzDMAqAccBXAIZhpALDgCfa+RwirQq16xQ94Y/RrFBKrxdpL19NeRIpU6l4\nEneDPVY3l1ZeW1vr0uE+fPhwsx1uR0r5l19+CUB0dDQ5OTlWZ7t79+507NgRm83m19ckIsGn5Vj4\nET8aeQGxsTXtOs6LlO+hYOXOPNd/AP5N/ZRcKcDPgYuABxt2eYz6CuI7+W4qrkPA615sr7RTOH7g\nQuk6xea09YDTk9Esd/7eoZZeL+IpX015EklTqXgSd0MxVkdHR5OdnU12djZDhtQXj6qtreXIkSMc\nPnzYWhqnlNfW1nLw4EEOHjxorUtMTLQ62t26daNbt24kJib6/TVJeFAF8eaFwnFuS7Hwmksv0fdQ\nGHBn5LoLsAzIAUqAzcAlpmmuBjBN8yHDMJKARUA69VXEJ5qmWeXdJounwvkDFyrXKTbHVwec4fz3\nFvGUrzI0IjHzw5O4G8qx2sExKp2Tk8N559VX9q2urqawsJBDhw5Zy7Fjx1zuV15ezq5du9i1a5e1\nLiMjg+7du9O9e3d69OhBly5dNLotbdJaBfFIvd46lI57fBELI/F7KBi5M8/1aUsPmqaZC+S2q0UR\nwt9n1iLhAxfK1yl6O8h68vcO9pRNEW/wVYaGPzM/gmlkxpO46+59gun1tiQ2NtbqJDuUl5dz+PBh\nDh06xMGDBzl06BCnTp1yuZ9jfm5HOnl8fDzdunWjR48e9OjRg27duqk6ubSouQrikdqxDsXjXG8f\ntyoDMTi095pr8UAgzqzpAxf8vBlkPfl7h2LKpkikCaWRGW8I5debmJhIv3796NevH1A/3VdxcbHV\n2T548CAFBQUu6eSVlZXs2bOHPXv2WOsyMzOtzvaAAQOIj4/3+2uR4OVcQTyS6ThXgoU6134WimfW\nAiUURiu8KRhebzikbIq0xlcZGv7I/AjF74/2xLVQfL2tsdlsdOrUiU6dOvG9730PqE8nz8/Ptzrb\nBw4coLS01OV+R44c4ciRI2zatImMjAxuuukmpY6LSBPKQAwOUYFuQKQ53Zk1Xxkz+mJ2fdr08Xd9\n+iFjRjdtjzuqqqp4+51VvP3OKqqqvHOJ/Zq167hrzny2V8awvTKGu+bMZ83adV557GDk7dfbnr+3\nYwT9kgnjQ+7gVeR0HBkaq5c9w/4d37B/xzesXvZMuzM0fPW4zgL1/eGp9sa1UHu9LWntOzI2Npae\nPXsyYsQIfvazn3HXXXdx++2385Of/IShQ4eSk5Pj0pEuKytrMhe3iPj2ODdU+ON7SE5PI9cRIpSK\nZoXbaMXp+OL1KsVbpGW+ytBQ5sd3Ii2Ot8Td70ibzUZ6ejrp6emcffbZQP17eejQIYqKiujZs6fm\nzxZpho576ul7KPDUufazQKZsBEPRrLaItOtmfPV6FWBFWuarAoi+LKwYSil/3ohrofR6m+Ot78i4\nuDj69OlDnz59fNVUkbDgz+OeYLiUryWhXOA3HLgzz/XvgKsAAygH1gGzTNPc0Wi/ucAN1E/HtRb4\ntWmauxAg8GfWAl00S/xLAVYkfAT6+8PfQv316jtSxP/8cdwTyoUWxffcueb6ImAhcAEwHogF3jEM\no4NjB8MwZgG3AjMb9isD3jYMQ6UtnYwaOYJHcu/lrPgazoqv4ZHce/WBdBJp181E2usVEc95+v3h\ni9oYrfFWXNP3pYgEE+eMlJ79B9Cz/wB+OG0my//9jl9iqwQ/d+a5/pHz74Zh/BI4Anwf+NgwDBtw\nBzDPNM2VDftMAwqBScByL7U5LITDiKKvUvZCfbTCXZH2ekWkfdz9/gjEKIsjrr383NOc2fC8Oz/9\nkGsuvcTtuBaq35ehntYuIk0pI0VOpz3XXKc33B5ruO0DZAHvOnYwTfOEYRjrgeGocx12fNkpjLTr\nhSPt9YqIfwS6sFhdXR07v/7S+jmS6MSpiEjk8ahzbRhGFPAY8LFpmtsaVmc33BY22r3QaZsEmLcL\nMPiyUxiqoxWeirTXKyK+F6hRFkenfvx1N7msj7Rq4TpxKuEgmIt3+ZsyUuR0PB25fgIYCPygDfva\ngMg6XR2kfJUaqE6hiIg4U+rkd/QdKaFMxbtcKSNFTsftzrVhGH8GfgxcZJrmYadNBQ23WbiOXmcB\nn3vcQvGKQKcGhhKdoRWRcKFRFhHxlI4dm6eMFGlNm6uFG4Zha+hYXwH80DTNfY12yaO+gz3O6T6p\nwDDgEy+0VdrhdKMIUm/N2nXcNWc+2ytj2F4Zw11z5rNm7bpAN0tExCOOUZbVy55h/45v2L/jG1Yv\ne8bnoyyaBUEk9OnYsWWOjJRLJoxXx1pcuDNy/QQwhfrOdZlhGI7rqI+bpllhmqbdMIzHgPsNw9gJ\n7AXmAYeA173YZhGf0BlaEQlHgRhlUeqkiIhEInfmub4RSAU+AA47LT9z7GCa5kPUz4W9CNgAdAAm\nmqapid8CTKMIzXOe+/WdVe/qDK2ItMjfc0V7UyBGWTRHtUho07GjiPvcmee6TR1x0zTzW7HoAAAg\nAElEQVRzgVyPWyQ+oVGEphoX6fji3bfp3LNvk2sTRURU1MczKuYlErp07CjivvbMcy0hRgUYvtNS\nCvgLDz/IBRMuJdbpfVHhH5HIpktGJNBUaFMCRceOIu5R5zrChPIogjcPLloq0nHR5T9h+cNzuOin\n/w3oDK2IaFopCSxlTUighfKxo4i/qXMtIcFvBxc2G9dP+Smx0TWAztCKiEjgKGtCRCS0uFPQTCQg\nnA8uevYfQM/+A/jhtJks//c7HhcVaq1Ix4SxYzW9gohYVNRHAkVTIYmIhBaNXEvQ80VKpop0iEhb\nKV6IiIhIW6hzLRFLRTpEpK0ULyQQxoy+mJVz5jeZxUKFNkVEgpNbnWvDMC4CfgN8H8gBrjRN841G\n+8wFbgDSgbXAr03T3OWd5kok8uXBhYp0iEhbKV6IvylrQkQktLg7ct0B+AJYAqwA7M4bDcOYBdwK\nTAP2AvOAtw3DGGiaZmW7WysRSQcXIiISqZQ1ISISOtzqXJum+R/gPwCGYbhsMwzDBtwBzDNNc2XD\numlAITAJWO6F9kqE0sGFiIQrzWEsp6OsCQkkxSiRtvNmtfA+QBbwrmOFaZongPXAcC8+j0Qox8GF\nqniLSLhYs3Ydd82Zz/bKGLZXxnDXnPmsWbsu0M0SEQEUo0Tc5c2CZtkNt4WN1hc6bRMRERE0h7GI\nBDfFKBH3+WOeaxuNrs0WERGJdJrDWESCmWKUiPu82bkuaLjNarQ+y2mbiIiIiIiISNjxZuc6j/pO\n9DjHCsMwUoFhwCdefB4REZGQN2b0xez6tOnoz65PP2TM6KajRSIi/qQYJeI+d+e5TgLOdFrV1zCM\nc4FvTdM8YBjGY8D9hmHs5LupuA4Br3upvSIiImFB0wyKSDBTjBJxn7sFzYYCqxt+tgOPNPy8FLje\nNM2HGjrgi4B0YA0w0TTNKi+0VUREJKxomkERCWaKUSLucXee6w84TSq5aZq5QG472iQiIhIxNIex\niAQzxSiRtvNHtXARERERERGRsKbOtYiIiIiIiEg7qXMtIiIiIiIi0k7qXIuIiIiIiIi0k7vVwn2q\nrq7OduLECT777LNAN0VEpFUnTpygrq7O5sl9FetEJFQo1olIJGhPrHMWVJ3rysrKuCuuuILdu3cH\nuikiIq264oorWLRokUfzkSjWiUioUKwTkUjQnljnzCeda8MwbgZ+A2QBm4FbTdNs02nLM844g7PP\nPtsXzZIIsnr1apYuXcqyZcsC3RSRZinWiTco1kmwU6wTb1Csk1Dh9WuuDcO4Gvg/6ue6HkJ95/pt\nwzC6ePu5RILVM888w+TJk/nXv/4V6KaIiPiMYp2IRALFOmkrXxQ0uwtYZJrmc6ZpfgPcCJwCrvfB\nc4kEnfXr17Nz5046duyIzdbuSzdERIKSYp2IRALFOnGHVzvXhmHEAd8H3nWsM03T3vD7cG8+l4SO\njRs3Mm3aNOx2OwB5eXlMnjyZF154wdrnySef5PHHH7d+3759O/fffz9Tpkxh5syZLFmyhMrKSmt7\ndXU1zz33HL/61a+YOnUqv/3tb9m6dWuLbSgpKeGee+7hoYceorq62gevst63337LkiVLuOOOO4iJ\nCaqSBiLiY4p1IhIJFOtEWubt/5IMIBoobLT+CDDAy88lIeKss86ivLycvLw8+vbty9atW0lJSXEJ\nmtu2bePKK68EoKCggAcffJCf//zn3HLLLZSUlLB48WIWL17MzTffDMDixYs5dOgQ/+///T86duzI\n+vXrefDBB3nkkUfIyclxef6jR48yZ84cBgwYwE033dTiWcdnnnmGjz76qNXX8re//a3FbXV1dSxY\nsIBJkybRvXv3Nr03IhI+FOtEJBIo1om0LNhOwXQOdAPE+5KSkujTpw9btmyhb9++bNu2jf/6r//i\nlVdeobKyktLSUgoKChg0aBAAK1as4KKLLuLSSy8FIDs7m+uvv54HHniAGTNmcPz4cd5//32eeeYZ\nOnbsCMDll1/OF198werVq5k6dar13IcOHWLu3LlceOGFXHfdda2285prruGKK67w+HW+/vrrxMTE\n8OMf/9jjx5CQ42nMUqwLQ4p1EsYU68SiWCdhrN0xy9ud66NALfVVwp1lAfltuH81EO/lNkkQGDhw\nIFu2bOHyyy9n+/btTJ06lXXr1rFt2zZKS0vp2LEj2dnZAOzdu5f9+/c3e7bxyJEjFBQUUFdXxy23\n3OKyraamhpSUFOv3qqoqfv/73zNq1KjTBmCAtLQ00tLSPHp9u3fv5s033+RPf/qTy3pHypSELU9z\n0RTrwpRinYQpxTpxoVgnYard1xh4tXNtmmaVYRibgHHAPwEMw4gCxgILTnf/AwcO/BDY4M02SXAY\nNGgQq1evZu/evURHR9OtWzcGDRrE1q1bKSsrc5mmo7KykgkTJjR7pjAjI4O9e/cSFRXFww8/TFSU\na9mAxMRE6+fY2FgGDx7Mxo0bueKKK+jUqVOrbTxd+pDNZnO5nsjZ9u3bOXHiBDNnzrTW1dXV8dxz\nz/Hmm2/y1FNPtfrcEpoaYpan91OsC0OKdYp14UixThpTrFOsC0eexjpnvkgLfwR4zjCMjcBnwB1A\nIvBXHzyXhAjH9TkrV6600oQGDRrEihUrKCsrc0nb6du3LwcOHLDOeDbWp08f6urqKCkp4ayzzmrx\nOW02G7fddhuPPvooubm5zJ0710o3ak570ocuvvhiBg8ebP1ut9uZN28eo0ePZsyYMR49poiEHsU6\nEYkEinUizfP6VFymab4C3A3MBb4AzgEmmqZZ5O3nktCRnJxMr169WLNmjRWEBw4cSF5eHvn5+Qwc\nONDad9KkSZimyeLFi8nLy+Pw4cNs2LCBxYsXA9C1a1dGjRrFggULWL9+PYWFhezcuZMVK1awadMm\nl+e12Wzcfvvt9OrVi9zcXI4fP95iG9PS0sjOzm51aUlKSgo9evSwlp49exITE0N6ejpdu3Ztz1sn\nIiFEsU5EIoFinUjzfFLQzDTNJ4AnfPHYEroGDRrEvn37rFSh5ORkevToQUlJiUug6tWrF3PnzuXF\nF1/k97//PXa7nezsbEaOHGntc8stt/CPf/yDpUuXcuzYMVJTU+nfvz/nn3++tY+jemR0dDR33nkn\njzzyCLNnz2bu3Lmkpqb66VWLSKRRrBORSKBYJ9KULZguzLfZbENfeumlDc7XaYiIBKMtW7YwZcqU\nYXa7/TN376tYJyKhQrFORCJBe2KdM6+nhYuIiIiIiIhEGnWuRURERERERNrJJ9dct0PCBx98wO7d\nuwPdDhGRVh04cAAgwcO7K9aJSEhQrBORSNDOWGcJts413bt3p1+/foFuhohIq9pbr0KxTkRCgWKd\niEQCb9UhC7bOdcUZZ5yBCl+ISIio8PR+inUiEkIU60QkEnga6yy65lpERERERMLK6tWrmTZtml+f\n84EHHuCvf/2rX59TgkuwjVxLEHjggQfo06cP1113nV+eb+HChZw6dYpZs2b59Hm2bt3KG2+8QV5e\nHsXFxdxzzz0MGzasyX4vvfQS7733HmVlZQwYMIAZM2aQk5Njba+qquK5555j7dq1VFdXc+655zJj\nxgzS0tKsfU6ePMmSJUvYtGkTNpuNCy+8kOuvv56EhO8u5SgqKmLRokVs3bqVhIQERo8ezdSpU4mO\njgbqvxSWLl3KsmXLmrRx8uTJzJo1i6FDh3rzLRKJeOEa/5YvX87f//53l3XdunXj8ccfd1nnj/i3\nZcsWZs+ezbJly+jQoYPL8994441cdtllXHbZZd5+C0QiVrjGtYULF/Lhhx8C9XNfZ2RkMHr0aK66\n6irrWErE3zRyLR6x2+3U1tYGuhluqaqqok+fPtxwww0A2Gy2Jvu89tprvPXWW8ycOZP//d//JT4+\nnnnz5lFdXW3t89e//pWNGzdy9913M2/ePIqLi3nooYdcHufxxx/n4MGD5Obmcu+997Jt2zaefvpp\na3ttbS3z58+3bm+99Vbef/99Xn75ZR+9ehHxllCMfwA9evRgyZIl1vLggw+6bPdX/GuNzWZrNjaL\niG+FYlyz2WwMGTKEJUuW8MQTT3DFFVfwyiuv8M9//jPQTZMIppFrcbFw4UK2bdvGtm3bePPNN7HZ\nbDz55JMUFhYye/Zs7rvvPl588UX279/PAw88wMCBA3nttddYtWoVx48fp2vXrkyePJnhw4cDUFdX\nx1NPPcWWLVs4fvw4GRkZTJw4kUsvvRSoH01xnHWcPHkyAHPmzGHQoEFef21DhgxhyJAhLW632+38\n61//YvLkydaI8G233cb06dPZsGEDI0eOpKysjNWrV3PnnXda15DdfPPN3H777ezYsYP+/ftz8OBB\nvvzySx566CH69u0LwPTp05k/fz7XXnstHTt2ZPPmzRw8eJDZs2eTlpZG7969mTJlCs8//zzXXHON\nzriKBEA4xz+oH9lxHmF25s/4JyL+E85xzW63ExMTY8W1CRMmsH79ej777DOuvPLKJvsXFBSwdOlS\ndu7cSUVFBd27d2fq1Kmcc8451j7V1dW8/PLLfPzxx5SUlNC5c2euuuoqxo4dC8D+/ftZtmwZ27dv\nJyEhgcGDB3PdddeRkpJiPUZtbS1/+ctf+Oijj4iJiWHChAlMmTLF2l5aWsqzzz7Lpk2bqK6uZuDA\ngUyfPt0lS0hClzrX4uL/s3fncVJVZ/7HP9X7vkF3s28CFxpFCZtAUIiAxmTUmTgaQ4YYNe4ZHWdc\nQ4KowV80MS5xgZCEEDMGMxqjMxqXoIisAgphO+yLDd0sDb13V1d3/f7ormtV79Vd3bX09/161aua\nW7eqThVVT93nnuecc+ONN3L8+HEGDx7Mt7/9bQBSU1MpLCwE4I9//CPz5s0jNzeX5ORkXnvtNVav\nXs2tt95K37592bFjB88++yxpaWmMGTMGt9tN7969uffee0lJScEYw0svvURmZiZTp07lyiuvJD8/\nn8rKSu68804AkpOTm23ba6+9xuuvv95q+5999ll69erVoddeWFhIcXGxT5BNSkpixIgRGGOYNm0a\nBw4coLa21mef/v3707t3b/vg0hhDcnKyfWAJMHbsWBwOB3v37mXSpEkYYxg8eLDPge7555/PkiVL\nOHr0KEOGDOnQaxCRjov0+Hf8+HF+8IMfEBsbi2VZzJ07l969ewPdG/9EpPtEelxrXOkSGxtLaWlp\ns/tWVVUxfvx45s6dS2xsLB9++CGPP/44zz33nB0Ln332Wfbs2cONN97IkCFDOHnyJGfPngWgvLyc\nBQsWMHv2bG644Qaqq6v5wx/+wC9+8Qsefvhh+3k++ugjLrnkEn72s5+xf/9+XnrpJbKzs5k1axYA\nv/rVrygoKODBBx8kISGBl19+mZ/+9Kc888wz6lyJAEquxUdSUhIxMTHExcU128Nx7bXX2gdWNTU1\n/OUvf2HBggWMHDkSgJycHHbt2sX777/PmDFjiI6O5tprr7Xvn5OTw+7du1m7di1Tp04lISGB2NhY\nampqWuxR8bj00kuZNm1aq/tkZGT4+5JtnuDZ+DHS09Pt286ePUtMTEyTcYIZGRk++6SlpfncHh0d\nTUpKis8+jZ/H8+8zZ84ouRYJgkiOfyNHjuTOO++kf//+FBUV8eqrrzJ//nx++ctfkpiY2K3xT0S6\nTyTHNfhy+SS32822bdvYunUrl19+ebP7DhkyxOf46rrrrmPjxo18+umnfP3rX+fYsWOsW7eOBQsW\ncN5559mvz+Odd95h2LBhfOc737G33XHHHdxyyy0cP37c7nnu3bu3Pb69X79+HD58mLfeeotZs2Zx\n7NgxNm3axKJFi+z3+K677uKWW25h48aNdoWAhC8l1+KX4cOH238fP36c6upqFi5c6LOPy+Xy6bV4\n5513WLlyJadOncLpdOJyuRg6dKjfz52SkkJKSkrHG99Bbre7S8YABmo9PRHpHuEc/7yHxAwaNIgR\nI0Zw6623snbtWrvcsTldFf9EJDSEc1wD2Lx5M3PnzqW2tha328306dO55pprmt23srKSV199lS1b\ntnDmzBlqa2txOp2cOnUKgIMHDxIVFUVeXl6z9z906BDbt29n7ty5PtsdDgcFBQV2cu1Jmj1GjhzJ\nW2+9hdvtJj8/n+joaEaMGGHfnpqaSr9+/cjPz+/w+yChQ8m1+CU+Pt7+u6qqfim4H/3oR2RlZfns\nFxsbC8Ann3zC8uXLuf7667Esi4SEBP7617+yd+9en/3bc/DW1WXhnrOjjXuVi4uL7R+VjIwMXC4X\nFRUVPr033vfJyMigpKTE57Fra2spKyvz2Wf//v0++3h6dTxjEpOSkqiurm7SzvLycvt2Eek+kRT/\nkpOT6devHwUFBUD3xj/PfRs/DtTHN8U2ke4T7nHt3HPP5eabbyY2NpbMzEyiolqeq3n58uVs27aN\n733ve/Tp04e4uDh+/vOf43K5AIiLi2u1LVVVVUycOJHvfve7TW7znk9CnSc9m5JraSImJoa6uro2\n9xs4cCCxsbGcPHmyxbN8u3fvxrIsLr30Unvb8ePHmzxfe2ao7Oqy8NzcXDIyMti2bZtdNlRRUcG+\nffu47LLLABg2bBjR0dFs27aNCy+8EID8/HxOnTqFZVkAWJZFeXk5Bw4csA9K//GPf+B2u+0zlaNG\njeL111+nuLjYLpvaunUrSUlJDBgwAKgvJaqtrfV5HIADBw7Yt4tIYPWU+FdZWcnx48e5+OKLge6N\nf3379sXhcLB//357nCPUTzZUUVGh2CYSYJEc1+Lj4+nTp0+bzwX1bZ85c6Y990NlZSWFhYX2ZGuD\nBw/G7XazY8cOn7klPIYNG8b69evJzs5udWx04xMNe/bsseNe//79qa2tZc+ePXbcLC0t5dixY/bx\nn4S3difXlmXFAI8C3wZygWPAMmPMY432ewS4CcgA1gC3GWP2BazF0uVycnLYu3cvJ06cICEhwWcG\nRG+JiYlcccUVLFu2DLfbzahRo6ioqGD37t0kJSUxY8YM+vXrx6pVq/j888/Jyclh1apV7N+/n9zc\nXPtxcnNz2bp1K8eOHSMlJYXk5ORmg1Zny4eqqqp8fgAKCws5ePAgqamp9O7dG4fDwTe/+U1ee+01\n+vbtS05ODq+88gpZWVl2IE5OTuaSSy5h2bJlpKSkkJiYyG9+8xssy7IPHAcMGMAFF1zAiy++yC23\n3ILL5WLp0qV89atftc9snn/++QwYMIBnn32Wf/u3f+PMmTP86U9/4rLLLiMmpv5rOWjQIM4//3xe\neOEFvve975GTk8OxY8f47W9/y7Rp0zTrrkgXiNT49/vf/54JEyaQnZ1NUVERK1asICYmhunTpwN0\na/xLTExk1qxZLFu2jKioKAYNGsSpU6d4+eWXGTlypH3AKSKBEalxzV99+/Zl/fr1TJgwAYBXXnnF\n5/acnBxmzJjB888/z4033sjgwYM5efIkJSUlTJ06la9//et88MEH/PKXv+Sqq64iJSWF48ePs3bt\nWm6//Xa7t/7UqVMsW7aM2bNnc+DAAd555x2uv/56oL5jZOLEibz44ovceuut9oRmvXr1sldqkPDm\nT8/1Q9QnzfOAHcBE4HeWZRUbY54DsCzrfuCHDfscoj4Zf9eyrDxjTNP6VglJV1xxBb/61a+4++67\nqamp4YUXXgCaL/G57rrrSEtL4/XXX6ewsNCeJfZb3/oWALNnz+bgwYM89dRTOBwOvvrVr3LZZZfx\n2Wef2Y8xa9Ystm/fzn333Ud1dTUPP/xwlyzZsG/fPns2R4fDwbJlywCYOXMmd9xxBwBXXXUVVVVV\nvPTSS1RUVDB69Gjmz59vl0MBfP/738fhcPDkk0/icrm44IILuPnmm32e6+6772bp0qU8/PDDOBwO\npkyZwo033mjfHhUVxUMPPcSSJUt46KGHiI+PZ+bMmfZMnh733HMPK1as4KWXXuLMmTP06tWLyZMn\n86//+q8Bf39EJHLj3+nTp3n66acpLS0lLS2N0aNH8/jjj/scZHdX/AO44YYb+Mtf/sLLL7/MyZMn\nycjI4Pzzz/eZKEhEAiNS45rD4Wiz/Nz79uuvv54XXniBhx56iLS0NK666ioqKyt99r/55pv54x//\nyK9//WtKS0vJzs7mX/7lX4D60u+f/vSn/OEPf+DRRx+lpqaG7Oxsxo0b5/M8M2bMwOl08sADDxAd\nHc03v/lNZs+ebd9+55138tvf/pZFixbhcrnIy8vjRz/6kWYKjxCO9o4LsCzrLaDAGPMDr22vAeXG\nmHmWZTmo781+0hjzVMPtaUAhcL0xZkWbjXE4Jr7yyisbPetnioiEqu3bt3PddddNcrvdn/p7X8U6\nEQkXinUi0hN0JtZ5a3nUf1PvALMsyxoBYFnW+cC0hu0AQ6kvF//AcwdjTAmwAdC88iIiIiIiIhKx\n2p1cG2NeAFYAxrIsJ7AF+KUxxjNgwTObQGGjuxZ63SYiIiIiIiIScfwpC/934EHgLurHXI8Dngbu\nMcYstyxrKvAJ0NcYU+h1v1eBWmPMdW09x8iRI0ujo6NT2lp0XkQk2IqLi6mtrS3bs2dP8zPDtEKx\nTkTChWKdiPQEnYl13vyZ0OxHwEJjzKsN/95hWdZg6hPu5UBBw/ZcfHuvc6nv5W53ezyzJYuIhLiO\nBivFOhEJJ4p1ItITdDpY+fMADqDxonV1DdsBDlKfYM8CtoE9odkk4Pl2PYHDcbxfv35D//73v/vR\nLBGR7nfJJZfwxRdfHG97z6YU60QkXCjWiUhP0JlY582f5PoNYL5lWUeBndSXhf8H8BsAY4zbsqyn\nG/bZy5dLceU33FdEREREREQkIvmTXP8HUEJ9L3Qu9ctuvQQ84tnBGPOEZVnJwBIgA1gNXGaMcQas\nxSIiIiIiIiIhpt3JtTGmHPivhktr+y0AFnSyXSIiIiIiIiJhw591rkVERERERESkGUquRURERERE\nRDpJybWIiIiIiIhIJym5FhEREREREekkJdciIiIiIiIinaTkWkRERERERKST2r0Ul2VZh4BBzdz0\ngjHmTsuyHMBC4Cbq17heA9xmjNkXgHaKiIgEjNPp5MOPVgEwc8bFxMXFBblFIiIiEu786bkeD/Tx\nusxu2P5qw/V9wA+BW4DJQDnwrmVZ8YFpqoiISOetXrOWexYuYld1DLuqY7hn4SJWr1kb7GaJiIhI\nmGt3z7Ux5rT3vy3L+idgnzHm44Ze67uBR40xbzXcPg8oBK4CVgSuySIiIh3jdDpZ8fZ7fG3eLfa2\nQSNHsWL5YiZPnKAebBEREemwDo25tiwrDvgu8NuGTUOBXOADzz7GmBJgAzClk20UEREJiA8/WsXw\nCy9usn34hRfbZeIiIiIiHdHRCc2uAtKBZQ3/7tNwXdhov0Kv20REREREREQiUkeT6xuBt40xBW3s\n5wDcHXwOERGRgJo542L2rW/aQ71v/Spmzmjaoy0iIiLSXu0ec+1hWdZg4BLgn702e5LsXHx7r3OB\nLR1unYiISADFxcVx7eVzWLF8sV0evm/9Kq69fI7GW4uIiEin+J1cA9+nPoH+P69tB6lPsGcB2wAs\ny0oDJgHPd7KNIiIiATN92lQmT5xgj7G+fcFDSqxFRESk0/xKri3LiqI+uf69MabOs90Y47Ys62lg\nvmVZe4FDwKNAPvBG4JorIiLSeXFxcVw6Z3bbO4qIiIi0k79jrmcBA/hylnCbMeYJ4DlgCbARSAIu\nM8Y4O9tIERERERERkVDmV8+1MeY9ILqV2xcACzrbKBERkVDndDrt0vKZMy5WabmIiEgP19HZwkVE\nRHqs1WvWcs/CReyqjmFXdQz3LFzE6jVrg90sERERCaKOTGgmIiLSYzmdTla8/R5fm3eLvW3QyFGs\nWL6YyRMnqAdbRCSEqepIupJ6rkVERPzw4Uer7GW8vA2/8GL7gE1EREKPqo6kq6nnWkREREREIpqq\njqQ7qOdaRETEDzNnXMy+9U17qPetX8XMGU17tEVEJPhUdSTdwd91rvsDPwMuo36prX3A940xm732\neQS4CcgA1gC3GWP2BazFIiIiQRQXF8e1l89hxfLF9oHavvWruPbyOer5EBER6cHa3XNtWVYm9cly\nNfXJ9WjgHuCM1z73Az8EbgEmA+XAu5ZlxQewzSIiIkE1fdpUnlrwEKPjXYyOd/HUgoeYPm1qsJsl\nIiIt8FQd1TidbFu3mm3rVlPjdKrqSALKn57r+4HDxpgbvbYd9vxhWZYDuBt41BjzVsO2eUAhcBWw\novPNlVCgWRZFROp7sC+dMzvYzRARkXaIi4sjb/AAVjzzBNOv+BYAK555glmTxulYVgLGnzHXVwCb\nLcv6s2VZhZZlbbEs6yav24cCucAHng3GmBJgAzAlIK2VoNMsiyIiIiISbpxOJzsPf8F3753PYGs0\ng63RfPfe+ew8/AVOpzPYzZMI4U9yPQy4DTDAHOBF4NmG3mmAPg3XhY3uV+h1m4Qx71kWB40cxaCR\no/javFtY8fZ7CkoiIiIiErI0oZl0B3+S6yhgszFmvjFmqzHm18CvgVvbuJ8DcHe0gRI6FJRERERE\nRESa509yfQzY2WjbbmBQw98FDde5jfbJ9bpNRERERESkW2kZRekO/iTXa4BRjbaNBA41/H2Q+iR6\nludGy7LSgEnAuo43UUKFgpKIiIiIhCPPMoorly/myJ7dHNmzm5XLF2sZRQkof2YL/yWw1rKsB4E/\nU580/6DhgjHGbVnW08B8y7L2Up90PwrkA28EstESHFrbVURERETC1fRpU5k8cYI9nPH2BQ/pGFYC\nqt3JtTFmk2VZ/ww8DvwEOADcZYx5xWufJyzLSgaWABnAauAyY4xmu4oQCkoiItJeWrpRREKNllGU\nruRPzzXGmP8D/q+NfRYACzrTKAltCkoiItKW1WvWsuLt9+xKp7cWLuLay+cwfdrUILdMRESka/iV\nXIuEEvWIiIiEJu+lGz0GjRzFiuWLGXf+WNasrZ+KRbFbREQiiT8TmomEjNVr1mQ3v2IAACAASURB\nVHLPwkXsqo5hV3UM9yxcxOo1a4PdLBERoeWlG+OycrjjRw+3GrudTifvvvc+7773Pk6nRpWJiEj4\nUM+1hJ3WekQmT5ygXhARkRBU43Ry7PBBrrnjP+1tjWO3SslFRCScqedawk5LPSLDL7zYLhMXEZHg\naW7pxl2bNzB51mVN9vXEbu8Tp4NGjmLQyFF8bd4trHj7PfVgi0izVOkioUbJtYiIiARUc+vJblv5\nNxw4WryPTpyKiD80RFBCkZJrCTvN9YhA/ZrbM2c0PTAT/+gssHQnfd7Ch7//V9OnTeWpBQ8xOt7F\n6HgXy194ln0bPm6yn2K3iDSnrKyMx594ksefeJKysjKf28Kx0kW/dz1Du5Nry7IetiyrrtFlZ6N9\nHrEs65hlWRWWZb1vWdbwwDdZerrmekRWLl/Mv8yeyYcfrVLQ6gSdBZbupM9b+Ojo/5Vn6cZL58wm\nJSWl2dh97eVziIuL04lTkS4QrgndC0uWMu/u+0kYNZ6EUeOZd/f9vLBkqX27P5UuofAe6Peu5/B3\nQrPtwCyvf7s8f1iWdT/wQ2AecAh4FHjXsqw8Y0x1J9sp4mP6tKlMnjjBDqBj58zk9fc+1CQ4naCJ\n4qQ76fMWPgL5f9U4dt++4CH7/p4TpyuWL7Zj+b71q+zkW0T8E64TBJaVlfHBxs/47r3z7W2D753P\ny08+xrzvlJGSktLuxwqF90C/dz2Lv8l1rTHmROONlmU5gLuBR40xbzVsmwcUAlcBKzrbUJHGPD0i\nTqeTexYuUtDqpLbOAl86Z3YQWiWRqjOfN61x3z087/OWzz9nyPimB6IdjQ2e2N2c1pJvEWm/cE7o\nnnvhRaZf8a0m26df8S2ee+FFHrzvXmbOuJi3Fi5i0MhRPvvsW7+K2xc8BITOe9De3zv9tkUGf8dc\nj7AsK9+yrP2WZb1sWdbAhu1DgVzgA8+OxpgSYAMwJTBNFWmeJsER6TlUWtc9vN/nxFHj2fTR++z5\nfHO3PLd3KbkOLkU6JtKPjVoaIuhd6RJO74F+2yKHP8n1euB7wKXAbdQn1Ksty0oB+jTsU9joPoVe\nt4lICNN4R+lOHfm8heMENuGoyftsjeaaO+5h56b11Hi9z4oNItIVfnj7bax+87Um21e/+Ro/vP02\n+9+NJ018asFDIVny3tbvnX7bIku7k2tjzN+MMa8ZY7YbY94DLgcygGtauZsDcHeyjSKt6uqkMBQm\nwugO7TkL3JKe8h5J4HTk8xZOvRDhrKX3edz0mXz85mt+xQYRCY5wPmGekpLCrEnjePnJxzhsdnHY\n7OLlJx9j1qRxTcZbN6508T4emTZ1Ski8B2393um3LbL4O+baZowptixrD3AO8GHD5lx8e69zgS0d\nb55I27pyEpxQmAijO3VkvGNPe48kcDS+Nrw4HFEMiK1ldLxL/1ciIS7cJwi8/eabmPedMp574UUA\nlj/9szYnMmtyPPL4z8kbPICVIfAe6Peu5+hwct1QDj4CWG6MOWhZVgH1M4lva7g9DZgEPB+Ihkrw\nhMMEC10RtEJlIozu1tpkQ4311PdIAsefz1t7JrCRzmvtfX5KB4Q+wuH3UXqucE/oUlJSePC+e9u1\nb0vHIyuXL+b/PfhfrFm7Dgjue9DS751+2yJLu5Nry7J+DrwJHAH6AQsBJ/BKwy5PA/Mty9rLl0tx\n5QNvBLC90s3CqVfSn4P09tDs2W3TeyTdKS4ujrzBA3j5ycfsmWRXv/kasyaNC6sDxlAX7j1e3SWc\nfh+l5wr0sVGoau14ZM3adSH9HijmRhZ/eq77U59I9wJOAquBC40xpwGMMU9YlpUMLKF+LPZq4DJj\njAZgdpNAn0FXr6SIhBKn08nOw19w7V33sWvzBgCuves+Vv/pdzidzlZjknoY/RPuPV5dTb+P0tNF\nckwNxmtTzI0c/kxodp0xpr8xJsEYM9AY8x1jzMFG+ywwxvQ1xiQaY+YYY/YFvsnSnK6Ywr+nT7AQ\nzpOB+KMzk5H1lPdIQoMnJsXGxTF2ynTGTplObFxcmzEpmEuctOf7FaoTAmpJrJb19N9H6dlCcdmo\nQB2PBPO1KeZGhg6PuZbQoTPoXaMnlOl0tqyxJ7xHEt6CGR/b8/1SabGIhJNQPeYMxPFIqL42CS/+\nrHMtIaqrzqCHW69kV/T+hMsaih0RqHUVI/k9ktDSkZgUrB7G9ny/unNt01DtHQ9X4fb7KBIooVy1\n0dnjkVB+bR6K5aFPPdfSonDqlezK3p9InQwkkJORRep7JKElnGJSe75f3TUhoHrHAy+cPosiPUkk\nH48olocH9VxHgK48g95VvZKBPPPW1b0/OksoEjr8jUk9vYdR8bHrqGpHeqJIjqmh/Nr8ieU9OS6H\nAvVcR4DOnEFvz4yIgT4LGOgzb13Z+xPJZwm1rqIES3V1NWfOnCE5OZmkpCSio6P9ur8/MSlYPYzt\n+X51x3dQ8bFrRXIvmUhzwqVqw+12U11dTVVVFZWVlT7XVVVVOJ1OampqcDqduFwu+98jcrPY8t+L\nSUhNA7ebqrJShmSmsGTJEtxuNw6Ho9lLdHQ0sbGxxMTENLnEx8cTHx9PQkKCz98JCQkkJSW1+jvo\nOU7f8vnnDBnfNLY2juWKy8Gn5DpCdGQK/2B8AcNpsohwamtHhMsPpESWs2fP8vzzz+NyuextiYmJ\npKSkkJyc7HPd3N9RUf4XXAVjiZP2fL/C+TsY6fFRRFoWzGWj3G43ZWVlFBcXU1JSQklJCWVlZZSX\nlze5rqur69BzxEY5qC0vtf8uLy+nvLw8kC+jifj4ePuEc3JyMsnJyZw5e5Zte/aTa51H3OBRbF71\nPjXOaqxxE5p9DMXl0NDh5NqyrAeARcAzxpj/8Nr+CHAT9WtdrwFu05Jc3cOfM+jB+gJ2RS9KV/X+\ndNd4yGDSuorS3UpKSnwSa4DKykoqKys5efJkm/f3TrZbuqSmphIXF4fD4bDvF4wexvZ8v7r6O6j4\nKNKzdNcazV0RU51OJys//AiXy8WYvNGUlpZSVFTEmTNnKC4uthPqjibN/nI4HERFRREVFeXTs+x2\nu3G73bhcLtxucDjodJuqq6uprq6mqKjIZ3tqjIOK/dsBsLJSqNq9GXNsP/Gp6cQlp5K/azuzrr2a\nwsJCNm/5THE5BHQoubYsayJwM7ANcHttvx/4ITAPOAQ8CrxrWVaeMaa6062VgImkA6Nw7v0JBSpr\nlO40cOBAvvWtb3Hw4MEmPQ2Nk+7meHoQCgsLW90vJiaG1NRUO9n2Trw916mpqSQmJvok4YHWnu9X\nV34HFR9Feo5wKgmura2lqKiIkydPcvLkSXbu2kX+8QJiY2OgtpbNmz7t0OM6HA77JGxSUhKJiYl2\nCXZCQoL97/j4eGJjY+1LXFycXdYdHR1NVFRUi78Njd/nfetXcc3XZzP1wsm4XC5qampwuVz2357E\n2VOm7vm7srKSiooKKioqKC8vp6KigqqqqlZfX5TDgbO0GGdpMQApMQ5ee+21L19/bBwH8vcTn5ZO\nfGoG8WkZ1DmrcMf5N/xKOs7v5NqyrBTgZep7p3/std0B3A08aox5q2HbPKAQuApYEYgGS3jrql6U\nruj96crxkN11Zlkk1DgcDs4991zOPfdcn+1utxun02kn2o0vjbe31Uvgcrk4c+YMZ86caXW/qKgo\nn2S7pUtCQkKXJuFdKdzio4j4L1RLgt1uNyUlJRQUFFBQUMCJEyc4efIkp0+fbhLHY6McUFvb4mMl\nJiaSlpZGenq6fZ2UlMS+/fuJjY1l5owZpKend2msbul9fnX5Yi6cNNFO4tv7WB9+tIrMrF72sWBt\nbS0VFRW89/4HHCivJSMzg5rKcmoqKnBVllNxtoi66soWH9Nd46S8MJ/ywnyf7Z/FxXEs/wtyc3Pt\nS05ODvHx8R17I6RFHem5fh74X2PMSsuyfuK1fSiQC3zg2WCMKbEsawMwBSXXISVYB0Zd2YsS6N6f\nrmprOJ1ZFukuDofDnuglKyur1X3dbjdVVVWUlZVRWlrabDLu2d5WL0BdXZ09bq810dHRdqKdlpZm\nJ+RpaWk+17GxsX6/9u4QLvFRRDqmKysS29sh4Ha7KSoqIj8/306mCwoKqKxsORn04XAQm5RCfEoa\ncanpVFRWMSgljku+NpOMjIwmz7t6zVp++9qb9uue/+TTXX48Faj3ubVjwdTUVP7pm9/gnoWLOMcr\niQdYuXwxv/jJgzidTvsE8tmzZzlz5gxFRUUcLyjAVVPT5PmcTidHjx7l6NGjPtszMjLo27cv/fr1\nsy/tPTkgzfMrubYs69vABcDEhk1ur5v7NFw3rtUr9LpNQkQwD4zCaZxvoNsaqmeWRcKJw+EgMTGR\nxMREsrOzW93X5XL5JNvNXZeWllJRUdHq49TW1nL27FnOnj3b6n4JCQnNJt1paWn230lJSWHbC+4t\nnGK5iHRMa0lgeXk5+fn5Ppe2TmhC/cnK3r17k52dTe/evck/dpzj0SkMPvcCorzGNh/Zs5useBc5\nOTlNHiOcj6fa0/bWjtM9J6JTU1MZNGhQk8c/e/YsH3zwdyorK8nO7k1RUREnTpyguLi42X3Pnj3L\nrl277G1ZWVl2oj1w4ED69u3r96oePVm7k2vLsgYCzwCzjDGeRdMcDZfWOIDumXkgjNTU1LBjxw4c\nDgf9+vWjV69eHZoFtzOCeWAUTuN8A9nWSBjrrpJ2CScxMTFkZGSQkZHR6n61tbXNJt2NL231wHiW\neWltcrbo6GifZNvzt/elozOjd7dwiuUikawrKhK9k0C3242ztJiUaRfz9rvvsWXTp22ebIT6SSj7\n9u1Lbm6ufZ2VleUT35xOJ/csXMTQ88e32XZ/l6YKtEC8z+09FuzocXpGRgZXX/2tJturqqo4ceIE\nhYWFPpeaRj3dRUVFFBUVsX17/URqMTExdqLtuSQlJbXrtfZE/vRcjweygS2WZXm2RQPTLcu6A/B8\nynLx7b3OBbZ0sp0RZ82aNaxa9eVC9XFxcfTt29cuzejTp0+3JNw6MBJ/qKRdIlV0dDTp6emkp6e3\nup/L5bIT7ZKSEp/E2/PvkpISalsZN1hbW9vmePCoqKhmE2/vsYbJyckR0QMuIp0X6IpEt9vN//7f\n2wwcdg6HP3mPipOFuBrG+iZEO5pNrJOTk+nfvz/9+/e3j2lTUlIC1nbvY5DEUeNZ/8E7uGqcjLxg\nfEsPHXDdXfkZyOP0hIQEBg0a5NPbXVdXx+nTpzl27Bj5+fkcP36cgoICnwlGXS4XR44c4ciRI/a2\n3r17M2TIEIYOHcqQIUOUbHvxJ7n+APCegcYB/A7YBfwMOAgUALOon0Ucy7LSgEnUj9MWL6mpqT7/\ndjqdHD58mMOHD9vbYmNjyc3NpU+fPvYlOztbPYVhLJwnAWpcxuSuqyMnN4c33vozFWX1CYVlWYwd\nOzbILRXpOjExMWRmZpKZmdniPm63m8rKSjvR9k66vbe1Vj5ZV1dnLz3TEs84cO+E23PtucTHxysB\nF+khOlOR6Ha7OXPmDAcPHrQvnuEyzUUqh8PBgAED6N+/v33dmcnE2mp7s6XU1mjeWPo8Q/POI7Zh\n3+44nups5WcoHQtGRUWRnZ1NdnY2559/PlB/AvjkyZN88cUX9jjtxieDT506xalTp9i0aRMAOTk5\ndrI9cuTIsKi86irtTq6NMWXATu9tlmVVAEXGmJ0N/34amG9Z1l6+XIorH3gjUA2OFOPHjycnJ4fD\nhw9z7Ngxjh071uQgqqamhi+++IIvvvjCZ3uvXr3sWf48l8zMzKB9kANRJtxTSo3DcRIgt9tNaWkp\n7/ztbwwceg5H139EVXER1cVncNfVkhbjYP369QDs3r2bvLw8YmI6tMqfSERwOBwkJSWRlJREbm5u\ni/s5nc4mCbfn4lnPtbUy9PaMA4+Li7MT7caJt2ebxtKJRA5/ejqrqqo4cOAA+/bt48CBA62ezIuK\njSWpdx+Ss/uwe/NGFj10b8B7K1tre0ul1OOmz+TjN19jxNhx3Xo81Zke5VA/FoyOjrY79SZMmABA\naWkpX3zxBUeOHOHo0aMcO3YMt/vLqbdOnDjBiRMn2LhxI6NHj+aaa64JVvODrrNHwG68JjUzxjxh\nWVYysATIAFYDl3mN0RYvnnELHuXl5Rw7dswuySgoKGi2bPD06dOcPn2anTu/PNfhmRwiJyeH3r17\n25devXp16YFTIMqEe1qpcahOAuR2uykuLubkyZOcOnXK59q7h621qUqUWIu0X1xcnB2rW1JTU9Ns\n0u39d2s94E6n015HtiWpqalkZGQ0Sbw927RUi0hkcLvdnDhxgr1797Jv3z6OHDnikyB5i4+PZ8iQ\nIdS5YcOO3QwZdyEOh4PtDUlgqJQBOxxRDIitZXS8K2SOp9ojVI8FW5Kamsro0aMZPXo0ANXV1Rw5\ncoRDhw5x6NAhjh8/bn+WioqKgtnUoOvUUbAxZmYz2xYACzrzuD1VcnIyI0aMYMSIEfa2qqoqCgoK\nKCwstNcHPHHihM9YCKjvwfBMTODN4XCQmZlJr1696NWrF1lZWfbfaWlpnSoXDMRMjeE822NnBGus\nu9vtpqKigqKiIvskjfffjT9XrYlPTSchoxfHDu7n+uuuYcCAAaSlpXVh6yUS9JQqlUCJjY21Y3Zz\nnE4n/7lwEVOuvIaaijJqKsqpqSjjyLbNjDpnqJ2It/bd9owZb7xEi0dCQoJP8u2ZIM7zd2JiokrP\nRUKUy+XiwIEDGGPYu3cvpaWlze4XHR3NoEGDGDp0KEOHDqVfv352ReTVXnE7WElga6XUT4V4YtqS\ncJ73KD4+3idnqaqq4vDhwxQVFTFy5Mggty641MUU4hISEhgyZAhDhgyxt9XV1dnT6p84cYKTJ09y\n4sQJTp8+3eQMpGfNwaKiIvbu3etzW3R0NBkZGWRlZflcey5trXMXiJmvI2H27FDjcrnsUlHv9Q89\nayA6nf4VkqSlpdnDD86cLWbt1u0MnvhVHFHRdhlTXl5eF70aiSQ9rUqlO3z40SrOufBiEtIzSUj/\nchx4dXwKfeJdfO9737NPqnnGcDd3KS8vb/E5PCd5CwoKmr09NjbW57ej8UXJt0j3qqysZO/evXZC\n3Xg2aI+srCxGjBjB8OHDGTx4MLGxsc3uFwpJYKiXUvd0CQkJeE143aMpuQ5DUVFRdimhd1Ljcrk4\nffq0PcmA96W5Xova2lq7x7I58fHxdu9E45lq09LSqKutRR+hwGlPj573ckHepaGe8tDi4mLKysr8\nfu6oqCi7qsF77cnevXs3KQm96sorGtoZXiVYElw9tUqlIwLdu+9wOEhOTiY5OZl+/fo1u4+n/Pzs\n2bM+Sbfn3yUlJdTVNb+qZk1NTaul5y0l35mZmXbyLSKdU1ZWxq5du9i1axeHDh1qttw7JiaGIUOG\nMHz4cEaMGEFWVlYQWtpx4VZKLT2TMqMIEhMTQ25ubpMJdDwTUnmXAHt6s8+cOdNiuWB1dXWzpebe\n6txuqvb9g9jEJGISk4hJSCR/+1amX/VP7Nu3j5SUFHtin+bG4vozY2KklpPW1dXx4UereHPlKgbm\nnU+dq4YPHl3EmOFDyczIoLy8nLKyMkpKSlrtXWqLw+GwKxQyMzPJysqyx+VnZGS0e0K8UDiDLeFH\nVSrt42/vfqBmnW2r/Lyuro6ysjK7KsaTdHtft7T8WFvJd3x8PNHR0cTHJzB8+Dn06tXLnpE9IyOj\nxd40kZ6uvLycXbt2sWPHDg4fPtxsQp2YmIhlWYwaNYphw4aF/fdJxyChK1KP0/2l5LoHcDgcdm/z\n0KFDfW5zu92UlZXZJcOegyTv3ouWeisAohwOqkvOUF3y5cRrKTEO/vd//7fJvnFxcSQlJZGYmGhf\nEhISmDByKOteXkzvoSNwRMdQsHcXl06dyJkzZ4iLiyM2NpZPN2/htfdWhlw5qdvtpra2FqfT6XOp\nrq6mqqqKqqoq++/q6moqKyubvQBkxToo37sNqH8PDx86xOHWnrwZnmV5musdSk9P16zAIiGsI737\n3VUqGRUVZf+OeK+R6uH5LfFOvr0T79aS7+rqagAqKir49NOmE+EkJyfbybYnnnn+7uzcISLhpqKi\nwk6oW+qhzsjIYNSoUYwaNYqBAwf26GWRpHto2NeXlFz3cA6Hg9TUVFJTU5s9YPL0VjSendYzAY7n\n0p6JsDyJZ3PLxqTEOKg6ug+A9FgHWzZtYkvD2nke2XEOSrauISo6hn7pKbz3/nv8Y+vnxMbGEhMT\nQ1RUFNHR0fYlKioKh8OBw+Gw/27u9dXPcAg5OdlA/UFiXV0dtbW1PheXy4XL5aKmpsbnuqWZNgPJ\n4XCQkpJCWlqa/f/lSaS9S/WVPEuoCqV1PUNVR3v3Q6FU0vu3xHsVDA/vE7meZLuoqIhPP/uc5NQ0\nairKoIVYWl5eTnl5eZNlKeHLuUO8k2/vS+N1ctWrIu0Rap8Vl8vFnj172LZtG3v37m220yMrK4sx\nY8aQl5dHbm6uTjpJt9GwL1/tTq4ty7oNuBUY0rBpB/CIMeZvXvs8AtxE/TJca4DbjDH7AtZa6Xbe\nvRUtcbvdVFdXU1paapcwew6GysrKqKyspKKiwr5UVlZ2OCF1e5JdqolxOFpdXsZfp04F7rHaIyEh\ngcTERGpqaqiKiiO1V2+i4xOIiU8kJiGRotOnGZ4UxZw5s0lKStIPpYQ1TUbTtUK9VLK5E7nvvvc+\n2RMuZtDIUbjr6qipLMdZVkrB/j1k1VaQlZVpT8jY0lwSbc0d4un1rnG5OJB/nN7DRhEVn8D/LlzE\nv359Nhd9dVqXvWYJT6HSA+d2uzly5Ahbt25l586ddoWHt8zMTMaMGcOYMWOUUEvQaNiXL396ro8C\n9wN7AQdwPfCmZVnjjDE7LMu6H/ghMA84BDwKvGtZVp4xpmlEkIjhcDhISEggISGB7OzsNvf3JONV\nVVV2WbSnbLq6upqKigre/ehj+o8YTZ2rpuHi4vTxL0jLzIK6OupqXdQ6nTjcda2WrQdSdHQ0MTEx\ndk+55zouLq7ZS0JCAvHx8T7X3uXwnjItp9PJPQsXMfryq32e77PVi7krBCbrCLUz+BK+QqGHNZT1\n5N59R1QUccmp9ZfiEgbGu3wOyGpqanxWPmi8EkJLsyF7TvQCJEc7qDxsAOgd52DlB++zfdtWe4x3\nVlaWPSdFenq6Sml7oFDogTt79iyff/45n3/+OcXFxU1uT0lJ4bzzzuPcc8+lb9++3ZJQ6zhApP3a\nnVwbYxoPop3f0Js9ybKsncDdwKPGmLcALMuaBxQCVwErAtTeiNGTA5V3Mp6RkdHk9nffe5++Ey9m\nQKMDzJg9uzl7+iRjp0wHYOXyxTy14CFiY2Opq6vzKeX2/O12u+1LXV0dbrcbh8PBJ2vWcsAZQ98h\n9WPQHQ1l48cOHWBEXC0zZ85otsS8K4Ryj16onMGXyBHqPazB1N5YECm/H/6cTIiNjSU7O7vZE7hu\nt5vy8nKfxNv70tK6vo6G6qfmKqCioqLsCSAbXzIyMvwaghMp/189QbB64FwuF7t37+azzz7jwIED\nTW6PjY0lLy+PsWPHMmTIkG498aPjAGlLTz4x3JwOjbm2LCsa+FcgHlgNDAVygQ88+xhjSizL2gBM\nQcm1DwWqjnG76ziZ/wVH9uxucsDpSYLbOwtmUlIS0dExPuvCAkTFJRAX7yIlJSXg7W9NKPbohcIZ\nfJGepq1YEEm/H4E6seiZkyIlJaXZ8d7vvPM3dpa66NW7N86yEpxlJVSXlVBRdAq3s6rZYUp1dXX2\nqhrNPV96erpPwt2rVy+719s78Y6k/y8JvMLCQrZs2cK2bduoqqryuc3hcHDOOecwduxYLMsKym+u\njgOkPUK5kygY/EquLcs6D1hHfVJdCVxjjNlnWZbnV6Lxmk2FQJ9OtzKCBDtQhcMZ9JbPgH3M16dN\nJja28+srh+JZtlDp0fN8RrZ8/jlDxjc9AOypY2hEuktLsaAzvx+hGvs7emLRn9dzySVf4/8WLmKo\n1/sG9dVPP//xA1RVVdlLU3pfFxUVNVtu7na77UnZGvcyehLvXr16kZ6ezscbNzNh1teJT00nLjlV\niUmI645jA5fLxc6dO9m0aRNHjx5tcntmZiYXXHABF1xwQavz3XQHjaXtXqEap9sjFDuJgsXfnuvd\nwFggnfqe6z9ZljWjlf0dQPcMiA0TwQxU4XIGvaUzYN/+xqUBa6vOsjXP+zOSOGo86z94B1eNk5EX\njA9200R6vI7+foR67Pf3xKK/r6e1eN/aECVPubkn0fZcTp8+TVFREU6ns9n7eBJvaFhW8eOGeV8d\nDuKSU+nbO4vf/W4Z48ZdYK8tnp6ersmoQkBXHhsUFRWxadMmPv/8c3sJTo+YmBjy8vIYN24cgwcP\n1mehBwr1ON0eodJJFGx+JdfGmBrAc5r2M8uyJgK3AYsatuXi23udC2zpbCOl84LdY+6v7jgDprNs\nvpr9jFijeWPp8wzNO4/Yhvemp46hEQlH4Rb729LR19OReO9dbt54qUq3201FRUWThNtzaW5mZ9xu\nnGUlABSUwDvvHLdviomJ8Skx975otYjuFchjA7fbzd69e9m4cSP79+9vcntOTg4TJkzgvPPOIyEh\noVPt7gqhWOUXiSItTvd0nV3nOhqIMsYctCyrAJgFbAOwLCsNmAQ838nniCjBClThWNrTHWfAIv0s\nmz8lRi19RsZNn8nHb77GiLHj1LsvEkQd+f0Ix9jfms68nkDGe4fDQXJyMsnJyU3GeXsS78LCQp7/\n3XLOGTsOZ2kJ1aXFVJcW4651NXk8l8vFiRMnOHHiRJPbEhIS7ER7/PjxTRJ9CbzOflaqq6v5/PPP\n2bhxY5Nx+9HR0eTl5TFhwgQGDhzY6RMnXVlKHI5VfuFYWh1pcbqn82ed68eBt6lfkisV+A5wEfBY\nwy5PUz+D+F6+XIorH3gjgO0Ne+EYqCQ8BarEyOGIYkBsLaPjOz/WXUQ6Qv6rMAAAIABJREFUTr8f\n4cGTeA8bNoyrLptjx+H47AEcXb+Kqy65iOHDhtm93Z51us+cOUNtbW2Tx6uqqiI/P5/8/Hx27drF\n/fff79ds5dJ9ioqK2LhxI5999lmTYQMZGRmMHz+ecePGkZycHJDn645S4nCq8ouE0moJf/70XGcD\ny4G+QDGwFbjUGLMSwBjzhGVZycASIIP6WcQvM8Y0HZTUwwUjUKm0p3nheIazPTpSYtTaZ+SpEP4x\nFelJ/P39iLTYH26vp7X/ryFDhvjsW1dXR3FxsZ1se1886x3n5OSoRNxLKPyGu91ujhw5wrp16zDG\nNLl92LBhTJ48mREjRgT0/647S4nDocovnEurwy2uSev8Wef6pnbsswBY0KkW9RDdHajCscejq380\nw+0MZyBKvFsrMQrHz4hIqOmOg31/fj8i7Xsdjq+nvf9fUVFRZGZmkpmZyfDhw31uq6mpoaysjIyM\nDCXXDYL9G15XV4cxhjVr1pCfn+9zW0xMDGPHjmXy5Mnk5OR0+DkaxxPA/ndNrUulxF7CubQ6HOOa\ntKyzY64ljKi050vhdoazuw4iwukzIhJqgn2w35JI+15H2utpj9jYWDIzM4PdjJARzN/wmpoatm7d\nyrp165qMp05LS2PixIl85StfISkpqVPP0zie/On++VSUljLlymsA+PjPr3DR1d/t1HNI6OiJcS1S\nKbnuYVTaUy+cznAGusS7rRKjcPiMiISaUD9hF0rf60D07ofS65HuF4zf8KqqKjZu3MiGDRuoqKjw\nuS0nJ4dp06YxZsyYgIyHbymevLH0efoOGUZsXBzX3ruAFc88wbV33ceuzRsAGD1+co8tJY6E0mrF\ntcig5FpCTjglvt1BJd4ioU9xq31CtXdfpCUVFRWsX7+ejRs3NllibejQoUydOpVzzjknoOX6LcWT\nr1x0Cbs2b2DslOnExsWRlJbGK798nIuvqu/NXvHME8yaNK5H/s7ruEdChZJr6ZFaO8N504P/xbvv\nvW/vF65BWSVGIhJs3r3U06ZO6VDvfihMWiWhpTt6KUtLS1m3bh2bNm2ipqbG3u5wOMjLy2Pq1Kn0\n69cvIM/VETVOJy6Xi3n3fznV0eB757Ny+WKcTmeP/J7ouEdCQVSwGyDS2MwZF7Nv/aom2/etX2VP\n6NFZnjOcK5cv5sie3RzZs5uVyxeTN3gADzz+c3ZVx7CrOoZ7Fi5i9Zq1rT6W0+nk3ffe59333m+y\n9EYgdOb98JQYXTpntn5gRLpQd8StcLN6zVruWbjIjqd3/Ohh4rKaTu7k6d1vz2O0JyZL5GvpNzwQ\nvZTFxcW8/fbbPPPMM6xbt85OrKOiohg3bhx33nknV199dZcm1i3Fky0f/53R4ycDsGvzBibPuqzJ\nPq19n3oCHfdIsKnnWkJOd5X2ND7DedOD/8UDj//cr16V7ihxVKmTSOjT99RXS2NGX33+KWqcTmLb\n8Z6E+jh2Ca5A91KWlpayevVqtmzZ4rPeeHR0NF/5yleYNm0a6enpnW53ezQXT7aufIfq0lKOHzoA\nwLaVf9OEZiIhqN3JtWVZDwL/AlhAJbAWuN8Ys6fRfo8AN1G/1vUa4DZjzL6AtVh6hO4q7fGePOLd\n9973a8xkdx74qdRJJPTpe/qllsaMTp51mT1m1KOlUl6NY5e2BGICqLKyMj755BM2bdrkk1THxsYy\nceJEpkyZQkpKSmeb6rcm8eRnj+F0OnnuhRcBWPr0z/nJL55lkDXa535dMYGXhmY0pfdEWuJPz/VF\nwHPAp0AssAh4z7KsPGNMBYBlWfcDPwTmAYeAR4F3G/apbvZRRVoQ6rMmdveBX6i/HyKi72lbHDjY\ntvJvZPTKBtrRu+92t2+biJ/Ky8tZs2YNn376KS6Xy94eGxvL5MmTmTJlSqeX0+os73jSuFLuJ794\nlrzBA1jZxdUymoSwKb0n0pp2J9fGmK97/9uyrOuBE8BXgE8sy3IAdwOPGmPeathnHlAIXAWsCFCb\npRN0pq1lkbCMg4hIKGgxnm74mOUvPMuateuA1nv3p02dwuK77+e798732f7xm69x09M/65qGS8Sr\nqqpi3bp1PuOpAWJiYpg0aRJTp04lOTk5iC1sqqVKuZXLF/P/Hvyvdn2fAvm8PXloht4TaUtnxlxn\nNFwXNVwPBXKBDzw7GGNKLMvaAExByXXQ6Uxb6/wdM6lkXESkea3F05SUlHb17q9Zu44ho8fwxtLn\n+cpFlwD1EzoNGT2GNWvXqUJA/OJyufj0009ZvXo1lZWV9vaYmBgmTJjAtGnTglL+3R6tVcp15XdB\nQzOa0nsibelQcm1ZVhTwNPCJMWZnw+Y+DdeFjXYv9LpNgkRn2trHnzGTmsBIRKRlgRiDPmjkaCbP\n+Qa7Nm8A4BvzftAwoZOr9TuKNKirq2Pr1q189NFHlJSU2NujoqIYP34806dPJzU1NYgtFJFI0tGl\nuJ4H8oBvt2NfB6ABUkHW1pm2nqq5ZbT8WcZh+rSpPLXgIUbHuxgd7+KpBQ91uBKgq5f0EhHpbp1Z\nFsezHFFsXBxjp0xn7JTpxMbF9ejlzaT93G43u3fv5sUXX+TNN9/0SazHjh3LnXfeyeWXXx4WiXWw\nlvrTEoNN6T2Rtvjdc21Z1q+Ay4GLjDHHvG4qaLjOxbf3OhfY0uEWinSRQJXJB2ICI5Xsi4j4UnWQ\ndFR+fj7vv/8+hw8f9tk+YsQILrnkEnJzc4PUso4J1ndB38Gm9J5IW/xZistB/WzhVwIzjDGHG+1y\nkPoEexawreE+acAk6nu6JYg0PthXKJXJh1JbREQCqbOTaGp5M/HH2bNnWblyJf/4xz98tg8cOJBL\nLrmEwYMHB6llnRes74K+g03pPZHW+NNz/TxwHfXJdbllWZ5x1GeNMVXGGLdlWU8D8y3L2suXS3Hl\nA28EsM3SAV15pi0cZyAPpQkpQqktIj1FOMatcBNK1UHhTp/X1lVVVfHJJ5+wfv16n7Wqs7KymD17\nNpZl4XA4gtjCwAjWd0Hfwab0nkhL/Emub6V+7PRHjbZfDywHMMY8YVlWMrCE+tnEVwOXGWM0gDQE\ndMWZNpUzi0i4UdzqeqrICRx9Xlu3f/9+Xn/9dSoqKuxtiYmJXHzxxUyYMIHo6Oggtk5Eehp/1rlu\n1+RnxpgFwIIOt0i6VCDPtIXzwVMolcmHUltEIl04x61wooqcwNDntW1/+9vf7MQ6OjqaSZMmcdFF\nF5GQkBDklolIT9TR2cJFwnoGck+Z/MrlizmyZzdH9uxm5fLFQZmQIpTaIhLpwjluSc+jz2vb8vLy\niIuLY8yYMdxxxx3MmTNHibWIBE2H1rkWiQShNCFFKLVFRKSzVJEj3WXmzJnMnDkz2M0QEQHUcy2d\nEAlr/XVmDdZIbotIpArFuBWJa9yrIicwQvHzKiIiLVPPtXSY1voTkXATanErkierUkVO54Xa51VE\nRFqn5Fo6RQdPIhJuQiVu9YTJqrRcTeeFyudVRETa5ldybVnWRcC9wFeAvsA/G2P+2mifR4CbqF+K\naw1wmzFmX2CaK6FIB08iEm5CIW5pRm1pr1D4vIqISNv8HXOdBHwG3NHwb7f3jZZl3Q/8ELgFmAyU\nA+9alhXfyXaKiIgEVSSOjRYREZHA8Su5Nsb8zRjzE2PMG41vsyzLAdwNPGqMecsY8w9gHtAPuCog\nrRUREQmC1WvWcs/CReyqjmFXdQz3LFzE6jVrO/WYmqxKREQksgRyzPVQIBf4wLPBGFNiWdYGYAqw\nIoDPJSIi0i26amy0JqsSERGJLIFMrvs0XBc22l7odZtIhzmdTntCl5kzLtbBp4h0i64cG63JqkRE\nRCJHd8wW7gDquuF5JIJF8nI1ItKzabIqERGRyODvhGatKWi4zm20PdfrNhG/eZdkDho5ikEjR/G1\nebew4u33NKmQiHQ5jY0WERGR9ghkcn2Q+iR6lmeDZVlpwCRgXQCfR3qYtkoyRUS6kmds9Mrlizmy\nZzdH9uxm5fLFGhstIiIiPvxd5zoZGOG1aZhlWRcAp40xRy3LehqYb1nWXuAQ8CiQDzSZXVxERCRc\naGy0iIiItMXfnuuJwJaGixt4quHvhQDGmCeA54AlwEbq18W+zBij2l3pMJVkikgo8IyNvnTObCXW\nIiIi0oRfPdfGmI9oIyE3xiwAFnSiTSI+tFyNiIiIiIiEuu6YLVyk01SSKSIiIiIioUzJtYQNLVcj\nIiIiIiKhKpCzhYuIiIiIiIj0SEquRURERERERDoppMrC6+rqHCUlJXz66afBboqISKtKSkqoq6tz\ndOS+inUiEi4U60SkJ+hMrPMWUsl1dXV13JVXXsn+/fuD3RQRkVZdeeWVLFmypEOz6inWiUi4UKwT\nkZ6gM7HOW5ck15Zl3QHcC+QCW4EfGmPaddpy+PDhnHvuuV3RLBGRkKFYJyI9gWKdiPQkAR9zbVnW\ntcAvqF/rehz1yfW7lmVlB/q5RFqycuVK5s2bF+xmiIh0KcU6EekJFOskXHRFz/U9wBJjzO8BLMu6\nFfgGcAPwsy54PpGQUFFRwR/+8Ac2bdpEWVkZOTk5fOMb32DOnDnBbpqISMAo1olIT6BYJx0R0OTa\nsqw44CvATz3bjDFuy7I+AKYE8rlEQs3vfvc7du7cyd13301OTg5bt25lyZIlZGZmMnHixGA3T0Qk\nIBTrRKQnUKyTjgh0WXhvIBoobLT9BNAnwM8lYWLTpk3MmzcPt9sNwMGDB7n66qt5+eWX7X1eeOEF\nnnnmGfvfu3btYv78+Vx33XXccsst/OY3v6G6utq+vaamht///vf84Ac/YO7cuTzwwAPs2LGjxTYU\nFxdz33338cQTT1BTU9MFrxL27dvHzJkzGTNmDNnZ2cyaNYshQ4awb9++Lnk+EQktinWKdSI9gWKd\nYp20LKRmCwd6BbsBEnijR4+msrKSgwcPMmzYMHbs2EFqaqpP0Ny5cyf//M//DEBBQQGPPfYY3/nO\nd7jzzjspLi5m6dKlLF26lDvuuAOApUuXkp+fz3/+53+SmZnJhg0beOyxx3jqqafo27evz/OfOnWK\nhQsXMmrUKG6//XYcjuZn2V+8eDEff/xxq6/lj3/8Y4u3jRs3jk8//ZSvfe1rZGZmsn37do4dO8b3\nv//9dr1PEpY6GrMU6yKQYp1iXQRTrBObYp1iXQTrdMwKdHJ9CqilfpZwb7nA8XbcvwaID3CbJMiS\nk5MZOnQo27dvZ9iwYezcuZN/+qd/4tVXX6W6upqysjIKCgoYM2YMAK+//joXXXQR3/jGNwDo06cP\nN9xwAz/5yU+4+eabOXv2LB9++CGLFy8mMzMTgCuuuILPPvuMlStXMnfuXPu58/PzeeSRR7jwwgvb\nDIbf/va3ufLKKzv8Ov/t3/6NZ555hptvvpno6GgcDge33XYbo0eP7vBjSsjr6OlyxboIpFinWBfB\nFOvEplinWBfBOl0GEdDk2hjjtCxrMzALeBPAsqwo4BLg2bbuf/To0a8BGwPZJgkNeXl5bN++nSuu\nuIJdu3Yxd+5c1q5dy86dOykrKyMzM5M+fepHDhw6dIgjR440e7bxxIkTFBQUUFdXx5133ulzm8vl\nIjU11f630+nkxz/+MdOnT2/XWcb09HTS09M7/BqXLVvG/v37efDBB8nOzmbHjh38+te/JjMzk7Fj\nx3b4cSV0NcSsjt5PsS4CKdYp1kUixTppTLFOsS4SdTTWeeuKsvCngN9blrUJ+BS4G0gEftcFzyVh\nYsyYMaxcuZJDhw4RHR1N//79GTNmDDt27KC8vNxnDczq6mrmzJnD5Zdf3uRxevfuzaFDh4iKiuLJ\nJ58kKsp32oDExET779jYWM4//3w2bdrElVdeSVZWVqttbKt8yOFw+Iwn8lZVVcXbb7/NAw88wPjx\n4wEYNGgQhw4d4s0331QQFukhFOsU60R6AsU6xTppXsCTa2PMqw1rWj9C/SRmnwGXGWNOBvq5JHx4\nxue89dZbdpnQmDFjeP311ykvL/cp2xk2bBhHjx61z3g2NnToUOrq6iguLm61NMfhcPDv//7v/PKX\nv2TBggU88sgjdrlRczpTPuSZ1KPxj4LD4bBvE5HIp1gnIj2BYp1I8wI9WzgAxpjnjTFDjDEJxpgp\nxphPu+J5JHykpKQwePBgVq9ebQfhvLw8Dh48yPHjx8nLy7P3veqqqzDGsHTpUg4ePMixY8fYuHEj\nS5cuBaBfv35Mnz6dZ599lg0bNlBYWMjevXt5/fXX2bx5s8/zOhwO7rrrLgYPHsyCBQs4e/Zsi21M\nT0+nT58+rV5akpiYyHnnncfy5cvZsWMHhYWFrFy5klWrVjF58uTOvHUiEkYU60SkJ1CsE2leqM0W\nLhFszJgxHD582C4VSklJYeDAgRQXF9OvXz97v8GDB/PII4/w3//93/z4xz/G7XbTp08fpk2bZu9z\n55138j//8z8sW7aMoqIi0tLSGDlyJBMmTLD38cweGR0dzX/8x3/w1FNP8fDDD/PII4+QlpYW8Nd3\n11138cc//pFnnnmG0tJScnJymDt3LnPmzAn4c4lI6FKsE5GeQLFOpClHKJU2OByOia+88spG73Ea\nIiKhaPv27Vx33XWT3G6335U5inUiEi4U60SkJ+hMrPPWJWXhIiIiIiIiIj2JkmsRERERERGRTgq1\nMdcJH330Efv37w92O0REWnX06FGAhA7eXbFORMKCYp2I9ASdjHW2UEuuGTBgAOecc06wmyEi0qrO\nzlehWCci4UCxTkR6gkDNQxZqyXXV8OHD0cQXIhImqjp6P8U6EQkjinUSdlauXMmyZctYvnx5tz3n\nT37yE4YOHcr3v//9bntOCaiOxjpbqCXXEgK6OzA899xzVFRUcP/993fp8+zYsYO//vWvHDx4kDNn\nznDfffcxadKkJvu98sor/P3vf6e8vJxRo0Zx880307dvX/t2p9PJ73//e9asWUNNTQ0XXHABN998\nM+np6fY+paWl/OY3v2Hz5s04HA4uvPBCbrjhBhISvqw2OXnyJEuWLGHHjh0kJCQwY8YM5s6dS3R0\nNND6j8LVV1/N/fffz8SJEwP5Fon0eJEa/1asWMGf//xnn239+/fnmWee8dnWHfFv+/btPPzww/x/\n9u48Pqry3uP4Z7LvCZAQCJCELYdFBRRBoChWXGqt0OVqUYu2esW1Lrdqq7YoWLxVr1qtVKltKdUq\n3uty9aoFLYooioqIsh0IJBBCFgJJIOtkmftHMseZbGSSmcyS7/v1mleSc87MPJNkfvOsv2fVqlXE\nxcW5Pf91113HRRddxEUXXeTtX4FIvxWqce3JJ59k/fr1QMv2XKmpqcyZM4cf/OAHVl1KpK8poZn0\niMPhoKmpyd/F8IjdbmfkyJFcc801wDf7Jbp69dVXefvtt1m0aBH/+Z//SXR0NEuXLqWhocG65q9/\n/Suff/45v/jFL1i6dCnl5eU89NBDbo/z+9//noMHD7J48WLuvvtuduzYwdNPP22db2pqYtmyZdbX\nm2++mffee48XX3zRR69eRLwlGOMfwIgRI/jzn/9s3R544AG3830V/7pis9k6jM0i4lvBGNdsNhtT\npkzhz3/+M0899RTz5s3jpZde4vXXX/d30aQf08i1uHnyySfZsWMHO3bs4M0338Rms7F8+XJKSkq4\n7777uOeee/jHP/7BgQMH+M1vfsOECRN49dVXeeedd6ioqCAjI4Mf/ehHzJgxA4Dm5mb++Mc/sm3b\nNioqKkhNTeWCCy7gu9/9LtAymuLsdfzRj34EwP3338/EiRO9/tqmTJnClClTOj3vcDj4v//7P370\nox9ZI8I///nPufrqq/n000+ZNWsW1dXVrFu3jttuu82a5nbjjTdyyy23sHv3bnJycjh48CBffvkl\nDz30EKNGjQLg6quvZtmyZVx55ZUMGDCArVu3cvDgQe677z6Sk5PJzs5mwYIF/P3vf+fHP/6xelxF\n/CCU4x+0jOy4jjC76sv4JyJ9J5TjmsPhICIiwopr5513Hps2beKzzz7j+9//frvri4uLWblyJXv2\n7KGuro7hw4dz+eWXc8opp1jXNDQ08OKLL/Lhhx9SWVnJoEGD+MEPfsA555wDwIEDB1i1ahU7d+4k\nJiaGSZMm8dOf/pTExETrMZqamvjTn/7EBx98QEREBOeddx4LFiywzldVVfGXv/yFzZs309DQwIQJ\nE7j66qvdZglJ8FLjWtxcffXVFBUVkZWVxY9//GMAEhMTKSkpAeD5559n4cKFpKenEx8fz8svv8yG\nDRu47rrrGDp0KNu3b+eJJ54gKSmJiRMn4nA4SE1N5Y477iAhIQHTNHn66acZMGAAM2fOZN68eRQW\nFlJbW8tNN90EQHx8fIdle/nll3nllVe6LP8TTzzBoEGDevTaS0pKqKysdAuycXFxjB07FtM0mTVr\nFvv27aOpqcntmmHDhpGammpVLk3TJD4+3qpYApxyyinYbDb27NnDtGnTME2TrKwst4rupEmTWLFi\nBQUFBWRnZ/foNYhIz4V6/CsqKuLf//3fiYyMxDAMLr/8clJTU4G+jX8i0ndCPa61nekSGRnJ8ePH\nO7y2rq6O0047jcsvv5zIyEjee+89HnzwQZ588kkrFj7xxBPs3r2bq6++muzsbA4fPkxFRQUA1dXV\nLF68mHPPPZef/exn1NfX8/e//53/+q//4r777rOe5/333+ecc87hd7/7HXv37uXpp58mLS2NuXPn\nAvCHP/yB4uJifvWrXxETE8Nzzz3Hb3/7W37/+99rcCUEqHEtbuLi4oiIiCAqKqrDEY5LL73Uqlg1\nNDTw6quvsnjxYnJycgAYPHgwO3fu5J133mHixImEh4dz6aWXWvcfPHgwu3btYuPGjcycOZOYmBgi\nIyNpaGjodETF6fzzz2fWrFldXpOSkuLpS7Y4g2fbx0hOTrbOVVRUEBER0W6dYEpKits1SUlJbufD\nw8NJSEhwu6bt8zh/Li8vV+NaxA9COf7l5ORw0003MWzYMI4ePcpLL73Evffey2OPPUZsbGyfxj8R\n6TuhHNfgmwzPDoeDr776iq1bt3LhhRd2eG12drZb/WrBggV8+umnfPbZZ3znO9/h0KFDfPzxxyxe\nvJiTTz7Zen1Ob7/9NqNGjeKyyy6zjt14440sWrSIoqIia+Q5NTXVWt+ekZHB/v37eeONN5g7dy6H\nDh3i888/Z9myZdbv+JZbbmHRokV8+umn1gwBCV5qXItHxowZY31fVFREfX09999/v9s1jY2NbqMW\nb7/9NuvWraOsrAy73U5jYyMjR470+LkTEhJISEjoeeF7yOFw+GQNoLdS/otI3wjm+Oe6JCYzM5Ox\nY8dy3XXXsXHjRmu6Y0d8Ff9EJDAEc1wD2Lx5M5dffjlNTU04HA5mz57NJZdc0uG1tbW1vPTSS3zx\nxReUl5fT1NSE3W6nrKwMgLy8PMLCwpgwYUKH98/Pz2fbtm1cfvnlbsdtNhvFxcVW49rZaHbKycnh\njTfewOFwUFhYSHh4OGPHjrXOJyYmkpGRQWFhYY9/DxI41LgWj0RHR1vf19W1ZKu/5557GDhwoNt1\nkZGRAHz44YesWrWKq666CsMwiImJ4X//93/Zs2eP2/Xdqbz5elq4s3e07ahyZWWl9aGSkpJCY2Mj\nNTU1bqM3rvdJSUnh2LFjbo/d1NREVVWV2zV79+51u8Y5quNckxgXF0d9fX27clZXV1vnRaTvhFL8\ni4+PJyMjg+LiYqBv45/zvm0fB1rim2KbSN8J9rh20kknce211xIZGcmAAQMIC+s8V/OqVav46quv\nuPLKKxkyZAhRUVE88sgjNDY2AhAVFdVlWerq6jj99NO54oor2p1zzSehwZP+TY1raSciIoLm5uYT\nXjdixAgiIyM5fPhwp718u3btwjAMzj//fOtYUVFRu+frToZKX08LT09PJyUlha+++sqaNlRTU0Nu\nbi4XXHABAKNGjSI8PJyvvvqKM844A4DCwkLKysowDAMAwzCorq5m3759VqX066+/xuFwWD2V48aN\n45VXXqGystKaNrV161bi4uIYPnw40DKVqKmpye1xAPbt22edFxHv6i/xr7a2lqKiIs466yygb+Pf\n0KFDsdls7N2711rnCC3JhmpqahTbRLwslONadHQ0Q4YMOeFzQUvZzz77bCv3Q21tLSUlJVaytays\nLBwOB9u3b3fLLeE0atQoPvnkE9LS0rpcG922o2H37t1W3Bs2bBhNTU3s3r3bipvHjx/n0KFDVv1P\ngpsa19LO4MGD2bNnD6WlpcTExLhlQHQVGxvLxRdfzMqVK3E4HIwbN46amhp27dpFXFwcc+bMISMj\ng/Xr1/Pll18yePBg1q9fz969e0lPT7ceJz09na1bt3Lo0CESEhKIj4/vMGj1dvpQXV2d2wdASUkJ\neXl5JCYmkpqais1m46KLLuLll19m6NChDB48mBdeeIGBAwdagTg+Pp5zzjmHlStXkpCQQGxsLH/+\n858xDMOqOA4fPpzJkyfzxz/+kUWLFtHY2Mizzz7Lt771Latnc9KkSQwfPpwnnniCn/zkJ5SXl/Pi\niy9ywQUXEBHR8rbMzMxk0qRJLF++nCuvvJLBgwdz6NAh/vKXvzBr1ixl3RXxgVCNf3/729+YOnUq\naWlpHD16lNWrVxMREcHs2bMB+jT+xcbGMnfuXFauXElYWBiZmZmUlZXx3HPPkZOTY1U4RcQ7QjWu\neWro0KF88sknTJ06FYAXXnjB7fzgwYOZM2cOTz31FFdffTVZWVkcPnyYY8eOMXPmTL7zne/w7rvv\n8thjjzF//nwSEhIoKipi48aN3HDDDdZofVlZGStXruTcc89l3759vP3221x11VVAy8DI6aefzh//\n+Eeuu+46K6HZoEGDrJ0aJLh1u3FtGEYEsBT4MZAOHAJWmqb5QJvrlgDXACnAR8D1pmnmeq3E4nMX\nX3wxf/jDH7j11ltpaGhg+fLlQMdTfBYsWEBSUhKvvPIKJSUlVpYU1tTwAAAgAElEQVTYH/7whwCc\ne+655OXl8eijj2Kz2fjWt77FBRdcwJYtW6zHmDt3Ltu2bePOO++kvr6e++67zydbNuTm5lrZHG02\nGytXrgTg7LPP5sYbbwRg/vz51NXV8fTTT1NTU8P48eO59957relQAD/96U+x2Ww8/PDDNDY2Mnny\nZK699lq357r11lt59tlnue+++7DZbMyYMYOrr77aOh8WFsbdd9/NihUruPvuu4mOjubss8+2Mnk6\n3X777axevZqnn36a8vJyBg0axPTp0/m3f/s3r/9+RCR049+RI0d4/PHHOX78OElJSYwfP54HH3zQ\nrZLdV/EP4Gc/+xmvvvoqzz33HIcPHyYlJYVJkya5JQoSEe8I1bhms9lOOP3c9fxVV13F8uXLufvu\nu0lKSmL+/PnU1ta6XX/ttdfy/PPP86c//Ynjx4+TlpbGD37wA6Bl6vdvf/tb/v73v7N06VIaGhpI\nS0tjypQpbs8zZ84c7HY7v/zlLwkPD+eiiy7i3HPPtc7fdNNN/OUvf2HZsmU0NjYyYcIE7rnnHmUK\nDxG27q4LMAzjN8DNwEJgO3A68FfgHtM0n2y95i7gl63X5NPSGD8ZmGCaZvvFo20LY7Od/sILL3zq\n3D9TRCRQbdu2jQULFkxzOByfeXpfxToRCRaKdSLSH/Qm1rnyZFr46cBrpmm+3frzAcMwLms9jmEY\nNuBWYKlpmm+0HlsIlADzgdW9KaiIiIiIiIhIoOo8pV57bwNzDcMYC2AYxiRgVutxgJG0TBd/13kH\n0zSPAZsAbdomIiIiIiIiIavbjWvTNJfTMvpsGoZhB74AHjNN05kNwJmqr6TNXUtczomIiIiIiIiE\nHE/WXP8c+BVwCy1rrqcAjwO3m6a5yjCMmcCHwFDTNEtc7vcS0GSa5oITPUdOTs7x8PDwBOfWRCIi\ngaqyspKmpqaq3bt3d5x2tQuKdSISLBTrRKQ/6E2sc+XJmut7gPtN03yp9efthmFk0dLgXgUUtx5P\nx330Op2WUe5ul8e5FZGISIDrabBSrBORYKJYJyL9Qa+DlScPYAPa7gjf3HocII+WBvZc4CsAwzCS\ngGnAU916AputKCMjY+S//vUvD4olItL3zjnnHA4ePFh04ivbU6wTkWChWCci/UFvYp0rTxrXrwH3\nGoZRAOygZVr4bcCfAUzTdBiG8XjrNXv4Ziuuwtb7ioiIiIiIiIQkTxrXtwHHaBmFTgcOAU8DS5wX\nmKb5kGEY8cAKIAXYAFxgmqbdayUWERERERERCTDdblybplkN/KL11tV1i4HFvSyXiIiIiIiISNDw\nZJ9rEREREREREemAGtciIiIiIiIivaTGtYiIiIiIiEgvqXEtIiIiIiIi0ktqXIuIiIiIiIj0Urez\nhRuGkQ9kdnBquWmaNxmGYQPuB66hZRuuj4DrTdPM9UI5RURERERERAKWJyPXpwFDXG7nth5/qfXr\nncDNwCJgOlANrDEMI9o7RRUREREREREJTJ7sc33E9WfDML4H5Jqm+UHrqPWtwFLTNN9oPb8QKAHm\nA6u9V2QRERERERGRwNKjNdeGYUQBVwB/aT00EkgH3nVeY5rmMWATMKOXZRQREREREREJaD1NaDYf\nSAZWtv48pPVrSZvrSlzOiYiIiIiIiISkbk8Lb+Nq4C3TNItPcJ0NaO7hc4iIiIiIiHiN3W7nvffX\nA3D2nLOIioryc4kklHg8cm0YRhZwDvCsy2FnIzu9zeXpLudERERERET8YsNHG7n9/mXsrI9gZ30E\nt9+/jA0fbfR3sSSE9GTk+qe0TPd+0+VYHi2N6LnAVwCGYSQB04CnellGERERERGRHrPb7ax+ay3f\nXrjIOpaZM47Vq55h+ulTNYItXuHRyLVhGGG0NK7/ZpqmNd3bNE0H8Dhwr2EY3zMM42RgFVAIvObF\n8oqIiIiIiHjkvffXM+aMs9odH3PGWdY0cZHe8nTkei4wnG+yhFtM03zIMIx4YAWQAmwALjBN097r\nUoqIiHiR1tyJiIiIt3nUuDZNcy0Q3sX5xcDi3hZKRETEVzZ8tJHVb621RjDeuH8Zl154HrNnzfRz\nyURExFfOnnMWb9y/jMyccW7Hcz9Zzw2L7/ZTqSTU9DRbuIiISNDRmjsRkf4pKiqKSy88j9WrnrE6\nV3M/Wc+lF56n2C9eo8a1eEzTKUUkWJ1ozd35553b7cdSLBQRCS6zZ81k+ulTrdh9w+K7FbvFq9S4\nFo9oOqWIiGKhiEiwioqK8qgjVcQTHu9zLf2X63TKzJxxZOaM49sLF7H6rbXY7cpbJyKB7+w5Z5H7\nSfussLmfrOfsOe1HtDuiWCgiIiIdUeNauk1bGIhIsHOuuVu36hkO7N7Fgd27WLfqGY/W3CkW9o7d\nbmfN2ndYs/YddUaISJ9TDBJf0rRwERHpV7Tmzn80nV5E/EkxSHzNo8a1YRjDgN8BFwBxQC7wU9M0\nN7tcswS4hpa9rj8CrjdNM9drJRa/0RYGIhIqerPmTrGwZ5SpXUT8STFI+kK3p4UbhjGAlsZyPS2N\n6/HA7UC5yzV3ATcDi4DpQDWwxjCMaC+WWfzEG9MpRUSCnWJhz2g6vYj4k2KQ9AVPRq7vAvabpnm1\ny7H9zm8Mw7ABtwJLTdN8o/XYQqAEmA+s7n1xxd80nVJERLFQRMTXtN2hBCNPEppdDGw2DOO/DcMo\nMQzjC8MwrnE5PxJIB951HjBN8xiwCZjhldJKQHBOpzz/vHMV6ESk31Is9Iw3MrWLSP+w4aON3H7/\nMnbWR7CzPoLb71/Gho829uoxFYOkL3gycj0KuB74L+ABYBrwhGEYdtM0VwFDWq8raXO/EpdzIiIi\n0g85p9OvXvWMNTUz95P1mk4vIm58tTZaMUj6gieN6zDgU9M07239eathGCcB1wGrurifDWjuYflE\nREQkRGg6vYicyInWRvc0GSUoBonvedK4PgTsaHNsF/DD1u+LW7+m4z56nQ580aPSiYiISEjpTaZ2\nEZHeUgwSX/JkzfVHwLg2x3KA/Nbv82hpYM91njQMI4mW6eMf97yIIiIiIiISKOx2O2vWvsOate9g\nt9u9+tihujbal78zCRyejFw/Bmw0DONXwH/T0mj+99Ybpmk6DMN4HLjXMIw9tDS6lwKFwGveLLSI\niIj4ljL1igQ/X7yPN3y0kdVvrbWmbr9x/zIuvfA8Zs+a2evHht6tjQ7UuOXr35kEjm43rk3T/Nww\njO8DDwK/AfYBt5im+YLLNQ8ZhhEPrABSgA3ABaZpqntGREQkSKgiKBL8fPE+9lWysbZ6sjY6UONW\nX/3OJDB4MnKNaZpvAm+e4JrFwOLeFEpERET8w5sVwUAdRRIJdb5q0Pky2VhbnqyNDuQGbF/+zsT/\nPFlzLSIiIiHuRBXB7vLFPrUi0j3eeh8Hi/72eiVwqXEtQcNfiSD6WwKK/vZ6RcT7XEeRMnPGkZkz\njm8vXMTqt9YqrogEsb5MNhYq9ZFQTdAmHVPjWk4oEIKbv0ZA+tvIS397vSLSnjcqghpFEvEvXzXo\nnMnG1q16hgO7d3Fg9y7WrXqmW8nGPOFpfSSQG7Dd/Z0FQn1bes+jNdfS/wRCcgh/raMJ5PU7vtDf\nXq+IdKw3mXpFJDD48n3ck2RjnuhJfSTQ49aJfmeBUN8W71DjWjoVKI0tfyWC8OXzBmKSHyXcEBGn\n3laez55zFm/cv4zMnHFux3M/Wc8Ni+/2allFpGO+bAR7kmzMU57UR9rWpzp6vYFS5+rsdxYo9W3x\njm43rg3DuI+WLbhc7TJNc4LLNUuAa2jZhusj4HrTNHO9UE7xAzW2fEO9kyISDHpTeQ70USSR/sKX\njWB/66w+5fp6g6HOpfp2aPF0zfU2YIjL7VvOE4Zh3AXcDCwCpgPVwBrDMKK9U1Tpr/y1jsYXzxvI\nSX4Ceb2ShC6tMQtds2fN5NHFdzM+upHx0Y08uvjugKrQikhg6k59pDv1qUCuc0no8rRx3WSaZqnL\n7SiAYRg24FZgqWmab5im+TWwEMgA5nu3yNIZb1dSA6Wx1VfJM/rieQM5yY+/fs/Sf/U0gV5VVRUP\nPvQwDz70MFVVVd16rv7eiPfX63eOmp1/3rkhGUf6+/+ViC90pz7SnfpUINe5XAVKfVu8w9M112MN\nwygE6oCPgV+ZplkAjATSgXedF5qmecwwjE3ADGC1l8ornfDFtJdAmtbn6+QZgfa8/tLfXq/4T0/X\nmC1f8SzvfrqF2Rf/EICFt97F3GlTuOHaazp9rmCYFuhL/f31+4p+ryK+05/qI4FU35be86Rx/Qlw\nJWDSMiK9GNhgGMZJtEwRByhpc58Sl3PiwpvJFXyZCCGQgpu/1g1583mDIclPT15voCQLkeDRkzVm\nVVVVvPvpFq64417rWNYd9/Lcww+w8LIqEhIS2t3H34li/P3e8PfrD1X6vYr4Xlf1ke7Up4KhzuUU\nSPVt6Z1uTws3TfOfpmm+bJrmNtM01wIX0pK47JIu7mYDHL0sY8jx9l7Cvp72EurT+vpSKE691t7Y\n0leeXP5Ha8Ta1eyLf8iTy//Y4X38OS0wEN4bwTItMtjo9yriX92pTwVbnUv17dDQ4624TNOsNAxj\nNzAaeK/1cDruo9fpwBc9L17oUW938PH2yFMo9U7q/1l6KphGFHoiFN8b/h6FFxFxNXvWTKZMOsXq\nXP3PX/2i3QymUKpzgeJwMPA0oZnFMIwEYCxQZJpmHlAMzHU5nwRMo2VttrTyRW+3EiH4jq9GnkKl\nd1KjN9JTPRlRuPmG69nw+svtjm94/WVuvuH6Du/jr/gYKO8Nb73+QBiFDyT63BXxD9ckguveX88v\nH3yE2PFTiR0/lV8++EiHcSlU6lyKw8HBk32uHwFeBw7Qsub6fsAOvNB6yePAvYZh7AHygaVAIfCa\nF8srHVAiBN8IxZEnkUDi6YhCQkICc6dN4bmHH7Cmh294/WXmTpvS4XprUHz0xutXLGyvv/9fifhD\n2ySCH7z0EtPO/Y41AyqU45LicPDwZOR6GC0N6V20ZP8+DJxhmuYRANM0HwKeBFYAnwJxwAWmaWpv\nChfO3m6Hw8HxQweoPXoYh8PBno/fp6GhocfbeWg/0Z5tz9OVQBl5CmQavZHe8nRE4YZrr2HV47+j\nbtdm6nZtZtXjv+syUzj4Jz4G0nujt69fsbBj+twV6Tsd7Vl9xR33suPzT2hwqTeHalxSHA4e3R65\nNk1zQTeuWUxLFnHphLO3+/XnV5AQYQPA3tRMc00Vu+0RhDVH9Hg7D39l0w4EPdmeR3pPozfiDwkJ\nCfzqzjs8uk9fx8dAe2/0588HX9LvVaRvdNa4PPXMc9i5eROnzJjth1KJtNfjNdfSc7NnzWTe+dby\ndKLCwxgyIImqbZtwHC7g9LnfYfVba3s0gt0fuW7Pk2WMJ8sYzxV33Mu7n27p1Qh2II08+ZLr+iXN\nmhDxnlB5b3QVC2fNnNGr+CEi4tTb+giEXh3Nqb/USUOBGtd+MmvWLObNm0dCQqJ1zNHczLGCPPI/\n+CeD46L468qVFBUV4XBoN7Ou9GR7nu4Iti0ceqInyTE6+vALlWQhIt7WnfeGNyqUvtRZLJyQNZxf\nPviIkuuISK+dqD7SWePyg//9HxKSUkKyjuYqGOqkgf5Z1ld6vBWX9I7NZmPy5MmUlB5mW4WdGIed\nirw9NNbXAuBobKC4qIgVK1aQlpbGKaecwsknn0xycrKfS96/hNoWDq56khyjbTKRni5hEJEWwfKe\nahsLr/nVL/jlg48ouY6I9Fp36iOdLbW56SeX0FBvBxo9qqM5HA6amppobGykoaGBhoYGGhsbaWxs\npKmpiebmZuvm/LntYJfNZrO+DwsLIywsjPDwcOur8xYZGWndIiIiCA8P79HvKZDrpMHyWdYX1Lj2\nM+der99euIghp0zj+KEDlOfvprIgz3rTHj58mH/961/861//Yvjw4Zx00klMmDCBxMTEEzz6iYXC\nfnk333A9C2+9i6w77nU7vuH1l1n1+O96/fihuqbuRMkx2r5mZaoU8a5ge0+5xsI1a9/xKH54wlef\nS6HweScSirpbH2nbuLz2njtpaGigpqaG2tpa9uzZY31fU1NDfX19pzd/jqyGhYVZje3GpiYiwsNJ\nS0sjNjaW6OhooqOjiY2NJS4uzu3mPBZoddJg+yzzNTWu/ayjnri9+/L4wblnk5QQz1dffUVBQYF1\n/cGDBzl48CD//Oc/yc7OZuLEiYwbN67TbWi6Eiq9TD3Znkc852ljXES6pvdUe776XAqVzzuR/qC5\nqZGGmhoaj1dw+FgtH374IVVVVdaturqaqqoqPvk4OJehNDc3W418J09yBMXGxpKQkNDulpiYSHJy\nMklJSSQmJvZ4hNxT+ixzp8Z1AOhqmsfUqVMpLy/n66+/Zvv27ZSWllr3y8/PJz8/nzfffJMRI0Yw\nfvx4xo0bx4ABA074nKHWy3TDtdew8LIqa431qsd/p4b1CThnTTj3h3TK/WQ9Nyy+26PHqq2tYevW\nrWRkZJCWlubNYopIAPJm/HDy1edSqH3eiQS7hoYGKioqKC8vp7y8nKbGBko//4j6vO001FTTVF9n\nXbsX2Ju7xyvP6xwVdk4zd07Tdn513lyndrtO97bZbDQ1NfHGO+sYc9p0vpkl7mDfF59y3lnfAnCb\nSt526nlDQwN2u52DRUXExCXQ3GCnubHBo9dRW1tLbW0thw8f7vQam81GQkICSUlJJCcnk5KSwoAB\nA6xbcnJynzW++5seN64Nw/glsAz4vWmat7kcXwJcA6QAHwHXm6aZ29uChrquph4PGDCAM888kzPP\nPJPS0lK2b9/Otm3bOHr0qHVNQUEBBQUFrF27liFDhmAYBjk5OQwdOtRtTYhTKPYy9WR7nlDX1TRI\nT7cKcq1MO5qbqTlSyrHCfI7s3EqlzcbWL78kLi6O//iP/yAsTLkSRU6kpw3UQJje7Iutxnz1uRSK\nn3cinQmE+ADQ2NhIeXk5R44ccbsdPXq0w1Ha6DAbdeVHuvXYUVFRxMfHk5CQQHx8vNuUadevrtOs\no6KiOqwPe2rN2ncYNu0s0trE7bqIWJqaG7sVT9asfYfajBwr9juam2lubKDA3El2eD2nnTrFakDX\n1NRYt9raWqqrq6murub48eM0NjZ2+hwOh4Pjx49z/PhxCgsL25232WwkJSUxYMAABg4cSGpqKoMG\nDSI1NZWUlBSP6nG+6GwNZj1qXBuGcTpwLfAV4HA5fhdwM7AQyAeWAmsMw5hgmmZ9Bw8lHho8eDCD\nBw9mzpw5FBcXs3PnTnbt2uXWe1VcXExxcTHr168nISGBsWPHkpOTw6hRo9RD3490ZxqkJ8kxmpqa\nmHPqKXz2/DPERkfhaO1pjXD5sIqJifHKh5dIf9CTBmogTW8O5OQ6Iv2RP+JDQ0MDZWVllJaWcvjw\nYetWUVHh8W43NpuNyMhIoqKiGTFiOMnJySQnJ5OYmEhiYqI1/TnU4owtLIzwqGjComOIj44gKyvr\nhPdxOBzY7XaOHz9uTZc/duyYdausrKSyspLq6upO7++8Jj8/3+1cWFiY1eBOS0uz2h6DBg3qcLTb\nF52twczjxrVhGAnAc7SMTv/a5bgNuBVYaprmG63HFgIlwHxgtTcKLC1sNhtDhw5l6NChfPvb36as\nrMxqaB86dMi6rqqqii1btrBlyxbCw8PJzMxk9OjRTBg/jjee/nO/72UKlB5eb/NkGmRnsyaam5sp\nLi4mNzeX3NxcDh48iMPhICbcZjWsoeV/MTMzE8MwmDRpkhrXEnBc17fZ7Xa3m3OKnt1ut6buuX51\nZo7t6OaaTdaZSdY1o6zD4egwu6zzPeL8Pid9ILU7P8OGjZNGDGHXju3s3rXTyjTrnKZos9n44uvt\nGBMmEFZdji08gpNOO53X/7mWyPAwYmNjremOrjfnyI0vpgB6M+Gjr0Y/NKoi/YGvlz84HA4qKioo\nLi6mpKSE4uJiSktLKS8v9+hxEhISGDBggDVN2XW6cmJiYsDPfPNGPPHGY9hsNiu2p6amdnpdY2Mj\nx44dc5uG7/p9bW1tu/s0NzdTVlZGWVkZu3btso6HhYUxaNAgq7E9ZMgQhgwZQmJiojpbXfRk5Pop\n4P9M01xnGMZvXI6PBNKBd50HTNM8ZhjGJmAGalz7VGpqKrNnz2b27NkcO3aM3bt3s2fPHvbt22dN\nG2lqaiIvL4+8vDwAhiVE89nzzzBg1DgiEpPZ98WmftXLFEgjQN7Wk2mQDoeD8vJy9u3bZ/2fdBR0\nASIjIxkzZgyGYTB27Fji4uK8/hpEXDU1NbWbJuf8uba2lrq6unY3Z4O6ocGz9Wz+VFtb0+X5mHAb\nR3N3uh1LiLCxZs2aEz52RESEVRmLjo4mJiaG2NhYYmJi3L53nVrpvPXF2jxfjX5oVEX6A28uf3A2\nrg4dOkRRUZHVmHZNwNWVqKgoa5rxwIEDGTRokHWLjo7udjkCkTfiSV/GpIiICAYOHMjAgQM7PF9b\nW2tN2S8rK3Obwt/U1OR2bXNzszUzYfv27dbxuLg4q6E9LGMo48eP79fruT1qXBuG8WNgMnB66yHX\nLvkhrV9L2tytxOWc9IGkpCSmTp3K1KlTaWhoIC8vj927d5Obm0tlZaV1nd1uJybcRu1+E4CxgwdQ\nfqSMr7/+muzsbK9s9RWolODmm8b0/v372b9/P/n5+W7/H22lpqYyZswYxo4dS2ZmJhERyocovdPQ\n0GBNZ3NObXOuJ6upqbG+r66upq6u7sQP2IdsNpuV6Mb1e+eoS9sRaifnaLbr6HbbEfCO9lP1BudI\nfGfTBLviXOPY2c05ZTMxMdHj+Nl2BpEvRj80qiLSMWdd4NChQxQWFloN6u50SkZGRlrThtPS0qxb\ncnJySM9i80Y8CZSYFBsby/Dhwxk+fLjb8ebmZioqKjh8+LA15b+0tJSysrJ2je6amhr27dvHvn37\nABgzZgyXX355n72GQNPt2rFhGCOA3wNzTdN0bg5na711xQY096x40luRkZHk5OSQk5ODw+Hg6NGj\n5ObmWqOTrsGzoqLCmkIOkJKSQmZmJiNGjCAzM5O0tLSQCZahnuCmoylHjuZm8jetZ/p3z+fll19m\n//79HD9+vNPHiImJITs7m1GjRjF27FhSUlL6ougS4urr63nttdfIy8vr9ihIT7iOzrqO1rpmiW37\nvWvW2I6yx7refD110eFwWFlmndPQa2trWfbEH5l20Q9pbmqkuakRR1MjX7+3hgXzLqK5ubndtHfn\nzXUk33nzpAHvfJzuTAGNioqyGtrOLWFcvyYlJZGQkEBYWFinM4h8EYO9OYVdJNB0d6qx3W7n0KFD\nFBQUWNu71tR0PWMGwBYZTXhsPJXlR5g+eRJnzzmLAQMGhEy90FPeiCfdeQx/LV90rrseOHAghmFY\nx5ubmzly5AglJSXWrIaioiK3/6Hu/D+FMk+Gnk4D0oAvXH7J4cBswzBuBJzv5nTcR6/TgS96WU7x\nApvNZk3LmT59Oo2NjRw8eNDa0uvgwYNuvVEVFRVUVFTw1VdfAS2NrWHDhrnd4uPj/fVypAtRUVHM\nP+dM3vr7Mwwenklj9TEaj1cwINLG2rVrO7yPc03+qFGjGDlyJEOHDg34tU8SfAoKCtzWcJ1IdHS0\nlQ22q4ywrlObo6Kigv5/12azWQ17p6SkJH504Xms/r+X200lnDp1qkeP70yG45xS7/q17dT7ttlq\nT8Rut3P06FG3HS3aCgsLIzExkcPlFYwdO5bIumNExiVw+rnf5X/eepPTpkzWchMRD3Q21fj755zF\nnj172L9/PwcPHqS4uPiEHWvJyclkZGSQnp7Oa++8x4xLriQiOsY6v27VM3zvou/224Z1XwnE5Yth\nYWHWDIWTTjoJaPk8qaqqoqioiKqqKkaPHu238gUCTxrX7wInufxsA/4K7AR+B+QBxcBcWrKIYxhG\nEjCNlnXaEmAiIiLIzs4mOzsbaJmiWVBQQF5eHgUFBRQWFrql+a+rq2Pv3r3s3bvXOpaSksLQoUOt\ntRZDhw4lISHBKwHXl711oZTgxrndQlFRkTWdyxngkiNt1JcUALT7m0RGRpKZmUlWVhZZWVlkZGRo\nqrf4XHZ2NuPHj6e0tNTK/Op6a7u9iv4n3XlrKqFrMhxPNDU1uU3Zd07ld81Y6/y+q5kJzc3NVFZW\nEhVmo2K/+26dA6NsPPzwwyQmJrbbm9U5khIXF6eKvYRsUtKemj1rJuNyxrJm7TscP3YMY8ggPtzw\nQZf3cU4LHjZsGBkZGWRkZFgDJ2vWvkPW9LPcGtYQOrP8AlkwLV+02WxWRnfxoHFtmmYVsMP1mGEY\nNcBR0zR3tP78OHCvYRh7+GYrrkLgNW8VWHwnMjKSUaNGMWrUKKClElVUVMSBAwesfbTbrtVzjm7v\n3PlNgh1nYgPnGhznV08qcb7urQvWBDe1tbWUlZVRUlJCaWkppaWllJSUdGs9anJysrWuZsSIERqZ\nFr+IiIjgkksu8Xcxgpo/pzeHh4d3uxL1/gcbeHXNvxgx8RSa7XYO791FalIClVVVxMYn0FhbTVcR\nyLlHa0FBQbtzUVFRVkPbNVnSoEGDiI2N7cUrlGARiKN6/lBdXU1eXp613K+ioqLL6wcPHmzVA4YP\nH86gQYPUURWAQn35Yijr7ZCAA5ekZqZpPmQYRjywAkgBNgAXuKzRliASHh7uluTAuSdeYWGhlfTi\n0KFD7ZJetE1s4JSUlGRlj3TNINl2s/q+6q0LlGQSbTU2NlrbJDizNzpv3U1EFBMTY23V5vwbqkdR\nRPqK3W7nf9b8yy2Oj5lxFs89/ACX3nInkVFRNNjtvLXqT5z/b5fRUFOFvfo4DdVVFGzbwqgRw7rc\no9Vut1NcXExxcXG7c7GxsaSmpra7tf2s6SsaXfU+X9cTAvlvZrfbyc/Pt3b1KClpm0f4G2FhYWRk\nZFgz1EaMGEFMTEyn17cVSrP8RPpKrxrXpmme3cGxxcDi3imL6j0AACAASURBVDyuBCabzUZKSgop\nKSlMnDgRaJnad/ToUYqKiqyKTnFxcYfJDJwb27dtdIeFhZGUlGQ9dlnZETLHjqOqpJCI2HgiY+MI\nj4wKmd66+vp6Kisrrd+H83vnnoPHjh3z6PESExNJT08nPT2doUOHkpGRQUpKinqiRYJUIFfsu6uz\nUZfZF/+QnZs3ccqM2URGRTF+6hm8/txfOWPud8AWRu7WLW6jj3a73epsdK7jdsbK8vLyDteO1tbW\nWrOtXIWHh5OamuqW2Xjw4ME+bXRrdNU3fDmqF2h/M4fDQWlpKbm5uezdu5f9+/fT3NxxnmDnoEh2\ndjZZWVkMHz6cyMjIHj93sM7yCwXq2AheWswmvRIWFmaNCpx88snAN4kNXNP3O7/vaA2eM91/26lM\nea1bhAGERURCeATboyM5VllhJTdyrst0JjNyZgaOiYkhIiKiywZmbz9AHQ4HDQ0N2O126uvr3fbc\ndd5ctxZyrkfs6Z678fHx1sh/eno6gwcPJj09XVMgRUJIoFXsfS1n8mlEREZRu2szp06e3G4GUVRU\nFIMHD2bw4MHt7tvU1ERFRYXbvqxHjx6lrKysw50QmpqaKCkpaTfSFxERQVpamltcTU9P73XCzmBa\nMyktAuVv5sxx42xQd7WzR0ZGBiNHjmTkyJFkZmb2qjHdkUCd5Rfq1LERvNS4Fq9zTWzgXL8NLY3R\nmpqadhWh8vJyKioqulw33NzYAI0NHK+vZceOHZ1e17YckZGR7W7OfWn37j/AyBHDaS7KA2BkVib/\nXLuWvL25hIWF4XA43LbBcX7v3JKmp43krsTFxbVL3uNsUKsRLRLaAqVi7w2djbpseP1lLr3lTrdj\n+Zs38mgPKuzh4eHWMqO26uvr2y2rOXz4MEeOHGk32t3Y2GglgnQVHx9vJet03gYOHNjtUW6tmfQd\nX43q+fNvdvToUXbv3s3u3bu7HJ1OSUlh9OjRjB49muzs7D6pG2gbO/9Qx0ZwUuNa+ozNZiM+Pp74\n+HgyMzPbna+rq6OiooL33l/P1rwC6qqOMXjIUJrt9dQeqyAyLAwb3d+X1bnVjN3e8ZL/qDAbVSWF\n7Y7l5eV59sK6KSYmxsqI7NzrNTk52fp+wIABHmfuFZHQEUqNsc5GXeZOm8KGF//q85GY6OhoK/Ox\nq8bGRo4cOeI2s6q0tLTDbcOqq6vb7ZARERFhLcFxLsNJS0sjPDzcq+WXroXCqF5zczMHDx7ENE12\n795NWVlZh9dFREQwcuRIRo8ezZgxYxg4cKCWffUj6tgIPmpc9zOBspavo3LExMRYIwND4gczNHsU\nOzdvAuCU875PUf4+ciLqmTnjDGsrGOfX+vp66urqrK91dXXW6LLrrbGx8YT7O3YkPDzcukVFRbW7\nOaeiO/fcdd6cnQnx8fHaUkhE/MYfsb+zURfXsnRnJMabZXc2jtPT092ONzQ0uO3AUFpaSnFxcbt9\nvRsbG62knk7h4eFuOS+GDRtGWlqa1kz6mC9G9Xz9N2tqaiIvL4+dO3dimmanCftSUlLIyckhJyeH\nrKws1R9Egojerf1IoKzlO1E5XD/cTpkx27qf88MtKiqKlJSUHj+/w+Ggrq6OOx74HWct+CkOl6lX\nG1b/jd/edTtRUVHYbDYiIiKsaeQiIr7kq4q9P2N/R6MunozE9FXZIyMjGTZsGMOGDbOOORwOjh8/\n7pass7i4mPLycrf7NjU1WbtnbN682Xq8IUOGcOqYLD567hmGTZqGLSqGvZs+CKrR1UDn7VE9X4yI\n2+12cnNz2bVrF7t37+4w94zNZmP48OHk5ORgGAapqandrnf0RcdZoAzMSPfo7+Vfalz3E4Gylq87\n5fD1dC+bzUZsbGzLc7zQfnpiUlJSr59DRMRTvqrYB0Ls7wl/l91ms1nLdnJycqzjdXV1FBcXc+jQ\nIWut9pEjR9zu29DQYGUrT4qwcXz7Z0RERDBjwlhobhm9zMjI0FKgAOSNEfGGhgZyc3PZvn07u3fv\n7jBHS0REBGPGjGHcuHGMHTuWuLg4j8vaF51PgTIwI92jv5f/dbtxbRjG9cB1QHbroe3AEtM0/+ly\nzRLgGlr2uP4IuN40zVyvlVZ6LFDW8nW3HH2RxEGJIkQk0Hg7LgVK7O+JQC17TEwM2dnZZGdnW8fq\n6+spKiqyRrALCwvb7YDR2NhIbm4uubnfVIvS09MZPnw4mZmZjBgxQtsoBoiejIg7/77bt2/HNM0O\nG9TR0dEYhsG4ceMYPXp0r97bfdH55O8OLvGM/l6BwZOR6wLgLmAPYAOuAl43DGOKaZrbDcO4C7gZ\nWAjkA0uBNYZhTDBNs/0cGJET6IskDkoUISKBRnEp+ERHR7drcFdXV1sN7cLCQg4ePNhuVwzn1mDO\n6eQJCQmMGDGCESNGkJWVxZAhQ3y2B7f0XnNzM/n5+Xz99dfs3LmzwynfsbGxjB8/ngkTJpCdne21\n5Hd90fkUqB1c0jH9vQJDtxvXpmn+X5tD97aOZk8zDGMHcCuw1DTNNwAMw1gIlADzgdVeKq/0UKAk\nVgmUckjntFZHpOcC7f0TzDE3mMsOLVt5jR07lrFjxwIta7iPHj3KwYMHrVtJSYlbks2qqip27tzJ\nzp07gZaOlhEjRpCZmWnd1Nj2L4fDQVFREV9//TXbtm2jqqqq3TUxMTGMHz+eiRMnerVBLSKBr0dr\nrg3DCAf+DYgGNgAjgXTgXec1pmkeMwxjEzADNa79LlC2rQiUckjHtFZHpOcC8f0TFRXFhKzhPPfw\nA8y++Ict5Xz9ZeZOmxLwMTfUPi9sNpu1L/ekSZOAlunkhYWFFBQUUFBQwMGDB91GP+12u9t2YNnZ\n2Vx55ZV+KX9/V1lZydatW/n666873DYrOjraalCPHDnSJw1q1867WTNn8MaDj/i08ynYO7j6G/29\nAoNHjWvDME4GPqalUV0LXGKaZq5hGM6aQ0mbu5QAQ3pdSvGKQFljHCjlEHdaqyPSc4H6/rHb7ezY\nf5BLb7nT2trw0lvuZMOLf8Vutwf8+zrUPy+io6MZNWoUo0aNAlqmGR8+fJj9+/dz4MAB9u/f7zYy\nevDgQZqamjQS2kcaGhrYuXMnW7duZd++fe3Oh4WFkZOTw8knn8zYsWOJjIz0WVnadd49+AgTsoaz\nzoedT6HWwRXq9PcKDJ6OXO8CTgGSaRm5ftEwjDldXG8Dmrs4L30sUNbyBUo5vMFb00D9PZ1Ua3VE\nei5Q3z/OckVGRbltbejvcnmio88Lf8dLXwkLC7P24Z42bRoOh4Py8nIOHDhAaWkpo0ePVsPaxxwO\nBwUFBXz55Zds374du93e7pqsrCxOPvlkJkyYQGxsrM/L1Fnn3bpVz/Cfv/oFH238GFDy12Dnjbim\nv5f/edS4Nk2zAXB23W0xDON04HpgWeuxdNxHr9OBL3pbSJFA5a1poIE4nVREJBD1p3hps9kYOHAg\nAwcO9HdRQl51dTVffvklW7Zsabe1GsCAAQOYPHkyp5xyCikpKX1atq467z7a+LGSv4YAb8Y1/b38\nq7f7XIcDYaZp5hmGUQzMBb4CMAwjCZgGPNXL5xAvCtXefn/w1jTQQJlO2tlanT0fv8+YWdNZs/Yd\n/c+IdCJQ17oFarl6KlDipYSG5uZm9u3bxxdffIFpmjQ3u0+2jIqKYuLEiUyePJkRI0ZomzTxCcW1\n0OLJPtcPAm/RsiVXInAZcCbwQOslj9OSQXwP32zFVQi85sXySi/0p97+3uhuB4S3poEGynTSjtbq\nbF33NjXHj5PbHAv1+p8R6UygrnXzd7m83aEbKPFSgtvx48f54osv2LJlC5WVle3OZ2dnM2XKFMaP\nH+/TddTddfacs3jxrnvbdZJtXfc2N/zugU7uJT3V1wNRimuhxZOR6zRgFTAUqAS2AuebprkOwDTN\nhwzDiAdWACm0ZBG/wDTN9otVpM+pV6x7+nsHhOtanYamRszoaL533W3Wef3PiHQuUNe6+atc/T2e\nSmBxOBzk5+fz2WefsWvXLrct0KBl67TJkydz6qmnBuQ0/Jrjx3nt2ac49cxzAPjig39Rf/y4n0sV\nehS3pLc82ef6mm5csxhY3KsSiU+oV6xjbbe18KQDwlvTLQNt2qZzrc6ate9gzPp2u/P9/X9GpCuB\nutatr8vlqw7dQIuXEvjq6ur48ssv+fzzzztcSz127FimTJlCTk5OwCaLe+/99cyYdwlDs0dZWf+/\nu/DfKcrfp89jL/IkbnlzdFtxLbT0ds21SNBq2zv5j3vuY1DmqHbXddaY9NZ0S39P2xQR8TZfdegq\nXkp3lZSUsGnTJr7++msaGxvdzsXHx3Pqqady2mmnkZyc7KcSeq5t1n/xru7GLW+PbiuuhRY1rvsJ\n9Yq566x38qWnHqXBbieym8HMW9MtA3E6qf5nxBeUVFF6KxDjpQSG5uZmdu/ezaZNm8jPz293Pjs7\nm6lTpzJu3LiAHaXuiL8/jxW3v+GrWTmKa6FDjet+ItB7xQIlecT0uRewc/Mmt57hE314eWu6ZaBN\nJw30/xkJPlrL1n/4ujEQaPFS/Ku2tpYtW7bw2WefUVFR4XYuOjqaU045hdNPP520tDQ/lbB3/Pl5\n3J/idnfili+XWSquhQY1rvuRQO0VC6TAbcPGV+v+Scqglg/g/t6YDNT/GQk+SqrYv6hzTvrK1q1b\nefPNN2loaHA7PmjQIKZNm8akSZOIjo72U+m8xx+fx/0tbituiTeocd3PBFqvmL8Cd6e9k5s+YNXy\nJ/ho48eAGpMQeP8zEpyUVLH/Ueec9IUPPvjArWE9ZswYpk+fzujRo0NuX+q+/jwOxrjd25mQJ4pb\n/p6iL4HPk32ufwX8ADCAWmAjcJdpmrvbXLcEuIaW7bg+Aq43TTPXayWWkOKvwN1V72RCQkJAfmCI\niAQbdc6Jr82YMYPNmzczYsQIpk+fzqBBg/xdJPETb82E7CpuaXRbTiTMg2vPBJ4EpgPnApHAWsMw\n4pwXGIZxF3AzsKj1umpgjWEYwT8fR0LO7FkzeXTx3YyPbmR8dCOPLr47JNcQiQSCs+ecRe4n69sd\nz/1kPWfPad/BJiLSHVOnTmXRokVceOGF/bJhbbfbWbP2HdasfQe73e7Vxw6muO06EzIzZxyZOeP4\n9sJFrH5rrdd/L6o/Slc82ef6O64/G4ZxFVAKnAp8aBiGDbgVWGqa5hut1ywESoD5wGovlVlCiL+n\n1wTSqIqycUooU2+/iIh3+TpnTTDF7b6eCRlI9UcJLL1Zc53S+vVo69eRQDrwrvMC0zSPGYaxCZiB\nGtfSgWAK3L4USEndRHxFa3BFRLyjr3LWKG6LeKZHjWvDMMKAx4EPTdPc0Xp4SOvXkjaXl7icE2mn\nvwfu/paNU/q3QOnt10wREQlmfTlSGyhxuyv+ngkp4tTTkeungAnAt7pxrQ1o7uHzSD8RDIHbV4Ix\nG6dIMNNMERGR0KKZkBIoPEloBoBhGH8ALgTONk3zkMup4tav6W3uku5yTkRExG/6MumNeIcvEzaJ\nBKtgSjbWV5RoTAJBtxvXhmHYWhvW84Bvm6a5v80lebQ0oue63CcJmAZ87IWyioQkfUCK9J0TzRSR\nwLLho43cfv8ydtZHsLM+gtvvX8aGjzb6u1h9Th0M0pZzpHbdqmc4sHsXB3bvYt2qZ/r9SK1zJuT5\n553br38P4j+eTAt/ClhAS+O62jAM5zrqCtM060zTdBiG8Thwr2EYe4B8YClQCLzmxTKLhBRNZRIR\naU/5KFpoGYN0pr/nrBEJRJ40rq8DHMD7bY5fBawCME3zIcMw4oEVtGQT3wBcYJqmullFuqAPSJG+\nEYhJb5RcrWPKR6EOBjmx/pyzRiQQebLPdbemkJumuRhY3OMSifRT+oAU8b1AmymiUUnpijoYRESC\nS2/2uRYBNOoiIsElUGaKaFSya4E4y0CkL6l+JRJ8PM4WLuJKyWZEJBgFQtIbJVfrmhI2KeFlf6b6\nlUhw0si19JhGXURExJcCZZaBvwTaMgbpG6pfiQQvjVxLj2nURUSk5zQq2T2BMMvAn7R3b/+j+pVI\n8NLItYiIiB9oVFK6SwkvRUSCg0aupcc06iIi0jsalRSRtlS/EgleHo1cG4ZxJnAHcCowFPi+aZr/\n2+aaJcA1tOxz/RFwvWmaud4prgQSjbqIiPSeRiVFxJXqVyLBy9Np4XHAFuDPwCuAw/WkYRh3ATcD\nC4F8YCmwxjCMCaZp1ve6tBJw+nuyGRERERFvU/1KJDh51Lg2TfOfwD8BDMNwO2cYhg24FVhqmuYb\nrccWAiXAfGC1F8orAUijLiIiIiLepfqVSPDx5prrkUA68K7zgGmax4BNwAwvPo/0U3a7nTVr32HN\n2new2+3+Lo6IiIiIiIjFm43rIa1fS9ocL3E5J9IjGz7ayO33L2NnfQQ76yO4/f5lbPhoo7+LJSIi\nIiIiAvTNVlw2oLkPnkdClN1uZ/Vba/n2wkXWscyccaxe9QzTT5+qNUgiIiIiIuJ33hy5Lm79mt7m\neLrLORGPvff+eitbpqsxZ5xlJfoQERERERHxJ282rvNoaUTPdR4wDCMJmAZ87MXnERER6XPK+yAi\nIiJd8ahxbRhGvGEYkw3DmNx6aFTrzyNM03QAjwP3GobxPcMwTgZWAYXAa94ttvQnZ885i9xP2o9Q\n536ynrPntB/RFhHxNuV9EBERkRPxdM316cC61u8dwKOt368Efmaa5kOGYcQDK4AUYANwgWma6uKX\nHouKiuLSC89j9apnrOnhuZ+s59ILz9N6axHxOeV9EBERke7wdJ/r9znBaLdpmouBxb0ok0g7s2fN\nZPrpU6011jcsvlsVWhHpEyfK+6B9aEVERAT6Jlu4iFdERUWpEisiIiIiIgHJmwnNREREQo7yPoiI\niEh3aORaRESkC8r7ICIiIt2hxrWIiMgJKO+DiIiInEhANa6bm5ttx44d47PPPvN3UUREunTs2DGa\nm5ttPbmvYl3wGjggBYCtW7f6uSQifUOxTkT6g97EOlc+aVwbhnEjcAeQDmwFbjZN84SRtb6+Pmre\nvHns3bvXF8USEfGaefPmsWLFih4NXSrWiUiwUKwTkf6gN7HOldcb14ZhXAr8F7AI2ATcBqwxDMMw\nTfPwie4/ZswYTjrpJG8XS0LcunXrWLlyJatWrfJ3UUS6RbFOekKxToKNYp30hGKdBCtfjFzfDqww\nTfNvAIZhXAd8F/gZ8DsfPJ+IX3zyySesXbuWvXv3Ul1dzSOPPEJ2drZ1vqqqihdffJGtW7dSVlZG\nUlIS06ZNY8GCBcTFxfmv4CIiHlCsE5H+QLFOvMGrjWvDMKKAU4HfOo+ZpukwDONdYIY3n0vE3+rr\n65kwYQIzZ87k6aefbnf+6NGjlJeXc9VVVzF8+HBKS0tZsWIF5eXl/OIXv/BDiUVEPKdYJyL9gWKd\neIO3R65TgXCgpM3xUmCcl59LAtTnn3/OE088wd/+9jdsNht5eXnccccdzJ8/nyuuuAKA5cuX09DQ\nwC233ALAzp07ef7559m7d6/VE3jFFVcQHR0NQENDA//4xz/48MMPqampYcSIEfzkJz9h4sSJHZah\nsrKS3/72t6SmpnLbbbcRGRnp9dd51lktW/KUlpZ2eD4zM5M77rjD+jk9PZ3LLruM3//+9zQ3NxMW\npm3mRYKZYl0LxTqR0KZY10KxTrojoLKFA4P8XQDpvfHjx1NbW0teXh6jRo1i+/btJCYmsn37duua\nHTt28P3vfx+A4uJiHnjgAS677DJuuukmKisrefbZZ3n22We58cYbAXj22WcpLCzkP/7jPxgwYACb\nNm3igQce4NFHH2Xo0KFuz19WVsb999/PuHHjuOGGG7DZOk7898wzz/DBBx90+Vqef/753vwq2qmu\nriYuLk4BOHT0NGYp1oUAxbrOKdaFHMW6fkyxrnOKdSGn1zHL243rMqCJlizhrtKBom7cvwGI9nKZ\npI/Fx8czcuRItm3bxqhRo9ixYwff+973eOmll6ivr6eqqori4mKrd/KVV17hzDPP5Lvf/S4AQ4YM\n4Wc/+xm/+c1vuPbaa6moqOC9997jmWeeYcCAAQBcfPHFbNmyhXXr1nH55Zdbz11YWMiSJUs444wz\n+OlPf9plOX/84x8zb948H/0W2jt27Bj//d//zbnnnttnzyk+19CL+ynWBTnFuo4p1oUkxbp+TLGu\nY4p1Iamnsc7i1ca1aZp2wzA2A3OB1wEMwwgDzgGeONH9CwoKvg186s0yiX9MmDCBbdu2cfHFF7Nz\n504uv/xyNm7cyI4dO6iqqmLAgAEMGTIEgPz8fA4cONBhb2NpaSnFxcU0Nzdz0003uZ1rbGwkMTHR\n+tlut/PrX/+a2bNnnzAAAyQnJ5OcnNzLV9o9NTU1LFu2jMzMTC655JI+eU7xvdaY1dP7KdaFAMU6\nd4p1oUmxThTr3CnWhaaexjpXvpgW/ijwN8MwPgc+A24FYoG/+uC5JEBNnDiRdevWkZ+fT3h4OMOG\nDWPixIls376d6upqt2056uvrOe+887jwwgvbPU5qair5+fmEhYXx8MMPt5t2Exsba30fGRnJpEmT\n+Pzzz5k3bx4DBw7ssownmj5ks9l47rnnuvuSO1VbW8sDDzxAXFwcd955J+Hh4b1+TBEJDIp131Cs\nEwldinXfUKyTrni9cW2a5kuGYaQBS4AhwBbggu7scS2hw7k+54033rCmCU2cOJFXXnmF6upqt2k7\no0aNoqCgwOrxbGvkyJE0NzdTWVnJ+PHjO31Om83Gz3/+cx577DEWL17MkiVLrOlGHemL6UM1NTUs\nXbqUqKgofvnLX/okAYeI+I9iXQvFOpHQpljXQrFOTsQnq+9N03zKNM1s0zRjTNOcYZrmZ754Hglc\nCQkJZGVlsWHDBisIT5gwgby8PIqKipgwYYJ17fz58zFNk2effZa8vDwOHTrEp59+yrPPPgtARkYG\ns2fP5oknnmDTpk2UlJSwZ88eXnnlFTZv3uz2vDabjVtuuYWsrCwWL15MRUVFp2VMTk5myJAhXd66\nUlVVRV5eHgUFBUDLuqC8vDzrOWtqaliyZAn19fVcf/31VFdXU15eTnl5Oc3NzZ7/UkUk4CjWKdaJ\n9AeKdYp10j2Bli1cQsjEiRPZv3+/NVUoISGBESNGUFlZSUZGhnVdVlYWS5Ys4R//+Ae//vWvcTgc\nDBkyhFmzZlnX3HTTTfzP//wPK1eu5OjRoyQlJZGTk8PUqVOta5zZI8PDw7ntttt49NFHue+++1iy\nZAlJSUlef32ffvopy5cvt577scceA+CSSy7hkksuYd++feTm5mKz2dzWFdlsNpYvX05aWprXyyQi\nfU+xTrFOpD9QrFOskxOzORwOf5fBYrPZTn/hhRc+dV23ISISiLZt28aCBQumORwOj2fmKNaJSLBQ\nrBOR/qA3sc6VNmUTERERERER6SU1rkVERERERER6KdDWXMe8//777N2719/lEBHpUmvCk5ge3l2x\nTkSCgmKdiPQHvYx1lkBrXDN8+HD+n737jpOqvvc//prt7C7sAss2OkEOJVFRQYUgIEUsURKNxnIx\nihFrLL+oV2IE1JAbzbVGLMGIxEQx1xKJGoogIigoKkjxUKRtZSnby2yZ3x+7c5zZPrvT9/18POYx\ns+ec2fOd3ZnPnO/38y0/+MEPAl0MEZFWdXa+CsU6EQkFinUi0hV4ax6yYKtcVw4dOhRNfCEiIaKy\no89TrBOREKJYJyJdQUdjnUVjrkVEREREJKysWbOGWbNm+fWcDz74IC+//LJfzynBJdgy1xIEHnzw\nQQYPHsx1113nl/M988wzlJeXc9999/n0PDt27OBf//oX+/fv58SJE9x7772MHTu2yXGvvfYaH374\nIWVlZQwfPpwbb7yRjIwMa7/dbueVV15hw4YNVFdXc+qpp3LjjTeSlJRkHVNSUsJLL73Eli1bsNls\nnHXWWVx//fXExX0/lKOgoIAXX3yRHTt2EBcXx6RJk7j66quJjIwE6r8UlixZwtKlS5uU8bLLLuO+\n++5jzJgx3vwTiXR54Rr/li1bxj//+U+3bX379uWpp55y2+aP+Ld9+3bmz5/P0qVLiY+Pdzv/TTfd\nxEUXXcRFF13k7T+BSJcVrnHtmWeeYd26dUD9WtgpKSlMmjSJn/3sZ9a1lIi/KXMtHeJwOKitrQ10\nMTxit9sZPHgwN9xwAwA2m63JMW+//TYffPABc+bM4X/+53+IjY3l4Ycfprq62jrm5Zdf5osvvuA3\nv/kNDz/8MCdOnODRRx91+z1PPfUUWVlZzJs3j7lz57Jz506ef/55a39tbS0LFy607m+//XbWrl3L\n66+/7qNXLyLeEorxD6B///689NJL1u2RRx5x2++v+Ncam83WbGwWEd8Kxbhms9kYPXo0L730Es8+\n+yyXXHIJb7zxBu+++26giyZdmDLX4uaZZ55h586d7Ny5k/feew+bzcaiRYvIz89n/vz5/Pa3v+Uf\n//gHhw4d4sEHH2TkyJG8/fbbrFq1isLCQjIzM7nssss4++yzAairq+O5555j+/btFBYWkpKSwowZ\nM7jwwguB+myKs9XxsssuA2DBggWMGjXK669t9OjRjB49usX9DoeDf//731x22WVWRvjXv/41s2fP\nZvPmzYwfP56ysjLWrFnDXXfdZY0hu/XWW7njjjvYvXs3w4YNIysri6+//ppHH32UIUOGADB79mwW\nLlzItddeS8+ePdm6dStZWVnMnz+fpKQkBg0axJVXXsnf/vY3fvGLX6jFVSQAwjn+QX1mxzXD7Mqf\n8U9E/Cec45rD4SAqKsqKa9OnT2fTpk18/vnn/PSnP21yfF5eHkuWLGHPnj1UVlbSr18/rr76ak4+\n+WTrmOrqal5//XU++eQTioqK6N27Nz/72c+YMmUKAIcOHWLp0qXs2rWLuLg4TjnlFK677jq6d+9u\n/Y7a2lr+8pe/8PHHHxMVFcX06dO58sorrf2lpaX8rCG2GQAAIABJREFU9a9/ZcuWLVRXVzNy5Ehm\nz57t1ktIQpcq1+Jm9uzZ5ObmMnDgQH7xi18A0L17d/Lz8wH4+9//zqxZs0hLSyMhIYE333yT9evX\nc9NNN5GRkcGOHTt4+umn6dGjB6NGjcLhcJCSksI999xDYmIipmny/PPP07NnT8aNG8cll1xCdnY2\nFRUV3HbbbQAkJCQ0W7Y333yTt956q9XyP/300/Tu3btDrz0/P5+ioiK3IBsfH89JJ52EaZqMHz+e\n7777jtraWrdj+vbtS0pKinVxaZomCQkJ1oUlwMknn4zNZmPPnj2MHTsW0zQZOHCg24XuKaecwosv\nvsjhw4cZNGhQh16DiHRcuMe/3NxcfvWrXxEdHY1hGFx99dWkpKQA/o1/IuI/4R7XGvd0iY6OpqSk\npNljKysrOf3007n66quJjo5m7dq1/OEPf+CZZ56xYuHTTz/N7t27mT17NoMGDaKgoIDCwkIAysrK\nmDdvHtOmTeP666+nqqqKv/3tb/zv//4v8+fPt87z0UcfMWXKFP74xz+yb98+nn/+efr06cPUqVMB\n+POf/0xeXh73338/cXFxvPrqq/z+97/nqaeeUnIlDKhyLW7i4+OJiooiJiam2QzHFVdcYV1YVVdX\n8/bbbzNv3jyGDRsGQGpqKrt27WLVqlWMGjWKyMhIrrjiCuv5qampfPvtt2zcuJFx48YRFxdHdHQ0\n1dXVLWZUnM477zzGjx/f6jHJycmevmSLM3g2/h1JSUnWvsLCQqKiopqME0xOTnY7pkePHm77IyMj\nSUxMdDum8XmcP584cUKVa5EACOf4N2zYMG677Tb69u3L8ePHeeONN3jggQd44okn6Natm1/jn4j4\nTzjHNfh++SSHw8G2bdvYunUrF1xwQbPHDho0yO366sorr2Tz5s18/vnnnH/++eTk5PDpp58yb948\nfvSjH1mvz+mDDz5gyJAhXHXVVda2W2+9lTlz5pCbm2tlnlNSUqzx7ZmZmRw8eJDly5czdepUcnJy\n+OKLL1i4cKH1N77jjjuYM2cOmzdvtnoISOhS5Vo8MnToUOtxbm4uVVVVLFiwwO2Ympoat6zFBx98\nwJo1azh69Ch2u52amhoGDx7s8bkTExNJTEzseOE7yOFw+GQMoLfW0xMR/wjl+Oc6JGbAgAGcdNJJ\n3HTTTWzcuNHq7tgcX8U/EQkOoRzXALZs2cLVV19NbW0tDoeDCRMmcPnllzd7bEVFBW+88QZffvkl\nJ06coLa2FrvdztGjRwHYv38/ERERjBw5stnnHzhwgO3bt3P11Ve7bbfZbOTl5VmVa2el2WnYsGEs\nX74ch8NBdnY2kZGRnHTSSdb+7t27k5mZSXZ2dof/DhI8VLkWj8TGxlqPKyvrl4L77W9/S69evdyO\ni46OBuCTTz5h6dKl/PKXv8QwDOLi4vjXv/7Fnj173I5vz8Wbr7uFO1tHG2eVi4qKrC+V5ORkampq\nKC8vd8veuD4nOTmZ4uJit99dW1tLaWmp2zH79u1zO8aZ1XGOSYyPj6eqqqpJOcvKyqz9IuI/4RT/\nEhISyMzMJC8vD/Bv/HM+t/Hvgfr4ptgm4j+hHtd++MMfcuONNxIdHU3Pnj2JiGh5rualS5eybds2\nrr32WtLT04mJieFPf/oTNTU1AMTExLRalsrKSsaMGcM111zTZJ/rfBJKnnRtqlxLE1FRUdTV1bV5\nXP/+/YmOjqagoKDFVr5vv/0WwzA477zzrG25ublNzteeGSp93S08LS2N5ORktm3bZnUbKi8vZ+/e\nvcyYMQOAIUOGEBkZybZt2zjrrLMAyM7O5ujRoxiGAYBhGJSVlfHdd99ZF6XffPMNDofDaqkcPnw4\nb731FkVFRVa3qa1btxIfH0+/fv2A+q5EtbW1br8H4LvvvrP2i4h3dZX4V1FRQW5uLhMnTgT8G/8y\nMjKw2Wzs27fPGucI9ZMNlZeXK7aJeFk4x7XY2FjS09PbPBfUl33y5MnW3A8VFRXk5+dbk60NHDgQ\nh8PBjh073OaWcBoyZAifffYZffr0aXVsdOOGht27d1txr2/fvtTW1rJ7924rbpaUlJCTk2Nd/0lo\na3fl2jCMKOBh4BdAGpADLDFN85FGxz0E3AAkAxuAm03T3Ou1EovPpaamsmfPHo4cOUJcXJzbDIiu\nunXrxsUXX8ySJUtwOBwMHz6c8vJyvv32W+Lj45k0aRKZmZmsW7eOr7/+mtTUVNatW8e+fftIS0uz\nfk9aWhpbt24lJyeHxMREEhISmg1ane0+VFlZ6fYFkJ+fz/79++nevTspKSnYbDYuuugi3nzzTTIy\nMkhNTeW1116jV69eViBOSEhgypQpLFmyhMTERLp168ZLL72EYRjWhWO/fv049dRTee6555gzZw41\nNTUsXryYH//4x1bL5imnnEK/fv14+umn+a//+i9OnDjB66+/zowZM4iKqv9YDhgwgFNOOYVFixZx\n7bXXkpqaSk5ODn/9618ZP368Zt0V8YFwjX+vvPIKZ5xxBn369OH48eMsW7aMqKgoJkyYAODX+Net\nWzemTp3KkiVLiIiIYMCAARw9epRXX32VYcOGWRecIuId4RrXPJWRkcFnn33GGWecAcBrr73mtj81\nNZVJkybx7LPPMnv2bAYOHEhBQQHFxcWMGzeO888/n9WrV/PEE08wc+ZMEhMTyc3NZePGjdxyyy1W\ntv7o0aMsWbKEadOm8d133/HBBx/wy1/+EqhPjIwZM4bnnnuOm266yZrQrHfv3tZKDRLaPMlcz6W+\n0jwL2AGMAV42DKPINM1nAAzDuA+4veGYA9RXxlcYhjHSNM2m/VslKF188cX8+c9/5s4776S6uppF\nixYBzXfxufLKK+nRowdvvfUW+fn51iyxl156KQDTpk1j//79PP7449hsNn784x8zY8YMvvrqK+t3\nTJ06le3bt3PvvfdSVVXF/PnzfbJkw969e63ZHG02G0uWLAFg8uTJ3HrrrQDMnDmTyspKnn/+ecrL\nyxkxYgQPPPCA1R0K4LrrrsNms/HYY49RU1PDqaeeyo033uh2rjvvvJPFixczf/58bDYbZ599NrNn\nz7b2R0REMHfuXF588UXmzp1LbGwskydPtmbydLr77rtZtmwZzz//PCdOnKB3796ceeaZ/PznP/f6\n30dEwjf+HTt2jCeffJKSkhJ69OjBiBEj+MMf/uB2ke2v+Adw/fXX8/bbb/Pqq69SUFBAcnIyp5xy\nittEQSLiHeEa12w2W5vdz133//KXv2TRokXMnTuXHj16MHPmTCoqKtyOv/HGG/n73//OX/7yF0pK\nSujTpw8/+9nPgPqu37///e/529/+xsMPP0x1dTV9+vRh9OjRbueZNGkSdrud//7v/yYyMpKLLrqI\nadOmWftvu+02/vrXv7Jw4UJqamoYOXIkv/3tbzVTeJiwtXdcgGEYy4E80zR/5bLtTaDMNM1ZhmHY\nqM9mP2aa5uMN+3sA+cAvTdNc1mZhbLYxr7322mbn+pkiIsFq+/btXHnllWMdDsfnnj5XsU5EQoVi\nnYh0BZ2Jda5aHvXf1AfAVMMwTgIwDOMUYHzDdoDB1HcXX+18gmmaxcAmQPPKi4iIiIiISNhqd+Xa\nNM1FwDLANAzDDnwJPGGapnPAgnM2gfxGT8132SciIiIiIiISdjzpFv5r4H7gDurHXI8GngTuNk1z\nqWEY44BPgAzTNPNdnvcGUGua5pVtnWPYsGElkZGRiW0tOi8iEmhFRUXU1taW7t69u/mZYVqhWCci\noUKxTkS6gs7EOleeTGj2W2CBaZpvNPy8wzCMgdRXuJcCeQ3b03DPXqdRn+Vud3mcsyWLiAS5jgYr\nxToRCSWKdSLSFXQ6WHnyC2xA40Xr6hq2A+ynvoI9FdgG1oRmY4Fn23UCmy03MzNz8IcffuhBsURE\n/G/KlClkZWXltn1kU4p1IhIqFOtEpCvoTKxz5Unl+h3gAcMwDgM7qe8WfhfwEoBpmg7DMJ5sOGYP\n3y/Fld3wXBEREREREZGw5Enl+i6gmPosdBr1y249DzzkPMA0zUcNw0gAXgSSgfXADNM07V4rsYiI\niIiIiEiQaXfl2jTNMuA3DbfWjpsHzOtkuURERERERERChifrXIuIiIiIiIhIM1S5FhEREREREekk\nVa5FREREREREOkmVaxEREREREZFOUuVaREREREREpJNUuRYRERERERHppHYvxWUYxgFgQDO7Fpmm\neZthGDZgAXAD9WtcbwBuNk1zrxfKKSIiIiIiIhK0PMlcnw6ku9ymNWx/o+H+XuB2YA5wJlAGrDAM\nI9Y7RRUREREREREJTu3OXJumecz1Z8MwfgLsNU3z44as9Z3Aw6ZpLm/YPwvIB2YCy7xXZBERERER\nEZHg0qEx14ZhxADXAH9t2DQYSANWO48xTbMY2ASc3ckyioiIiIiIiAS1jk5oNhNIApY0/JzecJ/f\n6Lh8l30iIiIiIiIiYand3cIbmQ28b5pmXhvH2YC6Dp5DRETEJ+x2O2s/WgfA5EkTiYmJCXCJRERE\nJNR5nLk2DGMgMAVY7LLZWclOa3R4mss+ERGRgFu/YSN3L1jIrqoodlVFcfeChazfsDHQxRIREZEQ\n15HM9XXUd/d+z2Xbfuor0VOBbQCGYfQAxgLPdrKMEmSU8RGRUGW321n2/krOnTXH2jZg2HCWLX2B\nM8ecoXjmB/oOERGRcOVR5towjAjqK9evmKZpdfc2TdMBPAk8YBjGTwzD+BGwFMgG3vFieSXAlPER\nkVC29qN1DD1rYpPtQ8+aaFX4xHf0HSIiIuHM08z1VKAf388SbjFN81HDMBKAF4FkYD0wwzRNe6dL\nKUFBGR8REekofYeIiEi48yhzbZrmStM0I03T3NvC/nmmaWaYptnNNM3pLR0noUkZn9Bjt9tZsXIV\nK1auwm5XO5fI5EkT2ftZ03i197N1TJ7UNL6J9+g7REREwl1HZwsXkSC3fsNGlr2/0rqYXb5gIVdc\nMJ0J48cFuGQigRMTE8MVF0xn2dIXrM/G3s/WccUF05U5FRERkU7p6DrX0gUp4xM6XLtfDhg2nAHD\nhnPurDkse3+lMtjS5U0YP47H581lRGwNI2JreHzeXDU6+YG+Q0REJNwpcy3tpoxP6Gir++V506cF\noFQiwSMmJkafAz/Td4iIiIQ7Va7FIxPGj+PMMWdY4+NumTdXF0UiItIu+g4REZFwpsq1eEwZn+A3\nedJEli9YyIBhw9227/1sHbfMmxugUomEF63X3DH6DhERkXDlUeXaMIy+wB+BGUA8sBe4zjTNLS7H\nPATcQP1yXBuAmzVruIh/qfuliG9pwkARERFprN2Va8MwelJfWf6Q+sp1AXAScMLlmPuA24FZwAHg\nYWCFYRgjTdOs8l6xRaQt6n4p4htar1lERDyl3k5dgyeZ6/uAg6ZpznbZdtD5wDAMG3An8LBpmssb\nts0C8oGZwLLOF1dEPKHulyLepwkDRUTEE+rt1HV4Urm+GPiPYRj/BM4BsoFFpmkubtg/GEgDVjuf\nYJpmsWEYm4CzUeVaRERERES6EPV26lo8Wed6CHAzYALTgeeApxuy0wDpDff5jZ6X77JPREQkpHWV\n9ZrtdjsrVq5ixcpV2O32QBdHRMQr/B3b2urtJOHFk8x1BLDZNM0HGn7eahjGD4GbgKWtPM8G1HWw\nfCIiIkGlK0wYqC6MIhKOFNvE1zypXOcAOxtt+xa4tOFxXsN9Gu7Z6zTgyw6VTkREJAiF84SB6sIo\nEro0aVbLAhXbtDxq1+JJt/ANwPBG24ZRPys4wH7qK9hTnTsNw+gBjAU+7XgRRXxH3R5FpKOcEwae\nN31aWF3AerMLo2KsiP+s37CRuxcsZFdVFLuqorh7wULWb9jok3OF4mc7UN2znb2d1ix9gUO7v+XQ\n7m9Zs/SFsOrtJN/zJHP9BLDRMIz7gX9SX2n+VcMN0zQdhmE8CTxgGMYevl+KKxt4x5uFFvEGdQ0S\nEfEdxVgR//FnVlafbc+Fc28ncdfuzLVpml8APwWuBL4BfgvcYZrmay7HPAo8A7wIbAbigRmmaYZG\nk5aElM60mrp+CQ0YNpwBw4Zz7qw5LHt/Zci0wIpI+4ViliVQvDFhm2KsiH/5Kysbyp/tQE9GGa69\nncSdJ5lrTNN8D3ivjWPmAfM6UyiRtnS21VTr1Ip0HcqyeMYbE7YpxoqEp1D+bHeFySgl8DyqXIv3\naMKJjtNkOyLSXooXHaMujCKhJRwnzfLFtbJim/iaJxOaiZf4c8KJUNZSN05vdH0KdNcgEfEPrS/a\ncZ3pwqgYK+Jf/po0y1+f7fUbNnLHgw/z7uff8O7n33DHgw93+Fq58fWkumeLLylz7WfKorSPr7tx\nqmuQiIjvKMaK+J8/srL++Gzb7Xb+/PLfiO3Zm9NOHg3Alx9/yJ9f/pvH18oaFiT+psq1n4XyWBV/\naasBwltdn9Q1SCT8BbqrZLAMAQpEORRjRfzPmZX1JV9/tleuWo0tPpGZN9xqbRswbDhvPPs4K1et\n5qILL7C2txbblNCSQFDlWoJOexogvNVq6o8vIRFpma8rfYHMoAZLxsRb5ejI/0oxViQ8+fKz/c2O\nHZw59fwm28+cej7f7NhiVa7bim1KaEkgqHLtZ4HOooQLZUREQp+/Kp+BiBfBkjHxVjmCpaFARMLf\nj370Q/ZUO5rZ4+BHP/ohEDwxVqSxdk9oZhjGfMMw6hrddjY65iHDMHIMwyg3DGOVYRhDvV/k0Oav\nCSe8yd/rw7Z3sgxNSOEbWg9Y/MHfa6X6O14Ey0Rq3ihHKK9rKyKhZ/qUKWxbu6LJ9m1rVzB9yhSg\nfbFNEytKIHg6W/h2IN3l9mPnDsMw7gNuB+YAZwJlwArDMGK9U9TwMWH8OB6fN5cRsTWMiK3h8Xlz\ng7b1PxAzm4diA0S40Ez24i/BUvmUtul/JSL+FBMTw/WXzeTDV57nkLmLQ+YuPnzlea6/bKZH14G6\nnpRA8LRbeK1pmkcabzQMwwbcCTxsmubyhm2zgHxgJrCsswUNN43Hqnhj3KG3xy4GssuNun37j/N9\nU11bw3vrNjLtulusfepiJcEoWCYJa40nQ4B8+Xo0FMm3QuG9KBKKmlwHzv+t2+ervbEt1K8nFWNC\nj6eZ65MMw8g2DGOfYRivGobRv2H7YCANWO080DTNYmATcLZ3ihq+vJEt9EXGMdDZCk+7cXakO3N7\nnhPO3aRd3zd7q2M5UV7J7q+3uB0z6PRx/O+TT4Xl65fA6Wh3vVDpXdHejImvX483MjfqWtm8UHkv\nioSq1q4DnbGtcXb7Z9Mms/ajdW7XLL4YFuSPa0PFmNDkSeb6M+BawAQygXnAesMwfkh9F3Goz1S7\nynfZJy5qa2sxTROg09lhTerQscl22vOccJ7Ep9n3jTGCdxY/y+CRPyI6JobdX2/h6w3rOGvq+eyq\nsoXV65fA6sgs3qEW69rKmPjr9XQ2c6M1q5sKtfeiSLiqq6tjzzdfA3AkL4+//es9Tjm3fqZxX12z\n+OPaUDEmdLW7cm2a5n9cftxuGMYm4CBwOfBtC0+zAXUdL1742rBhA2vXrgWgT0wE+z58l8S0fnRP\n70u3Xn08WibAV0sNhEp3wo4EoPY8J9wDW0vvm9POmcKuLZsYcfqZ7PziMy6/9W5rXzi9fgk8Tyt9\nobisSmvL1fjz9XR22ZxQ71rpbaH4XhQJJ85rNOdQtmq7nfeW/oWfNFob29vXLP66NlSMCV2edgu3\nmKZZBOwGfgDkNmxOa3RYGpDX0XOEs9hY13neHJQX5HFk+xfsW/0vdr79CmV7t5Obk0Nubi51dYFp\nnwiViSA60n29Pc9Z+9E6hp55TtNjzjwnrCfxcTjqKMjO4uN332T0hMlN9msSI/EmzfofOvS/EpFg\n0fg6bteWTZx2zpQmx3n7miXQQyYl+HW4cm0YRiJwEpBrmuZ+6ivRU1329wDGAp92tpDhaOzYsVx1\n1VWMHj2aGof7Wn511dXUFB/n4MEDvPjiizz22GO8/vrrfPrpp2RnZ1NbW+t2vC/Hw4XSzObeVl1b\ng4Om6yw6cFBdWxOAEnlXy++bjzn/1OH0i67FZms+RHz59dcagy1+F25jf8Ph9YTznBStCYf/nYgE\nF9d4On7c2YoxIard3cINw/gT8C5wiPox1wsAO/BawyFPAg8YhrEHOAA8DGQD73ixvGHDZrNx0kkn\ncdJJJ9Gzdwr/fH8l/YYOo6a4kKoTR4iw2axjKysrMU3TGqMdHR1Nv379GDBgAAMGDKBv374+HQ/X\n2e6E3tLSjInO7usZg4awa8smAEacfmar3dfb1eW9zsGm1f9hoDHS7ZhNq//DSec1zeiGmpbGUf7i\nwvOYMH4c06dN5e5m/kYf/+v/GHfBJeyqimL5goXMGD+W3j17cvToUYYOHcqIESMC8XKkC3C+Z19/\n5XlOanjP7ml4z4ZiJjXUxzL7atxhKMyOG+r/O5FQ1/g6bsTpZ/Le0r/4fCijr4ZMNomnf/gTIwf2\nY00IxZhQiN3+4MmEZn2pr0j3BgqA9cBZpmkeAzBN81HDMBKAF4Hkhv0zTNPsOk3ZHeQ2lq1vHyZN\nnE1RUREHDhywbhUVFdbx1dXV7N+/n/3791vbUlNTmTZ2NMVF2XRP7M7ND97fqOt5aGvtIi4mJoaR\nA/ux7KlHmXDxpQAse+pRpo4d3eIHuz0XRtHR0WQOHMw7i5+1uhp9+fGHZA4cTHR0tK9fsl+0No6y\n8d/I4XDw2Xtvc/pZZ9Gtuozy3HxS46LY8vnn1u/bunUr999/P5GRkQF5PdI1uE5gE6hhM94SqmOZ\nfTXuMJQmkQzV/51IOGjuOq7qxDGWP/+ENaGZLyqjvmhYaymerln6Av9z/2/YsLG+E3Awx5hQit2+\n5smEZle245h51M8iLh5qnB1OTU0lNTWVsWPH4nA4KCgo4ODBgxw+fJiDBw9SXFzs9vwjR45w5Mj3\nS5B/++0uMjIyyMzMpG/fvmRmZpKUlER1dbXXW5V83VLV1kUcwM6DWVxzzwPW/oH3PMCapS9gt9tb\nLE9bF0aTJ01k+boNXDjrV1ZG/MJZv2L96y8zedINXn2NgdRSz4Ty8nIy09O4dOpEvvnmG06cOMHI\nzBRqsr/jaAu/6wc/+IEq1uIzjSewcQr1SfaCpXeQJ3wx2U4oTiIZiv87CS/+yBYGa0ayyXXcoqcA\nfN7g5e2Gtdbi6YaNnwZ9jAnF2O1LnmSuJUBsNptV2R4zZgwARUVFVmU7KyuL/Px8HC5jt6uqqqys\nt1N0dDRlVXYS0/sTGZ/I+wsWcun50zjnx+M7XDZ/tFS1Z/KIjl7ktXZhZLVOvv6y9fvXv/5yUHfJ\n6QiHw0FJSQl5eXnk5uZat8YNOM2JSeyBIyaOfsmJnDd9OqmpqX4osXRVmj01vOn/K+IZf1yDBXtG\nsrnrOH/ECjWsfU+x250q1yEqKSmJk08+mZNPPhmobzXKyckhKyuLrKwscnJyKCkpcXtOdXU1MRE2\n7EeyAOgVY2PN6lV8u3MHaWlppKWlkZqaSlpaGgkJCW2WoSu0VIVbt7+qqioKCgo4cuQI+fn51q2y\nsrLN58bFxVFcXkHfH55GfO8+xPdOIyquG2uWvsBv5swO6b+LiHgmVJZqFAlX/rgG6wrXeYFUV1dH\ndXU1Y8ecwXt/eorUtDQctTXU1dbiqKvl0KZ1TLniUr755htqamqoqanBbreze/ceHA4HAwb0B6C2\ntpa6urpmbw6Hw7pvfGvPsCqbzYatYR4o52PnLSIigoiICPLy8iiviyTrRD4R0dH0HGLQLbm3T/92\nwUyV6zARExPDoEGDGDRokLWtpKSEnJwccnJy2LptG8WlZThqqt2eZ7PZrEylq/j4eFJSUkhJSaFP\nnz7W46SkJOtD5q+WqvZcxPnyIi/UWicdDgdlZWUcO3aMo0ePUlBQYN23JxsN9a85IyOD9PR0a2hB\nr169+GTjp1YLdmHZwaCfXEPCSzBV6Gpra6mqqnK72e12qqurm9xXV1dbF0a1tbVuj523uro66765\niyBnzyTXixznvfMCJyIigsjISLfHUVFRREVFERkZSWRkJNHR0c3eYmJiiImJITY2tsl9RETTVQN8\nMe4wmP6/IsHOH9dg/s5IBmv3c6e6ujoqKyupqqqy7ht/B7g+dr05vxNcf3at3PaOsbHng3+6nS8p\n2sZbb73VYnkOHz7ks9faESeO5wNQmp/NsPN/3mVjtyrXYax79+4YhoFhGNira9hZGUnmgIFUnDhK\n5YmjVJw4RmlBLnVVTbOW5eXlHDp0iEOH3D+4UVFR9OzZk969e1NUXIw9KoHSpO7EJHQnOj4RWzMX\nYZ3Vnou4rjZrq8PhoLS0lBMnTli348ePc+zYMY4dO0ZVVVW7f1f37t2tXgsZGRlkZGTQq1cv6+Ld\nVbhl8iW0+KJCV1tbS3l5eYu3yspKKisrqaiooKKiwvq5pib0l+Nrr9jYWOLi4txu3bp1o1u3bvx8\n+mQOHjpMdFQUF113DcnJyVRUVBAXF9dsDGmNZuAW6br81f3c4XBQVVVFRUUF5eXlbveuMb7xz5WV\nlVRXV7d9AqEuMpo1S1/osrFblesuwpkRGGjMIbrbAHpkDgBg1cuLmPHjM6mx2xnQvz/Hjh3jyJEj\nFBQUUFpa2uT31NTUUFBQQEFBgbVt/6E99Q9sNqK7JVBWWsLAU09h7dq1JCUl0aNHD+u+ozOYt1Wp\nC7VKX1uts9XV1ZSWllJUVOR2Ky4uprCwkMLCQo8v7uPi4ujTp491S09PJzU1lfj4eI9+T6hl8iW8\ntPezbrfbKSkpoaSkhOLiYkpKSigtLaW0tJSysjLrvry83N8voUWuWejG3e9sNpuVvXa9d3btc958\nwZmJKSoqavW43btN63FERATx8fEkJiaSkJBAYmIiiYmJdO/ene7du7s9jor6/lKkuf8vwIqVq4DO\nZbOCPSsmwSMU3iv+6Onhr94knel+7nA4qKjuwbEsAAAgAElEQVSoaBLXnfeNH1dUVARklYmIiAi3\nXkKNew9FRUVZ983dzN27yauNIiWzH7aISGwREdgiIijIyWZQTB1nn32WWy8m15vzu6W575XWGkEb\nf9c0163ctet5VVUVn3+xhbi4uC49XLDDlWvDMP4bWAg8ZZrmXS7bHwJuoH45rg3AzaZp7u1sQaVz\nmssIfPqvN4jv3p39jgSITmDtO+9xxQXTmTVjBlC/vrZrt2JnVvTEiRPU1tY2PYnDQXV5KTERNrZt\n29ZiOVwvrJwXXPHx8SQkJJCQkGA9jo6OdvvQt1WpC/ZKX3V1NRUVFXyy8VNWffIpGUOH46ipZs0j\nCxnSry8J8d2sikB7xkC3JDk5md69e1s3Z2U6ISHB40ySSDCKjIxk7JgzKCwsZOfOnW6NT8XFxRQV\nFWG3e38VyOjoaLp160ZcXByxsbFNbq4XTY3vG18oObtpO7txR0ZGeuXz6bzIcXY/b3xfU1NjdVV3\n3pxdFBt3Z3T+7MziVFZWNh/7m1FXV2c1ZrQlISGBHj16uN36ZmaQlJTEx598wjsfftzpbFawT8ok\nwSNU3iv+6Onhr94kzXU/r6upZvDosfzr3eWMHDGc0tJSSkpKKCsrs+5LS0spLy/3aWU5Ojra6rHj\n2ounue8A1++Cxt8JnY3xhUXFHK+KInngULftJ0rK6B5bQ9++fTv7Ur0iWMoRSB2qXBuGMQa4EdgG\nOFy23wfcDswCDgAPAysMwxhpmmb7+6mKT7hmBKqrq9mTmsqUa2+y9jduJYyLi6Nfv37069fP7ffU\n1dVRXFzs1g15z549VFVVUVtb22rF0G63W89pS0REhHUh6+yCGBcX5zY20Bm8Grf8OR87L1qbu4Bt\nnBFybYlzvUB13lwvUF3HUjovTp0Xoa6Pnd2KKioq3DLNydE2Kg7WZ3niI23k5ea0+/8YFRVFcnIy\nPXv2dLv16tWLnj17umWB/CUUWvkldDgcDsrLyzl+/DjHjx/nxIkTVo+NwsJCiouL3VZH6IioqCi3\nrGp8fHyTmzPuOGNPsCwz19rnzWazWTHPF5/DmpoaK6Y17lLpzAw5s0fOW1sXvs7jGs/94ZQaF0nF\n7q+JSeiOMWIE7/5nJUndE+nTpw89e/YkLi6u1d8f6EmZFB9DR6DfK57yR689b5/DOS+MszG0uLiY\nQ4cOUl5Rw/7sfVRXlFNdUUZddX0D6U5g547tnX4dUVFRTeJ74/vG15vdunULmrivOSlCh8dX4YZh\nJAKvUp+d/p3LdhtwJ/CwaZrLG7bNAvKBmcAybxRYOseZ3V2xchUnnT2pyf72TFIRERFBcnIyycnJ\nDBkyBIAZDdluqM94Oy+AG2eTnC2P7ckq1dXVWRddXUVkZCTdu3enR48eVpfJpKQkt1t8fHxQZaBD\npZVfgk9FRYU18d6xY8esBrsTJ050OPMcFRVlZT+dnyHXm7NCHRMTE1Sfo/Zq6fPmevHrywpcVFSU\n9bdsD4fDQWVlpdUt39lV3/nY+f1QUlLSYoOJo64Oe2kx9tJiyIfEKBtvv/22tT8+Pp5evXrRu3fv\nJvcxMTEBXSZG8TG0hOKSQv7otefJOSorK63eRIWFhRQVFVFSUmJdD5aUlLTYA8aTEc0RERFWj0dn\nI2njHpCu98HWMOIpT3oRqEEvsDqS4noW+LdpmmsMw3jQZftgIA1Y7dxgmmaxYRibgLNR5brLiIuL\nIz09nfT09BaPsdvt1sWVs1tP4/Exzm6IFRUVPuni6Q+RkZFuGbDikhJKbTEkp6UTFRdPVGwcUXHd\nKMjLY3hCBBdccH5IXfCHWiu/BJ7dbuf9999n9+7dVFRUePz8bt260bNnT5KTk0lKSiI5Odma1yEp\nKYlu3bqF1GfIEy193v782EO8/t4Kq8E0mCpwNpvNin+pqaktHufsRu5siC0sLOSbb7ZzrMJOpM1B\ndVkpdTXNX3o7vzeysrKa7EtKSgJsVMQkcCyijtgeycT16ElUXDdvvcQWKT5KuHE2ljl7ETl7FblW\npD2ZULVZtggiYmKorKigX0Y6gwcNanY4YTjH+pa0pxeBGvQCz6PKtWEYvwBOBcY0bHJtZnbWpPIb\nPS3fZZ8EiUB3L4mJiaFXr1706tWrXcc7lz9wLn3QeLkD1+VunI9dl7dxfQxNJ2dwXbPP9bHruMjG\nS9k0npCiuTE40dHRbq/Dbrdz94KFDJ92sdv2zSv+ze3z5obcF0UotvJLYB06dIitW7e2uN9ms5Gc\nnGwNc3DeOyvUHZ0UMRw093mrttuxxSe2OsQn2DSXVYmIiLB6HDiHIo0ZM4a7Fyzk3FlzcDgc1Nqr\nsJcW89UHb/OTKZMoKiqyVkooKSlp9lzfT8JWSE5BtrU9KrYb5RXlDB1zBl988QWpqamkpqa22cXc\nE4qPoSfQ10a+4GkWs7a21qo4N74VFhZ2qvIcFxdnTXDr7F3k+jg2NpZPP9uEzWZrsaxdPSvbWi8C\nNegFh3ZXrg3D6A88BUw1TdOZRrQ13FpjA/w/LZ+0KtSWPHHOPOvpzNbBJtT+7iLeNmDAAAYPHsyx\nY8fcuu+mpKTQu3dvkpKSgmaMWyjYtWUTZ049v8n2YK3AeZJVaS1eNj7ebrdz4sQJa06P48ePc/To\nUY4ePdrsPCA1VRXERNjYsmWL2/akpCRrJYX09HTS0tJaXJpQwk+4fUe39Hk7a+wYq2Gq8a2tVQFa\nEhkZafUiatyjyFmhbs/fcMZ50z1+PcrK1lODXnDwJHN9OtAH+NIwDOe2SGCCYRi3As5mvjTcs9dp\nwJedLKf4QKgtXxUuwunvHo6t/OJbMTExzJo1K9DFCEktfd7cO5EFr45kVdobL2NiYkhLSyMtLc1t\nu3PypKNHj5KXl8fWrdsoryintqam2fk8nGNFTfP7ZcViYmKsoU4ZGRlkZGSQkpLSZiOQ4mNoCpfv\n6IqKCv7v/ZWcMfV87CWFVJUUMahfX1atWsma1as8/n0REREkJSXRs2dPqwLt7FGUnJxMYmKiTxuh\n2hs/unpmWwLPk8r1auCHLj/bgJeBXcAfgf1AHjCV+lnEMQyjBzCW+nHaEoQCtXxVVw9+wbxsmCf/\nm3Br5RcJBi19Bpv7vOV/+w0Hq6oYaIx0+x3BWIFrKasy6PRx/O+TT3Haqac2G3M6Ey9tNps1RnPQ\noEGcddZZ1r7y8nKOHDlCQUEB+fn5HDlyhPz8/CZzfNjtdg4dOsShQ4esbZGRkaSnp5OZmWndUlJS\niIiIcCu34qNv+epaIpi/oxurqKiwemk4J4c8evQox48fp1eMjYMf/8ft+MhWKsBxcXFuQ3Fcbz16\n9HB7f/tbe7KyXT2zrQa94NDuyrVpmqXUz4hvMQyjHDhumubOhp+fBB4wDGMP3y/FlQ28460CS+jr\n6sEvmHXkfxMurfwiwaCtz2CTz9tDv2PT51+EbAVu99db+HrDOs6aej67qmx+/T6Ij49n0KBBDBo0\nyNrmcDgoLCwkLy+P/Px88vPzyc3NbdJVtra2luzsbLKzvx/HHR0dTUZGBn379rWWsVR89J2udC3h\ncDgoLS2loKCAo0ePUlBQYD32dEUVW2QUCd3iGDJkiDX3jfPWrZvvJ/rzFY03VoNesOjsgrgOXPqj\nmab5qGEYCcCLQDKwHpjhMkZbujgFv+DVmf9NKLXyiwSr9n4GG3/e2lOBC1RvIdfzjh93Nsv/8Ccr\nq1Jtt7Pzi8+4/Na7reMD/X1gs9msTN2IESOs7eXl5eTl5ZGbm2vdHzt2zO251dXVTTLc3bt3tyra\n/fv3D2jmL5yE67WEcxiDs0eF876goKDZuQNaEhUVRa9evTiUm0//H51GbPckYrsnEdM9iY+XvcLj\n994Tcn+jtrKyoTje2BdxWQ16gdepyrVpmpOb2TYPmNeZ3yvhKxSDX1eh/41IYHXmM9haA1egMnxN\nzvuHPzFyYD/WNGRV9mz7itETmlxGBGXMiY+PZ8iQIQwZMsTaVllZSW5uLjk5OeTk5JCdnd0kw11S\nUsKuXbvYtWsXUN+dPDMzk/79+9O/f38GDBgQ8hN1BkI4fF9VVVVZQxGOHDliVaTLy8vb/TsSEhLo\n06ePNSmk85aUlITNZnP7DJYcP8He998J2SxmuGVlfRmXlfAIrM5mrkVERCRIBSrD19J51yx9gf+5\n/zds2PgpFdG12Gwdy+QGw7wdcXFxDB48mMGDB1vbysrKyMrKIisry+o27jqGu7a2lsOHD3P48GFr\nW0pKCgMHDmTgwIEMGDCgYW1uCRd1dXWcOHHCGmrgrFAXFha2+3ckJSWRkpJCnz59rFtKSkqb3bjD\nLYvZ2usJpfHG4drzQuqpci1+FUrBr728dZEX6IvFcPzfiIQSX3wG1360jqFnntNk+9Azz/Fphq+1\n827Y+CnnTZ/G5EkTubsdr7dxbNz0+RdBO9Y2ISEBwzBwrqpSV1fH0aNHrQr14cOHOX78uNtznBNR\nOZcFS05OZuDAgdZ48OTkZL+/jmAXrN9Xdrud/Px8qyKdl5fHkSNHqK6ubtfzExMTSU1NpU+fPtba\n63369CE2NrbDZQq3LGZLryeUMtvh0PNCWqbKtfhVKAW/9vBWt55gmJgl3P43IqHGF5/B6toaHDTN\nDjtwUF1b06nydva87Xm9jWPju/N/z9EjR7jinget3xnMGZ+IiAirknT66acDUFpaSlZWljU+Ozc3\nl7q6Ous5hYWFFBYWsnXrVqC+su2saI8YMSLoXmMgBMP3VXl5udsY/Ly8vCbj8FsSExNjvS+cS8il\npqaG9IRiwSDcMvUSmlS5Fr8LpuDXXLa4vRlkb3XrCabuQcH0vxHpirz+GaxzsGn1f5os1bVp9X84\n6bym4529pp3nbe31thQb33j2cartdqJd/i6hlPFJTExk+PDhDB9en3W12+1kZWVx8OBBDh06RFZW\nFjU13zd8FBYW8vXXX/P111+zceNGbr755kAVPaj48/uqtLTUGl+fm5tLbm4uxcXF7Xpuz549SU9P\ntyrRaWlpJCcn+3RN6K4sFDL1LfW82PPpRwwdfyYrVq7qksvUhgtVriUggiH4NZctHjmwHzsPZrUr\ng+ytbj3B1j0oGP43Il2ZNz+D0dHRZA4czDuLn+W0c6YA8OXHH5I5cDDR0dFeOUdnz9vS620pNp45\n9Xx2bdnEyWdP8E3h/SwmJsZtsrSamhqys7M5cOAABw4c4PDhw9TW1gJQXFxMbW0tkZGRgSxy0PDF\n91VZWZk1SZ2zQl1SUtLm8yIjI63Kc3p6ulWh7kyXbglPzfW82LrmA8pLSthb1w2qgmu4i3hGlWvp\nklrKiLz62CNccce9VkYkmLsbikh48OV8C5MnTWT5ug1cOOtX7NqyCYALZ/2K9a+/zORJN3jtPP49\nr6PJlkCPtfWmqKgoa4KziRMnUlNTQ1ZWFvn5+QwYMEAVay+qqqoiNzeX7OzsFmd8b05MTAzp6elk\nZGRY9ykpKT7/35SWlvLMoucAuP2Wm0lMTPTp+cR3XHteVNfWYMbG8pOb7rL26/ozdLW7cm0Yxs3A\nTcCghk07gIdM0/yPyzEPATdQv8b1BuBm0zT3eq20Il7SUkZkwsWXNsmItJRB9taEKsE6MYuI+J6v\n51uwMiSvv2ydY/3rL/t8bKo3zttSbNy2dgVxsbEc6t0HCP+5IaKioqwx19JxdXV1HDlyxG0m94KC\ngjafFxMTQ2ZmJhkZGWRkZJCZmUmvXr383q170YuLWb35KyZcfCkAs+68j6ljR3PLjb5rJBPfcva8\nWLFyFcb4c5vsD6XhLvI9TzLXh4H7gD2ADfgl8K5hGKNN09xhGMZ9wO3ALOAA8DCwwjCMkaZpVnm1\n1CJBwFsTqgTDxCwi4n/+mm8hUHMpdPa8LcXG6y+bqbkhpE0lJSUcPnzYqkzn5ua2OWt3dHS0VYF2\n3gJRkW6stLSU1Zu/4pp7HrC2DbznAV597BFmXVWqDLZIEGl35do0zX832vRAQzZ7rGEYO4E7gYdN\n01wOYBjGLCAfmAks81J5RbyipYzI+nff5Io77nXb1loG2VsXrZpITKTr8ed8C4GaS6Gz520tNiqb\nI061tbXk5uZaa4xnZWW12b07IiKCtLQ0MjMz6du3L5mZmfTp04eIiI6tve5Lzyx6zspYu5pw8aU8\ns+g57r/3ngCUKnz5e2lU9WAMLx0ac20YRiTwcyAWWA8MBtKA1c5jTNMsNgxjE3A2qlxLkGkpIzJ1\n7GjWu3RjbE8G2VsXrZpITESkKcVGac3mzZtZtWqV2wzrzUlOTqZv37707duXfv36kZ6e7tNJ/SQ0\nBWJpVPVgDC8eVa4Nw/gR8Cn1leoK4HLTNPcahuF8x+U3eko+kN7pUorX+Ls1Lpi1lBFx/Rspgyzi\nXYpB31O2QqTzNm3a1KRiHR0dbVWi+/XrR9++fUO66/Ttt9zMrDvvY6BLt3Co72239Mk/BqhU4SeQ\nS6OqB2P48DRz/S1wMpBEfeb6dcMwJrVyvA2o61jRxNsC0RoX7JrLiChLIuIbXSEGedJ4oGyFSOed\ne+65fP755yQlJVmV6bS0tKDs3t1RiYmJTB07mlcfe8TqHr7+3TeZOna0XxoNukqjaKCXRtX1Z3jw\nqHJtmmY18F3Dj18ZhjEGuBlY2LAtDffsdRrwZWcLKZ0XyNY4EZGuEIM60nigbIVI54waNYpRo0YF\nuhg+d8uNNzDrqu+X4lr65B/9UrHuCo2iIt7U2Wa9SCDCNM39QB4w1bnDMIwewFjqu5FLgLXVGici\n4kvhHoNcGw8GDBvOgGHDOXfWHJa9vxK73d7qc53ZivOmT1PFWkRalJiYyP333sP9997jt4x1R+Na\nKJo8aSJ7P2v6fbT3s3VMntT0+0ukOZ6sc/0H4H3ql+TqDlwFnAM80nDIk9TPIL6H75fiygbe8WJ5\nRUREgk6guxOKiHhbV4trGqoj3uBJ5roPsJT6cdergdOB80zTXANgmuajwDPAi8BmIB6YYZpm+DVt\nhSC1xolIICkGiYhIsJswfhyPz5vLiNgaRsTW8Pi8uc12gbfb7axYuYoVK1eFZRZfOs6Tda5vaMcx\n84B5nSqR+IRa44JfV5kwRLqmcI9BmvlbRMJNV41rbU0spnHo0poOrXMtoUkT5wQvBWrpCsI5BoV7\n44GIdD2Ka011hck5pXNUue5iNM1/8FGglq4knGNQODceiEjXpLjmrquNQxfPqXItEmAK1CLhI5wb\nD0Ska1JcE2m/zi7FJSIiEnI0GY2IiHhKk3NKW1S5FgkwBWoR/1q/YSN3L1jIrqoodlVFcfeChazf\nsDHQxRJpkRqDpCV6b/iXcxz6mqUvcGj3txza/S1rlr7Qpcehizt1CxcJME0YIuI/muNAQo0mvJSW\n6L0RGBqHLq1pd+XaMIz7gZ8BBlABbATuM01zd6PjHgJuAJKBDcDNpmnu9VqJRcKQArWIf2iOAwkl\nagzq2lpbolPvjcDSOHRpiSfdws8BngHOBKYB0cBKwzDinQcYhnEfcDswp+G4MmCFYRixXiuxSJhy\nBurzpk/Tl6KIiLTZGCThq63hK3pviASndmeuTdM83/VnwzB+CRwBTgM+MQzDBtwJPGya5vKGY2YB\n+cBMYJmXyiwiItIhkydNZPmChQwYNtxt+97P1nHLvLkBKpWIyPeUlRYJXZ2Z0Cy54f54w/1gIA1Y\n7TzANM1iYBNwdifOIyIi4hWajEZCiSa87Jrak5XWe0MkOHVoQjPDMCKAJ4FPTNPc2bA5veE+v9Hh\n+S77REREAkpzHEio0ISX0hK9N0SCU0dnC38WGAn8uB3H2oC6Dp5HRETE6zQZjYQKNQZ1Pe0dvqL3\nhkjw8bhybRjGn4ELgHNM08xx2ZXXcJ+Ge/Y6DfiywyUUERER6cLUGNS1eJKV1ntDJLh4shSXjfrZ\nwi8BJpmmebDRIfupr2BPBbY1PKcHMJb6TLeEqdaWihARERERzygrLRKaPMlcPwtcSX3luswwDOc4\n6kLTNCtN03QYhvEk8IBhGHuAA8DDQDbwjhfLLEFk/YaNLHt/pdWyunzBQq64YDoTxo/z+rlUiRcR\nEZGuQllpkdDjyWzhNwE9gI+AHJfb5c4DTNN8lPrs9ovAZiAemGGapt1L5ZUg4rpUxIBhwxkwbDjn\nzprDsvdXYrd791/e1nqPIiIiIiIigeTJOtftqoibpjkPmNfhEknIaGupCG+1tmq9RxERERERCXad\nWedaxC/as96jiIiIiIhIIKlyLR02edJE9n7WtHK797N1TJ7UtDIsIiIiIiISrlS5lg5zLhWxZukL\nHNr9LYd2f8uapS80u1REZ6gSLyLBwG63s2LlKlasXOX1eSVEREQk9Hm8zrWIK38sFeHJeo8iIr7g\nz5URREREJDSpci2d5o+lIrTeo4gEiiZVFBERkfbwqHJtGMY5wD3AaUAG8FPTNP/V6JiHgBuAZGAD\ncLNpmnu9U1zpyrTeo4gEgr9WRhAREZHQ5umY63jgK+DWhp8drjsNw7gPuB2YA5wJlAErDMOI7WQ5\nRURERERERIKWR5Vr0zT/Y5rmg6ZpvtN4n2EYNuBO4GHTNJebpvkNMAvIBGZ6pbQiIiJ+pkkVRURE\npD28OVv4YCANWO3cYJpmMbAJONuL5xEREfEbf62MICIiIqHNmxOapTfc5zfanu+yT0REJORoUkUR\nERFpiz9mC7cBdX44j4iIiM9oUkURERFpjTe7hec13Kc12p7msk9EREREREQk7Hizcr2f+kr0VOcG\nwzB6AGOBT714HhEREREREZGg4uk61wnASS6bhhiGcSpwzDTNw4ZhPAk8YBjGHuAA8DCQDTSZXVxE\nREREREQkXHg65noMsKbhsQN4vOHxEuB60zQfbaiAvwgkA+uBGaZp2r1QVhERERER8SO73W5N5jh5\n0kRN5ijSCo8q16ZpfkQbXclN05wHzOtEmUREREREJMDWb9jIsvdXMvSsiQAsX7CQKy6YzoTx4wJc\nMpHg5I/ZwkVEREREJITY7XaWvb+Sc2fNsbYNGDacZUtf4MwxZyiDLdIMb05oJiIiIiIiYWDtR+us\njLWroWdNtLqJi4g7Va5FREREREREOkmVaxERERERcTN50kT2ftY0Q733s3VMntQ0oy0iQTbmuq6u\nzlZcXMznn38e6KKIiLSquLiYuro6W0eeq1gnIqFCsa5rO3lwf177nwfp/6PTADj8zZdMG38WW7du\nDXDJRLyrM7HOVVBVrquqqmIuueQS9u3bF+iiiIi06pJLLuHFF1/s0GwuinUiEioU67q2HokJXHre\nFOvnsZn1j/U/lXDTmVjnyieVa8MwbgXuAdKArcDtpmm2q9ly6NCh/PCHP/RFsSSMrVmzhiVLlrB0\n6dJAF0WkXRTrpCMU6yTUKNZJRyjWSajyeuXaMIwrgP8F5gCbgLuAFYZhGKZpFnj7fCKB8tlnn7Fy\n5Ur27dtHWVkZf/rTnxg0aFCT40zT5B//+Ad79+4lIiKCQYMG8bvf/U5LWIhISFCsE5GuQLFOvMEX\nmeu7gRdN03wFwDCMm4ALgeuBP/rgfCIBUVVVxciRIxk3bhzPP/98s8eYpskjjzzCpZdeyq9+9Ssi\nIiI4ePAgNlunh3SIiPiFYp2IdAWKdeINXq1cG4YRA5wG/N65zTRNh2EYq4GzvXkuCV5ffPEFTz/9\nNK+88go2m439+/dzzz33MHPmTK655hoAFi1aRHV1NXfccQcAu3bt4u9//zv79u2jR48ejB07lmuu\nuYbY2FgAqqur+cc//sEnn3xCeXk5/fv357/+678YNWpUs2UoKiri97//PSkpKdx1111ER0d7/XVO\nnFg/U+aRI0daPObll1/mwgsvZObMmda2zMxMr5dFRPxPse57inUi4Uux7nuKddIWb2euU4BIIL/R\n9iPAcC+fS4LUiBEjqKioYP/+/QwZMoQdO3bQvXt3duzYYR2zc+dOfvrTnwKQl5fHI488wlVXXcVt\nt91GUVERixcvZvHixdx6660ALF68mOzsbP7f//t/9OzZk02bNvHII4/w+OOPk5GR4Xb+o0ePsmDB\nAoYPH84tt9zSYmviCy+8wMcff9zqa/n73//e4b9DUVERe/fu5ZxzzmHu3Lnk5+fTt29frrrqKoYP\n18dBJNQp1tVTrBMJb4p19RTrpD2CarZwoHegCyCdl5CQwODBg9m+fTtDhgxh586d/OQnP+GNN96g\nqqqK0tJS8vLyrNbJt956i3POOYcLL7wQgPT0dK6//noefPBBbrzxRgoLC1m7di0vvPACPXv2BODi\niy/mq6++Ys2aNVx99dXWubOzs3nooYc466yzuO6661ot5y9+8QsuueQSH/0VID+/vo3pjTfe4Npr\nr2Xw4MGsXbuW+fPn88QTTzT58pCQ1NGYpVgXBhTr6inWdQmKdV2YYl09xbouodMxy9uV66NALfWz\nhLtKA3Lb8fxqINbLZZIAGDlyJNu3b+fiiy9m165dXH311WzcuJGdO3dSWlpKz549SU9PB+DAgQMc\nOnSo2dbGI0eOkJeXR11dHbfddpvbvpqaGrp37279bLfb+d3vfseECRPaDMAASUlJJCUldfKVtqyu\nrg6A6dOnM3nyZACuu+46vvnmmyZfHhKyqjvxPMW6MKBYp1jXRSjWdXGKdYp1XURHY53Fq5Vr0zTt\nhmFsAaYC7wIYhhEBTAGebuv5hw8fPhfY7M0ySWCMGjWKNWvWcODAASIjI+nbty+jRo1ix44dlJWV\nuS3LUVVVxfTp07ngggua/J6UlBQOHDhAREQEjz32GBEREW77u3XrZj2Ojo7mlFNO4YsvvuCSSy6h\nV69erZaxre5DNpuNV199tb0vuQlna2z//v3dtvfr14+jR492+PdK8GiIWR19nmJdGFCsU6zrChTr\nRLFOsa4r6Gisc+WLbuGPA68YhvEF8DlwJ9ANeNkH55Ig5Ryfs3z5cqub0KhRo3jrrbcoKytz67Yz\nZMgQDh8+bLV4NjZ48GDq6uooKipixF3ipNEAACAASURBVIgRLZ7TZrPx61//mieeeIJ58+bx0EMP\nWYGwOb7uPpSamkrPnj3Jyspy256Tk8Npp53ms/OKiP8o1inWiXQFinWKddI+EW0f4hnTNN8AfgM8\nBHwFnAzM0BrXXUtiYiIDBw5k/fr1VhAeOXIk+/fvJzc3l5EjR1rHzpw5E9M0Wbx4Mfv37ycnJ4fN\nmzezePFioH4WxgkTJvD000+zadMm8vPz2bNnD2+99RZbtmxxO6/NZuOOO+5g4MCBzJs3j8LCwhbL\nmJSURHp6equ31pSWlrJ//34OHz4M1I8L2r9/v3VOm83GJZdcwvvvv8+nn35Kbm4ur732Gjk5OUyZ\nMsXzP6qIBB3FOsU6ka5AsU6xTtrHJxOamab5LPCsL363hI5Ro0Zx8OBBq6tQYmIi/fv3p6ioyG3Z\ngoEDB/LQQw/xj3/8g9/97nc4HA7S09MZP368dcxtt93G//3f/7FkyRKOHz9Ojx49GDZsGGeccYZ1\njHP2yMjISO666y4ef/xx5s+fz0MPPUSPHj28/vo2b97MokWLrHM/8cQTAFx++eVcfvnlAFx00UVU\nV1ezZMkSSktLGTRoEA8++CBpaY2nJRCRUKVYp1gn0hUo1inWSdtsDocj0GWw2Gy2Ma+99tpm13Eb\nIiLBaPv27Vx55ZVjHQ7H554+V7FOREKFYp2IdAWdiXWuvN4tXERERERERKSrUeVaREREREREpJNU\nuRYRERERERHpJJ9MaNYJcR999BH79u0LdDlERFrVMJtoXAefrlgnIiFBsU5EuoJOxjpLsFWu6dev\nHz/4wQ8CXQwRkVZ1djJIxToRCQWKdSLSFXhrku9gq1xXDh06FM0qKSIhorKjz1OsE5EQolgnIWfN\nmjUsWbKEpUuX+u2cDz74IIMHD/7/7N15fJTV3f//12TfSALZCCDgxgGqolUWQRQUFVtva1vrUq3V\nYl1qrdbHXa3Ub5HqTe/a3q7Vqq3WUlvF/lxaqlZQFBEEFa2U7UAERCCEsIeQZLLM74/JXM5knUlm\ny8z7+XjMY2auua6ZM5OZT865Pmfh6quvjtprSlj1NNY54q1xLXEg2oHh4Ycf5vDhw9x+++0RfZ01\na9bw97//nc2bN7Nv3z5uu+02xo0b126/Z599ljfffJPa2lpGjhzJtddeS3l5ufO42+3mT3/6E0uX\nLqWxsZETTzyRa6+9loKCAmefmpoannzySVauXInL5WLChAl873vfIyvri94m1dXVPPHEE6xZs4as\nrCymTJnC5ZdfTmpqKtD1P4WLLrqI22+/nbFjx4bzIxJJeoka/+bNm8ff/va3gG2DBw/mwQcfDNgW\njfi3evVq7rrrLubOnUtOTk7A619//fWcf/75nH/++eH+CESSVqLGtYcffpjFixcD3rWwi4uLmTJl\nCt/4xjecupRItGlCM+kRj8dDc3NzrIsRErfbzZFHHsk111wDgMvlarfPSy+9xGuvvcZ1113H//7v\n/5KZmcndd99NY2Ojs88f//hHPvzwQ/77v/+bu+++m3379nHvvfcGPM+DDz7Itm3bmDVrFjNnzmTt\n2rU89thjzuPNzc3MmTPHub7pppt46623eO655yL07kUkXPpi/AM44ogjePLJJ53LPffcE/B4tOJf\nV1wuV4exWUQiqy/GNZfLxUknncSTTz7JI488wte+9jWef/55/vGPf8S6aJLElLmWAA8//DBr165l\n7dq1vPLKK7hcLh599FGqqqq46667+NnPfsZf//pXtm7dys9//nNGjx7NSy+9xMKFC9m/fz+DBg3i\noosu4tRTTwWgpaWF3/3ud6xevZr9+/dTXFzM9OnT+epXvwp4sym+s44XXXQRALNnz+ZLX/pS2N/b\nSSedxEknndTp4x6Ph3/+859cdNFFTkb4Rz/6ETNmzOD9999n0qRJ1NbWsmjRIn784x873dxuvPFG\nbr75ZjZs2MCIESPYtm0b//73v7n33ns56qijAJgxYwZz5szhu9/9Lv379+eTTz5h27Zt3HXXXRQU\nFDB8+HAuu+wy/vznP3PppZfqjKtIDCRy/ANvZsc/w+wvmvFPRKInkeOax+MhLS3NiWvnnHMOK1as\n4IMPPuDrX/96u/137tzJ008/zcaNG6mvr2fIkCFcfvnlnHDCCc4+jY2NPPfcc7z77rscOHCAoqIi\nvvGNb3DWWWcBsHXrVubOncu6devIyspizJgxXH311fTr1895jubmZn7/+9/zzjvvkJaWxjnnnMNl\nl13mPH7o0CGeeuopVq5cSWNjI6NHj2bGjBkBvYSk71LjWgLMmDGDyspKhg0bxqWXXgpAv379qKqq\nAuAvf/kLV155JWVlZeTm5vLCCy+wZMkSrr/+esrLy1mzZg0PPfQQ+fn5fOlLX8Lj8VBcXMxPfvIT\n8vLysNby2GOP0b9/fyZOnMjXvvY1tm/fTl1dHT/84Q8ByM3N7bBsL7zwAi+++GKX5X/ooYcoKirq\n0XuvqqriwIEDAUE2JyeHY489FmstkyZNYtOmTTQ3NwfsM3jwYIqLi53KpbWW3Nxcp2IJcMIJJ+By\nudi4cSPjxo3DWsuwYcMCKrpjxozhiSee4PPPP2f48OE9eg8i0nOJHv8qKyv5/ve/T3p6OsYYLr/8\ncoqLi4Hoxj8RiZ5Ej2tte7qkp6dTU1PT4b719fWcfPLJXH755aSnp/PWW2/xy1/+kocfftiJhQ89\n9BAbNmxgxowZDB8+nOrqavbv3w9AbW0ts2bN4uyzz+Z73/seDQ0N/PnPf+b//u//uOuuu5zXefvt\ntznrrLP41a9+xaeffspjjz1GSUkJ06ZNA+C3v/0tO3fu5I477iArK4tnnnmG//mf/+HBBx9UciUB\nqHEtAXJyckhLSyMjI6PDDMcll1ziVKwaGxt56aWXmDVrFiNGjACgtLSUdevWsXDhQr70pS+RmprK\nJZdc4hxfWlrK+vXrWbZsGRMnTiQrK4v09HQaGxs7zaj4nHvuuUyaNKnLfQoLC0N9yw5f8Gz7HAUF\nBc5j+/fvJy0trd04wcLCwoB98vPzAx5PTU0lLy8vYJ+2r+O7v2/fPjWuRWIgkePfiBEj+OEPf8jg\nwYPZu3cvzz//PHfeeSf3338/2dnZUY1/IhI9iRzX4IsZnj0eD6tWreKTTz7hK1/5Sof7Dh8+PKB+\nddlll/H+++/zwQcfcN5557Fjxw7ee+89Zs2axfHHH++8P5/XXnuNo446im9/+9vOthtvvJHrrruO\nyspKJ/NcXFzsjG8fNGgQn332GfPnz2fatGns2LGDDz/8kDlz5jif8c0338x1113H+++/7/QQkL5L\njWsJyTHHHOPcrqyspKGhgdmzZwfs09TUFJC1eO2111i0aBG7d+/G7XbT1NTEkUceGfJr5+XlkZeX\n1/PC95DH44nIGMBwTfkvItHRl+Of/5CYoUOHcuyxx3L99dezbNkyp7tjRyIV/0QkPvTluAawcuVK\nLr/8cpqbm/F4PEyePJmLL764w33r6up4/vnn+eijj9i3bx/Nzc243W52794NwObNm0lJSWH06NEd\nHr9lyxZWr17N5ZdfHrDd5XKxc+dOp3HtazT7jBgxgvnz5+PxeNi+fTupqakce+yxzuP9+vVj0KBB\nbN++vcefg8QPNa4lJJmZmc7t+nrvbPU/+9nPGDBgQMB+6enpALz77rvMnTuXq666CmMMWVlZ/P3v\nf2fjxo0B+wdTeYt0t3Df2dG2WeUDBw44/1QKCwtpamri8OHDAdkb/2MKCws5ePBgwHM3Nzdz6NCh\ngH0+/fTTgH18WR3fmMScnBwaGhralbO2ttZ5XESiJ5HiX25uLoMGDWLnzp1AdOOf79i2zwPe+KbY\nJhI9fT2uHXfccVx77bWkp6fTv39/UlI6n6t57ty5rFq1iu9+97sMHDiQjIwMfvOb39DU1ARARkZG\nl2Wpr69n7NixXHHFFe0e859PQsmT5KbGtbSTlpZGS0tLt/sdccQRpKenU11d3elZvvXr12OM4dxz\nz3W2VVZWtnu9YGaojHS38LKyMgoLC1m1apXTbejw4cNUVFQwffp0AI466ihSU1NZtWoVEyZMAGD7\n9u3s3r0bYwwAxhhqa2vZtGmTUyn9z3/+g8fjcc5Ujhw5khdffJEDBw443aY++eQTcnJyGDJkCODt\nStTc3BzwPACbNm1yHheR8EqW+FdXV0dlZSVnnHEGEN34V15ejsvl4tNPP3XGOYJ3sqHDhw8rtomE\nWSLHtczMTAYOHNjta4G37FOnTnXmfqirq6OqqsqZbG3YsGF4PB7WrFkTMLeEz1FHHcXy5cspKSnp\ncmx02xMNGzZscOLe4MGDaW5uZsOGDU7crKmpYceOHU79T/q2oBvXxpg04G7gUqAM2AE8ba29p81+\nvwCuAQqBpcAN1tqKsJVYIq60tJSNGzeya9cusrKyAmZA9Jednc0FF1zA008/jcfjYeTIkRw+fJj1\n69eTk5PDlClTGDRoEIsXL+bf//43paWlLF68mE8//ZSysjLnecrKyvjkk0/YsWMHeXl55Obmdhi0\nett9qL6+PuAfQFVVFZs3b6Zfv34UFxfjcrk4//zzeeGFFygvL6e0tJRnn32WAQMGOIE4NzeXs846\ni6effpq8vDyys7N58sknMcY4FcchQ4Zw4okn8rvf/Y7rrruOpqYm/vCHP3Daaac5ZzbHjBnDkCFD\neOihh/jOd77Dvn37eO6555g+fTppad6f5dChQxkzZgyPPvoo3/3udyktLWXHjh089dRTTJo0SbPu\nikRAosa/P/3pT5xyyimUlJSwd+9e5s2bR1paGpMnTwaIavzLzs5m2rRpPP3006SkpDB06FB2797N\nM888w4gRI5wKp4iER6LGtVCVl5ezfPlyTjnlFACeffbZgMdLS0uZMmUKjzzyCDNmzGDYsGFUV1dz\n8OBBJk6cyHnnnccbb7zB/fffz4UXXkheXh6VlZUsW7aMH/zgB062fvfu3Tz99NOcffbZbNq0idde\ne42rrroK8CZGxo4dy+9+9zuuv/56Z0KzoqIiZ6UG6dtCyVzPxNtovhJYA4wF/miMOWCtfRjAGHM7\ncFPrPlvwNsZfN8aMtta2798qcemCCy7gt7/9LbfccguNjY08+uijQMddfC677DLy8/N58cUXqaqq\ncmaJ/eY3vwnA2WefzebNm7nvvvtwuVycdtppTJ8+nY8//th5jmnTprF69Wpuu+02GhoauOuuuyKy\nZENFRYUzm6PL5eLpp58GYOrUqdx4440AXHjhhdTX1/PYY49x+PBhRo0axZ133ul0hwK4+uqrcblc\n/PrXv6apqYkTTzyRa6+9NuC1brnlFv7whz9w11134XK5OPXUU5kxY4bzeEpKCjNnzuSJJ55g5syZ\nZGZmMnXqVGcmT59bb72VefPm8dhjj7Fv3z6KiooYP3483/rWt8L++YhI4sa/PXv28MADD1BTU0N+\nfj6jRo3il7/8ZUAlO1rxD+B73/seL730Es888wzV1dUUFhYyZsyYgImCRCQ8EjWuuVyubruf+z9+\n1VVX8eijjzJz5kzy8/O58MILqaurC9j/2muv5S9/+Qu///3vqampoaSkhG984xuAt+v3//zP//Dn\nP/+Zu+++m8bGRkpKSjjppJMCXmfKlCm43W5++tOfkpqayvnnn8/ZZ5/tPP7DH/6Qp556ijlz5tDU\n1MTo0aP52c9+ppnCE4Qr2HEBxpj5wE5r7ff9tr0A1FprrzTGuPBms39trb2v9fF8oAq4ylo7r9vC\nuFxjn3322fd962eKiMSr1atXc9lll43zeDwfhHqsYp2I9BWKdSKSDHoT6/x1Puq/vdeAacaYYwGM\nMWOASa3bAY7E2138Dd8B1tqDwApA88qLiIiIiIhIwgq6cW2tfRSYB1hjjBv4CLjfWusbsOCbTaCq\nzaFVfo+JiIiIiIiIJJxQuoX/CLgDuBnvmOuTgAeAW621c40xE4F3gXJrbZXfcc8Dzdbay7p7jREj\nRtSkpqbmdbfovIhIrB04cIDm5uZDGzZs6HhmmC4o1olIX6FYJyLJoDexzl8oE5r9DJhtrX2+9f4a\nY8wwvA3uucDO1u1lBGavy/BmuYMuj2+2ZBGRONfTYKVYJyJ9iWKdiCSDXgerUJ7ABbRdtK6ldTvA\nZrwN7GnAKnAmNBsHPBLUC7hclYMGDTryzTffDKFYIiLRd9ZZZ7Ft27bK7vdsT7FORPoKxToRSQa9\niXX+QmlcvwzcaYz5HFiLt1v4j4EnAay1HmPMA637bOSLpbi2tx4rIiIiIiIikpBCaVz/GDiINwtd\nhnfZrceAX/h2sNbea4zJBZ4ACoElwHRrrTtsJRYRERERERGJM0E3rq21tcB/t1662m8WMKuX5RIR\nERERERHpM0JZ51pEREREREREOqDGtYiIiIiIiEgvqXEtIiIiIiIi0ktqXIuIiIiIiIj0khrXIiIi\nIiIiIr2kxrWIiIiIiIhILwW9FJcxZgswtIOHHrXW/tAY4wJmA9fgXeN6KXCDtbYiDOUUEREJG7fb\nzVtvLwZg6pQzyMjIiHGJREQkGhT/JZJCyVyfDAz0u5zduv351uvbgJuA64DxQC3wujEmMzxFFRER\n6b0lS5dx6+w5rGtIY11DGrfOnsOSpctiXSwREYkwxX+JtKAz19baPf73jTH/BVRYa99pzVrfAtxt\nrZ3f+viVQBVwITAvfEUWERHpGbfbzbxXF3Dmldc524aOGMm8uY8zfuwpymCIiCQoxX+Jhh6NuTbG\nZABXAE+1bjoSKAPe8O1jrT0IrABO7WUZRUREwuKttxdzzIQz2m0/ZsIZTjdBERFJPIr/Eg09ndDs\nQqAAeLr1/sDW66o2+1X5PSYiIiIiIiKSkHrauJ4BvGqt3dnNfi7A08PXEBERCaupU86gYnn7DEXF\n8sVMndI+oyEiIolB8V+iIegx1z7GmGHAWcDX/Tb7GtllBGavy4CPelw6ERGRMMrIyOCSr5zDvLmP\nO90DK5Yv5pKvnKPxdiIiCUzxX6Ih5MY1cDXeBvQrfts2421gTwNWARhj8oFxwCO9LKOI9JCWmxBp\nb/KkiYwfe4rz2/jBrJn6bYiIJAHFf4m0kBrXxpgUvI3rP1lrW3zbrbUeY8wDwJ3GmI3AFuBuYDvw\ncviKKyLBWrJ0GfNeXeCcnZ0/ew6XfOUcJk+aGOOSicReRkYG555zdvc7iohIQlH8l0gKNXM9DRjC\nF7OEO6y19xpjcoEngEJgCTDdWuvudSklrigbGv+03ISIiIiISHSF1Li21i4AUrt4fBYwq7eFkvil\nbGjf0N1yEzpjKyIiIiISXj0Zcy1JStlQERERERGRjvV0KS5JQt1lQyV+aLkJkchzu928vmAhry9Y\niNutEVAiIiLJTplrkQSk5SZEIktDZERERKQtZa4laMqG9i2TJ03kvlkzGZXZxKjMJu6bNVMVf5Ew\n8B8iM3TESIaOGMmZV17HvFcXKIMtIiKSxJS5lqApG9r3aLkJkfDThIEiIiLSETWuJSSTJ01k/NhT\nnDHWP5g1Uw1rERERERFJeiE1ro0xg4FfAdOBHKACuNpau9Jvn18A1+Bd63opcIO1tiJsJZaYUzZU\nRJLZ1ClnMH/2HIaOGBmwvWL5Yn4wa2aMSiUiIiKxFvSYa2NMf7yN5Qa8jetRwK3APr99bgduAq4D\nxgO1wOvGmMwwlllERCRmfENkFs19nK0b1rN1w3oWzX1cQ2RERESSXCiZ69uBz6y1M/y2fea7YYxx\nAbcAd1tr57duuxKoAi4E5vW+uCJfcLvdTvf0qVPOiFilNlqvE6+S/f1LYgj391hDZERERKStUGYL\nvwBYaYz5mzGmyhjzkTHmGr/HjwTKgDd8G6y1B4EVwKlhKa1IqyVLl3Hr7Dmsa0hjXUMat86ew5Kl\ny/rs68SrZH//khgi9T32DZE595yz1bAWEQkzt9vN6wsW8vqChUmxEkOyvd9EFUrm+ijgBuD/gHuA\nccBDxhi3tXYuMLB1v6o2x1X5PSbSa/7L4PgMHTGSeXMfZ/zYU8JWyY3W68SrZH//khj0PRYR6XuW\nLF3GvFcXOCszzJ89h0u+ck7CLimabO83kYWSuU4BVlpr77TWfmKt/T3we+D6bo5zAZ6eFjBR6exU\nz3W3DE5fe514lezvXxKDvsexpf91IhIq/5OiQ0eMZOiIkZx55XXMe3VBuziSCDEmlPcr8S+UxvUO\nYG2bbeuBoa23d7Zel7XZp8zvMUFdbUVEJPHpf52I9ESwJ0UTJcboJHBiCaVxvRQY2WbbCGBL6+3N\neBvR03wPGmPy8XYff6/nRUwsnZ2deu6V1/nnK6/26TNv0TJ1yhlULG8fbCqWL2bqlPbBKd5fJ974\nzgI3Njay8b232z2e6O9fEkuy/o5jTZkYEYmkzmLMX+e/xj9fey2gPp0I2W3pO0JpXN8PTDDG3GGM\nOcYY823g+8AjANZaD/AAcKcx5r+MMccDc4HtwMthLnef1fnZqdN57d/rI37mLRECTLSWwUnG5Xb8\nzwJXtGSze9cu5j92f9K8f0k8sf4dJ0LM7QllYkSkp4I5KdpRjNnw75XsO1xPRWOmU59+9Ik/xH12\nu6v3O2niqUn5P6QvC3pCM2vth8aYrwO/BH4ObAJuttY+67fPvcaYXOAJoBBYAky31urb0A2XK4WS\nwUOcs2+RmGwnkSZLiNYyOMm03E6HEz/95Ocs/OOjHJNWT3pqWkK/f0lcsfodJ1LMFRGJFt9J0Xlz\nH3fiZ8XyxV2eFG10u1n74XIuvvFWZ9vQESN55tf3cMnNt5Heelw8TmjZ2fsdPWwIP/3lb/Q/pI8J\nZbZwrLWvAK90s88sYFZvCpXIpk45g/mz5zB0RGAP+4/eeZOvXvl9577v7P6555wdltdNxBlzfcvg\nJMrrxFpnmSYz6UzSU5uS4jOQxBXt33EixtxQdPa/rmL5Yn4wa2aMSiUifUV3J0Xbxph1K1fw5dPP\nav88F3yTdStXcMKpk51t4a5jh0Pb93vNHf/NT3/5m6T9H9KXhdS4lt7znZ168c+P0z8rHVdaOrv2\nHWDk8Sc6Z9UiobsuevEUYERE+rpkj7k9yTyJiPjr6qRo2xhTvX0bBQOKolzC8PJ/v68vWJjU/0P6\nslDGXEuYTJ40kbMnjsPT3ERLQx3FORk0bl7Lxn/9f+xa+zHuQwd7NNlOrMb2JeuYwkTTm4mfPB4P\ndXV1tLS0RKp4ItLHTJ40kftmzWRUZhOjMpu4b9ZMdWcUkbDxjzHnnTiSiuXvtNtnyT9eYNTJ4wO2\nhWNCy57UfZOhvuzxaPVlZa5jZPLkyezdu5dPP/3U2Va/fy/1+/dSteoDjirKZ8WKFYwePZqiou7P\nxHU3ti9SXfQiOabQ7XY73WOmTjmj22xHqPtLoGAzTb7P2ePxMHrUSCoqKli7di179uzhiCOO4Kqr\nriIlReftJDiJ+rtNhG7Rbf82QMh/q3B0x0/U74hIJCTb78U/xhQUFrarw0wbdxJLnvtjWHvQ9KTu\nG+oxfeF/yOHDh6msrHQuO3bs4NChQ0yYMIGzzmrfRT9ZqHEdI/369eOKK66gpqaGNWvW8J///Icd\nO3Y4j9fU1LBo0SIWLVpEWVkZo0aNwhhDWVkZLpcr4LmCGdsXiS56kRxTGGoQ0sRB4dHdGKd33l3K\ni/9aSNngI2jav5sVywNX2duxY0e776dIZxL5d9vXu0W3/ds8d/udHK6p4dSvXQxE72+VyN8RkXBL\n9t9LZ3UY/xMOvZ3Qsid1354cE2//Q2pra9s1pA8cONDhvmvXrlXjWmKnX79+TJgwgQkTJrBv3z7W\nrFnD2rVrqaysdPapqqqiqqqKt99+m4KCAowxGGMYNmwYqampQY/tC/eMueEcU+gf+CZNPDWkIJTs\nEwf1Vkdnuf3/do2NjXz66aesW7eOf3/yCf3TXbh3bWv3PMOGDeP0009X41qCkgy/22jOUt5dtiqU\nbFZnf5uX//AI5cOPIj0jIyp/q2T4joiEi34vXh31lgnnhJY9qfv2tL4cq5UuampqAhrSlZWVHDx4\nsNvj0tLSKC8vZ8qUKREvYzwLunFtjLkL7xJc/tZba0f77fML4Bq8y3AtBW6w1laEoZxJoX///px2\n2mmcdtpp7Nu3j3Xr1rFu3Tq2bfuiIXPgwAHef/993n//fTIzMzn66KOpb2igJac4qNeIx5mv255p\n/evP7qJo6FHt9ussCCX7xEG90dlZ7uNGj2Ljxo1UVFSwefNmmpqaAEjxbzi7XOSVDqIpI5vRpYX8\n1/lfjcVbkD4qWX630Yi53WWrQs1mdfa3+fLpZwXMuhvpv1WyfEdEwkG/l8QUyf8hHo+HAwcOOA3o\nnTt3UllZyaFDh7o9Nj09nYEDB1JeXs6gQYMoLy+nuLhYwwIJPXO9Gpjmd7/Jd8MYcztwE3AlsAW4\nG3jdGDPaWtvQy3Imnf79+zNx4kQmTpzIgQMHsNZirWXLli3OpFENDQ2sXbu29YhP2bjtU/qVH0G/\ngUPILioN27iMzjIe4RgP0tmZ1ucfuY9GtzuiM6j7Xr+7bE6ijl/y/+xbmhqprd7JyOOOZ8HCBSx6\nY2HHB6WkkD9oKPmDhtFv8DDSMrPYumE9rtR6Xl/gPSaRPiOJT4n6m+yJ7rJVAPNeXcDkS69m3coV\nAEy+9GrmPffHpMpmiUjfE4+xvid133gYP+3xeNizZ09AQ3rnzp3U1dV1e2xGRgbl5eUBl6KiIjWk\nOxFq47rZWrur7UZjjAu4BbjbWju/dduVQBVwITCvtwVNZgUFBYwbN45x48ZRX19PRUUF1loqKiqo\nr6939qvft5v6fbupXvsxHo+HkeUlrFy5kqOPPpqSkpIeddftKuMRjvEgnZ1pHT9tert1CTsLQj0N\nWsFkcxJ1/FJLSwv/mP9Phgw7kk1vzufwnio8rSdt0tp8T/Ly8hgxYgRHH300j/7leY4/7ZyAxz9Z\n9Bo2MxMz6UwgcT4jiaxI/m6TWGEDbgAAIABJREFUSXfZKoCMAaW8Mvf3zhqwr8z9PcUDB3Wazers\nb/PRO2/y1Su/79yPVMXQV6FubGxk43vvxvWEPiLxIh4acOEUr7G+J3XfaI+fbm5uprq6ul1DurGx\nsdtjs7Ky2jWkBwwYoCF/IQi1cX2sMWY7UA+8B9xhrf0cOBIoA97w7WitPWiMWQGcihrXYZOVlcVx\nxx3HcccdR0tLC9u3b3e67/qP03a5XOzZs5sFCxYAkJOTw7Bhwxg+fDjDhg2jtLS02x9KMON3IjUe\nxIWLVYv+RWFRCdB1EOpJ0ArmvSXS+KXm5mYqKyv57LPPnItvGYiOupX065fP2LGncOyxxwZMotf2\nc9743tscrqnhv67/sXNsX/2MJLoi9buVQI2Njez4bDMX33irs83pHTRyeIfHdPS3+WTRazTU1FC5\nZRMQuYphYIU6jd27djH/sfsZc+Z5EX1dkb4u3ibA6o14j/U9qftGqr7sdrupqqoKaEjv2rUrqKVR\n8/LynK7dvktBQYEa0r3kCnY9MmPMdCAXsMAgYBYwGDgOOAF4Fyi31lb5HTMP8FhrLw3yNTYNGTLk\nyDfffDOkNyFeNTU1bNq0ybl0NWYiOzuboUOHMmTIEIYOHUp5eTnp6ekB+7y+YCHrGtLanQXdumE9\nozKbwjIGxO12c+vsOQEBFGDR3Mf53zv+m6XLvLNRh3sprmDeWzTef6Q0NDSwbds2Pv/8c+e6qzUV\nM/LyySsbTN7AwXz01kL+r4ug7/85NzY2UtGS3Sc/o94666yz2LZt22ZrbfsJArqhWPeFnvxuy4cf\n5XRxHnXyeCq3bEr471tnuoqh982ayYI332RjYwbDzOiAxz+zazk23c35553X5XP3dimuUHT2Xhb+\n8VG+esZE0lPT4qZrqE88dlsNN8W6viVa38lIvk5frn9Fkm/Gbl8meufOnezZsyeoYwsLCykvL3ca\n0wMHDqRfv34RLnHf0ptY5y/ozLW19l9+d1e3ZqU/Ay4G1ndymAvo/tSJhEW/fv0YM2YMY8aMwePx\nUF1dzaZNm9i8eTNbt24N6EJeV1fnjOMGSElJoby8nCFDhjBo0CAGDx4clYXguzrTmpeXF1IAjcfJ\n2qKhpaWF6upqduzYwfbt29m2bRtVVVVdHpObm8vw4cNpamlh2SdrOWrEiTQDK99a2O1Zbv/P+fUF\nCztOfYsEKdTf7dYN6/jonTfbdXEedfyxkSpiXOsuW5WemoarsX0WwoWL9NSuqwAd/W0iGWM76+Ju\nJp1Jemr8VajjtduqJLd4mERResfj8bBv3z5ngrGqqip27txJTU1Nt8e6XC6Ki4sZOHBgQEM6Ozs7\nCiUX6MVSXNbaA8aYDcDRwFutm8vwjrPG7/5HPS+e9JTL5aK0tJTS0lImTJhAS0sLVVVVfPbZZ2zZ\nsoWtW7cGTGLg62K+fft2Z1tWVhYHD9eRUXeQrP5FZBcWkdGvIOzjd2Kx1IBvbFLbDJj/e4vH8UvN\nzc3s3r3bOWO5Y8cOKisrux1Hk5eX5wwJGD58OEVFRU63n29c2PP1H+PxM5LENWniqTz+/Mtc8ZM7\nnW1DR4zkmV/fw6TrroxhyXqntxmgrmKo8xs1owKOqVjxjn6jvRDv3VZFIiUa3/1kqls0NTWxa9cu\np17na0h31dvQJzU1lbKysoCGdFlZWbueqNGSDD15gtHjxrUxJg84Fphrrd1sjNmJdybxVa2P5wPj\ngEfCUVDpHV9mury8nAkTJjizBm7dupXPP/+czz//vF3Xkvr6ejJSXFSv+7ezzePxcExJIQsXLqSk\npITS0lJKSkrIzc3tVfmCOdMazh9tRkYGo4cN4bkHf8XpF3wTgOce/BVnj/uy87yRHL/U3XvxeDzU\n1NRQXV3Nrl272LVrF1VVVezatYvm5uYun9vlclFWVsYRRxzhXLoaQ9Obs9yJNMZLomfLli3s3r2b\nfv36UVBQQH5+PtnZ2d2O81q67D3n9+rv9Au+ydJl78VdZjMY4coAdfY79sW6Z359D5NbP7sl/3iB\naeNOirvfaF+qUGvZI0lW0fjuJ2rdora21mk8+66rq6uD6imalZXlNKJ9DemioiJSU1Pb7RuLRq56\nM3whlHWufwP8A9iKd8z1bMANPNu6ywPAncaYjXyxFNd24OUwllfCxNdtpLi4mC9/+csAHD582Ola\n7Luura1td9yBAwf48MMPA7bn5uZSUlJCUVFRwKWwsLDDH36owv2jdbvdfLBuA9/5yf9ztn3nJ6OZ\n/9j9uN1uJxBFIqvu/148Lc28+os5nH7yGMpKStizZw979uxh165dNDQE19+6oKCAwYMHO935y8vL\nyczM7FUZQxGLngfSd23fvp0//elP7banpaWRn5/vNLb79esXcJ2fn++tgHTUAO+jk69EIwPkdrtZ\n+9k2Lrn5NqeXziU338aS5/4YEOviQaJWqEUkdH25btHS0sLevXvbNaSD6dYN3nqdf0N64MCBQU80\nFotGrnryBAolcz0Yb0O6CKgGlgATrLV7AKy19xpjcoEngMLWx6dba7vv1yAx0/bs1jHHHMMxxxwD\neLOnBw8eDJg4YefOnezfv7/d89TW1lJbW8uWLVsCtrtcLgoKCigsLKSwsJD+/ftTWFgYUIFOS+v6\naxiJH+2CN9/khKnnttt+wtRzWfDmmwGT/PQms+t2u6mpqeHgwYPs37+fPXv28OY77zJsUDm161bS\nVFfLgHQXq1etYnUQz+cbR+PrBlReXt7rXgPhkKzj3SV0mZmZpKSktJvJtKmpib1797J3795Oj3W5\nXDS1tODeso707FzSsnNIz85h28cfcO63L2Hnzp3k5eWRk5PTJ9bfjEYGyPca6RkZAUsbhus1wp0h\niWSFOpxl7UtZdpFwiuZ3vy/ULRoaGgIa0b5ehsEse5WSkkJJSUlAva4346Nj1chVT55AoUxodlkQ\n+8zCO4u49AHdnd3yNYwLCgowxjjH1dXVOd2Vq6urndtts9zgbaDv37+/wwa5T05ODvn5+eTl5ZGb\nm0tubq5zOycnh09WreLIk8bR7G4gJT3DOXPXmx/tf/6zmqyRJ3fwiIv//Gd1pzPotrS0UF9fT11d\nnXPxnVjwv/ga1P6TyPlkpbo4vHtnl+UrKCigpKTEufi63yfb2T9JPMXFxdx4441s2bKFgwcPcuDA\nAWpqajhw4AAHDx7scpyZx+Mh1eWibm81dVQ72/uluXj++eed+y6Xy4kjvot/XPG/9JWGeDyKVIYk\nEhXqcJdVWXZJVsn63ffVZ/0b0VVVVezbty+o433dusvKyigrK6O8vJzi4uJuE0yhUCM3PoTvLyp9\nSm/ObvmW8Ro6dGjA9rq6Ovbu3cuePXvYvXu3k4Xat29fh41Mn8OHD3P48OFuy7x2zQcApKSnk5KW\nQUtLC/9JS6VqZyXp6emkpaWRlpZGamqqc+1yuXC5XKSkpDi3PR4PAwoKWL3sTTLqDuDxePA0N+Np\nbubz9asZNrCUv/71r7jd7oBLQ0NDUBNMBCs1M4uM3H5k9iugzt3IEXmZnDl1CgMGDIhqt26RaBsw\nYAADBgxot93j8dDQ0MDBgwedE1S+S01NjXPp6ERe2+c5dOhQl8sR+svJyXEa2r6L735ubi7Z2dnO\n9uzsbNLT08OyDmg0MkCReo2+1A0wUmXty91WRXoj0b/7DQ0Nzlw3/pdg64D9+/dvl43Oz89v93/D\n7XZ7V12hb08App48gdS4TlKROLuVnZ3N4MGDGTx4cLvH6uvr2b9/P/v27WP//v0dVp6DXfqrpbGR\nltbuNrUNsHlzcBXotoqzM9i9flXAtoLMdPbv28f+IM9EdiY1NdUZJ+o/bjQvL48nnv0bp136PVL9\nZnNcNPdxbo2Tf06a7VFixeVykZWVRVZWFqWlpZ3u19zc7PQQ8V0OHTrkXPsutbW17bqfdyTYE3w+\naWlpZGdnd3rJyspyrtve9s+SRyMDFKnX6EsZkkiWNZLdVn09pXyXxsZGBg0aFLOZgEX8xUuX7d7U\nWXxLXrVtRAebjU5PT6e0tNRpRPuy0sEkSHram6ar9xurRm6y9mbojBrXEhX+sxx2xOPxUFdX51SI\nfde+rtdbt37O55WVZOfl42luorH+MBlpad3OnN1bLpeLjIwMMjMzycjIcCrK/pVl/y6mvm6nWVlZ\nnWa2LjrvAPOefSouA5Bme5S+wP/kVVc6iiu+27W1tRw+fDhgOEcwY+TAOz7c16gPVXp6OpmZmU78\nyMzMZPIJo9izbT2pqal8/czJpKW4+Oijj8jIyAiIP/6X9PT0oLuzJ3qWKZ54PB4aGxs77Pnk6/3k\nf7u+vt659m333e4oS1ZcXMwNN9ygoQwihFZnqaura5eNDnZsNHiH6/kaz76GdP/+/Xv0W+xpb5ru\n3m8sG7n6P/MFNa6TVKy7cHR05s3X7TKUYzweD7W1tdzxy98w6ZuX42nxdu9uaWnmw1de5Karv0Nq\naioej4eWlhY8Hk9AF/Hm5mY+/ve/cblSmDB+HNnZ2U6X8oyMDNLS0sLS/dNfvAagvtTNUyQYLper\n27jir7Gx0Wl0+xrevtt1dXUB177boZ7ga2xspLGxsdMu6zsrK4N+rrS0NKeh7btue0lLS3OuMzO8\n1x9//HGHw2jaXqekpJCamhpwSUlJISUlhdMnn8b8e34VV90APR6PE+ubm5ud65O/fBKv3P9bygYO\npKWlGU9LC56WZj5bsZjpV1yKtZampqYOL42NjZ1eu91u5+/pfzuSDh06FHQvr2QQqZ5W6sEVKB4/\nj67qLEcOG8revXsDljE9ePBgUM/rn432v2RlZYWt7D3pTRNsHS2Wdcx46c0Qa2pcJ6lYnt3qaXa0\nox+ty+Vi6bL3OHrCGWTk5gU8Nnz8GVR8uqnbH/rRRx/dg3fRO/EYgPpSN0+RSEhPT3dWNghWY2Nj\nwASHdXV1Tjde/9v+F192srdzOPgagLFSkuFi1bOP40pJAZeLluZmynPSeeCBBwLmugCcE5r+9zvj\nazz6X/tffCdKfbd9l64anUUZLja8+nzAtsJ0F/PmzevVZxBubXs2+HpLZWZmkp2dzXHHHReW5S0T\nQaR6WqkHV6B4/TwWvfU2R500noPbP6P+wF7q9++l/sBeSjJSePLJJ4N6jv79+1NWVhbQmB4wYEDY\nkyrhEEodLR7rmMlEjeskFouzW8qOiiS3eMyA9IYvQ9xdF/WOtLS0OF2B23YZ9t/mP6Gi7+Lf9dg/\ncxptLpcLPB7weEhxuWLe4I+2tj0G/Lvup6Wl8eGq1Rwx+gRS0tJJSc8gNT2dde+9w4zLLiY3N9dp\nSGdmZjrL1En3IlWXUB0lULx8Hv5dunft2sWuXbvYsWMHzc3NBDM4xzePR2lpqdOlu6SkJGaTx8a6\n96hEVo8b18aYnwJzgAettT/22/4L4Bq8a10vBW6w1lb0tqASGdE+uxWJ7Gi4g1SsKv+xbnQo2Euk\nxWsGJFZSUlKcORzCwePx0Nzc7HRP9l3adnH23W5ubqapqcm59t32XXzdqtve988W++77Z5T9M8u+\ni698/vc7Kn/bzLbv2n84j+9yuK4OFy7y8/s5XdZ9GfO23do76uLuu/i6yLe9pKamtute77udkZHh\nrEjRmdcXLGTg2DMobxNTj2h28fm27SH9v4v1/4d4E6meVurBFSjan0dtbS3/en0BdYcPU1Q0gD17\n9rBr167g57dwuWhqbuGE475EeXm5k5XuaKbuWOpJ71HV0fqOHjWujTFjgWuBVYDHb/vtwE3AlcAW\n4G7gdWPMaGttQ69LK9KBcHZxj1XlPx4aHZrtUSIpXjIgiczlcjkNw3A12ONR23i5ujVOJepJmnj4\n/yASTs3Nzc6YaN/ls61bOVxb6zSCP/206+fIz88nPT2Dz3dVU3T0KFKzc9n8yYd95rcRau9R1dH6\njpAb18aYPOAZvNnp/+e33QXcAtxtrZ3fuu1KoAq4EIivgU0SE5E68xaOLu6xqvzHU6MjXidbk75P\nGSEJh3iKl90Jx/+7vvR+oylSdQllBwP19vPweDzs37/faUBXV1eza9cudu/e3eFkkB1ll7Oyspxu\n3L5MdGlpqTPBmH+vjpv6WJ0l1N6jqqP1DT3JXD8C/NNau8gY83O/7UcCZcAbvg3W2oPGmBXAqahx\nLUT2zFtvu7jHqvIfb40OTYQhIvEq3uJlV8Lx/64vvd9oilRdQtnBQMF+Hh6Ph4MHDwY0oKurq6mu\nrg5+LghXCtmFA8gqHEBmwQCyCvqzZ89eRuelMP3cc7osYzL9DpLt/fZFITWujTGXAicCY1s3+Q+c\n8i1gXNXmsCq/x0R05k0kCSkjJMlI/+8iJ1Kfrf5mgfw/D4/Hw3duvYn9+/fz3nvvOQ3o6upqGhqC\nG/2ZkpJCUVERpaWllJSUUFpayoYNG9nkymOYGRWw776aWlyuwAkSNf+AxLugG9fGmCOAB4Fp1lrf\n+iGu1ktXXEBLz4oniSqYM2/RDqCxqvyr0SHJQBmh5BTuON4X42VvMk198f1GU6SyePGaHYxWvcjj\n8VBTUxPQePZlpFcsfy/o5xkwYIDTgPZdioqK2i0nd/TRR3Pr7DntGtdtv+eJPv+AThwkhlAy1ycD\nJcBHxhjftlRgsjHmRsAX+csIzF6XAR/1spySZGIRQGNV+VejQ5KFMkLJJRJxPNniZbK9X+lcJH5P\nbRvRvvHQu3btCjoTDVBQUBCQiS4tLaW4uJj09PSgjg/me57o8w8k+omDZBJK4/oN4Di/+y7gj8A6\n4FfAZmAnMA3vLOIYY/KBcXjHaYsEJZYBNFaVfzU6JFnEa0ZIwiuUOB5qtibZ4mWyvV9pr7f1Io/H\nw4EDB9ploqurq3G73V0e6y8/P99pRPsa0iUlJVHpjp/I8w8k+omDZBN049paewhY67/NGHMY2Gut\nXdt6/wHgTmPMRr5Yims78HK4CiyJL9YBNNTKf7i68ajRISKQGF0Du4rjC958k/RUb/UjPTODFxe8\n1WW2pqPPI9niZbK9XwnU5e9p4RtOhviM0ydTW1vrNJx3797tXAc9sRjeTLSvAe1/yczMDNt76kiy\nfs9jXe+V8OrROtd+PPhNamatvdcYkws8ARQCS4DpfmO0RRKKuvGISDglfEzxeHjq2b9x+re+Q1Nj\nI8uef54rfnKn83DbbE3Cfx4iPdDS3ERDzQEq13zMlv27KS0fTHP9Yd5btrTD5aw6U1hY2K4BXVxc\nHPFGdE9o/gHpK3rVuLbWTu1g2yxgVm+eV5JbXwmg6sYjIuGUSDGlszj+zj9e4JKfzCI9I4NV7y3h\n9K9d1O5YX7Zm6pQzEubzEOmJ+vp6qqurGdC/kMr5r9CycwsNB/fjrq0Bj4ccICc7k8b9u4GO14l2\nuVz079/faTj7N6L70m8o2PkH+mLPn75S75Xg9DZzLRJ2fWUCF3XjEZFwSqSYkpGRwehhQ3jm1/cw\n+YJvArD45efJK+xPutZ4FnH4Tyq2e/fugMuhQ4ec/XJSXdTs2Nrp87hSUsjIK6A5JZWy3CzGjx/n\nzM6dlpYY1f3uxmX31Z4ufaXeK8FJjF+bJBxN4CIi0ne53W7WfraNS26+jXUrVwBw2Y/v4KXf/5ZG\nt5v0jAxGnTyeV+b+vtNsjS/+iySCpqYm9u7d6zSc9+zZ49wOZVKxlJQUsrOzyczKojazH2VHHUtW\nfn8y8vJxpaSwdcN6jshs4vjjj4/gu4mdzsZlh7PnTyyy36r3Jg41riVutQ2g8dbVR914RCScEimm\n+LLO6RkZnHDqZGf7+GnTWbdyBSecOpn0jAxGnzKBZ359j9M93D9bk0ifhyQHj8fD4cOH2zWe9+zZ\nw759+/B4PN0/SaucnBxKSkooKioK6NKdn5+Py+XC7XZz6+w5jDj9nIDjkvX3Ea6eLsFmvyNRJ03W\nCd0SjRrX0ifEY1cfdeMRkXBKhpjiwsWqRf+isKgEgG2rPuSHV1xMY2vmzj9bkwyfh/RNzc3N7Nu3\nr10Wes+ePdTV1QX9PC6Xi8LCQoqLi9tdcnJyujxWv4/wCzb7HY91UokfalwnmXjL/gYjnif5UTce\nEQmnRIkpnWadV7zD3EcfYumy94Du31+ifB7S93g8HmprawMazr7boWah09PTO2xADxgwIKTx0G3r\ncPp9fCEcPV2CyX7Hc51U4oMa10mkr55pi/dJbdSNR0TCKRFiSldZtby8vJDeXyJ8HhK/GhsbnYaz\n/2X37t00NDSE9Fz5+fkUFRW1a0T369cvpCWyOtJVHU6/j+hl8uO9TiqxF3Tj2hhzA3A9MLx10xrg\nF9baf/nt8wvgGrxrXC8FbrDWVoSttNJjOtMmIpIY+koPJGXVJF60tLSwf//+DhvRBw8eDOm50tPT\nGTBggNNw9jWmi4qKIvb9Vh0uOL2NOZrnQcIhlMz158DtwEbABVwF/MMYc5K1do0x5nbgJuBKYAtw\nN/C6MWa0tTa0U38Sdn35TJuCnYj0FZFu+Pa1HkjKOku0+Ja02rNnD3v37m133dLSEtLzFRQUOI1m\n/wa0b0KxaOrLdbho603MCSb7rTqpdCfoxrW19p9tNt3Zms0eZ4xZC9wC3G2tnQ9gjLkSqAIuBOaF\nqbyShDRph4j0BZFu+Cp7JdKetZbFixeze/duGhsbQzo2KysroAHtuwwYMID09PQIlVjiWXfZb9VJ\npTs9GnNtjEkFvgVkAkuAI4Ey4A3fPtbag8aYFcCpqHEdc5E80xaNLorqXigi8SwaDV9lr0TaW7hw\nIXv27On08bS0tIBGs38jursZueOFsqXR1V32W3VS6UpIjWtjzPHAe3gb1XXAxdbaCmOM77R8VZtD\nqoCBvS6l9FqkzrRFs4uiuheKSLxSw1ckNk466SSWLl1KdnZ2hw3oWHTjDjdlS+OP6qTSmVAz1+uB\nE4ACvJnr54wxU7rY3wWENshFIibcZ9rURTG8+sokRSISG8peibQ3adIkJk2aFOtiRFwss6Wqn4gE\nLyWUna21jdbaTdbaj621M4EVwA1AZesuZW0OKQN29r6YEi6+M23nnnN2r4Njd5kaCd6Spcu4dfYc\n1jWksa4hjVtnz2HJ0mWxLpaIBGnqlDOoWN4+7lUsX8zUKe3jZE/4sleL5j7O1g3r2bphPYvmPq7s\nlUiSCGcdLliqn4iEprfrXKcCKdbazcaYncA0YBWAMSYfGAc80svXEElo6gEg0vdFq9umxvqJSLSo\nfiISulDWuf4l8CreJbn6Ad8GTgfuad3lAbwziG/ki6W4tgMvh7G8EkfURTE8NFZTJPoi0c0xWg1f\njfUTkWhQ/UQkdKFkrkuAuUA5cAD4BDjXWrsIwFp7rzEmF3gCKMQ7i/h0a607vEWWeKEJNkSkL4rk\nRIxq+IqIiCSvUNa5viaIfWYBs3pVIulT1EWx99QDQCR61M1RRPqKWE8kpvqJSOh6O+ZaRJmaXlIP\nAJHoUTdHEekLornUaWdUPxEJnRrXInFAPQBEREQE4quHjeonIqEJaSkuEYmcWCyxIZJsorFklohI\nb8TbUqeqn4gET5lrERFJGurmKCIiIpGizLWIiCSVyZMmct+smYzKbGJUZhP3zZoZ1XGMIiJdCaWH\njdvt5vUFC3l9wULcbi3QIxJroaxzfQfwDcAAdcAy4HZr7YY2+/0CuAbvclxLgRustRVhK7GIiEgv\n9WQixljP3CsiySHYHjbxMOmZiAQKJXN9OvAwMB44G0gHFhhjcnw7GGNuB24CrmvdrxZ43RiTGbYS\ni4iIRNmSpcu4dfYc1jWksa4hjVtnz2HJ0mWxLpaIJKjuetj4T3o2dMRIho4YyZlXXse8Vxcogy0S\nQ6Gsc32e/31jzFXALuDLwLvGGBdwC3C3tXZ+6z5XAlXAhcC8MJVZREQkauJp5l4RSR5d9bDRsoIi\n8ak3Y64LW6/3tl4fCZQBb/h2sNYeBFYAp/bidURERGIm3mbuFRERkfjUo8a1MSYFeAB411q7tnX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"text/plain": [ - "" + "" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -1375,27 +1414,54 @@ } ], "source": [ + "#Using lmplot for trellis grid\n", + "sns.set(font_scale=2)\n", "plt.figure(figsize=(12,12))\n", - "bbp = RPlot(cdystonia, x='age', y='twstrs')\n", - "bbp.add(TrellisGrid(['week', 'treat']))\n", - "bbp.add(GeomScatter())\n", - "bbp.add(GeomPolyFit(degree=2))\n", - "_ = bbp.render(plt.gcf())" + "\n", + "sns.lmplot(\"age\",\"twstrs\",cdystonia,col=\"treat\",row=\"week\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x=\"age\",y=\"twstrs\",data=cdystonia,hue=\"treat\",palette=\"Set1\",col=\"week\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the `RPlot` class to represent more than just trellis graphics. It is also useful for displaying multiple variables on the same panel, using combinations of color, size and shapes to do so." + "We can use the seaborn to represent more than just trellis graphics. It is also useful for displaying multiple variables on the same panel, using combinations of color, size and shapes to do so." ] }, { "cell_type": "code", - "execution_count": 131, - "metadata": { - "collapsed": true - }, + "execution_count": 79, + "metadata": {}, "outputs": [], "source": [ "cdystonia['site'] = cdystonia.site.astype(float)" @@ -1403,14 +1469,24 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 176, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+A1UsXFnFtAeflf/YA+CeQTsjQ221fEZGD2AdxcVvEfKM2ns3G4/tsGmtEKJ1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9/Enjc3kdpe/M5kMhmzB8ax9Jc8bqu0ee1JyiGITalDufuvzyJzU71WhwPvgMkRijt+0f1YYNCg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\nOjedVWsJcJJvhEgjRSIiEaURJBEREZEACpBEREREAihAEhEREQmgAElER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"text/plain": [ - "" + "" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -1418,12 +1494,12 @@ } ], "source": [ - "plt.figure(figsize=(6,6))\n", - "cp = RPlot(cdystonia, x='age', y='twstrs')\n", - "cp.add(GeomPoint(colour=ScaleGradient('site', colour1=(1.0, 1.0, 0.5), colour2=(1.0, 0.0, 0.0)),\n", - " size=ScaleSize('week', min_size=10.0, max_size=200.0),\n", - " shape=ScaleShape('treat')))\n", - "_ = cp.render(plt.gcf())" + "import seaborn as sns\n", + "sns.set(style=\"white\")\n", + "\n", + "sns.pointplot(y=\"twstrs\",x=\"week\",data=cdystonia, hue=\"treat\")\n", + "\n", + "\n" ] }, { @@ -1452,7 +1528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.15" } }, "nbformat": 4, diff --git a/Lectures/Lecture4-Matplotlib/normalvars.png b/Lectures/Lecture4-Matplotlib/normalvars.png index 208a25dd34fa65224cb468e22a2babdedc588082..31540331f47c3531fde18339b989270812842767 100644 GIT binary patch literal 39074 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changed, 583 insertions(+), 213 deletions(-) diff --git a/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb b/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb index c49c6d0..e2eaf97 100644 --- a/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb +++ b/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb @@ -22,6 +22,7 @@ "## Map Reduce and the New Software Stack\n", "

    \n", "
  • Covered in Chapter 2 in the Ullman book\n", + "
  • Covered in Chapter 24 of the Joel Grus book\n", "
  • Python support packages https://pypi.python.org/pypi/mrjob\n", "
  • map() and reduce() are basic built in functions in python https://docs.python.org/2/library/functions.html\n", "
\n" @@ -69,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -80,7 +81,7 @@ "[0, 2, 4, 6, 8, 10, 12, 14]" ] }, - "execution_count": 2, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -103,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -112,7 +113,7 @@ "56" ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -140,10 +141,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "import re\n", @@ -174,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -870,7 +869,13 @@ "['arms' 6L]\n", "['around' 3L]\n", "['arranged' 1L]\n", - "['arrived' 1L]\n", + "['arrived' 1L]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "['arrow' 1L]\n", "['arrum' 1L]\n", "['as' 246L]\n", @@ -1293,13 +1298,7 @@ "['dig' 1L]\n", "['digging' 2L]\n", "['diligently' 1L]\n", - "['dinn' 1L]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "['dinn' 1L]\n", "['dinner' 2L]\n", "['dipped' 2L]\n", "['directed' 2L]\n", @@ -1558,7 +1557,13 @@ "['flown' 1L]\n", "['flung' 1L]\n", "['flurry' 1L]\n", - "['flustered' 1L]\n", + "['flustered' 1L]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "['fluttered' 1L]\n", "['fly' 3L]\n", "['flying' 1L]\n", @@ -2227,7 +2232,13 @@ "['own' 10L]\n", "['oyster' 1L]\n", "['p' 1L]\n", - "['pace' 1L]\n", + "['pace' 1L]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "['pack' 5L]\n", "['paint' 1L]\n", "['painting' 2L]\n", @@ -2346,13 +2357,7 @@ "['present' 3L]\n", "['presented' 1L]\n", "['presents' 2L]\n", - "['pressed' 3L]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "['pressed' 3L]\n", "['pressing' 1L]\n", "['pretend' 1L]\n", "['pretending' 1L]\n", @@ -2918,7 +2923,13 @@ "['this' 113L]\n", "['thistle' 2L]\n", "['thoroughly' 2L]\n", - "['those' 9L]\n", + "['those' 9L]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "['though' 12L]\n", "['thought' 74L]\n", "['thoughtfully' 4L]\n", @@ -3284,15 +3295,98 @@ "load \"import\" myfile.py into the current cell" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting mrjob\n", + " Downloading https://files.pythonhosted.org/packages/95/24/b55a4d4799f49c4c4413233a9437a855c384b8c40daea5e8dec8ec1733f0/mrjob-0.6.5-py2.py3-none-any.whl (308kB)\n", + "Collecting google-cloud-dataproc>=0.2.0 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/07/23/7ae8951f5bc68d1471b492a6b8ee938d3ec9d47ebb82dc8ea1e45899ec8e/google_cloud_dataproc-0.2.0-py2.py3-none-any.whl (131kB)\n", + "Requirement already satisfied: PyYAML>=3.08 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from mrjob) (3.12)\n", + "Collecting google-cloud-logging>=1.5.0 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/93/a5/220c51fa50520991b7de756730fcc7bf719a29f5b4339eb9854d56ffdaf3/google_cloud_logging-1.7.0-py2.py3-none-any.whl (100kB)\n", + "Collecting botocore>=1.6.0 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/22/fa/af1ad7aebe166932fe1cac5eae5413d090eca4d723829756c31fc53c174d/botocore-1.12.14-py2.py3-none-any.whl (4.7MB)\n", + "Collecting google-cloud-storage>=1.9.0 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/3b/cd/4f58416260817ed968b9875d97bd2f6974fe8fdce7b724b2cdd70107b815/google_cloud_storage-1.12.0-py2.py3-none-any.whl (54kB)\n", + "Collecting boto3>=1.4.6 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/8e/49/4616f3beaa9974aa275933218fe64b200f4177ff5f0753a3b72c5cec5dda/boto3-1.9.14-py2.py3-none-any.whl (128kB)\n", + "Collecting google-api-core[grpc]<2.0.0dev,>=0.1.0 (from google-cloud-dataproc>=0.2.0->mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/a5/be/de30100034c391f4c56e2543f1507eb1b30b3030bd9a6764dd6cfe7a954e/google_api_core-1.4.1-py2.py3-none-any.whl (53kB)\n", + "Collecting google-cloud-core<0.29dev,>=0.28.0 (from google-cloud-logging>=1.5.0->mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/0f/41/ae2418b4003a14cf21c1c46d61d1b044bf02cf0f8f91598af572b9216515/google_cloud_core-0.28.1-py2.py3-none-any.whl\n", + "Requirement already satisfied: docutils>=0.10 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from botocore>=1.6.0->mrjob) (0.14)\n", + "Collecting jmespath<1.0.0,>=0.7.1 (from botocore>=1.6.0->mrjob)\n", + " Downloading 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c:\\users\\ps\\anaconda2\\lib\\site-packages (from grpcio>=1.8.2; extra == \"grpc\"->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (1.1.6)\n", + "Collecting pyasn1<0.5.0,>=0.4.1 (from pyasn1-modules>=0.2.1->google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/d1/a1/7790cc85db38daa874f6a2e6308131b9953feb1367f2ae2d1123bb93a9f5/pyasn1-0.4.4-py2.py3-none-any.whl (72kB)\n", + "Building wheels for collected packages: googleapis-common-protos\n", + " Running setup.py bdist_wheel for googleapis-common-protos: started\n", + " Running setup.py bdist_wheel for googleapis-common-protos: finished with status 'done'\n", + " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\62\\45\\af\\649bbf07b6595fda010be1bda667cd56d0444d07afc6f8b687\n", + "Successfully built googleapis-common-protos\n", + "Installing collected packages: protobuf, cachetools, pyasn1, pyasn1-modules, rsa, google-auth, googleapis-common-protos, grpcio, google-api-core, google-cloud-dataproc, google-cloud-core, google-cloud-logging, jmespath, botocore, google-resumable-media, google-cloud-storage, s3transfer, boto3, mrjob\n", + "Successfully installed boto3-1.9.14 botocore-1.12.14 cachetools-2.1.0 google-api-core-1.4.1 google-auth-1.5.1 google-cloud-core-0.28.1 google-cloud-dataproc-0.2.0 google-cloud-logging-1.7.0 google-cloud-storage-1.12.0 google-resumable-media-0.3.1 googleapis-common-protos-1.5.3 grpcio-1.15.0 jmespath-0.9.3 mrjob-0.6.5 protobuf-3.6.1 pyasn1-0.4.4 pyasn1-modules-0.2.2 rsa-4.0 s3transfer-0.1.13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "distributed 1.21.8 requires msgpack, which is not installed.\n", + "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", + "You are using pip version 10.0.1, however version 18.0 is available.\n", + "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "! pip install mrjob" + ] + }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# %load code/MRWordFrequencyCount.py\n", + "# Do not run this cell it is just displaying the content of the code\n", "from mrjob.job import MRJob\n", "\n", "class MRWordFrequencyCount(MRJob):\n", @@ -3311,95 +3405,202 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "no configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:no configs found; falling back on auto-configuration\n", - "creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\n", - "INFO:mrjob.runner:creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\n", - "\n", - "WARNING:mrjob.runner:\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "WARNING:mrjob.runner:PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "\n", - "WARNING:mrjob.runner:\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00000\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00001\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00001\n", - "Counters from step 1:\n", - "INFO:mrjob.runner:Counters from step 1:\n", - " (no counters found)\n", - "INFO:mrjob.runner: (no counters found)\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper-sorted\n", - "INFO:mrjob.runner:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper-sorted\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00000' 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00001'\n", - "INFO:mrjob.runner:> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00000' 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-mapper_part-00001'\n", - "Piping files into sort for Windows compatibility\n", - "INFO:mrjob.runner:Piping files into sort for Windows compatibility\n", - "> sort\n", - "INFO:mrjob.runner:> sort\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-reducer_part-00000\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - "INFO:mrjob.runner:Counters from step 1:\n", - " (no counters found)\n", - "INFO:mrjob.runner: (no counters found)\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\output\\part-00000\n", - "INFO:mrjob.sim:Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\output\\part-00000\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\output\n", - "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\\output\n" + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "WARNING:mrjob.conf:No configs specified for inline runner\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "INFO:mrjob.sim:Running step 1 of 1...\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\"chars\"\t148685\n", + "\"chars\"\t148687\n", "\"lines\"\t12\n", - "\"words\"\t26474\n" + "\"words\"\t26475\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\n", - "INFO:mrjob.runner:removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20150922.022135.283000\n" + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n" ] } ], "source": [ - "%run code/MRWordFrequencyCount.py ./.mrjob.conf data/canterbury/alice29.txt" + "%run -G code/MRWordFrequencyCount.py ./.mrjob.conf data/canterbury/alice29.txt" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "# MRJOB Word count function" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "usage: usage: ipykernel_launcher.py [options] [input files]\n", + "ipykernel_launcher.py: error: unrecognized arguments: -f\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "2", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n" + ] + } + ], "source": [ "# %load code\\MRWordFreqCount.py\n", + "# Do not run this block of code just a display of the stored program\n", "from mrjob.job import MRJob\n", "import re\n", "\n", @@ -3434,77 +3635,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:no configs found; falling back on auto-configuration\n", - "creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "INFO:mrjob.runner:creating tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "\n", - "\n", - "\n", - "WARNING:mrjob.runner:\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "WARNING:mrjob.runner:PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "\n", - "\n", - "\n", - "WARNING:mrjob.runner:\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "INFO:mrjob.runner:Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - "INFO:mrjob.runner: (no counters found)\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper-sorted\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper-sorted\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper-sorted\n", - "INFO:mrjob.runner:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper-sorted\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000'\n", - "INFO:mrjob.runner:> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-mapper_part-00000'\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "INFO:mrjob.runner:Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - "INFO:mrjob.runner: (no counters found)\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\\part-00000\n", - "INFO:mrjob.sim:Moving c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\\part-00000\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\n", - "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\\output\n" + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "WARNING:mrjob.conf:No configs specified for inline runner\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "INFO:mrjob.sim:Running step 1 of 1...\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n" ] }, { @@ -3514,9 +3693,9 @@ "\"'\"\t1091\n", "\"'em\"\t3\n", "\"'tis\"\t5\n", - "\"_i_\"\t2\n", "\"2\"\t1\n", "\"9\"\t1\n", + "\"_i_\"\t2\n", "\"a\"\t632\n", "\"abide\"\t1\n", "\"able\"\t1\n", @@ -3569,8 +3748,8 @@ "\"alarm\"\t2\n", "\"alarmed\"\t1\n", "\"alas\"\t4\n", - "\"alice's\"\t12\n", "\"alice\"\t386\n", + "\"alice's\"\t12\n", "\"alive\"\t3\n", "\"all\"\t182\n", "\"allow\"\t3\n", @@ -3593,8 +3772,8 @@ "\"anger\"\t2\n", "\"angrily\"\t9\n", "\"angry\"\t5\n", - "\"animal's\"\t1\n", "\"animal\"\t1\n", + "\"animal's\"\t1\n", "\"animals\"\t4\n", "\"ann\"\t4\n", "\"annoy\"\t1\n", @@ -3723,11 +3902,11 @@ "\"bent\"\t1\n", "\"besides\"\t4\n", "\"best\"\t12\n", - "\"better'\"\t1\n", "\"better\"\t13\n", + "\"better'\"\t1\n", "\"between\"\t6\n", - "\"bill's\"\t4\n", "\"bill\"\t13\n", + "\"bill's\"\t4\n", "\"bird\"\t2\n", "\"birds\"\t10\n", "\"birthday\"\t1\n", @@ -3812,8 +3991,8 @@ "\"calmly\"\t1\n", "\"came\"\t40\n", "\"camomile\"\t1\n", - "\"can't\"\t28\n", "\"can\"\t35\n", + "\"can't\"\t28\n", "\"canary\"\t1\n", "\"candle\"\t3\n", "\"cannot\"\t1\n", @@ -3834,12 +4013,12 @@ "\"cart\"\t1\n", "\"cartwheels\"\t1\n", "\"case\"\t5\n", - "\"cat's\"\t2\n", "\"cat\"\t35\n", + "\"cat's\"\t2\n", "\"catch\"\t4\n", "\"catching\"\t2\n", - "\"caterpillar's\"\t1\n", "\"caterpillar\"\t27\n", + "\"caterpillar's\"\t1\n", "\"cats\"\t13\n", "\"cattle\"\t1\n", "\"caucus\"\t3\n", @@ -4077,9 +4256,9 @@ "\"dig\"\t1\n", "\"digging\"\t4\n", "\"diligently\"\t1\n", + "\"dinah\"\t11\n", "\"dinah'll\"\t2\n", "\"dinah's\"\t1\n", - "\"dinah\"\t11\n", "\"dinn\"\t2\n", "\"dinner\"\t2\n", "\"dipped\"\t2\n", @@ -4105,8 +4284,8 @@ "\"dodo\"\t13\n", "\"does\"\t9\n", "\"doesn't\"\t16\n", - "\"dog's\"\t1\n", "\"dog\"\t2\n", + "\"dog's\"\t1\n", "\"dogs\"\t3\n", "\"doing\"\t5\n", "\"don't\"\t61\n", @@ -4114,8 +4293,8 @@ "\"door\"\t30\n", "\"doors\"\t2\n", "\"doorway\"\t1\n", - "\"dormouse's\"\t1\n", "\"dormouse\"\t39\n", + "\"dormouse's\"\t1\n", "\"doth\"\t3\n", "\"double\"\t1\n", "\"doubled\"\t1\n", @@ -4152,8 +4331,8 @@ "\"drowned\"\t1\n", "\"drunk\"\t2\n", "\"dry\"\t8\n", - "\"duchess's\"\t3\n", "\"duchess\"\t38\n", + "\"duchess's\"\t3\n", "\"duck\"\t4\n", "\"dull\"\t3\n", "\"dunce\"\t1\n", @@ -4191,8 +4370,8 @@ "\"elbows\"\t1\n", "\"elegant\"\t1\n", "\"eleventh\"\t1\n", - "\"else's\"\t1\n", "\"else\"\t11\n", + "\"else's\"\t1\n", "\"elsie\"\t1\n", "\"emphasis\"\t1\n", "\"empty\"\t1\n", @@ -4222,8 +4401,8 @@ "\"ever\"\t20\n", "\"every\"\t13\n", "\"everybody\"\t8\n", - "\"everything's\"\t2\n", "\"everything\"\t12\n", + "\"everything's\"\t2\n", "\"evidence\"\t7\n", "\"evidently\"\t1\n", "\"exact\"\t1\n", @@ -4238,8 +4417,8 @@ "\"executed\"\t6\n", "\"executes\"\t1\n", "\"execution\"\t3\n", - "\"executioner's\"\t1\n", "\"executioner\"\t5\n", + "\"executioner's\"\t1\n", "\"executions\"\t2\n", "\"existence\"\t1\n", "\"expected\"\t1\n", @@ -4370,8 +4549,8 @@ "\"fond\"\t4\n", "\"foolish\"\t1\n", "\"foot\"\t10\n", - "\"footman's\"\t1\n", "\"footman\"\t13\n", + "\"footman's\"\t1\n", "\"footmen\"\t1\n", "\"footsteps\"\t2\n", "\"for\"\t153\n", @@ -4422,8 +4601,8 @@ "\"fury\"\t3\n", "\"gained\"\t1\n", "\"gallons\"\t1\n", - "\"game's\"\t1\n", "\"game\"\t12\n", + "\"game's\"\t1\n", "\"games\"\t1\n", "\"garden\"\t16\n", "\"gardeners\"\t8\n", @@ -4523,18 +4702,18 @@ "\"hatching\"\t1\n", "\"hate\"\t2\n", "\"hated\"\t1\n", - "\"hatter's\"\t1\n", "\"hatter\"\t55\n", + "\"hatter's\"\t1\n", "\"hatters\"\t1\n", "\"have\"\t80\n", "\"haven't\"\t8\n", "\"having\"\t10\n", + "\"he\"\t122\n", "\"he'd\"\t1\n", "\"he'll\"\t1\n", "\"he's\"\t3\n", - "\"he\"\t122\n", - "\"head's\"\t1\n", "\"head\"\t49\n", + "\"head's\"\t1\n", "\"heads\"\t10\n", "\"heap\"\t1\n", "\"hear\"\t14\n", @@ -4614,11 +4793,11 @@ "\"hurrying\"\t1\n", "\"hurt\"\t3\n", "\"hush\"\t3\n", + "\"i\"\t408\n", "\"i'd\"\t11\n", "\"i'll\"\t31\n", "\"i'm\"\t59\n", "\"i've\"\t34\n", - "\"i\"\t408\n", "\"idea\"\t15\n", "\"idiot\"\t1\n", "\"idiotic\"\t1\n", @@ -4674,9 +4853,9 @@ "\"irritated\"\t1\n", "\"is\"\t108\n", "\"isn't\"\t7\n", + "\"it\"\t530\n", "\"it'll\"\t8\n", "\"it's\"\t57\n", - "\"it\"\t530\n", "\"its\"\t56\n", "\"itself\"\t14\n", "\"iv\"\t1\n", @@ -4714,8 +4893,8 @@ "\"kills\"\t1\n", "\"kind\"\t7\n", "\"kindly\"\t2\n", - "\"king's\"\t2\n", "\"king\"\t61\n", + "\"king's\"\t2\n", "\"kings\"\t1\n", "\"kiss\"\t1\n", "\"kissed\"\t1\n", @@ -4792,8 +4971,8 @@ "\"lesson\"\t3\n", "\"lessons\"\t10\n", "\"lest\"\t1\n", - "\"let's\"\t5\n", "\"let\"\t17\n", + "\"let's\"\t5\n", "\"letter\"\t3\n", "\"letters\"\t1\n", "\"lewis\"\t1\n", @@ -4822,8 +5001,8 @@ "\"livery\"\t3\n", "\"lives\"\t4\n", "\"living\"\t2\n", - "\"lizard's\"\t1\n", "\"lizard\"\t5\n", + "\"lizard's\"\t1\n", "\"lobster\"\t7\n", "\"lobsters\"\t6\n", "\"lock\"\t1\n", @@ -4858,8 +5037,8 @@ "\"lullaby\"\t1\n", "\"lying\"\t8\n", "\"m\"\t4\n", - "\"ma'am\"\t1\n", "\"ma\"\t2\n", + "\"ma'am\"\t1\n", "\"mabel\"\t4\n", "\"machines\"\t1\n", "\"mad\"\t15\n", @@ -4891,8 +5070,8 @@ "\"may\"\t13\n", "\"maybe\"\t2\n", "\"mayn't\"\t1\n", - "\"me'\"\t2\n", "\"me\"\t66\n", + "\"me'\"\t2\n", "\"meal\"\t1\n", "\"mean\"\t10\n", "\"meaning\"\t8\n", @@ -4937,8 +5116,8 @@ "\"mock\"\t56\n", "\"moderate\"\t1\n", "\"modern\"\t1\n", - "\"moment's\"\t2\n", "\"moment\"\t29\n", + "\"moment's\"\t2\n", "\"month\"\t2\n", "\"moon\"\t1\n", "\"moral\"\t8\n", @@ -4951,8 +5130,8 @@ "\"mostly\"\t2\n", "\"mournful\"\t1\n", "\"mournfully\"\t1\n", - "\"mouse's\"\t1\n", "\"mouse\"\t43\n", + "\"mouse's\"\t1\n", "\"mouth\"\t10\n", "\"mouths\"\t4\n", "\"move\"\t3\n", @@ -4995,8 +5174,8 @@ "\"neither\"\t2\n", "\"nervous\"\t5\n", "\"nest\"\t1\n", - "\"never'\"\t1\n", "\"never\"\t47\n", + "\"never'\"\t1\n", "\"nevertheless\"\t1\n", "\"new\"\t5\n", "\"newspapers\"\t1\n", @@ -5018,10 +5197,10 @@ "\"nor\"\t3\n", "\"normans\"\t1\n", "\"northumbria\"\t2\n", - "\"nose'\"\t1\n", "\"nose\"\t7\n", - "\"not'\"\t1\n", + "\"nose'\"\t1\n", "\"not\"\t144\n", + "\"not'\"\t1\n", "\"note\"\t2\n", "\"nothing\"\t34\n", "\"notice\"\t5\n", @@ -5033,8 +5212,8 @@ "\"number\"\t5\n", "\"nurse\"\t3\n", "\"nursing\"\t3\n", - "\"o'clock\"\t3\n", "\"o\"\t3\n", + "\"o'clock\"\t3\n", "\"obliged\"\t3\n", "\"oblong\"\t1\n", "\"obstacle\"\t1\n", @@ -5057,8 +5236,8 @@ "\"oldest\"\t1\n", "\"on\"\t193\n", "\"once\"\t34\n", - "\"one's\"\t1\n", "\"one\"\t103\n", + "\"one's\"\t1\n", "\"ones\"\t1\n", "\"oneself\"\t1\n", "\"onions\"\t1\n", @@ -5074,8 +5253,8 @@ "\"or\"\t77\n", "\"orange\"\t1\n", "\"order\"\t3\n", - "\"ordered'\"\t1\n", "\"ordered\"\t3\n", + "\"ordered'\"\t1\n", "\"ordering\"\t2\n", "\"ornamented\"\t2\n", "\"other\"\t40\n", @@ -5232,8 +5411,8 @@ "\"prevent\"\t1\n", "\"printed\"\t1\n", "\"prison\"\t1\n", - "\"prisoner's\"\t1\n", "\"prisoner\"\t1\n", + "\"prisoner's\"\t1\n", "\"prize\"\t1\n", "\"prizes\"\t5\n", "\"proceed\"\t2\n", @@ -5260,8 +5439,8 @@ "\"pun\"\t1\n", "\"punching\"\t1\n", "\"punished\"\t1\n", - "\"puppy's\"\t1\n", "\"puppy\"\t6\n", + "\"puppy's\"\t1\n", "\"purple\"\t1\n", "\"purpose\"\t1\n", "\"purring\"\t2\n", @@ -5276,8 +5455,8 @@ "\"quarrel\"\t1\n", "\"quarrelled\"\t1\n", "\"quarrelling\"\t2\n", - "\"queen's\"\t7\n", "\"queen\"\t68\n", + "\"queen's\"\t7\n", "\"queens\"\t1\n", "\"queer\"\t12\n", "\"queerest\"\t1\n", @@ -5290,9 +5469,9 @@ "\"quietly\"\t5\n", "\"quite\"\t55\n", "\"quiver\"\t1\n", + "\"rabbit\"\t46\n", "\"rabbit'\"\t1\n", "\"rabbit's\"\t4\n", - "\"rabbit\"\t46\n", "\"rabbits\"\t1\n", "\"race\"\t6\n", "\"railway\"\t2\n", @@ -5497,10 +5676,10 @@ "\"sharks\"\t1\n", "\"sharp\"\t6\n", "\"sharply\"\t4\n", + "\"she\"\t540\n", "\"she'd\"\t2\n", "\"she'll\"\t3\n", "\"she's\"\t7\n", - "\"she\"\t540\n", "\"shedding\"\t1\n", "\"sheep\"\t1\n", "\"shelves\"\t2\n", @@ -5558,10 +5737,10 @@ "\"singers\"\t2\n", "\"singing\"\t2\n", "\"sink\"\t1\n", - "\"sir'\"\t1\n", "\"sir\"\t6\n", - "\"sister's\"\t1\n", + "\"sir'\"\t1\n", "\"sister\"\t8\n", + "\"sister's\"\t1\n", "\"sisters\"\t2\n", "\"sit\"\t8\n", "\"sits\"\t1\n", @@ -5576,8 +5755,8 @@ "\"skurried\"\t1\n", "\"sky\"\t5\n", "\"slate\"\t4\n", - "\"slates'll\"\t1\n", "\"slates\"\t7\n", + "\"slates'll\"\t1\n", "\"sleep\"\t6\n", "\"sleepy\"\t5\n", "\"slightest\"\t1\n", @@ -5782,9 +5961,9 @@ "\"than\"\t24\n", "\"thank\"\t4\n", "\"thanked\"\t1\n", + "\"that\"\t280\n", "\"that'll\"\t1\n", "\"that's\"\t34\n", - "\"that\"\t280\n", "\"thatched\"\t1\n", "\"the\"\t1642\n", "\"their\"\t52\n", @@ -5792,18 +5971,18 @@ "\"them\"\t88\n", "\"themselves\"\t3\n", "\"then\"\t94\n", - "\"there's\"\t24\n", "\"there\"\t75\n", + "\"there's\"\t24\n", "\"therefore\"\t1\n", "\"these\"\t14\n", + "\"they\"\t131\n", "\"they'd\"\t4\n", "\"they'll\"\t4\n", "\"they're\"\t13\n", "\"they've\"\t1\n", - "\"they\"\t131\n", "\"thick\"\t1\n", - "\"thimble'\"\t1\n", "\"thimble\"\t3\n", + "\"thimble'\"\t1\n", "\"thin\"\t1\n", "\"thing\"\t49\n", "\"things\"\t31\n", @@ -5883,8 +6062,8 @@ "\"trembled\"\t2\n", "\"trembling\"\t6\n", "\"tremulous\"\t1\n", - "\"trial's\"\t3\n", "\"trial\"\t7\n", + "\"trial's\"\t3\n", "\"trials\"\t1\n", "\"trickling\"\t1\n", "\"tricks\"\t1\n", @@ -5912,8 +6091,8 @@ "\"turned\"\t16\n", "\"turning\"\t12\n", "\"turns\"\t3\n", - "\"turtle's\"\t2\n", "\"turtle\"\t57\n", + "\"turtle's\"\t2\n", "\"turtles\"\t2\n", "\"tut\"\t2\n", "\"twelfth\"\t1\n", @@ -6034,28 +6213,34 @@ "\"waving\"\t5\n", "\"way\"\t56\n", "\"ways\"\t1\n", + "\"we\"\t30\n", "\"we're\"\t2\n", "\"we've\"\t2\n", - "\"we\"\t30\n", "\"weak\"\t2\n", "\"wearily\"\t1\n", "\"week\"\t3\n", - "\"weeks\"\t1\n", + "\"weeks\"\t1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\"welcome\"\t1\n", "\"well\"\t63\n", "\"went\"\t83\n", "\"wept\"\t1\n", - "\"were'\"\t1\n", "\"were\"\t84\n", + "\"were'\"\t1\n", "\"weren't\"\t1\n", "\"wet\"\t2\n", - "\"what's\"\t5\n", "\"what\"\t136\n", + "\"what's\"\t5\n", "\"whatever\"\t3\n", "\"when\"\t79\n", "\"whenever\"\t1\n", - "\"where's\"\t2\n", "\"where\"\t13\n", + "\"where's\"\t2\n", "\"whereupon\"\t1\n", "\"wherever\"\t2\n", "\"whether\"\t11\n", @@ -6070,8 +6255,8 @@ "\"whistling\"\t1\n", "\"white\"\t30\n", "\"whiting\"\t8\n", - "\"who's\"\t2\n", "\"who\"\t61\n", + "\"who's\"\t2\n", "\"whoever\"\t1\n", "\"whole\"\t13\n", "\"whom\"\t1\n", @@ -6084,8 +6269,8 @@ "\"wild\"\t2\n", "\"wildly\"\t2\n", "\"will\"\t33\n", - "\"william's\"\t1\n", "\"william\"\t7\n", + "\"william's\"\t1\n", "\"win\"\t1\n", "\"wind\"\t2\n", "\"window\"\t8\n", @@ -6102,9 +6287,9 @@ "\"wits\"\t1\n", "\"woke\"\t1\n", "\"woman\"\t2\n", - "\"won't'\"\t1\n", - "\"won't\"\t23\n", "\"won\"\t2\n", + "\"won't\"\t23\n", + "\"won't'\"\t1\n", "\"wonder\"\t18\n", "\"wondered\"\t1\n", "\"wonderful\"\t2\n", @@ -6150,11 +6335,11 @@ "\"yes\"\t13\n", "\"yesterday\"\t3\n", "\"yet\"\t25\n", + "\"you\"\t365\n", "\"you'd\"\t10\n", "\"you'll\"\t6\n", "\"you're\"\t23\n", "\"you've\"\t7\n", - "\"you\"\t365\n", "\"young\"\t5\n", "\"your\"\t62\n", "\"yours\"\t3\n", @@ -6168,10 +6353,118 @@ "name": "stderr", "output_type": "stream", "text": [ - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n", - "INFO:mrjob.runner:removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20150922.023122.602000\n" + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n" ] } ], @@ -6181,12 +6474,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { - "collapsed": true + "scrolled": true }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pyspark\n", + " Downloading https://files.pythonhosted.org/packages/5e/cb/d8ff49ba885e2c88b8cf2967edd84235ffa9ac301bffef657dfa5605a112/pyspark-2.3.2.tar.gz (211.9MB)\n", + "Collecting py4j==0.10.7 (from pyspark)\n", + " Downloading https://files.pythonhosted.org/packages/e3/53/c737818eb9a7dc32a7cd4f1396e787bd94200c3997c72c1dbe028587bd76/py4j-0.10.7-py2.py3-none-any.whl (197kB)\n", + "Building wheels for collected packages: pyspark\n", + " Running setup.py bdist_wheel for pyspark: started\n", + " Running setup.py bdist_wheel for pyspark: still running...\n", + " Running setup.py bdist_wheel for pyspark: finished with status 'done'\n", + " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\be\\7d\\34\\cd3cfbc75d8b6b6ae0658e5425348560b86d187fe3e53832cc\n", + "Successfully built pyspark\n", + "Installing collected packages: py4j, pyspark\n", + "Successfully installed py4j-0.10.7 pyspark-2.3.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "distributed 1.21.8 requires msgpack, which is not installed.\n", + "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", + "You are using pip version 10.0.1, however version 18.0 is available.\n", + "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "!pip install pyspark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to implement this is to use SPARK on your local box: \n", + "Need to load spark and pyspark to get all this working\n", + "\n", + "\n", + " \n", + "http://spark.apache.org/downloads.html\n", + "\n", + "\n", + " pyspark: pip install pyspark\n", + " \n", + " On windows you need to follow:\n", + " https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c\n", + " \n", + " \n", + "Test code From:\n", + "https://github.com/eBay/Spark/blob/master/examples/src/main/python/wordcount.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ + "# %load code\\pysparkcount.py\n", "#\n", "# Licensed to the Apache Software Foundation (ASF) under one or more\n", "# contributor license agreements. See the NOTICE file distributed with\n", @@ -6203,6 +6557,7 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "#\n", + "#https://github.com/apache/spark/blob/master/examples/src/main/python/wordcount.py\n", "\n", "from __future__ import print_function\n", "\n", @@ -6227,6 +6582,21 @@ "\n", " sc.stop()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Should be able to run using\n", + "%run code/pysparkcount.py data/canterbury/alice29.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -6245,7 +6615,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.15" } }, "nbformat": 4, From 01d8169541ba42f2a2bc4385927c2ce5697fdc88 Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 7 Oct 2018 10:23:12 -0400 Subject: [PATCH 06/10] WOrking on week 6lecture --- .../Homework 2/Homework2-python_tutorials | 34 - .../Homework6/Homework 6 - Streams.ipynb | 63 - .../Lecture 2 continued.ipynb | 368 - ...2-Introduction-to-Python-Programming.ipynb | 4094 ---------- .../Lecture2-Jupyter_and_python/mymodule.py | 29 - ...INKS to lectures on NUMPY and PANDAS.ipynb | 43 - .../NUMPY Tutorial-Copy1.ipynb | 241 - .../Data_Visualization.ipynb | 1536 ---- .../Links to Lecture4.ipynb | 39 - .../Data Visualization.ipynb | 303 - .../Lecture4-Matplotlib/data/baseball.csv | 101 - .../Lecture4-Matplotlib/data/cdystonia.csv | 632 -- Lectures/Lecture4-Matplotlib/data/titanic.xls | Bin 289792 -> 0 bytes Lectures/Lecture4-Matplotlib/normalvars.png | Bin 39074 -> 0 bytes .../MR_SORT-checkpoint.ipynb | 496 -- .../.ipynb_checkpoints/Tf_Id-checkpoint.ipynb | 147 - Lectures/Lecture5-MapReduce/.mrjob.conf | 11 - Lectures/Lecture5-MapReduce/MR_SORT.ipynb | 399 - .../Lecture5-MapReduce/MapReduce Intro.ipynb | 6623 ----------------- .../Lecture5-MapReduce/code/MRSortByInt.py | 15 - .../Lecture5-MapReduce/code/MRSortByString.py | 16 - .../code/MRWordFreqCount.py | 21 - .../code/MRWordFrequencyCount.py | 14 - .../Lecture5-MapReduce/code/pysparkcount.py | 40 - Lectures/Lecture5-MapReduce/code/untitled.txt | 0 Lectures/Lecture5-MapReduce/data/numbers.txt | 11 - Lectures/Lecture5-MapReduce/data/sortdata.txt | 8 - Lectures/Lecture5-MapReduce/data/vocab.txt | 1899 ----- 28 files changed, 17183 deletions(-) delete mode 100644 Homeworks/Homework 2/Homework2-python_tutorials delete mode 100644 Homeworks/Homework6/Homework 6 - Streams.ipynb delete mode 100644 Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb delete mode 100644 Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb delete mode 100644 Lectures/Lecture2-Jupyter_and_python/mymodule.py delete mode 100644 Lectures/Lecture3-numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb delete mode 100644 Lectures/Lecture3-numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb delete mode 100644 Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb delete mode 100644 Lectures/Lecture4-Matplotlib/Links to Lecture4.ipynb delete mode 100644 Lectures/Lecture4-Matplotlib/UnderConstruction/Data Visualization.ipynb delete mode 100644 Lectures/Lecture4-Matplotlib/data/baseball.csv delete mode 100644 Lectures/Lecture4-Matplotlib/data/cdystonia.csv delete mode 100644 Lectures/Lecture4-Matplotlib/data/titanic.xls delete mode 100644 Lectures/Lecture4-Matplotlib/normalvars.png delete mode 100644 Lectures/Lecture5-MapReduce/.ipynb_checkpoints/MR_SORT-checkpoint.ipynb delete mode 100644 Lectures/Lecture5-MapReduce/.ipynb_checkpoints/Tf_Id-checkpoint.ipynb delete mode 100644 Lectures/Lecture5-MapReduce/.mrjob.conf delete mode 100644 Lectures/Lecture5-MapReduce/MR_SORT.ipynb delete mode 100644 Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb delete mode 100644 Lectures/Lecture5-MapReduce/code/MRSortByInt.py delete mode 100644 Lectures/Lecture5-MapReduce/code/MRSortByString.py delete mode 100644 Lectures/Lecture5-MapReduce/code/MRWordFreqCount.py delete mode 100644 Lectures/Lecture5-MapReduce/code/MRWordFrequencyCount.py delete mode 100644 Lectures/Lecture5-MapReduce/code/pysparkcount.py delete mode 100644 Lectures/Lecture5-MapReduce/code/untitled.txt delete mode 100644 Lectures/Lecture5-MapReduce/data/numbers.txt delete mode 100644 Lectures/Lecture5-MapReduce/data/sortdata.txt delete mode 100644 Lectures/Lecture5-MapReduce/data/vocab.txt diff --git a/Homeworks/Homework 2/Homework2-python_tutorials b/Homeworks/Homework 2/Homework2-python_tutorials deleted file mode 100644 index 60ecc96..0000000 --- a/Homeworks/Homework 2/Homework2-python_tutorials +++ /dev/null @@ -1,34 +0,0 @@ -Make jupyter python 2 notebook pages that teaches: - -A. The difference between python lists and tuples -Lists are collections of data … more often homogenous or of more limited ranges of types. -The notion of tuple as a “record type” … sort of a lightweight object with attributes but no methods -The difference between list and tuple is that lists are mutable and tuples are immutable. - -Show how to use them in: -1) if statements -2) for loops -3) lambdas -be sure to demonstrate mutablity or immuability and issues that can be created - -B. Use of dictionaries - -1) creation of dict -2) access, modify, and delete dict items -3) merge dictionaires -4) loop over - - i. keys - ii. values - iii. Items - -For more than 3 points you can add meaningful sections to the two pages -and create more pages on other interesting aspects of basic python, for example random functions, regexp, map/reduce, file manipulations... -Be sure to name your file -LASTNAME_HOMEWORK2_LIST_AND_TUPLES_TUTORIAL_Fall_2018.ipynb -LASTNAME_HOMEWORK2_DICTIONARY_TUTORIAL_Fall_2018.ipynb -LASTNAME_HOMEWORK2_OTHER_TUTORIAL_Fall_2018.ipynb - -You can upload the files or the zip of all the files. -Or put them in your homework directory in your big-data-python-class -github repo and send me the url in the submissions or both. diff --git a/Homeworks/Homework6/Homework 6 - Streams.ipynb b/Homeworks/Homework6/Homework 6 - Streams.ipynb deleted file mode 100644 index 33ad4a7..0000000 --- a/Homeworks/Homework6/Homework 6 - Streams.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Homework 5\n", - "\n", - "Copy this notebook. Rename it as: YOURNAME-HW5-streams \n", - "\n", - "with your name replacing YOURNAME.\n", - "\n", - "Upload your completed jupyter notebook to elearning site as your homework submission. You can put this notebook on your github.\n", - "\n", - "5.1 Register for a stream of Twitter data\n", - "\n", - "5.2 Create a bloom filter classifying two days worth of twitters ( after removing stop words and urls )\n", - "\n", - "5.3 For another days worth of twitter data find the previous twitters that match in the bloom filter\n", - "(This means get two days of data in one file or directory , use that data for training the bloom filter, capture a different days data in a different file ( or do it in real time)and capture the match output then running the new twitter data through the filter.\n", - "\n", - "5.4 Plot a historgram of matches for each twitter in 5.3\n", - "\n", - "For the 4-5 grade.- Submit in a separate notebook - YourNAME-Homework5-Supplement\n", - "\n", - "1. Use a different machine learning training algorithm\n", - "2. Make a continous feed where you take two days of data and match the incoming stream ( do this for 5 days windowing the filter data)\n", - "3. Find new trends in the twitter feed (daily or hourly)\n", - "4. Or some other streaming exploration of your choosing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb b/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb deleted file mode 100644 index 914a480..0000000 --- a/Lectures/Lecture2-Jupyter_and_python/Lecture 2 continued.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lecture 2 continued" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### regular expressions\n", - "Provides a way to search text.\n", - "Looking for matching patterns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "import re\n", - "print all([\n", - " not re.match(\"a\",\"cat\"),\n", - " re.search(\"a\",\"cat\"),\n", - " not re.search(\"c\",\"dog\"),\n", - " 3 == len(re.split(\"[ab]\",\"carbs\")),\n", - " \"R-D-\" == re.sub(\"[0-9]\",\"-\",\"R2D2\")\n", - "]) # prints true if all are true" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Object-oriented programming\n", - "Creating classes of objects with data and methods(functions) that operate on the data." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "class Set:\n", - " def __init__(self, values=None):\n", - " \"\"\"This is the constructor\"\"\"\n", - " self.dict={}\n", - " if values is not None:\n", - " for value in values:\n", - " self.add(value)\n", - " def __repr__(self):\n", - " \"\"\"Sting to represent object in class like a to_string function\"\"\"\n", - " return \"set:\"+str(self.dict.keys())\n", - " def add(self, value):\n", - " self.dict[value] = True\n", - " def contains(self, value):\n", - " return value in self.dict \n", - " \n", - " def remove(self,value):\n", - " del self.dict[value]\n", - "\n", - "\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n" - ] - } - ], - "source": [ - "#using the class\n", - "s = Set([1,2,3])\n", - "s.add(4)\n", - "s.remove(3)\n", - "print s.contains(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "set:[1, 2, 4]\n" - ] - } - ], - "source": [ - "print s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functional Tools\n", - "sometimes you want to change behavior based on the passed value types" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8\n" - ] - } - ], - "source": [ - "def exp(base,power):\n", - " return base**power\n", - "\n", - "#exp(2,power)\n", - "def two_to_the(power):\n", - " return exp(2,power)\n", - "\n", - "print( two_to_the(3) )" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4, 10]\n" - ] - } - ], - "source": [ - "def multiply(x,y): return x*y\n", - "\n", - "products = map(multiply,[1,2],[4,5])\n", - "print products" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### enumerate\n", - "enumerates creates a tuple (index,element)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index:0 Item:a\n", - "index:1 Item:b\n" - ] - } - ], - "source": [ - "#example use\n", - "documents = [\"a\",\"b\"]\n", - "def do_something(index,item):\n", - " print \"index:\"+str(index)+\" Item:\"+item\n", - " \n", - "for i, document in enumerate(documents):\n", - " do_something(i,document)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index:0\n", - "index:1\n" - ] - } - ], - "source": [ - "def do_something(index ):\n", - " print \"index:\"+str(index) \n", - " \n", - "for i, _ in enumerate(documents): do_something(i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Zip and argument unpacking\n", - "zip forms two more more lists for other lists" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('a', 1), ('b', 2), ('c', 3)]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list1 = ['a','b','c']\n", - "list2 = [1,2,3]\n", - "zip(list1,list2)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('a', 'b', 'c')\n", - "(1, 2, 3)\n" - ] - } - ], - "source": [ - "pairs = [('a', 1), ('b', 2), ('c', 3)]\n", - "letters, numbers = zip(*pairs) # * performs argument unpacking\n", - "print letters\n", - "print numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### args and kwargs\n", - "higher order function support - functions on functions" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8\n" - ] - } - ], - "source": [ - "def doubler(f):\n", - " def g(x):\n", - " return 2 * f(x)\n", - " return g\n", - "\n", - "def f1(x):\n", - " return x+1\n", - "\n", - "g= doubler(f1)\n", - "\n", - "print g(3)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unamed args (1, 2)\n", - "Key word args {'key2': 'word2', 'key1': 'word1'}\n" - ] - } - ], - "source": [ - "def magic(*args, **kwargs):\n", - " print \"unamed args\", args\n", - " print \"Key word args\", kwargs\n", - " \n", - "magic(1,2,key1=\"word1\",key2=\"word2\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb b/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb deleted file mode 100644 index ec59145..0000000 --- a/Lectures/Lecture2-Jupyter_and_python/Lecture-2-Introduction-to-Python-Programming.ipynb +++ /dev/null @@ -1,4094 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to Python programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This crash course on python is take from two souces:\n", - "\n", - "[http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", - "and\n", - "Chapter 2 of the Datascience from scratch: First principles with python\n", - "Code from https://github.com/joelgrus/data-science-from-scratch\n", - "An official tutorial on ipyhthon: http://ipython.org/ipython-doc/2/interactive/tutorial.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python program files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Python code is usually stored in text files with the file ending \"`.py`\":\n", - "\n", - " myprogram.py\n", - "\n", - "* Every line in a Python program file is assumed to be a Python statement, or part thereof. \n", - "\n", - " * The only exception is comment lines, which start with the character `#` (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.\n", - "\n", - "\n", - "* To run our Python program from the command line we use:\n", - "\n", - " $ python myprogram.py\n", - "\n", - "* On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is concerned):\n", - "\n", - " #!/usr/bin/env python\n", - "\n", - " If we do, and if we additionally set the file script to be executable, we can run the program like this:\n", - "\n", - " $ myprogram.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example:\n", - "a. use some command line functions:\n", - "\n", - "in Windows:\n", - "\n", - "ls ..\\Scripts\\hello-world.py \n", - "\n", - "Mac or linux:\n", - "\n", - "ls ../Scripts/hello-world.py" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Volume in drive C is OS\n", - " Volume Serial Number is 7417-6934\n", - "\n", - " Directory of C:\\Users\\PS\\git\\big-data-python-class-2018\\Scripts\n", - "\n", - "09/05/2018 10:46 PM 19 hello-world.py\n", - "09/05/2018 10:46 PM 72 hello-world-in-swedish.py\n", - " 2 File(s) 91 bytes\n", - " 0 Dir(s) 321,133,596,672 bytes free\n" - ] - } - ], - "source": [ - "ls ..\\..\\Scripts\\hello-world*.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### built in magic commands start with #\n", - "\n", - "A good list of the commands are found in:\n", - "https://ipython.org/ipython-doc/3/interactive/magics.html" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "print \"hello world\"" - ] - } - ], - "source": [ - "%%sh \n", - "cat ../../Scripts/hello-world.py" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello world\n" - ] - } - ], - "source": [ - "!python ..\\..\\Scripts\\hello-world.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Character encoding" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The standard character encoding is ASCII, but we can use any other encoding, for example UTF-8. To specify that UTF-8 is used we include the special line\n", - "\n", - " # -*- coding: UTF-8 -*-\n", - "\n", - "at the top of the file." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#!/usr/bin/env python\r\n", - "# -*- coding: UTF-8 -*-\r\n", - "\r\n", - "print(\"Hej världen!\")" - ] - } - ], - "source": [ - "%%sh\n", - "cat ../../Scripts/hello-world-in-swedish.py" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hej världen!\n" - ] - } - ], - "source": [ - "!python ../../Scripts/hello-world-in-swedish.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other than these two *optional* lines in the beginning of a Python code file, no additional code is required for initializing a program. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Jupyter notebooks ( or old ipython notebooks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This file - an IPython notebook - does not follow the standard pattern with Python code in a text file. Instead, an IPython notebook is stored as a file in the [JSON](http://en.wikipedia.org/wiki/JSON) format. The advantage is that we can mix formatted text, Python code and code output. It requires the IPython notebook server to run it though, and therefore isn't a stand-alone Python program as described above. Other than that, there is no difference between the Python code that goes into a program file or an IPython notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modules" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most of the functionality in Python is provided by *modules*. The Python Standard Library is a large collection of modules that provides *cross-platform* implementations of common facilities such as access to the operating system, file I/O, string management, network communication, and much more." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " * The Python Language Reference: http://docs.python.org/2/reference/index.html\n", - " * The Python Standard Library: http://docs.python.org/2/library/\n", - "\n", - "To use a module in a Python program it first has to be imported. A module can be imported using the `import` statement. For example, to import the module `math`, which contains many standard mathematical functions, we can do:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import math" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This includes the whole module and makes it available for use later in the program. For example, we can do:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - } - ], - "source": [ - "import math\n", - "\n", - "x = math.cos(2 * math.pi)\n", - "\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix \"`math.`\" every time we use something from the `math` module:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - } - ], - "source": [ - "from math import *\n", - "\n", - "x = cos(2 * pi)\n", - "\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the `import math` pattern. This would elminate potentially confusing problems with name space collisions.\n", - "\n", - "As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character `*`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - } - ], - "source": [ - "from math import cos, pi\n", - "\n", - "x = cos(2 * pi)\n", - "\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Looking at what a module contains, and its documentation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a module is imported, we can list the symbols it provides using the `dir` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" - ] - } - ], - "source": [ - "import math\n", - "\n", - "print(dir(math))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And using the function `help` we can get a description of each function (almost .. not all functions have docstrings, as they are technically called, but the vast majority of functions are documented this way). " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function log in module math:\n", - "\n", - "log(...)\n", - " log(x[, base])\n", - " \n", - " Return the logarithm of x to the given base.\n", - " If the base not specified, returns the natural logarithm (base e) of x.\n", - "\n" - ] - } - ], - "source": [ - "help(math.log)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.302585092994046" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "log(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3.3219280948873626" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "log(10, 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use the `help` function directly on modules: Try\n", - "\n", - " help(math) \n", - "\n", - "Some very useful modules form the Python standard library are `os`, `sys`, `math`, `shutil`, `re`, `subprocess`, `multiprocessing`, `threading`. \n", - "\n", - "A complete lists of standard modules for Python 2 and Python 3 are available at http://docs.python.org/2/library/ and http://docs.python.org/3/library/, respectively." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Whitespacing formatting\n", - "Python uses indents and not bracing to delimit blocks of code\n", - "Makes code readable but it means you must be careful about your formatting" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n", - "4\n", - "done looping\n" - ] - } - ], - "source": [ - "for i in [1,2,3,4]:\n", - " print i\n", - "print \"done looping\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "White spacing is ignored inside parenteses and brackets:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "108" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "long_winded_computation = (1+2+ 3 + 4 + 5+ 6\n", - " + 7 + 8 + 9 + 10 + 11+\n", - " \n", - " 13 + 14 + 15)\n", - "long_winded_computation\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So use that to make you code easier to read" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_lists = [[1,2,3],[4,5,6],[7,8,9]]\n", - "easier_to_read_list_of_lists = [[1,2,3],\n", - " [4,5,6],\n", - " [7,8,9]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This may make it hard to cut and paste code since the indentation may have to adjusted to the block" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Variables and types" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Symbol names " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Variable names in Python can contain alphanumerical characters `a-z`, `A-Z`, `0-9` and some special characters such as `_`. Normal variable names must start with a letter. \n", - "\n", - "By convention, variable names start with a lower-case letter, and Class names start with a capital letter. \n", - "\n", - "In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:\n", - "\n", - " and, as, assert, break, class, continue, def, del, elif, else, except, \n", - " exec, finally, for, from, global, if, import, in, is, lambda, not, or,\n", - " pass, print, raise, return, try, while, with, yield\n", - "\n", - "Note: Be aware of the keyword `lambda`, which could easily be a natural variable name in a scientific program. But being a keyword, it cannot be used as a variable name." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functions\n", - "A function is a rule for taking zero or more inputes and return a corresponding output\n", - "Functions are first-class which means we can assign them to variables and pass them into functions just like any aguement" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def double(x):\n", - " \"\"\"this is where you put in the docstring that explains what the fuciton does:\n", - " this function multiplies its input by \"\"\"\n", - " return x * 2\n", - "\n", - "def apply_to_one(f):\n", - " \"\"\"calls the fuction f with 1 as its argument\"\"\"\n", - " return f(1)\n", - "\n", - "apply_to_one(double)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can create short anonymous fuctions or lambdas or even assign lambdas to variables but better to use a def" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "apply_to_one(lambda x: x + 4 )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "another_double = lambda x: 2 * x\n", - "def yet_another_double(x): return 2 * x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Assignment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "The assignment operator in Python is `=`. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.\n", - "\n", - "Assigning a value to a new variable creates the variable:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# variable assignments\n", - "x = 1.0\n", - "my_variable = 12.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value that was assigned to it." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "float" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we assign a new value to a variable, its type can change." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "x = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "int" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we try to use a variable that has not yet been defined we get an `NameError`:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'y' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined" - ] - } - ], - "source": [ - "print(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fundamental types" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "int" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# integers\n", - "x = 1\n", - "type(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "float" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# float\n", - "x = 1.0\n", - "type(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bool" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# boolean\n", - "b1 = True\n", - "b2 = False\n", - "\n", - "type(b1)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "complex" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# complex numbers: note the use of `j` to specify the imaginary part\n", - "x = 1.0 - 1.0j\n", - "type(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1-1j)\n" - ] - } - ], - "source": [ - "print(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1.0, -1.0)\n" - ] - } - ], - "source": [ - "print(x.real, x.imag)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Type utility functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The module `types` contains a number of type name definitions that can be used to test if variables are of certain types:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['BooleanType', 'BufferType', 'BuiltinFunctionType', 'BuiltinMethodType', 'ClassType', 'CodeType', 'ComplexType', 'DictProxyType', 'DictType', 'DictionaryType', 'EllipsisType', 'FileType', 'FloatType', 'FrameType', 'FunctionType', 'GeneratorType', 'GetSetDescriptorType', 'InstanceType', 'IntType', 'LambdaType', 'ListType', 'LongType', 'MemberDescriptorType', 'MethodType', 'ModuleType', 'NoneType', 'NotImplementedType', 'ObjectType', 'SliceType', 'StringType', 'StringTypes', 'TracebackType', 'TupleType', 'TypeType', 'UnboundMethodType', 'UnicodeType', 'XRangeType', '__all__', '__builtins__', '__doc__', '__file__', '__name__', '__package__']\n" - ] - } - ], - "source": [ - "import types\n", - "\n", - "# print all types defined in the `types` module\n", - "print(dir(types))" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = 1.0\n", - "\n", - "# check if the variable x is a float\n", - "type(x) is float" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check if the variable x is an int\n", - "type(x) is int" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use the `isinstance` method for testing types of variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(x, float)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Type casting" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1.5, )\n" - ] - } - ], - "source": [ - "x = 1.5\n", - "\n", - "print(x, type(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, )\n" - ] - } - ], - "source": [ - "x = int(x)\n", - "\n", - "print(x, type(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "((1+0j), )\n" - ] - } - ], - "source": [ - "z = complex(x)\n", - "\n", - "print(z, type(z))" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "can't convert complex to float", - "output_type": "error", - "traceback": [ - "\u001b[0;31m\u001b[0m", - "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: can't convert complex to float" - ] - } - ], - "source": [ - "x = float(z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Complex variables cannot be cast to floats or integers. We need to use `z.real` or `z.imag` to extract the part of the complex number we want:" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1.0, ' -> ', True, )\n", - "(0.0, ' -> ', False, )\n" - ] - } - ], - "source": [ - "y = bool(z.real)\n", - "\n", - "print(z.real, \" -> \", y, type(y))\n", - "\n", - "y = bool(z.imag)\n", - "\n", - "print(z.imag, \" -> \", y, type(y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operators and comparisons" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most operators and comparisons in Python work as one would expect:\n", - "\n", - "* Arithmetic operators `+`, `-`, `*`, `/`, `//` (integer division), '**' power\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, -1, 2, 0)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1 + 2, 1 - 2, 1 * 2, 1 / 2" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(3.0, -1.0, 2.0, 0.5)" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1.0 + 2.0, 1.0 - 2.0, 1.0 * 2.0, 1.0 / 2.0" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Integer division of float numbers\n", - "3.0 // 2.0" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Note! The power operators in python isn't ^, but **\n", - "2 ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: The `/` operator always performs a floating point division in Python 3.x.\n", - "This is not true in Python 2.x, where the result of `/` is always an integer if the operands are integers.\n", - "to be more specific, `1/2 = 0.5` (`float`) in Python 3.x, and `1/2 = 0` (`int`) in Python 2.x (but `1.0/2 = 0.5` in Python 2.x)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The boolean operators are spelled out as the words `and`, `not`, `or`. " - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "True and False" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "not False" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "True or False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Comparison operators `>`, `<`, `>=` (greater or equal), `<=` (less or equal), `==` equality, `is` identical." - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(True, False)" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2 > 1, 2 < 1" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(False, False)" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2 > 2, 2 < 2" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(True, True)" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "2 >= 2, 2 <= 2" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# equality\n", - "[1,2] == [1,2]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# objects identical?\n", - "l1 = l2 = [1,2]\n", - "\n", - "l1 is l2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compound types: Strings, List and dictionaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Strings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Strings are the variable type that is used for storing text messages. " - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = \"Hello world\"\n", - "type(s)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# length of the string: the number of characters\n", - "len(s)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello test\n" - ] - } - ], - "source": [ - "# replace a substring in a string with somethign else\n", - "s2 = s.replace(\"world\", \"test\")\n", - "print(s2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can index a character in a string using `[]`:" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'H'" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Heads up MATLAB users:** Indexing start at 0!\n", - "\n", - "We can extract a part of a string using the syntax `[start:stop]`, which extracts characters between index `start` and `stop` -1 (the character at index `stop` is not included):" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello'" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[0:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'o'" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[4:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we omit either (or both) of `start` or `stop` from `[start:stop]`, the default is the beginning and the end of the string, respectively:" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello'" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'world'" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[6:]" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello world'" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also define the step size using the syntax `[start:end:step]` (the default value for `step` is 1, as we saw above):" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello world'" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[::1]" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hlowrd'" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s[::2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This technique is called *slicing*. Read more about the syntax here: http://docs.python.org/release/2.7.3/library/functions.html?highlight=slice#slice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python has a very rich set of functions for text processing. See for example http://docs.python.org/2/library/string.html for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### String formatting examples" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('str1', 'str2', 'str3')\n" - ] - } - ], - "source": [ - "print(\"str1\", \"str2\", \"str3\") # The print statement concatenates strings with a space" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('str1', 1.0, False, -1j)\n" - ] - } - ], - "source": [ - "print(\"str1\", 1.0, False, -1j) # The print statements converts all arguments to strings" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "str1str2str3\n" - ] - } - ], - "source": [ - "print(\"str1\" + \"str2\" + \"str3\") # strings added with + are concatenated without space" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "value = 1.000000\n" - ] - } - ], - "source": [ - "print(\"value = %f\" % 1.0) # we can use C-style string formatting" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "value1 = 3.14. value2 = 1\n" - ] - } - ], - "source": [ - "# this formatting creates a string\n", - "s2 = \"value1 = %.2f. value2 = %d\" % (3.1415, 1.5)\n", - "\n", - "print(s2)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "value1 = 3.1415, value2 = 1.5\n" - ] - } - ], - "source": [ - "# alternative, more intuitive way of formatting a string \n", - "s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n", - "\n", - "print(s3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lists are very similar to strings, except that each element can be of any type.\n", - "Other languages call these arrays, simply an ordered collection of things.\n", - "\n", - "The syntax for creating lists in Python is `[...]`:" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[1, 2, 3, 4]\n" - ] - } - ], - "source": [ - "l = [1,2,3,4]\n", - "\n", - "print(type(l))\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the same slicing techniques to manipulate lists as we could use on strings:" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 2, 3, 4]\n", - "[2, 3]\n", - "[1, 3]\n" - ] - } - ], - "source": [ - "print(l)\n", - "\n", - "print(l[1:3])\n", - "\n", - "print(l[::2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Heads up MATLAB users:** Indexing starts at 0!" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "l[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Elements in a list do not all have to be of the same type:" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 'a', 1.0, (1-1j)]\n" - ] - } - ], - "source": [ - "l = [1, 'a', 1.0, 1-1j]\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python lists can be inhomogeneous and arbitrarily nested:" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, [2, [3, [4, [5]]]]]" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nested_list = [1, [2, [3, [4, [5]]]]]\n", - "\n", - "nested_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example the `range` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "start = 10\n", - "stop = 30\n", - "step = 2\n", - "\n", - "range(start, stop, step)" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# in python 3 range generates an interator, which can be converted to a list using 'list(...)'.\n", - "# It has no effect in python 2\n", - "list(range(start, stop, step))" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(range(-10, 10))" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hello world'" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# convert a string to a list by type casting:\n", - "s2 = list(s)\n", - "\n", - "s2" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']\n" - ] - } - ], - "source": [ - "# sorting lists\n", - "s2.sort()\n", - "\n", - "print(s2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Adding, inserting, modifying, and removing elements from lists" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['A', 'd', 'd']\n" - ] - } - ], - "source": [ - "# create a new empty list\n", - "l = []\n", - "\n", - "# add an elements using `append`\n", - "l.append(\"A\")\n", - "l.append(\"d\")\n", - "l.append(\"d\")\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can modify lists by assigning new values to elements in the list. In technical jargon, lists are *mutable*." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['A', 'p', 'p']\n" - ] - } - ], - "source": [ - "l[1] = \"p\"\n", - "l[2] = \"p\"\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['A', 'd', 'd']\n" - ] - } - ], - "source": [ - "l[1:3] = [\"d\", \"d\"]\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Insert an element at an specific index using `insert`" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['i', 'n', 's', 'e', 'r', 't', 'A', 'd', 'd']\n" - ] - } - ], - "source": [ - "l.insert(0, \"i\")\n", - "l.insert(1, \"n\")\n", - "l.insert(2, \"s\")\n", - "l.insert(3, \"e\")\n", - "l.insert(4, \"r\")\n", - "l.insert(5, \"t\")\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remove first element with specific value using 'remove'" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['i', 'n', 's', 'e', 'r', 't', 'd', 'd']\n" - ] - } - ], - "source": [ - "l.remove(\"A\")\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remove an element at a specific location using `del`:" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['i', 'n', 's', 'e', 'r', 't']\n" - ] - } - ], - "source": [ - "del l[7]\n", - "del l[6]\n", - "\n", - "print(l)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See `help(list)` for more details, or read the online documentation " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tuples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tuples are like lists, except that they cannot be modified once created, that is they are *immutable*. \n", - "\n", - "In Python, tuples are created using the syntax `(..., ..., ...)`, or even `..., ...`:" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "((10, 20), )\n" - ] - } - ], - "source": [ - "point = (10, 20)\n", - "\n", - "print(point, type(point))" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "((10, 20), )\n" - ] - } - ], - "source": [ - "point = 10, 20\n", - "\n", - "print(point, type(point))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can unpack a tuple by assigning it to a comma-separated list of variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('x =', 10)\n", - "('y =', 20)\n" - ] - } - ], - "source": [ - "x, y = point\n", - "\n", - "print(\"x =\", x)\n", - "print(\"y =\", y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we try to assign a new value to an element in a tuple we get an error:" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'tuple' object does not support item assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m\u001b[0m", - "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpoint\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" - ] - } - ], - "source": [ - "point[0] = 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dictionaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is `{key1 : value1, ...}`:" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}\n" - ] - } - ], - "source": [ - "params = {\"parameter1\" : 1.0,\n", - " \"parameter2\" : 2.0,\n", - " \"parameter3\" : 3.0,}\n", - "\n", - "print(type(params))\n", - "print(params)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "parameter1 = 1.0\n", - "parameter2 = 2.0\n", - "parameter3 = 3.0\n" - ] - } - ], - "source": [ - "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", - "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", - "print(\"parameter3 = \" + str(params[\"parameter3\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "parameter1 = A\n", - "parameter2 = B\n", - "parameter3 = 3.0\n", - "parameter4 = D\n" - ] - } - ], - "source": [ - "params[\"parameter1\"] = \"A\"\n", - "params[\"parameter2\"] = \"B\"\n", - "\n", - "# add a new entry\n", - "params[\"parameter4\"] = \"D\"\n", - "\n", - "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", - "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", - "print(\"parameter3 = \" + str(params[\"parameter3\"]))\n", - "print(\"parameter4 = \" + str(params[\"parameter4\"]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Control Flow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conditional statements: if, elif, else" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Python syntax for conditional execution of code uses the keywords `if`, `elif` (else if), `else`:" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "statement1 and statement2 are False\n" - ] - } - ], - "source": [ - "statement1 = False\n", - "statement2 = False\n", - "\n", - "if statement1:\n", - " print(\"statement1 is True\")\n", - " \n", - "elif statement2:\n", - " print(\"statement2 is True\")\n", - " \n", - "else:\n", - " print(\"statement1 and statement2 are False\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level. \n", - "\n", - "Compare to the equivalent C code:\n", - "\n", - " if (statement1)\n", - " {\n", - " printf(\"statement1 is True\\n\");\n", - " }\n", - " else if (statement2)\n", - " {\n", - " printf(\"statement2 is True\\n\");\n", - " }\n", - " else\n", - " {\n", - " printf(\"statement1 and statement2 are False\\n\");\n", - " }\n", - "\n", - "In C blocks are defined by the enclosing curly brakets `{` and `}`. And the level of indentation (white space before the code statements) does not matter (completely optional). \n", - "\n", - "But in Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Examples:" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "both statement1 and statement2 are True\n" - ] - } - ], - "source": [ - "statement1 = statement2 = True\n", - "\n", - "if statement1:\n", - " if statement2:\n", - " print(\"both statement1 and statement2 are True\")" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "expected an indented block (, line 4)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m print(\"both statement1 and statement2 are True\") # this line is not properly indented\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n" - ] - } - ], - "source": [ - "# Bad indentation!\n", - "if statement1:\n", - " if statement2:\n", - " print(\"both statement1 and statement2 are True\") # this line is not properly indented" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "statement1 = False \n", - "\n", - "if statement1:\n", - " print(\"printed if statement1 is True\")\n", - " \n", - " print(\"still inside the if block\")" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "now outside the if block\n" - ] - } - ], - "source": [ - "if statement1:\n", - " print(\"printed if statement1 is True\")\n", - " \n", - "print(\"now outside the if block\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loops" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Python, loops can be programmed in a number of different ways. The most common is the `for` loop, which is used together with iterable objects, such as lists. The basic syntax is:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **`for` loops**:" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n" - ] - } - ], - "source": [ - "for x in [1,2,3]:\n", - " print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `for` loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the `for` loop. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n" - ] - } - ], - "source": [ - "for x in range(4): # by default range start at 0\n", - " print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: `range(4)` does not include 4 !" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-3\n", - "-2\n", - "-1\n", - "0\n", - "1\n", - "2\n" - ] - } - ], - "source": [ - "for x in range(-3,3):\n", - " print(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "scientific\n", - "computing\n", - "with\n", - "python\n" - ] - } - ], - "source": [ - "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n", - " print(word)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To iterate over key-value pairs of a dictionary:" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "parameter4 = D\n", - "parameter1 = A\n", - "parameter3 = 3.0\n", - "parameter2 = B\n" - ] - } - ], - "source": [ - "for key, value in params.items():\n", - " print(key + \" = \" + str(value))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the `enumerate` function for this:" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0, -3)\n", - "(1, -2)\n", - "(2, -1)\n", - "(3, 0)\n", - "(4, 1)\n", - "(5, 2)\n" - ] - } - ], - "source": [ - "for idx, x in enumerate(range(-3,3)):\n", - " print(idx, x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List comprehensions: Creating lists using `for` loops:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A convenient and compact way to initialize lists:" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 1, 4, 9, 16]\n" - ] - } - ], - "source": [ - "l1 = [x**2 for x in range(0,5)]\n", - "\n", - "print(l1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `while` loops:" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "done\n" - ] - } - ], - "source": [ - "i = 0\n", - "\n", - "while i < 5:\n", - " print(i)\n", - " \n", - " i = i + 1\n", - " \n", - "print(\"done\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the `print(\"done\")` statement is not part of the `while` loop body because of the difference in indentation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A function in Python is defined using the keyword `def`, followed by a function name, a signature within parentheses `()`, and a colon `:`. The following code, with one additional level of indentation, is the function body." - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def func0(): \n", - " print(\"test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test\n" - ] - } - ], - "source": [ - "func0()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optionally, but highly recommended, we can define a so called \"docstring\", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body." - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def func1(s):\n", - " \"\"\"\n", - " Print a string 's' and tell how many characters it has \n", - " \"\"\"\n", - " \n", - " print(s + \" has \" + str(len(s)) + \" characters\")" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function func1 in module __main__:\n", - "\n", - "func1(s)\n", - " Print a string 's' and tell how many characters it has\n", - "\n" - ] - } - ], - "source": [ - "help(func1)" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test has 4 characters\n" - ] - } - ], - "source": [ - "func1(\"test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Functions that returns a value use the `return` keyword:" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def square(x):\n", - " \"\"\"\n", - " Return the square of x.\n", - " \"\"\"\n", - " return x ** 2" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "16" - ] - }, - "execution_count": 143, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "square(4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can return multiple values from a function using tuples (see above):" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def powers(x):\n", - " \"\"\"\n", - " Return a few powers of x.\n", - " \"\"\"\n", - " return x ** 2, x ** 3, x ** 4" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9, 27, 81)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "powers(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27\n" - ] - } - ], - "source": [ - "x2, x3, x4 = powers(3)\n", - "\n", - "print(x3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Default argument and keyword arguments" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In a definition of a function, we can give default values to the arguments the function takes:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def myfunc(x, p=2, debug=False):\n", - " if debug:\n", - " print(\"evaluating myfunc for x = \" + str(x) + \" using exponent p = \" + str(p))\n", - " return x**p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we don't provide a value of the `debug` argument when calling the the function `myfunc` it defaults to the value provided in the function definition:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "25" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "myfunc(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating myfunc for x = 5 using exponent p = 2\n" - ] - }, - { - "data": { - "text/plain": [ - "25" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "myfunc(5, debug=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called *keyword* arguments, and is often very useful in functions that takes a lot of optional arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating myfunc for x = 7 using exponent p = 3\n" - ] - }, - { - "data": { - "text/plain": [ - "343" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "myfunc(p=3, debug=True, x=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unnamed functions (lambda function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Python we can also create unnamed functions, using the `lambda` keyword:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "f1 = lambda x: x**2\n", - " \n", - "# is equivalent to \n", - "\n", - "def f2(x):\n", - " return x**2" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4, 4)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f1(2), f2(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This technique is useful for example when we want to pass a simple function as an argument to another function, like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9, 4, 1, 0, 1, 4, 9]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# map is a built-in python function\n", - "map(lambda x: x**2, range(-3,4))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9, 4, 1, 0, 1, 4, 9]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# in python 3 we can use `list(...)` to convert the iterator to an explicit list\n", - "list(map(lambda x: x**2, range(-3,4)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object. \n", - "\n", - "In Python a class can contain *attributes* (variables) and *methods* (functions).\n", - "\n", - "A class is defined almost like a function, but using the `class` keyword, and the class definition usually contains a number of class method definitions (a function in a class).\n", - "\n", - "* Each class method should have an argument `self` as its first argument. This object is a self-reference.\n", - "\n", - "* Some class method names have special meaning, for example:\n", - "\n", - " * `__init__`: The name of the method that is invoked when the object is first created.\n", - " * `__str__` : A method that is invoked when a simple string representation of the class is needed, as for example when printed.\n", - " * There are many more, see http://docs.python.org/2/reference/datamodel.html#special-method-names" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "class Point:\n", - " \"\"\"\n", - " Simple class for representing a point in a Cartesian coordinate system.\n", - " \"\"\"\n", - " \n", - " def __init__(self, x, y):\n", - " \"\"\"\n", - " Create a new Point at x, y.\n", - " \"\"\"\n", - " self.x = x\n", - " self.y = y\n", - " \n", - " def translate(self, dx, dy):\n", - " \"\"\"\n", - " Translate the point by dx and dy in the x and y direction.\n", - " \"\"\"\n", - " self.x += dx\n", - " self.y += dy\n", - " \n", - " def __str__(self):\n", - " return(\"Point at [%f, %f]\" % (self.x, self.y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a new instance of a class:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Point at [0.000000, 0.000000]\n" - ] - } - ], - "source": [ - "p1 = Point(0, 0) # this will invoke the __init__ method in the Point class\n", - "\n", - "print(p1) # this will invoke the __str__ method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To invoke a class method in the class instance `p`:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Point at [0.250000, 1.500000]\n", - "Point at [1.000000, 1.000000]\n" - ] - } - ], - "source": [ - "p2 = Point(1, 1)\n", - "\n", - "p1.translate(0.25, 1.5)\n", - "\n", - "print(p1)\n", - "print(p2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that calling class methods can modifiy the state of that particular class instance, but does not effect other class instances or any global variables.\n", - "\n", - "That is one of the nice things about object-oriented design: code such as functions and related variables are grouped in separate and independent entities. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modules" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the most important concepts in good programming is to reuse code and avoid repetitions.\n", - "\n", - "The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program is greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot. \n", - "\n", - "Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions and classes in a module. A python module is defined in a python file (with file-ending `.py`), and it can be made accessible to other Python modules and programs using the `import` statement. \n", - "\n", - "Consider the following example: the file `mymodule.py` contains simple example implementations of a variable, function and a class:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing mymodule.py\n" - ] - } - ], - "source": [ - "%%file mymodule.py\n", - "\"\"\"\n", - "Example of a python module. Contains a variable called my_variable,\n", - "a function called my_function, and a class called MyClass.\n", - "\"\"\"\n", - "\n", - "my_variable = 0\n", - "\n", - "def my_function():\n", - " \"\"\"\n", - " Example function\n", - " \"\"\"\n", - " return my_variable\n", - " \n", - "class MyClass:\n", - " \"\"\"\n", - " Example class.\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " self.variable = my_variable\n", - " \n", - " def set_variable(self, new_value):\n", - " \"\"\"\n", - " Set self.variable to a new value\n", - " \"\"\"\n", - " self.variable = new_value\n", - " \n", - " def get_variable(self):\n", - " return self.variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can import the module `mymodule` into our Python program using `import`:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "import mymodule" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `help(module)` to get a summary of what the module provides:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on module mymodule:\n", - "\n", - "NAME\n", - " mymodule\n", - "\n", - "FILE\n", - " c:\\users\\ps\\git\\big-data-python-class\\lectures\\lecture2-jupyter_and_python\\mymodule.py\n", - "\n", - "DESCRIPTION\n", - " Example of a python module. Contains a variable called my_variable,\n", - " a function called my_function, and a class called MyClass.\n", - "\n", - "CLASSES\n", - " MyClass\n", - " \n", - " class MyClass\n", - " | Example class.\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self)\n", - " | \n", - " | get_variable(self)\n", - " | \n", - " | set_variable(self, new_value)\n", - " | Set self.variable to a new value\n", - "\n", - "FUNCTIONS\n", - " my_function()\n", - " Example function\n", - "\n", - "DATA\n", - " my_variable = 0\n", - "\n", - "\n" - ] - } - ], - "source": [ - "help(mymodule)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mymodule.my_variable" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mymodule.my_function() " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_class = mymodule.MyClass() \n", - "my_class.set_variable(10)\n", - "my_class.get_variable()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we make changes to the code in `mymodule.py`, we need to reload it using `reload`:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reload(mymodule) # works only in python 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exceptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Python errors are managed with a special language construct called \"Exceptions\". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To generate an exception we can use the `raise` statement, which takes an argument that must be an instance of the class `BaseException` or a class derived from it. " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "ename": "Exception", - "evalue": "description of the error", - "output_type": "error", - "traceback": [ - "\u001b[0;31m\u001b[0m", - "\u001b[0;31mException\u001b[0mTraceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"description of the error\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mException\u001b[0m: description of the error" - ] - } - ], - "source": [ - "raise Exception(\"description of the error\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A typical use of exceptions is to abort functions when some error condition occurs, for example:\n", - "\n", - " def my_function(arguments):\n", - " \n", - " if not verify(arguments):\n", - " raise Exception(\"Invalid arguments\")\n", - " \n", - " # rest of the code goes here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the `try` and `except` statements:\n", - "\n", - " try:\n", - " # normal code goes here\n", - " except:\n", - " # code for error handling goes here\n", - " # this code is not executed unless the code\n", - " # above generated an error\n", - "\n", - "For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test\n", - "Caught an exception\n" - ] - } - ], - "source": [ - "try:\n", - " print(\"test\")\n", - " # generate an error: the variable test is not defined\n", - " print(test)\n", - "except:\n", - " print(\"Caught an exception\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get information about the error, we can access the `Exception` class instance that describes the exception by using for example:\n", - "\n", - " except Exception as e:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test\n", - "Caught an exception:name 'test' is not defined\n" - ] - } - ], - "source": [ - "try:\n", - " print(\"test\")\n", - " # generate an error: the variable test is not defined\n", - " print(test)\n", - "except Exception as e:\n", - " print(\"Caught an exception:\" + str(e))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Counter\n", - "\n", - "Turns a sequenc of values into a defaultdict(int)-like object mappings keys to counts ( good for histograms )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Counter({0: 2, 1: 1, 2: 1})\n" - ] - } - ], - "source": [ - "import collections as coll\n", - "c = coll.Counter([0,1,2,0])\n", - "print c" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Counter({'a': 2, 'different': 1, 'least': 1, 'words': 1, 'This': 1, 'of': 1, 'is': 1, 'but': 1, 'one': 1, 'duplicate': 1, 'at': 1, 'lot': 1, 'test': 1, 'document': 1, 'with': 1})\n" - ] - } - ], - "source": [ - "document = \"This is a test document with a lot of different words but at least one duplicate\".split(\" \")\n", - "word_counts = coll.Counter(document)\n", - "print word_counts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sorting" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X=[1, 2, 3, 4]\n", - "y=[1, 2, 3, 4]\n" - ] - } - ], - "source": [ - "x = [4,1,2,3]\n", - "y=sorted(x)\n", - "x.sort()\n", - "print \"X=\"+str(x)\n", - "print \"y=\"+str(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List Comprehensions" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 4, 16]\n" - ] - } - ], - "source": [ - "even_numbers = [x for x in range(5) if x %2 == 0]\n", - "squares = [x * x for x in range(5)]\n", - "even_squared = [x*x for x in even_numbers]\n", - "\n", - "print even_squared" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}\n" - ] - } - ], - "source": [ - "square_dict = { x: x*x for x in range(5) }\n", - "print square_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generators and Iterators" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(10) # works greate but sometimes we need a single number or set when we need them" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "2\n", - "4\n", - "6\n", - "8\n", - "10\n", - "12\n", - "14\n", - "16\n", - "18\n" - ] - } - ], - "source": [ - "def lazy_range(n): \n", - " \"\"\"a lazy version of ther range function to only create the value when evaluating it import when the range gets really big\"\"\"\n", - " i = 0\n", - " while i < n :\n", - " yield i\n", - " i += 1\n", - " \n", - "for i in lazy_range(10):\n", - " print str(double(i))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You need to recreate the the lazy generator to use it a second time or use a list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "### randomness" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.666163620485913, 0.6052510406021051, 0.0846219126472334, 0.7502505317712742]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import random as rand\n", - "[rand.random() for _ in range(4)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* [Lecuture 2 continued](./Lecture%202%20continued.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further reading" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* http://www.python.org - The official web page of the Python programming language.\n", - "* http://www.python.org/dev/peps/pep-0008 - Style guide for Python programming. Highly recommended. \n", - "* http://www.greenteapress.com/thinkpython/ - A free book on Python programming.\n", - "* [Python Essential Reference](http://www.amazon.com/Python-Essential-Reference-4th-Edition/dp/0672329786) - A good reference book on Python programming.\n", - "* [ZEN of Python](https://legacy.python.org/dev/peps/pep-0020)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Zen of Python, by Tim Peters\n", - "\n", - "Beautiful is better than ugly.\n", - "Explicit is better than implicit.\n", - "Simple is better than complex.\n", - "Complex is better than complicated.\n", - "Flat is better than nested.\n", - "Sparse is better than dense.\n", - "Readability counts.\n", - "Special cases aren't special enough to break the rules.\n", - "Although practicality beats purity.\n", - "Errors should never pass silently.\n", - "Unless explicitly silenced.\n", - "In the face of ambiguity, refuse the temptation to guess.\n", - "There should be one-- and preferably only one --obvious way to do it.\n", - "Although that way may not be obvious at first unless you're Dutch.\n", - "Now is better than never.\n", - "Although never is often better than *right* now.\n", - "If the implementation is hard to explain, it's a bad idea.\n", - "If the implementation is easy to explain, it may be a good idea.\n", - "Namespaces are one honking great idea -- let's do more of those!\n" - ] - } - ], - "source": [ - "import this" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Lecture2-Jupyter_and_python/mymodule.py b/Lectures/Lecture2-Jupyter_and_python/mymodule.py deleted file mode 100644 index 4e7017f..0000000 --- a/Lectures/Lecture2-Jupyter_and_python/mymodule.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -Example of a python module. Contains a variable called my_variable, -a function called my_function, and a class called MyClass. -""" - -my_variable = 0 - -def my_function(): - """ - Example function - """ - return my_variable - -class MyClass: - """ - Example class. - """ - - def __init__(self): - self.variable = my_variable - - def set_variable(self, new_value): - """ - Set self.variable to a new value - """ - self.variable = new_value - - def get_variable(self): - return self.variable \ No newline at end of file diff --git a/Lectures/Lecture3-numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb b/Lectures/Lecture3-numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb deleted file mode 100644 index 0811d25..0000000 --- a/Lectures/Lecture3-numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# Lecture 3. NUMPY and PANDAS\n", - "\n", - "\n", - "\n", - "For NUMPY we will use: Lecture-2-Numpy.ipynb \n", - "from https://github.com/jrjohansson/scientific-python-lectures\n", - "\n", - "\n", - "For Pandas we will use\": 1. Introduction to Pandas.ipynb\n", - "https://github.com/fonnesbeck/statistical-analysis-python-tutorial" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Lectures/Lecture3-numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb b/Lectures/Lecture3-numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb deleted file mode 100644 index c34effa..0000000 --- a/Lectures/Lecture3-numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NUMPY Tutorial \n", - "\n", - "https://docs.scipy.org/doc/numpy-dev/,\n", - "https://www.python-course.eu/numpy.php/\n", - "\n", - "## What is NUMPY?\n", - "NumPy is an acronym for \"Numeric Python\" or \"Numerical Python\"\n", - "A python package that supports the creation and manipulation of n-dimensional arrays (ndarray) of homogeneous data types.\n", - "Numpy objects are fixed at creation, adjusting the size creates a new object.\n", - "The objects in the array must all have the same type ( You can defeat this somewhat in that if you create numbpy array of lists, the lists themselves can contain different objects)\n", - "\n", - "This means that mathematical operations can happen quickly since the size and type of the array elements are fixed.\n", - "Numbpy was designed for using large arrays and matrices.\n", - "SciPy (Scientific Python) extends the use of the numby ndarray's with advanced fucntions: minimization, regression, Fourier-transformation, ...\n", - "\n", - "\n", - "## Setting up\n", - "\n", - "If you already have python and pip installed the use:\n", - " \n", - "pip install numpy scipy\n", - "\n", - "Or follow the intstructions from http://www.numpy.org/ or https://docs.scipy.org/doc/numpy-dev/user/index.html#user\n", - "\n", - "## Using NUMBPY\n", - "Import it to use it" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 25.3 24.8 26.9 23.9]\n" - ] - } - ], - "source": [ - "#import numbpy\n", - "import numpy as np\n", - "# One dimensional array\n", - "cvalues = [25.3, 24.8, 26.9, 23.9]\n", - "c=np.array(cvalues)\n", - "print (c)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "list expression result=[77.54, 76.64, 80.42, 75.02]\n", - "numbpy broadcast result=[ 77.54 76.64 80.42 75.02]\n" - ] - } - ], - "source": [ - "# Assume they are celsius measurements that we want to convert to Fahrenheit\n", - "# List expression evaluation\n", - "print \"list expression result=\"+str([ x*9/5+32 for x in cvalues] )\n", - "print \"numbpy broadcast result=\"+str(c*9/5+32) # simpler syntax" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating ranges using NUMBPY\n", - "\n", - "Python ranges\n", - "range([start], stop[, step])\n", - "vs xrange \n", - "Renames xrange() to range() and wraps existing range() calls with list\n", - "\n", - "\n", - "np.arange([start,] stop[, step,], dtype=None) the stop value is not include in the numbers returned. Default step is one, the dtype is based on the start number format unless specified in dtype." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 3, 5, 7, 9, 11, 13]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "range(1,15,2) # operates on integers only" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ -1.5 , 1.6415, 4.783 , 7.9245, 11.066 , 14.2075])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange(-1.5,15,3.1415) # can create any sequence of numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1. -2.5j, 2. -6.5j, 3.-10.5j])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange((1.0-2.5j),50,(1-4j)) #Even complex number sequences" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NUMPY objects\n", - "https://docs.scipy.org/doc/numpy-dev/reference/index.html#reference\n", - "\n", - "3. Array objects\n", - " \n", - " a. N-dimensional array\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.ndarray.html\n", - " \n", - " b. scalars\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.scalars.html\n", - " \n", - " c. indexing\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.indexing.html\n", - " \n", - " d. interating over arrays\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.nditer.html\n", - " \n", - " e. Standard array subclasses\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.classes.html\n", - " \n", - " f. the array interface\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.interface.html\n", - " \n", - " g. datetimes and timedeltas\n", - " https://docs.scipy.org/doc/numpy-dev/reference/arrays.datetime.html\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NUMPY Universal functions (ufunc)\n", - " a. broadcasting\n", - " b. output type\n", - " c. internal buffers\n", - " d. error handling\n", - " e. casting rules\n", - " f. overriding ufunc\n", - " g. avalable ufuncs\n", - "## NUMPY Routines Routines\n", - " a. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb b/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb deleted file mode 100644 index 531b8ca..0000000 --- a/Lectures/Lecture4-Matplotlib/Data_Visualization.ipynb +++ /dev/null @@ -1,1536 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Visualization\n", - "Adapted from from 3. Plotting and Visualization.ipynb from https://github.com/fonnesbeck/statistical-analysis-python-tutorial\n", - "\n", - "Visual communication of results from analysis.\n", - "The story told by the images should enhance the understanding of the viewer versus viewing raw data or equations.\n", - "\n", - "Visual representation of data:\n", - "\n", - "\"information that has been abstracted in some schematic form, including attributes or variables for the units of information\"\n", - "\n", - "- Michael Friendly (2008). \n", - "\"Milestones in the history of thematic cartography, statistical graphics, and data visualization\".\n", - "\n", - "Data visualization is both an art and a science. The are many good and bad examples of what should be done.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from IPython.core.display import HTML \n", - "Image(url= \"https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png\" \n", - " ,height=400, width=550)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plotting and Visualization\n", - "\n", - "There are a handful of third-party Python packages that are suitable for creating scientific plots and visualizations. These include packages like:\n", - "\n", - "* matplotlib\n", - "* Chaco\n", - "* PyX\n", - "* Bokeh\n", - "\n", - "Here, we will focus excelusively on matplotlib with seaborn and the high-level plotting availabel within pandas. It is currently the most robust and feature-rich package available.\n", - "Official sites: http://matplotlib.org/ and http://seaborn.pydata.org/index.html\n", - "\n", - "\n", - "### Visual representation of data\n", - "\n", - "We require plots, charts and other statistical graphics for the written communication of quantitative ideas.\n", - "\n", - "They allow us to more easily convey relationships and reveal deviations from patterns.\n", - "\n", - "Gelman and Unwin 2011:\n", - "\n", - "> A well-designed graph can display more information than a table of the same size, and more information than numbers embedded in text. Graphical displays allow and encourage direct visual comparisons." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matplotlib\n", - "\n", - "http://matplotlib.org/users/pyplot_tutorial.html\n", - "Be sure to intsll matplotlib \n", - "python -m pip install matplotlib\n", - "If you are having trouble read http://matplotlib.org/users/installing.html\n", - "\n", - "You can specify a custom graphical backend (*e.g.* qt, gtk, osx), but iPython generally does a good job of auto-selecting. Now matplotlib is ready to go, and you can access the matplotlib API via `plt`. If you do not start iPython in pylab mode, you can do this manually with the following convention:\n", - "\n", - " import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Set some Pandas options\n", - "pd.set_option('display.notebook_repr_html', False)\n", - "pd.set_option('display.max_columns', 20)\n", - "pd.set_option('display.max_rows', 25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that setup is complete we can start adding plots inline to the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'ro')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above plot simply shows two sets of random numbers taken from a normal distribution plotted against one another. The `'ro'` argument is a shorthand argument telling matplotlib that I wanted the points represented as red circles.\n", - "\n", - "This plot was expedient. We can exercise a little more control by breaking the plotting into a workflow:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with mpl.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 8}):\n", - " fig = plt.figure(figsize=(6,3))\n", - " ax1 = fig.add_subplot(121)\n", - " ax1.set_xlabel('some random numbers')\n", - " ax1.set_ylabel('more random numbers')\n", - " ax1.set_title(\"Random scatterplot\")\n", - " plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'r.')\n", - " ax2 = fig.add_subplot(122)\n", - " plt.hist(np.random.normal(size=100), bins=15)\n", - " ax2.set_xlabel('sample')\n", - " ax2.set_ylabel('cumulative sum')\n", - " ax2.set_title(\"Normal distrubution\")\n", - " plt.tight_layout()\n", - " plt.savefig(\"normalvars.png\", dpi=150)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "matplotlib is a relatively low-level plotting package, relative to others. It makes very few assumptions about what constitutes good layout (by design), but has a lot of flexiblility to allow the user to completely customize the look of the output.\n", - "\n", - "If you want to make your plots look pretty take a look at: http://matplotlib.org/users/customizing.html\n", - "Look at http://matplotlib.org/gallery.html#style_sheets to find styles you like.\n", - "\n", - "We can quickly switch using:\n", - "plt.style.use('AVAILABLE_PLOT_STYLE')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['seaborn-darkgrid', 'Solarize_Light2', 'seaborn-notebook', 'classic', 'seaborn-ticks', 'grayscale', 'bmh', 'seaborn-talk', 'dark_background', 'ggplot', 'fivethirtyeight', '_classic_test', 'seaborn-colorblind', 'seaborn-deep', 'seaborn-whitegrid', 'seaborn-bright', 'seaborn-poster', 'seaborn-muted', 'seaborn-paper', 'seaborn-white', 'fast', 'seaborn-pastel', 'seaborn-dark', 'tableau-colorblind10', 'seaborn', 'seaborn-dark-palette']\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('ggplot')\n", - "#plt.style.use('dark_background')\n", - "#plt.style.use('bmh')\n", - "#plt.style.use('fivethirtyeight')\n", - "plt.style.reload_library()\n", - "print plt.style.available\n", - "with mpl.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 8}):\n", - " fig = plt.figure(figsize=(6,3))\n", - " ax1 = fig.add_subplot(121)\n", - " ax1.set_xlabel('some random numbers')\n", - " ax1.set_ylabel('more random numbers')\n", - " ax1.set_title(\"Random scatterplot\")\n", - " plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'r.')\n", - " ax2 = fig.add_subplot(122)\n", - " plt.hist(np.random.normal(size=100), bins=15)\n", - " ax2.set_xlabel('sample')\n", - " ax2.set_ylabel('cumulative sum')\n", - " ax2.set_title(\"Normal distrubution\")\n", - " plt.tight_layout()\n", - " plt.savefig(\"normalvars.png\", dpi=150)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting in Pandas\n", - "\n", - "As we discussed last week, Pandas includes methods for DataFrame and Series objects that are relatively high-level, and that make reasonable assumptions about how the plot should look." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "normals = pd.Series(np.random.normal(size=10))\n", - "normals.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that by default a line plot is drawn, and a light grid is included. All of this can be changed, however:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "normals.cumsum().plot(grid=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, for a DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "variables = pd.DataFrame({'normal': np.random.normal(size=100), \n", - " 'gamma': np.random.gamma(1, size=100), \n", - " 'poisson': np.random.poisson(size=100)})\n", - "variables.cumsum(0).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an illustration of the high-level nature of Pandas plots, we can split multiple series into subplots with a single argument for `plot`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ],\n", - " dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "variables.cumsum(0).plot(secondary_y='normal')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we would like a little more control, we can use matplotlib's `subplots` function directly, and manually assign plots to its axes:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0,0.5,'cumulative sum')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n", - "for i,var in enumerate(['normal','gamma','poisson']):\n", - " variables[var].cumsum(0).plot(ax=axes[i], title=var)\n", - "axes[0].set_ylabel('cumulative sum')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bar plots\n", - "\n", - "Bar plots are useful for displaying and comparing measurable quantities, such as counts or volumes. In Pandas, we just use the `plot` method with a `kind='bar'` argument.\n", - "\n", - "For this series of examples, let's load up the Titanic dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " pclass survived name sex \\\n", - "0 1 1 Allen, Miss. Elisabeth Walton female \n", - "1 1 1 Allison, Master. Hudson Trevor male \n", - "2 1 0 Allison, Miss. Helen Loraine female \n", - "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", - "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", - "\n", - " age sibsp parch ticket fare cabin embarked boat body \\\n", - "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", - "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", - "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", - "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", - "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", - "\n", - " home.dest \n", - "0 St Louis, MO \n", - "1 Montreal, PQ / Chesterville, ON \n", - "2 Montreal, PQ / Chesterville, ON \n", - "3 Montreal, PQ / Chesterville, ON \n", - "4 Montreal, PQ / Chesterville, ON " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "titanic = pd.read_excel(\"data/titanic.xls\", \"titanic\")\n", - "titanic.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.groupby('pclass').survived.sum().plot(kind='bar')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.groupby(['sex','pclass']).survived.sum().plot(kind='barh')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "death_counts = pd.crosstab([titanic.pclass, titanic.sex], titanic.survived.astype(bool))\n", - "death_counts.plot(kind='bar', stacked=True, color=['black','gold'], grid=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way of comparing the groups is to look at the survival *rate*, by adjusting for the number of people in each group." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "death_counts.div(death_counts.sum(1).astype(float), axis=0).plot(kind='barh', stacked=True, color=['black','gold'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Histograms\n", - "\n", - "Frequenfly it is useful to look at the *distribution* of data before you analyze it. Histograms are a sort of bar graph that displays relative frequencies of data values; hence, the y-axis is always some measure of frequency. This can either be raw counts of values or scaled proportions.\n", - "\n", - "For example, we might want to see how the fares were distributed aboard the titanic:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.fare.hist(grid=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `hist` method puts the continuous fare values into **bins**, trying to make a sensible décision about how many bins to use (or equivalently, how wide the bins are). We can override the default value (10):" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.fare.hist(bins=30)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are algorithms for determining an \"optimal\" number of bins, each of which varies somehow with the number of observations in the data series." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(11, 36, 14)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sturges = lambda n: int(np.log2(n) + 1)\n", - "square_root = lambda n: int(np.sqrt(n))\n", - "from scipy.stats import kurtosis\n", - "doanes = lambda data: int(1 + np.log(len(data)) + np.log(1 + kurtosis(data) * (len(data) / 6.) ** 0.5))\n", - "\n", - "n = len(titanic)\n", - "sturges(n), square_root(n), doanes(titanic.fare.dropna())" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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uCQQCWTu2TLMpq2RXXpuyStOXt6io6Lb2S/kSkDFGe/fuVXFxsR577LHk9vLycnV3d0uSuru7VVFRkdx+8OBBGWN0/Phxud3umxYAAGBmpDwDGBwc1MGDB7VkyRK98MILkqQnn3xSmzZtUnNzszo7OxUIBFRfXy9JWrVqlfr6+lRbW6u8vDyFQqHMJAAApCTlAvjud7+rP/zhDzf9u5deeumGbQ6HQ88++2yqTwcAyDDuBAYAS1EAAGApCgAALEUBAIClKAAAsBQFAACWogAAwFJprwVkm8Rzj0/LeZ1vfTgt5wWAW2EGAACWogAAwFIUAABYigIAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIAAMBSFAAAWIoCAABLUQAAYClWA80S6a4yeu4W21llFMCtMAMAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIAAMBSFAAAWIoCAABLcSdwjkv3DuNb4Q5jYO6b8QI4fPiw2tvbNTU1pQ0bNmjTpk0zPQQAgGa4AKampvT222/rV7/6lfx+v3bu3Kny8nItXrx4JoeBDJiOmcV0zSqmaxY0HZhZYSbNaAF8/vnn+va3v61FixZJkh5++GFFIhEKAJJu/xf1rRa+A3BnZrQAotGo/H5/8rHf79eJEydmcggAkFHpzDD/vzczMzEbnNECMMbcsM3hcFz3OBwOKxwOS5IaGxtVVFSU+hP+d2/qxwJZJK1/B3PMnMs6h3/PzOjXQP1+v0ZGRpKPR0ZG5PV6r9snGAyqsbFRjY2NaT9fQ0ND2ueYK8iau2zKa1NWafbzzmgB3H///frXv/6loaEhXblyRZ9++qnKy8tncggAgP+Y0UtATqdT27Zt065duzQ1NaUf/vCHuu+++2ZyCACA/5jx+wBWr16t1atXz8hzBYPBGXmebEDW3GVTXpuySrOf12Fu9sksACDnsRYQAFgqJ9cCysXlJvbs2aO+vj4VFhaqqalJkhSPx9Xc3Kzz589rwYIF2rFjhzwej4wxam9vV39/v+bNm6dQKKSSkpJZTnD7hoeH1dbWpgsXLsjhcCgYDOrRRx/NybyTk5P69a9/rStXriiRSKiyslJbtmzR0NCQWlpaFI/HtXTpUtXU1Mjlcuny5cvavXu3Tp48qfnz56uurk4LFy6c7Rh3ZGpqSg0NDfL5fGpoaMjprD/72c90991366677pLT6VRjY2N2vY5NjkkkEubnP/+5OXv2rLl8+bL5xS9+YU6fPj3bw0rbwMCA+eKLL0x9fX1y2zvvvGM++OADY4wxH3zwgXnnnXeMMcYcOnTI7Nq1y0xNTZnBwUGzc+fOWRlzqqLRqPniiy+MMcZMTEyY2tpac/r06ZzMOzU1ZS5dumSMMeby5ctm586dZnBw0DQ1NZlPPvnEGGPMm2++aT7++GNjjDF/+ctfzJtvvmmMMeaTTz4xr7/++uwMPA0fffSRaWlpMa+88ooxxuR01lAoZEZHR6/blk2v45y7BPT15SZcLldyuYm5bsWKFfJ4PNdti0QiqqqqkiRVVVUlc/b29mrdunVyOBxavny5xsfHFYvFZnzMqfJ6vcl3Pvfcc4+Ki4sVjUZzMq/D4dDdd98tSUokEkokEnI4HBoYGFBlZaUkqbq6+rqs1dXVkqTKykodO3bspjdYZquRkRH19fVpw4YNkq7eHJqrWW8lm17HOVcAN1tuIhqNzuKIps/o6GjyRjqv16uLFy9KuvozCAQCyf3m8s9gaGhIp06d0gMPPJCzeaempvTCCy/o2WefVVlZmRYtWiS32y2n0ylJ8vl8yTxff307nU653W6NjY3N2tjv1P79+/XMM88kVwAYGxvL2azX7Nq1Sy+++GJyhYNseh3n3GcAN3uH8H+Xm8h1ufIz+Oqrr9TU1KStW7fK7Xbfcr+5nveuu+7Sq6++qvHxcb322mv65z//ect953LWQ4cOqbCwUCUlJRoYGPjG/edy1mt+85vfyOfzaXR0VL/97W//32UuZiNvzhXA7Sw3kSsKCwsVi8Xk9XoVi8VUUFAg6erPYHh4OLnfXPwZXLlyRU1NTXrkkUe0du1aSbmdV5Ly8/O1YsUKnThxQhMTE0okEnI6nYpGo/L5fJL+9/Xt9/uVSCQ0MTFxw6XBbDU4OKje3l719/drcnJSly5d0v79+3My6zXXshQWFqqiokKff/55Vr2Oc+4SkE3LTZSXl6u7u1uS1N3drYqKiuT2gwcPyhij48ePy+12z6lfiMYY7d27V8XFxXrssceS23Mx78WLFzU+Pi7p6jeCjh49quLiYpWWlqqnp0eS1NXVlXwNr1mzRl1dXZKknp4elZaWzpl3xU899ZT27t2rtrY21dXV6cEHH1RtbW1OZpWuzmAvXbqU/PNf//pXLVmyJKtexzl5I1hfX59+97vfJZeb2Lx582wPKW0tLS367LPPNDY2psLCQm3ZskUVFRVqbm7W8PCwAoGA6uvrk18ne/vtt3XkyBHl5eUpFArp/vvvn+0It+3vf/+7XnrpJS1ZsiT5D/7JJ5/UsmXLci7vl19+qba2Nk1NTckYo+9///t64okndO7cuRu+Gvmtb31Lk5OT2r17t06dOiWPx6O6urrk/68xlwwMDOijjz5SQ0NDzmY9d+6cXnvtNUlXP+D/wQ9+oM2bN2tsbCxrXsc5WQAAgG+Wc5eAAAC3hwIAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIAAMBS/wPUoE4WUBifiwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.fare.hist(bins=doanes(titanic.fare.dropna()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A **density plot** is similar to a histogram in that it describes the distribution of the underlying data, but rather than being a pure empirical representation, it is an *estimate* of the underlying \"true\" distribution. As a result, it is smoothed into a continuous line plot. We create them in Pandas using the `plot` method with `kind='kde'`, where `kde` stands for **kernel density estimate**." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.fare.dropna().plot(kind='kde', xlim=(0,600))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Often, histograms and density plots are shown together:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.fare.hist(bins=doanes(titanic.fare.dropna()), density=True, color='lightseagreen')\n", - "titanic.fare.dropna().plot(kind='kde', xlim=(0,600), style='r--')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we had to normalize the histogram (`normed=True`), since the kernel density is normalized by definition (it is a probability distribution)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will explore kernel density estimates more in the next section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Boxplots\n", - "\n", - "A different way of visualizing the distribution of data is the boxplot, which is a display of common quantiles; these are typically the quartiles and the lower and upper 5 percent values." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.boxplot(column='fare', by='pclass', grid=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can think of the box plot as viewing the distribution from above. The blue crosses are \"outlier\" points that occur outside the extreme quantiles." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One way to add additional information to a boxplot is to overlay the actual data; this is generally most suitable with small- or moderate-sized data series." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bp = titanic.boxplot(column='age', by='pclass', grid=False)\n", - "for i in [1,2,3]:\n", - " y = titanic.age[titanic.pclass==i].dropna()\n", - " # Add some random \"jitter\" to the x-axis\n", - " x = np.random.normal(i, 0.04, size=len(y))\n", - " plt.plot(x, y, 'r.', alpha=0.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When data are dense, a couple of tricks used above help the visualization:\n", - "\n", - "1. reducing the alpha level to make the points partially transparent\n", - "2. adding random \"jitter\" along the x-axis to avoid overstriking" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A related but inferior cousin of the box plot is the so-called dynamite plot, which is just a bar chart with half of an error bar." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "titanic.groupby('pclass')['fare'].mean().plot(kind='bar', yerr=titanic.groupby('pclass')['fare'].std())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why is this plot a poor choice?\n", - "\n", - "- bar charts should be used for measurable quantities (*e.g.* raw data), not estimates. The area of the bar does not represent anything, since these are estimates derived from the data.\n", - "- the \"data-ink ratio\" (*sensu* Edward Tufte) is very high. There are only 6 values represented here (3 means and 3 standard deviations).\n", - "- the plot hides the underlying data.\n", - "\n", - "A boxplot is **always** a better choice than a dynamite plot." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data1 = [150, 155, 175, 200, 245, 255, 395, 300, 305, 320, 375, 400, 420, 430, 440]\n", - "data2 = [225, 380]\n", - "\n", - "fake_data = pd.DataFrame([data1, data2]).transpose()\n", - "p = fake_data.mean().plot(kind='bar', yerr=fake_data.std(), grid=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fake_data = pd.DataFrame([data1, data2]).transpose()\n", - "p = fake_data.mean().plot(kind='bar', yerr=fake_data.std(), grid=False)\n", - "x1, x2 = p.xaxis.get_majorticklocs()\n", - "plt.plot(np.random.normal(x1, 0.01, size=len(data1)), data1, 'ro')\n", - "plt.plot([x2]*len(data2), data2, 'ro')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise\n", - "\n", - "Using the Titanic data, create kernel density estimate plots of the age distributions of survivors and victims." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatterplots\n", - "\n", - "To look at how Pandas does scatterplots, let's reload the baseball sample dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " id player year stint team lg g ab r h ... rbi sb cs \\\n", - "0 88641 womacto01 2006 2 CHN NL 19 50 6 14 ... 2.0 1.0 1.0 \n", - "1 88643 schilcu01 2006 1 BOS AL 31 2 0 1 ... 0.0 0.0 0.0 \n", - "2 88645 myersmi01 2006 1 NYA AL 62 0 0 0 ... 0.0 0.0 0.0 \n", - "3 88649 helliri01 2006 1 MIL NL 20 3 0 0 ... 0.0 0.0 0.0 \n", - "4 88650 johnsra05 2006 1 NYA AL 33 6 0 1 ... 0.0 0.0 0.0 \n", - "\n", - " bb so ibb hbp sh sf gidp \n", - "0 4 4.0 0.0 0.0 3.0 0.0 0.0 \n", - "1 0 1.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0 2.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0 4.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - "[5 rows x 23 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "baseball = pd.read_csv(\"data/baseball.csv\")\n", - "baseball.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scatterplots are useful for data exploration, where we seek to uncover relationships among variables. There are no scatterplot methods for Series or DataFrame objects; we must instead use the matplotlib function `scatter`." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 200)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(baseball.ab, baseball.h)\n", - "plt.xlim(0, 700); plt.ylim(0, 200)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can add additional information to scatterplots by assigning variables to either the size of the symbols or their colors." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 200)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(baseball.ab, baseball.h, s=baseball.hr*10, alpha=0.5)\n", - "plt.xlim(0, 700); plt.ylim(0, 200)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(baseball.ab, baseball.h, c=baseball.hr, s=40, cmap='hot')\n", - "plt.xlim(0, 700); plt.ylim(0, 200);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To view scatterplots of a large numbers of variables simultaneously, we can use the `scatter_matrix` function that was recently added to Pandas. It generates a matrix of pair-wise scatterplots, optiorally with histograms or kernel density estimates on the diagonal." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_ = pd.plotting.scatter_matrix(baseball.loc[:,'r':'sb'], figsize=(12,8), diagonal='kde')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Trellis Plots\n", - "\n", - "One of the enduring strengths of carrying out statistical analyses in the R language is the quality of its graphics. In particular, the addition of [Hadley Wickham's ggplot2 package](http://ggplot2.org) allows for flexible yet user-friendly generation of publication-quality plots. Its srength is based on its implementation of a powerful model of graphics, called the [Grammar of Graphics](http://vita.had.co.nz/papers/layered-grammar.pdf) (GofG). The GofG is essentially a theory of scientific graphics that allows the components of a graphic to be completely described. ggplot2 uses this description to build the graphic component-wise, by adding various layers.\n", - "\n", - "Pandas recently deprecated some really good graphing functions such as Trellis plots, and suggested that we use the seaborn package for such functions. I found https://seaborn.pydata.org/generated/seaborn.FacetGrid.html that demonstrates this setup and have integrated in this lecture:\n", - "\n", - "Chiefly, this allows for the easy creation of **trellis plots**, which are a faceted graphic that shows relationships between two variables, conditioned on particular values of other variables. This allows for the representation of more than two dimensions of information without having to resort to 3-D graphics, etc.\n", - "\n", - "For more help with seaborn\n", - "https://www.tutorialspoint.com/seaborn\n", - "https://seaborn.pydata.org/examples/index.html\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use the `titanic` dataset to create a trellis plot that represents 4 variables at a time. This consists of 4 steps:\n", - "\n", - "1. Setup the Seaborn package\n", - "2. Add a grid that will be used to condition the variables by both passenger class and sex\n", - "3. Add the actual plot that will be used to visualize each comparison\n", - "4. Draw the visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns; \n", - "sns.set(style=\"ticks\", color_codes=True)\n", - "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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rw7wArmFWgPrcKnydO3dWaGio2rZtq9DQULVr106lpaW136+srFRwcHCD68TFxSkuLq7OseLiYkVFRbkTC7A05gVwDbMC1OfWKd3+/ftr+/btMsbo5MmT+v777zVo0CAVFhZKkvLz8xUWFubRoAAAAHCPWzt8w4cP1+7duzV+/HgZY5SSkqKQkBAlJycrKytLoaGhiomJ8XRWAAAAuMHty7I89thj9Y7l5OQ0KQwAAAA8jwvlAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsrkmF7/Tp0xo6dKi+/PJLHTt2TJMmTdLkyZOVmpoqp9PpqYwAAABoArcLn8PhUEpKitq3by9JysjIUHx8vNatWydjjPLy8jwWEgAAAO5zu/AtXrxYEydOVLdu3SRJRUVFCg8PlyRFRkaqoKDAMwkBAADQJAHu3Ck3N1ddu3bVkCFD9MILL0iSjDGy2WySpMDAQJWXlze4jt1uV3Z2tjsRgFaHeQFcw6wA9dmMMaaxd7rnnntks9lks9l06NAhXXfddTp48KAOHjwoSdqyZYsKCgqUkpLS6EDFxcWKiopSXl6eQkJCGn1/oDVhXgDXMCto7dza4Vu7dm3tr2NjY5WWlqbMzEwVFhYqIiJC+fn5GjhwoMdCAgAAwH0euyxLQkKC7Ha7JkyYIIfDoZiYGE8tDQAAgCZwa4fv761Zs6b21zk5OU1dDgAAAB7GhZcBAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABbX5MuyAAAA142Z8/oleZw3lt51SR4HLQM7fAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWBzX4WuCplxLiesjAQCakzt/R/F3k3WxwwcAAGBxFD4AAACLo/ABAABYHIUPAADA4ih8AAAAFkfhAwAAsDguy9ICcTkYtHRN+TPsCv6cA0Bd7PABAABYnFs7fA6HQ0lJSSopKdGFCxc0c+ZMXX/99UpMTJTNZlPPnj2VmpoqPz/6JAAAgLe5Vfg2bdqkzp07KzMzU2fOnNG4cePUu3dvxcfHKyIiQikpKcrLy1N0dLSn8wIA0Gwa+3YD3j6AlsKtLbjbb79dDz30UO3X/v7+KioqUnh4uCQpMjJSBQUFnkkIAACAJnFrhy8wMFCSVFFRodmzZys+Pl6LFy+WzWar/X55eXmD69jtdmVnZ7sTAWh1mBfANcwKUJ/bn9I9ceKEZs2apcmTJ2vMmDHKzMys/V5lZaWCg4MbXCMuLk5xcXF1jhUXFysqKsrdWGgAn/BtuZgXwDXMClCfW6d0T506pfvuu0+PPvqoxo8fL0nq06ePCgsLJUn5+fkKCwvzXEoAAAC4za0dvpUrV+rs2bNasWKFVqxYIUl64oknlJ6erqysLIWGhiomJsajQa2mua9D1hzYHQQAoGVyq/DNmzdP8+bNq3c8JyenyYEAAADgWVwoDwAAwOL40WoALIdrqQFAXRQ+XBK8/w8AAO/hlC4AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABbHdfgAAIAkLlpuZezwAQAAWBw7fAAAuKkpP0XICtx5/uwKegeFD2gFmvs0TWv/Sw+A6yiJ3sEpXQAAAItjhw9Aq8eOAwCro/ABgBv4NCOAloRTugAAABbX6nf4eLO572vK7xG7Ku5hLgDAWlp94QOAS4FTwAC8iVO6AAAAFscOHwD4IHYEAXiSRwuf0+lUWlqaDh8+rLZt2yo9PV3XXnutJx8CAAAAjeTRU7pbtmzRhQsX9Oqrr2rOnDl66qmnPLk8AAAA3ODRHb49e/ZoyJAhkqS+ffvqwIEDjV6jpqZGklRaWuryfX618P1GPw5ah9tn/s7t+770RLRHMlx55ZUKCGied0+4Oi+Oc2XN8vjwHcXFxd6O0GTMCv4Vd/58u9MNPPX//UuhsfNiM8YYTz34E088oZEjR2ro0KGSpGHDhmnLli3/MpDdbld2dranHh7wSXl5eQoJCWnyOswLrI5ZAVzX2HnxaOHLyMjQzTffrNGjR0uSIiMjlZ+f36g1zp8/rwMHDujyyy+Xv7//P71NVFSU8vLympy3ufh6PomMnuJKxubctWhoXqzyGnobGZvO12dF8v3XUCKjJ/h6Pql55sWjk9WvXz998MEHGj16tPbu3atevXo1eo327dsrLCyswdt54l+BzcnX80lk9BRvZnRlXngNPYOMTefrsyL5/msokdETfD2f5PmMHi180dHR2rlzpyZOnChjjBYtWuTJ5QEAAOAGjxY+Pz8/Pfnkk55cEgAAAE3ET9oAAACwOP+0tLQ0b4dwR0REhLcj/CRfzyeR0VN8PaOv55PI6Cm+ntHX80lk9BRfz+jr+STPZ/Top3QBAADgezilCwAAYHEUPgAAAIuj8AEAAFgchQ8AAMDiKHwAAAAWR+EDAACwOAofAACAxVH4AAAALI7CBwAAYHEUPgAAAIuj8AEAAFgchQ8AAMDiKHwWYrfbZbfbXb59YmKiCgsLm/SYubm5SkxMbNIanrBz505NnTrV2zHQgrTGefnmm280ffp03XXXXRo3bpx27drltSxoWVrrvEybNk1jx47V3XffrUOHDnktiydQ+NCiOZ1Ovfzyy3rkkUfkdDq9HQfwaUuWLNGIESP0+uuva+nSpZo7d65qamq8HQvwSc8884xiYmK0adMmxcXFaf78+d6O1CQB3g7gq0pLSzV37lydO3dOfn5+mjdvnvr27av9+/crIyND58+fV5cuXTR//nx16dJFY8eO1cKFCzVo0CBNnz5dI0aM0D333FO73vvvv6/s7Ow6j9GjRw89++yzdY6NGDFCd9xxh3bu3KmAgAA98MADevnll3Xs2DElJCRo9OjROnLkiBYsWKBz586prKxMv/nNbzRp0qQ66+Tn52v58uWqrq5WSEiIFixYoC5durj03AcNGqTo6Gh9+umnCgwM1NNPP62QkBAVFBToqaeekjFG3bt319KlS+vcb/Pmzfrd736n8+fP68KFC1q0aJH69eun3/3ud9q4caP8/Pz0X//1X3ryySf1+eefKyUlRdXV1WrXrp0yMjJ03XXX1a514sQJzZgxo162tWvXKigoqPbrL7/8Ul9++aUWLFigNWvWuPT84HnMS8uYl+joaA0cOFCSdO2116qqqkrnzp1Tx44dXXqu8AzmpWXMy8KFC2t/XVxcrODgYJeeo88y+Kfsdrt58cUXjTHGbNu2zbz00kumqqrKjBkzxpSUlBhjjMnPzzdTp041xhhTUFBgRo4caXJycsz06dPdftzhw4eb1atXG2OMSUxMNJMmTTIOh8MUFhaau+66yxhjTHp6uikoKDDGGPOXv/zF9O3b1xhjzPLly83y5cvN6dOnzdixY823335rjDHmlVdeMUlJSfUeKyEhwXz00Uf1jvfq1cvk5uYaY4z5/e9/b+6//35TVVVlBg0aZA4ePGiMMebpp582v//9782GDRtMQkKCqampMVOmTDGnT582xhizfv16c//995vq6moTERFhLly4YGpqakxiYqIpLS01iYmJ5u233zbGGJObm2s2btzo9mtmjDEfffSRuffee5u0BtzHvLSseTHGmFWrVjEzXsK8tKx5iYmJMTfddJPJz89v0jrexg7fvzBo0CDFxcXp0KFDGjp0qO6991599dVXOn78uGbOnFl7u4qKitrbDxw4UFlZWdq8eXO99Vz9F5gkRUZGSpK6d++ubt26KSAgQN27d9fZs2clXXxvxPbt27Vq1SodOXJE586dq3P/ffv26cSJE5oyZYqki6c9O3Xq5PJzb9eunf7nf/5HkjRu3DhlZWXp8OHDuuKKK3TjjTdKkubMmSPp4nssJMnPz0/PPfectm7dqqNHj+rjjz+Wn5+f/P39dcstt2j8+PGKiorS//7v/+qKK67Q0KFD9eSTT2r79u0aMWKEhg8fXieDq/8Cg29gXlrWvKxevVqvvvqqcnJyXH6e8BzmpWXNyzvvvKNDhw7pvvvu0+bNm9W5c2eXn68vofD9C/3799dbb72lDz/8UG+//bY2btyohIQEhYSE6PXXX5ck1dTU6NSpU5IkY4yOHj2qyy67TEePHlW3bt3qrBcdHa3o6GiXHrtNmza1vw4IqP9bFB8fr+DgYA0fPlyjR4/Wm2++Wef7NTU16tevn1auXClJqqqqUmVlpcvP3c/PTzabTdLFYfb391ebNm1qj0lSeXl5nTUrKys1fvx4jR07VgMGDNANN9ygtWvXSpJWrFihvXv3Kj8/X7/61a/09NNP6/bbb9ctt9yiDz74QKtXr9aHH36o9PT02vWuuuqq2tcZvo95aTnzsmTJEm3btk1r167VlVde6fLzhOcwLy1jXj788EMNGDBAgYGBuvHGG9W9e3cdP368xRY+PrTxLyxZskSbNm3SuHHjlJKSooMHDyo0NFTfffedPvnkE0nShg0bNHfuXEnSunXr1KFDB61YsULJycmNGoDG2rlzp2bPnq3bbrtN+fn5klTnjdc333yz9u7dq6NHj0q6OBBLlixxef3vv/9eW7dulXTxX1iRkZHq0aOHTp8+rS+++EKS9NJLL+mVV16pvc9XX30lm82mGTNmKCIiQu+//75qampUVlam0aNHq1evXnrooYd066236vDhw4qPj9dnn32miRMn6qGHHtLBgweb/LrAe5iXljEvq1evVmFhoV555RXKnhcxLy1jXjZu3KjXXntNkvTFF1/o1KlTCg0NdWstX8AO378QGxurOXPmKDc3V/7+/lq8eLHatm2rZcuWaeHChaqqqlJQUJAWL16s48eP6/nnn9f69et11VVXafDgwcrMzFRaWlqzZIuLi9PkyZPVrl079e7dW1dffbWKi4trv3/55Zdr0aJFio+Pl9Pp1BVXXKHMzMxGPcY777yjZ555Rt26ddPixYvVrl07ZWZm6rHHHpPD4dA111yjJUuW6N1335Uk9e7dWzfeeKNGjRolm82mwYMHa8+ePeratasmTJig8ePH67LLLlOPHj30i1/8QgMGDNATTzyh5557Tm3atGm21wqXBvPi+/NijNFzzz2noKAgxcbG1h5/4YUXdMUVVzR6PbiPefH9eZGkpKQkJSUlaePGjWrXrp2WLl2qwMBAt9byBTZjjPF2CHhHYmKixo0bp4iIiDrHb7jhBh0+fNhLqQDfxLwArmNefA+ndAEAACyOHT4AAACLY4cPAADA4nyu8FVXV6u4uFjV1dXejgL4POYFcA2zgtbO5wpfaWmpoqKiVFpa6u0ogM9jXgDXMCto7Xyu8AEAAMCzKHwAAAAWR+EDAACwOAofAACAxVH4AAAALM6lwrdv377an7147NgxTZo0SZMnT1ZqaqqcTqckKTs7W+PHj9fEiRO1f//+5ksMAACARmmw8L344ouaN2+eqqqqJEkZGRmKj4/XunXrZIxRXl6eioqK9PHHH2v9+vXKysrS/Pnzmz04AAAAXBPQ0A2uueYa2e12PfbYY5KkoqIihYeHS5IiIyO1c+dO9ejRQ4MHD5bNZlP37t1VU1OjsrIyde3atXnTAwBgAWPmvO72fd9YepcHk8CqGix8MTExKi4urv3aGCObzSZJCgwMVHl5uSoqKtS5c+fa2/xwvKHCZ7fblZ2d7W52oFVhXgDXMCtAfQ0Wvn/k5/fjWeDKykoFBwcrKChIlZWVdY537NixwbXi4uIUFxdX51hxcbGioqIaGwuwPOYFcA2zAtTX6E/p9unTR4WFhZKk/Px8hYWFqV+/ftqxY4ecTqe+/vprOZ1OTucCAAD4iEbv8CUkJCg5OVlZWVkKDQ1VTEyM/P39FRYWpgkTJsjpdColJaU5sgIAAMANLhW+kJAQvfbaa5KkHj16KCcnp95t/tkWOgAAALyPCy8DAABYHIUPAADA4ih8AAAAFkfhAwAAsDgKHwAAgMU1+rIsAACXuD8oAAARGklEQVTAd/Bj2eAKdvgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsLsCdOzkcDiUmJqqkpER+fn5asGCBAgIClJiYKJvNpp49eyo1NVV+fvRJAAAAb3Or8G3btk3V1dX6wx/+oJ07d+rZZ5+Vw+FQfHy8IiIilJKSory8PEVHR3s6LwAAABrJrS24Hj16qKamRk6nUxUVFQoICFBRUZHCw8MlSZGRkSooKPBoUAAAALjHrR2+Dh06qKSkRKNGjdKZM2e0cuVK7d69WzabTZIUGBio8vLyBtex2+3Kzs52JwLQ6jAvgGuYFaA+twrf6tWrNXjwYM2ZM0cnTpzQ1KlT5XA4ar9fWVmp4ODgBteJi4tTXFxcnWPFxcWKiopyJxZgacwL4BpmBajPrVO6wcHB6tixoySpU6dOqq6uVp8+fVRYWChJys/PV1hYmOdSAgAAwG1u7fBNmzZNSUlJmjx5shwOhx5++GHddNNNSk5OVlZWlkJDQxUTE+PprAAAAHCDW4UvMDBQy5Ytq3c8JyenyYEAAADgWVwoDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcQHu3nHVqlXaunWrHA6HJk2apPDwcCUmJspms6lnz55KTU2Vnx99EgAAwNvcamSFhYX69NNP9corr2jNmjUqLS1VRkaG4uPjtW7dOhljlJeX5+msAAAAcINbhW/Hjh3q1auXZs2apRkzZmjYsGEqKipSeHi4JCkyMlIFBQUeDQoAAAD3uHVK98yZM/r666+1cuVKFRcXa+bMmTLGyGazSZICAwNVXl7e4Dp2u13Z2dnuRABaHeYFcA2z4roxc153+75vLL3Lg0nQ3NwqfJ07d1ZoaKjatm2r0NBQtWvXTqWlpbXfr6ysVHBwcIPrxMXFKS4urs6x4uJiRUVFuRMLsDTmBXANswLU59Yp3f79+2v79u0yxujkyZP6/vvvNWjQIBUWFkqS8vPzFRYW5tGgAAAAcI9bO3zDhw/X7t27NX78eBljlJKSopCQECUnJysrK0uhoaGKiYnxdFYAAAC4we3Lsjz22GP1juXk5DQpDAAAADyPC+UBAABYHIUPAADA4ih8AAAAFkfhAwAAsDgKHwAAgMVR+AAAACyOwgcAAGBxFD4AAACLo/ABAABYHIUPAADA4ih8AAAAFkfhAwAAsDgKHwAAgMVR+AAAACyOwgcAAGBxFD4AAACLo/ABAABYHIUPAADA4ih8AAAAFkfhAwAAsDgKHwAAgMVR+AAAACyOwgcAAGBxTSp8p0+f1tChQ/Xll1/q2LFjmjRpkiZPnqzU1FQ5nU5PZQQAAEATuF34HA6HUlJS1L59e0lSRkaG4uPjtW7dOhljlJeX57GQAAAAcJ/bhW/x4sWaOHGiunXrJkkqKipSeHi4JCkyMlIFBQWeSQgAAIAmCXDnTrm5ueratauGDBmiF154QZJkjJHNZpMkBQYGqry8vMF17Ha7srOz3YkAtDqtYV7GzHndrfu9sfQuDydBS9YaZgVoLLcK34YNG2Sz2bRr1y4dOnRICQkJKisrq/1+ZWWlgoODG1wnLi5OcXFxdY4VFxcrKirKnViApTEvgGuYFaA+twrf2rVra38dGxurtLQ0ZWZmqrCwUBEREcrPz9fAgQM9FhIAAADu89hlWRISEmS32zVhwgQ5HA7FxMR4amkAAAA0gVs7fH9vzZo1tb/Oyclp6nIAAADwMC68DAAAYHFN3uEDAG/j070A8NPY4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcXxoAwAANJq7H5aS+MCUN1D4WrGmDKvEwAIA0FJwShcAAMDiKHwAAAAWR+EDAACwOAofAACAxVH4AAAALI7CBwAAYHEUPgAAAIvjOnxexHXwAADApUDha8GaWhi9/fgUVgAALg1O6QIAAFgchQ8AAMDiKHwAAAAWR+EDAACwOD60AQDA3zTlw2h8EA2+zK3C53A4lJSUpJKSEl24cEEzZ87U9ddfr8TERNlsNvXs2VOpqany82MDEWiNvP0JcgBAXW4Vvk2bNqlz587KzMzUmTNnNG7cOPXu3Vvx8fGKiIhQSkqK8vLyFB0d7em8AAAAaCS3Ct/tt9+umJiY2q/9/f1VVFSk8PBwSVJkZKR27txJ4QPg09zdieTUHYCWxq3CFxgYKEmqqKjQ7NmzFR8fr8WLF8tms9V+v7y8vMF17Ha7srOz3YkAtDrMC+AaZgWoz+032Z04cUJTpkzRXXfdpTFjxtR5v15lZaWCg4MbXCMuLk6HDx+u819eXp67kQBLY14A1zArQH1u7fCdOnVK9913n1JSUjRo0CBJUp8+fVRYWKiIiAjl5+dr4MCBHg0KAIAv48NK8GVu7fCtXLlSZ8+e1YoVKxQbG6vY2FjFx8fLbrdrwoQJcjgcdd7jBwAAAO9xa4dv3rx5mjdvXr3jOTk5TQ4EAAAAz+JCeQAAABZH4QMAALA4Ch8AAIDFUfgAAAAszq0PbeAiPoLfNE19/fhpBwAAuIYdPgAAAIuj8AEAAFgchQ8AAMDieA8fAAC4pJryHm7ev+0edvgAAAAsjsIHAABgcRQ+AAAAi+M9fEAr4O77ZXivzD/H+48AtDTs8AEAAFgcO3wAcAld6p/Qw44iAIkdPgAAAMuj8AEAAFgcp3TRYjX11BinugAArQU7fAAAABZH4QMAALA4Ch8AAIDFtdj38Hni0ga8hwv4aZf6EiIA0Jxa80XT2eEDAACwOI/u8DmdTqWlpenw4cNq27at0tPTde2113ryITyK3QsAVseP1YPVeOvv7pa+O+jRwrdlyxZduHBBr776qvbu3aunnnpKzz//vCcfAvAY3hYAAGgtPFr49uzZoyFDhkiS+vbtqwMHDjR6jZqaGklSaWnpT97Oca6s8QEBDysuLm7wNldeeaUCAprn7bLMC5qLK3+2PY1ZgVU1xzw1dl5sxhjjqQd/4oknNHLkSA0dOlSSNGzYMG3ZsuVfBrLb7crOzvbUwwM+KS8vTyEhIU1eh3mB1TErgOsaOy8eLXwZGRm6+eabNXr0aElSZGSk8vPzG7XG+fPndeDAAV1++eXy9/f/p7eJiopSXl5ek/M2F1/PJ5HRU1zJ2Jy7Fg3Ni1VeQ28jY9P5+qxIvv8aSmT0BF/PJzXPvHh0svr166cPPvhAo0eP1t69e9WrV69Gr9G+fXuFhYU1eDtP/CuwOfl6PomMnuLNjK7MC6+hZ5Cx6Xx9ViTffw0lMnqCr+eTPJ/Ro4UvOjpaO3fu1MSJE2WM0aJFizy5PAAAANzg0cLn5+enJ5980pNLAgAAoIm48DIAAIDF+aelpaV5O4Q7IiIivB3hJ/l6PomMnuLrGX09n0RGT/H1jL6eTyKjp/h6Rl/PJ3k+o0c/pQsAAADfwyldAAAAi6PwAQAAWByFDwAAwOIofAAAABZH4QMAALC45vmhhc3A6XQqLS1Nhw8fVtu2bZWenq5rr73W27HkcDiUlJSkkpISXbhwQTNnztT111+vxMRE2Ww29ezZU6mpqfLz8363Pn36tH7+85/r5ZdfVkBAgM9lXLVqlbZu3SqHw6FJkyYpPDzcZzI6HA4lJiaqpKREfn5+WrBggU++hj9gXpqGWWka5sUzmBfPYF7+xrQQ7777rklISDDGGPPpp5+aGTNmeDnRRX/84x9Nenq6McaYsrIyM3ToUHP//febjz76yBhjTHJysnnvvfe8GdEYY8yFCxfMAw88YEaOHGm++OILn8v40Ucfmfvvv9/U1NSYiooKs3z5cp/K+P7775vZs2cbY4zZsWOHefDBB30q3z9iXtzHrDQd8+IZzEvTMS8/8o1/Xrlgz549GjJkiCSpb9++OnDggJcTXXT77bfroYceqv3a399fRUVFCg8PlyRFRkaqoKDAW/FqLV68WBMnTlS3bt0kyecy7tixQ7169dKsWbM0Y8YMDRs2zKcy9ujRQzU1NXI6naqoqFBAQIBP5ftHzIv7mJWmY148g3lpOublRy2m8FVUVCgoKKj2a39/f1VXV3sx0UWBgYEKCgpSRUWFZs+erfj4eBljZLPZar9fXl7u1Yy5ubnq2rVr7f/QJPlcxjNnzujAgQNatmyZ5s+fr7lz5/pUxg4dOqikpESjRo1ScnKyYmNjfSrfP2Je3MOseAbz4hnMS9MxLz9qMe/hCwoKUmVlZe3XTqdTAQG+Ef/EiROaNWuWJk+erDFjxigzM7P2e5WVlQoODvZiOmnDhg2y2WzatWuXDh06pISEBJWVldV+3xcydu7cWaGhoWrbtq1CQ0PVrl07lZaW1n7f2xlXr16twYMHa86cOTpx4oSmTp0qh8PhM/n+EfPiHmbFM5gXz2FemoZ5+VGL2eHr16+f8vPzJUl79+5Vr169vJzoolOnTum+++7To48+qvHjx0uS+vTpo8LCQklSfn6+wsLCvBlRa9euVU5OjtasWaMbb7xRixcvVmRkpE9l7N+/v7Zv3y5jjE6ePKnvv/9egwYN8pmMwcHB6tixoySpU6dOqq6u9rnf57/HvLiHWfEM5sUzmJemY15+1GJ+lu4Pn6I6cuSIjDFatGiR/vM//9PbsZSenq7NmzcrNDS09tgTTzyh9PR0ORwOhYaGKj09Xf7+/l5M+aPY2FilpaXJz89PycnJPpVxyZIlKiwslDFGDz/8sEJCQnwmY2VlpZKSkvTXv/5VDodDU6ZM0U033eQz+f4R89J0zIr7mBfPYF48g3m5qMUUPgAAALinxZzSBQAAgHsofAAAABZH4QMAALA4Ch8AAIDFUfgAAAAsjsIHAABgcRQ+AAAAi/ONnx0Dj6qurlZaWpr+9Kc/6dSpU7rhhhuUlZWl1157TTk5OerYsaNCQ0N1zTXXKC4uTvn5+Vq+fLmqq6sVEhKiBQsWqEuXLt5+GsAlwbwArmNeWi52+Czo008/VZs2bfTqq6/q/fffV3l5uV566SWtXbtWubm5WrdunY4dOyZJKisr09KlS/Xb3/5W//d//6fBgwfr6aef9vIzAC4d5gVwHfPScrHDZ0EDBgxQ586dtXbtWv35z3/WV199pYiICA0fPlxBQUGSpDvuuENnz57Vvn37dOLECU2ZMkXSxR8x1KlTJ2/GBy4p5gVwHfPSclH4LCgvL0/Lly/XlClT9POf/1xnzpxRx44ddfbs2Xq3rampUb9+/bRy5UpJUlVVlSorKy91ZMBrmBfAdcxLy8UpXQvatWuXRo0apV/84hcKDg5WYWGhJGnbtm2qqKjQhQsX9N5778lms+nmm2/W3r17dfToUUnSihUrtGTJEm/GBy4p5gVwHfPSctmMMcbbIeBZhw8f1ty5cyVJbdq00dVXX63Q0FB169ZN69atU4cOHdSlSxcNGDBAv/71r7V161YtW7ZMTqdTV1xxhTIzM3lTLVoN5gVwHfPSclH4WomjR49q27ZtmjZtmiRp5syZuvvuuzVixAjvBgN8EPMCuI55aRl4D18rcfXVV+uzzz7TnXfeKZvNpsGDB2v48OHejgX4JOYFcB3z0jKwwwcAAGBxfGgDAADA4ih8AAAAFkfhAwAAsDgKHwAAgMVR+AAAACyOwgcAAGBx/w+RvKbFfQt2gwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")\n", - "g = g.map(plt.hist, \"age\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the cervical dystonia dataset, we can simultaneously examine the relationship between age and the primary outcome variable as a function of both the treatment received and the week of the treatment by creating a scatterplot of the data, and fitting a polynomial relationship between `age` and `twstrs` (http://nevro.legehandboka.no/imagevault/publishedmedia/jipv0xim2ibb749yalju/6454-2-twstrs.pdf) :" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " patient obs week site id treat age sex twstrs\n", - "0 1 1 0 1 1 5000U 65 F 32\n", - "1 1 2 2 1 1 5000U 65 F 30\n", - "2 1 3 4 1 1 5000U 65 F 24\n", - "3 1 4 8 1 1 5000U 65 F 37\n", - "4 1 5 12 1 1 5000U 65 F 39" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cdystonia = pd.read_csv(\"data/cdystonia.csv\", index_col=None)\n", - "cdystonia.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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48bV/V3PjEb+Ij8TH9h4fJemHP/yhCgsLdfXVV+sPf/hDk2PbOt6FxjdUhjZlyhRZrdaIf/+oUaOUmZmpNWvWqLy8vMnHhbZHvCRedoR42RybzaaePXtKkkpKSpocG+ljr2379u266667NHnyZOXn52v69Ol66KGH6v3tSNK2bdt02223acKECcrNzdWUKVP04IMPNjmvgwcP6o477tDYsWM1YsQIXX311Vq8eHGDY91ut5599lnNmDFD+fn5GjVqlGbNmqW33367yccdC5Q2xMikSZO0ceNGLV++vE5d+IoVKyQFlxWuWbOmTh1Q6Mk/efJkSZLX69WNN96oJUuWKC0tTWeccYZM09Tq1as1f/583XDDDfVqHn/5y1/q9ddfV1JSkvLz85WYmKjVq1frrrvu0sqVK/X444+Hl5Y3ZOXKlfrxj38sm82m3//+9w2+KaytX79+mjFjRstOTjP27dun+++/X6effrp++MMf6p577mnV33+8GTNmaN26ddqxY4dGjBihPn36KCsrK/xzj8ejH/3oR0pKStLEiRO1bds2nXrqqZKkZ599Vr/97W9lt9uVn5+vjIwMrVmzRo899pgWL16sP/7xj+HlWaZp6qabbtInn3yirl276vTTT5fNZtOWLVu0dOlSLVu2TM8884ymTZumrKwszZgxQx999JEqKys1bdq08JLXxiQlJUX8b1H78TUmdNFy+PBhzZ07V19++aWqq6uVm5urH/3oRzr77LPDY7dt2ybTNHXqqafKYqmfvxw4cKAkaevWrZKCy3YPHz6sHj16KC0trdnxpmmqoKBAFosl/LPaTjnlFFksFhUUFMg0zSaf44g94uOJIz7GR3yUpOnTp+umm25STk6Odu7c2ei4aMS7r7/+WpLqrF4ISUlJUbdu3bRnzx4dPHhQWVlZKigoaHS8JA0YMEAlJSXatm0bn+LGGPHyxBEv4ydeNsfj8YQbeIcSCg1pyWMP+eyzz3TTTTepsrJSOTk5ysvL05YtW/Tiiy/qP//5jxYuXKiMjAxJ0tKlSzVv3jy53W4NGTJEI0eO1NatW/XSSy/pww8/1EsvvVRvBZppmpo1a5b27duncePGye12a/Xq1Vq1apXuuecezZo1Kzz20KFDuuaaa/T111+ra9euOvvss1VVVaVVq1bp888/17Jly+Krv4aJmFi3bp3pcrnMuXPn1jn+P//zP+Zpp51mulwu88EHH6zzs6lTp5ojRowwq6urTdM0zd/85jemy+Uyr732WrO0tDQ8rri42Jw2bZrpcrnMJUuWhI8vXLjQdLlc5kUXXWTu3r07fLykpMScOXOm6XK5zFdffTV8/I033jBdLpf5i1/8IjznESNGmHl5eebSpUtb72S00Ny5c838/Hxz27Ztpmma5i9+8QvT5XKZb7zxRsS/Y8WKFabL5TJ/97vfRTS+sfuYPHmy6XK5zJkzZ5oej8c0TdP0+/2maZrmsmXLTJfLZU6aNMncunVr+DYVFRXmDTfcYLpcLvPJJ58MH3/nnXdMl8tlXnHFFabb7Q4f9/v95kMPPWS6XC5zzpw5de4/9O9cXFwc8WNvLRMnTjRdLpfpcrnMCy64wJw3b555wQUXhI+98MIL4bHvvfee6XK5zJtvvrnB31VQUGC6XC7z4osvNk3TNDdu3Gi6XC7zsssua3C82+02XS6XOXr0aNM0TbO0tNR0uVzmuHHjGp3v+PHjTZfLZR49etQ0zeDfSWiuzQn9O69YsaLZsTh5xMcTR3w8Jpbx8XiheDNt2rR6P4tGvBsxYoTpcrnMysrKBsdffvnlpsvlMjdt2mSaphmO5V9//XWD43/84x+bLpfL/PDDD8PHIo2Td9xxR4ueX2ga8fLEES+PiWW8bC4meL1e87777jNdLpc5ZcoU0+v1mqZ57NzPnj07PLalj728vNycMGGC6XK5zL/97W/h4z6fL/xvdc8995imGXx+jx492hw6dKj5/vvvh8cGAgHz2WefDd9vbaHrzEmTJpk7duwIH1+5cqWZl5dn5ubm1jnnN910k+lyucybbrrJrKioCB8vKioyp06darpcLvOVV16J6LxGA6UNMZKXlxdeGuj1eiUFG52sXr1aZ511llJSUuos8yosLFRxcbHOOussORwOeTwe/eUvf1FCQoIee+wxpaenh8f26dMnvMVOqO5Skv70pz9Jkn7961/XyeZlZGTooYceknSsjv14W7du1Q9/+EN5PB49/fTTOuuss1rpTLTMK6+8oiVLluiWW25p8JOYWLnyyitlt9slKfyJe+h833XXXXWWyyUlJemhhx6S0+nUyy+/HF6SFggENGXKFP3sZz+T0+kMj7dYLJo5c6Ykaffu3VF5PM05dOiQ9u7dK5vNpieffFJvvfWWnn76ab311lv6zW9+I5vNpkcffTTcR6GyslKSGs1yJyQk1Bnndrtbdbyk8DmtqKiI/IEiJoiPJ4b4GB/xsaWiEe9Ct6l97hoaf/x9RDoesUO8PDHEy/iLl++//75+9rOfhb9uvfVWXX/99Zo4caJefvllJSYm6te//rVstsYX1Lf0sX/00Ufav3+/pk+frssvvzx83Gq16vbbb1e/fv105MgRSdLrr7+uw4cP66qrrqqzwscwDN14443Ky8vT2rVrtXbt2nrz+ulPf1pnpcKYMWM0a9YseTwevfHGG5KCJSMffPCB0tPT9eijjyopKSk8vn///nr44YclHXs+xAMSCTFiGIYmTpyoyspKrVu3TpL05Zdf6vDhwxo3bpzy8vK0efNmHT16VNKxWsdJkyaFxx49elSDBg1qcNnQ+PHjZbPZtGbNGvn9fu3fv1+FhYVKT0/XkCFD6o0fPHiwunfvrqKiIh04cKDOz3bu3KnrrrtOZWVlmjt3br0tqKJlx44devzxxzV69GjNnj07JnNozPHn1O/3h1+4x44dW298RkaGhg4dqvLy8nCJwHe+8516y/sqKyu1fv16vffee5IUvkiItYyMDC1fvlz/+te/dMEFF9T52fnnn69Zs2bJ7/frr3/9q6RjL4aNLXM0a+opQ/+NtPQgNL6hconGxiL+ER9bjvgYP/GxpaIR76xWqwzDiDgGtzRmI3aIly1HvIzPeLl161a99dZb4a9//etfWrVqlTIyMvT9739fb775pkaPHt3k72jpY1+1apWkY2U+taWlpemDDz7Qb37zG0kK93to6N9BUjgptnr16jrHrVarzjvvvHrjQ/cZ+vcN/XfixIn1tk+XgsmH7OxsFRcXa+/evQ3OIdrokRBDEydO1Jtvvqnly5frjDPOCNezjRkzRm63W8uXL9eaNWs0adIkffrppzIMIxx09+zZIyn4AtBUUxSfz6fDhw+Hn3BlZWXNNlHZs2ePsrOzw9+vWLEifBGycOFCzZkzR127do3oMYb2uY7Eli1bGv2Z3+/X7bffLkl65JFH4q7G/fi61rKyMlVVVUkKNqZqyp49ezR8+HBJwaaEr776qpYsWaLt27fr4MGDkiK/0GzKzp07G2y01ZAXX3yx0UAZkpGREa4ZO97kyZO1YMECffnll5IUzqqGzsnxQln00CdsoQBaXV3d4PjQ8dD40O9vbHxDt7FareGfmc30TfD7/ZKOfTKItkd8rIv42L7iY0tEI94lJibqyJEjqq6ubjCOhcaHfndzMfv48dKx5ENzyQXiaesjXtZFvGyf8XLevHmNbv/YEi157KFkV48ePZr9vaG/lRtvvDGicSHdu3dvcBVF6D73799f57+9e/du9Hf36dNHBw4c0IEDByKac1sjkRBDZ599tmw2m5YvX6758+drxYoVSklJ0dChQ8PLCletWqVx48Zp9erVysvLC2eLQ/u99unTRyNGjGj2vkIv3JmZmTrzzDObHHt8FsxiseiRRx7RihUrtGjRIv3617/Wo48+GtFjbK3mOB988IHWrl2rvn376re//W2dn4Uy8AsXLtRnn32mc889V+eee+5J32dLHP8JUeh8JyYmNtj1urbQi+zWrVs1e/ZsHTp0SFlZWcrLy9Opp56qoUOHqn///rrssstOao7RbI4TekyhF79u3bpJUjiYHy8UPEO3a258KPCHxqekpCgpKUmlpaXy+Xz1ArbP51NpaakSEhLUpUsXSXWf55WVlQ1mf0NCy4Nrb1uJtkV8jBzxsX3Fx+NFI95169ZNR44c0YEDB9SnT59m76Nbt27atGmTDh48GG721tR46djfRujvszHE09ZHvIwc8bJ9x8vmtPSx+3y+iH936N9i6tSpdZKoxzt+VUljSdNQ0jVUyhISyQdbocaasUYiIYZSU1M1cuRIrV27VkeOHNF///tfjRkzRlarVcOHD5fT6dSqVau0atUqVVdXh5ehSceCRd++ffXEE080e1+hOsbk5OSIxtd2wQUX6KKLLtLZZ5+tjz/+WH//+9914YUXRlTXdsYZZzTbiTcSofkXFxeruLi4wTGhuqT+/ftHPfAfLz09XXa7XT6fT48++midT78b88ADD+jQoUO66aabNH/+/DqBpKnseqQyMjJa/G/fmM8++0xvvvmmhg0bpjlz5tT7eahDeShbOmjQIFksFm3btq3B37d9+3ZJxzqEZ2RkKDMzU3v37lV5eblSUlLqjA/9ntB4wzA0aNAgrV+/XkVFRRo0aFCd8YWFhQoEAnU6kHfp0kVdunTRkSNHtGvXrka7kx86dEhHjx6V1Wqts7872hbxMXLEx/iKjydy320d7wYPHqyCggJt27atXiKhvLxc+/fvV0ZGRviif/DgwVq8eLEKCgrqfZpomqa2b98uq9VaJ8nQp08fbd68Wbt27Wry8YY6r/fq1avZc4PIEC8jR7xs3/GyOS197KHn/759+xr8ff/6179kt9s1ceJEdevWTUVFRbruuuta9Fw8vsQnJNSrIXRtGUoqN/a8lI5dX0c7QdMYeiTE2MSJE+X1erVgwQJVVlZqzJgxkoKZptNPP11fffWV/v3vf0uqW7+Tl5cnp9OpDRs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f+eyzzzRx4kQNGDBAAwcO1PTp07Vhw4Zq7+M///mPbrzxRg0aNEj9+/fXDTfcoOXLl1c7/vvvv9ett96q888/X/369dP48eP16aefVjk2MM89e/bU+DhvuOEGde/eXYsWLapxHJIX8fEE4mN0PPTQQ+rZs2eNY6IZ7yQpLy9P99xzjy666CKdffbZGjNmjN577z35fL4qxx88eFC//vWvNWLECOXm5urSSy/Va6+9JpfLVWlsuHFy7ty5dXp+IfEQL08gXp66QEw4+V+PHj3Up08fDR06VLfffnula8rAuR81alTM5xyu4cOHq3v37jpw4EC8p3JKSCQg4dntdt15550qKiqqdsz27ds1duxY/fnPf5YkDR06VF26dNHKlSv1y1/+Um+//XZU5vbOO+9o7ty5cjgcUbn9+nrooYf06aefxi2DvHnzZplMJo0ZM6bKfzk5OSHj586dq7vvvlvbtm3ToEGDZLPZtGzZMl177bVatmxZpdtftGiRpk6dqrVr1yo3N1f9+vXT2rVrdfPNN+uDDz6oNP6rr77Stddeqy+//FJnnnmmBg0apK1bt+q+++7TSy+9FLXzAEQb8bHu4h0fK3rvvfe0ePHiGsdEO95t2bJF11xzjf7nf/5Hp59+ui688EIdOHBAs2bN0gMPPFBp/IEDBzRhwgR98MEHatasmS6++GIdO3ZMr7zyiqZNmya3213/EwJEEfGy7hIhXnbs2DHkGnL06NEaNmyYWrVqpS+++EJTp07VwoUL4za/xswa7wkANdm/f7/uuusurV+/vsZxjzzyiIqLizVt2jTde++9slgskqSVK1fqlltu0YsvvqiLLrpIZ555ZkTnV92nNfEWz3m53W5t27ZNnTp10pw5c2odv3HjRr366qtq3769/vSnP6lt27aSpKVLl+r222/XzJkz9cUXXygjI0OSPzv++OOPKysrS3/84x9ls9kkSRs2bNDUqVP1zDPP6OKLLw7ejsPh0P333y9J+sMf/qDBgwdLknbv3q0bbrhB8+bN06hRo9S7d++InwsgmoiP9ZMI8/L5fHrllVc0b968GsdFO94ZhqEHHnhAZWVlev7553XllVdK8n9qO2XKFH366acaNWqULr300uCcnnjiCR04cEB33XWXfvWrX0mSysvLg58MLly4UDfddFMEzxZw6oiX9ZMI8zrnnHP03HPPVfm9jz76SDNnztTzzz+vyy67rNIHVYguViQgIXk8Hr333nsaO3as1q9fr44dO1Y7Ni8vT+vWrVOHDh1Cgr4kDRkyRNdee628Xq/+8Y9/xGLqjd727dvldrvVq1evsMa/8847kqQ77rgjeDEsSRdffLGuuuoqHT58WJ999lnw+HvvvSeXy6UpU6YEL6olKTc3VzfffLOcTmfIp3SffPKJCgsLNWbMmOBFtSR16tRJ9957rySRyUZSIT4mt/Xr1+u6667TG2+8ofbt29c4Ntrx7quvvtKPP/6ogQMHBpMIktSyZUs9/vjjlcbv3LlTS5cuVadOnXTrrbcGjzdp0kTPPPOMLBaL3nvvvbqeEiBqiJcN27hx49SrVy+5XC4tXbo03tNpdEgkRNm4cePUvXt3bdq0KeT49u3bg3U+R44cCfnekiVL1L17dz399NPBYx6PR++//76uvvpq9e3bV/3799ekSZP0f//3f9Xe96effqrrrrtO/fv3V9++fXXNNdfoL3/5S9jLk/7973+rV69e6tevn7755psaxwbq4sL5F45vv/1Ws2bNksfj0ZNPPhn81KMqRUVF6tu3r4YOHRoS9AM6d+4syf/JTiQNHz5cL7/8siTp1VdfDakBDdSEbtu2Tddff7169+6toUOHhtS0rl+/XrfffrsGDx6sPn366NJLL9VLL72ksrKyKu/vs88+07Rp0zRkyJBgzd6kSZP097//PTgmUBe2cuVKSdKNN94YVq1/uL+7cHoBBJ7r4SYSli9fLpPJpOHDh1f6XqC+rWJdYuAcjhw5sk7jR4wYUWn88OHDZbFY6lT3iMghPhIfG1t8lKS7775ba9eu1ejRo/XXv/61xrHRjnc13f6AAQPUqlUrffvtt8HzvmLFChmGoWHDhslsDr2EPP3009WzZ0/t3btX27dvr/Fxoe6Il8TLxhgvwxFIDp38/K9KOI+9ooMHD2r27NkaNWqUcnNzNWLECD388MNVnocDBw7o8ccf17Bhw9S7d29dcMEFeuihh5Sfn1/tfI4dO6ann35a559/vs4++2yNHz9en3zySZVjA0mxwGu3X79+uuaaa/T+++/L4/HU+tijgdKGKLv44ou1ceNGrVy5MqSZ0qpVqyT5lxV+++23IQ1BAsFh2LBhkvxLxW+77TYtX75czZs31znnnCPDMLRmzRrdcccduvXWW3X33XeH3O8jjzyiv/71r2rSpIlyc3OVkZGhNWvW6NFHH9Xq1av1wgsvyGQyVTvv1atX66677pLVatUbb7yhc845p8bH2alTJ40ZM6ZuJ6cGGRkZmjJliqZPn66WLVvWGHD69+9fZZ1owPfffy9JIZ92R8LIkSO1evVqbdmyRTabTd27d1enTp1CxsyYMUN2uz34PAj8cb1o0SI9+uijMgxDvXv3Vrt27bRhwwbNmzdPS5Ys0cKFC5WdnR28nSeeeEJ/+tOf1KRJE/Xv319NmjTRjh07tGbNGq1Zs0ZHjhzRjTfeqCZNmmjMmDFatWqVCgoKdN5556lVq1Zq0qRJjY8l3N/dyY+vKps3b5bkXw53xx13aO3atSorK5PNZtMNN9wQcl+HDh3S0aNHddppp6l58+aVbqtr166SpK1bt0ryv162b98us9kc/F5FnTt3ltls1vbt22UYhkwmk7Zt2yZJIZ/mBTRt2lRt2rTR/v37dfjwYbVu3TqMs4BIIT7WD/ExeeOj5P9k86qrrqr1eROLeBf4g7+q8ZLUpUsXFRYWaseOHTr77LOD47t161bl+K5du+r777/X1q1bI770u7EjXtYP8TK542U4duzYIUlq165djePCfewBW7Zs0bRp03T48GF17txZF198sXbt2qVFixZpyZIl+vDDD3XGGWdI8n+IdtNNN6moqEhdunTRsGHDtGfPHi1evFj/+te/9Pbbbys3N7fSnGbMmKH8/HwNGjRIZrNZq1at0gMPPKAff/wxpEeN0+nUzTffrK+//lqZmZnBFWerV6/WU089pSVLluiNN95QamrqKZ/POjEQVevXrzdsNpsxbdq0kOO/+tWvjLPOOsuw2WzG008/HfK9ESNGGP369TOcTqdhGIbx0ksvGTabzZg6dapRVFQUHJefn2+MHDnSsNlsxvLly4PHP/zwQ8NmsxlXXnmlsW/fvuDxwsJCY/z48YbNZjP+/Oc/B49/9NFHhs1mM2bOnBmcc79+/Yw+ffoYK1asiNzJOAUnzzFcO3bsMHr16mV0797d+PHHH2sdn5+fb9hsNuPBBx8M6/Zfe+01w2azGa+88krI8UmTJhk2m80YPny4UVJSYhiGYXi9XsMwDGP79u1Gr169jAEDBhjffPNN8GdcLpfx2GOPGTabzbjnnnuCxwPPoVGjRhmFhYUh9/POO+8Ev1fR5MmTDZvNZqxatSqsxxFJ1157rWGz2QybzWaMGDHCmDFjhnHVVVcZPXr0MGw2m/HMM88Ex27cuNGw2WzGuHHjqrwtu91u2Gw249xzzzUMwzCKiooMm81mDB48uNr7HzJkiGGz2YzS0lLDMAyjX79+hs1mM8rLy6scf8011xg2m83YvHlz8Fhg/vn5+TU+1sDv+aOPPqpxHKpGfIwM4mPyxMeq2Gw246yzzqp0PBbxbvTo0YbNZjO2bdtW5fi77rrLsNlsxhdffGEYhmFMnz7dsNlsxtKlS6sc//zzzxs2m82YP39+8Fi4cfKVV16p0/OrsSFeRgbxMnniZW0xwefzGW+++aZhs9mM/v37G8XFxYZhnDj3I0eODI6t62P3er3GFVdcYdhsNmPu3LmGz+cLfm/u3Lkhr0Wn02mMGDHCsNlsxsKFC0Nue/HixUb37t2NYcOGBV+HhmEYw4YNM2w2mzFgwABj48aNweNbt241Bg0aZNhsNuO7774LHp89e7Zhs9mMiRMnhsz/8OHDxrhx4wybzWa88MILtZ/UCKO0Icr69OkTXBoY6GTs8/m0Zs0anX/++WratGnIMq+8vDzl5+fr/PPPV2pqqlwul9577z2lpaXp+eefD8kqdujQIbh9TaDOXFKwo+xzzz0Xkp1r2bKlnnnmGUn+JkxV2bp1q375y1/K5XJp7ty5Ov/88yN0JmKvuLhYd955p9xut66++upqP3GJpquuukpZWVmSFFwGOn/+fLndbt15550aMGBAcGxKSooeffRRtW3bVp999pkOHjwoSSorK9Mll1yiu+++Wy1btgy5/QkTJkiS9u3bF4uHUyvDMLRlyxZJ0gMPPKB//vOfmjt3rhYtWqR33nlHWVlZmj9/vpYsWSLJ30FZUrCR4snS0tIk+Rt5hTNektLT0yX5l4tV/JnA8erGB+4DsUN8jB/iY+KLRbyL9nhEDvEyfoiX8fXNN9/ovvvuC/l32223afjw4XrxxRdltVo1a9asKle2BtT1sa9du1ZbtmxR7969NWPGjJBVN7fddpt69Oghj8cjl8ulf/7zn8rPz9eoUaM0adKkkNseO3asLrnkEu3du7fK8qGbb745pBS4W7duuuOOOyRJf/rTnyT5m+j++c9/ltVq1UsvvRQy/1atWumll16SxWLR+++/L6fTWev5jCQSCVFmMpk0dOhQlZeXBzvF/vDDDzp69GiwlmnLli0qLS2VdKLW8eKLLw6OLS0t1ZlnnlnlsushQ4bIarXq22+/ldfr1aFDh5SXl6fs7Gz16NGj0vhu3bqpbdu22rVrlwoKCkK+t2fPHt10003BbrUXXXRRJE9FTB05ckQ33XSTtm3bpl69eumxxx6Lyzyq+h2sXr1akjRo0KBK30tNTdXAgQPl8/mCFwTnnXee5s6dq8svvzw4zul0avPmzfr4449lMpkSZrstk8mkZcuWBWvQKgbewYMHB4Pj+++/HxwfDuN4HebJNbk1jQ2wWCwymUzV3ldg/Mk/h+gjPsYH8TE5xCLeBe4jWuMROcTL+CBexl9+fr4+/fTTkH8rV65Uamqqrr76an3wwQf6+c9/XuNt1PWxf/3115JOlAVVZLFY9Mknn+jdd99Vampqjb8HSbrwwgtDbrOiX/ziF5WOBV6zgd/bxo0b5XA4dPbZZ1dZvtGxY0f16dNH5eXlwfKbWKFHQgwMHTpUixcv1sqVK3XOOecE69kGDhwou92ulStX6ttvv9XFF1+sL7/8UiaTKRh09+/fL8n/BlBTYxmPx6OjR4/qwIEDkvzZ09oa0ezfvz9km5RVq1YFL0I+/PBDTZkyRS1atAjrMS5atEgPP/xwWGN//PHHsMbV108//aRbbrlFu3btUq9evfT222/X+IlONFWVHQ38jq644ooafzbwu5f82ciPPvpIS5Ys0fbt23Xw4MFgTWwkLtjCbVr07LPP6uqrr65xTFZWVjBrfrLhw4dr9uzZ+uGHHyRJmZmZklRtBjVwPPD7C9Tm1ZRxPflnMjIyVFJSIqfTGVzhUNX4inV/ZrNZPp+v1nPr9XolqcrbRXiIj6GIjw07PtZFLOJd4L/V7V1f3/EVn1OB5EO48TTmNb5JhHgZinjZOOLlVVddVe32j3VRl8ceSI6ddtpptd5u4Pw+/fTTIY1NTxb4fQVYLJYqEwOB+ww09Qz8t6Zdfjp06KB169bp8OHDtc43kkgkxMCFF14oq9WqlStX6o477tCqVavUtGlT9ezZM7hM8Ouvv9bgwYO1Zs0a9enTJ5gtDuzf2qFDB/Xr16/W+wq8Ebdq1UrnnXdejWMDf8QFmM1mPfvss1q1apUWLVqk5557Tr/5zW/CeoyRbo5TX+vWrdOtt96qoqIiDRw4UG+88YaaNm0at/lU9alN4Hc0evToGj+RDzRwOXjwoCZNmqTdu3erWbNmys3N1WWXXaYePXpo8ODBGjFiRPA26ytWzXECz+vARWibNm0kqdrAFwjkgQuUpk2bqkmTJioqKpLH45HVGhrCPB6PioqKlJaWpmbNmgXvo6SkRAUFBeS8WPkAACAASURBVOrQoUOt9yH5XxulpaXB12d1AsuJq0ucoHbEx9ghPtZPPJqHSbGJd23atNHmzZt1+PBh/exnPwtrvFR7zA6Mk068lsKNp4HHgsqIl7FDvKyfeMXL2tT1sddlF4TAayvQkLI6JzegtVgsleJ6RSkpKZJOJGFr+h3HKxFLIiEGsrKy1L9/f61du1YlJSX67rvvNHDgQFksFvXt21fp6en6+uuv9fXXX8vpdAaXtEgn3rw7duyoOXPm1HpfgbrEzMzMsMZXNHr0aF155ZW68MILtWTJEn388ce64oorwqprO+ecc2rtxBtty5cv14wZM+RwOPSLX/xCzz33XEJ+stGmTRvt3btX999/f1iZzt/97nfavXu3xo0bpyeffDIYWCR/zdepBn1JdX6uVGfTpk1asGCB2rRpo3vuuafS9wPb5QQ6Hrds2VKtWrXSgQMHVFZWVulNOtCJN1CPaDKZdOaZZ2rDhg3atWtXpaCcl5cnn88XUr/YrVs3bd++XTt27Kh0YV1WVqZDhw6pZcuWIUs9O3TooM2bN2vv3r3V1kL6fD7t3r1bUs1ZYtSM+BgbxMf6i1R8rKtYxLtu3bpp2bJl2r59e6VluYZhaOfOnbJYLMEkQ2C3huq2dzw5ZksKzmPv3r01Pt6ffvpJkn8bSVSNeBkbxMv6i1e8rE1dH3vg9XLyKoKApUuXqry8XOedd15w7NixY3XllVeGPSeXy6Xi4uKQfiXSiVgZuFYOJGZr2kYy8L1Y7z5Gj4QYGTp0qNxut+bPn6/y8nINHDhQkj9zdPbZZ2vTpk36xz/+ISm0HqdPnz5KT0/X999/X+X+qD/++KNGjRqlO+64Q4ZhqEOHDmrXrp327NkTfEOvqLCwUJdddpmmTJkSzP4HBIJky5Ytde+990qSHn/88Vo/RUgE69evDwb9yZMn68UXX4xJ0A+3xr+iwBvksmXLqvz+tGnTNHHiRG3YsEGSgrWQN998c0jgk6Svvvoq+P+BjGh95xUJFotFixcv1sKFC6vcv/jjjz+WdKJeLPD/Xq9X//73vyuN/+KLLyQppL4y8LOB753K+CVLlsjr9Vaq3+zfv78k1biv9urVq1VeXq5WrVpV+Ukewkd8jC7iY2LEx/qIdrwLjP/Xv/5Vafx3332nI0eOaMCAAcEkb2D8kiVLQs6p5G9UtnnzZrVv3z4k6RGIp0uWLKm2/rqsrExr1qyRVH2dMfyIl9FFvEzeeFmTuj72QNxasWJFpdsyDENPP/207r33Xnk8nuDvIdCX5GS/+93vdOWVV+rDDz+s9L3//Oc/lY59/vnnkhR8bffu3VsZGRnasGFDlc0wd+/erU2bNikrK6vKXhrRRCIhRgJZ4fnz50s68eSQ/G+aXq9Xn3zyidq2bRuyP3CTJk00fvx4lZWV6YEHHlBRUVHwe0VFRXr44Ye1e/dutWvXLvhinzx5snw+n+6///6QJ5zdbtfDDz+svLw8ZWZmVlqKVtH48ePVr18/5efn65VXXonIOYgWh8Ohe+65Rw6HQxMnTtTMmTNjFvgCNaiB5kbhuOGGG2Q2m/Xb3/42pMOyYRh69dVXtWLFCu3ZsycYDAL1U4GdDgLWrVunWbNmBb+uWEdbn3lFQvfu3dWvXz+Vl5fr0UcfDZnTl19+qQULFgT3dA649tprZTKZNGfOnJBs69KlS7V48WLl5ORo9OjRweNXX3210tLS9NZbb2njxo3B499//71+//vfKz09Xdddd13w+KWXXqpWrVpp8eLFIW+2+fn5evHFF2UymULmI/lfQ2lpafr444/13nvvVbpg3rlzp5544glJ0i233FKvc4UTiI/RQ3z0S4T4WB/RjncDBw5Ut27d9NVXX4Vc5B45ckRPPvmkJGnq1KnB4x07dtSFF16ovLw8vfzyy8HjgZjv9XpDxkv+3jhnnnmmdu3apUcffbTSjg4lJSV64IEHVF5eruHDh5OYrQXxMnqIl37JGi9rUtfHPmTIEHXp0kVr166ttDPJ66+/rvz8fJ133nlq3bq1fvGLXygnJ0d///vfg83EA5YvX663335bP/74o/r06VNpXs8991zIte+6dev0xhtvyGq1BneAyMjI0IQJE+TxeHTPPfeEvHaPHDmie+65Rz6fTxMmTKC0oaHq1q2b2rdvr7179yorKyskuAey7x6PJ2QZWsC9996rH374QcuXL9eoUaOUm5srq9Wqb775RseOHVPfvn31//7f/wuOnzx5stauXavPP/9cP//5z9WnTx81bdpU69at05EjR3TGGWcELxCqYzKZ9MQTT+jqq6/W/PnzNXr06JDtSRLJ4sWLg0vmi4uLdd9991U57txzz9XEiRMjet+BurMPPvhA+/bt05VXXqlRo0bV+DN9+vTRgw8+qOeee06TJk1Sz5491b59e23dulW7du1Senq6Xn755WAwuPHGG7VixQq98MIL+sc//qHTTz9de/bs0Q8//KDmzZsrJydHBQUFOnz4sDp27BgyryeffFJ/+9vfdO+99waPRVvgcf3v//6vvv32W+Xm5qqwsFBr166V1WrViy++GLLktm/fvpo2bZp+//vfa8yYMRo8eLCOHTumNWvWyGq1as6cOSGBsUOHDnrwwQf11FNP6b/+6780ePBgGYah1atXy+Px6De/+U1IjVrTpk01a9Ys3XnnnZo+fbrOPfdcZWZmatWqVbLb7br77rsrZXDPOOMMzZ49W4888ohmzZql//7v/1bPnj2VlpamPXv2aNOmTfL5fBo/frxuuOGG6J/UBo74GD3Ex8SKj3UV7XhnNps1e/ZsTZ48WY899pj++te/qk2bNvr666919OhRTZgwQcOHDw+Z0+OPP65rr71W8+bN05IlS9SlSxd99913Kigo0NChQ3XttdeGjE9NTdWLL76o2267TR9//LH++c9/qm/fvmrevLkOHz6s77//Xna7XWeffXaNjcrgR7yMHuJlcsfLmtT1sQcSNFOmTNFvfvMbLVq0SF27dtWOHTu0fft2tW7dWrNnz5bk/0P/d7/7naZPn66nnnpK8+fPV7du3XT48GGtW7dOkvTQQw/prLPOCplTSkqKWrdurdGjR2vIkCFyuVzB2P7rX/86pETsnnvu0aZNm7RmzRqNHDlS5557riR/T5Rjx47pggsuCHntxorlicDHaoi6Xbt26fvvv9d5550X0mG1devWevfdd+XxeDRjxgx16dIl5OdSUlJ0xRVXKDs7WwcOHNCmTZt04MABnXHGGbrpppv02GOPhXScN5lMuvTSS9W+fXsdOnRIW7Zs0Z49e3TaaadpwoQJmj17dsgepJs3b9a//vUv9ezZUyNGjAiZV0lJidauXavvv/9e48ePD2s7qmiobo6Sfw/jbdu2SfLXZ27durXKf9nZ2Ro5cmSN91NSUqIFCxborLPOqnWsJHXu3FmlpaXasWOHduzYofbt22vIkCFavHix9u7dq3HjxlVZ79m3b18NHDhQxcXF2rZtm7Zt26bMzEwNHz5cc+bMCXmT7dy5s3Jzc7Vv3z7l5eXpp59+UmZmpi655BLNmTNHpaWl2rBhgzp06KC+fftKknr16qUdO3Zo586d2rNnjwYNGqTOnTvX+ngiITs7W2PGjJHT6Qwut7Lb7Ro6dKjmzJlT5bLV888/Xx06dFB+fr42bNigsrIyDRo0SC+88EJweVlFubm56t27t/bt26cNGzaosLBQffr00dNPP13l761r164aMmSIDhw4oI0bNwZ7Hzz00EOVLnoDbDabLr30UqWkpOjQoUPatm2btm/fLqvVqoEDB+rhhx/W1KlT4/aaaGiIj/VHfEye+FiVV199VWazWbfffnuV3492vGvbtq1GjhypwsJC/fDDD9q1a5c6duyoO+64Q7fffnul53Xz5s11+eWXq6SkRFu2bNHWrVvVpk0bTZ06VTNnzqzyE7HWrVtr7Nixat68uYqLi7Vz505t3rxZLpdLNptNt912m2bOnEnj2jARL+uPeJk88TLQ7yPccxgQOPfNmzfXjTfeKKl+jz0nJ0eXX3657Ha7tm3bpk2bNslkMumyyy7Tiy++GNKX4vTTT9fPf/5z2e125eXlafPmzfJ4PBowYICefPLJSk0oA6VJn3zyiQ4dOqSVK1cqPz9fubm5euqpp0JW4kqS1WrV6NGjlZ2drYMHD2rDhg06ePCgunXrphkzZujBBx+ssXFjtJgMNvsFgvbs2aMRI0ZEbKsZAGgoiI8AEB7iJRoDPkoDAAAAAABhI5EAAAAAAADCRiIBAAAAAACEjR4JAAAAAAAgbI1u+8eCguTeBzURtWjRREVF5bUPRFRw/uMn3uc+Jyc2Hc6Jm5EX7+dOY8a5j694n3/iZnKK9/OmseP8x0+8z31NMZPSBpwyq9US7yk0apz/+OHco7547sQP5z6+OP+oD5438cX5j59EPvckEgAAAAAAQNhIJAAAAAAAgLCRSAAAAAAAAGEjkQAAAAAAAMJGIgEAAAAAAISNRAIAAAAAAAgbiQQAAAAAABA2EgkAAAAAACBsJBIAAAAAAEDYSCQAAAAAAICwkUgAAAAAAABhI5EAAAAAAADCRiIBAAAAAACEjUQCAAAAAAAIG4kEAAAAAAAQNhIJAAAAAAAgbCQSAAAAAABA2EgkAAAAAACAsJFIAAAAAAAAYSORAAAAAAAAwkYiAQAAAAAAhI1EAgAAAAAACBuJBAAAAAAAEDYSCQAAAAAAIGzWeE8Ajc/GvEKt2LBfBcV25WRn6ILcdurdpVW8pwUAAAAACAOJBMTUxrxCfbRsZ/Drg0X24NckEwAAAAAg8VHagJhasWF/nY4DAAAAABILiQTEVEGxvZrjjhjPBAAAAABQH5Q2IKZysjN0sKhyMiEnOz0Os0F16GMBJCdeuwAAIBZYkYCYuiC3XZ2OI/YCfSwOFtnlM070sdiYVxjvqQGoAa9dAAAQKyQSEFO9u7TSuIu6qm2LDJlNJrVtkaFxF3XlE7MEQh8LIDnx2gUAALFCaQNirneXViQOEhh9LIDkxGsXAADECisSAITIyc6o5jh9LIBExmsXAADECokEACHoYwEkJ167AAAgVihtABAiUHbi7/zuUE52Op3fgSTAaxcAAMQKiQQAldDHAkhOvHYBAEAsUNoAAAAAAADCxooEAACABLcxr/B42YpdOdkZlK0AAOKKRAIAAEAC25hXqI+W7Qx+fbDIHvyaZAIAIB4obQAAAEhgKzbsr9NxAACijUQCAABAAisotldz3BHjmQAA4EciAQAAIIHlZGdUczw9xjMBAMCPRAIAAEACuyC3XZ2OAwAQbTRbBAAASGCBhor+XRscyslOZ9cGAEBckUgAAABIcL27tCJxAABIGJQ2AAAAAACAsJFIAAAAAAAAYSORAAAAAAAAwkYiAQAAAAAAhI1mi0hqG/MKj3extisnO4Mu1gAAAAAQZSQSkLQ25hXqo2U7g18fLLIHvyaZAAAAAADRQWkDktaKDfvrdBwAAAAAcOpIJCBpFRTbqznuiPFMAAAAAKDxSKjShr/97W9asGCBtm3bpqysLPXv31933323unTpEjLu448/1rvvvqtdu3apWbNmuvzyy3XnnXcqMzMzTjNHPORkZ+hgUeVkQk52ehxmAwAAAKC+6H2WXBJmRcJLL72k+++/X6Wlpbruuus0cOBAffHFF5o4caL27NkTHPfmm2/qwQcflM/n06RJk9SjRw+9++67mjZtmlwuVxwfAWLtgtx2dToOAAAAIPEEep8dLLLLZ5zofbYxrzDeU0M1EmJFwoYNG/Tmm29q4MCBeuutt5Se7v9E+ZJLLtFdd92l1157Tc8++6z27dunV155Rf369dPChQuVkpIiSXr55Zf1+uuv68MPP9SkSZPi+VAQQ4EMpT9z6VBOdjqZSwAAACDJ1NT7jGv7xJQQiYT3339fkvTUU08FkwiSdNlll2nixIlq187/CfMHH3wgj8ej6dOnB5MIknTrrbdqwYIF+stf/kIioZHp3aUVwQUAAABIYvQ+Sz4JkUj48ssvZbPZKvVCkPzJhYA1a9ZIks4999yQMWlpaerbt69WrFih0tJSZWVlRXfCAAAAAICIoPdZ8ol7j4TCwkIdOXJE3bp1044dOzRjxgydc845GjBggO68807l5+cHx+7evVutW7dW06ZNK91O+/btJUl5eXkxmzsAAAAA4NTQ+yz5xD2RcOjQIUnSwYMHNX78eO3du1fjxo3TgAED9Pnnn2vixInau3evJKm4uLja1QaB42VlZTXen93pUbnDI59hRPBRAAAAAADqo3eXVhp3UVe1bZEhs8mkti0yNO6irpQwJ7C4lzaUl5dL8pctXHnllXr22WdlsVgkSQsXLtTTTz+t2bNn67XXXpPH41FqamqVtxM47nQ6a7y/zKZpcrh98pqk1DSrMtNTlJpiieAjapxycigniSfOf/w0hnPfokUTWa3EyUhrDM+dRMW5j6/GcP6Jm5HXGJ43iSwW539YTpaGDewc9ftJNon63I97IsFs9i+KsFgsmjlzZjCJIEnXX3+95s+fr2XLlslutys9PV1ut7vK2wls/ZiRkVHj/RUftavc4Qk5ZrWY1CTNqvRUq8xm06k8nEYpJydLBQWl8Z5GoxPYa7eozKUWTVPZsSIO4v3cj9UbS1FReUzupzGJ93OnMePcx1e8zz9xMznF+3nTGASuKwuK7crJzgi5ruT8x0+8zr1hGLI7vTqjY4tqx8Q9kRAoSWjfvr2ys7NDvmc2m9W9e3fl5+dr3759atasmUpLqz6RgeP1abTo8RoqKXertNyttFSLMlKtSksli4zEFdhrV5JSrObgXruSSCYAAAAgbBWvKyVxXdmIeX0+2Z1elTvc8tXSCSDuPRI6duwoi8VS7UoDj8e/eiAjI0OdO3dWYWGhHI7K24Ds3btXZrNZZ5xxRr3nYkhyuLwqKnPqULFdpeUueby+et8eEC017bULAAAAhIvrSrjcXhWXOXW42KEye+1JBCkBEglpaWnq3bu39u/fr127doV8z+PxaMuWLcrOzlbbtm01YMAA+Xw+ffPNNyHjnE6n1q1bpzPPPLPKHR3qw+czdMzh0eGjDhWVOuV0eSNyu0AksNcuAAAAIoHrysbJMAyVOzw6fNSuI6VOOVxe1WU7grgnEiRpwoQJkqRnnnkmZGXCH/7wBx04cEBjx46VxWLRmDFjZLFY9OqrrwZ7IkjSvHnzVFZWpokTJ0Zlfk63f5VCQbFdZXa3vD5WKSC+crKr7gXCXrsAAKCx2ZhXqHmfbNSs+Ws075ON2phXGO8pJRWuKxsXr8+n0nKXCortKil3yeOt326Gce+RIEnjxo3Tv//9b33xxRcaO3ashg4dqh07dmjZsmXq3LmzZsyYIUnq2rWrbrrpJr311lsaO3ashg0bpu3bt2vp0qXq379/MCERLV6foTK7W2V2t9JSLMpIsygtxSKTiQaNiK0LctuF1LJVPA4AANBYUN9/6riubBzcHp/KHe46rzyojuWJJ554IgK3c0pMJpMuvfRSNW/eXJs2bdLy5ctVVlamK664QnPmzFHz5s2DY4cMGaKWLVtq48aN+vLLL+VwODRu3DjNmjVLTZo0qfW+io7a5fac+ooCr8+Qw+WV3eWVz2fIYjY12h0fMjPTVF7uqn0gIqZNiyZq3TxdR0occnp8at0sXZcN6sQbZozF+7mfmZkWk/vh9R158X7uNGac+/iK9/knbianmp43i77cqWMn7cgmSUdKHDqnR5toT61BqHhdaXd61aZFRsh1Zbxft43ZqZ57w/D/zVpa7lKZ3V3n1QdtW1ffNiAhViRIktVq1ZQpUzRlypQax5lMJl1//fW6/vrrYzOxWgR6KRxzeJRqNSsjzar0VFYpIPp6d2ml3l1asSUPAABotKjvj4zAdSUaBo/XJ7vTI7vTE1bjxPpImERCQ+Dy+OTyuFRaLqWnWpWRZlWKNSHaUAA1qmnvYABIJMQrABXlZGfoYFHlZEI06/uJQ0hUDpdH5Q6PXBFYgV8bEglR4DOkcqdH5U6PrBaT0lOtSkuxkFRAQqK2EECyIF4BOFms6/uJQ0g0Pp8R/NvTF63lB1XgL9so83j9DRoLSxw6fHzXB4+XXR+QONg7GECyIF4BOFnvLq007qKuatsiQ2aTSW1bZGjcRV2j9kc9cQiJwu3x6miFnQVjmUSQWJEQU54Kuz6kWI73U0izyEw/BcQRtYUAkgXxCkBVYlnfTxxCPAWaJ5Y7PHLH+cNpEglx4vb65C53qdQuZaRa1STdKqulYS4QoY4sscWjthBA4xKp9wHiFYB4Iw4hHrw+n8od0W2eWFcN8y/XJGIc76dw+Kjj+JYrHhlGgjw7IiBQR3awyC6fcaKObGNeYbynhuOqqyFk72AAkRDJ9wHiFYB4Iw4hlhwuj4pKnSooduiYI3GSCBIrEhJKcNcHu0lN0qzKSLPIYk7uXE9NdWSsSkgMgd+D/9NCh3Ky01k1AiBiIvk+QLwCEG/EIUSbzzDkcB7ffUEmOd3eeE+pSiQSEpDveC+FY3a3UlMsapLu3/UhGVFHlhzYOxhAtET6fYB4BSDeiEOIBpfbK7vTI4fLqwRaeFAtEgkJzJDkdHvldHtlNZuUnmZVWopZKdbkSSpQRwYgUdG/JTZ4HwCAU8d7VsPkX33gVbnTLY83GdIHJyT3uvlGJLDjQ2GJU4eK7Tpa5pTd6ZHXl9hbSVJHBiAR0b8ldngfAIBTw3tWw+Px+lRyzKWCYrtKyl1Jl0SQWJGQlHw+Q3aXV3aXv17GajEpI82qjFSrzObE2kqSOjIAiYj+LbHD+wAAnBresxqGwNaNdqdHLk9ifxgcDhIJDYDHa6i03K2ycrfSUi3KSEusngrUkQFINPRviS3eBwCg/njPSm4er092Z2Jt3RgJJBIaEEOSw+WVw+WVxexfpdAkLfFWKQBILk6XV1arKel3kamIun0AQLLgPSs5OV1elTs9CbvrwqkikdBAeU/e+SHNqrTUxFmlACB5FB9zyjAks9mkVKvZ/y/FIqsleRMLF+S200fLdlZ5HOGj+RcARN8Fue303v9tVZndLY/XJ6vFrKYZKfV+zyJ2R4/X55Pd6T3ey64BLT+oAomEBq7izg+BVQoZaZYG9ckigNjw+YzgqifJXSGxYFFqijmpEgvU7Z+6QPOvgEDzL0mcRwCIsJPXF9d3vTGxO/IMw5DT7ZXd6W2wqw+qQiKhEQmsUiizu5XGKgUApyg0sSCZTVLK8aRCisWsFKtZJlPillZRt39qaP4FALGxYsN+padZlZ5mrXS8rvGW2B05wd4HLq98DXz1QVVIJDRSgVUKZrNJ6SkWpaX4L/4T+aIfQGLzGSdiS4DVYlKK1RJMLFgtJuJMA0HzLwCIjUjGW2L3qXO4PI1u9UFVSCQ0cj6foXKnR+VOj0ySUo8nFNKSvP75ZIlaC5ao8wIixeM15PF6FLhsMUmyHk8qpFj9sYaGsMmJ5l8AEBt1ibe1XVsSu+vHZxhyOD065mj4vQ/C1XD+UsQpC/RTKC136/BRhw4V21VyzCWnyyvDSN4XTKAW7GCRXT7jRC3YxrxC5gXEmCHJ7fWp3OnR0WMuHSq26/BRf6yxOz3yeJN/X+XGoromXzSsBIDICjfehnNtSeyuG4/Xp5Jylw4X21VS7iaJUAErElCtk1crVPwEMTUleXorJGotWKLOC4i1wKoFOf1fm02hqxZSraxaSEQ0rASA2Ag33oZzbUnsDo/T5dUxh1suDx9wVIdEAsJiSHJ5fHJ5fDrm8MhsktJSLEpPtSb8aoVErQVL1HkB8eYzTsSbAIvZFEwsWC3+Zo4kF+KPhpUAEBvhxNtwry2J3VUzDEN2p1flDrc8rDyoFYmERmbbnmJ9s+WQikqdapGVpnN6tFG3Dtl1vh2fIdldXtldXlkKy3XsmEvpqf6mjYkmUWvBIj0v+i2gIfP6DHkr7BAh+ZMLwZULx/9LcgEA0Fgl6jVvonN7ArsveJTgn48mFHokNCLb9hTr86/zVVjilM+QCkuc+vzrfG3bU3xKt+szDNmdHhWVOlVQbFeZ3Z1Qdc6JWgsWyXnRbwGNkdfn37e5zO5WUZlTh4rtOlRsV1GpU2V2txwuj7y+xIlFAOLH6/PJ7WncHdbR8CXqNW8i8hmGyh1uFR51qLDEoXInSYS6YkVCI/LNlkPVHq/PqoSqeH2GyuxuldndslpMSk+1Kj01vjtAJGotWCTnRb8FwM/nM+T0hW7JFOi5YK2wcqEh7UoDoDKvzyenyyen2yu3xyufIaVaed2jYUvUa95E4vZ4Ve7wyOHyirzBqSGR0IgUlTrrdPxUebwnkgqpVrMy0vxJhXjsIZ+otWCRmhf9FoDqVdVzwWSSUizmkKaOJBeA5Obfns0ru9MjdwKtjARiKVGveeMp0PuA2BBZJBIakRZZaSosqZw0aJGVFvX79l/Eu1RqNykj1aKMNCsX7RFETRxQN0bF5MLxsBhMLiTJygX6oiAcDf154vH65Pb4Vx44+YQRQAUer0/lDnofRAuJhEbknB5t9PnX+VUejxWfz9Axh0fHHB6lWs3+Bo2pFlnMiXuxngwuyG2nj5btrPI4gPAYVaxcMJukFKtFVsuJXSMSIV4F+qIEBPqiSGpQfyTi1DSk54nPZ8hnGPJ6DbmPJw/cXp98dFYHUIFhGHK4/KsP2LoxukgkNCKBPgiR2LUhEoIX7OX+fgqpKZak+BQwEVET13j4jjcYdHt8ysmJ92waPp8h/yed7hPHzGZTMFbFa7cI+qIgHMn4PDEMf4xzeXzyeg15vT55fQYrDQBUyzAMuTw+OVxeOV0ekV+MDRIJjUy3DtlxSxzUxOM15PF6gl8HmqNVrF0muVAzauIaHp/PkNdnyOP1J93cHq88Xt4d463Kho4VkwvH/5mj2A+GvigIR7I8TzxeDqChZAAAIABJREFUn1xuf3mCy015AoDwuNz+rehJHsQHiQSEZdue4piuZKiqOVpgiXFaikXpqZaofALY0GtJESpRft8+nxFcpuv1Hv+vz6CeLw7qG+uqSi7s2HdU3/54SEdKnGqTnaELz26nPl1bR2Se9EVBVU6OaalWsxzuykt74/08CfQ1cLm9cnooTwAQPrfHnzxwuLwhsSPWf6s0RIZhqKjUqZ8Olmr3wTLtPliq/545qtrxJBJQq217ikN6KxSWOINfx/IFemKJsVel5VKK1RzcXjISSYWGVEuK2sXr920YxomLaI//v14uohNCJGPdybe1r7BcHyzZoZJjLvXs3ErpmWlyurz1LougLwpOVlVMczg9MiRlpIVe7sXyeVIxUer2+OQ1m3X4aGKtiACQ2KpLHgQkyt8qycbt8Wnv4bJg0uCng2U6ZnfX/oPHkUhArb7Zcqja4/F6cRqqsBNEMKlwao0bk7GWFPUXy993oK9B4B8rDRJTJGNddbf19eZD+ln7bJUcc6mozL9dhMVsCinlCqcsgr4oOFlVMS39+LbLzTNTY/I88RnHkwYeXzBhenKilMQpgHD4fIbKnR7ZnZ5a40Yi/q2SiEqOuY6vNvCvONh3+FiN5za7aWqNt0ciAbUqKq28ZWRNx2PtRFLB37gxxWJWSopZaVZLnT7tS5ZaUkRGtH/fHu+J7cjcHh81v0kgkrGuLrfl9RnynlQWYTWb/NtQWs2ymv1xzGI2hcQz+qKgoupimsvt061X9o74/Xl9/r4GgaSBx2dQogDglBjHk5F2p0eOOmznmuh/q8SD1+fTgcJy/XR8tcHug6UqLnNVO95iNun01pnq1LapOrXNUqe2WWqeSSIBp6hFVpoKSyq/EFtkpcVhNrVzH68xL5e/eWNgm8n0VGuNSQVqjhuXSPy+vT4j2ADR6/N3F/d4DXl8PlYdJKFIxrpTvS2Pz5Dn+DLOikzyN6K1VtgxwmoxyRTFxo7RUt8eJeH8XKL0P4mlusS02s6Px3t8xwSfIUP+fi0+nyHDMOQz5I91JA2AhHTy67tDm6bac6gspvGwrjHY6fLK7vLUe9Vmsv2tEg3lDrd2HyrT7gP+EoU9BWVy17D9ZWZGijq1aaozTstSp7ZN1b51U6VY67aqm0QCanVOjzYhdUcVjyeDwGqFknK3Uq1mpab4Gzae/GKh5rhxqevvO1CeENg9wesz5JJJR6p440JyimSsi1bcNHQiWRr4k9FkklIqJBdSU8z1LvGKlfr2KAnn5xprv5twYprX59OGHYX6eHne8U/6DO07fEwfLNmukkEu2Tpks9UikMROjn+7DpRq3bbDapGVpvQ0a0ziYV1isNvjVWm5O6S5en0k+98qdeUzDBUU2/29DQ6U6qeDpTX2njGZpNNaNvGvNGjTVJ1Oy1LLrLRT/hCCRAJqFagtagidUANJhTK7W2aTlJZiUWqKRakpZmqOG5naft9eX6CruE8utl1sFCIZ62IZN41qdrkJbJtrTcCVC/XtURLOzzXWfjcVH/+hIrtaNU/XoJ5t1LFNlgqPOoIrpZau3VtlTezqTQfV9fTmsZ42gAg6Of6VHW+cV2p3K71C09VoxsPaYrDPMOQ8vuKuYknfqWhIf6tUxenyKr/gRInC7oNllVYsVpSWYgmWKJzRNksd2mQqPTXyf/aTSEBYunXIbjAvxgCfIdld/i6wkmS1mNSpbZam/jxbaSmWOM8OsdC7Syv1PKNlSEfxw8V2PpFrxCIZ6+IZN6vaQjdQFpGSYvavzrJGZxvdcNS3R0k4P9cY+t14fSdKD4L/vD6d1qKJrrqwa0j8sjs9IT9LLTHQcJ0c/zxeX8h/T4yLXjw8eQ6G4b+mOnDEriMljqj1jWoof6sEtmDcfbBMPx0s1b7Dx7SnoKzGko/WzdPVqW1TdWyTpTNOy1KbFhm1Nm2OBBIJwHEeryGP16Nyh8e/WiHVqvQUi1JSau+gjuTg+//s3Xl0G+W5P/DvLNos2fHu7BsUhxAMCVmApGQBLi0lBVqWc0oCtOWQtFA4t0CB0pYW6OlCb8vW3lBOWRJCLwRoofx6C5cWAglJSFgSUiBAcMhC7DhOHFux9pnfH6MZS9Y2srWMpO/nHEM0GkmvJfnV6Jn3eR5VRSg0EDQIRdi/nCpHbFpEf3SbJApGpwhZ0lIjChFcGGqNEjO3a6p14bOOPvT5QghHFMiSiGqXDRNGVg9/4Dmmt4PVVzyJggBR1AJBqqrVJtADBUo0WDDcQCdziYnK1+A5UpZEhMPaPBi/X/7qf+ljUFQVqqLVVQGAhhr7sFMYylEorODzg0fjuil407RglCUBY5s9mBAtiDiu2QOPy1bAEceMpSiPSmRxiqqdxfEFwsZZPLtNO4PHwEJp0HuXhxUFYb0dGYMGRHEiiorIoKKOoihAjnaJkCQx2p5SgCTmLsgw1Jo0Zm43ttmDdz8+aFwOhxUc7gtgbh7r3ShqTLHVaIBSn21UVctPBbRVIUq0cKGiqkVphVhpucRElWTwHOlx2dDTF0D1oC+aua7/paoqgtEOLid/oREvvPFZwj6cYzS9R4NGwEBfcZDus6CuxoGxje5oUcRqjGqoskwdJAYSiDKIPYt3NNoJQpYE2GQpWjVd6wFvldzjSqSoqhYs0FcaJOldTkTmKIqKoPH3k9g1Qm9FKQhaO0pRgPbv6P+FJJdFIb4+w1Br0pi53d4DXtRWO+CNWZHgcdmw94B32M+NtoJACxYYbQ8jCkppuin3XGKiSjZ4jpw4shpj20ZFuzbktv6XqqpGnYPYbgsTR9bgnNnjOMdAC9Z3HOo3CiJmasEoCgJGN1ZhQks1xrVo3RQmj6/HoUNHCzhq8xhIIBoCPQ3CqJoOGMuDuWoh9/SD94iixOQDq0U9q0dUiVQMLLXPlgAYQQVRFDCuuRpLz6mBJApQVe3Mfb8/FHcLUcSgoIWAaZMa0h4Ed/X44HLIcDnkQdsz5wTrc0pswECfbyKqWjapUOWSS0xEiTLNkcMVjijGqt1UU2KlzjH9/jB2H+gz34LRKRsFEce1eDC2KfsWjMXEQAJRDqgYKGx2FAPpEEbesawtES50cKFUeqnrucB6v/JwzBLhcHj4OcFEVHwqtGCBov0HGEKurABAiK6CEIXoyohogEFfAVFf7cABPWgQk1LQMMKJQCgCv3EAPBCUzEXtgVL38d4enkEkoqRC4Qi8vhCC0TbYxWaF+UpRVRzs8WN358Bqg3QBawFAS31VXDeF+prht2AsJgYSiPIgNh0ilmwUNpPgsOe317uVe6kriopgOMLWikSUFRXQincZlxK1HduYtAbAicc04HBfAIJNxpGjqZeWVqKP9/bEPWfdvQHjMoMJRJUnoigIBLVjtGBYQRBC2gKAhVSs+SoQimDvAW80aKC1YszUgnFcsyda28CDcc2evLRgLKby+m2ILC6sqAjrLSf7B2otiAKMZbtagTNh2EGGQvdSV1L0pTHSEqIrDIKhCIseElHesAZA9rZ8eCDldj5vRJVBUVUEghH4AmFLrDpIpRDzVWwLxt3R1Qb7D/WnbcHYMMKJCbEtGGtdRWuxXCgMJBAVkV5rIRUxJp84toK6vi1drGE4vdS1CuTRdmOKCi3pQFuWHFtALRzROiMEw5G0kysRUSFVan7uUB3uS2wHmW47EZW+UFg7sRWOdrUqlRow+Ziv9BaMWn0DLXjQl6kFY5OWoqCnKhSrBWMxMZBAplghF6kSKbH5xAAGV1AHAEWU0NPjM/rB220S7LKIploXOg71J+zfWOtERIkuDI6pR6C3Loso5iuQ831BRIOZnRc4f1hHXbUD3b2JB+F11Y4ijIaI8kFRVaOzlT8QLtnVobmYr/r6g/gsZrXBvq70LRhHuO0Y1+LBhGhtg5ENVZCl3KUnl+rnYcUFEn6+8i0IglYl0+20we2yoUr/t1OG22Uzrqtyyjl9k5Qq5k5aW2wV9WBYwVG/VuzxhEn12NuV2C5m2uQGU6sSMuH7gogGMzsvcP6wlplTmpPWlWDfd6LSparacWEw2p6xXOpRZTtfRRQVnYf6o0EDrcZButULegvGcS3VGB+tcVDryV9QtZQ/DysukJBquXcqDpsEtysm0BANMAwOOOiXHTappKtvJsPcydKjIv95wnxfENFgZucFzh/WwroSRKVPVVUEQhFj1UEoopRl2mmm+arfH8aeA33GioO9B7xpaz5UOWWMb67GhJFaisKYJjfsslSQ3wUo7c/DigskTJ1Yh8N9AfT7wzjqD2WMzgWiUbxDSZbQJCOJAtxOGVVOmxGAGLziIfZyldMGyeKFOJg7WbrymSfM9wURDWZ2XuD8YT2sK0FUevRVB/5AGP5Q5dSr0ucrRVVx8Igfuzv68Ozanfis05v2pLEAoLnOZdQ2mDCyGg01zqKeBC7lz8OKCyRc/dUT0O/Xitvpf3xHfSEc9YfR79f+r18+6g/hqC+M/sDA9nRtPgBt+Uxvfwi9/eZbpLgcUsaAgzsamKhy2mCXxYK+4Zk7ScnwfUE0fPnOiyx03qXZeYHzBxEN1fb2bqzbth9dPT401bowr21U3ttaF+Mxk4ldbRAOKwhHlBSNcMtTMBTBni6vURBx94E++AKZWzDqQQMrtmAs5c9Daz2TBSYIAhw2CQ6bhPoac7eJKEp0NcNAoEH7f8hY5aAFJQYCEqna4ul8gYj2R3DE3BhkSUgRcEiedlHlkIfVfoS5k5QM3xdEw5PvvMhi5F2anRc4fxDRUGxv78Yzaz81Lnce9hmX8/XFvhiPqRdGjEQUhKIFscs1VSEVVVXR4w0YdQ12d3rR0X00bUHwhhqn0UVhfIsHLXVVlm/BWMqfhxUdSBgKSRRRXWVHdZXd1P6qqsIfjMQFGbQAQ0zgwRdznT+EYCh979ZwRMWRo0EcORo0NQZBAFyO9HUe9NUO+mWbPFBkkrmTlAzfF0TDk++8yGLkXZqdFzh/ENFQrNu2P+X2fH2pL8RjhiOKsdogGC6fwojZCEe0Fox60GB3Zx/60qzwliUBY5o8mGAEDkqzBWMpfx4ykJBngiDA5ZDhcshoGOE0dZtQWBlIs4gJPsQFI/RAhC+E/kA4bYRSVbXCI/3+MLpMjtsuizHBBi3AMMLtwKgGN9wuG0JhBZ919GkFJqvsUFW17IpMUmbMqa0MP3tkM0RRgCQKEAUBohj9EaLb4q5D4r5C4v5izP7J7jf542nBXFGEiXEM/Ej6uGLvN8n4Ci3feZHFyrs0Oy9w/iCibKXKf89FN6p8PKaqal21IhEVYUWBoqjaj4ro/7WfSlppoOvrDxoBg89MtGCscdu1FIVo0GBUjlswFlOpfh4OKZAQDAahKAqcTu2LcW9vL5588kns378fbW1tOO+88yDLjFEMlU0WMcLjwAiTrUYUVYU/EIZ3cMDBF44LSMRuD0XSr3oIhhUEvUH0eM2tehAFIZpikb6+A1trEpWeTLVhyoEAxAcXooGHhKBD0iBJbIBDNIIp6W7vrrIjENLS2vQYrAABEACPU8am9zuTBF0ESALigiTGYwweX7Twb483CAgChJhftK7aAUVVITL4S0QlpqnWhc7DiV/sm2rNnazL9WOqqopwREE4ohqtuBVFRURRoKiAqqgVVcMglcEtGHd39uFQ2haMwKhGt7bSYIgtGAtdI6gSZf1t/4EHHsCf/vQn/PznP8e5554Lv9+PSy+9FLt27YKqqvjzn/+MZ555Bg8//DBsttJbXlKKtC/xNlQ5bUCty9RtguGIkVIxOMUiMQ0jDF8gnPb+FFWF1xeC1xcCkky2ySRrrTk44BDb+aIcW2sSlYLjJ9TFHBypUKNnUPRt+nZFjbmsn22J2Z7uTEOxqdAOdCJQgSLHTXqPBvHcuva83X9Hdz9+9NAmCAISVmUMBD6SBENSrS5JsRIl4d8pV6cgxfZk94UU202sThHAzxCiEjevbVRcvYLY7bkWjiiIKCpmH9+M59btAoBoUEAFVKDtmAYcOOxjoCAJXyCMPQf02gZ92HPAmzZ1u8ohx9Q2qMbYJjfstqG3YCxGjaBKlFUg4fnnn8cDDzwAu11byg4Aa9asQXt7OyZOnIilS5fipZdewptvvomVK1fi29/+dl4GTcNnlyXYqyXTFUEjigpfYHBNB+3fCoDuHl9CACLTl4ahttbU0y2q4uo9JNaAKIXWmkSl4PIvteZs2WVssCE2wBC7zDM2QJGwLe72SNgeGfRvdfD9qYMeL81jaLdP/xjJt2uFeRUFGYvtFpOqAhHV2gGeXNMCJOJAoMRIfUkWuECagMbgIEmaYEjK1SlI/dhJgy7xY4+7bYrVKQyeULnRaxJoHRT8aKp1ZuygoKhaaoE+X2vTsrZSQInO81qAXL8cnf+jtx/d6MFZM8cmnN2ePHoEgwjQnruDR/zRFAVttcGBNCcVE1owtlSjYURuWzAWo0ZQJcoqkLBmzRrYbDY89dRTmDJlCgDg73//OwRBwO23347TTjsNF198MRYtWoQXXniBgYQyIokCPC6bVsSkLv66+no3Dh06GrdNVVUEQ8pAwCFmdUN/kgKTR31hBEL5aK0ppy4wmSQNYzjRTyLKTBQEiJIAVMifmn5wqg4KVNTUuHDo0NGE4IMaPZBNFhBJFqzQAx3J9xkcHIGx3DZ1ECRJACZmTIN/j+SBlWI/66kpKqBkSO0rN3FBkySBkMTtyBDQSAxWJKtdkmp1Sk21Ez5fcGCFiZlVLzErUjLVVBEYPLEs4wt63Jd27Su9/m8YqV4Y+JKuArLTjyPeAFQAYxo9uGThsXEB7u4jA/UK1OiKAT1okIspqVRz2PMhGIpgb5c3rptCupXLdpuIcc0DtQ3GNXvgcuQ3Bb5YNYIqTVav4o4dOzB79mwjiHDkyBFs3boVbrcbc+bMAQDY7Xa0tbVhw4YNuR8tlQxBEOCwS3DYJdTXmMtbC0eUQS00kwcc+o30CzOtNbW0jINHzBXhsUli0oBD1eC0i+j1LofMPGMiSkkQtLoGgAA5JngywuNAJJg+ZaxUqdGD99hVHmaCD7F5xZlWo0SigZG092nsFxtoASRZhD/6+ZEuOJJpNUps8Mb6wRMVqKAq8Im1S7RAgyyLeOz2LxV7eDmlqtEz59GXV8VA4T415pt43BdqNX6/7B5PuwNF/6IePXM/8Jhq3L764wy3oKDTH4avAur16KyS36+1YAxiZ0cf3t/Zjd2dfdifoQVjfbVDW20wUgseFKMFY121A91JVjybXYlN5mQVSAgGg3C73cbl9evXQ1EUzJo1C6I4UDgvEolAUSor4k/DJ0siatx21Liza62ZrLBkwgoIk601QxFl6K01M9V7iAYgWGSSiMqZED0rLA4KnlhFslV0w6XXDdGDG0bAYXBgI21QIvnqEqPqe0wwxNyqldj7VZIGR5IGSmIDMrErZAYFaqwcllBUFUoECEeK98Wzx6t9idG/PMeecxj8hVq/bvCXcSX67V9FNAiQEDSgclTM/P5wRMH+7qP4rMMbLYzYl3YlsCwJGNPoialv4EF1lbnj+HyaOaU57jmM3U65k1UgYezYsfjoo4+My//85z8hCAK++MUvGtv6+/uxdetWjBkzJnejJEoitrVm4whztzHbWtNY+ZBNa80ec2Nw2KSBDhdpAg56yoXTziKTRERWpq08EVBJceLEOiHmUmfcbgd6en2DVo4AyqDbJqtFMjjdJnbViZkVL4X8KK2EbjeUH4XM7+/rD2pFETu0FIV9B70Ip1m9VF1lw/iWakxoqcaEkR6ManBb8gSZ/jxZYVVHOcsqkDB37lysXLkSt9xyC1paWvCPf/wDsizjnHPOAQC8/fbb+N3vfocjR47gkksuycuAiYYj69aaigp/MHNrzdhUjEytNfUik2bztERBGBRciE+xaGn0QA1H4lIyJNF6kzoREZWPodY70VaEFOeMpV3mZyNZX77y+xVFRefhfnzW2Yc90foG6QqeiwIwssGN8S0eTJ3ciAaPHbUee8mc3GJdi/zLKpBw7bXXYuvWrfjrX/9qbLvxxhvR0KBVSr3uuutw8OBBnHzyybj66qtzO1KiIhDFIbTWDEWMAEN/TIpFfMrFQPDBF0h/1kJRVfT5QujzmS8y6bRLMUUk9aDD4KKTA6sg7DaxZD4YiIiIiMpVrvL79RaMu6MFEfcc8KYtbO6KtmCcEE1RGNvkMYqQ5yMljEpfVoGE6upqrFy5Ev/7v/+Lrq4uzJo1CyeddJJx/QUXXIBRo0bh4osvht1e/PwYomKw2yTYbdm01lSM4pFG8CGm0GSyVRCZ2rX5gxH4g+Zba8qSEJdeURWXdhETkNCDEQ45ZeGcXBYIskqxIaJC4vve+vgaUSnj+9fahpLfr6oquo/4jS4KegvGdEeLzXUujG/2YMJIrZtCY45bMFL5yyqQ8OCDD+LYY4/F+eefn/T6G2+8MSeDIqokkiiiuspuujiNqqoI6KsefCGINhmdXX2JgYeY1RCZWmuGIyp6jwbRa7bIJACnI7HOQzAUwa6OPqN9mD8Uwf/b8BnOma3g+An1pu5bV8xiQ0TFwve99fE1olLG96/1mcnvD4Yj2HvgKPYc6DMKI/ana8EoixjbPLDaYHxLdd5bMFL5y+od9PDDD6O5uRlnnnlmvsZDRBkIggCnXYbTLqOhxqktN6tPn3YxuLVmugKTespFukUPKrJrrbnqxY9gk8X4wINjUKeLmAKTbqeMzR90Jr2vfBQbIrKKQhbZoqHha0SljO/f0jA4v7/HG8C2nQfxWXS1wf6D/WlboBstGKNBg5b6KkgFbsFI5S/r9o8TJkzI11iIKE+yba2pqCoCwUjS+g79gwtMRrcHwxlaa4YV9HiD6PGaW/UAwFjZoPUE1376+oN4Y/t+ttakspSvIluUO3yNqJTx/Wt9WgvGfuzu7DNSFdKtGI1twTiupRoTLNKCkfIvITQkRLcJgnYMHT1+HryfIAhJbhzdXxAgitq/M6W6ZBVI+NKXvoR//OMf+PDDDzFlypRsbpqVX/3qV3j44YexcuVKzJkzJ+66v/71r3j00Uexa9cu1NTU4Mtf/jKuu+46uN3uvI2HKJNyyzcUY1trmrxNKKzgiZd3oLs3EG25NdAezC6LGNXgjlsB4fOHM/bAVlRAiRgdtA0vvPFZ0v3jWmu6ktV8sEW7XphvrVlury1ZW66KbFH+VNJrxPmv/FTS+7dUeH2haEFELWiwtytDC0ZXtAXjSG3FwehGa7ZgrCT6iS9JFKP/H/hCLonaF3Ix+uV98FFnpmPhxgY3ZEWJu6EAWKKeRVaBhAULFuCdd97BRRddhFNOOQVTpkzBiBEjIKZoNbd8+fKsB7Rt2zY89thjSa978MEH8dvf/hatra1YsmQJPvroIzz66KPYunUrVq5cyQKPVBTMN9TYZBGnTxuVtEDQObPHJTwXiqLCFwwbtR1iO1r0+8PoONSPfV1erTd5NCCRSbatNSVRMIIKVc7ENIu+/iDe23kouhoCOHjEX5GvLRXOUIpsUWFVymvEz7byVCnvX6vSWzDqBRF3d3rR3Zs6RdRowdjsiQYPPKj1OCzxJbISCMBAYCD6owcJpJjLw3k9Mt1SfxwryiqQcP3110MQBKiqik2bNmHTpk0AEiMiqqpCEISsAwnBYBC33XYbIpHEwnCff/457rvvPkyfPh2rVq2CzWYDANx77734wx/+gKeeegpLlizJ6vGIcoH5hgPMFAjSiaJgdIMAktd4iD0bVuux4+QvNGFUQ1V88CFNm01/MH2RyYiioq8/hL5+8601BQFY/X8fYWR9FWqrnbBJQlwAQlvxMHDZLrO1JpmXzd8QFUelvEb8bCtPlfL+tQp/MBwXNMjcglHCuOZqrSjiSK0FoyPagpGGRwAgiAIkPV1WO62fGCyIriQwVhFQSlkFEq655pq8HhCvWLEC7e3tOP300/HGG2/EXffkk08iHA5j2bJlRhAB0FY9rFy5EmvWrGEggYqC+YbxBhcIysd91VWbu33S1pq+QQGIQa02M7XWVFUgGFKiBwbejGOQo4GGuJUPrvj6DlUmW2tSZcjl3xDlRyW8RvxsK1+V8P4tBlVV0d3rx+5OLz7r6DPVgrGp1oUJ0YKI41uq0Vjr5JfXLAkCIEXz+fUAgaAHBKIrByRp+CsHKFFWgYTvfe97+RoHPvzwQ/zxj3/EsmXL0NvbmxBI2Lx5MwBg1qxZcdsdDgdOPvlkrFu3Dn19faiuNvkNgyhHUuUb2iQBf375I0b8i2i4rTX/d+NnOHI0iIiiavUaFC3FQhQBl11GfyCccdVDOKLiyNEgjmTRWtPlkJMGHLTLA8GIE1tN3SURUdaYS0+UXjAcwb6uo1pRxA4vdh/oQ7/ffAvGcc3VqHKyBWMyArQl/bI8qOaAIEAQtNUCgoCcBAe2t3dj3bb96OrxoanWhXltozBtUkNufpEyl9W799Zbb8WMGTNw8cUXp91vxYoV2LhxIx599FFT9xuJRPDDH/4QEyZMwLJly3D33Xcn7LN79240NjbC4/EkXDdmzBgAQHt7O9ra2kw9JlGuJMs39AfDCASBQFiLQzO3tDQMbq256JSxaWs+1Ne7caCrb6CDhZFyEb/KYSANIwyfidaa/YEw+k201vzS3MnD/I2JiJJjLj1RvCPegNF+cXdnHz7P0IKxrtphtF+cwBaMKcnRgIEsiZAlAbIkFmz1wPb2bjyz9lPjcudhn3GZwYTMsgok/OUvf0EkEskYSNi8eTPefvtt0/f7pz/9CR988AGeeOKJlAUTe3p6MHbs2KTX6asQvN7My4yJci1ZvqG3X0QgSTtE5paWFjO5pLIkYoTbjhFZtNb0ByIDrTP9ofj6DnoAIjCQhhHK0FqTqJIZXayiB50Jba4AKABURY1bYqy3xlKhrUZSET3LFb1N2ESB13LGXHqqZBFFwf6D/dH2i1p9g3QrCyVRwJgmt5YQkPpsAAAgAElEQVSi0OzB+JHVqGELxjiCANgkUQsaiNGggSwWNZVj3bb9KbczkJBZ2kDCjTfeiAMH4ovtvPHGG7j88stT3sbr9eKDDz7A6NGjTQ2gvb0dDzzwAL7xjW9g+vTpKfcLh8Mpgwz69kAgc95e7QgXnFU8KM+1+vrKbr85p96NOW1jjMu/ePRNyFLixNjnC+Xluar05z+fBr+2gxXiuQ+GIvD6QvD2B9HXP/D/QhlR44LI1lI5V6l/t/rZJkVVoaral3hl0Jd8bcmqdoCp57bGFsPS/i9mfXYvoqhQVRWjG90Zz3ZFFBWBoJa+FI4oUKPpTSq0cVeCTPPfUBXrvW+XC1e0rqbGCbmAj1cJ8vm+6esP4tN9R/DpviPYufcIPuvoTRvEr3HbMXnMCBwzZgSOGVuLcS3VsMnl/Tlp5vkXheiKAmkgDUESBdhtEmwWLD592BtM+rr1HA2iqck66fJWGkustIGEhQsX4oYbbjAuC4KAgwcP4uDBg+nvVJZN1VNQVRW33XYbGhoa8P3vfz/tvk6nE6FQ8gPnYFCLELpcySu/x+o54kubv0TZq69349Cho8UehqXUVNmS5pY2uO05f67y8fyzd7g5hX7ve+wSPHYJqHUW7DEB4Eivr2K+OBVKuc+bArSWsLKsfdmXov21ZUmAEFGhRmCc+QcACYgGFlTt4BMCEO3gFAkD6SuRZKepqRpdXX1Z/z4CgNjDTTUaCFGigZBI9CcUVhCJKBW/oiGVYr737bKIprrMx4q50JumpR9lbyjvm1THMoqi4kCPz0hR+KzTi+40qYSCAIyqr8K4aG2DCS3VqKuOb8HY1+sb8u82HIU6Xkv1/EuiAIdNgsMmwWYTIUIAwkrCvF2cZyezOo8dnYcTR9dS58r6cyJfhvKZlevHTyVtIOErX/kKRo8eDUVRoKoqlixZgnnz5uE73/lO0v0FQYDD4cCYMWNQW5v5Tbx69Wq89dZb+OMf/wi3O32Uq6amBn19yZ9EfTsLLZJVlHJuKXuHE1EmemVsfUlqbH9tWRKH1PZU1Cpo5WO4eaEX+xIhaJGQQVRVRTiiIhxREIooiERUhCIKFAYYiPIu9lhGUVTsO9iPXa/uRHWVHd1H/GlbMDrtUlxtg7HN1mzBWMjjNQFaIE5fWaAHiK22wiBb89pGxdVIiN1OmWWskRCbbnDhhRdixowZOOWUU3Ly4C+++CIA4Oqrr056vZ5C8c9//hMTJ07E5s2b4ff74XTGn43bt28fRFHEhAkTcjIuouEq5dxS9g4nIkCrIaAtUY3msooiW2hlQRAE2GQBNllE7DlwVR1YvaAoqrGiQYl2h4koSjQFo2hDJypZqqriUG8A/7d5D3q8AQRDEYQjA39MydICm2qdRvvFCSXUgjFfx2uyKECKpifotQxGNrhhS9vIsjTpdRC0rg1+NNU62bUhC1kVW/zFL36R8rpIJIIjR46gvr7e9P1deOGFmD17dsL2119/HVu3bsWFF16IMWPGoKamBqeccgo2bdqELVu2YN68eca+gUAA7777Lo499tikHR2ICk1vSzNlfB2OH1836Ertf6Le73ZQr1tRxMD26IeYfjCpL/tVVcQdgFY5ZASccvQMoWD8H8DAQaqqIhLRD14V44A1GfYOJ6o8eiqCTRZhlyXIspaKQLknCEK0Mnn6/cIxqxjCEQXhMNMliAYLhRXs7fJiT6fXKIx4NE0KswBg0ugaoyDi+GYPqpy2wg04h4ZzvCaKAqRoDRq9bo2+0iBZoFgs424T0yY1MHAwRFk3L+3u7saf//xnLFq0CFOnTgUAPPHEE/iv//ov9Pf3Y8yYMfjxj3+M+fPnZ7yvr33ta0m39/b2GoGEOXPmAAAWL16MBx98EA888ABmz55tFFhcsWIFvF4vLr300mx/FSIA2uQoR5fl6l/C9QreCVTEFwSL/kcWtQNwWSr8mbq6GifCgewL7+nBhdhlt5GIgvoaBw4eYe9wonImCoBNlqKBA9GSRbAqndYKDXDE5E0oqlaDIRRWEAxFEAorZXiOkCi1I0eDWm2Djj58ZqIFo1boTwuQ2mwiRta58I2zWws44vypq3YkrccVe7wmCsDOz49gy4ddONTrR1OtC188aRROnNxYyKFSmcoqkNDR0YGLLroI3d3daGxsxNSpU/Hee+/hzjvvhKqqqKurw969e3HNNdfgqaeeMgINuTB58mR861vfwkMPPYQLLrgACxcuxCeffIJXX30VM2bMwCWXXJKzx6LyFXvwbJO0g+dyjrKmIwoCxCTLbs+aOQ5Pv7pTOzhVYVQonz21BQLAg1aiEiNA+1KqH0xztUHpEoWBwmZw2aJ1GBQEQtHgQjjClAgqGxFFwf7ufuzu7EPHYR8+3t2TsQXj6EY3JrRUwyYLeH/XYUiDOg7NOr4l38MumGT1uAQAc08ciRFuO+w2ER98dhgvbd4bvVJA1xE/nn2tHYIg8Cw8DVtWgYQHH3wQBw8exFe+8hUjveDJJ5+Eqqq4+uqr8f3vfx/r16/HVVddhYceegi/+93vcjrYG264AaNGjcITTzyBlStXoqmpCVdeeSWuvfbalK0hqTIJ0FIGJEHrUaufcZPZwi6jTPli+nLbsKIYZ8YiXG5LZClytN2WUUmbqw3KklaHQYItJk9Cm5cjCIYVBMMs7kil46g/hN2dXqObwt4DRxGKpG7B6HHZjC4K41uqMbrRHdfKb+KompKsVWWW/ru8vaMLh70BNNe5cMZJo+MCBOu27U9623Xb9jOQQMMmqKr52PXZZ58NQRDw4osvGksg586di0OHDmHt2rVobtYq0n/jG9/A7t27sW7duvyMehh2ftbN9o85VqxWTvpZNjmaUqBXkBXFyjrbVuy2MIBWiyE2jzdUIbm8xW7hd2JrYc6sbP+ok2c5cyyX7x1BgLZsNzoXanmulTMHZssKc2YhhSNaGkQgpFiia0Sx2z+2HtNUkMd6b0dnQR6nVCmqigOHfdgTbb+4u7MPBzO0YBxZX6UVRWz2YMLIxBaM5U4vgCvLolEEUZbSr6y987HNSWtiiYKAH18x0/RjV9q8aSXFfu6H3P5xsM7OTixcuND4o/33v/+N7u5ufOELXzCCCNoDNmHbtm1DHC5RIlGA0YNcKwgmxUWd82V7e3f0zLwPTbWuiq7kmu65EEUBDlGKa48UURQEQ9oZsVAoUhGBBaJ8EwBIkgBb9GDSHp0P84HzX3n4cPfhuNfxtGkjcdzYWs7NFvbx3p6yO5MeCEaw58BAQcQ9B7zwB9O3YBwXDRiMb67Gia3N8B2trKLPoijAEW25aLcNLUDcVOtC52Ffku3OJHsTZSerQEJNTQ28Xq9xee3atQCA0047LW6/ffv2sYMCDZkgADZpoFetLUO0NV+2t3fH9ZbtPOwzLlfawfRQngtJFOFyiHBFa/4YgYVQBAEutyXKSJ8L5eiPTdbOPhXiDBznv/KQ7HX86+vt+Pr8ycbrqCgqgnoqxKBWeVR4H+/tict77+4NGJdLJZigqioO9QWMgoh7DnjRcag/7cq2xhHOaIqCB+NbqtFU54pLyXI55IoIJIgC4LDLcNkl2G3DDxLPaxsVNwfEbicarqwCCccddxy2bNmCXbt2obGxEc899xwEQcCZZ55p7PPyyy9j+/btOOOMM3I+WCpPsctyHTaxYAfKmTCvbEAunouBwII27eh5vFqRsEjKdpRElUAvBKsvVS12TRfOf+XBzOsoigKcdhnOaKkpRVURCmmFG0NhLR2CqU2Fs+XDAym3WzWQEAor2HfQi90d0RUHB7w46kvdTcomiRjb7NbSFKLBA3eJtmAcLr31rlbTJvcrzDLVvSIajqwCCVdccQU2btyI8847DzabDT6fD1OnTjVaNC5fvhyvv/46RFHElVdemY/xUomLrWtgi0lTsKKunsSlYNr21Dl85Sofz4Xes74qurpODyoEgpG0xZWISp0kCnA5ZHhctmiKgvXqunD+Kw9DeR1FQYDDLsFhjy/gGNsqmMHf/Dncl/yse6rtxWC0YOzsw+5OLz4/eDRt0eVaj90IGkxo8WBkQ5Xl5rxCkiWt+4rdJsFegNa70yY1MHBAeZFVIGH+/Pm499578atf/QoHDx7EvHnzcOeddxrX79u3D3V1dfjpT3+K008/PeeDpdIS2znBZhO1SbOEepUXIq/MKjnImcZRiOdCrzzucdmgKCoCIW2pbSSiFW5kKgSVmth6BvoKA1nWOijU1zgRCaQ+Y1dszKstD7l6HfXAb2yrYAZ/86Ou2oHu3sSgQV21owij0dISO7r7jYKIuzv70OPN3IJRT1EY31KNEe7K7qwWW+vAYZMqtu34UFnlWJkSZRVIALTODWeffXbS6x544AGMGzcOYgVHGSuVJGpnMGySCEnMXEW2FOQ7r8wqOchmxlHoHDsxesbWFXPcpPVL13qmRxQVwZC27JbhBbKC2HoGRtBAEkomcDoY82rLQz5fx6TB3+i8zAKOQzdzSnNcjYTY7YXQH23BqBdF3Nt1FKFw6kCR22UzuiiMb/FgTKOnIMWwrU4A4HTIqHJIll15WwqscqxMyWUVSLjmmmuwcOFCzJ8/H01Nie1zJkyYkLOBkbUJAiCL2gFzY60L9jL8OpfvvDKr5CCbGYcVcuy0funCwAGKywZVVY0CYQEWCaMCEcVo14RoepZeDLGcWOFvnoavUK/jQPBXO6xUVBVhIx1i4N+coTPT6yAUomuDoqro6vFhd6fXKIyYtgUjgJb6Koxv8WiFEUdWo77CWjBmIgpaAMHtlCs6fSNXrHKsTMllFUj45z//iX/9618QBAHHH388Fi5ciAULFmDatGn5Gh9ZhADAbpPgtCe2oHHkoKqsVeUzr8wqOchmx2HFHDtB0PIMHTYJ1YBxVkw/M8aTYjRcet9ufWm3TR5aC65SZMW/ecpeMV5HURCiLesGjg/0VWUjPA74jwaMugucphN9YWxtXgIHgWAEe7q8cfUN0rVgdNgkI0VhQks1xja74bRnvZi57ImC9lw57TLsttJJ4S0FVjlWpuSymg2ef/55vPbaa3jttdfwzjvv4N///jd+//vfo7GxEQsWLMD8+fMxd+5cuFyuzHdGlieLgtG71m6T4trw0PA11brwWUcf+nwhhCMKZElEtcuGCSOry24chcpvG3xWjDm8lA0jPSHadrbYnROIyom+qsztsqEmmjOvqipCYcVYWcZ0tdxRVRWH+wLRFAUteGCmBaOx2iBJC0YawOBBYbBej7Vl3f7xuOOOw1VXXYX+/n688cYbeO2117Bu3TqsWbMGTz/9NGw2G2bPno2FCxfisssuy9e4KcdkUdAOnqMHzrYyqHFgdWObPXj344PG5XBYweG+AOYWOAc53+MoZn5bshxerlYggEEDIisQYlcuRNPVwhEtsBCOqIhEa+Kk6whAmlBYwecHj2J3Z58RPPBmaME4ptmtBQ2aPRjXUg2PqzJbMJoligKcNq2jSSkVDy9lrNdjbUNen1RVVYWzzjoLZ511FgBg586deOyxx/Dss89i/fr1WL9+PQMJFiSKAmRRgCTpB89aYUROhoW394AXtdUOeGNWAnhcNuw94C2rcVglvy12tYJeWyEQiiAQjPAgtQLIkgC7LA0ES1kMjMhytFULicXpVFU1AgqRiIqIogUYwtF2lJU4g/caLRi1woiZWjCOcNu11QYjtdUGoyq8BaNZAgCHXYLLIZd1Kq9VsV6PtQ0r0enjjz/G5s2bjZ/u7m6o0TVT48ePz8kAaWhEUcsdlyUBsihCkgRIYulWEC9HXT2+uGX4A9sLXyMhn+OwYn5bbG0FVGlncvzBMALBCKuNlwm9IKIjmprF1QZEpUsQBO14RgIw6KR57CqGYEhLkSi3WTyiqOg41G8URMzUglEUBIxurMKElmqMa6nGhBYPRniK0z6yVMmiAJdTOzZiekdxsV6PdWUVSPjggw/w5ptvYvPmzdiyZQuOHDliBA7Gjh2LCy+8EHPmzMGpp56KlpaWvAyYktNbLtpkEQ6bxLNtJcAqeV+5Hsfgegh2WYQ/lFifwEr5bVoRPTuqq4BwRIE/GIE/EGZQoUTIYrR7AlOzKEu5rN/CXufFEbuKwe1EXDefYEgpyfo4/f4w9hzow2fR2gZ7DnjTt2B0yhjfUm0URhzT5IadLQezprcydw4qFEpEyWUVSLjwwgshCAJEUcTkyZNx1llnYebMmZgzZw5GjWKuSqEkHDTLIqOlJcgqeV+5HEeyegj+QBgqkLDiwar5bVpqh5beEY4MpD+wCJh1SHoh2GjglEEDGopc1m9hr3PriFtxBq3FoR5UsOIcHteCMbraIN2KvdgWjHo3hfoatmAcKlEAnHZt5QFPwhFlJ6tAgiRJiEQiUBQtKup0OlFVVQWn0zpnFsuNABgtx/T8Xh40lwer5H3lchzJ6iE4HTKcdgkj3PaSy2+TJS1Y53baoKgqAkEWaywGURTgkEWjiwzzeikXclm/xSq1YCiRKAhw2mU47cUeiSYQimDvAW9cNwWzLRjHt3gwrtnDFow5YJdFuKLHJwzCEA1NVjORntawYcMGbNy4EatXr8bq1ashCAKOOeYYzJkzB7Nnz8asWbNQV1eXrzGXNQHalxe95SKrwpY3q+R95WocqeohBEMKlp8/bdj3X0yikNha0lNlh7fXx9UKOSYKiM5/WuCA9Q0oH3JZv8WKtWCo+PQWjLGrDfZnaMHYMMKJ8c0DRRGba108gZQjoijAFS2cyM8VouHLKpDgdruxYMECLFiwAABw6NAhbNy4ERs3bsSbb76Jxx9/3AgsHHvssXj++efzMeayY4sJHDBNgUqZVeo+FIJNllDjtiNQ4zRyckPshT4kggDYZQkOmxjNdeYBHqWWrBYBgKzrE+RyvqqkuY/MWf3SR9jd2Ye+NC0YZUnA2GYPWzDmmcMmweXQ0l14cq48sCaNNQxrbVR9fT3OPfdcnHvuuWhvb8cLL7yAlStXoq+vDx9//HGuxlh2REGb1OzRHD5GmqlcWKXuQ6HF5eRGe6GHou0lS7XYVz7pgQO7TYRdFhNavRGlkqwWweqXPoqrw2K2PkEu56tKnfsotX/vOpSwbYTbjnEtWuBgQks1RjZU8cx4ngjQUivdTq4+KDesSWMdQw4kdHV1YcOGDUaaQ0dHBwCtjsKsWbOMVQukkaWBLxqsBEvlyip1H4pNEKLFAPViX4oKf0x9hUpbraDXetGCp1qqAs8K0VAkq0Wgn/EdXNA1U32CXM5XnPtoML0FY2w3hVq2YMw7WRKixRMl1tMpU6xJYx1ZBRJefvllbNy4ERs2bMCnn2qRH1VV0dDQgAsuuABnnHEGvvjFL8Lj8eRlsKVEAIyDZvYwp0pilboPViKKAqqcMqqcMhRVRSikGEGFcmwxKYoCbJJeJFb7PwMHlAvJahGEU6z4MVOfIJfzFec+ivWTb85kC8YCkUXBKOzM4+3yx5o01pFVIOHaa68FoJ1tO+GEE7BgwQKcccYZaGtry8vgSo0oaqsOnNHK4jxwLj3MuaJ8EwWtT7XDrh1gxraYDKbpE25VsigMtKLV29EyXYvyJFktglRfHFifgIqJQYTceOWdvXjz/U70ByOoskuYPbUFC6eP1VIXooUTudK3srAmjXVkFUg455xzMG/ePCxatAgNDam/XK1YsQIbNmzAY489NuwBWp1NErUvBTYWCCt1zLmiYohtMRlRFPgCEfiDYYQj1lupIEuCVgxRErRxszgsFViyWgTVLlvSdCHWJyAqba+8sxevvr1PuyAI6PeH8erb++CwS7hg7mQGrSsUa9JYR1aBhBdffBFOpxMXX3xx2v02b96Md955Z1gDsypZFGCLtmVkocTywpwrKjZJFOFxifC4bAiFFfgCYfiDYRQ7+6HO42DQgCwhVS2CZNs4bxOVtjff7zT+LQBQoa2KfuO9Dnzti8cUbVxUXKxJYx1pAwk33ngjDhw4ELdt/fr1uPzyy1Pexuv14oMPPsDo0aNzM0ILsMsDqw6Ye1W+mHNFVmKTRdhkO6qrbAhGayoEQhFEihBV4LJRspJUtQh4EElUXvoDYePfsenCR9O01KTKwJo01pA2kLBw4ULccMMNxmVBEHDw4EEcPHgw/Z3KMr73ve/lZoQFJAiAJArGUmNbtEgYz8JVBuZckRUJg2oqhMIK/MEwAsFIkUdGRESUW3qx8iqnjOoqO7z9iUEDt8tW+IERUYK0gYSvfOUrGD16NBRFgaqqWLJkCebNm4fvfOc7SfcXBAEOhwNjxoxBbW1tXgacK4IAozCY/sM2MZWNOVdUCgZWKhR7JERERLkhigKqHPFtGxdMH4MX1u9K2HfB9DEFHh0RJZOxRsL06dONf1944YWYMWMGTjnllLwOKp9cdhlVDpkpCpSAOVdEREREhSNLAtxOG5x2KaHb2XmnTQQAvPrOPvT7w3C7bFgwfYyxnYiKK6tii7/4xS/yNY6CYWcFSoc5V0RERET55YimLzgy1OA577SJOO+0iWhqqkZXV1+BRkdEZmQVSCAiIiIiIsqWJGo1f1x2mSf2iMoAAwlERERERJRzogA4HTKDB2Vke3t3NA3Yh6ZaF9OAKxgDCURERERElDM2SUSVU05a+4BK1/b27rjC5J2HfcZlBhMqD0ODREREREQ0LIIAuBwyGmqcaBjhhMshM4hQZtZt25/VdipvXJFARERERERDIkta60anQ4bIwEFZ6+rxpdjuL/BIyAoYSChjzGEiIrI2ztNEVIoEAE67BJdDhj1D5wUqH021LnQeTgwmNNU6izAaKjYGEsoUc5iIiKyN8zQRlRpZFOByasUTRZGrDyrNvLZRcZ9bsdup8rBGQpliDhMRkbVxniaiUuGwSairdqCx1gW308YgQoWaNqkBX58/GS11LoiCgJY6F74+fzKD3xWKKxLKFHOYiIisjfM0EVmZAK11o9spQ5Z47pE00yY1MHBAABhIKFuVnMPEnGMiKgWVPE8TkXUJAFxOLYAgiQwgADy2JEqGs0OZSpWrVO45THrOcedhHxR1IOd4e3t3sYdGRBSnUudpIrImAYDbKaOp1oWaKjuDCFE8tiRKjisSypQeJdWip3401TorInqaLue43H93IiotlTpPE5H1uOwSPFU2Bg+S4LElUXIMJJSxSsxhYs4xEZWSSpynicg6HDYJHpcNNpkBhFR4bEmUHGcNKitNta4U25lzTERERARoAYSGGgfqqh0MImTAY0ui5DhzUFlhzjERERFRIkEAqhwyGkc4owEEqdhDKgk8tiRKjqkNVFaYc0xERESkEQDYbRKcdgkOuwRREIo9pJLDY0ui5BhIoLLDnGMiIiKqZLIkoMphg9PB4EEu8NiSKBEDCUREREREZUArnigzbYGKant7d3QFhw9NtS6u4ChTDCQQEREREZUwWRRQ7bbDYWMAgYpre3s3nln7qXG587DPuMxgQnlhsUUiIiIiohIkigJqqmxoGOFkEIEsYd22/Vltp9LFFQlERERERCVEFAV4nDJcDhkCayCQhXT1+FJs9xd4JJRvDCQQERERDRNzgqkQZEmA22mD0y4xgECW1FTrQufhxGBCU62zCKOhfGJqAxEREdEw6DnBnYd9UNSBnODt7d3FHhqVCYdNQl21A40jXFyFQJY2r21UVtupdHFFAhEREdEwpMsJ5qoEGioBgNMhw+2UIUs890elQZ/ztBVafjTVOrlCq0wxkEBEREQ0DMwJplwSBaDKaUOVQ4YocuUBlZ5pkxoYOKgADCQQERERDQNzgikXZFFAldMGl4P1D4jI+rhOioiIiGgYmBNMw2GXRdR5HGisdaHKyfoHRFQauCKBiIiIaBiYE0zZEgA47RKqnDbYZJ7XI6LSw0ACERER0TAxJ5jMctkluF02FlAkopLGQAIVHHttExERUaVx2iV4GEAgojLBQAIVlN5rW6f32gbAYAIRERGVHa5AIKJyxBmNCipdr20iIiKiciCJAjwuG5pqnRjhcTCIQERlhysSqKDYa5uIiIjKXVOtq9hDICLKKwYSqKDYa5sqGeuDEBERDQ8/S4msgeusqKDYa5sqlV4fpPOwD4o6UB9ke3t3sYdGRERUEvhZSmQdDCRQQU2b1ICvz5+MljoXREFAS50LX58/mZFkKnusD0JERDQ8/Cwlsg6mNlDBsdc2VSLWByEiIhoefpYSWYdlAgldXV24//77sXbtWnR3d2PEiBE47bTTcP3112PcuHFx+/71r3/Fo48+il27dqGmpgZf/vKXcd1118Htdhdp9ERE6bE+CBERUXqZ6h/ws5TIOiyR2tDV1YWLL74YTz75JI455hgsXboUJ554Il544QVcdNFF2LVrl7Hvgw8+iJtvvhmKomDJkiWYMmUKHn30UXz7299GMBgs3i9BRJQG64MQERGlZqb+AT9LiazDEisS7r//fuzfvx+33HILvvnNbxrbn3/+edx000345S9/iRUrVuDzzz/Hfffdh+nTp2PVqlWw2WwAgHvvvRd/+MMf8NRTT2HJkiXF+jWIiFLSz6hoZ1r8aKp1stI0ERFRVLr6BwtnTwTAz1IiK7FEIOHll19GfX09rrjiirjtX/3qV3H//fdj3bp1UBQFTz75JMLhMJYtW2YEEQBg+fLlWLlyJdasWcNAAhFZFuuDEBERJWe2/gE/S4msoeiBhEgkgmXLlkGWZYhiYqaF3W5HKBRCKBTC5s2bAQCzZs2K28fhcODkk0/GunXr0NfXh+rq6oKMnYiIiIiIho/1D4hKS9FrJEiShCuuuAKXXXZZwnU7d+7Ep59+ivHjx8PhcGD37t1obGyEx+NJ2HfMmDEAgPb29ryPmYiIiIiIcof1D4hKS9FXJKSiKAruvPNOKIqCSy65BADQ09ODsWPHJt1fX4Xg9XoLNkYiIiIiIho+1j8gKi2WDCSoqoqf/OQn2LBhA6ZNm2bUTgiHw4WZVJwAACAASURBVLDb7Ulvo28PBAJp77uurgqyLOV2wISmJqaTFBOf/+KphOee82Z+VMJ7x6r43BdXJTz/nDeHZmFTtVFYcbBKeN9YGZ//4rHqc2+5QEI4HMaPf/xjPPvssxg3bhz+8Ic/GEECp9OJUCiU9HZ660eXy5X2/g8f7s/tgAlNTdXo6uor9jAqFp//4in2c1+oDxbOm7lX7PdOJeNzX1zFfv45b5amYr9vKh2f/+Ip9nOfbs60VCDB5/Ph+uuvx9q1azFx4kQ88sgjaGlpMa6vqalBX1/yJ1LfzkKLRERERERERPlT9GKLuiNHjuCKK67A2rVrMXXqVDzxxBMYPXp03D4TJ05Ed3c3/H5/wu337dsHURQxYcKEQg2ZiIiIiIiIqOJYIpAQCASwbNkybN26FbNnz8aqVavQ0JBYWOWUU06BoijYsmVLwu3fffddHHvssUk7OhARERERERFRblgikPDb3/4W77zzDqZPn46HHnooZTBg8eLFkCQJDzzwgFETAQBWrFgBr9eLSy+9tFBDJiIiIiIiIqpIRa+R0NXVhdWrVwMAJk+ejIceeijpfldffTUmT56Mb33rW3jooYdwwQUXYOHChfjkk0/w6quvYsaMGUabSCIiIiIiIiLKj6IHErZu3Wp0YnjmmWdS7nfFFVfA4XDghhtuwKhRo/DEE09g5cqVaGpqwpVXXolrr702ZWtIIiIiIiIiIsqNogcSzjrrLOzYscP0/oIg4LLLLsNll12Wx1ERERERERERUTKWqJFARERERERERKWh6CsSiIiIKD+2t3dj3bb96OrxoanWhXltozBtUmJXJCIiIqJsMJBARERUhra3d+OZtZ8alzsP+4zLDCYQERHRcDC1gYiIqAyt27Y/q+1EREREZjGQQEREVIa6enwptvsLPBIiIiIqN0xtICIiKkNNtS50Hk4MJjTVOvP6uKzLQEREVP64IoGIiKgMzWsbldX2XNDrMnQe9kFRB+oybG/vzttjEhERUeFxRQIREVEZ0lcBaKsD/GiqdeZ9dUC6ugxclUBERFQ+GEggIiIqU9MmNRT0CzzrMhAREVUGpjYQERFRTjTVulJsz29dBiIiIiosBhKIiIgoJ4pRl4GIiIgKj6kNRERElBPFqMtAREREhcdAAhEREeVMoesyEBERUeExtYGIiIiIiIiITGMggYiIiIiIiIhMYyCBiIiIiIiIiExjIIGIiIiIiIiITGOxRSop29u7o9XAfWiqdbEaOBERERERUYExkEAlY3t7N55Z+6lxufOwz7jMYAIREREREVFhMLWBSsa6bfuz2k5ERERERES5x0AClYyuHl+K7f4Cj4SIiIiIiKhyMZBAJaOp1pViu7PAIyEiIiIiIqpcDCRQyZjXNiqr7URERERERJR7LLZIJUMvqKh1bfCjqdbJrg1EREREREQFxkAClZRpkxoYOCAiIiIiIioipjYQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpDCQQERERERERkWkMJBARERERERGRaQwkEBEREREREZFpcrEHQEREREREZFXb27uxbtt+dPX40FTrwry2UZg2qaHYwyIqKgYSiIiIiIiIktje3o1n1n5qXO487DMuM5hAlYypDUREREREREms27Y/q+1ElYKBBCIiIiIioiS6enwptvsLPBIia2EggYiIiIiIKImmWleK7c4Cj4TIWhhIICIiIiIiSmJe26isthNVChZbJCIiIiIiSkIvqKh1bfCjqdbJrg1EYCCBiIiIiIgopWmTGhg4IBqEqQ1EREREREREZBoDCURERERERERkGgMJRERERERERGQaAwlEREREREREZBoDCURERERERERkGgMJRERERERERGQaAwlEREREREREZBoDCURERERERERkGgMJRERERERERGQaAwlEREREREREZBoDCURERERERERkmqCqqlrsQRARERERERFRaeCKBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEIiIiIiIiIjKNgQQiIiIiIiIiMo2BBCIiIiIiIiIyjYEEohxQVbXYQ0jKquMiosph1XnIquMiospl1XnJquOi4mIggfDss8+itbUVt912W7GHktKhQ4dw6qmnZhzjyy+/jMsvvxwzZ87EiSeeiPPPPx+rV6/OagJsbW3F0qVLTe0biUSwevVq/OIXvzB9/4XQ19eHu+66C88//3xRHv/3v/89WltbU/6sWbMm4TZvvPEGLr/8csyZMwczZszA0qVL8frrr6d8jPfeew/Lly/H3LlzMX36dFx88cX429/+lnL/9vZ2fP/738f8+fNx0kknYfHixXj88cehKErCvosWLUJrays2bdqU9ve85ZZb0Nraivvvvz/tflS6OD/G4/yYe/fffz9aW1vR0dGRcp98zncA0NnZiZ/85Cc488wz0dbWhnPOOQe///3vEQwGk+7f29uLu+++G+eccw7a2tqwaNEi/PKXv4TX603Y1+w8qf+tmX1/kfVwvozH+XJ49PdTsp9p06Zh3rx5uOqqq/D3v/894batra2YOnVqwcds1tKlS9Ha2ootW7YUeyjDIhd7AESZ+Hw+XHfddTh8+HDa/X73u99hxYoVsNlsmDNnDlRVxZYtW3DHHXdgz549uOWWW3I+tr///e+44447cOGFF+b8vofj17/+NZ566qmifSB98MEHAICzzz4bTqcz4frx48fHXX722Wdx6623wm6349RTT4WiKNi0aROuuuoq3HHHHbj00kvj9l+/fj2WLVsGRVEwa9YsuFwubNiwATfeeCM++eQT/Od//mfc/h9++CEuu+wyeL1ezJgxAyeeeCI2bdqEO++8E++++y5+85vf5PgZICoMzo/ZK/b8GOvll1/GihUr0u6T7/muo6MDl156KTo6OjB16lSccMIJePvtt3Hfffdh48aNePjhh2Gz2Yz9vV4vlixZgh07dmDSpElYsGAB/v3vf+ORRx7B66+/jv/5n/9BdXV17p4kohzhfJk9K8yXDQ0NOP300+O2hcNhdHR0YN26dXj99dfx3nvv4eabby7SCCsXAwlkafv378f111+PrVu3pt1vw4YNWLFiBVpaWvDII4/gmGOOAQDs3LkTl112GR555BF89atfzXl0MtXZnWIr9rjef/99OBwO3HPPPZDl9NPMgQMHcPvtt6O6uhpPPPEEjjvuOADAtm3b8M1vfhM///nPsWDBArS0tAAA/H4/brrpJgDAww8/jFNPPRUAsHv3bixduhQrVqzA2WefjWnTpgHQluP94Ac/gNfrxa9//Wucf/75ALSzEldeeSX+9re/4eyzz8Y555yTl+eCKF84Pw6NVcaln30Mh8Mp9ynEfPfTn/4UHR0duP766/Hd734XANDf349rrrkGb7zxBlatWoVvfetbxv733HMPduzYgUsuuQQ/+9nPIIoiwuEwfvjDH+K5557DPffcgx//+Me5fbKIhonz5dBYYVzHHHNMyhM+69evx9VXX42HH34Y5513Hk444YQCj66yMbWBLCkcDuPxxx/HBRdcgK1bt2LcuHFp99fP6Pzyl780Jn1Am3yuuuoqjBo1Ctu3b8/rmEnT29uLffv2YcqUKRmDCADw+OOPIxgM4sorrzSCCADQ1taGq666CoFAAE8++aSx/bnnnkN3dzcWL15sHFQD2iqHG264AQCwatUqY/v69euxY8cOzJ492zioBoD6+nrcfvvtCfsTWR3nx9K2c+dOXH311bjjjjvg8XjgdrtT7pvv+e7TTz/Fq6++ivHjx2P58uXG9qqqKvz85z+HJEl4/PHHje29vb1Ys2YNPB4Pbr75ZoiidhgpyzJuv/12jBgxAk8//TT6+/uH+vQQ5RTny/I2d+5cnHnmmQCA//u//yvyaCoPAwl59vWvfx2tra14//3347Z/8sknaG1txZQpU3Do0KG46/71r3+htbUVd911l7EtHA5j9erV+NrXvoaTTz4ZM2bMwJIlS/DSSy+lfOy//e1v+MY3voEZM2bg5JNPxkUXXYQ1a9aYzu965ZVXcMIJJ2D69OkZc3jS5TEN/jHjrbfewp133olwOIyf/exnxlmSZA4fPow333wTxx13XMLSJwC46qqr8Oqrr+KSSy4x9dhmLV26FD/4wQ8AAH/5y1/ickD1nNDNmzdj+fLlaGtrw+mnn45nn33WuP3OnTtx0003Yd68eZg2bRoWLVqEu+66C93d3Ukfb926dbjmmmuM/WfMmIGLLrooIe+1tbUVTz/9NADg1ltvNZXrr9cEyPRjphaA/l43GxXW6yCcddZZCdedffbZAIDXXnstYX/9g2Pw7yFJUtL9k93/KaecgoaGBrz11ltJc3spvzg/cn6stPkR0FYArF27FnPnzsWzzz6L2tralPvme75bt24dVFXFwoULjaCAbvTo0Zg6dSr27duHTz75BACwefNm+P1+nHrqqfB4PHH7u91unHbaafD7/di8ebOZp4KywPmS82UlzpdmjB07FgAS3v/JmP3ddb29vbj33ntx7rnn4qSTTsL8+fNx3XXXYceOHQn7HjlyBHfffTf+4z/+AyeeeCJOPfVUXHfddfjwww9TjiccDuP+++/HwoULceKJJ2Lx4sVYtWoVIpFI0v2fe+4542+xra0NixcvxoMPPgi/35/xd88Hpjbk2YIFC7B9+3Zs2LAhbhnUxo0bAWjLEN966y3jCxMwcCCwcOFCAEAoFMJ3vvMdvP766xgxYgRmzpwJVVWxefNmfO9738Py5csTciRvu+02PP3006iqqkJbWxtcLhc2b96MH/3oR9i0aRPuvvtuCIKQctybNm3C9ddfD1mW8d///d+YOXNm2t9z/PjxWLx4cXZPThoulwtXXnklli1bhvr6+rgJc7APP/wQiqKgra0NgPbcrl27Fn19fTj22GNx/vnno66uLmdj051++ukIhUJ45513MG7cOJx88skJH2w/+tGP0NPTgzPOOAMffPABjj/+eADaRHbttdfC5/NhypQpmDFjBj766COsWrUKL7/8MlatWhUXNX/ooYfwm9/8BjabDTNnzkRNTQ327t2L9957D++99x727NmDW2+9FQCwePFibN26Fbt378b06dMxduxYNDY2pv1dzjrrLFMTsJkPbr0+gsvlws0334w333wT3d3dmDhxIi6++GJcdtllxgGrqqr45JNPIIoiJk+enHBfEydOhCiK+OSTT6CqKgRBwMcffwwAcasXdB6PB83Nzdi/fz8OHjyIxsZG4wA42f4AMGnSJHR3d2Pnzp046aSTMv5+lDucH4eG82Ppzo8AMG3aNHzzm9/EokWLMu6b7/lO3/8LX/hC0v0nT56M9957Dx999BGOPfZYU/sDwI4dOzB//vyMvx+Zx/lyaDhflvZ8aYY+L40aNSrtftn87oCWDnPllVdi165daGlpwfz589HZ2YkXX3wRr7zyClauXInp06cDAD7//HNcfvnl2LNnD8aMGYMzzjgD3d3deOmll/DKK6/gvvvuM/4OY/3sZz9De3s7Zs6cieOPPx4bNmzAXXfdhbfeegv33HOPsZ+iKLjpppvwwgsvwOFwYNasWXA6ndiyZQt++9vf4sUXX8Sjjz6KmpqaXDyl5qmUV1u3blWPO+449dvf/nbc9u9+97vq8ccf///Zu/PoNspzf+DfWbR4w3ZsOYQkJKGJnIYkJHESoIQUSlnaS4GWhpQCZTvdDoGWWygtvf21EJYLl5aWCz3l9JYSWu5lKVvb0xYu0ELDDQ1xEpIQ4uwhu+V90Toz7++P0ciSJduSbUkj6fs5xyfxaGyNxtI7M888z/MKr9cr7rnnnoTHzjvvPLFw4UIRCoWEEEI8/PDDwuv1iuuvv150dnbG1jt48KD49Kc/Lbxer/jHP/4RW/7cc88Jr9crLr30UnHkyJHY8vb2drFixQrh9XrFM888E1v+wgsvCK/XK+68887YNi9cuFDMmzdPrF27dvx2xhgM3sZ4zzzzjPB6veI//uM/xLe+9S3h9XoTvpYuXSree++9tJ/L6/WKq6++Oq11X375ZeH1esUdd9yRsPyOO+4QXq9XNDU1xf4Guq4LIcy/w5IlS8ScOXPEa6+9FvsZwzDEL37xC+H1esXKlStjy48dOyZOPfVUcfrpp4sDBw4kPM+rr74qvF6vOO2000Q4HI4tv/POO4XX6xUvvPBC2q97vNx2222xfb9s2TJx0003iSuuuEKceuqpwuv1ilWrVsX2RWdnp/B6veKMM84Y8vedeeaZwuv1it7eXiGEEAsXLhRer1f4/f6U63/xi18UXq9XfPjhh0IIIS6++GLh9XrFrl27Uq5vvWdef/312LJzzz1XeL1e8e677w77Wq2/8yOPPDLsepQax8fxwfGxcMbHVKzx5ujRo0mPZXu8+/rXvy68Xq/4+9//nnL9Bx98UHi9XrFmzRohhBCrV68WXq9X/O53v0u5/tNPPy28Xq+47777YsvSHSet93G6769Sw/FyfHC8LJzxMp0x4Y9//KPwer3i4x//uNi7d29subXMMprXbo2PP/jBDxKWv/jii8Lr9YqLLrootuzKK6+MvXc0TYstX7t2rZg3b55oamoSbW1tseVXX3218Hq94tRTTxVvvfVWwnaef/75wuv1ij/96U+x5WvWrBFer1dccMEF4uDBg7Hlvb294mtf+5rwer3i29/+9tA7M0tY2pBl8+bNi6USRiIRAGZU6b333sNZZ52FysrKhDSvffv24eDBgzjrrLPgdDoRDofxu9/9Di6XCw8++GBCCuSUKVNi09f85je/iS3/9a9/DcCs74qPzk2YMAH33nsvALNpUyo7d+7EV7/6VYTDYfznf/4nzjrrrHHaE9nT29sLAHj22Wfx1ltvYfXq1Vi3bh1ef/11XHPNNejq6sJNN92E1tbWnG/b+eefH/sbWHfhf//736O7uxtXX311wp0DSZLwzW9+E/PmzcOmTZuwadMmAEB7ezvOP/98rFq1Kmm2gwsuuAC1tbUIBAIjdiHOFSsj4Stf+QrefPNNPProo3j22Wfx+9//HieddBJee+01PP300wDMDsqAecdgKNasD/39/Qk/k2o2iPjlVo1uputT7nB8zD6Oj/YaHzOV7fEu0/Wtf4caszmeZg/Hy+zjeGnP8XLPnj247bbbEr5uvvlmXHjhhbFeMd/5zncwY8aMIX9Hpq/9+PHj+Nvf/gaPx4P/9//+X8LMNZ///OexbNkyVFdXo62tDZs3b0ZzczNOPfVUfOc734GiKLF1zzrrLFx11VXo7e2NlYnE+/znP4/ly5fHvp84cSLuvPNOAMB///d/x5Y/+eSTAMzPolXKAZiZaQ899BCqqqrwl7/8BUeOHBlxf44nBhKyTJIkLF++HH6/P9Yp9oMPPkB3dzfOOOMMzJs3Dzt27IgNXlat4znnnBNb10qpSpVOdOaZZ0JVVTQ3N0PXdbS2tmLfvn2oqanB7Nmzk9afNWsWJk6ciP3798Pn8yU8dujQIdxwww3o6urCjTfeWDBpiaFQCIBZx7R69WpcccUVmDBhAqZOnYp/+7d/w4UXXoiurq7YxWsupUrdsurLTj/99JQ/Yx1srRrTOXPm4OGHH8bVV18dWycSiWD37t144YUXYnVU1olFvj333HP44x//iDvvvDNh4J09e3bsRMX6WwyuyU1FDKrBVBQFkiQNmUpprW/9az1HuutT7nB8zD6Oj/YaHzOV7fEu2+vT+OF4mX0cL+05Xra3t+OPf/xjwtfbb7+NSCSCz3zmM3jyySdx4403Dvs7Mn3t1r49++yz4XQ6k37fr3/9azzzzDOor6+Prbt06dKUY+PZZ58NAFi/fn3SY//yL/+StOyss86Cw+HA+++/j0gkgqNHj+Lw4cM48cQTY6UU8aqqqrB8+fJYmVIusUdCDixfvhwvvfQS1q1bh8WLF8fq2ZYuXYpAIIB169ahubkZ55xzDt5++21IkhQbdI8ePQrAPAAMV0+kaRq6u7tx7NgxAEBXV9eI9UdHjx6Fx+OJff/uu+/GTlqee+45XHfddWnXgr344osJdUXDSdWgZCzKy8sBACeccAIuvvjipMe/9KUv4dVXXx2xQUw2pGqiZf1Nv/nNbw77s9Z6gPn3/fOf/4w///nP2LVrF44ePRob9KxBaywnbp/61Kdw+PDhEddbtWoVbr755mHXKS8vH7I+95Of/CQURcG+ffvg9/tjfzvr4J2K9Zh1B6ysrAw9PT0IhUJwuVxDrm/9buvfoRrRDF4fQEIPh+FYf4NU20Hp4fiYiONjcY+Pmcr2eJfu+tb4m+n6QObjaaqTdjJxvEzE8bI0xsulS5eOy+xambx2Kzh24oknjvh7rf37m9/8JiGjZzDrMxVv8uTJScscDgfq6upw7NgxdHZ2xjJgUq1rsbIU2traRtze8cRAQg6cffbZUFUV69atw80334x3330XlZWVmDNnTiytcP369TjjjDPw3nvvYd68ebFosdVBdMqUKSmjUINZH4i6urqUHWfjDZ5ySpZl3H///Xj33Xfx4osv4t///d/xwAMPpPUax7s5Tiasg9NQH7CTTjoJAPKSqpUqMmn9jc4777yEi9fBrDsAfr8f11xzDbZt24by8nLMnTsX55xzDrxeL5YuXYqvfvWrOHjw4Ji2M1fNcRwOB6qrq9HR0YFgMIja2lqUl5ejs7MTmqYlTRepaRo6OzvhcrliDWQaGhrQ09MDn8+XkN5lsQZ/66SmoaEBH374Idra2hKmchpqfWDgs2F9PodilVtUVVWl9fopGcfH7OL4WDjjYyrZHu8aGhoADH3yaa1vrZfu+mMZT3PeLKyAcLzMLo6XhT1eDifT1z7UrAmpWJ+tBQsWDDu96IQJE5KWDVVWZgU0VFWN/X+4pqb5CsQykJADVVVVWLRoETZt2oSenh5s3LgRS5cuhaIoWLBgAdxuN9avX4/169cjFArF0tCAgYPx1KlT8dBDD434XFZdYkVFRVrrx7v44otx6aWX4uyzz8abb76Jl19+GZdcckladW2LFy8esRNvtliD0eDUOot1wpPqA5wPDQ0N2L9/P2644Ya09tkTTzyBbdu2Yfny5Xj44YeTptyy0hjHwqrHGqvDhw/jsccegyzLCdNNWfr7+9HR0QG3243q6mpIkoSZM2diy5Yt2L9/P2bOnJmw/r59+2AYRkKGw6xZs7B7927s2bMn6cS6r68Pra2tmDBhQuzkadasWXjrrbewe/fupPQ/IQT27t0LRVESTrqnTJmCHTt2jBhVP3DgAICBkwvKHMfH7OL4aJ/xcTSyPd5Zsy9YXc8H27NnD4CBWSDSXT/+IsHabo6nY8fxMrs4Xhb2eDmcTF+7NaamyiIAzHKRo0eP4vTTT499tj75yU8OO71oKq2trQmBV8DM7Gpvb0dZWRmqq6tjAdzhgjzWY3V1dRk9/1ixR0KOLF++HJFIBGvWrIHf78fSpUsBmJGj0047Ddu3b8df//pXAEiYHmTevHlwu93YunVryghfS0sLzj//fNx8880QQmDKlCmYNGkSDh06FDugx2tvb8dFF12E6667Lhb9t1hRrAkTJsSal/zoRz8a8S5CvjU2NqKhoQFtbW0p64+s6Y+ycWAaLjo4FGs74uf+jvf9738fl19+Od544w0AiNVCfuUrX0ka+LZt24auri4Aialoo9mu8VBZWYlXXnkFzz//fOykMN4rr7wCwKzFtJrRWLVjr7/+etL61rL4+srh1n/zzTeh63rK9a39GW/jxo3o6OhAU1NTwr5dtGgRAODVV18d8rV+9NFH2LNnD1RVRVNT05Dr0cg4PmYPx0f7jI+jke3xzlr/zTffTJpD/ciRI/jwww8xefLkWJDXmnJs3bp1SQ0V+/v7sW7dOpSXlyeMidZ4+u6776Knpyfl69Q0LfY3tz7/lBrHy+zheFnY4+VwMn3t1ri1bt06aJqW9PseeeQR3H777Th+/HjC3yFVWcgzzzyDiy++GI899ljSY++8807SsjfeeAOapqGpqQmKouCkk07C5MmTcfz48VjjzHi9vb145513IMtyzoNwDCTkiBUVXrNmDYDEA+Xpp58OXdfxyiuvYOLEiQnzA5eXl2PFihXo6+vDd7/73YR0qs7OTnz/+9/HRx99hEmTJsU+7Ndee21svtH47p2BQADf//73sW/fPlRUVCSlosVbsWIFFi5ciIMHD+KRRx4Zl32QLbIs47rrrgMA/PCHP0x4zevXr8eaNWvgcrmwYsWKcX9uq2Y1kyjuypUrUVZWhieeeAL/+7//m/DYiy++iJdeegk7d+7EaaedBmBgXty//e1vCevu3bsXt99+e+z7+D4Do9mu8VBdXY2LLroIgDn3dPxJ47Zt2/Dzn/8csizjG9/4Rmz5F77wBbhcLvzqV7/Ctm3bYsu3bt2K//qv/4Lb7caX20UiPgAAIABJREFUv/zl2PILL7wQdXV1eOmll/DWW2/Flh88eBA/+clPIElS7P0AmJ+1WbNm4Z133sFzzz0XW97R0YG77roLAHD99dcnvI7LL78cdXV1WL9+PX76058iHA4nPH78+HF897vfhRACV155ZdJBiTLD8TF7OD6a7DA+jka2x7upU6fi7LPPxr59+/Dzn/88ttzv9+Pf/u3foOt6wvrl5eW47LLL0N3djbvuuit2gq1pGu6++2709PRg5cqVCWPivHnz8IlPfALd3d249dZbky5ig8EgVq9ejUOHDmHOnDmx4AalxvEyezhemgp1vBxOpq99+vTpOOuss3D06FE88MADCaUOL7/8MtavX48ZM2Zg3rx5OOOMMzB79mxs2rQJDz/8cELgYfv27Xj44Yexa9eulOUcv/zlLxPOfffv34/7778fgPn5s1j//973vodDhw7Flvf39+P2229HX18fLrjgglj2Qq6wtCFHZs2ahcmTJ+Pw4cOoqqpKGNyt9ENN0xLS0Czf+c538MEHH+Af//gHzj//fMyfPx+qqmLDhg3o7+/HggUL8O1vfzu2/rXXXotNmzbh1VdfxWc/+1nMmzcPlZWV2Lx5Mzo6OjBt2rTYCcVQJEnCj3/8Y3zhC1/AmjVrcPHFF+PUU08dn52RBddddx02b96M1157DRdddBHOOOMM9PX1YfPmzRBC4K677kqa7mU8TJs2DYB5N+cb3/gGzj33XKxcuXLYnznxxBNx//334/bbb8eqVavg9Xoxffp0HDhwAC0tLZBlGQ8++GAsrerLX/4yXnzxRTz99NNYv349TjnlFPh8PmzevBkOhwNTpkzBoUOH4PP5YneNrO167LHH0NzcjGuvvTZnd81/8IMf4IMPPsB7772H888/HwsXLoTf78eGDRtgGAbuvPNOLFiwILb+lClTcMcdd+Duu+/Gl770JZxxxhkQQuCf//wnNE3DAw88kJCqVVlZidWrV+OWW27B17/+dSxZsgQVFRV49913EQgEcOuttyZ0mJZlGffddx+uvfZa/PCHP8Tvf/97NDQ0YP369eju7sYVV1yBT33qUwmvoaamBj/5yU/w7W9/G48//jiee+45zJs3DxUVFTh+/Di2bt2KSCSCc889F//6r/+a/Z1a5Dg+ZhfHR/uMj5nKxXj3ox/9CFdeeSV++ctf4s0338SMGTOwceNG+Hw+LF++HFdeeWXC+rfeeiv++c9/4uWXX0ZzczPmzJmD7du34+DBg5gzZ07KBmr33Xcfvva1r2Ht2rX45Cc/iYULF6Kurg7d3d3YsmULent7MX36dPzsZz9LazafUsbxMrs4XhbueDmc0bz2e+65B1dddRWeeuopvPnmmzj11FNx+PBhbNu2DWVlZfjZz34WC7r99Kc/xbXXXovHH38cr7zyCubMmYO+vr7Yue8111yDT3/600nbNWvWLKxcuRJnnHFGrP9JKBTCjTfemDAt5DXXXINNmzbhL3/5Cz772c9iyZIlKCsrw4YNG9DZ2Yk5c+bgxz/+cU72ZTzlx/l41hK1f/9+bN26FZ/4xCdwySWXxJbX19fjySefhKZpWLVqVdI8qA6HA5dccglqampw7NgxbN++HceOHcO0adNwww034Ic//GFCkxVJknDhhRdi8uTJaG1txY4dO3Do0CGceOKJuOKKK3Dfffcl1Hd9+OGHeOONNzBnzhycd955CdvV09ODTZs2YevWrVixYkXeDvBDbaNFlmVcdNFFmDRpEo4dO4atW7eiu7sbixYtwt133x27S56ORx99FJMnT8YXvvCFEdetr6+Hy+XCrl27sGvXLlRWVuL888/H66+/jh07duD8888fctqkc889F319fdi9ezd27NgBVVXxiU98Ag888EBCHWF9fT0+8YlP4NixY/joo4+wd+9eqKqKs88+Gw8++CBqa2uxdu1aVFdXx+7kNDY24siRI9izZw8OHDiAj3/845g7d27a+2AsysrKcNlll0GSJBw+fDg2PdWSJUuwevXqlFPdzJ8/H3PnzsWRI0ewZcsWtLe3Y968ebjnnntSDrynnHIKzjzzTBw7dgzbtm3D4cOH4fV68b3vfS/ppBcw5+X99Kc/jfb2dnzwwQfYv38/pk6diptvvhk33XRTyvf11KlT8bnPfQ7l5eVoa2vDnj170NLSAiEE5s+fj1tvvRXf+ta32GF8nHB8HD2Oj4UzPqayZs0a9Pb24vrrr0+Z3ZTt8a66uhqf+cxn0NPTgx07dmDnzp1oaGjA9ddfjzvvvDNpjHO73bj44osRCoWwe/dubN++HSeccAJWrFiBe++9N+VrqKqqwuc//3k0NDSgt7cX+/btw/bt29Hf349p06bh+uuvx1133ZXzu2mFiuPl6HG8LJzx0vpbpbsP4z366KOQZRk33XQTgNG99qqqKlxyySXQdR179+7Ftm3bEAwGcc455+Chhx5KyDCYMGECLrnkEhiGgYMHD8bGt7lz5+KOO+7A9ddfn1Am8tJLL+Hw4cP43e9+B0mS8H//93/YvXs3Zs2ahe9+97u44YYbEl7P4M/itm3bcPDgQUydOhU33HADVq9enZfsWElwsl+iBI2NjeM21QwRUTHh+EhElB6Ol1TsmD9GRERERERERGljIIGIiIiIiIiI0sZAAhERERERERGljT0SiIiIiIiIiChtJTf9o89X2POg2lFtbTk6O/353oySxf2fP/ne9x5PVU6eh+Pm+Mv3e6eUcd/nV773P8fNwpTv902p4/7Pn3zv++HGTJY20JipqpLvTShp3P/5w31Po8X3Tv5w3+cX9z+NBt83+cX9nz923vcMJBARERERERFR2hhIICIiIiIiIqK0MZBARERERERERGljIIGIiIiIiIiI0sZAAhERERERERGljYEEIiIiIiIiIkobAwlERERERERElDYGEoiIiIiIiIgobQwkEBEREREREVHaGEggIiIiIiIiorQxkEBEREREREREaWMggYiIiIiIiIjSpuZ7A6j0bNvXjrVbjsLXFYCnpgzL5k/C3Bl1+d4sIiIiIqKSxPNzyhQDCZRT2/a144W39sa+P94ZiH3PwYqIiIiIKLd4fk6jwdIGyqm1W45mtJyIiIiIiLKH5+c0GgwkUE75ugJDLA/meEuIiIiIiIjn5zQaDCRQTnlqyoZY7s7xlhAREREREc/PaTQYSKCcWjZ/UkbLiYiIiIgoe3h+TqPBZouUU1bDFrMrbBCeGje7whIRERER5QnPz2k0GEignJs7o44DExERERGRTfD8nAYLhrVhH2dpAxERERERERHBMAS6+kLo6gsPux4zEoiIiIiIiIhKXCCkodcfhiFGXpeBBCIiIiIiIqISpRsGevojCEX0tH+GgQQiIiIiIiKiEuQPaugNhCHSyEKIx0ACERERERERUQnRdAM9/WGENWNUP89AAhEREREREVEJEEKgLxCBP6ghwySEBAwkEBERERERERW5UERHT38YejrdFEfAQAIRERERERFRkTIMgV5/GIFw+s0UR8JAAhEREREREVERymRKx0wwkEBERERkc9v2tWPtlqPwdQXgqSnDsvmTMHdGXb43i4iIbGqszRRHwkACERERkY1t29eOF97aG/v+eGcg9j2DCUREFE8Igf6ghv5AZEzNFEciZ/F3ExEREdEYrd1yNKPlRERUmjTdQHtPEH1ZDiIAzEggIiIisjVfV2CI5cEcbwkREdlVIKShxx+GyHYEIYqBBCIioiLBOvri5Kkpw/HO5GCCp8adh60hIiI7EUKg1x+BP6Tl9HlZ2kBERFQErDr6450BGGKgjn7bvvZ8bxqN0bL5kzJaTkREpcEqZch1EAFgRgIREVFRGK6OnlkJhc36+5nZJkF4atzMNiEiKnH+oDmtY44qGZIwkEBERFQEWEdf3ObOqGPggIiIoBsGevojCEX0vG4HSxuIiIiKgKembIjlrKMnIiIqBoGQhvbuYN6DCAADCUREREWBdfRERETFyRAC3X0hdPeHYeSrlmEQljYQEREVAdbRExERFZ+IZqC7LwTNLhGEKAYSiIiIigTr6ImIiIpHnz+Mjp5g3hoqDoeBBCponDOdiIiIiIiKiW4Y6O4LoxKSLYMIAAMJVMCsOdMt1pzpABhMICIiIiKighMImdM62qySIQmbLVLBGm7OdCIiIiIiokJhGAJdNmuoOBxmJFDB4pzpRERERERU6EIR3QwgFEIEIYoZCVSwOGc6EREREREVKiEEevxhdPaGCiqIADCQQAWMc6YTEREREVEh0nQDHT0h+INavjdlVFjaQAWLc6YTEREREVGhCYQ09PjDEIWVhJCAgQQqaJwznYiIiIiICoEhBHr7wwiE9XxvypgxkEBERERERESURYGQht5ApOB6IQyFgQQiIiIiIiKiLIhoBnr9YYQ1I9+bMq4YSCAiIiIiIiIaR4Yh0BuIIBAqzGaKI2EggYiIiIiIiGgcCCHgD2noC0QKupniSGw1/eMf/vAHfPGLX8Rpp52GZcuW4ZZbbsG+ffuS1nv55Zdx2WWXYcGCBVi+fDnuv/9+9Pf352GLiYiIiIiIiIBQREd7dxC9/uIOIgA2CiQ8/PDDuP3229Hb24svf/nLWLp0KV5//XWsXLkShw4diq33+OOP44477oBhGLj66qsxe/ZsPPnkk7jxxhsRDofz+AqIiIiIiIio1OiGga6+EDp7Q9CKpJniSGxR2rBlyxY8/vjjWLp0KX71q1/B7XYDAC644AJ861vfwmOPPYb7778fR44cwSOPPIKFCxfit7/9LRwOBwDg5z//OX7xi1/gueeew9VXX53Pl0JEREREREQlQAiB/qCG/kAEpRE+GGCLjISnn34aAHD33XfHgggAcNFFF2HlypU4+eSTAQDPPvssNE3D17/+9VgQAQC+8Y1voLKyEs8//3xuN5yIiIiIiIhKTkQz0N4dNHsh5Htj8sAWGQlvv/02vF4vZsyYkfTY3XffHfv/e++9BwBYsmRJwjoulwsLFizA2rVr0dvbi6qqquxuMGVk2752rN1yFL6uADw1ZVg2fxLmzqjL92YREVGO5Po4wOPO8Lh/iIjGpj8YQZ+/NAMIlrxnJLS3t6OjowOzZs3Cnj17sGrVKixevBhNTU245ZZbcPDgwdi6H330Eerr61FZWZn0eyZPngwAKZszxvMHzQ6aml5c83ja1bZ97Xjhrb043hmAIYDjnQG88NZebNvXnu9NIyKiHMj1cYDHneFx/xARjZ5hCHT2hsxmivnemDzLeyChtbUVAHD8+HGsWLEChw8fxuWXX46mpia8+uqrWLlyJQ4fPgwA6OrqGjLbwFre19c37PNphoG+QARt3UG0dQcYVMiytVuOZrSciIiKS66PAzzuDI/7h4hodMIRHW09QYQier43xRbyXtrg9/sBmGULl156Ke6//34oigIA+O1vf4t77rkH9913Hx577DFomgan05ny91jLQ6HQsM9XU10Gd3li4MAAIBQZZW4VbqcKh5r3+ErB8XhSB3g6+8Ip92dXf3jIn6HMcV/mTyns+9racqiqku/NKDql8N4Bcn8cSOf5SmXfp2KH43Ip7H+Om+OvFN43dlbq+7/XH0akP4yamtxfPk+YUJHz50xH3gMJsmwezBRFwZ133hkLIgDAVVddhTVr1uCtt95CIBCA2+1GJBJJ+XusqR/LysqGfb6u7gD8QW3YdVRFgtupwu1UoCoMKozE46mCz9eb8rHaSieOdwaSlk+sLRvyZygzw+1/yq587/tcHdQ7O/05eZ5Sku/3Ti7l+jgw0vOV0r5PJd/H5Xzvf46bhSnf7xs7yGdvE7vt/1zui2BYQ58/krcpHSdMqEBHR39enhsAJnuSWwpY8n6VbJUkTJ48GTU1NQmPybKMxsZGRCIRHDlyBCeccAJ6e1O/ia3l49FoUdPFQPlDl1n+ENFY/jAay+ZPymg5EREVl1wfB3jcGR73D1HhYW+TAbnaF9aMDF194bwFEewu7xkJU6dOhaIoQ2YaaJqZPVBWVobp06fjvffeQzAYTJgmEgAOHz4MWZYxbdq0cd0+zTCDCn2BCFRZgsupsPwhA1Z00IwaBuGpcbM7NBFRCcn1cYDHneFx/xAVnuF6m5TaZzfb+0II89rPH9RKvpniSPIeSHC5XJg7dy7ef/997N+/H9OnT489pmkaduzYgZqaGkycOBFNTU345z//iQ0bNmDZsmWx9UKhEDZv3oyZM2emnNFhvGiGgBbU0B/UGFTIwNwZdSU3yBER0YBcHwd43Bke9w9RYfF1JZcjmcuDOd6S/MvmvghHdPT0MwMhXba4Ar7iiisAAPfee29CZsITTzyBY8eO4bLLLoOiKPjc5z4HRVHw6KOPxnoiAMAvf/lL9PX1YeXKlTnbZs0Q6A9qaO8JwtcVMBtwsPyBiIiIiIjGkacmdQ84T4075fJilo19IYRAT38YHb0hBhEykPeMBAC4/PLL8be//Q2vv/46LrvsMixfvhx79uzBW2+9henTp2PVqlUAgFNOOQU33HADfvWrX+Gyyy7Dueeei927d+Pvf/87Fi1aFAtI5JoeDSr0BzUosgS3U4HbqcDBbr1UIPLZwIcoX/i+JyKiQrBs/iS88NbelMsHK/ZjWyb7Ih2haBaCzgBCxpQf//jHP873RkiShAsvvBDV1dXYvn07/vGPf6Cvrw+XXHIJHnroIVRXV8fWPfPMMzFhwgRs27YNb7/9NoLBIC6//HKsXr0a5eXlIz5XZ3cgq5kDQpjNOQIhHYGQBt0QkCVAkW2R/JEVFRUu+P3hkVekrBjr/rea1vRHa8H6gxo+PNCJ+mo3GmpH/kyVsny/9ysqXDl5nmL8fOf7fZ/v904p477Pr3zvf46bhSnf75t8a6gtR321Gx09QQRCOhpqy3DR6ScnBQiydWyz0/5Pd1+MxBACPf4Iev0RCBvHEMrKnAgEUvcSzIWJ9UO3DbBFRgIAqKqK6667Dtddd92w60mShKuuugpXXXVVbjZsDHRDwB/U4A9qkGUJbocCl1OBU5UhSVK+N48IABv4UGni+56otOiGAYN3HKmApdPbpFSObWPt8xKK6OjuD3NMGCPbBBKKnWEI+EMa/CENkgQ4VbP8weVQIMsMKlD+sIEPlSK+74mKl2EIRDQDEd2I/WsYAk42x6Yix2Pb8Awh0OuPIBDS8r0ptiaEwL6jvdi4sxXzGicOuR4DCXkghBkJC0V0AIBDkeGKBhU4AwTlmqemDMc7kw88pdjAh0rHUO97p0PGL1/ZVrS1pVR6ir1eWosGCzTdgKaLWNCAqBSlOrYFQho03cDqNe/FxgAART0upMJeCCPr7gth4842NO9sRUdPaMT1GUiwgYhuIBIw0BeIQJEluBxmUMHpYAkEZd94N60hKgSp3vfBkIZASEMwbAZ5j3cGYusU+wkWFSerXtpSyO9pIUQ0aGAGC7Ro8ICXBEQDBh/bAiENXb0h1Fa5YAhzDHj6tZ0QAMpc5mVgIY8L6WAWwvA03cCHBzrR3NKKXYe6M+oXwUCCzegpSiBcDgUup1zUDRspf6yDhhmZDsJT4y6JyDSlTwhRdEHNVO/77r4QgpHkZrzFVltKpaNQ66UHggYDpQmazpAB0UgGH9s03UBtlQtu18AlX2+0cV+ZK/Ey0O7jwmiwF8LQjrb3Y0OLD5t3tSUFWcpcKhbMqkeT1zPs72AgwcYSSiD8LIGg7Blr0xoqbr6uAGRJgqrKUBUZDkWGqkoFH9wc/L5fvea9lOuxtpQKVaHUSwth9jQIawbCER0RjZkGRKMVf2xbveY9DL6G1vTUs9fZbVwYC2YhpOYPanh/TxuaW3w40taf8JgEYNbUajQ1NuDj02qhKiOf4zGQUEDiSyDkaAmEUzWDC3KR3S0cb8VeI0qUTQKAZghoYR2AHlsuS4BDVeByyHCoClRFKujMhXz0C+HYRNlk1x44VsZBKBINHOiGradfIypUqcaAoS4Q8z0ujJV1PD3e4Ud1pQtNjR7MmlKT783KO8MQ2HOkGxt2+LB9f0dSj4gJVS40NTZgobceNZWZTY/LQEKBMgyBQEhDIARI/YBDlc1ZIJxKwd8lHG/FVCNKZCdGQuPYCCQAijyQuaAqUvTfwhiTct0vhGMTZZtdeuDEBw7k7gBauwIMHBDlQKoxoKrMkTLjp5B7Y23b147n/74HhiEgBNDWHcSr6w8CQMkGEzp6gmje6cPGFh+6+8MJjzkUGXNPmYCmxgZMn1Q16hvSDCQUAQGY6YCaAfgjUBUzW8HtVOBQlXxvXt4Vao0oUaEZKnNBksyDlqLIcKoyHKo9gwu57hfCsYmyLZ89cCKajlDEiJYs6LHAgbPMySACUY4MNQakWlaoxx3dMPBm8yHoKfqobNjRWlKBhLCm44N9HWhu8WHvkZ6kx0+eWIkmrwfzPlYHt3PsYQAGEoqQpgtouob+oAZZQrRZowKnqkCWCzfteLQKpUaUqFgJYQY7oRkIRGcTkmUJDsUMKlj/2mF8ymW/EI5NlAu5eE8bQkCLNkUMRxIDB0SUX0ONAYUaOIgXDGvo6Q+jrTv1cbOzd+QpDAudEAKHfP1obmnF+7vbo1miAyrLHFg4qx5NjQ1oqC0b1+dmIKHIGQIIhHUEotOZqYoEp0OBS1XgcMgl0VvBrjWiRKXMMARChp5wwFNkKZatoMhSrDyikPsuDIdjExWipBkVNAMaO6ITUQ4NbqZYW+VCe09y0KC2KrOa/0LS6w9j826zcWLroHMJWZLQeHINFjd64D25Jmtl7wwklBgrW8EP84NnBRacqly0GQt2qRElouHphoA+qCwCMLMXVFmCktB3ofBnjeDYRIVANxKDBpxRgYjyKRTR0dMfTmgauHh2Q6wnQrzFsxtyuWlZpxsCOw92obmlFTsOdMEYlPrlqSnD4kYPFsyqR1W5M+vbw0BCiRsILJis/gpWcKEY7gTms0aUiMbOMATChgC0xCmrVFmCIzpWybIERZYgy1LBZFpxbCK7sTINNN2ApgtEND1p6jgionwwDIG+QAT+FFM6Wn0QNuxoRWdvCLVVLiye3VA0/RFauwLY2NKKTTvb0BuIJDzmciiY/7E6NDV6MLWhMqfXbgwkUIL4/goSYAYUHDJcDsWWzdHSlcu6ZyLKDc0Q0KKz18SL779g9xIJjk2UL7HyBN2I9TdgXwMishshBPqDGvqDkWHHqFlTaoomcAAAobCOrXvbsXlPO/Ye7k56fMakE9DU6MHcUybAmafm+gwk0JAEBqZ260UEshzNVlDNwEIxlkEQUeFL1X8BMDMYrKkpzVkkpKwGSK05rX1dAXhqyvKSbWCHbSB7MMsSrJkUmGlARNkz2mPP4J9b+vEGTGmoglEiA5YQAvuP9aK5pRVb93YgMigTs7rCiUVeD5oaPZhwQv77KTGQQGkzDIFA3N0/hyLD5VRQPehknYjIjlJOTQnEAgrjGWDYtq89of/B8c5A7PtcXcjbYRsoP5htQET5MtpjT/zPCSFwuK0fL769DxcunVpUmQapdPeHsWmnD80tPrT3JM5AoSoSZk+rxeLGBsycXG2rG7kMJNCoRXQDkYABX1cAXZ1+uBwK3E4VLmd+0muIiDIlYJV0pQ4wmIEFM8Cgquk3eFy75eiQy3N1EW+HbaDsEkJANxJnUdB0g9kGRJQ3oz32WD9nGCKhkeKGHa1FGUjQdAMfHuhEc4sPuw51JQV7J9WVo6mxAecsORnhQDg/GzkCBhJoXMRPMylLiAUUiqVhIxGVlqECDLIEM3NBHfhKFVzwdSVP62guTz3XdTbYYRto7IQQMISAYYjoe9JshKjrBnRDcAYFIrKV0R57Wjv90HWRFAjt7E2e1rGQHW3vR3OLD5t3tSU1jixzKVgw0yxdOKm+AgBQWeZABwMJZAe7DnVlvaOpIQB/SIM/pEGSEO2roMDlTH3CTaNj1ZF19oVRW+nElIZKHGrty3stNGuyqZgZAghrBsJxdYuSBKhydGpK1cxeqK92ozXFSZOnJnc1jZ6aMhzvTD6hG89tGMvnnWNFMiEEwvFTLepGzmqDc3F+QJQtHE/sYzTHnlBExwkVLrR1Jx83a6tc47p9+eAPanh/TxuaW3w40taf8JgEYOaUajQ1NmDO9NqCam7PQEIJ2XWoK2GO1faeUOz7bJ0sCAEEwzqCYR3wm3U+bqeKMpfCoMIYxNeROVQZB471YvOuNtRUuVDmUvNWC82abCpFQkRLvXQAYTN7Ye4pdXht/UFAAiRJggQz4LBs/qScbdey+ZMSPo/xy8fDWD7vHCtMmm5A1wV0w0AoYiCs6XnpZZCP8wOi8cLxxF4yOfYYQqDXH0EgpKGp0ZMwDlkWz27IynZmmyEE9hzuRnOLD9v3d0DTEwf3CVUuLGr0YJHXg5rKwgyWMJBQQjbsaB1yea5OFDTdnAO2LxCBU5XhdipwqAocKoMKmRhcf2bNKdsXiKDMpSasl8uDKGuyiUyD57Suid7hbagtR0dPEKrLgb5ABIoswRGdSWK8WZ858y5dEJ4a97jepRvL573UxgrdsAIGIqGXgV3KEuxwfkA0WqU2nthduseecERHd3841g9h8HGzUDOjOnqC2LjTh407fejqSyxJcCgy5p4yAU2NHkyfdALkAi//ZiChhAxVY5Sv2qOB9OAI5GgJhMupwOVQ2FdhBIPrzzTdSPh3YL3c1kKzJptoQKo5rQ1DIGwI+EMa+qIBQGCg94KiyFBkySyTUMYeYJg7oy5rJ9Jj+bwX01hh9S/QDRHLLoh9b5iP2SVgMBS7nR8QZaKYxpNiMdKxpy8QQX8gkjQ2pjpuFoKwpmP7vk5saGnF3iM9SY9PbahEU6MH8z9WB7ezeC6/i+eV0Ihqq1xo70k+KbBD7VF8s0YJgMupwF3gQYV06vVGW9M3uP5MVWRompF00ZHLeuxU25Wv7SAqNFbvBQyaM1qWAIeQBkSSAAAgAElEQVSqJAQWVEXKy7g4eLxyqjKCESNpvXQ+74U2VujGQINDbVDAwE4zJIy2z4Gdzw+IRlJo40kpMwyB7v4wQkUwdbwQAod8/WhuacX7u9uTXlNFmQOLZtVjUaMHE2vL87SV2cVAQglZPLuhIGqPBAb6KljNGl0OBU5H4TRrTKdebyw1fYPrz6rKHOjsDaGyzJG0Xi5luyabqNQYwmxCFYokLpdlCao8EFxwqInBBSmhN8PYgw6pxqtgSIMAEsqpgPQ+73YaK4SIZgyIuICBEQ0a6AKaYeSlb0GmxtLnoFDOD4hSsdN4QkMLR3R09Ydz1kA2W/oCEWze1YYNLa1oHRTAkiWg8eRaNDV60HhyTcFct4wWAwklpBBrjxKaNQJQo/XEDtUMLNi1s2k69XpjqemL/x1d/WFMO7EKZ82fFJ21YfxrodOV7ZpsIjJZJRJhLTkjYDAJgCRLkCVAkWUoihmEUGQZsmwGGmRJMle01h8UfEg1XrldKtxOBdUVzow/7+M5VhhxJQRG3FSJVnDAYgUMZKeK9u4gdCEgCqDsIF1j6XNQiOcHRBaee9if1R+tUOmGwK6DXdjQ0oodB7pgDIoue2rcaGpswMJZ9agqd+ZpK3OPgYQSU6i1RxbNENCiJRCA2bTE7TLLIOwU9UunXm+sNX1W/ZnHUwWfrzfzjcySbNZkE1HmBABhCBgANF0HMjiXs2abONLWn/KC2x8U+MYlp5rBCFmCJJnTXCH6c7HfE///aMRi1pQaeKfURB8zl8X3eZEkxDIBhBjoRWAIYWYKaMaosgWCYR0RfeQATKEZa5+DQj8/oNLGcw970nQDPf3htILeduTrCqC5xYdNO32xxuYWl0PB/I/VoanRg6kNlQVbij0WDCRQWuw6v3RENxDxG+j1m93PnaoczVgwsxXy9aFOp17PU1OG/cd60ReIQNPN/gaVZQ5MP7Eql5tKRAUqF+OygHkRP1QNfU2VMxbYzZV0Xrddj1nZlI0+B6W4H4nsarR9tfJBCIH+oJayoaLdhcI6tu5tR3OLDweOJ9+omzGpCk2NDZg7YwKcDiUPW2gfDCTQiAplfmndELGGjYB5f0tRJDijZRBOVYEs5yawkE693pSGSmze1Rb7XtMMdPWGMIU1fUQ0glyPy3apoU/ndRfKMWu8jfffqFT3I5EdjaWvVq4Fwxr6/BFoBdQLQQiB/cd60dziw9a97YgMyqA4ocKJRbPq0dTYgLpqNvG0MJBAIyrU+aUFYKa/6hr80Zs0qiLB6VDgVLMbWEinXu9Qax9qq1zojctIqCpz4FBrX1a2iYiKR67HZbvU0Kfzugv1mDVW4/03KtX9SGRHY+mrlSvhiI6+QKSgyhi6+8PYtNOH5hYf2nsSS4sVWcLHp9dicWMDZk6uztnNyELCQAKNqJjml44FFqLfq7IERzSw4HKMb2BhpHo9X1fAbFY2qOM55z0mopHkY1y2Qw19Oq+7mI5ZmRrPv1Ep70ciuxlrX61simgG+gKRgpnSUdMN7DjQieYWH3Ye6krqszOprhxNjQ1YMLMO5W5H6l9CABhIoDQU8/zSmiGghTQEoi/PqcpwOc3pJrM9I0QxzHtcSPV6RMWkmMfl4aTzukt134w37kci+7DjOaOmG+gPRHLeJ2e0jnX40byjFZt2tcEf0hIeK3MpWDDTg6ZGD06qr8jTFhYe+7S5J9saqr6yGOeXDmtm48a27iDaugOxRojZMNT8xoUy77FVr3e8MwBDDNTrbdvXnu9NIyp6pTQux0vndZfqvhlv3I9E9mGnc8ZwREdnbwht3UHbBxECIQ3vfnAMj720FY/8fgve2XYsFkSQAMyaUo0vnTcT37uqCZ87azqDCBliRgKNyC61sbmm6SI2762qSHA7VbgcMhzq+HRoLfR5jwuhXq/YRDTD/NINRDQdHg9n+ChVpToup/O6S3XfjDfuRyL7yPc5o2EI+IMa/KEINN3eTRQNIbD3cA82tLRi+/6OpO2trXKhqdGDRV4PaiqZYTUWDCRQWuxQG5tPA0EFc25zp6rEggoOdfSJPYU877Gd6/WKgRACmm4grBkIR8zAQQE1QKYcKNVxOZ3XXar7ZryNdT8KIaAb5pdhmGOapCro6AnGlpuPGdB0c53Yct1I/FnDMB/XRdLP6rqAZsT/fPRn9cRlgIRHbjt3/HYQUQ7l45wxohkIhDREpH70+MM5fe5MdfYG0dziw8adPnT1JW6rQ5Ex95QJWNTowYxJJ0DO0/TwhUKSzH2mjnCNw0ACpYVzSQ8QAghF9GhTmQhkCdGZIBS4nDIUuTQqhuxYr5cpu/R40HQz0yD+5DmiGQU39zIRjT8hBAwB6NaFdMLFdOoLZj3FeuaFfOr1jCF+Z/zPxl+863r09yX8fPLzGoO7mBEVCbucP2SDEALBsA5/UEMkWt7rrrDnnfuIZuCD/R1obmnFnsM9SY9PbajEIq8Hp82sg9vJy97BJAAO1QwYOBQZqiJBkeW0m89zj9KIOJf08AwBBMM6gmEd8AOyLMGpynCoMpyqDFWRIRVh5HPZ/EkJcxrHLy8E+ZiT2cwyELHyBE0zoOkMGBDlgiHMC95gSIM/qMUumK0L4tQXw4Mutoe6W66nuNhOsd6wF//64J8fuPinzEgSoEZPhhVFgiKbX+NVmkilLR/nD7lgGAL+kAZ/MGLrDEghBA77+rGhpRVb9rSb599xKtwqFs4yGydOnFCep620L1mW4HYocDnNWevGco3CQAKNiHNJZ8YwxEBgAQPpQc7oNJOOMX5o7SLf9XpjlYseD1amQVhj0ICKg3l3PDm9PP4OtTHkXfGB9Ya6YDYGXWiPnNo+6Pfpyc9t/U47nxjbkQRAUSTzYlw271IpCRfm5vdyimXWclWRIMctU2QJFRUuRMLawM9aX0riz6Zabn0vyxJUedC2KQM/O1TasnMMpYhElmLrERXRDPiDEQTDuq3PUfoCEWze1YbmltakjFhZArxTa7F4tgeNJ9eUTHZwuhRZgsupwO1Q4HSMX0CVgQQaEeeSHhshzNkgwpqZHmalERVDYIE9HhIZhkAooiMc0RHSzAscolQMYWampLro1oZIYU/nglkbZr0h0+JT3Y1PESSwvqfMyJKUcJGrKnLCxXLsgnjwBXP0jro6+GJeSXGxneIiP/lnE9czL8blgcdTBAiyYcKECnR09GfldxPlQrH0iNJ0Az394dj5qR3phsCuQ11o3uHDjo86k45Bnho3mhobsHBWParKnXnaSvuxsqOd0euNbE1pz0ACjaiU5pLORS8IgYHAwq5DXWhuMZ+vvtqssVsws75gAwuFZKw9HoQwgwb9wUhsNgVeZOWW1chtqJTzIdPLU14wG4NSzQfWS3mnfcS75XEX74PuoOuGAEvHMyNJiF3kAma/ACHMZWVuFeUuddgL5vhl5eVOaGEt+ljcxXuqO+0p74hbF/KDL/wHrauYd8Y5nhMVl0LvESWEQH9QQ38gYtsMBF9XAM0tPmza5UOvP5LwmNMhY/7H6rG40YOpDZVFMcaO5fpDlgCHqkBVJDiiNyhzlZHBQAKNaPHshoQeCfHLi0mue0EMfj6rxq4vEMGp0yfEshYcqszuslmQSY8HK5U7Ei1PiP0ryUkHuGJ0pK0/lnY+1AXz4AvrwcuH/Vnr8cGp7cNd/Ots5DYaCanjigw1IQ1dHpRSnnh3O6308rhUdzVuvUzutA9eL/7u+OBx03Lh0qlpj9O8I05EY1GoPaKsJor9gQg0G974CIV1bN3bjuYWHw4c7016fPqkKjR5PZh3St24pufnW6bXH6oiwRmdNc7pyG+TdwYSaESlMpd0rntBjPR8Yc1Af1CDBEBVZDgcZkdVR7SBI41Nqh4PZ5w6ER87qRo9/WEzw0AICEPYNmKfK//5wtZ8b4KtyBISL66VoS6Gh05DLy9zQNP0xPTywT+bcFc8sf48ZWr6UBf/VhdmCQV/54Y9e4go3wqtR5RuGGYAIajZruRSCIEDx3vRvMOHrXvbk8osTqhwYtGseixq9KC+uixPW5ldwx3XGqfWwOVQUO5Wo6UKStbKzkaj5AIJj724FYYQKHOZqZBl0a+B/ysD37tVOIq0436mSmFO7lz3gkj3+QSAiG7EpuABok2wonf7XOUuhMI6VFXKa1SykJgZBgZOmVSNkxuqYhkGAkB3v73nSS4WiY3cUqWOD1PLLQ80covd3R4qFT3Fc1jrJ90tT6pRT75TP1wjt0zwrvjosGcPEdmB3XtEGUIgGNIRDGu27IHQ0x/Gpl0+bGjxob07sbeEIkv4+LRaLJ7dgJmTq2114ZwNg49fVtC/1x9BQ2056mvKICJanrZueCUXSNh1qDuj9RVZigUVhgw4xAcjorWaLqfCdPQCk+teEGN5PgFAMwQ0Q0evP4zOPvP3WHdKVSWatqwMNPYq1YDYwJSLeqyXgR1T+uzsX86cllx/riRebA9u9pbOnXYqTtnsNVNb5cKRtn70R6dvVGQZFW4VJ9VXjMvvT1cu+ukQEWUqounwBzVbzsCg6QZ2fGT2Btt5sCupV9CkunI0NXpw2sx6VLgdOduufI/nddVutPcEk3rqNNTaPwOj5AIJUzwV6O4PIxDS0pqbWTcEegMR9AYyq4OWALhTBBsGByNSBSmYtp4fue4FkY3nMwRg6AYiOgAkzqsrSdHu4XF3Xh2KXJCZDEKYzeoMIaLT0ZkzJljLrPniDcPsa2C3g2mhWTZ/EpsDUlqy3WvmxLpy7DjQGftej3YdX9ToGfPvTleu++kQEQ0nFNERCusIRnTblS4AwLEOP5pbWrFpVxv8wcQ762UuBafNrMfixoacB4SB/IznEgCnQ4HLYfZCO69pSkH23ABKMJBw25ULY2/isKYjENIRCGnwByMD/w9pCES//CENwZAOf9w6oYg+wrOYd4zN36ejA5mlXDpUecjMh3L30EEJl0Mp2bvO4yHXvSBy/XxCAHq0yz0GpblJEsyggjKQzWD+ECAgIEnjm9Ydz2pkGLv4j/07EDAwtz/xMSKyn2z3MDjW7kdVhRP+uIyEcreKY+3+Mf/udLFPAxHlWziiIxg2SxfseE4UCGl4f08bmlt8OOxLLOOTAHxscjWaGj2YE20uni+5Gs9lWYJLleFyKnA6ErPWC63nRrySCyTEc6oKnKqC6orM5h3VDSMWdIgPOMQHIoLRf/1BLWG9dD7sEc1AtxbOuFZblqRY4CEhyOBOkQUx6EthmjGA3PeCsEvvCSEGpqQciQRAkiWzhgsSrLFQkqLLrPdSNBBgPQZrsTGQQaAbhi0PgEQ0OtnuYdDZG4LbqcLtVJOW5wr7NBBRrgkhENbMpokhm2YeGEJg75EeNLe04oN9HUmZ37VVLjQ1erDI60FNpT2mkB/v8Xxw/yVrSsaRMn/t3nNjKCUdSBgtRZZRWSajsiyz+h0hBMIRIynjISEgEYwGJMKJQYh0LvAMa17YYOYNOVwOZchSi+F6Q+Qzikj5IRANBsS+IyIyZbvXTK572dh1G4io+BmGMMsWol92LTHs7A1h404fmlta0dWXeBNUVSTMnVGHpkYPZpx0gu36x411PLcyDZwOJe9TMeYDAwk5JEkSXE4FLqeS8QmHphuJpRbBCAJhPS74MDgoocMfiiAYSq/ZijVIDR4ARqLIEirKHHA7FZQ5rfKLFFkRcc0oy5wq3E57TV9SrPLdQGasCn37iUajkN/32e41k+teNnbdBiIqPlbWQTiiIxxJnK3LbiKage37O9Dc4sOew91J1xpTPBVoamzA/I/Vocxl38vNTMdzSTIz2q3+BqXe186+f1lKoCoyqsqdqCrPrAzDEAKhsJ6YBRHUEAhrCAT1hMyIwdkR6Taj7OkPoyfDWcwybkbpHCjRKPUPbboKvSFYoW8/0WgU+vs+271fct1bxq7bQETFIRy9kWfNKmXTpAMAZqDjcFs/mlt8eH93G4LhxJ5xFW4VC2d5sKjRgxMnlOdpKzMz3HguS4j2DjO/HKoEh6rkeYvthYGEImf2TVBHFQ2MaEbK0ovBpRmaAfT0hWLLBg8sqWSrGWUsGJEQfDDXKbVmlIXeEKzQt59oNIrhfZ/t3i926C1jh20gosIUCpslzHYuV4jXF4hg03sf4R+bDuNYR2JjW1kCvFNrsXi2B96pNQV5s2/WlBp4p9TAocoJX6VWpjAaDCTQkByqjGrVOWIzygkTKtDRMZCSoBsCwXBy34fYzBdh3WxGaWVGxK1jpDGijr4ZJYbIfIhmPwzKfCj0ZpSF3hCs0LefaDT4viciKj4RzUAoYp4L27FR4mC6IbDrUBead/iw46NOc8avOPXVbixubMACbz1OyDBb2i5kWYLbYZacO1W5pG42jhcGEmjcKbKECrcDFe7xaUbpD2qxwER8UCI+UyK9ZpQYv2aUzrjMh6GaUbpVOJT8DUyF3hCs0LefaDRK9X0vwaw9lSRzJhhZkhKmnY0P5orotLRW3FnXzZRgrQBOzomodIQjOoIRHaGwnnQhbldtXQE07/Rh404fev2RhMecDhnzT6lDU2MDTp5YmbPz2/HsGyTLUrSvm8IyhXHAQALZxng0o7SaTMam4oz1g4g2qgzHzYwRMh9LJ61sLM0ok4MPuWlGWegNwQp9+4lGo1je9+aUV0osAGDNACtJZmBAlqwpY6VYAGGsDCES7vSZY7s53awQArohoOkCLoc5vhbCXUEiKhzWDTEzeJDelO92EIro2La3Hc0tPuw/1pv0+PQTq7B80RTMmFgJlyO3F99j7RskS4BDNWdUcKoygwfjjIEEKgoDzSgBoCztn8tFM8reQAS9gciI68aLNaNMUWoxuBnlxP4IIqFILAhhTclZ6A3BCn37iUajUN/3DkU2T9QcChyqnJcpvmRJgqyM/Lz1NWUQETO9WNONhAwH3RDQdQOaLqAbRsFcCBBRflhTNAbDOsKR9GZKswMhBD463ocNLa3Yuqc9KbP3hHIHFno9aGr0oL66LKmMOVcy6RskyxJUWYKiyLFjUiH2bCgkowokhMNhGIYBt9sNAOjp6cGzzz6Lo0ePYv78+bj44ouhqoxRkP2NRzNKf9DMgBjIdhg8HWdioCLTZpTIsDbaochxwQcz+2HihHKUu1Qc9vWjoydUMM0o2dCMSpFd3/fWCZoiS1AVCYosQ1HMsgO7jR3pkGUJTnn4u1OGiA8smP/XowEIBhmISpMhBILR8z67z7QwWE9/GJt2+dDc4kNbdzDhMUWWMHtaLRY3esxZC2zQH2y4vkGSZJYeu6J9DvIRwC51GV89Pfroo/j1r3+Ne++9F5/97GcRDAaxcuVK7N+/H0II/M///A9eeOEFPPHEE3A4MquRJyok6TajHCzWjDKa+RBfapEqM2IgOJFmM0rdQKQ/jJ5RNKN0pyq3GLEZpcLOtkRFxAwUmEECVTanvFKz0O9l2752rN1yFL6uADw1ZVg2fxLmzqgb1+cYK1mSIKsKHCnOlswggxlU0I2BQINmCFuVTYxnfTFRqdINwyxbKIDMg8Gf+YVeD3RdoLmlFS0Hu5JKek+cUI6mRg8WzKrPuL9Ztg3VN6ihtgyemrJhgweFcIwpdBkFEv7whz/g0UcfhdPphIi+C59//nns27cP06dPxzXXXIPXXnsN69evx1NPPYUbb7wxKxtNVMjGuxmlrCpo6+iPfq+n6AeRfjNKf9D8mUwlNaN0mUEHqxnlQGAisT+Eg11yifLKuqPjVBU4VDPjIBd3dbbta8cLb+2NfX+8MxD7vlBO9MwggxQrJ4s30JPBgGEM9GkwBKIZDrnJaBhrfTFRqRJCIKwZCEUDB4XSzDX+Mx/RDOw90oMP9nUkjTdup4LTZtZjcaMHJ9VX2PZcbHDfIKvHzjkLJ48YRCj0Y0whyCiQ8Pzzz8PhcOC5557D7NmzAQB//vOfIUkSfvSjH+HMM8/EihUr8KlPfQp/+tOfGEggGkdDNaNMp27NakbpD2kIxpVaDNWM0h/NgAjmuRllyoaUrvFpRklUilRZgqqadaMOVc7blFdrtxwdcnkxnORJkln+MVx9rmEIRDTDzCLTjFjZxHhermRSX0xU6qx+B9ZXOuc/dvPPD46hPxCBP2SWXcSTAHxscjWaGj2YM31CyiCo3TSeXAunQ0bzDh/ae0Lw1LjTyiwo9mOMXWQUSGhpacHSpUtjQYTu7m68//77qKiowOmnnw4AcDqdmD9/PtatWzf+W0tEozLQjDKzMgyrGeXgvg+xaTlTlWTkshllimDDwPeDHo+WZLDxDpWSWBNEVYHDkZ8miKn4ugJDLA+mXF6MZDkaHEZinwYtmrEQ0YxYoGG0FzTD1RcTlbr4z1lY09M6b7EjQwjsO9KD5hYfth/oSnrczIRV8fVL5xbEVMJO1Wzc64o2722oKcPpHz8xo9/BY0xuZBRICIfDqKioiH3/zjvvwDAMLFmyBHJcjbSu6zCMkVOpicjextqMMmXfh6QZMMbYjBIZNqNU5aGDDdb/40oyjnf0Y/v+TnT3hTDhBDfri8nWrGkXnaocm+ZwtLJZX+qpKcPxzuQTPU+Ne1x+fyFTFTNjxB0X99UNA5omENGNjGaVGKq+uBAuJkpZtmu7S7V2XAgzC8icnlGHnkG5gh17jXT2hrBxpw8bd/pSBgfNklMHnA4Z9dVuW3/unaoMl1OB2zk+fbd4jMmNjK4OpkyZgp07d8a+f+ONNyBJEs4+++zYMr/fj/fffx+TJ08ev60kooLjUGU4VCdOGEUzylB4IOshVbAh1cwYgZCW1kmBefch82aUAHC03Y/tBzoxocqF2io3ak5wQwFiAYn4zAc2o6RckGUJ7mjH6vEsU8h2femy+ZMSfn/8ckqmyDIUJ5KyFwzDDCho0aaPmj7Q8BFIri+2LJ7dkJPtpsxl+7NXSrXjuhGX2RP9Gk3OgZ16jUQ0A9v3d6C5xYc9h7uTXk99tRsRzUCZS00IJNvtMy8BsayDbJSr8hiTGxkFEs466yw89dRT+N73voeJEyfir3/9K1RVxYUXXggA2LhxIx5++GF0d3fjiiuuyMoGE1FxU2QJ5W4Hyt0OoDr9n7OaUcbPdGH2hEieitMfTOwHMbiOcCiGAKAL+LqCGaXHWc0oh+z74GYzSkqPLEtm2qcqw6EqWatxzXZ9qfU7zLuiwbTrXimRLEuQ5eRZJaxmjzWVDXA7Faz74Bjau0OorXKiqTH/d1JpaNn+7BVr7fjgniORaKPT8ZDvXiNCCBxp68eGFh/e392WlLlZ7laxaJYHixo9OHFCuS2zJwBzdjCHagYOsj1dI48xuZFRIGHVqlV4//338fLLL8eW3XbbbairM/8ot9xyC9ra2rBgwQJ87WtfG98tJSIaRnwzyprKzNL3BjejtP7/v+8dhG4IGEKYHdcNxP7vVBUEctCMMlXfByv4MK9xYka/jwqTdeem3K3C5VBGXH885KK+dO6MOp7UZUl8s8clsydiyeyBsWJwD4ZczSBB6cn2Z69Yasc13ZyOMaIbiGR5VoV89RrpD0aweVcbmlt8ONbhT3hMkoDGqTVoamxA48k1Cf2fZk2psUXgABgoWXBmMfA9FB5jsi+jQEJVVRWeeuop/OUvf4HP58OSJUtw2mmnxR6/7LLLMGnSJKxYsQJOZ2bpzDT+7BqRJLKboZpRtnzUmbK+uO4EF678tBc1teU4eqwndd+HQTNiJPw/g2aUfYEI+oZoRvmtK5tG94LJ9iQJcKpm2qfLKee8PIb1pcUrVQ8GK7ig6SIWXMikfpzGT7Y/e4X22dZ0A7ou0Oc3SxJ1QyCi6TkNfuWy14hhCOw61IXmFh8+PNCZ9Dmsr3ajqdGDhbM8GZeO5kK2SxbIXjIKJDz++OOYOXMmLr300pSP33bbbeOyUTR2dqrnIipUI9UXxzejnJDh745oxtB9H4IaAmE9rkfEQM+IUGTkZpRUWCQJUGUZqirDochwqOad5HyWtbC+tLRYwYV4hjHQdyEWaDDEuKWLU2rZ/uzZ+bNtZcqENQMRTYeuD0yHKjlV+ENaXrYrF71G2roD2Njiw8ZdbUk9nJyqjHkfq0NTowfTJlbZsuTRGW1kne2SBbKXjAIJTzzxBBoaGnDeeedla3tonOS7nouoGFiflWxk9jhUGdWqE9VjaEZJhUWRzQCBokioqXRBRDSoimTLZpysLyVZluCUFTgdicsNIWIzR8Q3eNQNMapGdpQo2589O3y2hTDfO2HNgBbtZxAfNLCbbJ0LhCI6tu1tR3OLD/uP9SY9Pv3EKjQ1ejD3lLqclbVlQpYAt4tTa5eyjKd/nDZtWra2hcYR544mGh92qjUEBjWjJFuTJMDtVOFOMaNCRZkD/j77nRjGY30ppSJLEmR16AaPEc0MKmi6eZHIAEPmsv3Zy/Vn2xACkYiBsKaPafaEfBqvcwEhBD463ofmllZs2duOcCSx2fMJ5Q4s9HrQ5PWgvqZszM833iQALqeCMqcKp4MNoUtdRoGEiy66CH/961+xY8cOzJ49O1vbhAceeABPPPEEnnrqKZx++ukJj7388sv/v717j46qPvc//tl77rnfJhTCHZwgIgLh0lqKxMtpq7ZHipZzhKK1PSKraFelN+vP1lU92p5jtbbWotZThKLHa1vb0y5btaIgUBJupUIQDCIXSQgECOQ6s39/TGZMSAIzkLm/X2t1lXz3TvKdbeaZPc98v8+jpUuXavfu3crLy9NnP/tZ3XbbbcrOzo7ZfFIRvaORTKjXEcR1SH+GofDeUJfDxk1WnGytbej8hLVZ3gJPQlZPJMMcEqlrgceugp8+W2pt96ut3S+eEenv1C0KkdQESnfHT7Zp445Dqqqp06Gj3Qtb2kxDY4YVanK5V6MHF8hmGnp3b6P+WvVB0twvBGtJOeRx2ql7gLCoEgkzZ87Uxo0bde2116qiokJjxoxRfn6+zD6WZd5yyy1RT2jLli166qmnevKYv2sAACAASURBVD322GOP6cEHH1R5ebnmzZunHTt2aOnSpdq8ebOWLVtGgccu6B2NZEG9jiCuQ3oyTaOzrkGwvgGf0MTf1tqGbnu+Dx5pDn8drzfyyTCHZGUYhhx2I1ix3eNQcXG2Olrb1dbuV2tH/7XoQ+IEOyj41doeiHshxGTmDwRUs6dRVdvrteODIz2uy8eKslRR7tVFo0uU4/lolWGy3C8YktzOYLeoAUVZqvdTowndRZVI+PrXvy7DMGRZltatW6d169ZJUo+bJsuyZBhG1ImEtrY23XnnnfL38oe6f/9+/exnP9PEiRO1fPlyORzBJ9zDDz+sRx99VM8995zmzZsX1e9LZ7Hc2434MxT8pNM0jB7PN4fNlM0MPi9lGDI7z7OZhuz2YAEt0wjurQ8EJCn4/Az+rODzN/QjQy0OQy0PA4HgUtXQktXAWSxRpV5HENch9Z2aNHDYTT6ZSQKrthzoczxeb+KTYQ6pwuzS1lb66E1oW+eSd7pFJK9AwFJ7lw4ffj/tQ3tz8PBJVdfUa+O79TrR0r2ekdtp00WjS1Th86rMm91r4jnR9wt2m6Esl11ul53CiTitqBIJX/va12L6ScuSJUtUW1uriy++WG+//Xa3Y88++6w6Ojq0YMGCcBJBCq56WLZsmZ5//nkSCadItr3d6J2h4I2VLfS/zsSAzTRkhv53muedtyhLRgRZ4v7aUR9MLgRvIgKWJctSOOkQSjx0TThQryOI65BaTNOQszMRF0ockDRITvWNPVvZBcdbeh1P1zmkqlDHiKzOr0NvVts7ggmGVNxPnw4CnfUuQv8tWGlwei1tHdq8s0HVNXXaW3+ix/HRZfmaVO7VBcOLgqtzTiMR9wuh1QdZbrsc9uSu34PkEVUi4dZbb43VPLR9+3Y9/vjjWrBggY4dO9YjkbB+/XpJ0pQpU7qNu1wuTZgwQatWrdLx48eVm5sbszkCkTh19YBpGuGvg0mBromD1HtzEkxu9Cy01VVoT2yHPyBvgSd8k21JsjpvRDKtXgd1S5KbYUhOe7CugdPRsxUekpe3wKODR3q+kfcWuDNqDunCNA25zOBzUR5HsFBfR5fEgj8Qfh1B//EHAmpr/yh500HW4IwClqXa/cdUXVOvrbUNPWpBFOQ4NcnnVUW5V4W5kceCeN4vmIbkcdmV5bYnZQchJLeoEgl33HGHJk2apOuuu+605y1ZskRr167V0qVLI/q5fr9f3/ve9zRs2DAtWLBA//3f/93jnD179qikpEQ5OTk9jpWVlUmSamtrNX78+Ih+J3CuuvZ+DxWY4lPLoK57YisnlfXoWW1ZlionlSk/2xlcJtn5qUc63xxStyS52Du3/jjsppx2k09gUtj08QN7xJjQeCbNIV2ZhiGX46PEgqTOpfXBlXGhT82ptRC5rlsU2tlSErXGplZt2FGv6pr6HqsE7DZDY4cXafKYUo0clHdWWwPicb9gGursAMX2BZy9qBIJv/3tb+X3+8+YSFi/fr02bNgQ8c998skntW3bNj399NN9FkxsbGzU4MGDez0WWoXQ1NQU8e8EIhFaPRCqOWDrTBbY7cnZ+z0ZnalnddfmRl1vatrSrNIzdUsSyzQNueymnJ1vSEj4pY8zxZhMmUMmCW2H6Krb/v3O5ILfn9mtJwNWsI5Be4fVJflCTYOz0d4R0Du7D6u6pl679h3t8XdV5s0OFk4cVRKu/3G2Ynm/YJqGst12ZbnsFAbGOTvtX/o3v/lN1dV1L/jx9ttva/78+X1+T1NTk7Zt26ZBgwZFNIHa2lo98sgjuv766zVx4sQ+z+vo6OgzyRAab209896hgnyP3FmBM56H6BQVpXb7TZsZ/PTcYbfJ0bnCIJW2HXi9yb2lp9Kbq8qpw6P+vkDgo5ufrktbk+keKJq//WlF2Zo2viyGs4mN/DyPzBRc6u+wmcF+1y67nI7kW3GQ7M/bVBJtjInFtT/bOJeJ4vW3b3XZFtHeEVA83zcVFmbJHseVTqHH2tYRUHtn8coOf0Cym7Lbo/zkMEnF+17TsiztOXhcb285oPX//FAnW7sXTszxODRt3Md08YWDVFbac8X0uejv+4Vw+8ZzSCDwmpU4yXrtTxtXKisrtXjx4vDXhmHo0KFDOnTo0Ol/qN0eUT0Fy7J05513qri4WLfffvtpz3W73Wpvb+/1WFtbmyTJ4/H0eryrxqPNOnlKBVWcm6KibB0+3LOwTKIZhsJbDQzD6La6IFSjIFzLIGAo0BZQa1uHUq30ndebq/r644meRtzYLaszsWCpIxD8xClRRaAS/bdf5u3fG5e+HD3WnBLbTuw2Q87OZKDLYZMhS23NfrU1tyV6aj1k2vM2mXDtEyvh1z8nPnVpjhw5GbOfbXV5HQwWQ/Sn/eqLeL7enmxp16adh1S1vV4fHu7+39EwJN+QAlWUl2rM0ILwyphE3Qu8u7fxtCsXXI5gAUVTNp047teJs3zqJfx5m8ESfe1Pl8Q4bSLhqquu0qBBgxQIBGRZlubNm6fp06dr4cKFvZ5vGIZcLpfKyspUUHDm5TcrVqxQdXW1Hn/8cWVnnz7LmJeXp+PHe7+IoXEKLWYmQ5LNFmzLxtaD9Besv9Cz2GN7R7CHNcW4MkOoOKKjs86Bw26yzxNA2gkVLw7VgujoXGnAS1z/CgQs7dx3VFU1ddq2+0iPmhHF+W5NLvdqwnle5Wf3vkI63t7d29itlkLDsdbw1xeNKlGW207hYMTUGVc6dd1uMGvWLE2aNEkVFRX98stfeeUVSdLNN9/c6/HQForXXntNw4cP1/r169XS0iK3u3vl03379sk0TQ0bNqxf5oXkY0gftUa0GcEihzZDNhsJAwQFkwsfFeMKLWUNrVro8FNMKpXZOtsxOrokDwAg3XTdytdO0iDmGo62qLqmThvePaRjJ7qvXnPaTV04qlgV5V4NG5CbdDUFqrbX9RgzTWnrew361PjItpgD5yKqLVP3339/n8f8fr+OHj2qoqKiiH/erFmzNHXq1B7jb731ljZv3qxZs2aprKxMeXl5qqio0Lp161RVVaXp06eHz21tbdWmTZs0evToXjs6IHUYUvgNgt1mdrZPVJdtCMkVwJHcenuzaVmW/J29sVva/ElXbwFBXWNBsKsCxREBpJduNYD8FisN4qit3a+ttYdVVVOn3Qd6rnYe9rFcVfi8unBUcbBbSJLq2jEi1F7cMAwdOppqm3SRqqKuvdLQ0KBnnnlGl156qcaOHStJevrpp/WTn/xEJ0+eVFlZme666y5dcsklZ/xZX/jCF3odP3bsWDiRMG3aNEnS5z73OT322GN65JFHNHXq1HCBxSVLlqipqUlz5syJ9qEggUzT+Kj9Wmf1ZbuNZEGibK1t6Kw23ixvgSdtq40bhhFu1elx2RWwLLW2+cPFqeibnRhmeLVBsBWj3WYSC1JEpsQO4GyF6hl0+K2PtiYELNpVRihUA+DYyXblZTnOunuBZVn6oK5JVTX12rLrkNrauxdez/U4NKncqwqfVyUFZ665lgyK8906fKxVRmcCIcRb4D7Nd6UHXnuSQ1SJhA8//FDXXnutGhoaVFJSorFjx+of//iH7rnnHlmWpcLCQu3du1df+9rX9Nxzz4UTDf1h5MiRuummm/TEE0/ommuuUWVlpXbu3Kk33nhDkyZN0he/+MV++13oP4YRLPRit5mymQYJgyS0tbahW//zg0eaw1+ne1A2DUMel12eztpbofZhXVtQUmuhfxkKVo92OIJJRKfDZHtSisrk2AGcyZHjrWypO0ddawDYbUa3GgCRJhOOn2zTxncPqbqmTvWNLd2OmYah84cVqmKMV+cNLpAtBVa+GYbkcQXbN15WMbhbDA6ZPn5gAmYWP7z2JI+oEgmPPfaYDh06pKuuuiq8veDZZ5+VZVm6+eabdfvtt2v16tX66le/qieeeEIPPfRQv0528eLFGjhwoJ5++mktW7ZMXq9XN954oxYtWtRna0jEl2kanSsMDLkctqRsuYbuVm050Od4pgVk0zTkMm3hpYyWZamtI6DWdr9a2/zcEJ6FUExwOlhtkG6IHUDfWtv9iZ5CyuutBkBo/HSJBH8goJo9jaraXq8dHxzp0dVpQKFHk8eU6qLRJcrprKuU7ExDynI7gh0YOl9DQ3E2+Ml8i7wF7oz4ZJ7XnuQRVSJh1apVGjp0qB544IHwjeDf/vY3GYahefPmSZI++clPauLEiVq/fv1ZT+rOO+/UnXfe2WPcMAzNnTtXc+fOPeufjXN3auHDghyX1N4RrGuQAtlcdFff2NzHeEuv45nEMIIJMZfDJmUFO0M0d9ZW8PtJKpwqvNogtE2B1QZpjdgBIJa61gCIZPzg4ZOq3lGvje8e0onm7i3jXQ6bLhpdrMnlpSrzZqdMQttuM5TtdsjttPU653EjijPuzTOvPckjqkTCwYMHVVlZGf5D/uc//6mGhgadd955Ki0tDZ/n9Xq1ZcuW/p0pEsI0FK6SbjON8P93DWbZHodONrHyIFV5Czw6eKRnUD51jx370bp0hlBwtUJBgUftLW1qbfOrvSOzimSFVhp07aLisLPaIJNEGjsA4GwU5rrUcKxn0qAw1xX+d0tbh7bsalB1Tb0+qGvqce7IQXmqKPfqghFFctpT517V5bApy21P6mKPicJrT/KIKpGQl5enpqaPnqQrV66UJH3iE5/odt6+ffvooJCi7J3JAofdJqfDpP9sBpg+fuAZ99ixH60nwzDkdNiU7XYo2+1QwLLU1u5Xa3twK0Q6FdIKdVFwOmxydK44YPURIokdAHC2Jo8pDddE6Kqi3Kv39h9VdU29tr53WO3+7oUTC3KcmuTzapLPq6K81HlzaUhyu+zKdtu5/z4NXnuSR1SJBJ/Pp6qqKu3evVslJSX6/e9/L8MwdNlll4XPefXVV7V161bNmDGj3yeL/mMY6vw00ZSjs4q93W6G910hc0Syx479aGdmGobcTrvcneVaOvwf1VZI9dUK3gIPKw3QQ6buzwUQH6E6CFXb63S8uV25LpuyXA79ftXuHtsb7DZDY4cXaXJ5qUaW5aXU/WwogZDjsbMdMAK89iSPqBIJN9xwg9auXaurr75aDodDzc3NGjt2bLhF4y233KK33npLpmnqxhtvjMV8cRZCrdXsts5Wi3aDQIVuzrTHjv1o0bN3tjUNrVZobw+otcOvtrbUazNJEgF9ycT9uQDiZ/jH8tTc2qEt7x3WttrDPZLyZSXZqhjj1UWjSuRxRd3VPqFCHRiy3SQQosVrT3KI6hl3ySWX6OGHH9aPf/xjHTp0SNOnT9c999wTPr5v3z4VFhbq7rvv1sUXX9zvk8WZUeysf2VqXYBTH7fTblNLLxWoe9uPlqnX7HRMw5DLaZPLGSza6A8E1NYeUFtHQO3tqZdYANIF8QpITvsPnVBVTZ027zyk5tbu9x8uhy346b3NVFGeS8V57pRKIvTWgQFIRVE/66644gpdccUVvR575JFHNGTIEJm8cY0Lm9m5JSG0NaHz33x62D8ytS5Ab4+7ubUjvPSuq1P3o2XqNYuWzTTlcZnydNaLCicW2v3B+grkFYCYI14ByeVkS7s27WxQdU2dDjSc7HbMMCTf4AINKsnS9j2N4XvdhmOt4ToKp2sJmQxM01C2264sl517daSFqBIJX/va11RZWalLLrlEXq+3x/Fhw4b128TwEdM0ZDcN2e2m7KYphz3YepEsZmxlal2A3h63x2WX22EqP8d12v1omXrNztVHiYVgSG5r96uls76Cn6wCEBPEKyDxAgFLO/cdVXVNnd7ZfaTHa15xnlsV5V5VTh0qq92vZ17d0eub8KrtdUmbSDAMdRZmJoGA9BJVIuG1117T66+/LsMwdP7556uyslIzZ87UuHHjYjW/jHJqwsBuMyiAmECZWhegr8fd1mHpln89/XM9U69Zf3M6bHI6gtsgOvwBtbT51dbuV1tH4MzfDCAixCsgcRqOtai6pl4bd9Tr6Im2bsecdlPjRhZr8hivhg3IlWEYKsx16/DhEz2KLIb0NZ5oWS67cjwOOh0hLUWVSHj55Zf15ptv6s0339TGjRv1z3/+U7/4xS9UUlKimTNn6pJLLtEnP/lJeTyeWM03LZiGum1FCP2bIJNcMqlPbdd9wkdPtMluM3vsN4zkcWfSNYsXu81UjseUPA4FApZaOxMK7b3UrAAQOeIVEF9t7X5trT2s6po61R443uP40AE5mlxeqgtHFgdrCvWiMNelhmM9kwaFua5+n++5sJmG8rOdwQ8FEoxaMIiVqNs/+nw+ffWrX9XJkyf19ttv680339SqVav0/PPP64UXXpDD4dDUqVNVWVmpuXPnxmreKSGUMAi1WLR1dk0gYZAaMqVP7an7hB02M5zZ75pMiORxZ8o1SxTTNORx2cO1FQCcPeIVEHuWZemDuiZV19Rry64GtZ6SBM/1ODTR59Wkcq9KC878QeTkMaXhmginjicLj8uu3CxHUqwophYMYumsS5xmZWXp8ssv1+WXXy5J2rVrl5566im99NJLWr16tVavXp0xiQQSBukpU/rUnrpP2O2yq1BSuz8g0zCietyZcs0ApD7iFRA7x0+2adO7h1RVU99jG5FpGDp/WKEqyr06b0iBbFHcL4fqIFRtr9OR460qzHVp8pjSpKiPYJqG8rOcfa6mSARqwSCWzqlXyrvvvqv169eH/9fQ0CDLChZJGTp0aL9MMJkYRvDTWlu3LQkG7RXTWCb0qe1tn7DbZVeWYeiuGyZH/fMy4ZoBSA/EK6D/+AMB1expVHVNvWr2HOnRgai00KPJ5aWacF6JcjyOs/495w0uSIrEQVdup015Wc6k+xCRWjCIpagSCdu2bdPf//53rV+/XlVVVTp69Gg4cTB48GDNmjVL06ZN08c//nENGDAgJhOOB8NQsNihnYQB0h/7hAEAwNk6eOSkNtTUa8O7h3Siub3bMZfDpotGF6uivFSDvdlp17XAZhrKS7JVCF1xj4dYiiqRMGvWLBmGIdM0NXLkSF1++eWaPHmypk2bpoEDU29PYY+EgWnKbidhgMzCPmEAABCNlrYO/WNXg6pq6vVBXVOP4yMH5ami3KsLRhTJaU/ON9nnwpCU5Q52ZEjm5Egs7vEo3oiQqBIJNptNfr9fgUCwBZnb7VZWVpbc7tTJarkdNrnsNtk6VxoAmY59wgAA4EwClqXdB46puqZeW987rHZ/95bEBTlOTfR5VeHzqigvdd4bRMtpN5WX7UyJ9xH9fY9H8UZ0FVUiIbStYc2aNVq7dq1WrFihFStWyDAMjRo1StOmTdPUqVM1ZcoUFRYWxmrO5yQZ2rAAyYZ9wgAAoDeNTa3asKNeG2rqdfh499aLdpuhscOLNLm8VCPL8pKiU0GsmKahXI+jR3vsZNef93gUb0RXUT0TsrOzNXPmTM2cOVOSdPjwYa1du1Zr167V3//+d/3mN78JJxZGjx6tl19+ORZzBgAAABAjHf6A3tl9RNU1ddq596hOqZuoQSXZmlzu1UWjS1LujfXZSKaWjolE8UZ0dU7P/KKiIl155ZW68sorVVtbqz/+8Y9atmyZjh8/rnfffbe/5ggAAFIE+2eB1LX/0AlV19Rr085Dam7t6HYsy2XXhPNKVFHu1cDi7ATNML5spqG8bKdcrGiWFHnxRl4HMsNZJxLq6+u1Zs2a8DaHDz/8UFKwjsKUKVPCqxYAAEBmYP8skHpOtrRr084Gbaip0/6Gk92OGUaw3WJFuVfnDytMiboA/SVUTDHTVyF0FUnxRl4HMkdUiYRXX31Va9eu1Zo1a/Tee8E/CMuyVFxcrGuuuUYzZszQpz71KeXk5MRksgAAIHmxfxZIDYGApZ37jqq6pk7v7D4if6D75oWiPJcml5dqos+r/GxngmaZGHabofxspxxp2G3iXEVSvJHXgcwRVSJh0aJFkiTDMHTBBRdo5syZmjFjhsaPHx+TyQEAgNTB/lkguR0+1qLqmnpt2FGvoyfauh1z2E2NG1GkyWNKNfxjuUnd1jAWTEPK8TiV5U7/mg/n4kzFG3kdyBxRPVM+/elPa/r06br00ktVXNz3H9CSJUu0Zs0aPfXUU+c8QQAA0lW67SONdP8sgPhp6/Drn+8dVlVNvWoPHOtxfOiAHFWUl2r8yGK5nMn1Kfy7extVtb1Ox062Ky/LocljSnXe4IJ+/z0el125HodMM7OSJ7HA60DmiCqR8Morr8jtduu666477Xnr16/Xxo0bz2liAACks3TcRxrJ/lkAsWdZlj6oa1J1Tb227GpQa7u/2/FcjyNYOHFMqUoLPAma5em9u7dRr/z9A0nB7QYNx1rDX/dXMsHeWUyR9vD9h9eBzHHaRMI3v/lN1dXVdRtbvXq15s+f3+f3NDU1adu2bRo0aFD/zBAAgDSUjvtII9k/CyB2jp9s06adh1RdU6+6Uz4VNg1DY4YVaHJ5qc4bUiBbkn/6XrW9rs/xc00kGIaU7XYo223PuC0cscbrQOY4bSKhsrJSixcvDn9tGIYOHTqkQ4cOnf6H2u269dZb+2eGAACkoXTdR3qm/bMA+pc/ENCOPY2q3lGv7e83KmB1L5xYWujR5PJSTTivRDkeR4JmGb0jx1ujGo+EIcnNNoaY43UgM5w2kXDVVVdp0KBBCgQCsixL8+bN0/Tp07Vw4cJezzcMQy6XS2VlZSoo6P/9S0CySrd9zgBij32kAM5F3ZFmVdfUaeO7h9TU3N7tmMth00Wji1VR7tVgb05KfupemOtSw7GeSYPCXNdZ/Ty7aagg15VRLSyBWDpjjYSJEyeG/z1r1ixNmjRJFRUVMZ0UkErScZ8zgNhjHymAaLW0degf7x1WdU2d9hxs6nF85KA8Vfi8umBkkZwp3r5w8pjScE2EU8ej5XLYlJ/jlJmCCRUgWUVVbPH++++P1TyAlJWO+5wBxB77SAFEwrIs1R44ruqaOm2tPaz2jkC34/nZTk3yeVVR7lVRXvqsaArVQajaXqfjze0qznaeVdeGHI8jpbZ0AKmCRqnAOUrXfc4AYo99pAD6crSpVRt2HFL1jjodPmWJv91m6PxhRZo8xqtRg/LTdr//eYMLdN7gAhUVZevw4RNRfa/dNJSf45QjxVdmAMmKRALSTrzrFbDPGQAA9IcOf0Db3j+i6po6vbv3qE6pm6hBxVmqGFOqi0aVKMvNbXxvDElZbrtyPI6UrA0BpAoiENJKIuoVsM8ZAACci/2HTqh6R702vXtIza0d3Y55XHZNOK9EFT6vBpVkJ2iGqcHjtCnb46CgIhAHJBKQVhJRr4B9zgAAIFonWzq0eechVdfUaX/DyW7HDCO4rL+i3KvzhxXyxvgMnHZTuVlOOexcJyBeSCQgrSSqXgH7nAEAwJkEApZ27T+qqu31emf3YfkD3fcuFOW5VOEr1SRfifJzzq7NYSYxDCnX41CWm2KKQLyRSEBaoV4BAHwk3jVjAPTu8LEWVe+o14aaeh090dbtmMNuatyIIk0eU6rhH8tlX3+EXA6b8rIdspmsQgASgUQC0gr1CgAgKBE1YwB8pK3Dr3/WHlZ1Tb3e23+sx/GhA3JUUV6qC0cWye3kljxSpmko1+OQx8U1AxKJZyDSCvUKACAoETVjAAT97q33tHlng1rb/d3GczwOTfKVaFJ5qUoLPAmaXeryuOzKzXLIZNUGkHAkEpB2qFcAAImrGQNA+vu2uvC/TcPQmGEFqigvlW9IPkvxz4JhSAU5TlZuAEmEZyMAAGmImjHxRT0KnMpb4NHkcq8mnFei3CxnoqeTshw2U6WFWTpy+ETC5sDzG+iJRAIAAGmImjHxQz0KnGrhNRdosDeHwonnKNttV47HkdD2lzy/gd6xtgoAgDQ0bkSxZl8yUgMKPTINQwMKPZp9yUhufGPgdPUokJmGlNJ94VyYpqGiXJdys5wJv448v4HesSIBAIA0Rc2Y+KAeBdB/kq2gIs9voHesSAAAADgH3j6q71OPAoicvXMVQn62M2mSCBLPb6AvJBIAAADOQV91J6hHAZyZYQTbYhbnu+V02BI9nR54fgO9Y2sDAADAOQhtHwlWdW+Rt8BNVXcgAm6nTblZjqRuicnzG+gdiQQAAIBzRD0KIHKGIeVlOeVxpcZbEZ7fQE+p8exFWqEXLwAAQGZy2Ezl5zgT2tIRwLkjkYC4ohcvAABA5jEkZXscyvE4Ej0VAP2AVCDiil68AAAAmcVpN1Wc7yaJAKQRViQgrujFCwAAkBkMQ8r1OJTlJoEApBsSCYgrb4FHB4/0TCbQixcAACB9OO2m8rKjq4VAHS0gdbC1AXFFL14AAID0ZUjKy3KoKM8ddRLhxZXv6eCRZgWsj+poba1tiN1kAZw1EgmIq3EjijX7kpEaUOiRaRgaUOjR7EtGkm0GAABIcXaboeJ891ltZaCOFpBa2NqAuKMXLwAAQPowJGW57crxOGQYxln9DOpoAamFFQkAAAAAzkqoI0NulvOskwhSsI5W7+PU0QKSEYkEAAAAAFE521oIfaGOFpBa2NoAAAAAIGKmaagg2ymnw9ZvPzO07TXYtaFF3gI3XRuAJEYiAQAAAEBEnHZT+TlO2cz+X9hMHS0gdZBIAAAAAHBahqRsj0M5nug7MgBIPyQSAAAAAPTJZhoqyHHKYe+/rQwAUhuJBAAAAAC98rjsys1yyDyHjgwA0g+JBAAAAADdmIaUl+2U28nbBQA9ERkAAAAAhLkcNuVlO2JSUBFAeiCRAAAAAECmIeVmOeVxJe9bhK21DZ0tIpvlLfDQIhJIkOSNEgAAAADiwuWwKT/bKdNM3loIW2sb9OLK98JfHzzSHP6aZAIQX6xXAgAAADJYjsehwlxXUicRJGnVlgNRjQOIHVYkAAAAABnINA0VZDvldKRGW8f6xuY+xlviPBMASZNIqK+v189//nOtXLlSDQ0Nys/P1yc+8Ql9/etf15Aha0fkRwAAGSlJREFUQ7qd+7vf/U5Lly7V7t27lZeXp89+9rO67bbblJ2dnaDZI17YFwcAAHDunHZTBTnJvwqhK2+BRweP9EwmeAvcCZgNkNmSYmtDfX29rrvuOj377LMaNWqUvvSlL+nCCy/UH//4R1177bXavXt3+NzHHntM3/nOdxQIBDRv3jyNGTNGS5cu1Ve+8hW1tbUl7kEg5kL74g4eaVbA+mhf3NbahkRPDQAAIGXkeBwqynOnVBJBkqaPHxjVOIDYSYoVCT//+c914MABffe739WXv/zl8PjLL7+sb33rW/rRj36kJUuWaP/+/frZz36miRMnavny5XI4HJKkhx9+WI8++qiee+45zZs3L1EPAzF2un1xrEoAAAA4PcOQCrJdcjlTYyvDqUL3e8HVqS3yFrhZnQokSFKsSHj11VdVVFSkG264odv45z//eQ0dOlSrVq1SIBDQs88+q46ODi1YsCCcRJCkW265RTk5OXr++efjPXXEEfviAAAAzo7dNFSc507ZJELIuBHFuuVfx+muGybrln8dRxIBSJCEr0jw+/1asGCB7Ha7TLNnXsPpdKq9vV3t7e1av369JGnKlCndznG5XJowYYJWrVql48ePKzc3Ny5zR3yxLw4AACB6LodN+TlOmUZqbWUAkLwSviLBZrPphhtu0Ny5c3sc27Vrl9577z0NHTpULpdLe/bsUUlJiXJycnqcW1ZWJkmqra2N+ZyRGOyLAwAAiE6W2x5s7UgSAUA/SviKhL4EAgHdc889CgQC+uIXvyhJamxs1ODBg3s9P7QKoampKW5zRHyxLw4AACAyhqS8bKc8rqS93QeQwpIysliWpe9///tas2aNxo0bF66d0NHRIafT2ev3hMZbW1tP+7MLC7Nkt6f23rBk5PXGZztJpTdXlVOHx+V3pZJ4XX/0lAnXnrgZG5nwt5OsuPaJlQnXPy/PndC4aTMNFeW55XSkT+zOhL+bZMb1T5xkvfZJl0jo6OjQXXfdpZdeeklDhgzRo48+Gk4SuN1utbe39/p9odaPHo/ntD//yJGT/TthyOvNVX398URPI2Nx/RMn0dc+Xi8sxM3+l+i/nUzGtU+sRF//eMXNY8cSVwjaYTNVkOvU0cb0id2J/rvJdFz/xEn0tT9dzEyqREJzc7O+/vWva+XKlRo+fLh+/etfa8CAAeHjeXl5On689wsZGqfQIgAAADKR22lTfrZTBvUQAMRYwosthhw9elQ33HCDVq5cqbFjx+rpp5/WoEGDup0zfPhwNTQ0qKWlZ5Z33759Mk1Tw4YNi9eUAQAAgKSQm+VQQY6LJAKAuEiKREJra6sWLFigzZs3a+rUqVq+fLmKi3sW0KuoqFAgEFBVVVWP79+0aZNGjx7da0cHAAAAIB0ZhlSY41K225HoqQDIIEmRSHjwwQe1ceNGTZw4UU888USfyYDPfe5zstlseuSRR8I1ESRpyZIlampq0pw5c+I1ZQAAACCh7DZDxXluuZzpU1QRQGpIeI2E+vp6rVixQpI0cuRIPfHEE72ed/PNN2vkyJG66aab9MQTT+iaa65RZWWldu7cqTfeeEOTJk0Kt4kEAAAA0pnHZVdeloOtDAASIuGJhM2bN4c7Mbz44ot9nnfDDTfI5XJp8eLFGjhwoJ5++mktW7ZMXq9XN954oxYtWtRna0gAAAAgHRiGlJfllMeV8Nt4ABks4RHo8ssvV01NTcTnG4ahuXPnau7cuTGcFQAAAJBcnHZT+TlO2cyk2J0MIIMlPJEAAAAAoG+GpJwsBwUVASQNEgkAAABAkjJNQwXZTjkdFFQEkDxIJAAAAABJiK0MAJIViQQAAAAgydCVAUAyI5EAdLG1tkGrthxQfWOzvAUeTR8/UONGFCd6WgAAIEMYknKznMpyc5sOIHkRoYBOW2sb9OLK98JfHzzSHP6aZAIAAIg1m2moIMcph516CACSGxuugE6rthyIahwAAKC/ZLnsKsl3k0QAkBJYkQB0qm9s7mO8Jc4zAQAAmcJuGsqjKwOAFMOKBKCTt8DTx7g7zjMBAADpzpCU7barON9NEgFAyiGRAHSaPn5gVOMAAABnw2EzVZTnVm6Wk64MAFISWxuATqGCisGuDS3yFrjp2gAAAPqNISnb41COx5HoqQDAOSGRAHQxbkQxiQMAANDvHDZT+TlO2W0sCAaQ+kgkAAAAADFiSMrJcijbzSqEVLW1tqFzxWqzvAUeVqwCIpEAAAAAxITTbiovm1UIqWxrbYNeXPle+OuDR5rDX5NMQCYjqgEAAAD9yJC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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Using lmplot for trellis grid\n", - "sns.set(font_scale=2)\n", - "plt.figure(figsize=(12,12))\n", - "\n", - "sns.lmplot(\"age\",\"twstrs\",cdystonia,col=\"treat\",row=\"week\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.lmplot(x=\"age\",y=\"twstrs\",data=cdystonia,hue=\"treat\",palette=\"Set1\",col=\"week\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the seaborn to represent more than just trellis graphics. It is also useful for displaying multiple variables on the same panel, using combinations of color, size and shapes to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "cdystonia['site'] = cdystonia.site.astype(float)" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.set(style=\"white\")\n", - "\n", - "sns.pointplot(y=\"twstrs\",x=\"week\",data=cdystonia, hue=\"treat\")\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Lecture4-Matplotlib/Links to Lecture4.ipynb b/Lectures/Lecture4-Matplotlib/Links to Lecture4.ipynb deleted file mode 100644 index a41970c..0000000 --- a/Lectures/Lecture4-Matplotlib/Links to Lecture4.ipynb +++ /dev/null @@ -1,39 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Visualization\n", - "\n", - "We will be using: \n", - "\n", - "3. Plotting and Visualization.ipynb \n", - "from https://github.com/fonnesbeck/statistical-analysis-python-tutorial\n", - "\n", - "Download it locally into your jupyter notebook directory." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Lectures/Lecture4-Matplotlib/UnderConstruction/Data Visualization.ipynb b/Lectures/Lecture4-Matplotlib/UnderConstruction/Data Visualization.ipynb deleted file mode 100644 index 9126e5c..0000000 --- a/Lectures/Lecture4-Matplotlib/UnderConstruction/Data Visualization.ipynb +++ /dev/null @@ -1,303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization of Data\n", - "\n", - "Visual communication of results from analysis.\n", - "The story told by the images should enhance the understanding of the viewer versus viewing raw data or equations.\n", - "\n", - "Visual representation of data:\n", - "\n", - "\"information that has been abstracted in some schematic form, including attributes or variables for the units of information\"\n", - "\n", - "- Michael Friendly (2008). \n", - "\"Milestones in the history of thematic cartography, statistical graphics, and data visualization\".\n", - "\n", - "Data visualization is both an art and a science. The are many good and bad examples of what should be done." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from IPython.core.display import HTML \n", - "Image(url= \"https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png\" \n", - " ,height=400, width=550)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "The are a number of packages that we could use to present the data.\n", - "In this case we will be exploring the use of matplotlib. Use '%matplotlib inline' line in your ipython notebook to activate the inline viewing of the plots" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(np.arange(10))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Plots in matplotlib reside in a figure object. Create a new figure object with \n", - "plt.figure()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ">" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "fig.clear" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax1 = fig.add_subplot(2,2,1)\n", - "ax2 = fig.add_subplot(2,2,2)\n", - "ax3 = fig.add_subplot(2,2,3)\n", - "#Empty Matplotlib figure with three subplots\n", - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Y5nZ+SfOA9Sd563jb55fXvA94zPZXpmhjbsvT+bbndx5qWzYAtuz1aCZJ2wNfAnbooZmt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\neUREAySZR0Q0wP8HOpKY5mVC48wAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(randn(50).cumsum(),'k--')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "k-- is a style option for black dashed lines" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)\n", - "ax2.scatter(np.arange(30), np.arange(30) +3 * randn(30))" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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"nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Lecture4-Matplotlib/data/baseball.csv b/Lectures/Lecture4-Matplotlib/data/baseball.csv deleted file mode 100644 index aadbace..0000000 --- a/Lectures/Lecture4-Matplotlib/data/baseball.csv +++ /dev/null @@ -1,101 +0,0 @@ -id,player,year,stint,team,lg,g,ab,r,h,X2b,X3b,hr,rbi,sb,cs,bb,so,ibb,hbp,sh,sf,gidp -88641,womacto01,2006,2,CHN,NL,19,50,6,14,1,0,1,2.0,1.0,1.0,4,4.0,0.0,0.0,3.0,0.0,0.0 -88643,schilcu01,2006,1,BOS,AL,31,2,0,1,0,0,0,0.0,0.0,0.0,0,1.0,0.0,0.0,0.0,0.0,0.0 -88645,myersmi01,2006,1,NYA,AL,62,0,0,0,0,0,0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0 -88649,helliri01,2006,1,MIL,NL,20,3,0,0,0,0,0,0.0,0.0,0.0,0,2.0,0.0,0.0,0.0,0.0,0.0 -88650,johnsra05,2006,1,NYA,AL,33,6,0,1,0,0,0,0.0,0.0,0.0,0,4.0,0.0,0.0,0.0,0.0,0.0 -88652,finlest01,2006,1,SFN,NL,139,426,66,105,21,12,6,40.0,7.0,0.0,46,55.0,2.0,2.0,3.0,4.0,6.0 -88653,gonzalu01,2006,1,ARI,NL,153,586,93,159,52,2,15,73.0,0.0,1.0,69,58.0,10.0,7.0,0.0,6.0,14.0 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-57,5,12,6,5,10000U,44,F,51 -57,6,16,6,5,10000U,44,F,51 -58,1,0,6,6,Placebo,41,F,36 -58,2,2,6,6,Placebo,41,F,29 -58,3,4,6,6,Placebo,41,F,24 -58,4,8,6,6,Placebo,41,F,32 -58,5,12,6,6,Placebo,41,F,45 -58,6,16,6,6,Placebo,41,F,36 -59,1,0,6,7,5000U,51,F,59 -59,2,2,6,7,5000U,51,F,53 -59,3,4,6,7,5000U,51,F,45 -59,4,8,6,7,5000U,51,F,44 -59,5,12,6,7,5000U,51,F,50 -59,6,16,6,7,5000U,51,F,48 -60,1,0,6,8,Placebo,57,F,49 -60,2,2,6,8,Placebo,57,F,50 -60,3,4,6,8,Placebo,57,F,48 -60,4,8,6,8,Placebo,57,F,56 -60,5,12,6,8,Placebo,57,F,49 -60,6,16,6,8,Placebo,57,F,57 -61,1,0,6,9,10000U,42,F,50 -61,2,2,6,9,10000U,42,F,38 -61,3,4,6,9,10000U,42,F,42 -61,4,8,6,9,10000U,42,F,43 -61,5,12,6,9,10000U,42,F,42 -61,6,16,6,9,10000U,42,F,46 -62,1,0,6,10,Placebo,48,F,46 -62,2,2,6,10,Placebo,48,F,48 -62,3,4,6,10,Placebo,48,F,46 -62,4,8,6,10,Placebo,48,F,57 -62,5,12,6,10,Placebo,48,F,57 -62,6,16,6,10,Placebo,48,F,49 -63,1,0,6,11,10000U,57,M,55 -63,2,2,6,11,10000U,57,M,34 -63,3,4,6,11,10000U,57,M,26 -63,4,8,6,11,10000U,57,M,40 -63,5,12,6,11,10000U,57,M,49 -63,6,16,6,11,10000U,57,M,47 -64,1,0,6,12,5000U,39,M,46 -64,2,2,6,12,5000U,39,M,44 -64,3,4,6,12,5000U,39,M,47 -64,4,8,6,12,5000U,39,M,50 -64,5,12,6,12,5000U,39,M,46 -64,6,16,6,12,5000U,39,M,51 -65,1,0,6,13,10000U,67,M,34 -65,2,2,6,13,10000U,67,M,31 -65,3,4,6,13,10000U,67,M,25 -66,1,0,6,14,5000U,39,F,57 -66,2,2,6,14,5000U,39,F,48 -66,3,4,6,14,5000U,39,F,50 -66,4,8,6,14,5000U,39,F,50 -66,5,12,6,14,5000U,39,F,50 -66,6,16,6,14,5000U,39,F,49 -67,1,0,6,15,Placebo,69,M,41 -67,2,2,6,15,Placebo,69,M,40 -67,3,4,6,15,Placebo,69,M,42 -67,4,8,6,15,Placebo,69,M,38 -67,5,12,6,15,Placebo,69,M,50 -67,6,16,6,15,Placebo,69,M,56 -68,1,0,7,1,5000U,54,F,49 -68,2,2,7,1,5000U,54,F,25 -68,3,4,7,1,5000U,54,F,30 -68,4,8,7,1,5000U,54,F,41 -68,5,12,7,1,5000U,54,F,41 -68,6,16,7,1,5000U,54,F,31 -69,1,0,7,2,Placebo,67,F,42 -69,2,2,7,2,Placebo,67,F,30 -69,3,4,7,2,Placebo,67,F,40 -69,4,8,7,2,Placebo,67,F,43 -69,5,12,7,2,Placebo,67,F,36 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zxQcUglQBOL&4oc!hzGT~Y3FHa(4MB~Xz?;`zS6N$XN{ur68ya^fzAuRQO; sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "INFO:mrjob.runner:> sort 'c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-mapper_part-00000'\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "INFO:mrjob.sim:writing to c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - "INFO:mrjob.runner:Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - " (no counters found)\n", - "INFO:mrjob.runner: (no counters found)\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "INFO:mrjob.sim:Moving c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\step-0-reducer_part-00000 -> c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\\part-00000\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n", - "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\\output\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['1', '10']\n", - "['2', '11']\n", - "['3', '3']\n", - "['4', '12']\n", - "['5', '4']\n", - "['6', '1']\n", - "['7', '1']\n", - "['8', '41']\n", - "['9', '532']\n", - "['10', '2']\n", - "['11', '0']\n", - "\"0\"\t\"11\"\n", - "\"1\"\t\"6\"\n", - "\"10\"\t\"1\"\n", - "\"11\"\t\"2\"\n", - "\"12\"\t\"4\"\n", - "\"2\"\t\"10\"\n", - "\"3\"\t\"3\"\n", - "\"4\"\t\"5\"\n", - "\"41\"\t\"8\"\n", - "\"532\"\t\"9\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n", - "INFO:mrjob.runner:removing tmp directory c:\\users\\ps\\appdata\\local\\temp\\MRSortByString.PS.20150922.021119.320000\n" - ] - } - ], - "source": [ - "%run code/MRSortByString.py data/numbers.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (MRSortByInt.py, line 14)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"c:\\Users\\PS\\Documents\\GitHub\\big-data-python-class\\Lectures\\Lecture5-MapReduce\\code\\MRSortByInt.py\"\u001b[1;36m, line \u001b[1;32m14\u001b[0m\n\u001b[1;33m if __name__ == '__main__':\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "%run code/MRSortByInt.py data/numbers.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "sortdata.txt\n", - "\n", - "1 1\n", - "2 4\n", - "3 8\n", - "4 2\n", - "4 7\n", - "5 5\n", - "6 10\n", - "7 11" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:mrjob.job:mr() is deprecated and will be removed in v0.6.0. Use mrjob.step.MRStep directly instead.\n", - "WARNING:mrjob.job:mr() is deprecated and will be removed in v0.6.0. Use mrjob.step.MRStep directly instead.\n", - "WARNING:mrjob.job:mr() is deprecated and will be removed in v0.6.0. Use mrjob.step.MRStep directly instead.\n", - "WARNING:mrjob.job:mr() is deprecated and will be removed in v0.6.0. Use mrjob.step.MRStep directly instead.\n", - "WARNING:mrjob.job:mr() is deprecated and will be removed in v0.6.0. Use mrjob.step.MRStep directly instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting MRSortByString job on local\n", - "finished MRSortByString job\n", - "Sorting sortdata.txt\n", - "1: 1 \n", - "10: 6 \n", - "11: 7 \n", - "2: 4 \n", - "4: 2 \n", - "5: 5 \n", - "7: 4 \n", - "8: 3 \n" - ] - } - ], - "source": [ - "# -*- coding: utf-8 -*-\n", - "# Testing word frequency count\n", - "from MRSortByString import *\n", - "from mrjob.job import MRJob\n", - "'''\n", - "This is a simple wrapper that runs mrjob MapReduce jobs, the inputs are:\n", - "MRJobClass - the class of the job to be run\n", - "argsArr - an array of strings to be used when creating the MRJob.\n", - "@author: Peter Harrington if you have any questions: peter.b.harrington@gmail.com\n", - "'''\n", - "def runJob(MRJobClass, argsArr, loc='local'):\n", - " if loc == 'emr': \n", - " argsArr.extend(['-r', 'emr'])\n", - " print \"starting %s job on %s\" % (MRJobClass.__name__, loc)\n", - " mrJob = MRJobClass(args=argsArr)\n", - " runner = mrJob.make_runner()\n", - " runner.run()\n", - " print \"finished %s job\" % MRJobClass.__name__\n", - " return mrJob, runner\n", - " \n", - "def runParallelJob(MRJobClass, argsArr): #TO DO: add threading to allow jobs to run in \n", - " pass #parallel \n", - " #launch a new thread\n", - " #call runJob(MRJobClass, argsArr) on the new thread\n", - "\n", - "if __name__ == '__main__':\n", - "# pass in file from outside\n", - "# MRWordFrequencyCount.run()\n", - "#setup file here\n", - " mr_job, runner = runJob(MRSortByString,[\"C:\\data\\sortdata.txt\"],\"local\")\n", - " print \"Sorting sortdata.txt\"\n", - " for line in runner.stream_output(): \n", - " key, value = mr_job.parse_output_line(line)\n", - " print \"%s: %s \"%(key,value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the second column is reported by their string values" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Lectures/Lecture5-MapReduce/.ipynb_checkpoints/Tf_Id-checkpoint.ipynb b/Lectures/Lecture5-MapReduce/.ipynb_checkpoints/Tf_Id-checkpoint.ipynb deleted file mode 100644 index f8d16c9..0000000 --- a/Lectures/Lecture5-MapReduce/.ipynb_checkpoints/Tf_Id-checkpoint.ipynb +++ /dev/null @@ -1,147 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " stored in MRSortByString.py\n" - ] - } - ], - "source": [ - "from mrjob.job import MRJob\n", - "\n", - "class MRSortByString(MRJob):\n", - " def mapper(self, _, line):\n", - " \"\"\"\n", - " \"\"\"\n", - " l = line.split(' ')\n", - " yield l[1], l[0]\n", - "\n", - " def reducer(self, key, val):\n", - " yield key, [v for v in val][0]\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " print \" stored in MRSortByString.py\"\n", - " #MRSortByString.run()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "sortdata.txt\n", - "\n", - "1 1\n", - "2 4\n", - "3 8\n", - "4 2\n", - "4 7\n", - "5 5\n", - "6 10\n", - "7 11" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "No module named MRSortByString", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# Testing word frequency count\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mMRSortByString\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmrjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjob\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMRJob\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m '''\n", - "\u001b[1;31mImportError\u001b[0m: No module named MRSortByString" - ] - } - ], - "source": [ - "# -*- coding: utf-8 -*-\n", - "# Testing word frequency count\n", - "from MRSortByString import *\n", - "from mrjob.job import MRJob\n", - "'''\n", - "This is a simple wrapper that runs mrjob MapReduce jobs, the inputs are:\n", - "MRJobClass - the class of the job to be run\n", - "argsArr - an array of strings to be used when creating the MRJob.\n", - "@author: Peter Harrington if you have any questions: peter.b.harrington@gmail.com\n", - "'''\n", - "def runJob(MRJobClass, argsArr, loc='local'):\n", - " if loc == 'emr': \n", - " argsArr.extend(['-r', 'emr'])\n", - " print \"starting %s job on %s\" % (MRJobClass.__name__, loc)\n", - " mrJob = MRJobClass(args=argsArr)\n", - " runner = mrJob.make_runner()\n", - " runner.run()\n", - " print \"finished %s job\" % MRJobClass.__name__\n", - " return mrJob, runner\n", - " \n", - "def runParallelJob(MRJobClass, argsArr): #TO DO: add threading to allow jobs to run in \n", - " pass #parallel \n", - " #launch a new thread\n", - " #call runJob(MRJobClass, argsArr) on the new thread\n", - "\n", - "if __name__ == '__main__':\n", - "# pass in file from outside\n", - "# MRWordFrequencyCount.run()\n", - "#setup file here\n", - " mr_job, runner = runJob(MRSortByString,[\"C:\\data\\sortdata.txt\"],\"local\")\n", - " print \"Sorting sortdata.txt\"\n", - " for line in runner.stream_output(): \n", - " key, value = mr_job.parse_output_line(line)\n", - " print \"%s: %s \"%(key,value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the second column is reported by their string values" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Lectures/Lecture5-MapReduce/.mrjob.conf b/Lectures/Lecture5-MapReduce/.mrjob.conf deleted file mode 100644 index 72592a3..0000000 --- a/Lectures/Lecture5-MapReduce/.mrjob.conf +++ /dev/null @@ -1,11 +0,0 @@ -runners: - local: - upload_files: &upload_files - - $DATA_DIR/*.db - hadoop: - upload_files: *upload_files - emr: - upload_files: *upload_files - inline: - upload_files: *upload_files - - $DATA_DIR/*.db diff --git a/Lectures/Lecture5-MapReduce/MR_SORT.ipynb b/Lectures/Lecture5-MapReduce/MR_SORT.ipynb deleted file mode 100644 index 154550f..0000000 --- a/Lectures/Lecture5-MapReduce/MR_SORT.ipynb +++ /dev/null @@ -1,399 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# examine the data\n", - "First examine the data by loading it in with the %Load command\n", - "It will fill in the block with the content of the file in the block and comment out the %Load command" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load data/numbers.txt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# examine the sorting code \n", - "Do not run the block a second time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %load code/MRSortByString.py\n", - "from mrjob.job import MRJob\n", - "\n", - "class MRSortByString(MRJob):\n", - " def mapper(self, _, line):\n", - " \"\"\"\n", - " \"\"\"\n", - " l = line.split(' ')\n", - " print l\n", - " yield l[1], l[0]\n", - "\n", - " def reducer(self, key, val):\n", - " yield key, [v for v in val][0]\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " MRSortByString.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "creating tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\n", - "\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-mapper_part-00000\n", - "Counters from step 1:\n", - " (no counters found)\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-mapper-sorted\n", - "> sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-mapper_part-00000'\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - " (no counters found)\n", - "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\output\\part-00000\n", - "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\output\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['1', '10']\n", - "['2', '11']\n", - "['3', '3']\n", - "['4', '12']\n", - "['5', '4']\n", - "['6', '1']\n", - "['7', '1']\n", - "['8', '41']\n", - "['9', '532']\n", - "['10', '2']\n", - "['11', '0']\n", - "\"0\"\t\"11\"\n", - "\"1\"\t\"6\"\n", - "\"10\"\t\"1\"\n", - "\"11\"\t\"2\"\n", - "\"12\"\t\"4\"\n", - "\"2\"\t\"10\"\n", - "\"3\"\t\"3\"\n", - "\"4\"\t\"5\"\n", - "\"41\"\t\"8\"\n", - "\"532\"\t\"9\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "removing tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\n" - ] - } - ], - "source": [ - "%run code/MRSortByString.py data/numbers.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "How were they sorted?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# %load code/MRSortByInt.py\n", - "from mrjob.job import MRJob\n", - "\n", - "class MRSortByInt(MRJob):\n", - " def mapper(self, _, line):\n", - " \"\"\"\n", - " \"\"\"\n", - " l = line.strip('\\n').split()\n", - " yield '%01d'%int(l[1]), l[0]\n", - "\n", - " def reducer(self, key, val):\n", - " yield int(key), int(list(val)[0])\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " MRSortByInt.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "creating tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", - "creating tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", - "\n", - "\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "\n", - "\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper-sorted\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper-sorted\n", - "> sort 'c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000'\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000\n", - "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - "Moving c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\\part-00000\n", - "Moving c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\\part-00000\n", - "Streaming final output from c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\n", - "Streaming final output from c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\t11\n", - "1\t6\n", - "10\t1\n", - "11\t2\n", - "12\t4\n", - "2\t10\n", - "3\t3\n", - "4\t5\n", - "41\t8\n", - "532\t9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "removing tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", - "removing tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n" - ] - } - ], - "source": [ - "%run code/MRSortByInt.py data/numbers.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing data/sortdata.txt\n" - ] - } - ], - "source": [ - "%%writefile data/sortdata.txt\n", - "1 1\n", - "2 4\n", - "3 8\n", - "4 2\n", - "4 7\n", - "5 5\n", - "6 10\n", - "7 11" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Running code inline example" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "no configs found; falling back on auto-configuration\n", - "creating tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\n", - "creating tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\n", - "\n", - "\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", - "\n", - "\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper-sorted\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper-sorted\n", - "> sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000'\n", - "> sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000'\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000\n", - "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000\n", - "Counters from step 1:\n", - "Counters from step 1:\n", - " (no counters found)\n", - " (no counters found)\n", - "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\\part-00000\n", - "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\\part-00000\n", - "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\n", - "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting MRSortByString job on local\n", - "['1', '1']\n", - "['2', '4']\n", - "['3', '8']\n", - "['4', '2']\n", - "['4', '7']\n", - "['5', '5']\n", - "['6', '10']\n", - "['7', '11']\n", - "finished MRSortByString job\n", - "Sorting sortdata.txt\n", - "1: 1 \n", - "10: 6 \n", - "11: 7 \n", - "2: 4 \n", - "4: 2 \n", - "5: 5 \n", - "7: 4 \n", - "8: 3 \n" - ] - } - ], - "source": [ - "# -*- coding: utf-8 -*-\n", - "# Testing word frequency count\n", - "import os, sys\n", - "sys.path.append(os.path.join(os.getcwd(),\"code\"))\n", - "from MRSortByString import *\n", - "from mrjob.job import MRJob\n", - "'''\n", - "This is a simple wrapper that runs mrjob MapReduce jobs, the inputs are:\n", - "MRJobClass - the class of the job to be run\n", - "argsArr - an array of strings to be used when creating the MRJob.\n", - "@author: Peter Harrington if you have any questions: peter.b.harrington@gmail.com\n", - "'''\n", - "def runJob(MRJobClass, argsArr, loc='local'):\n", - " if loc == 'emr': \n", - " argsArr.extend(['-r', 'emr'])\n", - " print \"starting %s job on %s\" % (MRJobClass.__name__, loc)\n", - " mrJob = MRJobClass(args=argsArr)\n", - " runner = mrJob.make_runner()\n", - " runner.run()\n", - " print \"finished %s job\" % MRJobClass.__name__\n", - " return mrJob, runner\n", - " \n", - "def runParallelJob(MRJobClass, argsArr): #TO DO: add threading to allow jobs to run in \n", - " pass #parallel \n", - " #launch a new thread\n", - " #call runJob(MRJobClass, argsArr) on the new thread\n", - "\n", - "if __name__ == '__main__':\n", - "# pass in file from outside\n", - "# MRWordFrequencyCount.run()\n", - "#setup file here\n", - " mr_job, runner = runJob(MRSortByString,[os.path.join(os.path.join(os.getcwd(),\"data\"),\"sortdata.txt\")],\"local\")\n", - " print \"Sorting sortdata.txt\"\n", - " for line in runner.stream_output(): \n", - " key, value = mr_job.parse_output_line(line)\n", - " print \"%s: %s \"%(key,value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the second column is reported by their string values" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb b/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb deleted file mode 100644 index e2eaf97..0000000 --- a/Lectures/Lecture5-MapReduce/MapReduce Intro.ipynb +++ /dev/null @@ -1,6623 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Challenge of processing large amounts of data\n", - "

    \n", - "
  • How to process is quickly?\n", - "
  • So how do we go about making the problem map so that it can be distributed computation?\n", - "
  • Distributed/Parrallel Programming is hard\n", - "
\n", - "\n", - "##Mapreduce addresses all the challengs\n", - "
    \n", - "
  • Google's computational/data manipulation model\n", - "
  • Elegant way to work with big data\n", - "
\n", - "\n", - "## Map Reduce and the New Software Stack\n", - "
    \n", - "
  • Covered in Chapter 2 in the Ullman book\n", - "
  • Covered in Chapter 24 of the Joel Grus book\n", - "
  • Python support packages https://pypi.python.org/pypi/mrjob\n", - "
  • map() and reduce() are basic built in functions in python https://docs.python.org/2/library/functions.html\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Built-in Python Functional Programming Tools\n", - "https://docs.python.org/2/tutorial/datastructures.html\n", - "\n", - "### Python Map Function\n", - "For each value in a sequence, process each one and output a new result for each element." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def cube(x): return x*x*x\n", - "\n", - "map(cube,range(1,11))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If there are more parameters then each could be an array, and they are applied together one element at a time" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 2, 4, 6, 8, 10, 12, 14]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq = range(8)\n", - "def add(x,y): return x+y\n", - "map(add, seq,seq)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python Reduce Function\n", - "For an array passed in, compute a single result.\n", - "So a reduce function always has two paramters to carry the result foward with the next element in the sequence.\n", - "The operation starts by using the first two values then passes the result with the next element to the function..." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "56" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = map(add, seq,seq)\n", - "reduce(add, result) # adding each element of the result together" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If there is only one value in a sequence then that element is returned; if the sequence is empty, an exception is raised." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Word Count using python in a map reduce manner\n", - "Using the Canterbury Corpus Test file from http://compression.ca/act/files/canterbury.zip,\n", - "We will attempt to count the each unique word.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "import pandas as pd\n", - "import numpy as np\n", - "aliceFile = open('data/canterbury/alice29.txt','r')\n", - "map1=[]\n", - "\n", - "WORD_RE = re.compile(r\"[\\w']+\")\n", - "\n", - "# Create the map of words with prelminary counts\n", - "for line in aliceFile:\n", - " for w in WORD_RE.findall(line):\n", - " map1.append([w,1])\n", - "\n", - "#sort the map \n", - "map2 = sorted(map1)\n", - "\n", - "#Separate the map into groups by the key values\n", - "df = pd.DataFrame(map2)\n", - "\n", - "uniquewords = df[0].unique()\n", - "DataFrameDict = {elem : pd.DataFrame for elem in uniquewords}\n", - "\n", - "for key in DataFrameDict.keys():\n", - " DataFrameDict[key] = df[:][df[0] == key]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\"'\" 1091L]\n", - "[\"'TIS\" 1L]\n", - "[\"'Tis\" 2L]\n", - "[\"'em\" 3L]\n", - "[\"'tis\" 2L]\n", - "['2' 1L]\n", - "['9' 1L]\n", - "['A' 17L]\n", - "['ADVENTURES' 1L]\n", - "[\"ALICE'S\" 3L]\n", - "['ALL' 4L]\n", - "['AND' 3L]\n", - "['ARE' 6L]\n", - "['AT' 1L]\n", - "['Ada' 1L]\n", - "['Adventures' 2L]\n", - "['Advice' 1L]\n", - "['After' 6L]\n", - "['Ah' 5L]\n", - "['Ahem' 1L]\n", - "['Alas' 1L]\n", - "['Alice' 386L]\n", - "[\"Alice's\" 9L]\n", - "['All' 5L]\n", - "['Allow' 1L]\n", - "['Always' 1L]\n", - "['Ambition' 1L]\n", - "['An' 5L]\n", - "['And' 67L]\n", - "['Ann' 4L]\n", - "['Antipathies' 1L]\n", - 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"A framework that allows you to do mapreduce jobs without HADOOP but will run the same jobs in an hadoop environment.\n", - "( DUMBO and Pydoop give you lower level access to HADOOP)\n", - "\n", - "Before using it for the first time install this package using : pip install mrjob \n", - "\n", - "If that does not work use the alternatives in provided on https://pythonhosted.org/mrjob/guides/quickstart.html#installation\n", - "\n", - "%%writefile myfile.py\n", - "\n", - "write/save cell contents into myfile.py (use -a to append). Another alias: %%file myfile.py\n", - "%run myfile.py\n", - "\n", - "run myfile.py and output results in the current cell\n", - "%load myfile.py\n", - "\n", - "load \"import\" myfile.py into the current cell" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting mrjob\n", - " Downloading https://files.pythonhosted.org/packages/95/24/b55a4d4799f49c4c4413233a9437a855c384b8c40daea5e8dec8ec1733f0/mrjob-0.6.5-py2.py3-none-any.whl (308kB)\n", - "Collecting google-cloud-dataproc>=0.2.0 (from mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/07/23/7ae8951f5bc68d1471b492a6b8ee938d3ec9d47ebb82dc8ea1e45899ec8e/google_cloud_dataproc-0.2.0-py2.py3-none-any.whl (131kB)\n", - "Requirement already satisfied: PyYAML>=3.08 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from mrjob) (3.12)\n", - "Collecting google-cloud-logging>=1.5.0 (from mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/93/a5/220c51fa50520991b7de756730fcc7bf719a29f5b4339eb9854d56ffdaf3/google_cloud_logging-1.7.0-py2.py3-none-any.whl (100kB)\n", - "Collecting botocore>=1.6.0 (from mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/22/fa/af1ad7aebe166932fe1cac5eae5413d090eca4d723829756c31fc53c174d/botocore-1.12.14-py2.py3-none-any.whl (4.7MB)\n", - "Collecting google-cloud-storage>=1.9.0 (from mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/3b/cd/4f58416260817ed968b9875d97bd2f6974fe8fdce7b724b2cdd70107b815/google_cloud_storage-1.12.0-py2.py3-none-any.whl (54kB)\n", - "Collecting boto3>=1.4.6 (from mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/8e/49/4616f3beaa9974aa275933218fe64b200f4177ff5f0753a3b72c5cec5dda/boto3-1.9.14-py2.py3-none-any.whl (128kB)\n", - "Collecting google-api-core[grpc]<2.0.0dev,>=0.1.0 (from google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/a5/be/de30100034c391f4c56e2543f1507eb1b30b3030bd9a6764dd6cfe7a954e/google_api_core-1.4.1-py2.py3-none-any.whl (53kB)\n", - "Collecting google-cloud-core<0.29dev,>=0.28.0 (from google-cloud-logging>=1.5.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/0f/41/ae2418b4003a14cf21c1c46d61d1b044bf02cf0f8f91598af572b9216515/google_cloud_core-0.28.1-py2.py3-none-any.whl\n", - "Requirement already satisfied: docutils>=0.10 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from botocore>=1.6.0->mrjob) (0.14)\n", - "Collecting jmespath<1.0.0,>=0.7.1 (from botocore>=1.6.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/b7/31/05c8d001f7f87f0f07289a5fc0fc3832e9a57f2dbd4d3b0fee70e0d51365/jmespath-0.9.3-py2.py3-none-any.whl\n", - "Requirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in c:\\users\\ps\\anaconda2\\lib\\site-packages (from botocore>=1.6.0->mrjob) (2.7.3)\n", - "Requirement already satisfied: urllib3<1.24,>=1.20 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from botocore>=1.6.0->mrjob) (1.22)\n", - "Collecting google-resumable-media>=0.3.1 (from google-cloud-storage>=1.9.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/77/95/2e4020a54366423ddba715f89fb7ca456c8f048b15cada6cd6a54cf10e8c/google_resumable_media-0.3.1-py2.py3-none-any.whl\n", - "Collecting s3transfer<0.2.0,>=0.1.10 (from boto3>=1.4.6->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/d7/14/2a0004d487464d120c9fb85313a75cd3d71a7506955be458eebfe19a6b1d/s3transfer-0.1.13-py2.py3-none-any.whl (59kB)\n", - "Collecting protobuf>=3.4.0 (from google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/77/78/a7f1ce761e2c738e209857175cd4f90a8562d1bde32868a8cd5290d58926/protobuf-3.6.1-py2.py3-none-any.whl (390kB)\n", - "Collecting google-auth<2.0.0dev,>=0.4.0 (from google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/58/cb/96dbb4e50e7a9d856e89cc9c8e36ab1055f9774f7d85f37e2156c1d79d9f/google_auth-1.5.1-py2.py3-none-any.whl (65kB)\n", - "Requirement already satisfied: six>=1.10.0 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (1.11.0)\n", - 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"Collecting grpcio>=1.8.2; extra == \"grpc\" (from google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/12/d7/b7311f393c785dd877259e6427a3abbf7c75871b53da19c70a59ecf4266a/grpcio-1.15.0-cp27-cp27m-win_amd64.whl (1.5MB)\n", - "Collecting cachetools>=2.0.0 (from google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/0a/58/cbee863250b31d80f47401d04f34038db6766f95dea1cc909ea099c7e571/cachetools-2.1.0-py2.py3-none-any.whl\n", - "Collecting pyasn1-modules>=0.2.1 (from google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/19/02/fa63f7ba30a0d7b925ca29d034510fc1ffde53264b71b4155022ddf3ab5d/pyasn1_modules-0.2.2-py2.py3-none-any.whl (62kB)\n", - "Collecting rsa>=3.1.4 (from google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/02/e5/38518af393f7c214357079ce67a317307936896e961e35450b70fad2a9cf/rsa-4.0-py2.py3-none-any.whl\n", - "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (3.0.4)\n", - "Requirement already satisfied: idna<2.7,>=2.5 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (2.6)\n", - "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (2018.4.16)\n", - "Requirement already satisfied: enum34>=1.0.4 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from grpcio>=1.8.2; extra == \"grpc\"->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (1.1.6)\n", - "Collecting pyasn1<0.5.0,>=0.4.1 (from pyasn1-modules>=0.2.1->google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", - " Downloading https://files.pythonhosted.org/packages/d1/a1/7790cc85db38daa874f6a2e6308131b9953feb1367f2ae2d1123bb93a9f5/pyasn1-0.4.4-py2.py3-none-any.whl (72kB)\n", - "Building wheels for collected packages: googleapis-common-protos\n", - " Running setup.py bdist_wheel for googleapis-common-protos: started\n", - " Running setup.py bdist_wheel for googleapis-common-protos: finished with status 'done'\n", - " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\62\\45\\af\\649bbf07b6595fda010be1bda667cd56d0444d07afc6f8b687\n", - "Successfully built googleapis-common-protos\n", - "Installing collected packages: protobuf, cachetools, pyasn1, pyasn1-modules, rsa, google-auth, googleapis-common-protos, grpcio, google-api-core, google-cloud-dataproc, google-cloud-core, google-cloud-logging, jmespath, botocore, google-resumable-media, google-cloud-storage, s3transfer, boto3, mrjob\n", - "Successfully installed boto3-1.9.14 botocore-1.12.14 cachetools-2.1.0 google-api-core-1.4.1 google-auth-1.5.1 google-cloud-core-0.28.1 google-cloud-dataproc-0.2.0 google-cloud-logging-1.7.0 google-cloud-storage-1.12.0 google-resumable-media-0.3.1 googleapis-common-protos-1.5.3 grpcio-1.15.0 jmespath-0.9.3 mrjob-0.6.5 protobuf-3.6.1 pyasn1-0.4.4 pyasn1-modules-0.2.2 rsa-4.0 s3transfer-0.1.13\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "distributed 1.21.8 requires msgpack, which is not installed.\n", - "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", - "You are using pip version 10.0.1, however version 18.0 is available.\n", - "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" - ] - } - ], - "source": [ - "! pip install mrjob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %load code/MRWordFrequencyCount.py\n", - "# Do not run this cell it is just displaying the content of the code\n", - "from mrjob.job import MRJob\n", - "\n", - "class MRWordFrequencyCount(MRJob):\n", - " \n", - " def mapper(self, _ , line):\n", - " yield \"chars\", len(line)\n", - " yield \"words\", len(line.split())\n", - " yield \"lines\", 1\n", - " \n", - " def reducer(self, key, values):\n", - " yield key, sum(values)\n", - " \n", - "if __name__ == '__main__':\n", - " MRWordFrequencyCount.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "WARNING:mrjob.conf:No configs specified for inline runner\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "INFO:mrjob.sim:Running step 1 of 1...\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", - "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\"chars\"\t148687\n", - "\"lines\"\t12\n", - "\"words\"\t26475\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n" - ] - } - ], - "source": [ - "%run -G code/MRWordFrequencyCount.py ./.mrjob.conf data/canterbury/alice29.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "# MRJOB Word count function" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "usage: usage: ipykernel_launcher.py [options] [input files]\n", - "ipykernel_launcher.py: error: unrecognized arguments: -f\n" - ] - }, - { - "ename": "SystemExit", - "evalue": "2", - "output_type": "error", - "traceback": [ - "An exception has occurred, use %tb to see the full traceback.\n", - "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n" - ] - } - ], - "source": [ - "# %load code\\MRWordFreqCount.py\n", - "# Do not run this block of code just a display of the stored program\n", - "from mrjob.job import MRJob\n", - "import re\n", - "\n", - "WORD_RE = re.compile(r\"[\\w']+\")\n", - "\n", - "\n", - "class MRWordFreqCount(MRJob):\n", - "\n", - " def mapper(self, _, line):\n", - " for word in WORD_RE.findall(line):\n", - " yield word.lower(), 1\n", - "\n", - " def combiner(self, word, counts):\n", - " yield word, sum(counts)\n", - "\n", - " def reducer(self, word, counts):\n", - " yield word, sum(counts)\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " MRWordFreqCount.run()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "No configs found; falling back on auto-configuration\n", - "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "No configs specified for inline runner\n", - "WARNING:mrjob.conf:No configs specified for inline runner\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "Running step 1 of 1...\n", - "INFO:mrjob.sim:Running step 1 of 1...\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", - "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\"'\"\t1091\n", - "\"'em\"\t3\n", - "\"'tis\"\t5\n", - "\"2\"\t1\n", - "\"9\"\t1\n", - "\"_i_\"\t2\n", - "\"a\"\t632\n", - "\"abide\"\t1\n", - "\"able\"\t1\n", - "\"about\"\t94\n", - "\"above\"\t3\n", - "\"absence\"\t1\n", - "\"absurd\"\t2\n", - "\"acceptance\"\t1\n", - "\"accident\"\t2\n", - "\"accidentally\"\t1\n", - "\"account\"\t1\n", - "\"accounting\"\t1\n", - "\"accounts\"\t1\n", - "\"accusation\"\t1\n", - "\"accustomed\"\t1\n", - "\"ache\"\t1\n", - "\"across\"\t5\n", - "\"act\"\t1\n", - "\"actually\"\t1\n", - "\"ada\"\t1\n", - "\"added\"\t23\n", - "\"adding\"\t1\n", - "\"addressed\"\t2\n", - "\"addressing\"\t1\n", - "\"adjourn\"\t1\n", - "\"adoption\"\t1\n", - "\"advance\"\t3\n", - "\"advantage\"\t3\n", - "\"adventures\"\t7\n", - "\"advice\"\t2\n", - "\"advisable\"\t2\n", - 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"\"blew\"\t2\n", - "\"blow\"\t2\n", - "\"blown\"\t1\n", - "\"blows\"\t1\n", - "\"body\"\t2\n", - "\"boldly\"\t1\n", - "\"bone\"\t1\n", - "\"bones\"\t1\n", - "\"book\"\t11\n", - "\"books\"\t2\n", - "\"boon\"\t1\n", - "\"boots\"\t4\n", - "\"bore\"\t1\n", - "\"both\"\t14\n", - "\"bother\"\t1\n", - "\"bottle\"\t10\n", - "\"bottom\"\t4\n", - "\"bough\"\t1\n", - "\"bound\"\t1\n", - "\"bowed\"\t4\n", - "\"bowing\"\t1\n", - "\"box\"\t10\n", - "\"boxed\"\t1\n", - "\"boy\"\t3\n", - "\"brain\"\t1\n", - "\"branch\"\t1\n", - "\"branches\"\t2\n", - "\"brandy\"\t1\n", - "\"brass\"\t1\n", - "\"brave\"\t1\n", - "\"bread\"\t7\n", - "\"break\"\t2\n", - "\"breath\"\t4\n", - "\"breathe\"\t3\n", - "\"breeze\"\t1\n", - "\"bright\"\t8\n", - "\"brightened\"\t2\n", - "\"bring\"\t3\n", - "\"bringing\"\t3\n", - "\"bristling\"\t1\n", - "\"broke\"\t2\n", - "\"broken\"\t6\n", - "\"brother's\"\t1\n", - "\"brought\"\t3\n", - "\"brown\"\t2\n", - "\"brush\"\t1\n", - "\"brushing\"\t1\n", - "\"burn\"\t2\n", - "\"burning\"\t1\n", - 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"\"choked\"\t3\n", - "\"choking\"\t1\n", - "\"choosing\"\t1\n", - "\"chop\"\t1\n", - "\"chorus\"\t6\n", - "\"chose\"\t2\n", - "\"christmas\"\t1\n", - "\"chrysalis\"\t1\n", - "\"chuckled\"\t1\n", - "\"circle\"\t1\n", - "\"circumstances\"\t1\n", - "\"civil\"\t3\n", - "\"clamour\"\t1\n", - "\"clapping\"\t1\n", - "\"clasped\"\t1\n", - "\"classics\"\t1\n", - "\"claws\"\t2\n", - "\"clean\"\t1\n", - "\"clear\"\t2\n", - "\"cleared\"\t1\n", - "\"clearer\"\t1\n", - "\"clearly\"\t1\n", - "\"clever\"\t2\n", - "\"climb\"\t1\n", - "\"clinging\"\t1\n", - "\"clock\"\t2\n", - "\"close\"\t13\n", - "\"closed\"\t2\n", - "\"closely\"\t1\n", - "\"closer\"\t1\n", - "\"clubs\"\t1\n", - "\"coast\"\t1\n", - "\"coaxing\"\t2\n", - "\"coils\"\t1\n", - "\"cold\"\t1\n", - "\"collar\"\t1\n", - "\"collected\"\t2\n", - "\"come\"\t46\n", - "\"comes\"\t2\n", - "\"comfits\"\t2\n", - "\"comfort\"\t1\n", - "\"comfortable\"\t1\n", - "\"comfortably\"\t1\n", - "\"coming\"\t9\n", - "\"common\"\t1\n", - "\"commotion\"\t1\n", - "\"company\"\t1\n", - "\"complained\"\t1\n", - "\"complaining\"\t1\n", - "\"completely\"\t1\n", - "\"concert\"\t2\n", - "\"concluded\"\t2\n", - "\"conclusion\"\t2\n", - "\"condemn\"\t1\n", - "\"conduct\"\t1\n", - "\"confused\"\t4\n", - "\"confusing\"\t3\n", - "\"confusion\"\t5\n", - "\"conger\"\t1\n", - "\"conqueror\"\t2\n", - "\"conquest\"\t1\n", - "\"consented\"\t1\n", - "\"consider\"\t4\n", - "\"considered\"\t3\n", - "\"considering\"\t3\n", - "\"constant\"\t2\n", - "\"consultation\"\t1\n", - "\"contempt\"\t1\n", - "\"contemptuous\"\t1\n", - "\"contemptuously\"\t2\n", - "\"content\"\t1\n", - "\"continued\"\t9\n", - "\"contradicted\"\t1\n", - "\"conversation\"\t10\n", - "\"conversations\"\t1\n", - "\"cook\"\t13\n", - "\"cool\"\t2\n", - "\"corner\"\t4\n", - "\"corners\"\t1\n", - "\"cost\"\t1\n", - "\"could\"\t77\n", - "\"couldn't\"\t9\n", - "\"counting\"\t1\n", - "\"country\"\t1\n", - "\"couple\"\t1\n", - "\"couples\"\t1\n", - "\"courage\"\t3\n", - "\"course\"\t26\n", - "\"court\"\t18\n", - "\"courtiers\"\t2\n", - "\"coward\"\t1\n", - "\"crab\"\t3\n", - "\"crash\"\t3\n", - "\"crashed\"\t1\n", - "\"crawled\"\t1\n", - "\"crawling\"\t1\n", - "\"crazy\"\t1\n", - "\"creature\"\t4\n", - "\"creatures\"\t10\n", - "\"creep\"\t1\n", - "\"crept\"\t1\n", - "\"cried\"\t20\n", - "\"cries\"\t1\n", - "\"crimson\"\t2\n", - "\"crocodile\"\t1\n", - "\"croquet\"\t9\n", - "\"croqueted\"\t1\n", - "\"croqueting\"\t1\n", - "\"cross\"\t3\n", - "\"crossed\"\t3\n", - "\"crossly\"\t1\n", - "\"crouched\"\t1\n", - "\"crowd\"\t4\n", - "\"crowded\"\t5\n", - "\"crown\"\t3\n", - "\"crumbs\"\t4\n", - "\"crust\"\t1\n", - "\"cry\"\t3\n", - "\"crying\"\t2\n", - "\"cucumber\"\t2\n", - "\"cunning\"\t1\n", - "\"cup\"\t2\n", - "\"cupboards\"\t2\n", - "\"cur\"\t1\n", - "\"curiosity\"\t5\n", - "\"curious\"\t19\n", - "\"curiouser\"\t2\n", - "\"curled\"\t2\n", - "\"curls\"\t1\n", - "\"curly\"\t1\n", - "\"currants\"\t1\n", - "\"curtain\"\t1\n", - "\"curtsey\"\t1\n", - "\"curtseying\"\t1\n", - "\"curving\"\t1\n", - "\"cushion\"\t2\n", - "\"custard\"\t1\n", - "\"custody\"\t2\n", - "\"cut\"\t5\n", - "\"cutting\"\t1\n", - "\"d\"\t1\n", - "\"dainties\"\t1\n", - "\"daisies\"\t1\n", - "\"daisy\"\t1\n", - "\"dance\"\t13\n", - "\"dancing\"\t2\n", - "\"dare\"\t5\n", - "\"daresay\"\t1\n", - "\"dark\"\t3\n", - "\"darkness\"\t1\n", - "\"dates\"\t1\n", - "\"daughter\"\t1\n", - "\"day\"\t29\n", - "\"days\"\t4\n", - "\"dead\"\t4\n", - "\"deal\"\t12\n", - "\"dear\"\t29\n", - "\"dears\"\t3\n", - "\"death\"\t1\n", - "\"decided\"\t3\n", - "\"decidedly\"\t4\n", - "\"declare\"\t2\n", - "\"declared\"\t1\n", - "\"deep\"\t7\n", - "\"deepest\"\t1\n", - "\"deeply\"\t4\n", - "\"delay\"\t1\n", - "\"delight\"\t3\n", - "\"delighted\"\t2\n", - "\"delightful\"\t2\n", - "\"denial\"\t1\n", - "\"denied\"\t2\n", - "\"denies\"\t1\n", - "\"deny\"\t2\n", - "\"denying\"\t1\n", - "\"depends\"\t1\n", - "\"derision\"\t1\n", - "\"deserved\"\t1\n", - "\"desk\"\t1\n", - "\"desks\"\t1\n", - "\"despair\"\t1\n", - "\"desperate\"\t1\n", - "\"desperately\"\t1\n", - "\"diamonds\"\t1\n", - "\"did\"\t63\n", - "\"didn't\"\t14\n", - "\"die\"\t1\n", - "\"died\"\t1\n", - "\"different\"\t9\n", - "\"difficult\"\t2\n", - "\"difficulties\"\t1\n", - "\"difficulty\"\t4\n", - "\"dig\"\t1\n", - "\"digging\"\t4\n", - "\"diligently\"\t1\n", - "\"dinah\"\t11\n", - "\"dinah'll\"\t2\n", - "\"dinah's\"\t1\n", - "\"dinn\"\t2\n", - "\"dinner\"\t2\n", - "\"dipped\"\t2\n", - "\"directed\"\t2\n", - "\"direction\"\t5\n", - "\"directions\"\t3\n", - "\"directly\"\t2\n", - "\"disagree\"\t1\n", - "\"disappeared\"\t2\n", - "\"disappointment\"\t1\n", - "\"disgust\"\t1\n", - "\"dish\"\t4\n", - "\"dishes\"\t2\n", - "\"dismay\"\t1\n", - "\"disobey\"\t1\n", - "\"dispute\"\t2\n", - "\"distance\"\t8\n", - "\"distant\"\t2\n", - "\"distraction\"\t1\n", - "\"dive\"\t1\n", - "\"do\"\t81\n", - "\"dodged\"\t1\n", - "\"dodo\"\t13\n", - "\"does\"\t9\n", - "\"doesn't\"\t16\n", - "\"dog\"\t2\n", - "\"dog's\"\t1\n", - "\"dogs\"\t3\n", - "\"doing\"\t5\n", - "\"don't\"\t61\n", - "\"done\"\t15\n", - "\"door\"\t30\n", - "\"doors\"\t2\n", - "\"doorway\"\t1\n", - "\"dormouse\"\t39\n", - "\"dormouse's\"\t1\n", - "\"doth\"\t3\n", - "\"double\"\t1\n", - "\"doubled\"\t1\n", - "\"doubling\"\t1\n", - "\"doubt\"\t4\n", - "\"doubtful\"\t2\n", - "\"doubtfully\"\t2\n", - "\"down\"\t102\n", - "\"downward\"\t1\n", - "\"downwards\"\t1\n", - "\"doze\"\t1\n", - "\"dozing\"\t1\n", - "\"draggled\"\t1\n", - "\"draw\"\t7\n", - "\"drawing\"\t1\n", - "\"drawling\"\t3\n", - "\"dreadful\"\t2\n", - "\"dreadfully\"\t6\n", - "\"dream\"\t7\n", - "\"dreamed\"\t1\n", - "\"dreaming\"\t1\n", - "\"dreamy\"\t1\n", - "\"dressed\"\t1\n", - "\"drew\"\t5\n", - "\"dried\"\t1\n", - "\"driest\"\t1\n", - "\"drink\"\t7\n", - "\"drinking\"\t1\n", - "\"dripping\"\t1\n", - "\"drive\"\t2\n", - "\"drop\"\t1\n", - "\"dropped\"\t5\n", - "\"dropping\"\t1\n", - "\"drowned\"\t1\n", - "\"drunk\"\t2\n", - "\"dry\"\t8\n", - "\"duchess\"\t38\n", - "\"duchess's\"\t3\n", - "\"duck\"\t4\n", - "\"dull\"\t3\n", - "\"dunce\"\t1\n", - "\"dutchess\"\t1\n", - "\"e\"\t6\n", - "\"each\"\t8\n", - "\"eager\"\t3\n", - "\"eagerly\"\t8\n", - "\"eaglet\"\t3\n", - "\"ear\"\t6\n", - "\"earls\"\t2\n", - "\"earnestly\"\t2\n", - "\"ears\"\t5\n", - "\"earth\"\t4\n", - "\"easily\"\t3\n", - "\"easy\"\t2\n", - "\"eat\"\t18\n", - "\"eaten\"\t1\n", - "\"eating\"\t1\n", - "\"eats\"\t1\n", - "\"edgar\"\t1\n", - "\"edge\"\t3\n", - "\"edition\"\t1\n", - "\"editions\"\t2\n", - "\"educations\"\t1\n", - "\"edwin\"\t2\n", - "\"eel\"\t2\n", - "\"eels\"\t1\n", - "\"effect\"\t3\n", - "\"egg\"\t1\n", - "\"eggs\"\t5\n", - "\"eh\"\t1\n", - "\"either\"\t10\n", - "\"elbow\"\t3\n", - "\"elbows\"\t1\n", - "\"elegant\"\t1\n", - "\"eleventh\"\t1\n", - "\"else\"\t11\n", - "\"else's\"\t1\n", - "\"elsie\"\t1\n", - "\"emphasis\"\t1\n", - "\"empty\"\t1\n", - "\"encourage\"\t1\n", - "\"encouraged\"\t1\n", - "\"encouraging\"\t2\n", - "\"end\"\t18\n", - "\"ending\"\t2\n", - "\"energetic\"\t1\n", - "\"engaged\"\t1\n", - "\"engine\"\t1\n", - "\"england\"\t1\n", - "\"english\"\t6\n", - "\"engraved\"\t1\n", - "\"enjoy\"\t1\n", - "\"ennyworth\"\t1\n", - "\"enormous\"\t1\n", - "\"enough\"\t18\n", - "\"entangled\"\t2\n", - "\"entirely\"\t2\n", - "\"entrance\"\t1\n", - "\"escape\"\t4\n", - "\"esq\"\t1\n", - "\"est\"\t1\n", - "\"even\"\t19\n", - "\"evening\"\t5\n", - "\"ever\"\t20\n", - "\"every\"\t13\n", - "\"everybody\"\t8\n", - "\"everything\"\t12\n", - "\"everything's\"\t2\n", - "\"evidence\"\t7\n", - "\"evidently\"\t1\n", - "\"exact\"\t1\n", - "\"exactly\"\t8\n", - "\"examine\"\t2\n", - "\"examining\"\t1\n", - "\"excellent\"\t2\n", - "\"except\"\t4\n", - "\"exclaimed\"\t6\n", - "\"exclamation\"\t1\n", - "\"execute\"\t1\n", - "\"executed\"\t6\n", - "\"executes\"\t1\n", - "\"execution\"\t3\n", - "\"executioner\"\t5\n", - "\"executioner's\"\t1\n", - "\"executions\"\t2\n", - "\"existence\"\t1\n", - "\"expected\"\t1\n", - "\"expecting\"\t3\n", - "\"experiment\"\t2\n", - "\"explain\"\t10\n", - "\"explained\"\t1\n", - "\"explanation\"\t2\n", - "\"explanations\"\t1\n", - "\"expressing\"\t1\n", - "\"expression\"\t1\n", - "\"extra\"\t1\n", - "\"extraordinary\"\t2\n", - "\"extras\"\t1\n", - "\"extremely\"\t2\n", - "\"eye\"\t7\n", - "\"eyed\"\t1\n", - "\"eyelids\"\t1\n", - "\"eyes\"\t29\n", - "\"face\"\t15\n", - "\"faces\"\t5\n", - "\"fact\"\t8\n", - "\"fading\"\t1\n", - "\"failure\"\t1\n", - "\"faint\"\t1\n", - "\"fainting\"\t1\n", - "\"faintly\"\t1\n", - "\"fair\"\t1\n", - "\"fairly\"\t1\n", - "\"fairy\"\t1\n", - "\"fall\"\t7\n", - "\"fallen\"\t4\n", - "\"falling\"\t2\n", - "\"familiarly\"\t1\n", - "\"family\"\t1\n", - "\"fan\"\t10\n", - "\"fancied\"\t2\n", - "\"fancy\"\t7\n", - "\"fancying\"\t1\n", - "\"fanned\"\t1\n", - "\"fanning\"\t1\n", - "\"far\"\t13\n", - "\"farm\"\t1\n", - "\"farmer\"\t1\n", - "\"farther\"\t1\n", - "\"fashion\"\t2\n", - "\"fast\"\t4\n", - "\"faster\"\t3\n", - "\"fat\"\t1\n", - "\"father\"\t6\n", - "\"favoured\"\t1\n", - "\"favourite\"\t1\n", - "\"fear\"\t4\n", - "\"feared\"\t1\n", - "\"feather\"\t1\n", - "\"feathers\"\t1\n", - "\"feeble\"\t2\n", - "\"feebly\"\t1\n", - "\"feel\"\t8\n", - "\"feeling\"\t7\n", - "\"feelings\"\t2\n", - "\"feet\"\t19\n", - "\"fell\"\t6\n", - "\"fellow\"\t4\n", - "\"fellows\"\t1\n", - "\"felt\"\t23\n", - "\"fender\"\t1\n", - "\"ferrets\"\t2\n", - "\"fetch\"\t7\n", - "\"few\"\t9\n", - "\"fidgeted\"\t1\n", - "\"field\"\t1\n", - "\"fifteen\"\t1\n", - "\"fifteenth\"\t1\n", - "\"fifth\"\t1\n", - "\"fig\"\t1\n", - "\"fight\"\t2\n", - "\"fighting\"\t1\n", - "\"figure\"\t3\n", - "\"figures\"\t1\n", - "\"filled\"\t3\n", - "\"fills\"\t1\n", - "\"find\"\t21\n", - "\"finding\"\t3\n", - "\"finds\"\t1\n", - "\"fine\"\t2\n", - "\"finger\"\t5\n", - "\"finish\"\t5\n", - "\"finished\"\t12\n", - "\"finishing\"\t1\n", - "\"fire\"\t4\n", - "\"fireplace\"\t1\n", - "\"first\"\t51\n", - "\"fish\"\t8\n", - "\"fishes\"\t1\n", - "\"fit\"\t3\n", - "\"fits\"\t1\n", - "\"fitted\"\t1\n", - "\"five\"\t8\n", - "\"fix\"\t1\n", - "\"fixed\"\t1\n", - "\"flame\"\t1\n", - "\"flamingo\"\t5\n", - "\"flamingoes\"\t2\n", - "\"flapper\"\t1\n", - "\"flappers\"\t1\n", - "\"flashed\"\t1\n", - "\"flat\"\t2\n", - "\"flavour\"\t1\n", - "\"flew\"\t1\n", - "\"flinging\"\t1\n", - "\"flock\"\t1\n", - "\"floor\"\t3\n", - "\"flower\"\t2\n", - "\"flowers\"\t2\n", - "\"flown\"\t1\n", - "\"flung\"\t1\n", - "\"flurry\"\t1\n", - "\"flustered\"\t1\n", - "\"fluttered\"\t1\n", - "\"fly\"\t3\n", - "\"flying\"\t1\n", - "\"folded\"\t3\n", - "\"folding\"\t1\n", - "\"follow\"\t2\n", - "\"followed\"\t8\n", - "\"follows\"\t3\n", - "\"fond\"\t4\n", - "\"foolish\"\t1\n", - "\"foot\"\t10\n", - "\"footman\"\t13\n", - "\"footman's\"\t1\n", - "\"footmen\"\t1\n", - "\"footsteps\"\t2\n", - "\"for\"\t153\n", - "\"forehead\"\t2\n", - "\"forepaws\"\t1\n", - "\"forget\"\t2\n", - "\"forgetting\"\t3\n", - "\"forgot\"\t2\n", - "\"forgotten\"\t6\n", - "\"fork\"\t1\n", - "\"form\"\t1\n", - "\"fortunately\"\t1\n", - "\"forty\"\t1\n", - "\"forwards\"\t1\n", - "\"found\"\t32\n", - "\"fountains\"\t2\n", - "\"four\"\t8\n", - "\"fourteenth\"\t1\n", - "\"fourth\"\t1\n", - "\"frame\"\t1\n", - "\"frames\"\t1\n", - "\"france\"\t1\n", - "\"free\"\t3\n", - "\"french\"\t4\n", - "\"friend\"\t3\n", - "\"friends\"\t2\n", - "\"fright\"\t2\n", - "\"frighten\"\t1\n", - "\"frightened\"\t7\n", - "\"frog\"\t3\n", - "\"from\"\t36\n", - "\"front\"\t2\n", - "\"frontispiece\"\t1\n", - "\"frowning\"\t4\n", - "\"frying\"\t1\n", - "\"ful\"\t1\n", - "\"fulcrum\"\t1\n", - "\"full\"\t6\n", - "\"fumbled\"\t1\n", - "\"fun\"\t3\n", - "\"funny\"\t3\n", - "\"fur\"\t3\n", - "\"furious\"\t1\n", - "\"furiously\"\t1\n", - "\"furrow\"\t1\n", - "\"furrows\"\t1\n", - "\"further\"\t3\n", - "\"fury\"\t3\n", - "\"gained\"\t1\n", - "\"gallons\"\t1\n", - "\"game\"\t12\n", - "\"game's\"\t1\n", - "\"games\"\t1\n", - "\"garden\"\t16\n", - "\"gardeners\"\t8\n", - "\"gather\"\t1\n", - "\"gave\"\t15\n", - "\"gay\"\t1\n", - "\"gazing\"\t1\n", - "\"general\"\t3\n", - "\"generally\"\t7\n", - "\"gently\"\t3\n", - "\"geography\"\t1\n", - "\"get\"\t46\n", - "\"getting\"\t22\n", - "\"giddy\"\t2\n", - "\"girl\"\t4\n", - "\"girls\"\t3\n", - "\"give\"\t12\n", - "\"given\"\t1\n", - "\"giving\"\t2\n", - "\"glad\"\t11\n", - "\"glanced\"\t1\n", - "\"glaring\"\t1\n", - "\"glass\"\t10\n", - "\"globe\"\t1\n", - "\"gloomily\"\t1\n", - "\"gloves\"\t11\n", - "\"go\"\t50\n", - "\"goes\"\t7\n", - "\"going\"\t27\n", - "\"golden\"\t7\n", - "\"goldfish\"\t2\n", - "\"gone\"\t13\n", - "\"good\"\t27\n", - "\"goose\"\t2\n", - "\"got\"\t45\n", - "\"graceful\"\t1\n", - "\"grammar\"\t1\n", - "\"grand\"\t3\n", - "\"grant\"\t1\n", - "\"grass\"\t4\n", - "\"grave\"\t3\n", - "\"gravely\"\t3\n", - "\"gravy\"\t1\n", - "\"grazed\"\t1\n", - "\"great\"\t39\n", - "\"green\"\t4\n", - "\"grew\"\t1\n", - "\"grey\"\t1\n", - "\"grief\"\t1\n", - "\"grin\"\t6\n", - "\"grinned\"\t3\n", - "\"grinning\"\t1\n", - "\"grins\"\t1\n", - "\"ground\"\t8\n", - "\"grow\"\t13\n", - "\"growing\"\t11\n", - "\"growl\"\t3\n", - "\"growled\"\t1\n", - "\"growling\"\t1\n", - "\"growls\"\t1\n", - "\"grown\"\t7\n", - "\"grumbled\"\t1\n", - "\"grunt\"\t1\n", - "\"grunted\"\t4\n", - "\"gryphon\"\t54\n", - "\"guard\"\t1\n", - "\"guess\"\t3\n", - "\"guessed\"\t3\n", - "\"guests\"\t3\n", - "\"guilt\"\t1\n", - "\"guinea\"\t6\n", - "\"had\"\t178\n", - "\"hadn't\"\t8\n", - "\"hair\"\t7\n", - "\"half\"\t23\n", - "\"hall\"\t9\n", - "\"hand\"\t21\n", - "\"handed\"\t3\n", - "\"hands\"\t12\n", - "\"handsome\"\t1\n", - "\"handwriting\"\t1\n", - "\"hanging\"\t3\n", - "\"happen\"\t8\n", - "\"happened\"\t7\n", - "\"happening\"\t1\n", - "\"happens\"\t5\n", - "\"happy\"\t1\n", - "\"hard\"\t8\n", - "\"hardly\"\t12\n", - "\"hare\"\t31\n", - "\"harm\"\t1\n", - "\"has\"\t7\n", - "\"hasn't\"\t2\n", - "\"haste\"\t1\n", - "\"hastily\"\t16\n", - "\"hat\"\t1\n", - "\"hatching\"\t1\n", - "\"hate\"\t2\n", - "\"hated\"\t1\n", - "\"hatter\"\t55\n", - "\"hatter's\"\t1\n", - "\"hatters\"\t1\n", - "\"have\"\t80\n", - "\"haven't\"\t8\n", - "\"having\"\t10\n", - "\"he\"\t122\n", - "\"he'd\"\t1\n", - "\"he'll\"\t1\n", - "\"he's\"\t3\n", - "\"head\"\t49\n", - "\"head's\"\t1\n", - "\"heads\"\t10\n", - "\"heap\"\t1\n", - "\"hear\"\t14\n", - "\"heard\"\t30\n", - "\"hearing\"\t4\n", - "\"heart\"\t2\n", - "\"hearth\"\t1\n", - "\"hearthrug\"\t1\n", - "\"hearts\"\t8\n", - "\"heavy\"\t2\n", - "\"hedge\"\t2\n", - "\"hedgehog\"\t7\n", - "\"hedgehogs\"\t3\n", - "\"hedges\"\t1\n", - "\"heels\"\t1\n", - "\"height\"\t5\n", - "\"held\"\t4\n", - "\"help\"\t9\n", - "\"helped\"\t1\n", - "\"helpless\"\t1\n", - "\"her\"\t247\n", - "\"herald\"\t1\n", - "\"here\"\t51\n", - "\"hers\"\t4\n", - "\"herself\"\t83\n", - "\"hid\"\t1\n", - "\"hide\"\t1\n", - "\"high\"\t16\n", - "\"highest\"\t1\n", - "\"him\"\t43\n", - "\"himself\"\t6\n", - "\"hint\"\t2\n", - "\"hippopotamus\"\t1\n", - "\"his\"\t96\n", - "\"hiss\"\t1\n", - "\"histories\"\t1\n", - "\"history\"\t7\n", - "\"hit\"\t2\n", - "\"hjckrrh\"\t1\n", - "\"hm\"\t1\n", - "\"hoarse\"\t3\n", - "\"hoarsely\"\t1\n", - "\"hold\"\t10\n", - "\"holding\"\t3\n", - "\"hole\"\t5\n", - "\"holiday\"\t1\n", - "\"hollow\"\t1\n", - "\"home\"\t5\n", - "\"honest\"\t1\n", - "\"honour\"\t4\n", - "\"hookah\"\t5\n", - "\"hope\"\t3\n", - "\"hoped\"\t1\n", - "\"hopeful\"\t1\n", - "\"hopeless\"\t1\n", - "\"hoping\"\t3\n", - "\"horse\"\t1\n", - "\"hot\"\t7\n", - "\"hour\"\t2\n", - "\"hours\"\t4\n", - "\"house\"\t18\n", - "\"housemaid\"\t1\n", - "\"houses\"\t1\n", - "\"how\"\t68\n", - "\"however\"\t20\n", - "\"howled\"\t1\n", - "\"howling\"\t3\n", - "\"humble\"\t1\n", - "\"humbly\"\t2\n", - "\"hundred\"\t1\n", - "\"hung\"\t1\n", - "\"hungry\"\t3\n", - "\"hunting\"\t3\n", - "\"hurried\"\t11\n", - "\"hurriedly\"\t2\n", - "\"hurry\"\t11\n", - "\"hurrying\"\t1\n", - "\"hurt\"\t3\n", - "\"hush\"\t3\n", - "\"i\"\t408\n", - "\"i'd\"\t11\n", - "\"i'll\"\t31\n", - "\"i'm\"\t59\n", - "\"i've\"\t34\n", - "\"idea\"\t15\n", - "\"idiot\"\t1\n", - "\"idiotic\"\t1\n", - "\"if\"\t96\n", - "\"ignorant\"\t1\n", - "\"ii\"\t1\n", - "\"iii\"\t1\n", - "\"ill\"\t2\n", - "\"imagine\"\t2\n", - "\"imitated\"\t1\n", - "\"immediate\"\t1\n", - "\"immediately\"\t3\n", - "\"immense\"\t1\n", - "\"impatient\"\t1\n", - "\"impatiently\"\t5\n", - "\"impertinent\"\t1\n", - "\"important\"\t7\n", - "\"impossible\"\t3\n", - "\"improve\"\t1\n", - "\"in\"\t369\n", - "\"incessantly\"\t1\n", - "\"inches\"\t6\n", - "\"inclined\"\t1\n", - "\"indeed\"\t16\n", - "\"indignant\"\t1\n", - "\"indignantly\"\t4\n", - "\"injure\"\t1\n", - "\"ink\"\t1\n", - "\"inkstand\"\t1\n", - "\"inquired\"\t1\n", - "\"inquisitively\"\t1\n", - "\"inside\"\t2\n", - "\"insolence\"\t1\n", - "\"instance\"\t3\n", - "\"instantly\"\t5\n", - "\"instead\"\t3\n", - "\"insult\"\t1\n", - "\"interest\"\t1\n", - "\"interesting\"\t5\n", - "\"interrupt\"\t1\n", - "\"interrupted\"\t9\n", - "\"interrupting\"\t2\n", - "\"into\"\t67\n", - "\"introduce\"\t2\n", - "\"introduced\"\t1\n", - "\"invent\"\t1\n", - "\"invented\"\t1\n", - "\"invitation\"\t2\n", - "\"invited\"\t2\n", - "\"involved\"\t1\n", - "\"inwards\"\t1\n", - "\"irons\"\t1\n", - "\"irritated\"\t1\n", - "\"is\"\t108\n", - "\"isn't\"\t7\n", - "\"it\"\t530\n", - "\"it'll\"\t8\n", - "\"it's\"\t57\n", - "\"its\"\t56\n", - "\"itself\"\t14\n", - "\"iv\"\t1\n", - "\"ix\"\t1\n", - "\"jack\"\t1\n", - "\"jar\"\t2\n", - "\"jaw\"\t1\n", - "\"jaws\"\t2\n", - "\"jelly\"\t1\n", - "\"jogged\"\t1\n", - "\"join\"\t9\n", - "\"joined\"\t3\n", - "\"journey\"\t1\n", - "\"joys\"\t1\n", - "\"judge\"\t4\n", - "\"judging\"\t1\n", - "\"jug\"\t1\n", - "\"jumped\"\t6\n", - "\"jumping\"\t4\n", - "\"juror\"\t1\n", - "\"jurors\"\t4\n", - "\"jury\"\t22\n", - "\"jurymen\"\t4\n", - "\"just\"\t52\n", - "\"justice\"\t1\n", - "\"keep\"\t11\n", - "\"keeping\"\t2\n", - "\"kept\"\t13\n", - "\"kettle\"\t1\n", - "\"key\"\t9\n", - "\"kick\"\t3\n", - "\"kid\"\t5\n", - "\"kill\"\t1\n", - "\"killing\"\t1\n", - "\"kills\"\t1\n", - "\"kind\"\t7\n", - "\"kindly\"\t2\n", - "\"king\"\t61\n", - "\"king's\"\t2\n", - "\"kings\"\t1\n", - "\"kiss\"\t1\n", - "\"kissed\"\t1\n", - "\"kitchen\"\t4\n", - "\"knave\"\t9\n", - "\"knee\"\t5\n", - "\"kneel\"\t1\n", - "\"knelt\"\t1\n", - "\"knew\"\t14\n", - "\"knife\"\t3\n", - "\"knock\"\t1\n", - "\"knocked\"\t1\n", - "\"knocking\"\t3\n", - "\"knot\"\t2\n", - "\"know\"\t88\n", - "\"knowing\"\t2\n", - "\"knowledge\"\t3\n", - "\"known\"\t1\n", - "\"knows\"\t2\n", - "\"knuckles\"\t1\n", - "\"label\"\t2\n", - "\"labelled\"\t1\n", - "\"lacie\"\t1\n", - "\"lad\"\t1\n", - "\"ladder\"\t1\n", - "\"lady\"\t3\n", - "\"laid\"\t2\n", - "\"lamps\"\t1\n", - "\"land\"\t1\n", - "\"languid\"\t1\n", - "\"lap\"\t2\n", - "\"large\"\t33\n", - "\"larger\"\t7\n", - "\"largest\"\t1\n", - "\"lark\"\t1\n", - "\"last\"\t33\n", - "\"lasted\"\t2\n", - "\"lastly\"\t1\n", - "\"late\"\t6\n", - "\"lately\"\t1\n", - "\"later\"\t3\n", - "\"latin\"\t1\n", - "\"latitude\"\t2\n", - "\"laugh\"\t1\n", - "\"laughed\"\t2\n", - "\"laughing\"\t2\n", - "\"laughter\"\t1\n", - "\"law\"\t2\n", - "\"lay\"\t4\n", - "\"lazily\"\t1\n", - "\"lazy\"\t1\n", - "\"leaders\"\t1\n", - "\"leading\"\t1\n", - "\"leaning\"\t2\n", - "\"leant\"\t1\n", - "\"leap\"\t1\n", - "\"learn\"\t7\n", - "\"learned\"\t1\n", - "\"learning\"\t2\n", - "\"learnt\"\t2\n", - "\"least\"\t9\n", - "\"leave\"\t9\n", - "\"leaves\"\t6\n", - "\"leaving\"\t1\n", - "\"led\"\t4\n", - "\"ledge\"\t1\n", - "\"left\"\t14\n", - "\"lefthand\"\t2\n", - "\"legged\"\t2\n", - "\"legs\"\t3\n", - "\"length\"\t1\n", - "\"less\"\t4\n", - "\"lessen\"\t1\n", - "\"lesson\"\t3\n", - "\"lessons\"\t10\n", - "\"lest\"\t1\n", - "\"let\"\t17\n", - "\"let's\"\t5\n", - "\"letter\"\t3\n", - "\"letters\"\t1\n", - "\"lewis\"\t1\n", - "\"licking\"\t1\n", - "\"lie\"\t2\n", - "\"life\"\t12\n", - "\"lifted\"\t1\n", - "\"like\"\t85\n", - "\"liked\"\t6\n", - "\"likely\"\t5\n", - "\"likes\"\t1\n", - "\"limbs\"\t1\n", - "\"line\"\t2\n", - "\"lines\"\t1\n", - "\"linked\"\t1\n", - "\"lips\"\t1\n", - "\"list\"\t3\n", - "\"listen\"\t7\n", - "\"listened\"\t1\n", - "\"listeners\"\t1\n", - "\"listening\"\t3\n", - "\"lit\"\t1\n", - "\"little\"\t128\n", - "\"live\"\t8\n", - "\"lived\"\t3\n", - "\"livery\"\t3\n", - "\"lives\"\t4\n", - "\"living\"\t2\n", - "\"lizard\"\t5\n", - "\"lizard's\"\t1\n", - "\"lobster\"\t7\n", - "\"lobsters\"\t6\n", - "\"lock\"\t1\n", - "\"locked\"\t1\n", - "\"locks\"\t2\n", - "\"lodging\"\t1\n", - "\"london\"\t1\n", - "\"lonely\"\t2\n", - "\"long\"\t32\n", - "\"longed\"\t2\n", - "\"longer\"\t3\n", - "\"longitude\"\t2\n", - "\"look\"\t29\n", - "\"looked\"\t45\n", - "\"looking\"\t32\n", - "\"loose\"\t1\n", - "\"lory\"\t7\n", - "\"lose\"\t1\n", - "\"losing\"\t1\n", - "\"lost\"\t3\n", - "\"loud\"\t6\n", - "\"louder\"\t1\n", - "\"loudly\"\t3\n", - "\"love\"\t3\n", - "\"loveliest\"\t1\n", - "\"lovely\"\t2\n", - "\"loving\"\t1\n", - "\"low\"\t15\n", - "\"lower\"\t1\n", - "\"lowing\"\t1\n", - "\"luckily\"\t2\n", - "\"lullaby\"\t1\n", - "\"lying\"\t8\n", - "\"m\"\t4\n", - "\"ma\"\t2\n", - "\"ma'am\"\t1\n", - "\"mabel\"\t4\n", - "\"machines\"\t1\n", - "\"mad\"\t15\n", - "\"made\"\t30\n", - "\"magic\"\t1\n", - "\"magpie\"\t1\n", - "\"majesty\"\t12\n", - "\"make\"\t27\n", - "\"makes\"\t11\n", - "\"making\"\t8\n", - "\"mallets\"\t1\n", - "\"man\"\t5\n", - "\"manage\"\t7\n", - "\"managed\"\t4\n", - "\"managing\"\t1\n", - "\"manner\"\t2\n", - "\"manners\"\t1\n", - "\"many\"\t12\n", - "\"maps\"\t1\n", - "\"march\"\t34\n", - "\"marched\"\t1\n", - "\"mark\"\t3\n", - "\"marked\"\t6\n", - "\"marmalade'\"\t1\n", - "\"mary\"\t4\n", - "\"master\"\t4\n", - "\"matter\"\t9\n", - "\"matters\"\t2\n", - "\"may\"\t13\n", - "\"maybe\"\t2\n", - "\"mayn't\"\t1\n", - "\"me\"\t66\n", - "\"me'\"\t2\n", - "\"meal\"\t1\n", - "\"mean\"\t10\n", - "\"meaning\"\t8\n", - "\"means\"\t5\n", - "\"meant\"\t5\n", - "\"meanwhile\"\t1\n", - "\"measure\"\t1\n", - "\"meat\"\t1\n", - "\"meekly\"\t2\n", - "\"meet\"\t2\n", - "\"meeting\"\t1\n", - "\"melancholy\"\t6\n", - "\"memorandum\"\t1\n", - "\"memory\"\t1\n", - "\"men'\"\t1\n", - "\"mentioned\"\t3\n", - "\"mercia\"\t2\n", - "\"merely\"\t2\n", - "\"merrily\"\t1\n", - "\"messages\"\t2\n", - "\"met\"\t3\n", - "\"mice\"\t4\n", - "\"middle\"\t7\n", - "\"might\"\t28\n", - "\"mile\"\t2\n", - "\"miles\"\t3\n", - "\"milk\"\t2\n", - "\"millennium\"\t1\n", - "\"mind\"\t11\n", - "\"minded\"\t1\n", - "\"minding\"\t1\n", - "\"mine\"\t10\n", - "\"mineral\"\t1\n", - "\"minute\"\t21\n", - "\"minutes\"\t11\n", - "\"mischief\"\t1\n", - "\"miserable\"\t2\n", - "\"miss\"\t4\n", - "\"missed\"\t2\n", - "\"mistake\"\t3\n", - "\"mixed\"\t2\n", - "\"mock\"\t56\n", - "\"moderate\"\t1\n", - "\"modern\"\t1\n", - "\"moment\"\t29\n", - "\"moment's\"\t2\n", - "\"month\"\t2\n", - "\"moon\"\t1\n", - "\"moral\"\t8\n", - "\"morals\"\t1\n", - "\"morcar\"\t2\n", - "\"more\"\t49\n", - "\"morning\"\t5\n", - "\"morsel\"\t1\n", - "\"most\"\t8\n", - "\"mostly\"\t2\n", - "\"mournful\"\t1\n", - "\"mournfully\"\t1\n", - "\"mouse\"\t43\n", - "\"mouse's\"\t1\n", - "\"mouth\"\t10\n", - "\"mouths\"\t4\n", - "\"move\"\t3\n", - "\"moved\"\t5\n", - "\"moving\"\t3\n", - "\"much\"\t51\n", - "\"muchness\"\t3\n", - "\"muddle\"\t1\n", - "\"multiplication\"\t1\n", - "\"murder\"\t1\n", - "\"murdering\"\t1\n", - "\"muscular\"\t1\n", - "\"mushroom\"\t8\n", - "\"music\"\t3\n", - "\"must\"\t44\n", - "\"mustard\"\t3\n", - "\"muttered\"\t2\n", - "\"muttering\"\t3\n", - "\"my\"\t58\n", - "\"myself\"\t7\n", - "\"mystery\"\t2\n", - "\"name\"\t10\n", - "\"names\"\t2\n", - "\"narrow\"\t2\n", - "\"nasty\"\t1\n", - "\"natural\"\t4\n", - "\"natured\"\t1\n", - "\"naturedly\"\t1\n", - "\"nay\"\t1\n", - "\"near\"\t15\n", - "\"nearer\"\t5\n", - "\"nearly\"\t11\n", - "\"neat\"\t1\n", - "\"neatly\"\t2\n", - "\"neck\"\t7\n", - "\"needn't\"\t3\n", - "\"needs\"\t1\n", - "\"neighbour\"\t1\n", - "\"neighbouring\"\t1\n", - "\"neither\"\t2\n", - "\"nervous\"\t5\n", - "\"nest\"\t1\n", - "\"never\"\t47\n", - "\"never'\"\t1\n", - "\"nevertheless\"\t1\n", - "\"new\"\t5\n", - "\"newspapers\"\t1\n", - "\"next\"\t30\n", - "\"nibbled\"\t2\n", - "\"nibbling\"\t3\n", - "\"nice\"\t6\n", - "\"nicely\"\t2\n", - "\"night\"\t5\n", - "\"nile\"\t1\n", - "\"nine\"\t5\n", - "\"no\"\t90\n", - "\"nobody\"\t8\n", - "\"nodded\"\t1\n", - "\"noise\"\t3\n", - "\"noises\"\t1\n", - "\"none\"\t4\n", - "\"nonsense\"\t7\n", - "\"nor\"\t3\n", - "\"normans\"\t1\n", - "\"northumbria\"\t2\n", - "\"nose\"\t7\n", - "\"nose'\"\t1\n", - "\"not\"\t144\n", - "\"not'\"\t1\n", - "\"note\"\t2\n", - "\"nothing\"\t34\n", - "\"notice\"\t5\n", - "\"noticed\"\t8\n", - "\"noticing\"\t1\n", - "\"notion\"\t3\n", - "\"now\"\t60\n", - "\"nowhere\"\t2\n", - "\"number\"\t5\n", - "\"nurse\"\t3\n", - "\"nursing\"\t3\n", - "\"o\"\t3\n", - "\"o'clock\"\t3\n", - "\"obliged\"\t3\n", - "\"oblong\"\t1\n", - "\"obstacle\"\t1\n", - "\"occasional\"\t1\n", - "\"occasionally\"\t1\n", - "\"occurred\"\t2\n", - "\"odd\"\t1\n", - "\"of\"\t513\n", - "\"off\"\t73\n", - "\"offend\"\t1\n", - "\"offended\"\t10\n", - "\"offer\"\t2\n", - "\"officer\"\t1\n", - "\"officers\"\t4\n", - "\"often\"\t5\n", - "\"oh\"\t45\n", - "\"ointment\"\t1\n", - "\"old\"\t19\n", - "\"older\"\t2\n", - "\"oldest\"\t1\n", - "\"on\"\t193\n", - "\"once\"\t34\n", - "\"one\"\t103\n", - "\"one's\"\t1\n", - "\"ones\"\t1\n", - "\"oneself\"\t1\n", - "\"onions\"\t1\n", - "\"only\"\t50\n", - "\"oop\"\t7\n", - "\"ootiful\"\t4\n", - "\"open\"\t7\n", - "\"opened\"\t10\n", - "\"opening\"\t3\n", - "\"opinion\"\t1\n", - "\"opportunity\"\t8\n", - "\"opposite\"\t1\n", - "\"or\"\t77\n", - "\"orange\"\t1\n", - "\"order\"\t3\n", - "\"ordered\"\t3\n", - "\"ordered'\"\t1\n", - "\"ordering\"\t2\n", - "\"ornamented\"\t2\n", - "\"other\"\t40\n", - "\"others\"\t7\n", - "\"otherwise\"\t4\n", - "\"ou\"\t1\n", - "\"ought\"\t14\n", - "\"our\"\t8\n", - "\"ours\"\t1\n", - "\"ourselves\"\t1\n", - "\"out\"\t117\n", - "\"outside\"\t4\n", - "\"over\"\t40\n", - "\"overcome\"\t1\n", - "\"overhead\"\t1\n", - "\"owl\"\t3\n", - "\"own\"\t10\n", - "\"oyster\"\t1\n", - "\"p\"\t1\n", - "\"pace\"\t1\n", - "\"pack\"\t5\n", - "\"paint\"\t1\n", - "\"painting\"\t2\n", - "\"pair\"\t5\n", - "\"pairs\"\t1\n", - "\"pale\"\t4\n", - "\"pan\"\t1\n", - "\"panted\"\t1\n", - "\"panther\"\t3\n", - "\"panting\"\t2\n", - "\"paper\"\t4\n", - "\"parchment\"\t2\n", - "\"pardon\"\t6\n", - "\"pardoned\"\t1\n", - "\"paris\"\t2\n", - "\"part\"\t2\n", - "\"particular\"\t4\n", - "\"partner\"\t1\n", - "\"partners\"\t1\n", - "\"parts\"\t1\n", - "\"party\"\t10\n", - "\"pass\"\t1\n", - "\"passage\"\t4\n", - "\"passed\"\t5\n", - "\"passing\"\t1\n", - "\"passion\"\t3\n", - "\"passionate\"\t1\n", - "\"past\"\t3\n", - "\"pat\"\t3\n", - "\"patience\"\t1\n", - "\"patiently\"\t2\n", - "\"patriotic\"\t1\n", - "\"patted\"\t1\n", - "\"pattering\"\t3\n", - "\"pattern\"\t1\n", - "\"pause\"\t2\n", - "\"paused\"\t1\n", - "\"paw\"\t3\n", - "\"paws\"\t4\n", - "\"pebbles\"\t2\n", - "\"peeped\"\t3\n", - "\"peeping\"\t1\n", - "\"peering\"\t1\n", - "\"pegs\"\t1\n", - "\"pence\"\t1\n", - "\"pencil\"\t2\n", - "\"pencils\"\t1\n", - "\"pennyworth\"\t1\n", - "\"people\"\t13\n", - "\"pepper\"\t8\n", - "\"perfectly\"\t4\n", - "\"perhaps\"\t17\n", - "\"permitted\"\t1\n", - "\"persisted\"\t2\n", - "\"person\"\t4\n", - "\"personal\"\t2\n", - "\"persons\"\t1\n", - "\"pet\"\t1\n", - "\"picked\"\t3\n", - "\"picking\"\t2\n", - "\"picture\"\t1\n", - "\"pictured\"\t1\n", - "\"pictures\"\t4\n", - "\"pie\"\t3\n", - "\"piece\"\t6\n", - "\"pieces\"\t3\n", - "\"pig\"\t11\n", - "\"pigeon\"\t12\n", - "\"pigs\"\t6\n", - "\"pinch\"\t2\n", - "\"pinched\"\t2\n", - "\"pine\"\t1\n", - "\"pink\"\t1\n", - "\"piteous\"\t1\n", - "\"pitied\"\t1\n", - "\"pity\"\t3\n", - "\"place\"\t8\n", - "\"placed\"\t1\n", - "\"places\"\t2\n", - "\"plainly\"\t1\n", - "\"plan\"\t4\n", - "\"planning\"\t1\n", - "\"plate\"\t3\n", - "\"plates\"\t2\n", - "\"play\"\t8\n", - "\"played\"\t1\n", - "\"players\"\t4\n", - "\"playing\"\t2\n", - "\"pleaded\"\t3\n", - "\"pleasant\"\t1\n", - "\"pleasanter\"\t1\n", - "\"please\"\t19\n", - "\"pleased\"\t7\n", - "\"pleases\"\t1\n", - "\"pleasing\"\t1\n", - "\"pleasure\"\t2\n", - "\"plenty\"\t2\n", - "\"pocked\"\t1\n", - "\"pocket\"\t6\n", - "\"pointed\"\t1\n", - "\"pointing\"\t4\n", - "\"poison\"\t3\n", - "\"poker\"\t1\n", - "\"poky\"\t1\n", - "\"politely\"\t6\n", - "\"pool\"\t11\n", - "\"poor\"\t27\n", - "\"pop\"\t1\n", - "\"pope\"\t1\n", - "\"porpoise\"\t4\n", - "\"position\"\t2\n", - "\"positively\"\t1\n", - "\"possible\"\t1\n", - "\"possibly\"\t3\n", - "\"pot\"\t1\n", - "\"pounds\"\t1\n", - "\"pour\"\t1\n", - "\"poured\"\t1\n", - "\"powdered\"\t1\n", - "\"practice\"\t1\n", - "\"pray\"\t3\n", - "\"precious\"\t1\n", - "\"present\"\t3\n", - "\"presented\"\t1\n", - "\"presently\"\t2\n", - "\"presents\"\t2\n", - "\"pressed\"\t3\n", - "\"pressing\"\t1\n", - "\"pretend\"\t1\n", - "\"pretending\"\t1\n", - "\"pretexts\"\t1\n", - "\"prettier\"\t1\n", - "\"pretty\"\t1\n", - "\"prevent\"\t1\n", - "\"printed\"\t1\n", - "\"prison\"\t1\n", - "\"prisoner\"\t1\n", - "\"prisoner's\"\t1\n", - "\"prize\"\t1\n", - "\"prizes\"\t5\n", - "\"proceed\"\t2\n", - "\"procession\"\t5\n", - "\"processions\"\t1\n", - "\"produced\"\t1\n", - "\"producing\"\t1\n", - "\"promise\"\t1\n", - "\"promised\"\t1\n", - "\"promising\"\t1\n", - "\"pronounced\"\t1\n", - "\"proper\"\t3\n", - "\"proposal\"\t1\n", - "\"prosecute\"\t1\n", - "\"protection\"\t1\n", - "\"proud\"\t2\n", - "\"prove\"\t1\n", - "\"proved\"\t2\n", - "\"proves\"\t2\n", - "\"provoking\"\t1\n", - "\"puffed\"\t1\n", - "\"pulled\"\t1\n", - "\"pulling\"\t1\n", - "\"pun\"\t1\n", - "\"punching\"\t1\n", - "\"punished\"\t1\n", - "\"puppy\"\t6\n", - "\"puppy's\"\t1\n", - "\"purple\"\t1\n", - "\"purpose\"\t1\n", - "\"purring\"\t2\n", - "\"push\"\t1\n", - "\"puss\"\t1\n", - "\"put\"\t31\n", - "\"putting\"\t3\n", - "\"puzzle\"\t1\n", - "\"puzzled\"\t9\n", - "\"puzzling\"\t4\n", - "\"quadrille\"\t4\n", - "\"quarrel\"\t1\n", - "\"quarrelled\"\t1\n", - "\"quarrelling\"\t2\n", - "\"queen\"\t68\n", - "\"queen's\"\t7\n", - "\"queens\"\t1\n", - "\"queer\"\t12\n", - "\"queerest\"\t1\n", - "\"question\"\t17\n", - "\"questions\"\t4\n", - "\"quick\"\t2\n", - "\"quicker\"\t1\n", - "\"quickly\"\t2\n", - "\"quiet\"\t2\n", - "\"quietly\"\t5\n", - "\"quite\"\t55\n", - "\"quiver\"\t1\n", - "\"rabbit\"\t46\n", - "\"rabbit'\"\t1\n", - "\"rabbit's\"\t4\n", - "\"rabbits\"\t1\n", - "\"race\"\t6\n", - "\"railway\"\t2\n", - "\"raised\"\t2\n", - "\"raising\"\t1\n", - "\"ran\"\t16\n", - "\"rapidly\"\t2\n", - "\"rapped\"\t1\n", - "\"rat\"\t1\n", - "\"rate\"\t9\n", - "\"rather\"\t25\n", - "\"rats\"\t1\n", - "\"rattle\"\t1\n", - "\"rattling\"\t2\n", - "\"raven\"\t1\n", - "\"ravens\"\t1\n", - "\"raving\"\t2\n", - "\"raw\"\t1\n", - "\"reach\"\t4\n", - "\"reaching\"\t1\n", - "\"read\"\t11\n", - "\"readily\"\t1\n", - "\"reading\"\t3\n", - "\"ready\"\t8\n", - "\"real\"\t3\n", - "\"reality\"\t1\n", - "\"really\"\t13\n", - "\"rearing\"\t1\n", - "\"reason\"\t9\n", - "\"reasonable\"\t1\n", - "\"reasons\"\t1\n", - "\"received\"\t1\n", - "\"recognised\"\t1\n", - "\"recovered\"\t2\n", - "\"red\"\t3\n", - "\"reduced\"\t1\n", - "\"reeds\"\t1\n", - "\"reeling\"\t1\n", - "\"refreshments\"\t1\n", - "\"refused\"\t1\n", - "\"regular\"\t2\n", - "\"relief\"\t2\n", - "\"relieved\"\t1\n", - "\"remain\"\t1\n", - "\"remained\"\t3\n", - "\"remaining\"\t1\n", - "\"remark\"\t10\n", - "\"remarkable\"\t2\n", - "\"remarked\"\t10\n", - "\"remarking\"\t3\n", - "\"remarks\"\t3\n", - "\"remedies\"\t1\n", - "\"remember\"\t14\n", - "\"remembered\"\t5\n", - "\"remembering\"\t1\n", - "\"reminding\"\t1\n", - "\"removed\"\t2\n", - "\"repeat\"\t7\n", - "\"repeated\"\t10\n", - "\"repeating\"\t3\n", - "\"replied\"\t29\n", - "\"reply\"\t5\n", - "\"resource\"\t1\n", - "\"respect\"\t1\n", - "\"respectable\"\t1\n", - "\"respectful\"\t1\n", - "\"rest\"\t10\n", - "\"resting\"\t2\n", - "\"result\"\t1\n", - "\"retire\"\t1\n", - "\"returned\"\t2\n", - "\"returning\"\t1\n", - "\"rich\"\t1\n", - "\"riddle\"\t1\n", - "\"riddles\"\t2\n", - "\"ridge\"\t1\n", - "\"ridges\"\t1\n", - "\"ridiculous\"\t1\n", - "\"right\"\t32\n", - "\"righthand\"\t1\n", - "\"rightly\"\t1\n", - "\"ring\"\t2\n", - "\"ringlets\"\t2\n", - "\"riper\"\t1\n", - "\"rippling\"\t1\n", - "\"rise\"\t1\n", - "\"rises\"\t1\n", - "\"rising\"\t1\n", - "\"roared\"\t1\n", - "\"roast\"\t1\n", - "\"rock\"\t1\n", - "\"rocket\"\t1\n", - "\"rome\"\t2\n", - "\"roof\"\t6\n", - "\"room\"\t13\n", - "\"roots\"\t2\n", - "\"rope\"\t1\n", - "\"rose\"\t4\n", - "\"roses\"\t3\n", - "\"rosetree\"\t1\n", - "\"roughly\"\t1\n", - "\"round\"\t41\n", - "\"row\"\t2\n", - "\"royal\"\t2\n", - "\"rubbed\"\t1\n", - "\"rubbing\"\t2\n", - "\"rude\"\t2\n", - "\"rudeness\"\t1\n", - "\"rule\"\t5\n", - "\"rules\"\t3\n", - "\"rumbling\"\t1\n", - "\"run\"\t4\n", - "\"running\"\t8\n", - "\"rush\"\t2\n", - "\"rushed\"\t1\n", - "\"rustled\"\t1\n", - "\"rustling\"\t1\n", - "\"sad\"\t3\n", - "\"sadly\"\t5\n", - "\"safe\"\t2\n", - "\"sage\"\t1\n", - "\"said\"\t462\n", - "\"salmon\"\t1\n", - "\"salt\"\t2\n", - "\"same\"\t24\n", - "\"sand\"\t1\n", - "\"sands\"\t1\n", - "\"sang\"\t2\n", - "\"sat\"\t17\n", - "\"saucepan\"\t1\n", - "\"saucepans\"\t1\n", - "\"saucer\"\t1\n", - "\"savage\"\t4\n", - "\"save\"\t1\n", - "\"saves\"\t1\n", - "\"saw\"\t13\n", - "\"say\"\t52\n", - "\"saying\"\t15\n", - "\"says\"\t4\n", - "\"scale\"\t1\n", - "\"scaly\"\t1\n", - "\"school\"\t6\n", - "\"schoolroom\"\t1\n", - "\"scolded\"\t1\n", - "\"scrambling\"\t1\n", - "\"scratching\"\t1\n", - "\"scream\"\t2\n", - "\"screamed\"\t4\n", - "\"screaming\"\t1\n", - "\"scroll\"\t2\n", - "\"sea\"\t14\n", - "\"seals\"\t1\n", - "\"seaography\"\t1\n", - "\"search\"\t1\n", - "\"seaside\"\t1\n", - "\"seated\"\t1\n", - "\"second\"\t4\n", - "\"secondly\"\t2\n", - "\"secret\"\t1\n", - "\"see\"\t67\n", - "\"seeing\"\t1\n", - "\"seem\"\t8\n", - "\"seemed\"\t27\n", - "\"seems\"\t5\n", - "\"seen\"\t15\n", - "\"seldom\"\t1\n", - "\"sell\"\t2\n", - "\"send\"\t1\n", - "\"sending\"\t2\n", - "\"sends\"\t1\n", - "\"sensation\"\t2\n", - "\"sense\"\t3\n", - "\"sent\"\t2\n", - "\"sentence\"\t6\n", - "\"sentenced\"\t1\n", - "\"series\"\t1\n", - "\"seriously\"\t1\n", - "\"serpent\"\t9\n", - "\"serpents\"\t3\n", - "\"set\"\t14\n", - "\"setting\"\t1\n", - "\"settle\"\t1\n", - "\"settled\"\t3\n", - "\"settling\"\t1\n", - "\"seven\"\t6\n", - "\"several\"\t4\n", - "\"severely\"\t4\n", - "\"severity\"\t1\n", - "\"sh\"\t2\n", - "\"shade\"\t1\n", - "\"shake\"\t1\n", - "\"shakespeare\"\t1\n", - "\"shaking\"\t3\n", - "\"shall\"\t25\n", - "\"shan't\"\t6\n", - "\"shape\"\t1\n", - "\"shaped\"\t3\n", - "\"share\"\t1\n", - "\"shared\"\t1\n", - "\"sharing\"\t1\n", - "\"shark\"\t1\n", - "\"sharks\"\t1\n", - "\"sharp\"\t6\n", - "\"sharply\"\t4\n", - "\"she\"\t540\n", - "\"she'd\"\t2\n", - "\"she'll\"\t3\n", - "\"she's\"\t7\n", - "\"shedding\"\t1\n", - "\"sheep\"\t1\n", - "\"shelves\"\t2\n", - "\"shepherd\"\t1\n", - "\"shifting\"\t1\n", - "\"shilling\"\t1\n", - "\"shillings\"\t1\n", - "\"shingle\"\t1\n", - "\"shining\"\t1\n", - "\"shiny\"\t1\n", - "\"shiver\"\t1\n", - "\"shock\"\t1\n", - "\"shoes\"\t7\n", - "\"shook\"\t9\n", - "\"shore\"\t4\n", - "\"short\"\t4\n", - "\"shorter\"\t2\n", - "\"should\"\t27\n", - "\"shoulder\"\t4\n", - "\"shoulders\"\t4\n", - "\"shouldn't\"\t5\n", - "\"shouted\"\t9\n", - "\"shouting\"\t2\n", - "\"show\"\t3\n", - "\"shower\"\t2\n", - "\"showing\"\t2\n", - "\"shriek\"\t5\n", - "\"shrieked\"\t1\n", - "\"shrieks\"\t1\n", - "\"shrill\"\t5\n", - "\"shrimp\"\t1\n", - "\"shrink\"\t1\n", - "\"shrinking\"\t4\n", - "\"shut\"\t5\n", - "\"shutting\"\t2\n", - "\"shy\"\t1\n", - "\"shyly\"\t1\n", - "\"side\"\t17\n", - "\"sides\"\t4\n", - "\"sigh\"\t4\n", - "\"sighed\"\t5\n", - "\"sighing\"\t3\n", - "\"sight\"\t10\n", - "\"sign\"\t1\n", - "\"signed\"\t2\n", - "\"signifies\"\t1\n", - "\"signify\"\t1\n", - "\"silence\"\t14\n", - "\"silent\"\t7\n", - "\"simple\"\t5\n", - "\"simpleton\"\t1\n", - "\"simply\"\t3\n", - "\"since\"\t4\n", - "\"sing\"\t6\n", - "\"singers\"\t2\n", - "\"singing\"\t2\n", - "\"sink\"\t1\n", - "\"sir\"\t6\n", - "\"sir'\"\t1\n", - "\"sister\"\t8\n", - "\"sister's\"\t1\n", - "\"sisters\"\t2\n", - "\"sit\"\t8\n", - "\"sits\"\t1\n", - "\"sitting\"\t10\n", - "\"six\"\t2\n", - "\"sixpence\"\t1\n", - "\"sixteenth\"\t1\n", - "\"size\"\t13\n", - "\"sizes\"\t1\n", - "\"skimming\"\t1\n", - "\"skirt\"\t1\n", - "\"skurried\"\t1\n", - "\"sky\"\t5\n", - "\"slate\"\t4\n", - "\"slates\"\t7\n", - "\"slates'll\"\t1\n", - "\"sleep\"\t6\n", - "\"sleepy\"\t5\n", - "\"slightest\"\t1\n", - "\"slipped\"\t3\n", - "\"slippery\"\t1\n", - "\"slowly\"\t8\n", - "\"sluggard\"\t1\n", - "\"small\"\t10\n", - "\"smaller\"\t3\n", - "\"smallest\"\t2\n", - "\"smile\"\t2\n", - "\"smiled\"\t2\n", - "\"smiling\"\t2\n", - "\"smoke\"\t1\n", - "\"smoking\"\t2\n", - "\"snail\"\t3\n", - "\"snappishly\"\t1\n", - "\"snatch\"\t2\n", - "\"sneeze\"\t2\n", - "\"sneezed\"\t1\n", - "\"sneezes\"\t2\n", - "\"sneezing\"\t6\n", - "\"snorting\"\t1\n", - "\"snout\"\t1\n", - "\"so\"\t151\n", - "\"sob\"\t1\n", - "\"sobbed\"\t1\n", - "\"sobbing\"\t3\n", - "\"sobs\"\t4\n", - "\"soft\"\t1\n", - "\"softly\"\t1\n", - "\"soldier\"\t1\n", - "\"soldiers\"\t10\n", - "\"solemn\"\t3\n", - "\"solemnly\"\t4\n", - "\"soles\"\t1\n", - "\"solid\"\t1\n", - "\"some\"\t51\n", - "\"somebody\"\t7\n", - "\"somehow\"\t1\n", - "\"someone\"\t1\n", - "\"somersault\"\t2\n", - "\"something\"\t18\n", - "\"sometimes\"\t5\n", - "\"somewhere\"\t3\n", - "\"son\"\t1\n", - "\"song\"\t7\n", - "\"soo\"\t7\n", - "\"soon\"\t25\n", - "\"sooner\"\t2\n", - "\"soothing\"\t1\n", - "\"sorrow\"\t2\n", - "\"sorrowful\"\t2\n", - "\"sorrows\"\t1\n", - "\"sorry\"\t1\n", - "\"sort\"\t20\n", - "\"sorts\"\t3\n", - "\"sound\"\t4\n", - "\"sounded\"\t5\n", - "\"sounds\"\t4\n", - "\"soup\"\t18\n", - "\"sour\"\t1\n", - "\"spades\"\t1\n", - "\"speak\"\t15\n", - "\"speaker\"\t1\n", - "\"speaking\"\t5\n", - "\"spectacles\"\t3\n", - "\"speech\"\t3\n", - "\"speed\"\t1\n", - "\"spell\"\t1\n", - "\"spirited\"\t1\n", - "\"spite\"\t1\n", - "\"splash\"\t1\n", - "\"splashed\"\t1\n", - "\"splashing\"\t2\n", - "\"splendidly\"\t1\n", - "\"spoke\"\t17\n", - "\"spoken\"\t1\n", - "\"spoon\"\t2\n", - "\"spot\"\t1\n", - "\"sprawling\"\t1\n", - "\"spread\"\t3\n", - "\"spreading\"\t1\n", - "\"squeaked\"\t1\n", - "\"squeaking\"\t2\n", - "\"squeeze\"\t1\n", - "\"squeezed\"\t1\n", - "\"stairs\"\t3\n", - "\"stalk\"\t1\n", - "\"stamping\"\t2\n", - "\"stand\"\t6\n", - "\"standing\"\t1\n", - "\"star\"\t1\n", - "\"staring\"\t3\n", - "\"started\"\t2\n", - "\"startled\"\t2\n", - "\"state\"\t1\n", - "\"station\"\t1\n", - "\"stay\"\t5\n", - "\"stays\"\t1\n", - "\"steady\"\t1\n", - "\"steam\"\t1\n", - "\"sternly\"\t1\n", - "\"stick\"\t4\n", - "\"sticks\"\t1\n", - 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"\"terrier\"\t1\n", - "\"terror\"\t1\n", - "\"than\"\t24\n", - "\"thank\"\t4\n", - "\"thanked\"\t1\n", - "\"that\"\t280\n", - "\"that'll\"\t1\n", - "\"that's\"\t34\n", - "\"thatched\"\t1\n", - "\"the\"\t1642\n", - "\"their\"\t52\n", - "\"theirs\"\t1\n", - "\"them\"\t88\n", - "\"themselves\"\t3\n", - "\"then\"\t94\n", - "\"there\"\t75\n", - "\"there's\"\t24\n", - "\"therefore\"\t1\n", - "\"these\"\t14\n", - "\"they\"\t131\n", - "\"they'd\"\t4\n", - "\"they'll\"\t4\n", - "\"they're\"\t13\n", - "\"they've\"\t1\n", - "\"thick\"\t1\n", - "\"thimble\"\t3\n", - "\"thimble'\"\t1\n", - "\"thin\"\t1\n", - "\"thing\"\t49\n", - "\"things\"\t31\n", - "\"think\"\t53\n", - "\"thinking\"\t11\n", - "\"thirteen\"\t1\n", - "\"this\"\t134\n", - "\"thistle\"\t2\n", - "\"thoroughly\"\t2\n", - "\"those\"\t10\n", - "\"though\"\t13\n", - "\"thought\"\t74\n", - "\"thoughtfully\"\t4\n", - "\"thoughts\"\t2\n", - "\"thousand\"\t2\n", - "\"three\"\t28\n", - "\"threw\"\t2\n", - "\"throat\"\t2\n", - "\"throne\"\t1\n", - "\"through\"\t12\n", - "\"throw\"\t3\n", - "\"throwing\"\t2\n", - "\"thrown\"\t1\n", - "\"thump\"\t2\n", - "\"thunder\"\t1\n", - "\"thunderstorm\"\t1\n", - "\"thy\"\t1\n", - "\"tide\"\t1\n", - "\"tidy\"\t1\n", - "\"tie\"\t1\n", - "\"tied\"\t1\n", - "\"tight\"\t1\n", - "\"till\"\t21\n", - "\"tillie\"\t1\n", - "\"time\"\t71\n", - "\"times\"\t6\n", - "\"timid\"\t3\n", - "\"timidly\"\t9\n", - "\"tinkling\"\t1\n", - "\"tiny\"\t4\n", - "\"tipped\"\t1\n", - "\"tiptoe\"\t2\n", - "\"tired\"\t7\n", - "\"tittered\"\t1\n", - "\"to\"\t729\n", - "\"toast\"\t1\n", - "\"today\"\t1\n", - "\"toes\"\t3\n", - "\"toffee\"\t1\n", - "\"together\"\t9\n", - "\"told\"\t6\n", - "\"tomorrow\"\t1\n", - "\"tone\"\t40\n", - "\"tones\"\t2\n", - "\"tongue\"\t4\n", - "\"too\"\t26\n", - "\"took\"\t24\n", - "\"top\"\t7\n", - "\"tops\"\t1\n", - "\"tortoise\"\t3\n", - "\"toss\"\t1\n", - "\"tossing\"\t3\n", - "\"touch\"\t1\n", - "\"tougher\"\t1\n", - "\"towards\"\t1\n", - "\"toys\"\t1\n", - "\"trampled\"\t1\n", - "\"traps\"\t1\n", - "\"tray\"\t1\n", - "\"treacle\"\t7\n", - "\"treading\"\t2\n", - "\"treat\"\t1\n", - "\"treated\"\t1\n", - "\"tree\"\t8\n", - "\"trees\"\t7\n", - "\"tremble\"\t1\n", - "\"trembled\"\t2\n", - "\"trembling\"\t6\n", - "\"tremulous\"\t1\n", - "\"trial\"\t7\n", - "\"trial's\"\t3\n", - "\"trials\"\t1\n", - "\"trickling\"\t1\n", - "\"tricks\"\t1\n", - "\"tried\"\t19\n", - "\"trims\"\t1\n", - "\"triumphantly\"\t2\n", - "\"trot\"\t1\n", - "\"trotting\"\t2\n", - "\"trouble\"\t6\n", - "\"true\"\t4\n", - "\"trumpet\"\t3\n", - "\"trusts\"\t1\n", - "\"truth\"\t1\n", - "\"truthful\"\t1\n", - "\"try\"\t12\n", - "\"trying\"\t14\n", - "\"tucked\"\t3\n", - "\"tulip\"\t1\n", - "\"tumbled\"\t1\n", - "\"tumbling\"\t2\n", - "\"tunnel\"\t1\n", - "\"tureen\"\t1\n", - "\"turkey\"\t1\n", - "\"turn\"\t11\n", - "\"turned\"\t16\n", - "\"turning\"\t12\n", - "\"turns\"\t3\n", - "\"turtle\"\t57\n", - "\"turtle's\"\t2\n", - "\"turtles\"\t2\n", - "\"tut\"\t2\n", - "\"twelfth\"\t1\n", - "\"twelve\"\t4\n", - "\"twentieth\"\t1\n", - "\"twenty\"\t3\n", - "\"twice\"\t5\n", - "\"twinkle\"\t8\n", - "\"twinkled\"\t1\n", - "\"twinkling\"\t4\n", - "\"twist\"\t2\n", - "\"two\"\t40\n", - "\"ugh\"\t2\n", - "\"uglification\"\t2\n", - "\"uglify\"\t1\n", - "\"uglifying\"\t1\n", - "\"ugly\"\t2\n", - "\"unable\"\t1\n", - "\"uncivil\"\t1\n", - "\"uncomfortable\"\t4\n", - "\"uncomfortably\"\t1\n", - "\"uncommon\"\t1\n", - "\"uncommonly\"\t1\n", - "\"uncorked\"\t1\n", - "\"under\"\t16\n", - "\"underneath\"\t1\n", - "\"understand\"\t6\n", - "\"understood\"\t1\n", - "\"undertone\"\t2\n", - "\"undo\"\t1\n", - "\"undoing\"\t1\n", - "\"uneasily\"\t2\n", - "\"uneasy\"\t1\n", - "\"unfolded\"\t2\n", - "\"unfortunate\"\t3\n", - "\"unhappy\"\t2\n", - "\"unimportant\"\t5\n", - "\"unjust\"\t1\n", - "\"unless\"\t2\n", - "\"unlocking\"\t1\n", - "\"unpleasant\"\t2\n", - "\"unrolled\"\t2\n", - "\"until\"\t5\n", - "\"untwist\"\t1\n", - "\"unusually\"\t1\n", - "\"unwillingly\"\t1\n", - "\"up\"\t100\n", - "\"upon\"\t26\n", - "\"upright\"\t1\n", - "\"upset\"\t3\n", - "\"upsetting\"\t1\n", - "\"upstairs\"\t1\n", - "\"us\"\t14\n", - "\"use\"\t18\n", - "\"used\"\t13\n", - "\"useful\"\t2\n", - "\"using\"\t2\n", - "\"usual\"\t5\n", - "\"usually\"\t2\n", - "\"usurpation\"\t1\n", - "\"v\"\t1\n", - "\"vague\"\t1\n", - "\"vanished\"\t4\n", - "\"vanishing\"\t1\n", - "\"variations\"\t1\n", - "\"various\"\t1\n", - "\"vegetable\"\t1\n", - "\"velvet\"\t1\n", - "\"venture\"\t3\n", - "\"ventured\"\t4\n", - "\"verdict\"\t4\n", - "\"verse\"\t4\n", - "\"verses\"\t4\n", - "\"very\"\t144\n", - "\"vi\"\t1\n", - "\"vii\"\t1\n", - "\"viii\"\t1\n", - "\"vinegar\"\t1\n", - "\"violence\"\t1\n", - "\"violent\"\t2\n", - "\"violently\"\t4\n", - "\"visit\"\t1\n", - "\"voice\"\t48\n", - "\"voices\"\t2\n", - "\"vote\"\t1\n", - "\"vulgar\"\t1\n", - "\"w\"\t1\n", - "\"wag\"\t1\n", - "\"wags\"\t1\n", - "\"waist\"\t1\n", - "\"waistcoat\"\t2\n", - "\"wait\"\t1\n", - "\"waited\"\t11\n", - "\"waiting\"\t9\n", - "\"wake\"\t2\n", - "\"walk\"\t5\n", - "\"walked\"\t10\n", - "\"walking\"\t5\n", - "\"walrus\"\t1\n", - "\"wander\"\t1\n", - "\"wandered\"\t2\n", - "\"wandering\"\t2\n", - "\"want\"\t9\n", - "\"wanted\"\t4\n", - "\"wants\"\t2\n", - "\"warning\"\t1\n", - "\"was\"\t357\n", - "\"wash\"\t2\n", - "\"washing\"\t3\n", - "\"wasn't\"\t11\n", - "\"waste\"\t1\n", - "\"wasting\"\t2\n", - "\"watch\"\t8\n", - "\"watched\"\t2\n", - "\"watching\"\t3\n", - "\"water\"\t5\n", - "\"waters\"\t1\n", - "\"waving\"\t5\n", - "\"way\"\t56\n", - "\"ways\"\t1\n", - "\"we\"\t30\n", - "\"we're\"\t2\n", - "\"we've\"\t2\n", - "\"weak\"\t2\n", - "\"wearily\"\t1\n", - "\"week\"\t3\n", - "\"weeks\"\t1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\"welcome\"\t1\n", - "\"well\"\t63\n", - "\"went\"\t83\n", - "\"wept\"\t1\n", - "\"were\"\t84\n", - "\"were'\"\t1\n", - "\"weren't\"\t1\n", - "\"wet\"\t2\n", - "\"what\"\t136\n", - "\"what's\"\t5\n", - "\"whatever\"\t3\n", - "\"when\"\t79\n", - "\"whenever\"\t1\n", - "\"where\"\t13\n", - "\"where's\"\t2\n", - "\"whereupon\"\t1\n", - "\"wherever\"\t2\n", - "\"whether\"\t11\n", - "\"which\"\t49\n", - "\"while\"\t25\n", - "\"whiles\"\t1\n", - "\"whiskers\"\t3\n", - "\"whisper\"\t3\n", - "\"whispered\"\t5\n", - "\"whispers\"\t1\n", - "\"whistle\"\t1\n", - "\"whistling\"\t1\n", - "\"white\"\t30\n", - "\"whiting\"\t8\n", - "\"who\"\t61\n", - "\"who's\"\t2\n", - "\"whoever\"\t1\n", - "\"whole\"\t13\n", - "\"whom\"\t1\n", - "\"whose\"\t2\n", - "\"why\"\t40\n", - "\"wide\"\t2\n", - "\"wider\"\t1\n", - "\"wife\"\t1\n", - "\"wig\"\t2\n", - "\"wild\"\t2\n", - "\"wildly\"\t2\n", - "\"will\"\t33\n", - "\"william\"\t7\n", - "\"william's\"\t1\n", - "\"win\"\t1\n", - "\"wind\"\t2\n", - "\"window\"\t8\n", - "\"wine\"\t2\n", - "\"wings\"\t1\n", - "\"wink\"\t2\n", - "\"winter\"\t1\n", - "\"wise\"\t2\n", - "\"wish\"\t21\n", - "\"with\"\t180\n", - "\"within\"\t2\n", - "\"without\"\t26\n", - "\"witness\"\t10\n", - "\"wits\"\t1\n", - "\"woke\"\t1\n", - "\"woman\"\t2\n", - "\"won\"\t2\n", - "\"won't\"\t23\n", - "\"won't'\"\t1\n", - "\"wonder\"\t18\n", - "\"wondered\"\t1\n", - "\"wonderful\"\t2\n", - "\"wondering\"\t7\n", - "\"wonderland\"\t3\n", - "\"wood\"\t8\n", - "\"wooden\"\t1\n", - "\"word\"\t10\n", - "\"words\"\t21\n", - "\"wore\"\t1\n", - "\"work\"\t8\n", - "\"works\"\t1\n", - "\"world\"\t7\n", - "\"worm\"\t1\n", - "\"worried\"\t1\n", - "\"worry\"\t1\n", - "\"worse\"\t3\n", - "\"worth\"\t4\n", - "\"would\"\t83\n", - "\"wouldn't\"\t13\n", - "\"wow\"\t6\n", - "\"wrapping\"\t1\n", - "\"wretched\"\t2\n", - "\"wriggling\"\t1\n", - "\"write\"\t6\n", - "\"writhing\"\t1\n", - "\"writing\"\t6\n", - "\"written\"\t6\n", - "\"wrong\"\t5\n", - "\"wrote\"\t3\n", - "\"x\"\t1\n", - "\"xi\"\t1\n", - "\"xii\"\t1\n", - "\"yard\"\t1\n", - "\"yards\"\t1\n", - "\"yawned\"\t2\n", - "\"yawning\"\t2\n", - "\"ye\"\t1\n", - "\"year\"\t2\n", - "\"years\"\t1\n", - "\"yelp\"\t1\n", - "\"yer\"\t4\n", - "\"yes\"\t13\n", - "\"yesterday\"\t3\n", - "\"yet\"\t25\n", - "\"you\"\t365\n", - "\"you'd\"\t10\n", - "\"you'll\"\t6\n", - "\"you're\"\t23\n", - "\"you've\"\t7\n", - "\"young\"\t5\n", - "\"your\"\t62\n", - "\"yours\"\t3\n", - "\"yourself\"\t10\n", - "\"youth\"\t6\n", - "\"zealand\"\t1\n", - "\"zigzag\"\t1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", - "Traceback (most recent call last):\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", - " shutil.rmtree(self._local_tmp_dir)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", - " rmtree(fullname, ignore_errors, onerror)\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", - " onerror(os.remove, fullname, sys.exc_info())\n", - " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", - " os.remove(fullname)\n", - "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n" - ] - } - ], - "source": [ - "%run code/MRWordFreqCount.py data/canterbury/alice29.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting pyspark\n", - " Downloading https://files.pythonhosted.org/packages/5e/cb/d8ff49ba885e2c88b8cf2967edd84235ffa9ac301bffef657dfa5605a112/pyspark-2.3.2.tar.gz (211.9MB)\n", - "Collecting py4j==0.10.7 (from pyspark)\n", - " Downloading https://files.pythonhosted.org/packages/e3/53/c737818eb9a7dc32a7cd4f1396e787bd94200c3997c72c1dbe028587bd76/py4j-0.10.7-py2.py3-none-any.whl (197kB)\n", - "Building wheels for collected packages: pyspark\n", - " Running setup.py bdist_wheel for pyspark: started\n", - " Running setup.py bdist_wheel for pyspark: still running...\n", - " Running setup.py bdist_wheel for pyspark: finished with status 'done'\n", - " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\be\\7d\\34\\cd3cfbc75d8b6b6ae0658e5425348560b86d187fe3e53832cc\n", - "Successfully built pyspark\n", - "Installing collected packages: py4j, pyspark\n", - "Successfully installed py4j-0.10.7 pyspark-2.3.2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "distributed 1.21.8 requires msgpack, which is not installed.\n", - "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", - "You are using pip version 10.0.1, however version 18.0 is available.\n", - "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" - ] - } - ], - "source": [ - "!pip install pyspark" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way to implement this is to use SPARK on your local box: \n", - "Need to load spark and pyspark to get all this working\n", - "\n", - "\n", - " \n", - "http://spark.apache.org/downloads.html\n", - "\n", - "\n", - " pyspark: pip install pyspark\n", - " \n", - " On windows you need to follow:\n", - " https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c\n", - " \n", - " \n", - "Test code From:\n", - "https://github.com/eBay/Spark/blob/master/examples/src/main/python/wordcount.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %load code\\pysparkcount.py\n", - "#\n", - "# Licensed to the Apache Software Foundation (ASF) under one or more\n", - "# contributor license agreements. See the NOTICE file distributed with\n", - "# this work for additional information regarding copyright ownership.\n", - "# The ASF licenses this file to You under the Apache License, Version 2.0\n", - "# (the \"License\"); you may not use this file except in compliance with\n", - "# the License. You may obtain a copy of the License at\n", - "#\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License.\n", - "#\n", - "#https://github.com/apache/spark/blob/master/examples/src/main/python/wordcount.py\n", - "\n", - "from __future__ import print_function\n", - "\n", - "import sys\n", - "from operator import add\n", - "\n", - "from pyspark import SparkContext\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " if len(sys.argv) != 2:\n", - " print(\"Usage: wordcount \", file=sys.stderr)\n", - " exit(-1)\n", - " sc = SparkContext(appName=\"PythonWordCount\")\n", - " lines = sc.textFile(sys.argv[1], 1)\n", - " counts = lines.flatMap(lambda x: x.split(' ')) \\\n", - " .map(lambda x: (x, 1)) \\\n", - " .reduceByKey(add)\n", - " output = counts.collect()\n", - " for (word, count) in output:\n", - " print(\"%s: %i\" % (word, count))\n", - "\n", - " sc.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Should be able to run using\n", - "%run code/pysparkcount.py data/canterbury/alice29.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Lecture5-MapReduce/code/MRSortByInt.py b/Lectures/Lecture5-MapReduce/code/MRSortByInt.py deleted file mode 100644 index 03bf223..0000000 --- a/Lectures/Lecture5-MapReduce/code/MRSortByInt.py +++ /dev/null @@ -1,15 +0,0 @@ -from mrjob.job import MRJob - -class MRSortByInt(MRJob): - def mapper(self, _, line): - """ - """ - l = line.strip('\n').split() - yield '%01d'%int(l[1]), l[0] - - def reducer(self, key, val): - yield int(key), int(list(val)[0]) - - -if __name__ == '__main__': - MRSortByInt.run() \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/code/MRSortByString.py b/Lectures/Lecture5-MapReduce/code/MRSortByString.py deleted file mode 100644 index a76457d..0000000 --- a/Lectures/Lecture5-MapReduce/code/MRSortByString.py +++ /dev/null @@ -1,16 +0,0 @@ -from mrjob.job import MRJob - -class MRSortByString(MRJob): - def mapper(self, _, line): - """ - """ - l = line.split(' ') - print l - yield l[1], l[0] - - def reducer(self, key, val): - yield key, [v for v in val][0] - - -if __name__ == '__main__': - MRSortByString.run() \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/code/MRWordFreqCount.py b/Lectures/Lecture5-MapReduce/code/MRWordFreqCount.py deleted file mode 100644 index 58362a7..0000000 --- a/Lectures/Lecture5-MapReduce/code/MRWordFreqCount.py +++ /dev/null @@ -1,21 +0,0 @@ -from mrjob.job import MRJob -import re - -WORD_RE = re.compile(r"[\w']+") - - -class MRWordFreqCount(MRJob): - - def mapper(self, _, line): - for word in WORD_RE.findall(line): - yield word.lower(), 1 - - def combiner(self, word, counts): - yield word, sum(counts) - - def reducer(self, word, counts): - yield word, sum(counts) - - -if __name__ == '__main__': - MRWordFreqCount.run() \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/code/MRWordFrequencyCount.py b/Lectures/Lecture5-MapReduce/code/MRWordFrequencyCount.py deleted file mode 100644 index d0bb8fd..0000000 --- a/Lectures/Lecture5-MapReduce/code/MRWordFrequencyCount.py +++ /dev/null @@ -1,14 +0,0 @@ -from mrjob.job import MRJob - -class MRWordFrequencyCount(MRJob): - - def mapper(self, _ , line): - yield "chars", len(line) - yield "words", len(line.split()) - yield "lines", 1 - - def reducer(self, key, values): - yield key, sum(values) - -if __name__ == '__main__': - MRWordFrequencyCount.run() \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/code/pysparkcount.py b/Lectures/Lecture5-MapReduce/code/pysparkcount.py deleted file mode 100644 index 96d0332..0000000 --- a/Lectures/Lecture5-MapReduce/code/pysparkcount.py +++ /dev/null @@ -1,40 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -#https://github.com/apache/spark/blob/master/examples/src/main/python/wordcount.py - -from __future__ import print_function - -import sys -from operator import add - -from pyspark import SparkContext - - -if __name__ == "__main__": - if len(sys.argv) != 2: - print("Usage: wordcount ", file=sys.stderr) - exit(-1) - sc = SparkContext(appName="PythonWordCount") - lines = sc.textFile(sys.argv[1], 1) - counts = lines.flatMap(lambda x: x.split(' ')) \ - .map(lambda x: (x, 1)) \ - .reduceByKey(add) - output = counts.collect() - for (word, count) in output: - print("%s: %i" % (word, count)) - - sc.stop() \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/code/untitled.txt b/Lectures/Lecture5-MapReduce/code/untitled.txt deleted file mode 100644 index e69de29..0000000 diff --git a/Lectures/Lecture5-MapReduce/data/numbers.txt b/Lectures/Lecture5-MapReduce/data/numbers.txt deleted file mode 100644 index b4f229a..0000000 --- a/Lectures/Lecture5-MapReduce/data/numbers.txt +++ /dev/null @@ -1,11 +0,0 @@ -1 10 -2 11 -3 3 -4 12 -5 4 -6 1 -7 1 -8 41 -9 532 -10 2 -11 0 \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/data/sortdata.txt b/Lectures/Lecture5-MapReduce/data/sortdata.txt deleted file mode 100644 index 0ba6fc2..0000000 --- a/Lectures/Lecture5-MapReduce/data/sortdata.txt +++ /dev/null @@ -1,8 +0,0 @@ -1 1 -2 4 -3 8 -4 2 -4 7 -5 5 -6 10 -7 11 \ No newline at end of file diff --git a/Lectures/Lecture5-MapReduce/data/vocab.txt b/Lectures/Lecture5-MapReduce/data/vocab.txt deleted file mode 100644 index 27f64a3..0000000 --- a/Lectures/Lecture5-MapReduce/data/vocab.txt +++ /dev/null @@ -1,1899 +0,0 @@ -1 aa -2 ab -3 abil -4 abl -5 about -6 abov -7 absolut -8 abus -9 ac -10 accept -11 access -12 accord -13 account -14 achiev -15 acquir -16 across -17 act -18 action -19 activ -20 actual -21 ad -22 adam -23 add -24 addit -25 address -26 administr -27 adult -28 advanc -29 advantag -30 advertis -31 advic -32 advis -33 ae -34 af -35 affect -36 affili -37 afford -38 africa -39 after -40 ag -41 again -42 against -43 agenc -44 agent -45 ago -46 agre -47 agreement -48 aid -49 air -50 al -51 alb -52 align -53 all -54 allow -55 almost -56 alon -57 along -58 alreadi -59 alsa -60 also -61 altern -62 although -63 alwai -64 am -65 amaz -66 america -67 american -68 among -69 amount -70 amp -71 an -72 analysi -73 analyst -74 and -75 ani -76 anim -77 announc -78 annual -79 annuiti -80 anoth -81 answer -82 anti -83 anumb -84 anybodi -85 anymor -86 anyon -87 anyth -88 anywai -89 anywher -90 aol -91 ap -92 apolog -93 app -94 appar -95 appear -96 appl -97 appli -98 applic -99 appreci -100 approach -101 approv -102 apt -103 ar -104 archiv -105 area -106 aren -107 argument -108 arial -109 arm -110 around -111 arrai -112 arriv -113 art -114 articl -115 artist -116 as -117 ascii -118 ask -119 asset -120 assist -121 associ -122 assum -123 assur -124 at -125 atol -126 attach -127 attack -128 attempt -129 attent -130 attornei -131 attract -132 audio -133 aug -134 august -135 author -136 auto -137 autom -138 automat -139 avail -140 averag -141 avoid -142 awai -143 awar -144 award -145 ba -146 babi -147 back -148 background -149 backup -150 bad -151 balanc -152 ban -153 bank -154 bar -155 base -156 basenumb -157 basi -158 basic -159 bb -160 bc -161 bd -162 be -163 beat -164 beberg -165 becaus -166 becom -167 been -168 befor -169 begin -170 behalf -171 behavior -172 behind -173 believ -174 below -175 benefit -176 best -177 beta -178 better -179 between -180 bf -181 big -182 bill -183 billion -184 bin -185 binari -186 bit -187 black -188 blank -189 block -190 blog -191 blood -192 blue -193 bnumber -194 board -195 bodi -196 boi -197 bonu -198 book -199 boot -200 border -201 boss -202 boston -203 botan -204 both -205 bottl -206 bottom -207 boundari -208 box -209 brain -210 brand -211 break -212 brian -213 bring -214 broadcast -215 broker -216 browser -217 bug -218 bui -219 build -220 built -221 bulk -222 burn -223 bush -224 busi -225 but -226 button -227 by -228 byte -229 ca -230 cabl -231 cach -232 calcul -233 california -234 call -235 came -236 camera -237 campaign -238 can -239 canada -240 cannot -241 canon -242 capabl -243 capillari -244 capit -245 car -246 card -247 care -248 career -249 carri -250 cartridg -251 case -252 cash -253 cat -254 catch -255 categori -256 caus -257 cb -258 cc -259 cd -260 ce -261 cell -262 cent -263 center -264 central -265 centuri -266 ceo -267 certain -268 certainli -269 cf -270 challeng -271 chanc -272 chang -273 channel -274 char -275 charact -276 charg -277 charset 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-1878 write -1879 written -1880 wrong -1881 wrote -1882 www -1883 ximian -1884 xml -1885 xp -1886 yahoo -1887 ye -1888 yeah -1889 year -1890 yesterdai -1891 yet -1892 york -1893 you -1894 young -1895 your -1896 yourself -1897 zdnet -1898 zero -1899 zip From 277c3f45b3acf1f55d489fd18d8d8799057c9ee6 Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 7 Oct 2018 10:24:31 -0400 Subject: [PATCH 07/10] Reorganizing name spaces --- .../Homework2/Homework2-python_tutorials | 34 + .../Statistics and Probablity Homework.ipynb | 131 + .../Homework7/Homework 6 - Streams.ipynb | 63 + .../Lecture 2 continued.ipynb | 368 + ...2-Introduction-to-Python-Programming.ipynb | 4094 ++++++++++ .../mymodule.py | 29 + ...INKS to lectures on NUMPY and PANDAS.ipynb | 43 + .../NUMPY Tutorial-Copy1.ipynb | 241 + .../Data_Visualization.ipynb | 1536 ++++ .../Links to Lecture4.ipynb | 39 + .../Data Visualization.ipynb | 303 + .../data/baseball.csv | 101 + .../data/cdystonia.csv | 632 ++ .../data/titanic.xls | Bin 0 -> 289792 bytes .../normalvars.png | Bin 0 -> 39074 bytes .../Week 5 - MapReduce - MRJob/.mrjob.conf | 11 + .../Week 5 - MapReduce - MRJob/MR_SORT.ipynb | 399 + .../MapReduce Intro.ipynb | 6623 +++++++++++++++++ .../code/MRSortByInt.py | 15 + .../code/MRSortByString.py | 16 + .../code/MRWordFreqCount.py | 21 + .../code/MRWordFrequencyCount.py | 14 + .../code/pysparkcount.py | 40 + .../code/untitled.txt | 0 .../data/canterbury/alice29.txt | 1 + .../data/numbers.txt | 11 + .../data/sortdata.txt | 8 + .../Week 5 - MapReduce - MRJob/data/vocab.txt | 1899 +++++ .../Linear Algebra Tutorial.ipynb | 522 ++ .../Probabilty Tutorial.ipynb | 198 + .../Statistics.ipynb | 223 + .../code/linear_algebra.py | 126 + 32 files changed, 17741 insertions(+) create mode 100644 Homeworks/Homework2/Homework2-python_tutorials create mode 100644 Homeworks/Homework6/Statistics and Probablity Homework.ipynb create mode 100644 Homeworks/Homework7/Homework 6 - Streams.ipynb create mode 100644 Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture 2 continued.ipynb create mode 100644 Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture-2-Introduction-to-Python-Programming.ipynb create mode 100644 Lectures/Week 2 - Python and Jupyter for Big-Data/mymodule.py create mode 100644 Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb create mode 100644 Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/Data_Visualization.ipynb create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/Links to Lecture4.ipynb create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/UnderConstruction/Data Visualization.ipynb create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/data/baseball.csv create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/data/cdystonia.csv create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/data/titanic.xls create mode 100644 Lectures/Week 4 - Data Visualization - Matplotlib/normalvars.png create mode 100644 Lectures/Week 5 - MapReduce - MRJob/.mrjob.conf create mode 100644 Lectures/Week 5 - MapReduce - MRJob/MR_SORT.ipynb create mode 100644 Lectures/Week 5 - MapReduce - MRJob/MapReduce Intro.ipynb create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/MRSortByInt.py create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/MRSortByString.py create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/MRWordFreqCount.py create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/MRWordFrequencyCount.py create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/pysparkcount.py create mode 100644 Lectures/Week 5 - MapReduce - MRJob/code/untitled.txt create mode 100644 Lectures/Week 5 - MapReduce - MRJob/data/canterbury/alice29.txt create mode 100644 Lectures/Week 5 - MapReduce - MRJob/data/numbers.txt create mode 100644 Lectures/Week 5 - MapReduce - MRJob/data/sortdata.txt create mode 100644 Lectures/Week 5 - MapReduce - MRJob/data/vocab.txt create mode 100644 Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb create mode 100644 Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb create mode 100644 Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Statistics.ipynb create mode 100644 Lectures/Week 6 - Linear Algebra, Statistics, and Probability/code/linear_algebra.py diff --git a/Homeworks/Homework2/Homework2-python_tutorials b/Homeworks/Homework2/Homework2-python_tutorials new file mode 100644 index 0000000..60ecc96 --- /dev/null +++ b/Homeworks/Homework2/Homework2-python_tutorials @@ -0,0 +1,34 @@ +Make jupyter python 2 notebook pages that teaches: + +A. The difference between python lists and tuples +Lists are collections of data … more often homogenous or of more limited ranges of types. +The notion of tuple as a “record type” … sort of a lightweight object with attributes but no methods +The difference between list and tuple is that lists are mutable and tuples are immutable. + +Show how to use them in: +1) if statements +2) for loops +3) lambdas +be sure to demonstrate mutablity or immuability and issues that can be created + +B. Use of dictionaries + +1) creation of dict +2) access, modify, and delete dict items +3) merge dictionaires +4) loop over + + i. keys + ii. values + iii. Items + +For more than 3 points you can add meaningful sections to the two pages +and create more pages on other interesting aspects of basic python, for example random functions, regexp, map/reduce, file manipulations... +Be sure to name your file +LASTNAME_HOMEWORK2_LIST_AND_TUPLES_TUTORIAL_Fall_2018.ipynb +LASTNAME_HOMEWORK2_DICTIONARY_TUTORIAL_Fall_2018.ipynb +LASTNAME_HOMEWORK2_OTHER_TUTORIAL_Fall_2018.ipynb + +You can upload the files or the zip of all the files. +Or put them in your homework directory in your big-data-python-class +github repo and send me the url in the submissions or both. diff --git a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb new file mode 100644 index 0000000..7ae361c --- /dev/null +++ b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HW 6.1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 6.1.1\n", + "Write a function, flip_sum, which generates $n$ random coin flips from a fair coin and then returns the number of heads. \n", + "\n", + "A fair coin is defined to be a coin where $P($heads$)=\\frac{1}{2}$\n", + "\n", + "The output type should be a numpy integer, hint: use random.rand()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1.2 Test it \n", + "Check it by showing the results of 100 coins being flipped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1.3 Create and display a histogram of 200 experiments of flipping 5 coins." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.2\n", + "\n", + "Write a function, estimate_prob, that uses flip_sum to estimate the following probability:\n", + "\n", + "$P( $k_1$ <=$number of heads in $n$ flips$ < $k_2$ ) $\n", + "\n", + "The function should estimate the probability by running $m$ different trials of flip_sum(n), probably using a for loop.\n", + "\n", + "In order to receive full credit estimate_prob call flip_sum (aka: flip_sum is located inside the estimate_prob function)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "https://github.com/VSerpak/DSE210x-Statistics-and-Probability-in-Data-Science-using-Python/blob/master/Week%201%20Introduction%20to%20Statistics%20and%20Probability/1.8%20HW_1.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Homeworks/Homework7/Homework 6 - Streams.ipynb b/Homeworks/Homework7/Homework 6 - Streams.ipynb new file mode 100644 index 0000000..33ad4a7 --- /dev/null +++ b/Homeworks/Homework7/Homework 6 - Streams.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Homework 5\n", + "\n", + "Copy this notebook. Rename it as: YOURNAME-HW5-streams \n", + "\n", + "with your name replacing YOURNAME.\n", + "\n", + "Upload your completed jupyter notebook to elearning site as your homework submission. You can put this notebook on your github.\n", + "\n", + "5.1 Register for a stream of Twitter data\n", + "\n", + "5.2 Create a bloom filter classifying two days worth of twitters ( after removing stop words and urls )\n", + "\n", + "5.3 For another days worth of twitter data find the previous twitters that match in the bloom filter\n", + "(This means get two days of data in one file or directory , use that data for training the bloom filter, capture a different days data in a different file ( or do it in real time)and capture the match output then running the new twitter data through the filter.\n", + "\n", + "5.4 Plot a historgram of matches for each twitter in 5.3\n", + "\n", + "For the 4-5 grade.- Submit in a separate notebook - YourNAME-Homework5-Supplement\n", + "\n", + "1. Use a different machine learning training algorithm\n", + "2. Make a continous feed where you take two days of data and match the incoming stream ( do this for 5 days windowing the filter data)\n", + "3. Find new trends in the twitter feed (daily or hourly)\n", + "4. Or some other streaming exploration of your choosing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture 2 continued.ipynb b/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture 2 continued.ipynb new file mode 100644 index 0000000..914a480 --- /dev/null +++ b/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture 2 continued.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 2 continued" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### regular expressions\n", + "Provides a way to search text.\n", + "Looking for matching patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "import re\n", + "print all([\n", + " not re.match(\"a\",\"cat\"),\n", + " re.search(\"a\",\"cat\"),\n", + " not re.search(\"c\",\"dog\"),\n", + " 3 == len(re.split(\"[ab]\",\"carbs\")),\n", + " \"R-D-\" == re.sub(\"[0-9]\",\"-\",\"R2D2\")\n", + "]) # prints true if all are true" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Object-oriented programming\n", + "Creating classes of objects with data and methods(functions) that operate on the data." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "class Set:\n", + " def __init__(self, values=None):\n", + " \"\"\"This is the constructor\"\"\"\n", + " self.dict={}\n", + " if values is not None:\n", + " for value in values:\n", + " self.add(value)\n", + " def __repr__(self):\n", + " \"\"\"Sting to represent object in class like a to_string function\"\"\"\n", + " return \"set:\"+str(self.dict.keys())\n", + " def add(self, value):\n", + " self.dict[value] = True\n", + " def contains(self, value):\n", + " return value in self.dict \n", + " \n", + " def remove(self,value):\n", + " del self.dict[value]\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "#using the class\n", + "s = Set([1,2,3])\n", + "s.add(4)\n", + "s.remove(3)\n", + "print s.contains(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set:[1, 2, 4]\n" + ] + } + ], + "source": [ + "print s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functional Tools\n", + "sometimes you want to change behavior based on the passed value types" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "def exp(base,power):\n", + " return base**power\n", + "\n", + "#exp(2,power)\n", + "def two_to_the(power):\n", + " return exp(2,power)\n", + "\n", + "print( two_to_the(3) )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 10]\n" + ] + } + ], + "source": [ + "def multiply(x,y): return x*y\n", + "\n", + "products = map(multiply,[1,2],[4,5])\n", + "print products" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### enumerate\n", + "enumerates creates a tuple (index,element)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index:0 Item:a\n", + "index:1 Item:b\n" + ] + } + ], + "source": [ + "#example use\n", + "documents = [\"a\",\"b\"]\n", + "def do_something(index,item):\n", + " print \"index:\"+str(index)+\" Item:\"+item\n", + " \n", + "for i, document in enumerate(documents):\n", + " do_something(i,document)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index:0\n", + "index:1\n" + ] + } + ], + "source": [ + "def do_something(index ):\n", + " print \"index:\"+str(index) \n", + " \n", + "for i, _ in enumerate(documents): do_something(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zip and argument unpacking\n", + "zip forms two more more lists for other lists" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('a', 1), ('b', 2), ('c', 3)]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list1 = ['a','b','c']\n", + "list2 = [1,2,3]\n", + "zip(list1,list2)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 'b', 'c')\n", + "(1, 2, 3)\n" + ] + } + ], + "source": [ + "pairs = [('a', 1), ('b', 2), ('c', 3)]\n", + "letters, numbers = zip(*pairs) # * performs argument unpacking\n", + "print letters\n", + "print numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### args and kwargs\n", + "higher order function support - functions on functions" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "def doubler(f):\n", + " def g(x):\n", + " return 2 * f(x)\n", + " return g\n", + "\n", + "def f1(x):\n", + " return x+1\n", + "\n", + "g= doubler(f1)\n", + "\n", + "print g(3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unamed args (1, 2)\n", + "Key word args {'key2': 'word2', 'key1': 'word1'}\n" + ] + } + ], + "source": [ + "def magic(*args, **kwargs):\n", + " print \"unamed args\", args\n", + " print \"Key word args\", kwargs\n", + " \n", + "magic(1,2,key1=\"word1\",key2=\"word2\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture-2-Introduction-to-Python-Programming.ipynb b/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture-2-Introduction-to-Python-Programming.ipynb new file mode 100644 index 0000000..ec59145 --- /dev/null +++ b/Lectures/Week 2 - Python and Jupyter for Big-Data/Lecture-2-Introduction-to-Python-Programming.ipynb @@ -0,0 +1,4094 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Python programming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This crash course on python is take from two souces:\n", + "\n", + "[http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", + "and\n", + "Chapter 2 of the Datascience from scratch: First principles with python\n", + "Code from https://github.com/joelgrus/data-science-from-scratch\n", + "An official tutorial on ipyhthon: http://ipython.org/ipython-doc/2/interactive/tutorial.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python program files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Python code is usually stored in text files with the file ending \"`.py`\":\n", + "\n", + " myprogram.py\n", + "\n", + "* Every line in a Python program file is assumed to be a Python statement, or part thereof. \n", + "\n", + " * The only exception is comment lines, which start with the character `#` (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.\n", + "\n", + "\n", + "* To run our Python program from the command line we use:\n", + "\n", + " $ python myprogram.py\n", + "\n", + "* On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is concerned):\n", + "\n", + " #!/usr/bin/env python\n", + "\n", + " If we do, and if we additionally set the file script to be executable, we can run the program like this:\n", + "\n", + " $ myprogram.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example:\n", + "a. use some command line functions:\n", + "\n", + "in Windows:\n", + "\n", + "ls ..\\Scripts\\hello-world.py \n", + "\n", + "Mac or linux:\n", + "\n", + "ls ../Scripts/hello-world.py" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Volume in drive C is OS\n", + " Volume Serial Number is 7417-6934\n", + "\n", + " Directory of C:\\Users\\PS\\git\\big-data-python-class-2018\\Scripts\n", + "\n", + "09/05/2018 10:46 PM 19 hello-world.py\n", + "09/05/2018 10:46 PM 72 hello-world-in-swedish.py\n", + " 2 File(s) 91 bytes\n", + " 0 Dir(s) 321,133,596,672 bytes free\n" + ] + } + ], + "source": [ + "ls ..\\..\\Scripts\\hello-world*.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### built in magic commands start with #\n", + "\n", + "A good list of the commands are found in:\n", + "https://ipython.org/ipython-doc/3/interactive/magics.html" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "print \"hello world\"" + ] + } + ], + "source": [ + "%%sh \n", + "cat ../../Scripts/hello-world.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n" + ] + } + ], + "source": [ + "!python ..\\..\\Scripts\\hello-world.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Character encoding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The standard character encoding is ASCII, but we can use any other encoding, for example UTF-8. To specify that UTF-8 is used we include the special line\n", + "\n", + " # -*- coding: UTF-8 -*-\n", + "\n", + "at the top of the file." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#!/usr/bin/env python\r\n", + "# -*- coding: UTF-8 -*-\r\n", + "\r\n", + "print(\"Hej världen!\")" + ] + } + ], + "source": [ + "%%sh\n", + "cat ../../Scripts/hello-world-in-swedish.py" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hej världen!\n" + ] + } + ], + "source": [ + "!python ../../Scripts/hello-world-in-swedish.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other than these two *optional* lines in the beginning of a Python code file, no additional code is required for initializing a program. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jupyter notebooks ( or old ipython notebooks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This file - an IPython notebook - does not follow the standard pattern with Python code in a text file. Instead, an IPython notebook is stored as a file in the [JSON](http://en.wikipedia.org/wiki/JSON) format. The advantage is that we can mix formatted text, Python code and code output. It requires the IPython notebook server to run it though, and therefore isn't a stand-alone Python program as described above. Other than that, there is no difference between the Python code that goes into a program file or an IPython notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most of the functionality in Python is provided by *modules*. The Python Standard Library is a large collection of modules that provides *cross-platform* implementations of common facilities such as access to the operating system, file I/O, string management, network communication, and much more." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " * The Python Language Reference: http://docs.python.org/2/reference/index.html\n", + " * The Python Standard Library: http://docs.python.org/2/library/\n", + "\n", + "To use a module in a Python program it first has to be imported. A module can be imported using the `import` statement. For example, to import the module `math`, which contains many standard mathematical functions, we can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This includes the whole module and makes it available for use later in the program. For example, we can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "x = math.cos(2 * math.pi)\n", + "\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix \"`math.`\" every time we use something from the `math` module:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "from math import *\n", + "\n", + "x = cos(2 * pi)\n", + "\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the `import math` pattern. This would elminate potentially confusing problems with name space collisions.\n", + "\n", + "As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character `*`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "from math import cos, pi\n", + "\n", + "x = cos(2 * pi)\n", + "\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Looking at what a module contains, and its documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once a module is imported, we can list the symbols it provides using the `dir` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" + ] + } + ], + "source": [ + "import math\n", + "\n", + "print(dir(math))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And using the function `help` we can get a description of each function (almost .. not all functions have docstrings, as they are technically called, but the vast majority of functions are documented this way). " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function log in module math:\n", + "\n", + "log(...)\n", + " log(x[, base])\n", + " \n", + " Return the logarithm of x to the given base.\n", + " If the base not specified, returns the natural logarithm (base e) of x.\n", + "\n" + ] + } + ], + "source": [ + "help(math.log)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.302585092994046" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3219280948873626" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log(10, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the `help` function directly on modules: Try\n", + "\n", + " help(math) \n", + "\n", + "Some very useful modules form the Python standard library are `os`, `sys`, `math`, `shutil`, `re`, `subprocess`, `multiprocessing`, `threading`. \n", + "\n", + "A complete lists of standard modules for Python 2 and Python 3 are available at http://docs.python.org/2/library/ and http://docs.python.org/3/library/, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Whitespacing formatting\n", + "Python uses indents and not bracing to delimit blocks of code\n", + "Makes code readable but it means you must be careful about your formatting" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "done looping\n" + ] + } + ], + "source": [ + "for i in [1,2,3,4]:\n", + " print i\n", + "print \"done looping\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "White spacing is ignored inside parenteses and brackets:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "108" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "long_winded_computation = (1+2+ 3 + 4 + 5+ 6\n", + " + 7 + 8 + 9 + 10 + 11+\n", + " \n", + " 13 + 14 + 15)\n", + "long_winded_computation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So use that to make you code easier to read" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "list_of_lists = [[1,2,3],[4,5,6],[7,8,9]]\n", + "easier_to_read_list_of_lists = [[1,2,3],\n", + " [4,5,6],\n", + " [7,8,9]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This may make it hard to cut and paste code since the indentation may have to adjusted to the block" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variables and types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Symbol names " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Variable names in Python can contain alphanumerical characters `a-z`, `A-Z`, `0-9` and some special characters such as `_`. Normal variable names must start with a letter. \n", + "\n", + "By convention, variable names start with a lower-case letter, and Class names start with a capital letter. \n", + "\n", + "In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:\n", + "\n", + " and, as, assert, break, class, continue, def, del, elif, else, except, \n", + " exec, finally, for, from, global, if, import, in, is, lambda, not, or,\n", + " pass, print, raise, return, try, while, with, yield\n", + "\n", + "Note: Be aware of the keyword `lambda`, which could easily be a natural variable name in a scientific program. But being a keyword, it cannot be used as a variable name." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions\n", + "A function is a rule for taking zero or more inputes and return a corresponding output\n", + "Functions are first-class which means we can assign them to variables and pass them into functions just like any aguement" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def double(x):\n", + " \"\"\"this is where you put in the docstring that explains what the fuciton does:\n", + " this function multiplies its input by \"\"\"\n", + " return x * 2\n", + "\n", + "def apply_to_one(f):\n", + " \"\"\"calls the fuction f with 1 as its argument\"\"\"\n", + " return f(1)\n", + "\n", + "apply_to_one(double)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can create short anonymous fuctions or lambdas or even assign lambdas to variables but better to use a def" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apply_to_one(lambda x: x + 4 )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "another_double = lambda x: 2 * x\n", + "def yet_another_double(x): return 2 * x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "The assignment operator in Python is `=`. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.\n", + "\n", + "Assigning a value to a new variable creates the variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# variable assignments\n", + "x = 1.0\n", + "my_variable = 12.2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value that was assigned to it." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we assign a new value to a variable, its type can change." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "x = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we try to use a variable that has not yet been defined we get an `NameError`:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'y' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fundamental types" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integers\n", + "x = 1\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# float\n", + "x = 1.0\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bool" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# boolean\n", + "b1 = True\n", + "b2 = False\n", + "\n", + "type(b1)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "complex" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# complex numbers: note the use of `j` to specify the imaginary part\n", + "x = 1.0 - 1.0j\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1-1j)\n" + ] + } + ], + "source": [ + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1.0, -1.0)\n" + ] + } + ], + "source": [ + "print(x.real, x.imag)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Type utility functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The module `types` contains a number of type name definitions that can be used to test if variables are of certain types:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BooleanType', 'BufferType', 'BuiltinFunctionType', 'BuiltinMethodType', 'ClassType', 'CodeType', 'ComplexType', 'DictProxyType', 'DictType', 'DictionaryType', 'EllipsisType', 'FileType', 'FloatType', 'FrameType', 'FunctionType', 'GeneratorType', 'GetSetDescriptorType', 'InstanceType', 'IntType', 'LambdaType', 'ListType', 'LongType', 'MemberDescriptorType', 'MethodType', 'ModuleType', 'NoneType', 'NotImplementedType', 'ObjectType', 'SliceType', 'StringType', 'StringTypes', 'TracebackType', 'TupleType', 'TypeType', 'UnboundMethodType', 'UnicodeType', 'XRangeType', '__all__', '__builtins__', '__doc__', '__file__', '__name__', '__package__']\n" + ] + } + ], + "source": [ + "import types\n", + "\n", + "# print all types defined in the `types` module\n", + "print(dir(types))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = 1.0\n", + "\n", + "# check if the variable x is a float\n", + "type(x) is float" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check if the variable x is an int\n", + "type(x) is int" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the `isinstance` method for testing types of variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "isinstance(x, float)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Type casting" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1.5, )\n" + ] + } + ], + "source": [ + "x = 1.5\n", + "\n", + "print(x, type(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, )\n" + ] + } + ], + "source": [ + "x = int(x)\n", + "\n", + "print(x, type(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((1+0j), )\n" + ] + } + ], + "source": [ + "z = complex(x)\n", + "\n", + "print(z, type(z))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "can't convert complex to float", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: can't convert complex to float" + ] + } + ], + "source": [ + "x = float(z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Complex variables cannot be cast to floats or integers. We need to use `z.real` or `z.imag` to extract the part of the complex number we want:" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1.0, ' -> ', True, )\n", + "(0.0, ' -> ', False, )\n" + ] + } + ], + "source": [ + "y = bool(z.real)\n", + "\n", + "print(z.real, \" -> \", y, type(y))\n", + "\n", + "y = bool(z.imag)\n", + "\n", + "print(z.imag, \" -> \", y, type(y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operators and comparisons" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most operators and comparisons in Python work as one would expect:\n", + "\n", + "* Arithmetic operators `+`, `-`, `*`, `/`, `//` (integer division), '**' power\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, -1, 2, 0)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 + 2, 1 - 2, 1 * 2, 1 / 2" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3.0, -1.0, 2.0, 0.5)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1.0 + 2.0, 1.0 - 2.0, 1.0 * 2.0, 1.0 / 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Integer division of float numbers\n", + "3.0 // 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note! The power operators in python isn't ^, but **\n", + "2 ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: The `/` operator always performs a floating point division in Python 3.x.\n", + "This is not true in Python 2.x, where the result of `/` is always an integer if the operands are integers.\n", + "to be more specific, `1/2 = 0.5` (`float`) in Python 3.x, and `1/2 = 0` (`int`) in Python 2.x (but `1.0/2 = 0.5` in Python 2.x)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The boolean operators are spelled out as the words `and`, `not`, `or`. " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True and False" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "not False" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True or False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Comparison operators `>`, `<`, `>=` (greater or equal), `<=` (less or equal), `==` equality, `is` identical." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 > 1, 2 < 1" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(False, False)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 > 2, 2 < 2" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, True)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 >= 2, 2 <= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# equality\n", + "[1,2] == [1,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# objects identical?\n", + "l1 = l2 = [1,2]\n", + "\n", + "l1 is l2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compound types: Strings, List and dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Strings are the variable type that is used for storing text messages. " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = \"Hello world\"\n", + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# length of the string: the number of characters\n", + "len(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello test\n" + ] + } + ], + "source": [ + "# replace a substring in a string with somethign else\n", + "s2 = s.replace(\"world\", \"test\")\n", + "print(s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can index a character in a string using `[]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'H'" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Heads up MATLAB users:** Indexing start at 0!\n", + "\n", + "We can extract a part of a string using the syntax `[start:stop]`, which extracts characters between index `start` and `stop` -1 (the character at index `stop` is not included):" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello'" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'o'" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[4:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we omit either (or both) of `start` or `stop` from `[start:stop]`, the default is the beginning and the end of the string, respectively:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello'" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'world'" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[6:]" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also define the step size using the syntax `[start:end:step]` (the default value for `step` is 1, as we saw above):" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[::1]" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hlowrd'" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This technique is called *slicing*. Read more about the syntax here: http://docs.python.org/release/2.7.3/library/functions.html?highlight=slice#slice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python has a very rich set of functions for text processing. See for example http://docs.python.org/2/library/string.html for more information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### String formatting examples" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('str1', 'str2', 'str3')\n" + ] + } + ], + "source": [ + "print(\"str1\", \"str2\", \"str3\") # The print statement concatenates strings with a space" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('str1', 1.0, False, -1j)\n" + ] + } + ], + "source": [ + "print(\"str1\", 1.0, False, -1j) # The print statements converts all arguments to strings" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "str1str2str3\n" + ] + } + ], + "source": [ + "print(\"str1\" + \"str2\" + \"str3\") # strings added with + are concatenated without space" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value = 1.000000\n" + ] + } + ], + "source": [ + "print(\"value = %f\" % 1.0) # we can use C-style string formatting" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value1 = 3.14. value2 = 1\n" + ] + } + ], + "source": [ + "# this formatting creates a string\n", + "s2 = \"value1 = %.2f. value2 = %d\" % (3.1415, 1.5)\n", + "\n", + "print(s2)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value1 = 3.1415, value2 = 1.5\n" + ] + } + ], + "source": [ + "# alternative, more intuitive way of formatting a string \n", + "s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n", + "\n", + "print(s3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists are very similar to strings, except that each element can be of any type.\n", + "Other languages call these arrays, simply an ordered collection of things.\n", + "\n", + "The syntax for creating lists in Python is `[...]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "l = [1,2,3,4]\n", + "\n", + "print(type(l))\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the same slicing techniques to manipulate lists as we could use on strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4]\n", + "[2, 3]\n", + "[1, 3]\n" + ] + } + ], + "source": [ + "print(l)\n", + "\n", + "print(l[1:3])\n", + "\n", + "print(l[::2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Heads up MATLAB users:** Indexing starts at 0!" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elements in a list do not all have to be of the same type:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'a', 1.0, (1-1j)]\n" + ] + } + ], + "source": [ + "l = [1, 'a', 1.0, 1-1j]\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python lists can be inhomogeneous and arbitrarily nested:" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, [2, [3, [4, [5]]]]]" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nested_list = [1, [2, [3, [4, [5]]]]]\n", + "\n", + "nested_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example the `range` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "start = 10\n", + "stop = 30\n", + "step = 2\n", + "\n", + "range(start, stop, step)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# in python 3 range generates an interator, which can be converted to a list using 'list(...)'.\n", + "# It has no effect in python 2\n", + "list(range(start, stop, step))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(range(-10, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert a string to a list by type casting:\n", + "s2 = list(s)\n", + "\n", + "s2" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']\n" + ] + } + ], + "source": [ + "# sorting lists\n", + "s2.sort()\n", + "\n", + "print(s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adding, inserting, modifying, and removing elements from lists" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'd', 'd']\n" + ] + } + ], + "source": [ + "# create a new empty list\n", + "l = []\n", + "\n", + "# add an elements using `append`\n", + "l.append(\"A\")\n", + "l.append(\"d\")\n", + "l.append(\"d\")\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can modify lists by assigning new values to elements in the list. In technical jargon, lists are *mutable*." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'p', 'p']\n" + ] + } + ], + "source": [ + "l[1] = \"p\"\n", + "l[2] = \"p\"\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'd', 'd']\n" + ] + } + ], + "source": [ + "l[1:3] = [\"d\", \"d\"]\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert an element at an specific index using `insert`" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't', 'A', 'd', 'd']\n" + ] + } + ], + "source": [ + "l.insert(0, \"i\")\n", + "l.insert(1, \"n\")\n", + "l.insert(2, \"s\")\n", + "l.insert(3, \"e\")\n", + "l.insert(4, \"r\")\n", + "l.insert(5, \"t\")\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove first element with specific value using 'remove'" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't', 'd', 'd']\n" + ] + } + ], + "source": [ + "l.remove(\"A\")\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove an element at a specific location using `del`:" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't']\n" + ] + } + ], + "source": [ + "del l[7]\n", + "del l[6]\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See `help(list)` for more details, or read the online documentation " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tuples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tuples are like lists, except that they cannot be modified once created, that is they are *immutable*. \n", + "\n", + "In Python, tuples are created using the syntax `(..., ..., ...)`, or even `..., ...`:" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((10, 20), )\n" + ] + } + ], + "source": [ + "point = (10, 20)\n", + "\n", + "print(point, type(point))" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((10, 20), )\n" + ] + } + ], + "source": [ + "point = 10, 20\n", + "\n", + "print(point, type(point))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can unpack a tuple by assigning it to a comma-separated list of variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('x =', 10)\n", + "('y =', 20)\n" + ] + } + ], + "source": [ + "x, y = point\n", + "\n", + "print(\"x =\", x)\n", + "print(\"y =\", y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we try to assign a new value to an element in a tuple we get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpoint\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "source": [ + "point[0] = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is `{key1 : value1, ...}`:" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}\n" + ] + } + ], + "source": [ + "params = {\"parameter1\" : 1.0,\n", + " \"parameter2\" : 2.0,\n", + " \"parameter3\" : 3.0,}\n", + "\n", + "print(type(params))\n", + "print(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter1 = 1.0\n", + "parameter2 = 2.0\n", + "parameter3 = 3.0\n" + ] + } + ], + "source": [ + "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", + "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", + "print(\"parameter3 = \" + str(params[\"parameter3\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter1 = A\n", + "parameter2 = B\n", + "parameter3 = 3.0\n", + "parameter4 = D\n" + ] + } + ], + "source": [ + "params[\"parameter1\"] = \"A\"\n", + "params[\"parameter2\"] = \"B\"\n", + "\n", + "# add a new entry\n", + "params[\"parameter4\"] = \"D\"\n", + "\n", + "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", + "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", + "print(\"parameter3 = \" + str(params[\"parameter3\"]))\n", + "print(\"parameter4 = \" + str(params[\"parameter4\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Control Flow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conditional statements: if, elif, else" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Python syntax for conditional execution of code uses the keywords `if`, `elif` (else if), `else`:" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "statement1 and statement2 are False\n" + ] + } + ], + "source": [ + "statement1 = False\n", + "statement2 = False\n", + "\n", + "if statement1:\n", + " print(\"statement1 is True\")\n", + " \n", + "elif statement2:\n", + " print(\"statement2 is True\")\n", + " \n", + "else:\n", + " print(\"statement1 and statement2 are False\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level. \n", + "\n", + "Compare to the equivalent C code:\n", + "\n", + " if (statement1)\n", + " {\n", + " printf(\"statement1 is True\\n\");\n", + " }\n", + " else if (statement2)\n", + " {\n", + " printf(\"statement2 is True\\n\");\n", + " }\n", + " else\n", + " {\n", + " printf(\"statement1 and statement2 are False\\n\");\n", + " }\n", + "\n", + "In C blocks are defined by the enclosing curly brakets `{` and `}`. And the level of indentation (white space before the code statements) does not matter (completely optional). \n", + "\n", + "But in Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "both statement1 and statement2 are True\n" + ] + } + ], + "source": [ + "statement1 = statement2 = True\n", + "\n", + "if statement1:\n", + " if statement2:\n", + " print(\"both statement1 and statement2 are True\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "expected an indented block (, line 4)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m print(\"both statement1 and statement2 are True\") # this line is not properly indented\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n" + ] + } + ], + "source": [ + "# Bad indentation!\n", + "if statement1:\n", + " if statement2:\n", + " print(\"both statement1 and statement2 are True\") # this line is not properly indented" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "statement1 = False \n", + "\n", + "if statement1:\n", + " print(\"printed if statement1 is True\")\n", + " \n", + " print(\"still inside the if block\")" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "now outside the if block\n" + ] + } + ], + "source": [ + "if statement1:\n", + " print(\"printed if statement1 is True\")\n", + " \n", + "print(\"now outside the if block\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, loops can be programmed in a number of different ways. The most common is the `for` loop, which is used together with iterable objects, such as lists. The basic syntax is:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **`for` loops**:" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n" + ] + } + ], + "source": [ + "for x in [1,2,3]:\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `for` loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the `for` loop. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n" + ] + } + ], + "source": [ + "for x in range(4): # by default range start at 0\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: `range(4)` does not include 4 !" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3\n", + "-2\n", + "-1\n", + "0\n", + "1\n", + "2\n" + ] + } + ], + "source": [ + "for x in range(-3,3):\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scientific\n", + "computing\n", + "with\n", + "python\n" + ] + } + ], + "source": [ + "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n", + " print(word)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To iterate over key-value pairs of a dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter4 = D\n", + "parameter1 = A\n", + "parameter3 = 3.0\n", + "parameter2 = B\n" + ] + } + ], + "source": [ + "for key, value in params.items():\n", + " print(key + \" = \" + str(value))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the `enumerate` function for this:" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, -3)\n", + "(1, -2)\n", + "(2, -1)\n", + "(3, 0)\n", + "(4, 1)\n", + "(5, 2)\n" + ] + } + ], + "source": [ + "for idx, x in enumerate(range(-3,3)):\n", + " print(idx, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List comprehensions: Creating lists using `for` loops:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A convenient and compact way to initialize lists:" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 4, 9, 16]\n" + ] + } + ], + "source": [ + "l1 = [x**2 for x in range(0,5)]\n", + "\n", + "print(l1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `while` loops:" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "done\n" + ] + } + ], + "source": [ + "i = 0\n", + "\n", + "while i < 5:\n", + " print(i)\n", + " \n", + " i = i + 1\n", + " \n", + "print(\"done\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `print(\"done\")` statement is not part of the `while` loop body because of the difference in indentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A function in Python is defined using the keyword `def`, followed by a function name, a signature within parentheses `()`, and a colon `:`. The following code, with one additional level of indentation, is the function body." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def func0(): \n", + " print(\"test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n" + ] + } + ], + "source": [ + "func0()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optionally, but highly recommended, we can define a so called \"docstring\", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body." + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def func1(s):\n", + " \"\"\"\n", + " Print a string 's' and tell how many characters it has \n", + " \"\"\"\n", + " \n", + " print(s + \" has \" + str(len(s)) + \" characters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function func1 in module __main__:\n", + "\n", + "func1(s)\n", + " Print a string 's' and tell how many characters it has\n", + "\n" + ] + } + ], + "source": [ + "help(func1)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test has 4 characters\n" + ] + } + ], + "source": [ + "func1(\"test\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Functions that returns a value use the `return` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def square(x):\n", + " \"\"\"\n", + " Return the square of x.\n", + " \"\"\"\n", + " return x ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "square(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can return multiple values from a function using tuples (see above):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def powers(x):\n", + " \"\"\"\n", + " Return a few powers of x.\n", + " \"\"\"\n", + " return x ** 2, x ** 3, x ** 4" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9, 27, 81)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "powers(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27\n" + ] + } + ], + "source": [ + "x2, x3, x4 = powers(3)\n", + "\n", + "print(x3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default argument and keyword arguments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a definition of a function, we can give default values to the arguments the function takes:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def myfunc(x, p=2, debug=False):\n", + " if debug:\n", + " print(\"evaluating myfunc for x = \" + str(x) + \" using exponent p = \" + str(p))\n", + " return x**p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we don't provide a value of the `debug` argument when calling the the function `myfunc` it defaults to the value provided in the function definition:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myfunc(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "evaluating myfunc for x = 5 using exponent p = 2\n" + ] + }, + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myfunc(5, debug=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called *keyword* arguments, and is often very useful in functions that takes a lot of optional arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "evaluating myfunc for x = 7 using exponent p = 3\n" + ] + }, + { + "data": { + "text/plain": [ + "343" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myfunc(p=3, debug=True, x=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unnamed functions (lambda function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python we can also create unnamed functions, using the `lambda` keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "f1 = lambda x: x**2\n", + " \n", + "# is equivalent to \n", + "\n", + "def f2(x):\n", + " return x**2" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 4)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1(2), f2(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This technique is useful for example when we want to pass a simple function as an argument to another function, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 4, 1, 0, 1, 4, 9]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# map is a built-in python function\n", + "map(lambda x: x**2, range(-3,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 4, 1, 0, 1, 4, 9]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# in python 3 we can use `list(...)` to convert the iterator to an explicit list\n", + "list(map(lambda x: x**2, range(-3,4)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object. \n", + "\n", + "In Python a class can contain *attributes* (variables) and *methods* (functions).\n", + "\n", + "A class is defined almost like a function, but using the `class` keyword, and the class definition usually contains a number of class method definitions (a function in a class).\n", + "\n", + "* Each class method should have an argument `self` as its first argument. This object is a self-reference.\n", + "\n", + "* Some class method names have special meaning, for example:\n", + "\n", + " * `__init__`: The name of the method that is invoked when the object is first created.\n", + " * `__str__` : A method that is invoked when a simple string representation of the class is needed, as for example when printed.\n", + " * There are many more, see http://docs.python.org/2/reference/datamodel.html#special-method-names" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Point:\n", + " \"\"\"\n", + " Simple class for representing a point in a Cartesian coordinate system.\n", + " \"\"\"\n", + " \n", + " def __init__(self, x, y):\n", + " \"\"\"\n", + " Create a new Point at x, y.\n", + " \"\"\"\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " def translate(self, dx, dy):\n", + " \"\"\"\n", + " Translate the point by dx and dy in the x and y direction.\n", + " \"\"\"\n", + " self.x += dx\n", + " self.y += dy\n", + " \n", + " def __str__(self):\n", + " return(\"Point at [%f, %f]\" % (self.x, self.y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a new instance of a class:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Point at [0.000000, 0.000000]\n" + ] + } + ], + "source": [ + "p1 = Point(0, 0) # this will invoke the __init__ method in the Point class\n", + "\n", + "print(p1) # this will invoke the __str__ method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To invoke a class method in the class instance `p`:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Point at [0.250000, 1.500000]\n", + "Point at [1.000000, 1.000000]\n" + ] + } + ], + "source": [ + "p2 = Point(1, 1)\n", + "\n", + "p1.translate(0.25, 1.5)\n", + "\n", + "print(p1)\n", + "print(p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that calling class methods can modifiy the state of that particular class instance, but does not effect other class instances or any global variables.\n", + "\n", + "That is one of the nice things about object-oriented design: code such as functions and related variables are grouped in separate and independent entities. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the most important concepts in good programming is to reuse code and avoid repetitions.\n", + "\n", + "The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program is greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot. \n", + "\n", + "Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions and classes in a module. A python module is defined in a python file (with file-ending `.py`), and it can be made accessible to other Python modules and programs using the `import` statement. \n", + "\n", + "Consider the following example: the file `mymodule.py` contains simple example implementations of a variable, function and a class:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing mymodule.py\n" + ] + } + ], + "source": [ + "%%file mymodule.py\n", + "\"\"\"\n", + "Example of a python module. Contains a variable called my_variable,\n", + "a function called my_function, and a class called MyClass.\n", + "\"\"\"\n", + "\n", + "my_variable = 0\n", + "\n", + "def my_function():\n", + " \"\"\"\n", + " Example function\n", + " \"\"\"\n", + " return my_variable\n", + " \n", + "class MyClass:\n", + " \"\"\"\n", + " Example class.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " self.variable = my_variable\n", + " \n", + " def set_variable(self, new_value):\n", + " \"\"\"\n", + " Set self.variable to a new value\n", + " \"\"\"\n", + " self.variable = new_value\n", + " \n", + " def get_variable(self):\n", + " return self.variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can import the module `mymodule` into our Python program using `import`:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import mymodule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `help(module)` to get a summary of what the module provides:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on module mymodule:\n", + "\n", + "NAME\n", + " mymodule\n", + "\n", + "FILE\n", + " c:\\users\\ps\\git\\big-data-python-class\\lectures\\lecture2-jupyter_and_python\\mymodule.py\n", + "\n", + "DESCRIPTION\n", + " Example of a python module. Contains a variable called my_variable,\n", + " a function called my_function, and a class called MyClass.\n", + "\n", + "CLASSES\n", + " MyClass\n", + " \n", + " class MyClass\n", + " | Example class.\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self)\n", + " | \n", + " | get_variable(self)\n", + " | \n", + " | set_variable(self, new_value)\n", + " | Set self.variable to a new value\n", + "\n", + "FUNCTIONS\n", + " my_function()\n", + " Example function\n", + "\n", + "DATA\n", + " my_variable = 0\n", + "\n", + "\n" + ] + } + ], + "source": [ + "help(mymodule)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mymodule.my_variable" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mymodule.my_function() " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_class = mymodule.MyClass() \n", + "my_class.set_variable(10)\n", + "my_class.get_variable()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we make changes to the code in `mymodule.py`, we need to reload it using `reload`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reload(mymodule) # works only in python 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exceptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python errors are managed with a special language construct called \"Exceptions\". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To generate an exception we can use the `raise` statement, which takes an argument that must be an instance of the class `BaseException` or a class derived from it. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "description of the error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mException\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"description of the error\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mException\u001b[0m: description of the error" + ] + } + ], + "source": [ + "raise Exception(\"description of the error\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A typical use of exceptions is to abort functions when some error condition occurs, for example:\n", + "\n", + " def my_function(arguments):\n", + " \n", + " if not verify(arguments):\n", + " raise Exception(\"Invalid arguments\")\n", + " \n", + " # rest of the code goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the `try` and `except` statements:\n", + "\n", + " try:\n", + " # normal code goes here\n", + " except:\n", + " # code for error handling goes here\n", + " # this code is not executed unless the code\n", + " # above generated an error\n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n", + "Caught an exception\n" + ] + } + ], + "source": [ + "try:\n", + " print(\"test\")\n", + " # generate an error: the variable test is not defined\n", + " print(test)\n", + "except:\n", + " print(\"Caught an exception\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get information about the error, we can access the `Exception` class instance that describes the exception by using for example:\n", + "\n", + " except Exception as e:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n", + "Caught an exception:name 'test' is not defined\n" + ] + } + ], + "source": [ + "try:\n", + " print(\"test\")\n", + " # generate an error: the variable test is not defined\n", + " print(test)\n", + "except Exception as e:\n", + " print(\"Caught an exception:\" + str(e))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Counter\n", + "\n", + "Turns a sequenc of values into a defaultdict(int)-like object mappings keys to counts ( good for histograms )" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counter({0: 2, 1: 1, 2: 1})\n" + ] + } + ], + "source": [ + "import collections as coll\n", + "c = coll.Counter([0,1,2,0])\n", + "print c" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counter({'a': 2, 'different': 1, 'least': 1, 'words': 1, 'This': 1, 'of': 1, 'is': 1, 'but': 1, 'one': 1, 'duplicate': 1, 'at': 1, 'lot': 1, 'test': 1, 'document': 1, 'with': 1})\n" + ] + } + ], + "source": [ + "document = \"This is a test document with a lot of different words but at least one duplicate\".split(\" \")\n", + "word_counts = coll.Counter(document)\n", + "print word_counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sorting" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X=[1, 2, 3, 4]\n", + "y=[1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "x = [4,1,2,3]\n", + "y=sorted(x)\n", + "x.sort()\n", + "print \"X=\"+str(x)\n", + "print \"y=\"+str(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List Comprehensions" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 4, 16]\n" + ] + } + ], + "source": [ + "even_numbers = [x for x in range(5) if x %2 == 0]\n", + "squares = [x * x for x in range(5)]\n", + "even_squared = [x*x for x in even_numbers]\n", + "\n", + "print even_squared" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}\n" + ] + } + ], + "source": [ + "square_dict = { x: x*x for x in range(5) }\n", + "print square_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generators and Iterators" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(10) # works greate but sometimes we need a single number or set when we need them" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n", + "12\n", + "14\n", + "16\n", + "18\n" + ] + } + ], + "source": [ + "def lazy_range(n): \n", + " \"\"\"a lazy version of ther range function to only create the value when evaluating it import when the range gets really big\"\"\"\n", + " i = 0\n", + " while i < n :\n", + " yield i\n", + " i += 1\n", + " \n", + "for i in lazy_range(10):\n", + " print str(double(i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You need to recreate the the lazy generator to use it a second time or use a list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### randomness" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.666163620485913, 0.6052510406021051, 0.0846219126472334, 0.7502505317712742]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random as rand\n", + "[rand.random() for _ in range(4)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [Lecuture 2 continued](./Lecture%202%20continued.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* http://www.python.org - The official web page of the Python programming language.\n", + "* http://www.python.org/dev/peps/pep-0008 - Style guide for Python programming. Highly recommended. \n", + "* http://www.greenteapress.com/thinkpython/ - A free book on Python programming.\n", + "* [Python Essential Reference](http://www.amazon.com/Python-Essential-Reference-4th-Edition/dp/0672329786) - A good reference book on Python programming.\n", + "* [ZEN of Python](https://legacy.python.org/dev/peps/pep-0020)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Week 2 - Python and Jupyter for Big-Data/mymodule.py b/Lectures/Week 2 - Python and Jupyter for Big-Data/mymodule.py new file mode 100644 index 0000000..4e7017f --- /dev/null +++ b/Lectures/Week 2 - Python and Jupyter for Big-Data/mymodule.py @@ -0,0 +1,29 @@ +""" +Example of a python module. Contains a variable called my_variable, +a function called my_function, and a class called MyClass. +""" + +my_variable = 0 + +def my_function(): + """ + Example function + """ + return my_variable + +class MyClass: + """ + Example class. + """ + + def __init__(self): + self.variable = my_variable + + def set_variable(self, new_value): + """ + Set self.variable to a new value + """ + self.variable = new_value + + def get_variable(self): + return self.variable \ No newline at end of file diff --git a/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb b/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb new file mode 100644 index 0000000..0811d25 --- /dev/null +++ b/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/LINKS to lectures on NUMPY and PANDAS.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Lecture 3. NUMPY and PANDAS\n", + "\n", + "\n", + "\n", + "For NUMPY we will use: Lecture-2-Numpy.ipynb \n", + "from https://github.com/jrjohansson/scientific-python-lectures\n", + "\n", + "\n", + "For Pandas we will use\": 1. Introduction to Pandas.ipynb\n", + "https://github.com/fonnesbeck/statistical-analysis-python-tutorial" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb b/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb new file mode 100644 index 0000000..c34effa --- /dev/null +++ b/Lectures/Week 3 - Data aquisition and clean up -numpy_and_pandas/Underconstruction/NUMPY Tutorial-Copy1.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NUMPY Tutorial \n", + "\n", + "https://docs.scipy.org/doc/numpy-dev/,\n", + "https://www.python-course.eu/numpy.php/\n", + "\n", + "## What is NUMPY?\n", + "NumPy is an acronym for \"Numeric Python\" or \"Numerical Python\"\n", + "A python package that supports the creation and manipulation of n-dimensional arrays (ndarray) of homogeneous data types.\n", + "Numpy objects are fixed at creation, adjusting the size creates a new object.\n", + "The objects in the array must all have the same type ( You can defeat this somewhat in that if you create numbpy array of lists, the lists themselves can contain different objects)\n", + "\n", + "This means that mathematical operations can happen quickly since the size and type of the array elements are fixed.\n", + "Numbpy was designed for using large arrays and matrices.\n", + "SciPy (Scientific Python) extends the use of the numby ndarray's with advanced fucntions: minimization, regression, Fourier-transformation, ...\n", + "\n", + "\n", + "## Setting up\n", + "\n", + "If you already have python and pip installed the use:\n", + " \n", + "pip install numpy scipy\n", + "\n", + "Or follow the intstructions from http://www.numpy.org/ or https://docs.scipy.org/doc/numpy-dev/user/index.html#user\n", + "\n", + "## Using NUMBPY\n", + "Import it to use it" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 25.3 24.8 26.9 23.9]\n" + ] + } + ], + "source": [ + "#import numbpy\n", + "import numpy as np\n", + "# One dimensional array\n", + "cvalues = [25.3, 24.8, 26.9, 23.9]\n", + "c=np.array(cvalues)\n", + "print (c)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "list expression result=[77.54, 76.64, 80.42, 75.02]\n", + "numbpy broadcast result=[ 77.54 76.64 80.42 75.02]\n" + ] + } + ], + "source": [ + "# Assume they are celsius measurements that we want to convert to Fahrenheit\n", + "# List expression evaluation\n", + "print \"list expression result=\"+str([ x*9/5+32 for x in cvalues] )\n", + "print \"numbpy broadcast result=\"+str(c*9/5+32) # simpler syntax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating ranges using NUMBPY\n", + "\n", + "Python ranges\n", + "range([start], stop[, step])\n", + "vs xrange \n", + "Renames xrange() to range() and wraps existing range() calls with list\n", + "\n", + "\n", + "np.arange([start,] stop[, step,], dtype=None) the stop value is not include in the numbers returned. Default step is one, the dtype is based on the start number format unless specified in dtype." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 3, 5, 7, 9, 11, 13]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(1,15,2) # operates on integers only" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ -1.5 , 1.6415, 4.783 , 7.9245, 11.066 , 14.2075])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(-1.5,15,3.1415) # can create any sequence of numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. -2.5j, 2. -6.5j, 3.-10.5j])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange((1.0-2.5j),50,(1-4j)) #Even complex number sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NUMPY objects\n", + "https://docs.scipy.org/doc/numpy-dev/reference/index.html#reference\n", + "\n", + "3. Array objects\n", + " \n", + " a. N-dimensional array\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.ndarray.html\n", + " \n", + " b. scalars\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.scalars.html\n", + " \n", + " c. indexing\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.indexing.html\n", + " \n", + " d. interating over arrays\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.nditer.html\n", + " \n", + " e. Standard array subclasses\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.classes.html\n", + " \n", + " f. the array interface\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.interface.html\n", + " \n", + " g. datetimes and timedeltas\n", + " https://docs.scipy.org/doc/numpy-dev/reference/arrays.datetime.html\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NUMPY Universal functions (ufunc)\n", + " a. broadcasting\n", + " b. output type\n", + " c. internal buffers\n", + " d. error handling\n", + " e. casting rules\n", + " f. overriding ufunc\n", + " g. avalable ufuncs\n", + "## NUMPY Routines Routines\n", + " a. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 4 - Data Visualization - Matplotlib/Data_Visualization.ipynb b/Lectures/Week 4 - Data Visualization - Matplotlib/Data_Visualization.ipynb new file mode 100644 index 0000000..531b8ca --- /dev/null +++ b/Lectures/Week 4 - Data Visualization - Matplotlib/Data_Visualization.ipynb @@ -0,0 +1,1536 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Visualization\n", + "Adapted from from 3. Plotting and Visualization.ipynb from https://github.com/fonnesbeck/statistical-analysis-python-tutorial\n", + "\n", + "Visual communication of results from analysis.\n", + "The story told by the images should enhance the understanding of the viewer versus viewing raw data or equations.\n", + "\n", + "Visual representation of data:\n", + "\n", + "\"information that has been abstracted in some schematic form, including attributes or variables for the units of information\"\n", + "\n", + "- Michael Friendly (2008). \n", + "\"Milestones in the history of thematic cartography, statistical graphics, and data visualization\".\n", + "\n", + "Data visualization is both an art and a science. The are many good and bad examples of what should be done.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from IPython.core.display import HTML \n", + "Image(url= \"https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png\" \n", + " ,height=400, width=550)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting and Visualization\n", + "\n", + "There are a handful of third-party Python packages that are suitable for creating scientific plots and visualizations. These include packages like:\n", + "\n", + "* matplotlib\n", + "* Chaco\n", + "* PyX\n", + "* Bokeh\n", + "\n", + "Here, we will focus excelusively on matplotlib with seaborn and the high-level plotting availabel within pandas. It is currently the most robust and feature-rich package available.\n", + "Official sites: http://matplotlib.org/ and http://seaborn.pydata.org/index.html\n", + "\n", + "\n", + "### Visual representation of data\n", + "\n", + "We require plots, charts and other statistical graphics for the written communication of quantitative ideas.\n", + "\n", + "They allow us to more easily convey relationships and reveal deviations from patterns.\n", + "\n", + "Gelman and Unwin 2011:\n", + "\n", + "> A well-designed graph can display more information than a table of the same size, and more information than numbers embedded in text. Graphical displays allow and encourage direct visual comparisons." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matplotlib\n", + "\n", + "http://matplotlib.org/users/pyplot_tutorial.html\n", + "Be sure to intsll matplotlib \n", + "python -m pip install matplotlib\n", + "If you are having trouble read http://matplotlib.org/users/installing.html\n", + "\n", + "You can specify a custom graphical backend (*e.g.* qt, gtk, osx), but iPython generally does a good job of auto-selecting. Now matplotlib is ready to go, and you can access the matplotlib API via `plt`. If you do not start iPython in pylab mode, you can do this manually with the following convention:\n", + "\n", + " import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set some Pandas options\n", + "pd.set_option('display.notebook_repr_html', False)\n", + "pd.set_option('display.max_columns', 20)\n", + "pd.set_option('display.max_rows', 25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that setup is complete we can start adding plots inline to the notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'ro')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above plot simply shows two sets of random numbers taken from a normal distribution plotted against one another. The `'ro'` argument is a shorthand argument telling matplotlib that I wanted the points represented as red circles.\n", + "\n", + "This plot was expedient. We can exercise a little more control by breaking the plotting into a workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with mpl.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 8}):\n", + " fig = plt.figure(figsize=(6,3))\n", + " ax1 = fig.add_subplot(121)\n", + " ax1.set_xlabel('some random numbers')\n", + " ax1.set_ylabel('more random numbers')\n", + " ax1.set_title(\"Random scatterplot\")\n", + " plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'r.')\n", + " ax2 = fig.add_subplot(122)\n", + " plt.hist(np.random.normal(size=100), bins=15)\n", + " ax2.set_xlabel('sample')\n", + " ax2.set_ylabel('cumulative sum')\n", + " ax2.set_title(\"Normal distrubution\")\n", + " plt.tight_layout()\n", + " plt.savefig(\"normalvars.png\", dpi=150)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "matplotlib is a relatively low-level plotting package, relative to others. It makes very few assumptions about what constitutes good layout (by design), but has a lot of flexiblility to allow the user to completely customize the look of the output.\n", + "\n", + "If you want to make your plots look pretty take a look at: http://matplotlib.org/users/customizing.html\n", + "Look at http://matplotlib.org/gallery.html#style_sheets to find styles you like.\n", + "\n", + "We can quickly switch using:\n", + "plt.style.use('AVAILABLE_PLOT_STYLE')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['seaborn-darkgrid', 'Solarize_Light2', 'seaborn-notebook', 'classic', 'seaborn-ticks', 'grayscale', 'bmh', 'seaborn-talk', 'dark_background', 'ggplot', 'fivethirtyeight', '_classic_test', 'seaborn-colorblind', 'seaborn-deep', 'seaborn-whitegrid', 'seaborn-bright', 'seaborn-poster', 'seaborn-muted', 'seaborn-paper', 'seaborn-white', 'fast', 'seaborn-pastel', 'seaborn-dark', 'tableau-colorblind10', 'seaborn', 'seaborn-dark-palette']\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('ggplot')\n", + "#plt.style.use('dark_background')\n", + "#plt.style.use('bmh')\n", + "#plt.style.use('fivethirtyeight')\n", + "plt.style.reload_library()\n", + "print plt.style.available\n", + "with mpl.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 8}):\n", + " fig = plt.figure(figsize=(6,3))\n", + " ax1 = fig.add_subplot(121)\n", + " ax1.set_xlabel('some random numbers')\n", + " ax1.set_ylabel('more random numbers')\n", + " ax1.set_title(\"Random scatterplot\")\n", + " plt.plot(np.random.normal(size=100), np.random.normal(size=100), 'r.')\n", + " ax2 = fig.add_subplot(122)\n", + " plt.hist(np.random.normal(size=100), bins=15)\n", + " ax2.set_xlabel('sample')\n", + " ax2.set_ylabel('cumulative sum')\n", + " ax2.set_title(\"Normal distrubution\")\n", + " plt.tight_layout()\n", + " plt.savefig(\"normalvars.png\", dpi=150)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting in Pandas\n", + "\n", + "As we discussed last week, Pandas includes methods for DataFrame and Series objects that are relatively high-level, and that make reasonable assumptions about how the plot should look." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "normals = pd.Series(np.random.normal(size=10))\n", + "normals.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that by default a line plot is drawn, and a light grid is included. All of this can be changed, however:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "normals.cumsum().plot(grid=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for a DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variables = pd.DataFrame({'normal': np.random.normal(size=100), \n", + " 'gamma': np.random.gamma(1, size=100), \n", + " 'poisson': np.random.poisson(size=100)})\n", + "variables.cumsum(0).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an illustration of the high-level nature of Pandas plots, we can split multiple series into subplots with a single argument for `plot`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variables.cumsum(0).plot(subplots=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, we may want to have some series displayed on the secondary y-axis, which can allow for greater detail and less empty space:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variables.cumsum(0).plot(secondary_y='normal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we would like a little more control, we can use matplotlib's `subplots` function directly, and manually assign plots to its axes:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'cumulative sum')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n", + "for i,var in enumerate(['normal','gamma','poisson']):\n", + " variables[var].cumsum(0).plot(ax=axes[i], title=var)\n", + "axes[0].set_ylabel('cumulative sum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bar plots\n", + "\n", + "Bar plots are useful for displaying and comparing measurable quantities, such as counts or volumes. In Pandas, we just use the `plot` method with a `kind='bar'` argument.\n", + "\n", + "For this series of examples, let's load up the Titanic dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " pclass survived name sex \\\n", + "0 1 1 Allen, Miss. Elisabeth Walton female \n", + "1 1 1 Allison, Master. Hudson Trevor male \n", + "2 1 0 Allison, Miss. Helen Loraine female \n", + "3 1 0 Allison, Mr. Hudson Joshua Creighton male \n", + "4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \n", + "\n", + " age sibsp parch ticket fare cabin embarked boat body \\\n", + "0 29.0000 0 0 24160 211.3375 B5 S 2 NaN \n", + "1 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN \n", + "2 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "3 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 \n", + "4 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN \n", + "\n", + " home.dest \n", + "0 St Louis, MO \n", + "1 Montreal, PQ / Chesterville, ON \n", + "2 Montreal, PQ / Chesterville, ON \n", + "3 Montreal, PQ / Chesterville, ON \n", + "4 Montreal, PQ / Chesterville, ON " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic = pd.read_excel(\"data/titanic.xls\", \"titanic\")\n", + "titanic.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.groupby(['sex','pclass']).survived.sum().plot(kind='barh')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "death_counts = pd.crosstab([titanic.pclass, titanic.sex], titanic.survived.astype(bool))\n", + "death_counts.plot(kind='bar', stacked=True, color=['black','gold'], grid=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way of comparing the groups is to look at the survival *rate*, by adjusting for the number of people in each group." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "death_counts.div(death_counts.sum(1).astype(float), axis=0).plot(kind='barh', stacked=True, color=['black','gold'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histograms\n", + "\n", + "Frequenfly it is useful to look at the *distribution* of data before you analyze it. Histograms are a sort of bar graph that displays relative frequencies of data values; hence, the y-axis is always some measure of frequency. This can either be raw counts of values or scaled proportions.\n", + "\n", + "For example, we might want to see how the fares were distributed aboard the titanic:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.hist(grid=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `hist` method puts the continuous fare values into **bins**, trying to make a sensible décision about how many bins to use (or equivalently, how wide the bins are). We can override the default value (10):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.hist(bins=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are algorithms for determining an \"optimal\" number of bins, each of which varies somehow with the number of observations in the data series." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11, 36, 14)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sturges = lambda n: int(np.log2(n) + 1)\n", + "square_root = lambda n: int(np.sqrt(n))\n", + "from scipy.stats import kurtosis\n", + "doanes = lambda data: int(1 + np.log(len(data)) + np.log(1 + kurtosis(data) * (len(data) / 6.) ** 0.5))\n", + "\n", + "n = len(titanic)\n", + "sturges(n), square_root(n), doanes(titanic.fare.dropna())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.hist(bins=doanes(titanic.fare.dropna()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A **density plot** is similar to a histogram in that it describes the distribution of the underlying data, but rather than being a pure empirical representation, it is an *estimate* of the underlying \"true\" distribution. As a result, it is smoothed into a continuous line plot. We create them in Pandas using the `plot` method with `kind='kde'`, where `kde` stands for **kernel density estimate**." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.dropna().plot(kind='kde', xlim=(0,600))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often, histograms and density plots are shown together:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.hist(bins=doanes(titanic.fare.dropna()), density=True, color='lightseagreen')\n", + "titanic.fare.dropna().plot(kind='kde', xlim=(0,600), style='r--')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we had to normalize the histogram (`normed=True`), since the kernel density is normalized by definition (it is a probability distribution)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will explore kernel density estimates more in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boxplots\n", + "\n", + "A different way of visualizing the distribution of data is the boxplot, which is a display of common quantiles; these are typically the quartiles and the lower and upper 5 percent values." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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b2Odh0/7yl78gLi7ObJmIyBY1uKewZMkSfPjhh1ixYgUAID4+Hj179sTatWvRs2dPxMfHAwBSUlKQlZWFtWvXYsqUKdi0aVPjVP6Q6NatG/bt24fIyEjk5OQgMjIS+/btQ7du3axdGhFRDfc9fJScnIyQkBAAQEhICJKTkwEAx44dQ3BwMBQKBbp27Yri4mLk5eVZptqHUEJCggyGXr16yUBISEiwdmlERDU0+IZ477//PgBg6NCh0Ol0uHXrFjQaDQBAo9GgoKAAAKDX6+Hh4SG302q10Ov1sm21hIQE+cFY3ft4VDEAiOhh0aBQ+Nvf/gZ3d3fcunULS5curfNue0KIGusUCkWNdTqdjtMyiYhsTIOGj9zd3QEAarUaQUFBuHTpEtRqtRwWysvLg5ubG4CqnkFOTo7cNjc3t0YvgYiIbFO9oWAwGFBaWir/furUKXTs2BGBgYE4cOAAAODAgQMICgoCAAQGBiIpKQlCCFy4cAHOzs4MBSKih0S9w0e3bt2S8+qNRiMGDRqE3r1747HHHsPq1auxf/9+eHh4YM6cOQCAPn364MSJE5g5cyYcHBwwbdq0xj0DIiKyGIWo7SKAFfDJa0REjYdPXiMionvGUCAiIomhQEREEkOBiIgkhgIREUkMBSIikhgKREQkMRSIiEhiKBARkcRQICIiiaFAREQSQ4GIiCSGAhERSQwFIiKSGApERCQxFIiISGIoEBGRxFAgIiKJoUBERBJDgYiIJIYCERFJDAUiIpIYCkREJDEUiIhIYigQEZHEUCAiIomhQEREEkOBiIgkhgIREUl2DW1oMpkwf/58uLu7Y/78+cjOzsaaNWtQVFSEzp07Y8aMGbCzs0NFRQXWrVuHy5cvw9XVFbNmzULr1q0b8xyIiMhCGtxT+O677+Dt7S2Xt2zZgrCwMKxduxYuLi7Yv38/AGD//v1wcXHBxx9/jLCwMGzdutXyVRMRUaNoUCjk5ubixIkTGDJkCABACIGzZ8+if//+AIDQ0FAkJycDAI4dO4bQ0FAAQP/+/XHmzBkIIRqhdCIisrQGhUJcXBwmTJgAhUIBACgsLISzszOUSiUAwN3dHXq9HgCg1+uh1WoBAEqlEs7OzigsLGyM2omIyMLqvaZw/PhxqNVq+Pr64uzZs/XusLZeQXWY3C4hIQEJCQkAgBUrVjSkViIiamT1hsL58+dx7NgxpKSkoLy8HKWlpYiLi0NJSQmMRiOUSiX0ej3c3d0BAFqtFrm5udBqtTAajSgpKYFKpaqxX51OB51OZ/kzIiKi+1bv8NG4ceMQExOD6OhozJo1Cz169MDMmTPh7++Pw4cPAwASExMRGBgIAAgICEBiYiIA4PDhw/D396+1p0BERLbnvr+nMH78eOzevRszZsxAUVERBg8eDAAYPHgwioqKMGPGDOzevRvjx4+3WLFERNS4FMJGpgZlZmZauwQiokeWl5dXg9rxG81ERCQxFIiISGIoEBGRxFAgIiKJoUBERBJDgYiIJIYCERFJDAUiIpIYCkREJDEUiIhIYigQEZHEUCAiIomhQEREEkOBiIgkhgIREUkMBSIikhgKREQkMRSIiEhiKBARkcRQICIiiaFAREQSQ4GIiCSGAhERSQwFIiKSGApERCQxFIiISGIoEBGRxFAgIiKJoUBERJJdfQ3Ky8uxZMkSVFZWwmg0on///ggPD0d2djbWrFmDoqIidO7cGTNmzICdnR0qKiqwbt06XL58Ga6urpg1axZat27dFOdCREQPSCGEEHU1EEKgrKwMTk5OqKysxOLFixEZGYndu3ejX79+eOqpp/Dpp5+iU6dOePbZZ7F3716kpaVhypQp+O9//4ujR49i9uzZ9RaSmZlpsZMiIiJzXl5eDWpX7/CRQqGAk5MTAMBoNMJoNEKhUODs2bPo378/ACA0NBTJyckAgGPHjiE0NBQA0L9/f5w5cwb15A4REdmIeoePAMBkMmHevHnIysrCsGHD0KZNGzg7O0OpVAIA3N3dodfrAQB6vR5arRYAoFQq4ezsjMLCQri5uTXSKRARkaU0KBRatGiBDz/8EMXFxfjoo4+QkZFx17a19QoUCkWNdQkJCUhISAAArFixoqH1EhFRI2pQKFRzcXFB9+7dcfHiRZSUlMBoNEKpVEKv18Pd3R0AoNVqkZubC61WC6PRiJKSEqhUqhr70ul00Ol0ljkLIiKyiHqvKRQUFKC4uBhA1Uyk06dPw9vbG/7+/jh8+DAAIDExEYGBgQCAgIAAJCYmAgAOHz4Mf3//WnsKRERke+qdfZSWlobo6GiYTCYIITBgwACMGTMGN27cqDEl1d7eHuXl5Vi3bh2uXLkClUqFWbNmoU2bNvUWwtlHRESNp6Gzj+oNhabCUCAiajwWm5JKRETNB0OBiIgkhgIREUkMBSIikhgKREQkMRSIiEi6p2800/3x9vausa6uW4UQEVkLewqNrDoQlEoldu7cKW8iWFtQEBFZG3sKTUCpVCI9PR0AkJ6ejo4dO8JoNFq5KiKimthTaALbt2+vc5mIyFbwNheNzNvb26ynAED2FHhdgYiaCm9zYUOMRiM6duyIn3/+mUNHRGTT2FNoApx9RETWxp6CjRg3bhwAICIiAqmpqYiIiDBbT0RkSzj7qJElJSUhIiJCPnK0+s8tW7ZYsywiolpx+KiReXt7IzU1FWq1Wq67desWunfvziEkImoyHD6yEQqFAsuXLzdbt3z5cj6ilIhsEoePGllwcDA2b94MAFiwYAGWL1+OzZs3IyQkxMqVERHVxOGjJjBu3DgkJSVBCAGFQoHg4GBs27bN2mURUTPCZzQTEZHU0FDg8FETCAoKMgs9Ly8vJCcnW7EiIqLa8UJzI6sOhICAABw7dgwBAQHIzMxEUFCQtUsjIqqBodDIqgNh165daNeuHXbt2iWDgYjI1jAUmsCGDRvqXCYishUMhSYwderUOpeJiGwFZx9ZiKWepMZvORNRY+DsoyZW14c5Zx8R0cOCodAEqgPAOHkklBt3WbkaIqK74zUFIiKSGApERCTVO3yUk5OD6Oho5OfnQ6FQQKfTYfjw4SgqKsLq1atx8+ZNeHp6Yvbs2VCpVBBCIDY2FikpKXB0dMS0adPg6+vbFOdCREQPqN6eglKpREREBFavXo33338fe/fuxbVr1xAfH4+ePXti7dq16NmzJ+Lj4wEAKSkpyMrKwtq1azFlyhRs2rSp0U+CiIgso95Q0Gg08l/6LVu2hLe3N/R6PZKTk+Xtn0NCQuTF1GPHjiE4OBgKhQJdu3ZFcXEx8vLyGvEUiIjIUu5p9lF2djauXLmCLl264NatW9BoNACqgqOgoAAAoNfr4eHhIbfRarXQ6/WybbWEhAQkJCQA+N8jKomIyLoaHAoGgwFRUVGIjIyEs7PzXdvV9l242p4yptPpoNPpGnp4IiJqAg2afVRZWYmoqCg8/fTT6NevHwBArVbLYaG8vDy4ubkBqOoZ5OTkyG1zc3Nr9BKIiMg21RsKQgjExMTA29sbI0aMkOsDAwNx4MABAMCBAwfkraADAwPlU8YuXLgAZ2dnhgIR0UOi3uGj8+fPIykpCR07dsTcuXMBAC+//DJGjRqF1atXY//+/fDw8MCcOXMAAH369MGJEycwc+ZMODg4YNq0aY17BkREZDG8IV4T4m0uiMhaGnpDPH6jmYiIJIYCERFJDAUiIpIYCkREJDEUiIhIYigQEZHEUCAiIomhQEREEkOBiIgkhgIREUkMBSIikhgKREQkMRSIiEhiKBARkcRQICIiiaFAREQSQ4GIiCSGAhERSQwFIiKSGApERCQxFIiISGIoEBGRxFAgIiKJoUBERBJDgYiIJIYCERFJDAUiIpIYCkREJDEUiIhIsquvwSeffIITJ05ArVYjKioKAFBUVITVq1fj5s2b8PT0xOzZs6FSqSCEQGxsLFJSUuDo6Ihp06bB19e30U+CiIgsQyGEEHU1SE1NhZOTE6Kjo2UobNmyBSqVCqNGjUJ8fDyKioowYcIEnDhxAv/+97+xYMECXLx4EXFxcVi2bFmDCsnMzHzws2kkxj+PA0qKrF1GFWcVlH/fZu0qiOgh4+Xl1aB29fYUunfvjuzsbLN1ycnJeOeddwAAISEheOeddzBhwgQcO3YMwcHBUCgU6Nq1K4qLi5GXlweNRnPvZ2BLSoqg3LjL2lUAAIyTR1q7BCJ6hN3XNYVbt27JD3qNRoOCggIAgF6vh4eHh2yn1Wqh1+stUCYRETWFensK96K2kSiFQlFr24SEBCQkJAAAVqxYYckyiIjoPt1XKKjVajkslJeXBzc3NwBVPYOcnBzZLjc3965DRzqdDjqd7n4OT0REjeS+ho8CAwNx4MABAMCBAwcQFBQk1yclJUEIgQsXLsDZ2fnhv55ARNSM1NtTWLNmDVJTU1FYWIjXX38d4eHhGDVqFFavXo39+/fDw8MDc+bMAQD06dMHJ06cwMyZM+Hg4IBp06Y1+gkQEZHl1DsltanY9JTUySNtavaRrdRCRA+Phk5J5TeaiYhIYigQEZFk0SmpRI86Pz8/lJSUyGVnZ2dcvHjRihURWRZ7CkQNVB0I7du3x6FDh9C+fXuUlJTAz8/P2qXRffD29q7xQwwFogarDoQjR46gc+fOOHLkiAwGerhUB4CjoyN2794NR0dHs/XNGYePiO5BWVmZ2QeHp6enFauhB+Ho6IjLly8DAC5fvgxfX1+UlZVZuSrrY0+B6B7cvHkTAQEBOHbsGAICAnDz5k1rl0T36Z///Gedy80VQ4HoHt24cQMGgwE3btywdin0AF544YU6l5srDh8R3QMnJydcu3YNgwYNkssGg8HKVdH9uHMokKqwp0B0D/z9/ZGRkSF//P39rV0S3QcHB4d7Wt+csKfQAN/pvgS251u7jCq6L/F/1q6hmfLy8sLx48cxcuRIbNiwAVOnTsXx48cbfPsAsh3l5eXw8PDAL7/8Itf16tXL7C7PzRXvfdQAtnS/IVuqpTkKCgoy+2/Vy8sLycnJVqyI7oe3tzcSExPNvmNy8eJFhIaGIiMjw4qVNR6LPY6TiP6HAfDoCA0NtXYJNonXFIioWYuLi7N2CTaFoUBEzVpkZKS1S7ApDAUiapacnJzqXG6uGApE1CwZDAazmxvy+yZVeKGZ6A6W/ELTozqT5VFx+xcRqQpDgegODfkg59RgelRx+IiIiCSGAhE1a9HR0dYuwaZw+KiBjJNHWruEKs4qa1dA9EiZPn26tUuwKQyFBrDU2DHHoYlsS0BAgNl9rIj3PmpSDAXbYPzzOKCkyNplAM4qKP++zdpVPNI4k+x/eO8jorspKbKJcLaZIclH2N0+yOsKi4f9w/9B8UIzETU7d/vgb+6BALCnQETNVHUAcFjXHEOBmh2beWgSH5j0wCx1feiBh/IeoetDDAVqdoYnTLR2CVWcVcDYR+ODxGp4fcjiGiUUTp48idjYWJhMJgwZMgSjRo1qjMMQ3RdLfIhwyIEeVRYPBZPJhM8++wyLFi2CVqvFggULEBgYiPbt21v6UETUzHEo0PIsHgqXLl1C27Zt0aZNGwDAwIEDkZyczFAgIov7v7GtHngf7PWZs/iUVL1eD61WK5e1Wi30er2lD0NERI3A4j2F2r4grVAoaqxLSEhAQkICAGDFihWWLqPJNfibk/W04zxp67PUewnw/bQ2vpf3zuKhoNVqkZubK5dzc3Oh0WhqtNPpdNDpdJY+vNU0l/9gmgO+l48Ovpf3zuLDR4899hiuX7+O7OxsVFZW4qeffkJgYKClD0NERI3A4j0FpVKJV155Be+//z5MJhOeeeYZdOjQwdKHISKiRsC7pBIRNQMNvUsqb4hHREQSQ4GIiCSGAhERSQwFIiKSGApERCTZzOwjIiKyPvYUmtD8+fOtXQJZCN/LRwffS3MMBSIikhgKREQkMRSa0KN0A8Dmju/lo4PvpTleaCYiIok9BSIikix+l1Sq6ZNPPsGJEyegVqsRFRVl7XLoPuXk5CA6Ohr5+flQKBTQ6XQYPny4tcui+1ReXo4lS5agsrISRqMR/fv3R3h4uLXLsjoOHzWB1NRUODk5ITo6mqHwEMvLy0NeXh58fX1RWlqK+fPnY+7cuXz++EOYWzxkAAAEt0lEQVRKCIGysjI4OTmhsrISixcvRmRkJLp27Wrt0qyKw0dNoHv37lCpVNYugx6QRqOBr68vAKBly5bw9vbm88cfYgqFAk5OTgAAo9EIo9FY66ODmxsOHxHdh+zsbFy5cgVdunSxdin0AEwmE+bNm4esrCwMGzYMfn5+1i7J6thTILpHBoMBUVFRiIyMhLOzs7XLoQfQokULfPjhh4iJicFvv/2G9PR0a5dkdQwFontQWVmJqKgoPP300+jXr5+1yyELcXFxQffu3XHy5Elrl2J1DAWiBhJCICYmBt7e3hgxYoS1y6EHVFBQgOLiYgBVM5FOnz4Nb29vK1dlfZx91ATWrFmD1NRUFBYWQq1WIzw8HIMHD7Z2WXSPfv31VyxevBgdO3aUFyRffvllPPnkk1aujO5HWloaoqOjYTKZIITAgAEDMGbMGGuXZXUMBSIikjh8REREEkOBiIgkhgIREUkMBSIikhgKREQkMRSI6vHOO+/ghx9+sHYZRE2CoUBERBJDgYiIJN4llZqV6dOnQ6fTISkpCfn5+QgKCsJrr70GBwcHJCcn46uvvkJ2djbc3Nzw6quvonfv3mbbZ2VlYcOGDUhLS4NCoUCvXr3w6quvwsXFBQAQHx+P77//HqWlpdBoNHjttdfQs2dPXLp0CZs2bcL169fh4OCAQYMGYdKkSdb4FRDViaFAzc6hQ4ewcOFCODk54YMPPsDXX3+NwMBArFu3Dm+99RZ69OiB/Px8lJaW1rr9888/j27duqG0tBRRUVHYsWMHIiMjkZmZib1792L58uVwd3dHdnY2TCYTACA2NhbDhw9HcHAwDAYD78ZJNouhQM3OsGHD4OHhAaDqAz42NhYFBQV45pln8Ic//AEA4O7uXuu2bdu2Rdu2bQEA9vb2CAsLw86dOwFU3Ya5oqIC165dg5ubG1q3bi23s7OzQ1ZWFgoKCuDm5tbsn+5FtouhQM1OdSAAgKenJ/R6PXJzc9GnT596t7116xZiY2Nx7tw5GAwGmEwm+VS9tm3bIjIyEjt27MC1a9fQq1cvTJw4Ee7u7nj99dexfft2zJ49G61bt8aYMWMQEBDQaOdIdL8YCtTs5OTkmP3d3d0dWq0WWVlZ9W67bds2AMBHH30EV1dXHD16FJ9//rl8fdCgQRg0aBBKSkrw6aefYuvWrZgxYwbatWuHWbNmwWQy4ejRo1i1ahU+++wz+ThIIlvB2UfU7Ozduxe5ubkoKirCN998gwEDBmDw4MFITEzE6dOnYTKZoNfrkZGRUWPb0tJSODk5wcXFBXq9Ht9++618LTMzE2fOnEFFRQUcHBzg4OCAFi2q/hdLSkpCQUEBWrRoIZ/WVv0akS1hT4GanUGDBmHp0qXIy8tDYGAgXnjhBTg6OmLatGn44osvkJ2dDbVajVdffbXGQ1defPFFrFu3DpMmTULbtm0RHByMPXv2AAAqKiqwdetWZGRkQKlU4vHHH8eUKVMAACdPnsSXX36JsrIyeHp64s9//jMcHBya/NyJ6sPnKVCzMn36dEydOlVeUCYic+y/EhGRxFAgIiKJw0dERCSxp0BERBJDgYiIJIYCERFJDAUiIpIYCkREJDEUiIhI+n9xJomMeU8a+QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.boxplot(column='fare', by='pclass', grid=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can think of the box plot as viewing the distribution from above. The blue crosses are \"outlier\" points that occur outside the extreme quantiles." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to add additional information to a boxplot is to overlay the actual data; this is generally most suitable with small- or moderate-sized data series." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bp = titanic.boxplot(column='age', by='pclass', grid=False)\n", + "for i in [1,2,3]:\n", + " y = titanic.age[titanic.pclass==i].dropna()\n", + " # Add some random \"jitter\" to the x-axis\n", + " x = np.random.normal(i, 0.04, size=len(y))\n", + " plt.plot(x, y, 'r.', alpha=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When data are dense, a couple of tricks used above help the visualization:\n", + "\n", + "1. reducing the alpha level to make the points partially transparent\n", + "2. adding random \"jitter\" along the x-axis to avoid overstriking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A related but inferior cousin of the box plot is the so-called dynamite plot, which is just a bar chart with half of an error bar." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.groupby('pclass')['fare'].mean().plot(kind='bar', yerr=titanic.groupby('pclass')['fare'].std())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why is this plot a poor choice?\n", + "\n", + "- bar charts should be used for measurable quantities (*e.g.* raw data), not estimates. The area of the bar does not represent anything, since these are estimates derived from the data.\n", + "- the \"data-ink ratio\" (*sensu* Edward Tufte) is very high. There are only 6 values represented here (3 means and 3 standard deviations).\n", + "- the plot hides the underlying data.\n", + "\n", + "A boxplot is **always** a better choice than a dynamite plot." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data1 = [150, 155, 175, 200, 245, 255, 395, 300, 305, 320, 375, 400, 420, 430, 440]\n", + "data2 = [225, 380]\n", + "\n", + "fake_data = pd.DataFrame([data1, data2]).transpose()\n", + "p = fake_data.mean().plot(kind='bar', yerr=fake_data.std(), grid=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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h6A3ARikA18PQjQHYKAXgegh6A7BRCsD1EPQGiN/kuqF2ANmFoDfBbHeEZcTdYQCcRtAbIGcydkPtALILQW8AzrgBcD0EvQE44wbA9bCO3gCccQPgegh6Q3DGDYDZMHQDAIYj6AHAcAQ9ABiOoAcAwxH0AGA4gh4ADEfQA4DhCHoAMBxBDwCGI+gBwHAEPQAYjqAHAMMR9ABgOIIeAAxH0AOA4eY8j/7IkSPq7OxUcXGx6uvrJUnDw8NqaGjQhQsXtHz5cu3Zs0eFhYWybVvHjh1TV1eXlixZopqaGq1ZsybtDwEAmN2cPfqtW7fq+eefn9bW0tKiyspKNTY2qrKyUi0tLZKkrq4u9fX1qbGxUc8884yOHj2anqoBAPM2Z9CvW7dOhYWF09ra29u1ZcsWSdKWLVvU3t4uSero6NDmzZtlWZbWrl2rkZERRaPRNJQNAJivz3WV4MDAgLxeryTJ6/VqcHBQkhSJRFRaWpp4nc/nUyQSSbz2s0KhkEKhkCQpGAx+njIAAPOQ0jtjbdue0WZZVtLXBgIBBQKBVL49ACCJz7Xqpri4ODEkE41GVVRUJOlKD/7ixYuJ14XD4aS9eQDAwvlcQV9VVaW2tjZJUltbm+69995E+6lTp2Tbts6fPy+Px0PQA4DD5hy6OXjwoN5//30NDQ3pBz/4gZ544gnt2LFDDQ0Nam1tVWlpqfbu3StJWr9+vTo7O7Vr1y7l5eWppqYm7Q8AALg+y042sO6A3t5ep0uYU+z733K6BKO4fvNHp0swxmPH/+10CUZ5c+edTpcwL+Xl5fN6HTtjAcBwBD0AGI6gBwDDEfQAYDiCHgAMR9ADgOEIegAwHEEPAIYj6AHAcAQ9ABiOoAcAwxH0AGA4gh4ADEfQA4DhCHoAMBxBDwCGI+gBwHAEPQAYjqAHAMMR9ABgOIIeAAxH0AOA4Qh6ADAcQQ8AhiPoAcBwBD0AGI6gBwDDEfQAYDiCHgAMR9ADgOEIegAwHEEPAIYj6AHAcAQ9ABiOoAcAwxH0AGA4gh4ADEfQA4DhctPxTf/5z3/q2LFjisfjeuCBB7Rjx450vA0AYB5S3qOPx+N65ZVX9Pzzz6uhoUGnT5/WJ598kuq3AQDMU8qD/sMPP9TNN9+sFStWKDc3V1//+tfV3t6e6rcBAMxTyoduIpGIfD5f4mOfz6cPPvhgxutCoZBCoZAkKRgMqry8PNWlpN6fO5yuAEiq/blF8P8Hjkl5j9627RltlmXNaAsEAgoGgwoGg6kuIevV1dU5XQKQFP82nZHyoPf5fAqHw4mPw+GwvF5vqt8GADBPKQ/6L3/5y/r000/V39+vyclJvfvuu6qqqkr12wAA5inlY/Qul0vf+9739NJLLykej+v+++/Xl770pVS/Da4jEAg4XQKQFP82nWHZyQbVAQDGYGcsABiOoAcAwxH0AGC4tJx1g4XT09Oj9vZ2RSIRWZYlr9erqqoqrVq1yunSAGQIJmMXsZaWFp0+fVr33XefSkpKJF3ZmTzVxmFyACR69IvaO++8o/r6euXmTv9rfPTRR7V3716CHhnrnXfe0f333+90GVmDMfpFzLIsRaPRGe3RaDTpsRNApnj99dedLiGr0KNfxJ566im9+OKLWrlyZeIguYsXL6qvr09PP/20w9Uh2/3oRz9K2m7btgYGBha4muxG0C9id999t371q1/pww8/VCQSkSSVlJTo9ttvV04Ov6zBWQMDA9q/f78KCgqmtdu2rZ/85CcOVZWdCPpFLicnR2vXrnW6DGCGe+65R+Pj47rttttmfG7dunULX1AWY9UNABiO3+8BwHAEPQAYjqAHAMMR9ABguP8H9Roa9yoV56kAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fake_data = pd.DataFrame([data1, data2]).transpose()\n", + "p = fake_data.mean().plot(kind='bar', yerr=fake_data.std(), grid=False)\n", + "x1, x2 = p.xaxis.get_majorticklocs()\n", + "plt.plot(np.random.normal(x1, 0.01, size=len(data1)), data1, 'ro')\n", + "plt.plot([x2]*len(data2), data2, 'ro')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "Using the Titanic data, create kernel density estimate plots of the age distributions of survivors and victims." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatterplots\n", + "\n", + "To look at how Pandas does scatterplots, let's reload the baseball sample dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " id player year stint team lg g ab r h ... rbi sb cs \\\n", + "0 88641 womacto01 2006 2 CHN NL 19 50 6 14 ... 2.0 1.0 1.0 \n", + "1 88643 schilcu01 2006 1 BOS AL 31 2 0 1 ... 0.0 0.0 0.0 \n", + "2 88645 myersmi01 2006 1 NYA AL 62 0 0 0 ... 0.0 0.0 0.0 \n", + "3 88649 helliri01 2006 1 MIL NL 20 3 0 0 ... 0.0 0.0 0.0 \n", + "4 88650 johnsra05 2006 1 NYA AL 33 6 0 1 ... 0.0 0.0 0.0 \n", + "\n", + " bb so ibb hbp sh sf gidp \n", + "0 4 4.0 0.0 0.0 3.0 0.0 0.0 \n", + "1 0 1.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0 2.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0 4.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseball = pd.read_csv(\"data/baseball.csv\")\n", + "baseball.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scatterplots are useful for data exploration, where we seek to uncover relationships among variables. There are no scatterplot methods for Series or DataFrame objects; we must instead use the matplotlib function `scatter`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 200)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(baseball.ab, baseball.h)\n", + "plt.xlim(0, 700); plt.ylim(0, 200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add additional information to scatterplots by assigning variables to either the size of the symbols or their colors." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 200)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(baseball.ab, baseball.h, s=baseball.hr*10, alpha=0.5)\n", + "plt.xlim(0, 700); plt.ylim(0, 200)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(baseball.ab, baseball.h, c=baseball.hr, s=40, cmap='hot')\n", + "plt.xlim(0, 700); plt.ylim(0, 200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To view scatterplots of a large numbers of variables simultaneously, we can use the `scatter_matrix` function that was recently added to Pandas. It generates a matrix of pair-wise scatterplots, optiorally with histograms or kernel density estimates on the diagonal." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = pd.plotting.scatter_matrix(baseball.loc[:,'r':'sb'], figsize=(12,8), diagonal='kde')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trellis Plots\n", + "\n", + "One of the enduring strengths of carrying out statistical analyses in the R language is the quality of its graphics. In particular, the addition of [Hadley Wickham's ggplot2 package](http://ggplot2.org) allows for flexible yet user-friendly generation of publication-quality plots. Its srength is based on its implementation of a powerful model of graphics, called the [Grammar of Graphics](http://vita.had.co.nz/papers/layered-grammar.pdf) (GofG). The GofG is essentially a theory of scientific graphics that allows the components of a graphic to be completely described. ggplot2 uses this description to build the graphic component-wise, by adding various layers.\n", + "\n", + "Pandas recently deprecated some really good graphing functions such as Trellis plots, and suggested that we use the seaborn package for such functions. I found https://seaborn.pydata.org/generated/seaborn.FacetGrid.html that demonstrates this setup and have integrated in this lecture:\n", + "\n", + "Chiefly, this allows for the easy creation of **trellis plots**, which are a faceted graphic that shows relationships between two variables, conditioned on particular values of other variables. This allows for the representation of more than two dimensions of information without having to resort to 3-D graphics, etc.\n", + "\n", + "For more help with seaborn\n", + "https://www.tutorialspoint.com/seaborn\n", + "https://seaborn.pydata.org/examples/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use the `titanic` dataset to create a trellis plot that represents 4 variables at a time. This consists of 4 steps:\n", + "\n", + "1. Setup the Seaborn package\n", + "2. Add a grid that will be used to condition the variables by both passenger class and sex\n", + "3. Add the actual plot that will be used to visualize each comparison\n", + "4. Draw the visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns; \n", + "sns.set(style=\"ticks\", color_codes=True)\n", + "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "g = sns.FacetGrid(data, col=\"pclass\", row=\"sex\")\n", + "g = g.map(plt.hist, \"age\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the cervical dystonia dataset, we can simultaneously examine the relationship between age and the primary outcome variable as a function of both the treatment received and the week of the treatment by creating a scatterplot of the data, and fitting a polynomial relationship between `age` and `twstrs` (http://nevro.legehandboka.no/imagevault/publishedmedia/jipv0xim2ibb749yalju/6454-2-twstrs.pdf) :" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " patient obs week site id treat age sex twstrs\n", + "0 1 1 0 1 1 5000U 65 F 32\n", + "1 1 2 2 1 1 5000U 65 F 30\n", + "2 1 3 4 1 1 5000U 65 F 24\n", + "3 1 4 8 1 1 5000U 65 F 37\n", + "4 1 5 12 1 1 5000U 65 F 39" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cdystonia = pd.read_csv(\"data/cdystonia.csv\", index_col=None)\n", + "cdystonia.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Using lmplot for trellis grid\n", + "sns.set(font_scale=2)\n", + "plt.figure(figsize=(12,12))\n", + "\n", + "sns.lmplot(\"age\",\"twstrs\",cdystonia,col=\"treat\",row=\"week\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x=\"age\",y=\"twstrs\",data=cdystonia,hue=\"treat\",palette=\"Set1\",col=\"week\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the seaborn to represent more than just trellis graphics. It is also useful for displaying multiple variables on the same panel, using combinations of color, size and shapes to do so." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "cdystonia['site'] = cdystonia.site.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "sns.set(style=\"white\")\n", + "\n", + "sns.pointplot(y=\"twstrs\",x=\"week\",data=cdystonia, hue=\"treat\")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Week 4 - Data Visualization - Matplotlib/Links to Lecture4.ipynb b/Lectures/Week 4 - Data Visualization - Matplotlib/Links to Lecture4.ipynb new file mode 100644 index 0000000..a41970c --- /dev/null +++ b/Lectures/Week 4 - Data Visualization - Matplotlib/Links to Lecture4.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Visualization\n", + "\n", + "We will be using: \n", + "\n", + "3. Plotting and Visualization.ipynb \n", + "from https://github.com/fonnesbeck/statistical-analysis-python-tutorial\n", + "\n", + "Download it locally into your jupyter notebook directory." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 4 - Data Visualization - Matplotlib/UnderConstruction/Data Visualization.ipynb b/Lectures/Week 4 - Data Visualization - Matplotlib/UnderConstruction/Data Visualization.ipynb new file mode 100644 index 0000000..9126e5c --- /dev/null +++ b/Lectures/Week 4 - Data Visualization - Matplotlib/UnderConstruction/Data Visualization.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization of Data\n", + "\n", + "Visual communication of results from analysis.\n", + "The story told by the images should enhance the understanding of the viewer versus viewing raw data or equations.\n", + "\n", + "Visual representation of data:\n", + "\n", + "\"information that has been abstracted in some schematic form, including attributes or variables for the units of information\"\n", + "\n", + "- Michael Friendly (2008). \n", + "\"Milestones in the history of thematic cartography, statistical graphics, and data visualization\".\n", + "\n", + "Data visualization is both an art and a science. The are many good and bad examples of what should be done." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from IPython.core.display import HTML \n", + "Image(url= \"https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png\" \n", + " ,height=400, width=550)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The are a number of packages that we could use to present the data.\n", + "In this case we will be exploring the use of matplotlib. Use '%matplotlib inline' line in your ipython notebook to activate the inline viewing of the plots" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(np.arange(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plots in matplotlib reside in a figure object. Create a new figure object with \n", + "plt.figure()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ">" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "fig.clear" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wdW4QklwK3ArcCLwOeATYm+S0FVb5RWAvcAkwBfw58Jkk5w6UWJIkDd0gWxBu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\nMY2DHK9k34EAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax1 = fig.add_subplot(2,2,1)\n", + "ax2 = fig.add_subplot(2,2,2)\n", + "ax3 = fig.add_subplot(2,2,3)\n", + "#Empty Matplotlib figure with three subplots\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Y5nZ+SfOA9Sd563jb55fXvA94zPZXpmhjbsvT+bbndx5qWzYAtuz1aCZJ2wNfAnbooZmt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\neUREAySZR0Q0wP8HOpKY5mVC48wAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(randn(50).cumsum(),'k--')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "k-- is a style option for black dashed lines" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)\n", + "ax2.scatter(np.arange(30), np.arange(30) +3 * randn(30))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Week 4 - Data Visualization - Matplotlib/data/baseball.csv b/Lectures/Week 4 - Data Visualization - Matplotlib/data/baseball.csv new file mode 100644 index 0000000..aadbace --- /dev/null +++ b/Lectures/Week 4 - Data Visualization - Matplotlib/data/baseball.csv @@ -0,0 +1,101 @@ +id,player,year,stint,team,lg,g,ab,r,h,X2b,X3b,hr,rbi,sb,cs,bb,so,ibb,hbp,sh,sf,gidp +88641,womacto01,2006,2,CHN,NL,19,50,6,14,1,0,1,2.0,1.0,1.0,4,4.0,0.0,0.0,3.0,0.0,0.0 +88643,schilcu01,2006,1,BOS,AL,31,2,0,1,0,0,0,0.0,0.0,0.0,0,1.0,0.0,0.0,0.0,0.0,0.0 +88645,myersmi01,2006,1,NYA,AL,62,0,0,0,0,0,0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0 +88649,helliri01,2006,1,MIL,NL,20,3,0,0,0,0,0,0.0,0.0,0.0,0,2.0,0.0,0.0,0.0,0.0,0.0 +88650,johnsra05,2006,1,NYA,AL,33,6,0,1,0,0,0,0.0,0.0,0.0,0,4.0,0.0,0.0,0.0,0.0,0.0 +88652,finlest01,2006,1,SFN,NL,139,426,66,105,21,12,6,40.0,7.0,0.0,46,55.0,2.0,2.0,3.0,4.0,6.0 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zxQcUglQBOL&4oc!hzGT~Y3FHa(4MB~Xz?;`zS6N$XN{ur68ya^fzAuRQO; sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-mapper_part-00000'\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-reducer_part-00000\n", + "Counters from step 1:\n", + " (no counters found)\n", + "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\output\\part-00000\n", + "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\\output\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['1', '10']\n", + "['2', '11']\n", + "['3', '3']\n", + "['4', '12']\n", + "['5', '4']\n", + "['6', '1']\n", + "['7', '1']\n", + "['8', '41']\n", + "['9', '532']\n", + "['10', '2']\n", + "['11', '0']\n", + "\"0\"\t\"11\"\n", + "\"1\"\t\"6\"\n", + "\"10\"\t\"1\"\n", + "\"11\"\t\"2\"\n", + "\"12\"\t\"4\"\n", + "\"2\"\t\"10\"\n", + "\"3\"\t\"3\"\n", + "\"4\"\t\"5\"\n", + "\"41\"\t\"8\"\n", + "\"532\"\t\"9\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "removing tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.015754.754000\n" + ] + } + ], + "source": [ + "%run code/MRSortByString.py data/numbers.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "How were they sorted?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# %load code/MRSortByInt.py\n", + "from mrjob.job import MRJob\n", + "\n", + "class MRSortByInt(MRJob):\n", + " def mapper(self, _, line):\n", + " \"\"\"\n", + " \"\"\"\n", + " l = line.strip('\\n').split()\n", + " yield '%01d'%int(l[1]), l[0]\n", + "\n", + " def reducer(self, key, val):\n", + " yield int(key), int(list(val)[0])\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " MRSortByInt.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "creating tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", + "creating tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", + "\n", + "\n", + "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", + "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", + "\n", + "\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000\n", + "Counters from step 1:\n", + "Counters from step 1:\n", + " (no counters found)\n", + " (no counters found)\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper-sorted\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper-sorted\n", + "> sort 'c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000'\n", + "> sort 'c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-mapper_part-00000'\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000\n", + "writing to c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000\n", + "Counters from step 1:\n", + "Counters from step 1:\n", + " (no counters found)\n", + " (no counters found)\n", + "Moving c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\\part-00000\n", + "Moving c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\\part-00000\n", + "Streaming final output from c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\n", + "Streaming final output from c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\\output\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\t11\n", + "1\t6\n", + "10\t1\n", + "11\t2\n", + "12\t4\n", + "2\t10\n", + "3\t3\n", + "4\t5\n", + "41\t8\n", + "532\t9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "removing tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n", + "removing tmp directory c:\\cygwin64\\tmp\\MRSortByInt.PS.20171002.020226.416000\n" + ] + } + ], + "source": [ + "%run code/MRSortByInt.py data/numbers.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing data/sortdata.txt\n" + ] + } + ], + "source": [ + "%%writefile data/sortdata.txt\n", + "1 1\n", + "2 4\n", + "3 8\n", + "4 2\n", + "4 7\n", + "5 5\n", + "6 10\n", + "7 11" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Running code inline example" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "no configs found; falling back on auto-configuration\n", + "creating tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\n", + "creating tmp directory c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\n", + "\n", + "\n", + "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", + "PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols\n", + "\n", + "\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000\n", + "Counters from step 1:\n", + "Counters from step 1:\n", + " (no counters found)\n", + " (no counters found)\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper-sorted\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper-sorted\n", + "> sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000'\n", + "> sort 'c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-mapper_part-00000'\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000\n", + "writing to c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000\n", + "Counters from step 1:\n", + "Counters from step 1:\n", + " (no counters found)\n", + " (no counters found)\n", + "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\\part-00000\n", + "Moving c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\step-0-reducer_part-00000 -> c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\\part-00000\n", + "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\n", + "Streaming final output from c:\\cygwin64\\tmp\\MRSortByString.PS.20171002.023747.748000\\output\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting MRSortByString job on local\n", + "['1', '1']\n", + "['2', '4']\n", + "['3', '8']\n", + "['4', '2']\n", + "['4', '7']\n", + "['5', '5']\n", + "['6', '10']\n", + "['7', '11']\n", + "finished MRSortByString job\n", + "Sorting sortdata.txt\n", + "1: 1 \n", + "10: 6 \n", + "11: 7 \n", + "2: 4 \n", + "4: 2 \n", + "5: 5 \n", + "7: 4 \n", + "8: 3 \n" + ] + } + ], + "source": [ + "# -*- coding: utf-8 -*-\n", + "# Testing word frequency count\n", + "import os, sys\n", + "sys.path.append(os.path.join(os.getcwd(),\"code\"))\n", + "from MRSortByString import *\n", + "from mrjob.job import MRJob\n", + "'''\n", + "This is a simple wrapper that runs mrjob MapReduce jobs, the inputs are:\n", + "MRJobClass - the class of the job to be run\n", + "argsArr - an array of strings to be used when creating the MRJob.\n", + "@author: Peter Harrington if you have any questions: peter.b.harrington@gmail.com\n", + "'''\n", + "def runJob(MRJobClass, argsArr, loc='local'):\n", + " if loc == 'emr': \n", + " argsArr.extend(['-r', 'emr'])\n", + " print \"starting %s job on %s\" % (MRJobClass.__name__, loc)\n", + " mrJob = MRJobClass(args=argsArr)\n", + " runner = mrJob.make_runner()\n", + " runner.run()\n", + " print \"finished %s job\" % MRJobClass.__name__\n", + " return mrJob, runner\n", + " \n", + "def runParallelJob(MRJobClass, argsArr): #TO DO: add threading to allow jobs to run in \n", + " pass #parallel \n", + " #launch a new thread\n", + " #call runJob(MRJobClass, argsArr) on the new thread\n", + "\n", + "if __name__ == '__main__':\n", + "# pass in file from outside\n", + "# MRWordFrequencyCount.run()\n", + "#setup file here\n", + " mr_job, runner = runJob(MRSortByString,[os.path.join(os.path.join(os.getcwd(),\"data\"),\"sortdata.txt\")],\"local\")\n", + " print \"Sorting sortdata.txt\"\n", + " for line in runner.stream_output(): \n", + " key, value = mr_job.parse_output_line(line)\n", + " print \"%s: %s \"%(key,value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the second column is reported by their string values" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Week 5 - MapReduce - MRJob/MapReduce Intro.ipynb b/Lectures/Week 5 - MapReduce - MRJob/MapReduce Intro.ipynb new file mode 100644 index 0000000..e2eaf97 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/MapReduce Intro.ipynb @@ -0,0 +1,6623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Challenge of processing large amounts of data\n", + "

    \n", + "
  • How to process is quickly?\n", + "
  • So how do we go about making the problem map so that it can be distributed computation?\n", + "
  • Distributed/Parrallel Programming is hard\n", + "
\n", + "\n", + "##Mapreduce addresses all the challengs\n", + "
    \n", + "
  • Google's computational/data manipulation model\n", + "
  • Elegant way to work with big data\n", + "
\n", + "\n", + "## Map Reduce and the New Software Stack\n", + "
    \n", + "
  • Covered in Chapter 2 in the Ullman book\n", + "
  • Covered in Chapter 24 of the Joel Grus book\n", + "
  • Python support packages https://pypi.python.org/pypi/mrjob\n", + "
  • map() and reduce() are basic built in functions in python https://docs.python.org/2/library/functions.html\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built-in Python Functional Programming Tools\n", + "https://docs.python.org/2/tutorial/datastructures.html\n", + "\n", + "### Python Map Function\n", + "For each value in a sequence, process each one and output a new result for each element." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def cube(x): return x*x*x\n", + "\n", + "map(cube,range(1,11))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there are more parameters then each could be an array, and they are applied together one element at a time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 2, 4, 6, 8, 10, 12, 14]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq = range(8)\n", + "def add(x,y): return x+y\n", + "map(add, seq,seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python Reduce Function\n", + "For an array passed in, compute a single result.\n", + "So a reduce function always has two paramters to carry the result foward with the next element in the sequence.\n", + "The operation starts by using the first two values then passes the result with the next element to the function..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = map(add, seq,seq)\n", + "reduce(add, result) # adding each element of the result together" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there is only one value in a sequence then that element is returned; if the sequence is empty, an exception is raised." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Word Count using python in a map reduce manner\n", + "Using the Canterbury Corpus Test file from http://compression.ca/act/files/canterbury.zip,\n", + "We will attempt to count the each unique word.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import pandas as pd\n", + "import numpy as np\n", + "aliceFile = open('data/canterbury/alice29.txt','r')\n", + "map1=[]\n", + "\n", + "WORD_RE = re.compile(r\"[\\w']+\")\n", + "\n", + "# Create the map of words with prelminary counts\n", + "for line in aliceFile:\n", + " for w in WORD_RE.findall(line):\n", + " map1.append([w,1])\n", + "\n", + "#sort the map \n", + "map2 = sorted(map1)\n", + "\n", + "#Separate the map into groups by the key values\n", + "df = pd.DataFrame(map2)\n", + "\n", + "uniquewords = df[0].unique()\n", + "DataFrameDict = {elem : pd.DataFrame for elem in uniquewords}\n", + "\n", + "for key in DataFrameDict.keys():\n", + " DataFrameDict[key] = df[:][df[0] == key]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\"'\" 1091L]\n", + "[\"'TIS\" 1L]\n", + "[\"'Tis\" 2L]\n", + "[\"'em\" 3L]\n", + "[\"'tis\" 2L]\n", + "['2' 1L]\n", + "['9' 1L]\n", + "['A' 17L]\n", + "['ADVENTURES' 1L]\n", + "[\"ALICE'S\" 3L]\n", + "['ALL' 4L]\n", + "['AND' 3L]\n", + "['ARE' 6L]\n", + "['AT' 1L]\n", + "['Ada' 1L]\n", + "['Adventures' 2L]\n", + "['Advice' 1L]\n", + "['After' 6L]\n", + "['Ah' 5L]\n", + "['Ahem' 1L]\n", + "['Alas' 1L]\n", + "['Alice' 386L]\n", + "[\"Alice's\" 9L]\n", + "['All' 5L]\n", + "['Allow' 1L]\n", + "['Always' 1L]\n", + "['Ambition' 1L]\n", + "['An' 5L]\n", + "['And' 67L]\n", + "['Ann' 4L]\n", + "['Antipathies' 1L]\n", + "['Anything' 1L]\n", + "['Are' 4L]\n", + "['Arithmetic' 1L]\n", + "['As' 17L]\n", + "['At' 9L]\n", + "['Atheling' 1L]\n", + "['Australia' 1L]\n", + "['BE' 1L]\n", + "['BEE' 1L]\n", + "['BEFORE' 1L]\n", + "['BEG' 1L]\n", + "['BEST' 2L]\n", + "['BOOTS' 1L]\n", + "['BUSY' 1L]\n", + "['Back' 1L]\n", + "['Be' 2L]\n", + "['Beau' 4L]\n", + "['Beautiful' 5L]\n", + "['Because' 1L]\n", + "['Before' 1L]\n", + "['Begin' 1L]\n", + "['Behead' 1L]\n", + "['Besides' 1L]\n", + "['Between' 1L]\n", + "['Bill' 12L]\n", + "[\"Bill's\" 4L]\n", + "['Birds' 1L]\n", + "['Boots' 1L]\n", + "['Brandy' 1L]\n", + "['Bring' 1L]\n", + "['But' 37L]\n", + "['By' 4L]\n", + "['C' 1L]\n", + "['CAN' 4L]\n", + "['CHAPTER' 12L]\n", + "['CHORUS' 2L]\n", + "['COULD' 4L]\n", + "['COURT' 1L]\n", + "['CURTSEYING' 1L]\n", + "['Call' 4L]\n", + "['Can' 1L]\n", + "[\"Can't\" 1L]\n", + "['Canary' 1L]\n", + "['Canterbury' 1L]\n", + "['Carroll' 1L]\n", + "['Cat' 24L]\n", + "[\"Cat's\" 2L]\n", + "['Catch' 1L]\n", + "['Caterpillar' 26L]\n", + "[\"Caterpillar's\" 1L]\n", + "['Caucus' 3L]\n", + "['Certainly' 1L]\n", + "['Cheshire' 7L]\n", + "['Chorus' 1L]\n", + "['Christmas' 1L]\n", + "['Classics' 1L]\n", + "['Coils' 1L]\n", + "['Collar' 1L]\n", + "['Come' 21L]\n", + "['Coming' 1L]\n", + "['Conqueror' 2L]\n", + "['Consider' 3L]\n", + "['Crab' 2L]\n", + "['Croquet' 1L]\n", + "['Curiouser' 1L]\n", + "['D' 1L]\n", + "['DOES' 1L]\n", + "[\"DON'T\" 1L]\n", + "['DOTH' 1L]\n", + "['DRINK' 2L]\n", + "['Dear' 1L]\n", + "['Derision' 1L]\n", + "['Did' 3L]\n", + "['Digging' 2L]\n", + "['Dinah' 11L]\n", + "[\"Dinah'll\" 2L]\n", + "[\"Dinah's\" 1L]\n", + "['Dinn' 1L]\n", + "['Distraction' 1L]\n", + "['Do' 13L]\n", + "['Dodo' 13L]\n", + "['Does' 2L]\n", + "[\"Don't\" 9L]\n", + "['Dormouse' 39L]\n", + "[\"Dormouse's\" 1L]\n", + "['Down' 3L]\n", + "['Drawling' 3L]\n", + "['Drink' 1L]\n", + "['Drive' 1L]\n", + "['Duchess' 38L]\n", + "[\"Duchess's\" 3L]\n", + "['Duck' 3L]\n", + "['Dutchess' 1L]\n", + "['EAT' 1L]\n", + "['EDITION' 1L]\n", + "['END' 1L]\n", + "['ESQ' 1L]\n", + "['EVEN' 1L]\n", + "['EVER' 1L]\n", + "['EVERYBODY' 1L]\n", + "['Each' 1L]\n", + "['Eaglet' 3L]\n", + "['Edgar' 1L]\n", + "['Edwin' 2L]\n", + "['Either' 1L]\n", + "['Elsie' 1L]\n", + "['England' 1L]\n", + "['English' 6L]\n", + "['Even' 1L]\n", + "['Everybody' 2L]\n", + "['Everything' 1L]\n", + "[\"Everything's\" 1L]\n", + "['Evidence' 1L]\n", + "['Exactly' 3L]\n", + "['Explain' 2L]\n", + "['FATHER' 2L]\n", + "['FENDER' 1L]\n", + "['FIT' 2L]\n", + "['FOOT' 1L]\n", + "['FROM' 1L]\n", + "['FUL' 1L]\n", + "['FULCRUM' 1L]\n", + "['Fainting' 1L]\n", + "['Father' 2L]\n", + "['Fetch' 1L]\n", + "['Fifteenth' 1L]\n", + "['First' 7L]\n", + "['Fish' 2L]\n", + "['Five' 7L]\n", + "['Footman' 10L]\n", + "[\"Footman's\" 1L]\n", + "['For' 13L]\n", + "['Forty' 1L]\n", + "['Found' 2L]\n", + "['Fourteenth' 1L]\n", + "['France' 1L]\n", + "['French' 4L]\n", + "['Frog' 1L]\n", + "['From' 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mapreduce jobs without HADOOP but will run the same jobs in an hadoop environment.\n", + "( DUMBO and Pydoop give you lower level access to HADOOP)\n", + "\n", + "Before using it for the first time install this package using : pip install mrjob \n", + "\n", + "If that does not work use the alternatives in provided on https://pythonhosted.org/mrjob/guides/quickstart.html#installation\n", + "\n", + "%%writefile myfile.py\n", + "\n", + "write/save cell contents into myfile.py (use -a to append). Another alias: %%file myfile.py\n", + "%run myfile.py\n", + "\n", + "run myfile.py and output results in the current cell\n", + "%load myfile.py\n", + "\n", + "load \"import\" myfile.py into the current cell" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting mrjob\n", + " Downloading https://files.pythonhosted.org/packages/95/24/b55a4d4799f49c4c4413233a9437a855c384b8c40daea5e8dec8ec1733f0/mrjob-0.6.5-py2.py3-none-any.whl (308kB)\n", + "Collecting google-cloud-dataproc>=0.2.0 (from mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/07/23/7ae8951f5bc68d1471b492a6b8ee938d3ec9d47ebb82dc8ea1e45899ec8e/google_cloud_dataproc-0.2.0-py2.py3-none-any.whl (131kB)\n", + "Requirement already satisfied: PyYAML>=3.08 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from mrjob) (3.12)\n", + "Collecting google-cloud-logging>=1.5.0 (from mrjob)\n", + " Downloading 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c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (3.0.4)\n", + "Requirement already satisfied: idna<2.7,>=2.5 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (2.6)\n", + "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (2018.4.16)\n", + "Requirement already satisfied: enum34>=1.0.4 in c:\\users\\ps\\anaconda2\\lib\\site-packages (from grpcio>=1.8.2; extra == \"grpc\"->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob) (1.1.6)\n", + "Collecting pyasn1<0.5.0,>=0.4.1 (from pyasn1-modules>=0.2.1->google-auth<2.0.0dev,>=0.4.0->google-api-core[grpc]<2.0.0dev,>=0.1.0->google-cloud-dataproc>=0.2.0->mrjob)\n", + " Downloading https://files.pythonhosted.org/packages/d1/a1/7790cc85db38daa874f6a2e6308131b9953feb1367f2ae2d1123bb93a9f5/pyasn1-0.4.4-py2.py3-none-any.whl (72kB)\n", + "Building wheels for collected packages: googleapis-common-protos\n", + " Running setup.py bdist_wheel for googleapis-common-protos: started\n", + " Running setup.py bdist_wheel for googleapis-common-protos: finished with status 'done'\n", + " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\62\\45\\af\\649bbf07b6595fda010be1bda667cd56d0444d07afc6f8b687\n", + "Successfully built googleapis-common-protos\n", + "Installing collected packages: protobuf, cachetools, pyasn1, pyasn1-modules, rsa, google-auth, googleapis-common-protos, grpcio, google-api-core, google-cloud-dataproc, google-cloud-core, google-cloud-logging, jmespath, botocore, google-resumable-media, google-cloud-storage, s3transfer, boto3, mrjob\n", + "Successfully installed boto3-1.9.14 botocore-1.12.14 cachetools-2.1.0 google-api-core-1.4.1 google-auth-1.5.1 google-cloud-core-0.28.1 google-cloud-dataproc-0.2.0 google-cloud-logging-1.7.0 google-cloud-storage-1.12.0 google-resumable-media-0.3.1 googleapis-common-protos-1.5.3 grpcio-1.15.0 jmespath-0.9.3 mrjob-0.6.5 protobuf-3.6.1 pyasn1-0.4.4 pyasn1-modules-0.2.2 rsa-4.0 s3transfer-0.1.13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "distributed 1.21.8 requires msgpack, which is not installed.\n", + "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", + "You are using pip version 10.0.1, however version 18.0 is available.\n", + "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "! pip install mrjob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load code/MRWordFrequencyCount.py\n", + "# Do not run this cell it is just displaying the content of the code\n", + "from mrjob.job import MRJob\n", + "\n", + "class MRWordFrequencyCount(MRJob):\n", + " \n", + " def mapper(self, _ , line):\n", + " yield \"chars\", len(line)\n", + " yield \"words\", len(line.split())\n", + " yield \"lines\", 1\n", + " \n", + " def reducer(self, key, values):\n", + " yield key, sum(values)\n", + " \n", + "if __name__ == '__main__':\n", + " MRWordFrequencyCount.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "WARNING:mrjob.conf:No configs specified for inline runner\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "INFO:mrjob.sim:Running step 1 of 1...\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n", + "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000\\output...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"chars\"\t148687\n", + "\"lines\"\t12\n", + "\"words\"\t26475\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFrequencyCount.PS.20181001.022038.947000...\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFrequencyCount.PS.20181001.022038.947000\\\\step\\\\000\\\\cache\\\\MRWordFrequencyCount.py'\n" + ] + } + ], + "source": [ + "%run -G code/MRWordFrequencyCount.py ./.mrjob.conf data/canterbury/alice29.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# MRJOB Word count function" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "usage: usage: ipykernel_launcher.py [options] [input files]\n", + "ipykernel_launcher.py: error: unrecognized arguments: -f\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "2", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n" + ] + } + ], + "source": [ + "# %load code\\MRWordFreqCount.py\n", + "# Do not run this block of code just a display of the stored program\n", + "from mrjob.job import MRJob\n", + "import re\n", + "\n", + "WORD_RE = re.compile(r\"[\\w']+\")\n", + "\n", + "\n", + "class MRWordFreqCount(MRJob):\n", + "\n", + " def mapper(self, _, line):\n", + " for word in WORD_RE.findall(line):\n", + " yield word.lower(), 1\n", + "\n", + " def combiner(self, word, counts):\n", + " yield word, sum(counts)\n", + "\n", + " def reducer(self, word, counts):\n", + " yield word, sum(counts)\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " MRWordFreqCount.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "No configs found; falling back on auto-configuration\n", + "INFO:mrjob.conf:No configs found; falling back on auto-configuration\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "No configs specified for inline runner\n", + "WARNING:mrjob.conf:No configs specified for inline runner\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "Running step 1 of 1...\n", + "INFO:mrjob.sim:Running step 1 of 1...\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "INFO:mrjob.runner:Creating temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "INFO:mrjob.runner:job output is in c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n", + "INFO:mrjob.runner:Streaming final output from c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000\\output...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"'\"\t1091\n", + "\"'em\"\t3\n", + "\"'tis\"\t5\n", + "\"2\"\t1\n", + "\"9\"\t1\n", + "\"_i_\"\t2\n", + "\"a\"\t632\n", + "\"abide\"\t1\n", + "\"able\"\t1\n", + "\"about\"\t94\n", + "\"above\"\t3\n", + "\"absence\"\t1\n", + "\"absurd\"\t2\n", + "\"acceptance\"\t1\n", + "\"accident\"\t2\n", + "\"accidentally\"\t1\n", + "\"account\"\t1\n", + "\"accounting\"\t1\n", + "\"accounts\"\t1\n", + "\"accusation\"\t1\n", + "\"accustomed\"\t1\n", + "\"ache\"\t1\n", + "\"across\"\t5\n", + "\"act\"\t1\n", + "\"actually\"\t1\n", + "\"ada\"\t1\n", + "\"added\"\t23\n", + 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+ "\"dismay\"\t1\n", + "\"disobey\"\t1\n", + "\"dispute\"\t2\n", + "\"distance\"\t8\n", + "\"distant\"\t2\n", + "\"distraction\"\t1\n", + "\"dive\"\t1\n", + "\"do\"\t81\n", + "\"dodged\"\t1\n", + "\"dodo\"\t13\n", + "\"does\"\t9\n", + "\"doesn't\"\t16\n", + "\"dog\"\t2\n", + "\"dog's\"\t1\n", + "\"dogs\"\t3\n", + "\"doing\"\t5\n", + "\"don't\"\t61\n", + "\"done\"\t15\n", + "\"door\"\t30\n", + "\"doors\"\t2\n", + "\"doorway\"\t1\n", + "\"dormouse\"\t39\n", + "\"dormouse's\"\t1\n", + "\"doth\"\t3\n", + "\"double\"\t1\n", + "\"doubled\"\t1\n", + "\"doubling\"\t1\n", + "\"doubt\"\t4\n", + "\"doubtful\"\t2\n", + "\"doubtfully\"\t2\n", + "\"down\"\t102\n", + "\"downward\"\t1\n", + "\"downwards\"\t1\n", + "\"doze\"\t1\n", + "\"dozing\"\t1\n", + "\"draggled\"\t1\n", + "\"draw\"\t7\n", + "\"drawing\"\t1\n", + "\"drawling\"\t3\n", + "\"dreadful\"\t2\n", + "\"dreadfully\"\t6\n", + "\"dream\"\t7\n", + "\"dreamed\"\t1\n", + "\"dreaming\"\t1\n", + "\"dreamy\"\t1\n", + "\"dressed\"\t1\n", + "\"drew\"\t5\n", + "\"dried\"\t1\n", + "\"driest\"\t1\n", + "\"drink\"\t7\n", + "\"drinking\"\t1\n", + "\"dripping\"\t1\n", + "\"drive\"\t2\n", + "\"drop\"\t1\n", + "\"dropped\"\t5\n", + "\"dropping\"\t1\n", + "\"drowned\"\t1\n", + "\"drunk\"\t2\n", + "\"dry\"\t8\n", + "\"duchess\"\t38\n", + "\"duchess's\"\t3\n", + "\"duck\"\t4\n", + "\"dull\"\t3\n", + "\"dunce\"\t1\n", + "\"dutchess\"\t1\n", + "\"e\"\t6\n", + "\"each\"\t8\n", + "\"eager\"\t3\n", + "\"eagerly\"\t8\n", + "\"eaglet\"\t3\n", + "\"ear\"\t6\n", + "\"earls\"\t2\n", + "\"earnestly\"\t2\n", + "\"ears\"\t5\n", + "\"earth\"\t4\n", + "\"easily\"\t3\n", + "\"easy\"\t2\n", + "\"eat\"\t18\n", + "\"eaten\"\t1\n", + "\"eating\"\t1\n", + "\"eats\"\t1\n", + "\"edgar\"\t1\n", + "\"edge\"\t3\n", + "\"edition\"\t1\n", + "\"editions\"\t2\n", + "\"educations\"\t1\n", + "\"edwin\"\t2\n", + "\"eel\"\t2\n", + "\"eels\"\t1\n", + "\"effect\"\t3\n", + "\"egg\"\t1\n", + "\"eggs\"\t5\n", + "\"eh\"\t1\n", + "\"either\"\t10\n", + "\"elbow\"\t3\n", + "\"elbows\"\t1\n", + "\"elegant\"\t1\n", + "\"eleventh\"\t1\n", + "\"else\"\t11\n", + "\"else's\"\t1\n", + "\"elsie\"\t1\n", + "\"emphasis\"\t1\n", + "\"empty\"\t1\n", + "\"encourage\"\t1\n", + "\"encouraged\"\t1\n", + "\"encouraging\"\t2\n", + "\"end\"\t18\n", + "\"ending\"\t2\n", + "\"energetic\"\t1\n", + "\"engaged\"\t1\n", + "\"engine\"\t1\n", + "\"england\"\t1\n", + "\"english\"\t6\n", + "\"engraved\"\t1\n", + "\"enjoy\"\t1\n", + "\"ennyworth\"\t1\n", + "\"enormous\"\t1\n", + "\"enough\"\t18\n", + "\"entangled\"\t2\n", + "\"entirely\"\t2\n", + "\"entrance\"\t1\n", + "\"escape\"\t4\n", + "\"esq\"\t1\n", + "\"est\"\t1\n", + "\"even\"\t19\n", + "\"evening\"\t5\n", + "\"ever\"\t20\n", + "\"every\"\t13\n", + "\"everybody\"\t8\n", + "\"everything\"\t12\n", + "\"everything's\"\t2\n", + "\"evidence\"\t7\n", + "\"evidently\"\t1\n", + "\"exact\"\t1\n", + "\"exactly\"\t8\n", + "\"examine\"\t2\n", + "\"examining\"\t1\n", + "\"excellent\"\t2\n", + "\"except\"\t4\n", + "\"exclaimed\"\t6\n", + "\"exclamation\"\t1\n", + "\"execute\"\t1\n", + "\"executed\"\t6\n", + "\"executes\"\t1\n", + "\"execution\"\t3\n", + "\"executioner\"\t5\n", + "\"executioner's\"\t1\n", + "\"executions\"\t2\n", + "\"existence\"\t1\n", + "\"expected\"\t1\n", + "\"expecting\"\t3\n", + "\"experiment\"\t2\n", + "\"explain\"\t10\n", + "\"explained\"\t1\n", + "\"explanation\"\t2\n", + "\"explanations\"\t1\n", + "\"expressing\"\t1\n", + "\"expression\"\t1\n", + "\"extra\"\t1\n", + "\"extraordinary\"\t2\n", + "\"extras\"\t1\n", + "\"extremely\"\t2\n", + "\"eye\"\t7\n", + "\"eyed\"\t1\n", + "\"eyelids\"\t1\n", + "\"eyes\"\t29\n", + "\"face\"\t15\n", + "\"faces\"\t5\n", + "\"fact\"\t8\n", + "\"fading\"\t1\n", + "\"failure\"\t1\n", + "\"faint\"\t1\n", + "\"fainting\"\t1\n", + "\"faintly\"\t1\n", + "\"fair\"\t1\n", + "\"fairly\"\t1\n", + "\"fairy\"\t1\n", + "\"fall\"\t7\n", + "\"fallen\"\t4\n", + "\"falling\"\t2\n", + "\"familiarly\"\t1\n", + "\"family\"\t1\n", + "\"fan\"\t10\n", + "\"fancied\"\t2\n", + "\"fancy\"\t7\n", + "\"fancying\"\t1\n", + "\"fanned\"\t1\n", + "\"fanning\"\t1\n", + "\"far\"\t13\n", + "\"farm\"\t1\n", + "\"farmer\"\t1\n", + "\"farther\"\t1\n", + "\"fashion\"\t2\n", + "\"fast\"\t4\n", + "\"faster\"\t3\n", + "\"fat\"\t1\n", + "\"father\"\t6\n", + "\"favoured\"\t1\n", + "\"favourite\"\t1\n", + "\"fear\"\t4\n", + "\"feared\"\t1\n", + "\"feather\"\t1\n", + "\"feathers\"\t1\n", + "\"feeble\"\t2\n", + "\"feebly\"\t1\n", + "\"feel\"\t8\n", + "\"feeling\"\t7\n", + "\"feelings\"\t2\n", + "\"feet\"\t19\n", + "\"fell\"\t6\n", + "\"fellow\"\t4\n", + "\"fellows\"\t1\n", + "\"felt\"\t23\n", + "\"fender\"\t1\n", + "\"ferrets\"\t2\n", + "\"fetch\"\t7\n", + "\"few\"\t9\n", + "\"fidgeted\"\t1\n", + "\"field\"\t1\n", + "\"fifteen\"\t1\n", + "\"fifteenth\"\t1\n", + "\"fifth\"\t1\n", + "\"fig\"\t1\n", + "\"fight\"\t2\n", + "\"fighting\"\t1\n", + "\"figure\"\t3\n", + "\"figures\"\t1\n", + "\"filled\"\t3\n", + "\"fills\"\t1\n", + "\"find\"\t21\n", + "\"finding\"\t3\n", + "\"finds\"\t1\n", + "\"fine\"\t2\n", + "\"finger\"\t5\n", + "\"finish\"\t5\n", + "\"finished\"\t12\n", + "\"finishing\"\t1\n", + "\"fire\"\t4\n", + "\"fireplace\"\t1\n", + "\"first\"\t51\n", + "\"fish\"\t8\n", + "\"fishes\"\t1\n", + "\"fit\"\t3\n", + "\"fits\"\t1\n", + "\"fitted\"\t1\n", + "\"five\"\t8\n", + "\"fix\"\t1\n", + "\"fixed\"\t1\n", + "\"flame\"\t1\n", + "\"flamingo\"\t5\n", + "\"flamingoes\"\t2\n", + "\"flapper\"\t1\n", + "\"flappers\"\t1\n", + "\"flashed\"\t1\n", + "\"flat\"\t2\n", + "\"flavour\"\t1\n", + "\"flew\"\t1\n", + "\"flinging\"\t1\n", + "\"flock\"\t1\n", + "\"floor\"\t3\n", + "\"flower\"\t2\n", + "\"flowers\"\t2\n", + "\"flown\"\t1\n", + "\"flung\"\t1\n", + "\"flurry\"\t1\n", + "\"flustered\"\t1\n", + "\"fluttered\"\t1\n", + "\"fly\"\t3\n", + "\"flying\"\t1\n", + "\"folded\"\t3\n", + "\"folding\"\t1\n", + "\"follow\"\t2\n", + "\"followed\"\t8\n", + "\"follows\"\t3\n", + "\"fond\"\t4\n", + "\"foolish\"\t1\n", + "\"foot\"\t10\n", + "\"footman\"\t13\n", + "\"footman's\"\t1\n", + "\"footmen\"\t1\n", + "\"footsteps\"\t2\n", + "\"for\"\t153\n", + "\"forehead\"\t2\n", + "\"forepaws\"\t1\n", + "\"forget\"\t2\n", + "\"forgetting\"\t3\n", + "\"forgot\"\t2\n", + "\"forgotten\"\t6\n", + "\"fork\"\t1\n", + "\"form\"\t1\n", + "\"fortunately\"\t1\n", + "\"forty\"\t1\n", + "\"forwards\"\t1\n", + "\"found\"\t32\n", + "\"fountains\"\t2\n", + "\"four\"\t8\n", + "\"fourteenth\"\t1\n", + "\"fourth\"\t1\n", + "\"frame\"\t1\n", + "\"frames\"\t1\n", + "\"france\"\t1\n", + "\"free\"\t3\n", + "\"french\"\t4\n", + "\"friend\"\t3\n", + "\"friends\"\t2\n", + "\"fright\"\t2\n", + "\"frighten\"\t1\n", + "\"frightened\"\t7\n", + "\"frog\"\t3\n", + "\"from\"\t36\n", + "\"front\"\t2\n", + "\"frontispiece\"\t1\n", + "\"frowning\"\t4\n", + "\"frying\"\t1\n", + "\"ful\"\t1\n", + "\"fulcrum\"\t1\n", + "\"full\"\t6\n", + "\"fumbled\"\t1\n", + "\"fun\"\t3\n", + "\"funny\"\t3\n", + "\"fur\"\t3\n", + "\"furious\"\t1\n", + "\"furiously\"\t1\n", + "\"furrow\"\t1\n", + "\"furrows\"\t1\n", + "\"further\"\t3\n", + "\"fury\"\t3\n", + "\"gained\"\t1\n", + "\"gallons\"\t1\n", + "\"game\"\t12\n", + "\"game's\"\t1\n", + "\"games\"\t1\n", + "\"garden\"\t16\n", + "\"gardeners\"\t8\n", + "\"gather\"\t1\n", + "\"gave\"\t15\n", + "\"gay\"\t1\n", + "\"gazing\"\t1\n", + "\"general\"\t3\n", + "\"generally\"\t7\n", + "\"gently\"\t3\n", + "\"geography\"\t1\n", + "\"get\"\t46\n", + "\"getting\"\t22\n", + "\"giddy\"\t2\n", + "\"girl\"\t4\n", + "\"girls\"\t3\n", + "\"give\"\t12\n", + "\"given\"\t1\n", + "\"giving\"\t2\n", + "\"glad\"\t11\n", + "\"glanced\"\t1\n", + "\"glaring\"\t1\n", + "\"glass\"\t10\n", + "\"globe\"\t1\n", + "\"gloomily\"\t1\n", + "\"gloves\"\t11\n", + "\"go\"\t50\n", + "\"goes\"\t7\n", + "\"going\"\t27\n", + "\"golden\"\t7\n", + "\"goldfish\"\t2\n", + "\"gone\"\t13\n", + "\"good\"\t27\n", + "\"goose\"\t2\n", + "\"got\"\t45\n", + "\"graceful\"\t1\n", + "\"grammar\"\t1\n", + "\"grand\"\t3\n", + "\"grant\"\t1\n", + "\"grass\"\t4\n", + "\"grave\"\t3\n", + "\"gravely\"\t3\n", + "\"gravy\"\t1\n", + "\"grazed\"\t1\n", + "\"great\"\t39\n", + "\"green\"\t4\n", + "\"grew\"\t1\n", + "\"grey\"\t1\n", + "\"grief\"\t1\n", + "\"grin\"\t6\n", + "\"grinned\"\t3\n", + "\"grinning\"\t1\n", + "\"grins\"\t1\n", + "\"ground\"\t8\n", + "\"grow\"\t13\n", + "\"growing\"\t11\n", + "\"growl\"\t3\n", + "\"growled\"\t1\n", + "\"growling\"\t1\n", + "\"growls\"\t1\n", + "\"grown\"\t7\n", + "\"grumbled\"\t1\n", + "\"grunt\"\t1\n", + "\"grunted\"\t4\n", + "\"gryphon\"\t54\n", + "\"guard\"\t1\n", + "\"guess\"\t3\n", + "\"guessed\"\t3\n", + "\"guests\"\t3\n", + "\"guilt\"\t1\n", + "\"guinea\"\t6\n", + "\"had\"\t178\n", + "\"hadn't\"\t8\n", + "\"hair\"\t7\n", + "\"half\"\t23\n", + "\"hall\"\t9\n", + "\"hand\"\t21\n", + "\"handed\"\t3\n", + "\"hands\"\t12\n", + "\"handsome\"\t1\n", + "\"handwriting\"\t1\n", + "\"hanging\"\t3\n", + "\"happen\"\t8\n", + "\"happened\"\t7\n", + "\"happening\"\t1\n", + "\"happens\"\t5\n", + "\"happy\"\t1\n", + "\"hard\"\t8\n", + "\"hardly\"\t12\n", + "\"hare\"\t31\n", + "\"harm\"\t1\n", + "\"has\"\t7\n", + "\"hasn't\"\t2\n", + "\"haste\"\t1\n", + "\"hastily\"\t16\n", + "\"hat\"\t1\n", + "\"hatching\"\t1\n", + "\"hate\"\t2\n", + "\"hated\"\t1\n", + "\"hatter\"\t55\n", + "\"hatter's\"\t1\n", + "\"hatters\"\t1\n", + "\"have\"\t80\n", + "\"haven't\"\t8\n", + "\"having\"\t10\n", + "\"he\"\t122\n", + "\"he'd\"\t1\n", + "\"he'll\"\t1\n", + "\"he's\"\t3\n", + "\"head\"\t49\n", + "\"head's\"\t1\n", + "\"heads\"\t10\n", + "\"heap\"\t1\n", + "\"hear\"\t14\n", + "\"heard\"\t30\n", + "\"hearing\"\t4\n", + "\"heart\"\t2\n", + "\"hearth\"\t1\n", + "\"hearthrug\"\t1\n", + "\"hearts\"\t8\n", + "\"heavy\"\t2\n", + "\"hedge\"\t2\n", + "\"hedgehog\"\t7\n", + "\"hedgehogs\"\t3\n", + "\"hedges\"\t1\n", + "\"heels\"\t1\n", + "\"height\"\t5\n", + "\"held\"\t4\n", + "\"help\"\t9\n", + "\"helped\"\t1\n", + "\"helpless\"\t1\n", + "\"her\"\t247\n", + "\"herald\"\t1\n", + "\"here\"\t51\n", + "\"hers\"\t4\n", + "\"herself\"\t83\n", + "\"hid\"\t1\n", + "\"hide\"\t1\n", + "\"high\"\t16\n", + "\"highest\"\t1\n", + "\"him\"\t43\n", + "\"himself\"\t6\n", + "\"hint\"\t2\n", + "\"hippopotamus\"\t1\n", + "\"his\"\t96\n", + "\"hiss\"\t1\n", + "\"histories\"\t1\n", + "\"history\"\t7\n", + "\"hit\"\t2\n", + "\"hjckrrh\"\t1\n", + "\"hm\"\t1\n", + "\"hoarse\"\t3\n", + "\"hoarsely\"\t1\n", + "\"hold\"\t10\n", + "\"holding\"\t3\n", + "\"hole\"\t5\n", + "\"holiday\"\t1\n", + "\"hollow\"\t1\n", + "\"home\"\t5\n", + "\"honest\"\t1\n", + "\"honour\"\t4\n", + "\"hookah\"\t5\n", + "\"hope\"\t3\n", + "\"hoped\"\t1\n", + "\"hopeful\"\t1\n", + "\"hopeless\"\t1\n", + "\"hoping\"\t3\n", + "\"horse\"\t1\n", + "\"hot\"\t7\n", + "\"hour\"\t2\n", + "\"hours\"\t4\n", + "\"house\"\t18\n", + "\"housemaid\"\t1\n", + "\"houses\"\t1\n", + "\"how\"\t68\n", + "\"however\"\t20\n", + "\"howled\"\t1\n", + "\"howling\"\t3\n", + "\"humble\"\t1\n", + "\"humbly\"\t2\n", + "\"hundred\"\t1\n", + "\"hung\"\t1\n", + "\"hungry\"\t3\n", + "\"hunting\"\t3\n", + "\"hurried\"\t11\n", + "\"hurriedly\"\t2\n", + "\"hurry\"\t11\n", + "\"hurrying\"\t1\n", + "\"hurt\"\t3\n", + "\"hush\"\t3\n", + "\"i\"\t408\n", + "\"i'd\"\t11\n", + "\"i'll\"\t31\n", + "\"i'm\"\t59\n", + "\"i've\"\t34\n", + "\"idea\"\t15\n", + "\"idiot\"\t1\n", + "\"idiotic\"\t1\n", + "\"if\"\t96\n", + "\"ignorant\"\t1\n", + "\"ii\"\t1\n", + "\"iii\"\t1\n", + "\"ill\"\t2\n", + "\"imagine\"\t2\n", + "\"imitated\"\t1\n", + "\"immediate\"\t1\n", + "\"immediately\"\t3\n", + "\"immense\"\t1\n", + "\"impatient\"\t1\n", + "\"impatiently\"\t5\n", + "\"impertinent\"\t1\n", + "\"important\"\t7\n", + "\"impossible\"\t3\n", + "\"improve\"\t1\n", + "\"in\"\t369\n", + "\"incessantly\"\t1\n", + "\"inches\"\t6\n", + "\"inclined\"\t1\n", + "\"indeed\"\t16\n", + "\"indignant\"\t1\n", + "\"indignantly\"\t4\n", + "\"injure\"\t1\n", + "\"ink\"\t1\n", + "\"inkstand\"\t1\n", + "\"inquired\"\t1\n", + "\"inquisitively\"\t1\n", + "\"inside\"\t2\n", + "\"insolence\"\t1\n", + "\"instance\"\t3\n", + "\"instantly\"\t5\n", + "\"instead\"\t3\n", + "\"insult\"\t1\n", + "\"interest\"\t1\n", + "\"interesting\"\t5\n", + "\"interrupt\"\t1\n", + "\"interrupted\"\t9\n", + "\"interrupting\"\t2\n", + "\"into\"\t67\n", + "\"introduce\"\t2\n", + "\"introduced\"\t1\n", + "\"invent\"\t1\n", + "\"invented\"\t1\n", + "\"invitation\"\t2\n", + "\"invited\"\t2\n", + "\"involved\"\t1\n", + "\"inwards\"\t1\n", + "\"irons\"\t1\n", + "\"irritated\"\t1\n", + "\"is\"\t108\n", + "\"isn't\"\t7\n", + "\"it\"\t530\n", + "\"it'll\"\t8\n", + "\"it's\"\t57\n", + "\"its\"\t56\n", + "\"itself\"\t14\n", + "\"iv\"\t1\n", + "\"ix\"\t1\n", + "\"jack\"\t1\n", + "\"jar\"\t2\n", + "\"jaw\"\t1\n", + "\"jaws\"\t2\n", + "\"jelly\"\t1\n", + "\"jogged\"\t1\n", + "\"join\"\t9\n", + "\"joined\"\t3\n", + "\"journey\"\t1\n", + "\"joys\"\t1\n", + "\"judge\"\t4\n", + "\"judging\"\t1\n", + "\"jug\"\t1\n", + "\"jumped\"\t6\n", + "\"jumping\"\t4\n", + "\"juror\"\t1\n", + "\"jurors\"\t4\n", + "\"jury\"\t22\n", + "\"jurymen\"\t4\n", + 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+ "\"ou\"\t1\n", + "\"ought\"\t14\n", + "\"our\"\t8\n", + "\"ours\"\t1\n", + "\"ourselves\"\t1\n", + "\"out\"\t117\n", + "\"outside\"\t4\n", + "\"over\"\t40\n", + "\"overcome\"\t1\n", + "\"overhead\"\t1\n", + "\"owl\"\t3\n", + "\"own\"\t10\n", + "\"oyster\"\t1\n", + "\"p\"\t1\n", + "\"pace\"\t1\n", + "\"pack\"\t5\n", + "\"paint\"\t1\n", + "\"painting\"\t2\n", + "\"pair\"\t5\n", + "\"pairs\"\t1\n", + "\"pale\"\t4\n", + "\"pan\"\t1\n", + "\"panted\"\t1\n", + "\"panther\"\t3\n", + "\"panting\"\t2\n", + "\"paper\"\t4\n", + "\"parchment\"\t2\n", + "\"pardon\"\t6\n", + "\"pardoned\"\t1\n", + "\"paris\"\t2\n", + "\"part\"\t2\n", + "\"particular\"\t4\n", + "\"partner\"\t1\n", + "\"partners\"\t1\n", + "\"parts\"\t1\n", + "\"party\"\t10\n", + "\"pass\"\t1\n", + "\"passage\"\t4\n", + "\"passed\"\t5\n", + "\"passing\"\t1\n", + "\"passion\"\t3\n", + "\"passionate\"\t1\n", + "\"past\"\t3\n", + "\"pat\"\t3\n", + "\"patience\"\t1\n", + "\"patiently\"\t2\n", + "\"patriotic\"\t1\n", + "\"patted\"\t1\n", + "\"pattering\"\t3\n", + "\"pattern\"\t1\n", + "\"pause\"\t2\n", + "\"paused\"\t1\n", + "\"paw\"\t3\n", + "\"paws\"\t4\n", + "\"pebbles\"\t2\n", + "\"peeped\"\t3\n", + "\"peeping\"\t1\n", + "\"peering\"\t1\n", + "\"pegs\"\t1\n", + "\"pence\"\t1\n", + "\"pencil\"\t2\n", + "\"pencils\"\t1\n", + "\"pennyworth\"\t1\n", + "\"people\"\t13\n", + "\"pepper\"\t8\n", + "\"perfectly\"\t4\n", + "\"perhaps\"\t17\n", + "\"permitted\"\t1\n", + "\"persisted\"\t2\n", + "\"person\"\t4\n", + "\"personal\"\t2\n", + "\"persons\"\t1\n", + "\"pet\"\t1\n", + "\"picked\"\t3\n", + "\"picking\"\t2\n", + "\"picture\"\t1\n", + "\"pictured\"\t1\n", + "\"pictures\"\t4\n", + "\"pie\"\t3\n", + "\"piece\"\t6\n", + "\"pieces\"\t3\n", + "\"pig\"\t11\n", + "\"pigeon\"\t12\n", + "\"pigs\"\t6\n", + "\"pinch\"\t2\n", + "\"pinched\"\t2\n", + "\"pine\"\t1\n", + "\"pink\"\t1\n", + "\"piteous\"\t1\n", + "\"pitied\"\t1\n", + "\"pity\"\t3\n", + "\"place\"\t8\n", + "\"placed\"\t1\n", + "\"places\"\t2\n", + "\"plainly\"\t1\n", + "\"plan\"\t4\n", + "\"planning\"\t1\n", + "\"plate\"\t3\n", + "\"plates\"\t2\n", + "\"play\"\t8\n", + "\"played\"\t1\n", + "\"players\"\t4\n", + "\"playing\"\t2\n", + "\"pleaded\"\t3\n", + "\"pleasant\"\t1\n", + "\"pleasanter\"\t1\n", + "\"please\"\t19\n", + "\"pleased\"\t7\n", + "\"pleases\"\t1\n", + "\"pleasing\"\t1\n", + "\"pleasure\"\t2\n", + "\"plenty\"\t2\n", + "\"pocked\"\t1\n", + "\"pocket\"\t6\n", + "\"pointed\"\t1\n", + "\"pointing\"\t4\n", + "\"poison\"\t3\n", + "\"poker\"\t1\n", + "\"poky\"\t1\n", + "\"politely\"\t6\n", + "\"pool\"\t11\n", + "\"poor\"\t27\n", + "\"pop\"\t1\n", + "\"pope\"\t1\n", + "\"porpoise\"\t4\n", + "\"position\"\t2\n", + "\"positively\"\t1\n", + "\"possible\"\t1\n", + "\"possibly\"\t3\n", + "\"pot\"\t1\n", + "\"pounds\"\t1\n", + "\"pour\"\t1\n", + "\"poured\"\t1\n", + "\"powdered\"\t1\n", + "\"practice\"\t1\n", + "\"pray\"\t3\n", + "\"precious\"\t1\n", + "\"present\"\t3\n", + "\"presented\"\t1\n", + "\"presently\"\t2\n", + "\"presents\"\t2\n", + "\"pressed\"\t3\n", + "\"pressing\"\t1\n", + "\"pretend\"\t1\n", + "\"pretending\"\t1\n", + "\"pretexts\"\t1\n", + "\"prettier\"\t1\n", + "\"pretty\"\t1\n", + "\"prevent\"\t1\n", + "\"printed\"\t1\n", + "\"prison\"\t1\n", + "\"prisoner\"\t1\n", + "\"prisoner's\"\t1\n", + "\"prize\"\t1\n", + "\"prizes\"\t5\n", + "\"proceed\"\t2\n", + "\"procession\"\t5\n", + "\"processions\"\t1\n", + "\"produced\"\t1\n", + "\"producing\"\t1\n", + "\"promise\"\t1\n", + "\"promised\"\t1\n", + "\"promising\"\t1\n", + "\"pronounced\"\t1\n", + "\"proper\"\t3\n", + "\"proposal\"\t1\n", + "\"prosecute\"\t1\n", + "\"protection\"\t1\n", + "\"proud\"\t2\n", + "\"prove\"\t1\n", + "\"proved\"\t2\n", + "\"proves\"\t2\n", + "\"provoking\"\t1\n", + "\"puffed\"\t1\n", + "\"pulled\"\t1\n", + "\"pulling\"\t1\n", + "\"pun\"\t1\n", + "\"punching\"\t1\n", + "\"punished\"\t1\n", + "\"puppy\"\t6\n", + "\"puppy's\"\t1\n", + "\"purple\"\t1\n", + "\"purpose\"\t1\n", + "\"purring\"\t2\n", + "\"push\"\t1\n", + "\"puss\"\t1\n", + "\"put\"\t31\n", + "\"putting\"\t3\n", + "\"puzzle\"\t1\n", + "\"puzzled\"\t9\n", + "\"puzzling\"\t4\n", + "\"quadrille\"\t4\n", + "\"quarrel\"\t1\n", + "\"quarrelled\"\t1\n", + "\"quarrelling\"\t2\n", + "\"queen\"\t68\n", + "\"queen's\"\t7\n", + "\"queens\"\t1\n", + "\"queer\"\t12\n", + "\"queerest\"\t1\n", + "\"question\"\t17\n", + "\"questions\"\t4\n", + "\"quick\"\t2\n", + "\"quicker\"\t1\n", + "\"quickly\"\t2\n", + "\"quiet\"\t2\n", + "\"quietly\"\t5\n", + "\"quite\"\t55\n", + "\"quiver\"\t1\n", + "\"rabbit\"\t46\n", + "\"rabbit'\"\t1\n", + "\"rabbit's\"\t4\n", + "\"rabbits\"\t1\n", + "\"race\"\t6\n", + "\"railway\"\t2\n", + "\"raised\"\t2\n", + "\"raising\"\t1\n", + "\"ran\"\t16\n", + "\"rapidly\"\t2\n", + "\"rapped\"\t1\n", + "\"rat\"\t1\n", + "\"rate\"\t9\n", + "\"rather\"\t25\n", + "\"rats\"\t1\n", + "\"rattle\"\t1\n", + "\"rattling\"\t2\n", + "\"raven\"\t1\n", + "\"ravens\"\t1\n", + "\"raving\"\t2\n", + "\"raw\"\t1\n", + "\"reach\"\t4\n", + "\"reaching\"\t1\n", + "\"read\"\t11\n", + "\"readily\"\t1\n", + "\"reading\"\t3\n", + "\"ready\"\t8\n", + "\"real\"\t3\n", + "\"reality\"\t1\n", + "\"really\"\t13\n", + "\"rearing\"\t1\n", + "\"reason\"\t9\n", + "\"reasonable\"\t1\n", + "\"reasons\"\t1\n", + "\"received\"\t1\n", + "\"recognised\"\t1\n", + "\"recovered\"\t2\n", + "\"red\"\t3\n", + "\"reduced\"\t1\n", + "\"reeds\"\t1\n", + "\"reeling\"\t1\n", + "\"refreshments\"\t1\n", + "\"refused\"\t1\n", + "\"regular\"\t2\n", + "\"relief\"\t2\n", + "\"relieved\"\t1\n", + "\"remain\"\t1\n", + "\"remained\"\t3\n", + "\"remaining\"\t1\n", + "\"remark\"\t10\n", + "\"remarkable\"\t2\n", + "\"remarked\"\t10\n", + "\"remarking\"\t3\n", + "\"remarks\"\t3\n", + "\"remedies\"\t1\n", + "\"remember\"\t14\n", + "\"remembered\"\t5\n", + "\"remembering\"\t1\n", + "\"reminding\"\t1\n", + "\"removed\"\t2\n", + "\"repeat\"\t7\n", + "\"repeated\"\t10\n", + "\"repeating\"\t3\n", + "\"replied\"\t29\n", + "\"reply\"\t5\n", + "\"resource\"\t1\n", + "\"respect\"\t1\n", + "\"respectable\"\t1\n", + "\"respectful\"\t1\n", + "\"rest\"\t10\n", + "\"resting\"\t2\n", + "\"result\"\t1\n", + "\"retire\"\t1\n", + "\"returned\"\t2\n", + "\"returning\"\t1\n", + "\"rich\"\t1\n", + "\"riddle\"\t1\n", + "\"riddles\"\t2\n", + "\"ridge\"\t1\n", + "\"ridges\"\t1\n", + "\"ridiculous\"\t1\n", + "\"right\"\t32\n", + "\"righthand\"\t1\n", + "\"rightly\"\t1\n", + "\"ring\"\t2\n", + "\"ringlets\"\t2\n", + "\"riper\"\t1\n", + "\"rippling\"\t1\n", + "\"rise\"\t1\n", + "\"rises\"\t1\n", + "\"rising\"\t1\n", + "\"roared\"\t1\n", + "\"roast\"\t1\n", + "\"rock\"\t1\n", + "\"rocket\"\t1\n", + "\"rome\"\t2\n", + "\"roof\"\t6\n", + "\"room\"\t13\n", + "\"roots\"\t2\n", + "\"rope\"\t1\n", + "\"rose\"\t4\n", + "\"roses\"\t3\n", + "\"rosetree\"\t1\n", + "\"roughly\"\t1\n", + "\"round\"\t41\n", + "\"row\"\t2\n", + "\"royal\"\t2\n", + "\"rubbed\"\t1\n", + "\"rubbing\"\t2\n", + "\"rude\"\t2\n", + "\"rudeness\"\t1\n", + "\"rule\"\t5\n", + "\"rules\"\t3\n", + "\"rumbling\"\t1\n", + "\"run\"\t4\n", + "\"running\"\t8\n", + "\"rush\"\t2\n", + "\"rushed\"\t1\n", + "\"rustled\"\t1\n", + "\"rustling\"\t1\n", + "\"sad\"\t3\n", + "\"sadly\"\t5\n", + "\"safe\"\t2\n", + "\"sage\"\t1\n", + "\"said\"\t462\n", + "\"salmon\"\t1\n", + "\"salt\"\t2\n", + "\"same\"\t24\n", + "\"sand\"\t1\n", + "\"sands\"\t1\n", + "\"sang\"\t2\n", + "\"sat\"\t17\n", + "\"saucepan\"\t1\n", + "\"saucepans\"\t1\n", + "\"saucer\"\t1\n", + "\"savage\"\t4\n", + "\"save\"\t1\n", + "\"saves\"\t1\n", + "\"saw\"\t13\n", + "\"say\"\t52\n", + "\"saying\"\t15\n", + "\"says\"\t4\n", + "\"scale\"\t1\n", + "\"scaly\"\t1\n", + "\"school\"\t6\n", + "\"schoolroom\"\t1\n", + "\"scolded\"\t1\n", + "\"scrambling\"\t1\n", + "\"scratching\"\t1\n", + "\"scream\"\t2\n", + "\"screamed\"\t4\n", + "\"screaming\"\t1\n", + "\"scroll\"\t2\n", + "\"sea\"\t14\n", + "\"seals\"\t1\n", + "\"seaography\"\t1\n", + "\"search\"\t1\n", + "\"seaside\"\t1\n", + "\"seated\"\t1\n", + "\"second\"\t4\n", + "\"secondly\"\t2\n", + "\"secret\"\t1\n", + "\"see\"\t67\n", + "\"seeing\"\t1\n", + "\"seem\"\t8\n", + "\"seemed\"\t27\n", + "\"seems\"\t5\n", + "\"seen\"\t15\n", + "\"seldom\"\t1\n", + "\"sell\"\t2\n", + "\"send\"\t1\n", + "\"sending\"\t2\n", + "\"sends\"\t1\n", + "\"sensation\"\t2\n", + "\"sense\"\t3\n", + "\"sent\"\t2\n", + "\"sentence\"\t6\n", + "\"sentenced\"\t1\n", + "\"series\"\t1\n", + "\"seriously\"\t1\n", + "\"serpent\"\t9\n", + "\"serpents\"\t3\n", + "\"set\"\t14\n", + "\"setting\"\t1\n", + "\"settle\"\t1\n", + "\"settled\"\t3\n", + "\"settling\"\t1\n", + "\"seven\"\t6\n", + "\"several\"\t4\n", + "\"severely\"\t4\n", + "\"severity\"\t1\n", + "\"sh\"\t2\n", + "\"shade\"\t1\n", + "\"shake\"\t1\n", + "\"shakespeare\"\t1\n", + "\"shaking\"\t3\n", + "\"shall\"\t25\n", + "\"shan't\"\t6\n", + "\"shape\"\t1\n", + "\"shaped\"\t3\n", + "\"share\"\t1\n", + "\"shared\"\t1\n", + "\"sharing\"\t1\n", + "\"shark\"\t1\n", + "\"sharks\"\t1\n", + "\"sharp\"\t6\n", + "\"sharply\"\t4\n", + "\"she\"\t540\n", + "\"she'd\"\t2\n", + "\"she'll\"\t3\n", + "\"she's\"\t7\n", + "\"shedding\"\t1\n", + "\"sheep\"\t1\n", + "\"shelves\"\t2\n", + "\"shepherd\"\t1\n", + "\"shifting\"\t1\n", + "\"shilling\"\t1\n", + "\"shillings\"\t1\n", + "\"shingle\"\t1\n", + "\"shining\"\t1\n", + "\"shiny\"\t1\n", + "\"shiver\"\t1\n", + "\"shock\"\t1\n", + "\"shoes\"\t7\n", + "\"shook\"\t9\n", + "\"shore\"\t4\n", + "\"short\"\t4\n", + "\"shorter\"\t2\n", + "\"should\"\t27\n", + "\"shoulder\"\t4\n", + "\"shoulders\"\t4\n", + "\"shouldn't\"\t5\n", + "\"shouted\"\t9\n", + "\"shouting\"\t2\n", + "\"show\"\t3\n", + "\"shower\"\t2\n", + "\"showing\"\t2\n", + "\"shriek\"\t5\n", + "\"shrieked\"\t1\n", + "\"shrieks\"\t1\n", + "\"shrill\"\t5\n", + "\"shrimp\"\t1\n", + "\"shrink\"\t1\n", + "\"shrinking\"\t4\n", + "\"shut\"\t5\n", + "\"shutting\"\t2\n", + "\"shy\"\t1\n", + "\"shyly\"\t1\n", + "\"side\"\t17\n", + "\"sides\"\t4\n", + "\"sigh\"\t4\n", + "\"sighed\"\t5\n", + "\"sighing\"\t3\n", + "\"sight\"\t10\n", + "\"sign\"\t1\n", + "\"signed\"\t2\n", + "\"signifies\"\t1\n", + "\"signify\"\t1\n", + "\"silence\"\t14\n", + "\"silent\"\t7\n", + "\"simple\"\t5\n", + "\"simpleton\"\t1\n", + "\"simply\"\t3\n", + "\"since\"\t4\n", + "\"sing\"\t6\n", + "\"singers\"\t2\n", + "\"singing\"\t2\n", + "\"sink\"\t1\n", + "\"sir\"\t6\n", + "\"sir'\"\t1\n", + "\"sister\"\t8\n", + "\"sister's\"\t1\n", + "\"sisters\"\t2\n", + "\"sit\"\t8\n", + "\"sits\"\t1\n", + "\"sitting\"\t10\n", + "\"six\"\t2\n", + "\"sixpence\"\t1\n", + "\"sixteenth\"\t1\n", + "\"size\"\t13\n", + "\"sizes\"\t1\n", + "\"skimming\"\t1\n", + "\"skirt\"\t1\n", + "\"skurried\"\t1\n", + "\"sky\"\t5\n", + "\"slate\"\t4\n", + "\"slates\"\t7\n", + "\"slates'll\"\t1\n", + "\"sleep\"\t6\n", + "\"sleepy\"\t5\n", + "\"slightest\"\t1\n", + "\"slipped\"\t3\n", + "\"slippery\"\t1\n", + "\"slowly\"\t8\n", + "\"sluggard\"\t1\n", + "\"small\"\t10\n", + "\"smaller\"\t3\n", + "\"smallest\"\t2\n", + "\"smile\"\t2\n", + "\"smiled\"\t2\n", + "\"smiling\"\t2\n", + "\"smoke\"\t1\n", + "\"smoking\"\t2\n", + "\"snail\"\t3\n", + "\"snappishly\"\t1\n", + "\"snatch\"\t2\n", + "\"sneeze\"\t2\n", + "\"sneezed\"\t1\n", + "\"sneezes\"\t2\n", + "\"sneezing\"\t6\n", + "\"snorting\"\t1\n", + "\"snout\"\t1\n", + "\"so\"\t151\n", + "\"sob\"\t1\n", + "\"sobbed\"\t1\n", + "\"sobbing\"\t3\n", + "\"sobs\"\t4\n", + "\"soft\"\t1\n", + "\"softly\"\t1\n", + "\"soldier\"\t1\n", + "\"soldiers\"\t10\n", + "\"solemn\"\t3\n", + "\"solemnly\"\t4\n", + "\"soles\"\t1\n", + "\"solid\"\t1\n", + "\"some\"\t51\n", + "\"somebody\"\t7\n", + "\"somehow\"\t1\n", + "\"someone\"\t1\n", + "\"somersault\"\t2\n", + "\"something\"\t18\n", + "\"sometimes\"\t5\n", + "\"somewhere\"\t3\n", + "\"son\"\t1\n", + "\"song\"\t7\n", + "\"soo\"\t7\n", + "\"soon\"\t25\n", + "\"sooner\"\t2\n", + "\"soothing\"\t1\n", + "\"sorrow\"\t2\n", + "\"sorrowful\"\t2\n", + "\"sorrows\"\t1\n", + "\"sorry\"\t1\n", + "\"sort\"\t20\n", + "\"sorts\"\t3\n", + "\"sound\"\t4\n", + "\"sounded\"\t5\n", + "\"sounds\"\t4\n", + "\"soup\"\t18\n", + "\"sour\"\t1\n", + "\"spades\"\t1\n", + "\"speak\"\t15\n", + "\"speaker\"\t1\n", + "\"speaking\"\t5\n", + "\"spectacles\"\t3\n", + "\"speech\"\t3\n", + "\"speed\"\t1\n", + "\"spell\"\t1\n", + "\"spirited\"\t1\n", + "\"spite\"\t1\n", + "\"splash\"\t1\n", + "\"splashed\"\t1\n", + "\"splashing\"\t2\n", + "\"splendidly\"\t1\n", + "\"spoke\"\t17\n", + "\"spoken\"\t1\n", + "\"spoon\"\t2\n", + "\"spot\"\t1\n", + "\"sprawling\"\t1\n", + "\"spread\"\t3\n", + "\"spreading\"\t1\n", + "\"squeaked\"\t1\n", + "\"squeaking\"\t2\n", + "\"squeeze\"\t1\n", + "\"squeezed\"\t1\n", + "\"stairs\"\t3\n", + "\"stalk\"\t1\n", + "\"stamping\"\t2\n", + "\"stand\"\t6\n", + "\"standing\"\t1\n", + "\"star\"\t1\n", + "\"staring\"\t3\n", + "\"started\"\t2\n", + "\"startled\"\t2\n", + "\"state\"\t1\n", + "\"station\"\t1\n", + "\"stay\"\t5\n", + "\"stays\"\t1\n", + "\"steady\"\t1\n", + "\"steam\"\t1\n", + "\"sternly\"\t1\n", + "\"stick\"\t4\n", + "\"sticks\"\t1\n", + "\"stiff\"\t1\n", + "\"stigand\"\t1\n", + "\"still\"\t13\n", + "\"stingy\"\t1\n", + "\"stirring\"\t2\n", + "\"stockings\"\t1\n", + "\"stole\"\t2\n", + "\"stolen\"\t1\n", + "\"stood\"\t7\n", + "\"stool\"\t1\n", + "\"stoop\"\t2\n", + "\"stop\"\t6\n", + "\"stopped\"\t3\n", + "\"stopping\"\t1\n", + "\"story\"\t8\n", + "\"straight\"\t2\n", + "\"straightened\"\t1\n", + "\"straightening\"\t1\n", + "\"strange\"\t5\n", + "\"strength\"\t1\n", + "\"stretched\"\t2\n", + "\"stretching\"\t2\n", + "\"strings\"\t1\n", + "\"struck\"\t2\n", + "\"stuff\"\t4\n", + "\"stupid\"\t6\n", + "\"stupidest\"\t1\n", + "\"stupidly\"\t1\n", + "\"subdued\"\t1\n", + "\"subject\"\t6\n", + "\"subjects\"\t1\n", + "\"submitted\"\t1\n", + "\"succeeded\"\t3\n", + "\"such\"\t41\n", + "\"sudden\"\t5\n", + "\"suddenly\"\t13\n", + "\"suet\"\t1\n", + "\"sugar\"\t2\n", + "\"suit\"\t3\n", + "\"sulkily\"\t2\n", + "\"sulky\"\t3\n", + "\"summer\"\t2\n", + "\"sun\"\t2\n", + "\"supple\"\t1\n", + "\"suppose\"\t14\n", + "\"suppress\"\t1\n", + "\"suppressed\"\t4\n", + "\"sure\"\t24\n", + "\"surprise\"\t5\n", + "\"surprised\"\t7\n", + "\"swallow\"\t1\n", + "\"swallowed\"\t1\n", + "\"swallowing\"\t1\n", + "\"swam\"\t5\n", + "\"sweet\"\t1\n", + "\"swim\"\t5\n", + "\"swimming\"\t2\n", + "\"t\"\t1\n", + "\"table\"\t18\n", + "\"tail\"\t9\n", + "\"tails\"\t3\n", + "\"take\"\t22\n", + "\"taken\"\t4\n", + "\"takes\"\t2\n", + "\"taking\"\t5\n", + "\"tale\"\t4\n", + "\"tales\"\t1\n", + "\"talk\"\t14\n", + "\"talking\"\t17\n", + "\"taller\"\t2\n", + "\"tart\"\t1\n", + "\"tarts\"\t7\n", + "\"taste\"\t2\n", + "\"tasted\"\t3\n", + "\"tastes\"\t1\n", + "\"taught\"\t4\n", + "\"tea\"\t19\n", + "\"teaching\"\t1\n", + "\"teacup\"\t3\n", + "\"teacups\"\t2\n", + "\"teapot\"\t1\n", + "\"tears\"\t11\n", + "\"teases\"\t1\n", + "\"teeth\"\t1\n", + "\"telescope\"\t3\n", + "\"telescopes\"\t1\n", + "\"tell\"\t32\n", + "\"telling\"\t2\n", + "\"tells\"\t2\n", + "\"temper\"\t5\n", + "\"tempered\"\t2\n", + "\"ten\"\t6\n", + "\"terms\"\t1\n", + "\"terribly\"\t1\n", + 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"\"throne\"\t1\n", + "\"through\"\t12\n", + "\"throw\"\t3\n", + "\"throwing\"\t2\n", + "\"thrown\"\t1\n", + "\"thump\"\t2\n", + "\"thunder\"\t1\n", + "\"thunderstorm\"\t1\n", + "\"thy\"\t1\n", + "\"tide\"\t1\n", + "\"tidy\"\t1\n", + "\"tie\"\t1\n", + "\"tied\"\t1\n", + "\"tight\"\t1\n", + "\"till\"\t21\n", + "\"tillie\"\t1\n", + "\"time\"\t71\n", + "\"times\"\t6\n", + "\"timid\"\t3\n", + "\"timidly\"\t9\n", + "\"tinkling\"\t1\n", + "\"tiny\"\t4\n", + "\"tipped\"\t1\n", + "\"tiptoe\"\t2\n", + "\"tired\"\t7\n", + "\"tittered\"\t1\n", + "\"to\"\t729\n", + "\"toast\"\t1\n", + "\"today\"\t1\n", + "\"toes\"\t3\n", + "\"toffee\"\t1\n", + "\"together\"\t9\n", + "\"told\"\t6\n", + "\"tomorrow\"\t1\n", + "\"tone\"\t40\n", + "\"tones\"\t2\n", + "\"tongue\"\t4\n", + "\"too\"\t26\n", + "\"took\"\t24\n", + "\"top\"\t7\n", + "\"tops\"\t1\n", + "\"tortoise\"\t3\n", + "\"toss\"\t1\n", + "\"tossing\"\t3\n", + "\"touch\"\t1\n", + "\"tougher\"\t1\n", + "\"towards\"\t1\n", + "\"toys\"\t1\n", + "\"trampled\"\t1\n", + "\"traps\"\t1\n", + "\"tray\"\t1\n", + "\"treacle\"\t7\n", + "\"treading\"\t2\n", + "\"treat\"\t1\n", + "\"treated\"\t1\n", + "\"tree\"\t8\n", + "\"trees\"\t7\n", + "\"tremble\"\t1\n", + "\"trembled\"\t2\n", + "\"trembling\"\t6\n", + "\"tremulous\"\t1\n", + "\"trial\"\t7\n", + "\"trial's\"\t3\n", + "\"trials\"\t1\n", + "\"trickling\"\t1\n", + "\"tricks\"\t1\n", + "\"tried\"\t19\n", + "\"trims\"\t1\n", + "\"triumphantly\"\t2\n", + "\"trot\"\t1\n", + "\"trotting\"\t2\n", + "\"trouble\"\t6\n", + "\"true\"\t4\n", + "\"trumpet\"\t3\n", + "\"trusts\"\t1\n", + "\"truth\"\t1\n", + "\"truthful\"\t1\n", + "\"try\"\t12\n", + "\"trying\"\t14\n", + "\"tucked\"\t3\n", + "\"tulip\"\t1\n", + "\"tumbled\"\t1\n", + "\"tumbling\"\t2\n", + "\"tunnel\"\t1\n", + "\"tureen\"\t1\n", + "\"turkey\"\t1\n", + "\"turn\"\t11\n", + "\"turned\"\t16\n", + "\"turning\"\t12\n", + "\"turns\"\t3\n", + "\"turtle\"\t57\n", + "\"turtle's\"\t2\n", + "\"turtles\"\t2\n", + "\"tut\"\t2\n", + "\"twelfth\"\t1\n", + "\"twelve\"\t4\n", + "\"twentieth\"\t1\n", + "\"twenty\"\t3\n", + "\"twice\"\t5\n", + "\"twinkle\"\t8\n", + "\"twinkled\"\t1\n", + "\"twinkling\"\t4\n", + "\"twist\"\t2\n", + "\"two\"\t40\n", + "\"ugh\"\t2\n", + "\"uglification\"\t2\n", + "\"uglify\"\t1\n", + "\"uglifying\"\t1\n", + "\"ugly\"\t2\n", + "\"unable\"\t1\n", + "\"uncivil\"\t1\n", + "\"uncomfortable\"\t4\n", + "\"uncomfortably\"\t1\n", + "\"uncommon\"\t1\n", + "\"uncommonly\"\t1\n", + "\"uncorked\"\t1\n", + "\"under\"\t16\n", + "\"underneath\"\t1\n", + "\"understand\"\t6\n", + "\"understood\"\t1\n", + "\"undertone\"\t2\n", + "\"undo\"\t1\n", + "\"undoing\"\t1\n", + "\"uneasily\"\t2\n", + "\"uneasy\"\t1\n", + "\"unfolded\"\t2\n", + "\"unfortunate\"\t3\n", + "\"unhappy\"\t2\n", + "\"unimportant\"\t5\n", + "\"unjust\"\t1\n", + "\"unless\"\t2\n", + "\"unlocking\"\t1\n", + "\"unpleasant\"\t2\n", + "\"unrolled\"\t2\n", + "\"until\"\t5\n", + "\"untwist\"\t1\n", + "\"unusually\"\t1\n", + "\"unwillingly\"\t1\n", + "\"up\"\t100\n", + "\"upon\"\t26\n", + "\"upright\"\t1\n", + "\"upset\"\t3\n", + "\"upsetting\"\t1\n", + "\"upstairs\"\t1\n", + "\"us\"\t14\n", + "\"use\"\t18\n", + "\"used\"\t13\n", + "\"useful\"\t2\n", + "\"using\"\t2\n", + "\"usual\"\t5\n", + "\"usually\"\t2\n", + "\"usurpation\"\t1\n", + "\"v\"\t1\n", + "\"vague\"\t1\n", + "\"vanished\"\t4\n", + "\"vanishing\"\t1\n", + "\"variations\"\t1\n", + "\"various\"\t1\n", + "\"vegetable\"\t1\n", + "\"velvet\"\t1\n", + "\"venture\"\t3\n", + "\"ventured\"\t4\n", + "\"verdict\"\t4\n", + "\"verse\"\t4\n", + "\"verses\"\t4\n", + "\"very\"\t144\n", + "\"vi\"\t1\n", + "\"vii\"\t1\n", + "\"viii\"\t1\n", + "\"vinegar\"\t1\n", + "\"violence\"\t1\n", + "\"violent\"\t2\n", + "\"violently\"\t4\n", + "\"visit\"\t1\n", + "\"voice\"\t48\n", + "\"voices\"\t2\n", + "\"vote\"\t1\n", + "\"vulgar\"\t1\n", + "\"w\"\t1\n", + "\"wag\"\t1\n", + "\"wags\"\t1\n", + "\"waist\"\t1\n", + "\"waistcoat\"\t2\n", + "\"wait\"\t1\n", + "\"waited\"\t11\n", + "\"waiting\"\t9\n", + "\"wake\"\t2\n", + "\"walk\"\t5\n", + "\"walked\"\t10\n", + "\"walking\"\t5\n", + "\"walrus\"\t1\n", + "\"wander\"\t1\n", + "\"wandered\"\t2\n", + "\"wandering\"\t2\n", + "\"want\"\t9\n", + "\"wanted\"\t4\n", + "\"wants\"\t2\n", + "\"warning\"\t1\n", + "\"was\"\t357\n", + "\"wash\"\t2\n", + "\"washing\"\t3\n", + "\"wasn't\"\t11\n", + "\"waste\"\t1\n", + "\"wasting\"\t2\n", + "\"watch\"\t8\n", + "\"watched\"\t2\n", + "\"watching\"\t3\n", + "\"water\"\t5\n", + "\"waters\"\t1\n", + "\"waving\"\t5\n", + "\"way\"\t56\n", + "\"ways\"\t1\n", + "\"we\"\t30\n", + "\"we're\"\t2\n", + "\"we've\"\t2\n", + "\"weak\"\t2\n", + "\"wearily\"\t1\n", + "\"week\"\t3\n", + "\"weeks\"\t1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"welcome\"\t1\n", + "\"well\"\t63\n", + "\"went\"\t83\n", + "\"wept\"\t1\n", + "\"were\"\t84\n", + "\"were'\"\t1\n", + "\"weren't\"\t1\n", + "\"wet\"\t2\n", + "\"what\"\t136\n", + "\"what's\"\t5\n", + "\"whatever\"\t3\n", + "\"when\"\t79\n", + "\"whenever\"\t1\n", + "\"where\"\t13\n", + "\"where's\"\t2\n", + "\"whereupon\"\t1\n", + "\"wherever\"\t2\n", + "\"whether\"\t11\n", + "\"which\"\t49\n", + "\"while\"\t25\n", + "\"whiles\"\t1\n", + "\"whiskers\"\t3\n", + "\"whisper\"\t3\n", + "\"whispered\"\t5\n", + "\"whispers\"\t1\n", + "\"whistle\"\t1\n", + "\"whistling\"\t1\n", + "\"white\"\t30\n", + "\"whiting\"\t8\n", + "\"who\"\t61\n", + "\"who's\"\t2\n", + "\"whoever\"\t1\n", + "\"whole\"\t13\n", + "\"whom\"\t1\n", + "\"whose\"\t2\n", + "\"why\"\t40\n", + "\"wide\"\t2\n", + "\"wider\"\t1\n", + "\"wife\"\t1\n", + "\"wig\"\t2\n", + "\"wild\"\t2\n", + "\"wildly\"\t2\n", + "\"will\"\t33\n", + "\"william\"\t7\n", + "\"william's\"\t1\n", + "\"win\"\t1\n", + "\"wind\"\t2\n", + "\"window\"\t8\n", + "\"wine\"\t2\n", + "\"wings\"\t1\n", + "\"wink\"\t2\n", + "\"winter\"\t1\n", + "\"wise\"\t2\n", + "\"wish\"\t21\n", + "\"with\"\t180\n", + "\"within\"\t2\n", + "\"without\"\t26\n", + "\"witness\"\t10\n", + "\"wits\"\t1\n", + "\"woke\"\t1\n", + "\"woman\"\t2\n", + "\"won\"\t2\n", + "\"won't\"\t23\n", + "\"won't'\"\t1\n", + "\"wonder\"\t18\n", + "\"wondered\"\t1\n", + "\"wonderful\"\t2\n", + "\"wondering\"\t7\n", + "\"wonderland\"\t3\n", + "\"wood\"\t8\n", + "\"wooden\"\t1\n", + "\"word\"\t10\n", + "\"words\"\t21\n", + "\"wore\"\t1\n", + "\"work\"\t8\n", + "\"works\"\t1\n", + "\"world\"\t7\n", + "\"worm\"\t1\n", + "\"worried\"\t1\n", + "\"worry\"\t1\n", + "\"worse\"\t3\n", + "\"worth\"\t4\n", + "\"would\"\t83\n", + "\"wouldn't\"\t13\n", + "\"wow\"\t6\n", + "\"wrapping\"\t1\n", + "\"wretched\"\t2\n", + "\"wriggling\"\t1\n", + "\"write\"\t6\n", + "\"writhing\"\t1\n", + "\"writing\"\t6\n", + "\"written\"\t6\n", + "\"wrong\"\t5\n", + "\"wrote\"\t3\n", + "\"x\"\t1\n", + "\"xi\"\t1\n", + "\"xii\"\t1\n", + "\"yard\"\t1\n", + "\"yards\"\t1\n", + "\"yawned\"\t2\n", + "\"yawning\"\t2\n", + "\"ye\"\t1\n", + "\"year\"\t2\n", + "\"years\"\t1\n", + "\"yelp\"\t1\n", + "\"yer\"\t4\n", + "\"yes\"\t13\n", + "\"yesterday\"\t3\n", + "\"yet\"\t25\n", + "\"you\"\t365\n", + "\"you'd\"\t10\n", + "\"you'll\"\t6\n", + "\"you're\"\t23\n", + "\"you've\"\t7\n", + "\"young\"\t5\n", + "\"your\"\t62\n", + "\"yours\"\t3\n", + "\"yourself\"\t10\n", + "\"youth\"\t6\n", + "\"zealand\"\t1\n", + "\"zigzag\"\t1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "INFO:mrjob.runner:Removing temp directory c:\\users\\ps\\appdata\\local\\temp\\MRWordFreqCount.PS.20181001.022200.276000...\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "ERROR:mrjob.runner:[Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\site-packages\\mrjob\\runner.py\", line 615, in _cleanup_local_tmp\n", + " shutil.rmtree(self._local_tmp_dir)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 261, in rmtree\n", + " rmtree(fullname, ignore_errors, onerror)\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 266, in rmtree\n", + " onerror(os.remove, fullname, sys.exc_info())\n", + " File \"C:\\Users\\PS\\anaconda2\\lib\\shutil.py\", line 264, in rmtree\n", + " os.remove(fullname)\n", + "WindowsError: [Error 5] Access is denied: u'c:\\\\users\\\\ps\\\\appdata\\\\local\\\\temp\\\\MRWordFreqCount.PS.20181001.022200.276000\\\\step\\\\000\\\\cache\\\\MRWordFreqCount.py'\n" + ] + } + ], + "source": [ + "%run code/MRWordFreqCount.py data/canterbury/alice29.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pyspark\n", + " Downloading https://files.pythonhosted.org/packages/5e/cb/d8ff49ba885e2c88b8cf2967edd84235ffa9ac301bffef657dfa5605a112/pyspark-2.3.2.tar.gz (211.9MB)\n", + "Collecting py4j==0.10.7 (from pyspark)\n", + " Downloading https://files.pythonhosted.org/packages/e3/53/c737818eb9a7dc32a7cd4f1396e787bd94200c3997c72c1dbe028587bd76/py4j-0.10.7-py2.py3-none-any.whl (197kB)\n", + "Building wheels for collected packages: pyspark\n", + " Running setup.py bdist_wheel for pyspark: started\n", + " Running setup.py bdist_wheel for pyspark: still running...\n", + " Running setup.py bdist_wheel for pyspark: finished with status 'done'\n", + " Stored in directory: C:\\Users\\PS\\AppData\\Local\\pip\\Cache\\wheels\\be\\7d\\34\\cd3cfbc75d8b6b6ae0658e5425348560b86d187fe3e53832cc\n", + "Successfully built pyspark\n", + "Installing collected packages: py4j, pyspark\n", + "Successfully installed py4j-0.10.7 pyspark-2.3.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "distributed 1.21.8 requires msgpack, which is not installed.\n", + "grin 1.2.1 requires argparse>=1.1, which is not installed.\n", + "You are using pip version 10.0.1, however version 18.0 is available.\n", + "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "!pip install pyspark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to implement this is to use SPARK on your local box: \n", + "Need to load spark and pyspark to get all this working\n", + "\n", + "\n", + " \n", + "http://spark.apache.org/downloads.html\n", + "\n", + "\n", + " pyspark: pip install pyspark\n", + " \n", + " On windows you need to follow:\n", + " https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c\n", + " \n", + " \n", + "Test code From:\n", + "https://github.com/eBay/Spark/blob/master/examples/src/main/python/wordcount.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %load code\\pysparkcount.py\n", + "#\n", + "# Licensed to the Apache Software Foundation (ASF) under one or more\n", + "# contributor license agreements. See the NOTICE file distributed with\n", + "# this work for additional information regarding copyright ownership.\n", + "# The ASF licenses this file to You under the Apache License, Version 2.0\n", + "# (the \"License\"); you may not use this file except in compliance with\n", + "# the License. You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "#\n", + "#https://github.com/apache/spark/blob/master/examples/src/main/python/wordcount.py\n", + "\n", + "from __future__ import print_function\n", + "\n", + "import sys\n", + "from operator import add\n", + "\n", + "from pyspark import SparkContext\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " if len(sys.argv) != 2:\n", + " print(\"Usage: wordcount \", file=sys.stderr)\n", + " exit(-1)\n", + " sc = SparkContext(appName=\"PythonWordCount\")\n", + " lines = sc.textFile(sys.argv[1], 1)\n", + " counts = lines.flatMap(lambda x: x.split(' ')) \\\n", + " .map(lambda x: (x, 1)) \\\n", + " .reduceByKey(add)\n", + " output = counts.collect()\n", + " for (word, count) in output:\n", + " print(\"%s: %i\" % (word, count))\n", + "\n", + " sc.stop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Should be able to run using\n", + "%run code/pysparkcount.py data/canterbury/alice29.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByInt.py b/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByInt.py new file mode 100644 index 0000000..03bf223 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByInt.py @@ -0,0 +1,15 @@ +from mrjob.job import MRJob + +class MRSortByInt(MRJob): + def mapper(self, _, line): + """ + """ + l = line.strip('\n').split() + yield '%01d'%int(l[1]), l[0] + + def reducer(self, key, val): + yield int(key), int(list(val)[0]) + + +if __name__ == '__main__': + MRSortByInt.run() \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByString.py b/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByString.py new file mode 100644 index 0000000..a76457d --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/code/MRSortByString.py @@ -0,0 +1,16 @@ +from mrjob.job import MRJob + +class MRSortByString(MRJob): + def mapper(self, _, line): + """ + """ + l = line.split(' ') + print l + yield l[1], l[0] + + def reducer(self, key, val): + yield key, [v for v in val][0] + + +if __name__ == '__main__': + MRSortByString.run() \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFreqCount.py b/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFreqCount.py new file mode 100644 index 0000000..58362a7 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFreqCount.py @@ -0,0 +1,21 @@ +from mrjob.job import MRJob +import re + +WORD_RE = re.compile(r"[\w']+") + + +class MRWordFreqCount(MRJob): + + def mapper(self, _, line): + for word in WORD_RE.findall(line): + yield word.lower(), 1 + + def combiner(self, word, counts): + yield word, sum(counts) + + def reducer(self, word, counts): + yield word, sum(counts) + + +if __name__ == '__main__': + MRWordFreqCount.run() \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFrequencyCount.py b/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFrequencyCount.py new file mode 100644 index 0000000..d0bb8fd --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/code/MRWordFrequencyCount.py @@ -0,0 +1,14 @@ +from mrjob.job import MRJob + +class MRWordFrequencyCount(MRJob): + + def mapper(self, _ , line): + yield "chars", len(line) + yield "words", len(line.split()) + yield "lines", 1 + + def reducer(self, key, values): + yield key, sum(values) + +if __name__ == '__main__': + MRWordFrequencyCount.run() \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/pysparkcount.py b/Lectures/Week 5 - MapReduce - MRJob/code/pysparkcount.py new file mode 100644 index 0000000..96d0332 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/code/pysparkcount.py @@ -0,0 +1,40 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +#https://github.com/apache/spark/blob/master/examples/src/main/python/wordcount.py + +from __future__ import print_function + +import sys +from operator import add + +from pyspark import SparkContext + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: wordcount ", file=sys.stderr) + exit(-1) + sc = SparkContext(appName="PythonWordCount") + lines = sc.textFile(sys.argv[1], 1) + counts = lines.flatMap(lambda x: x.split(' ')) \ + .map(lambda x: (x, 1)) \ + .reduceByKey(add) + output = counts.collect() + for (word, count) in output: + print("%s: %i" % (word, count)) + + sc.stop() \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/code/untitled.txt b/Lectures/Week 5 - MapReduce - MRJob/code/untitled.txt new file mode 100644 index 0000000..e69de29 diff --git a/Lectures/Week 5 - MapReduce - MRJob/data/canterbury/alice29.txt b/Lectures/Week 5 - MapReduce - MRJob/data/canterbury/alice29.txt new file mode 100644 index 0000000..733052d --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/data/canterbury/alice29.txt @@ -0,0 +1 @@ + ALICE'S ADVENTURES IN WONDERLAND Lewis Carroll THE MILLENNIUM FULCRUM EDITION 2.9 CHAPTER I Down the Rabbit-Hole Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, `and what is the use of a book,' thought Alice `without pictures or conversation?' So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her. There was nothing so VERY remarkable in that; nor did Alice think it so VERY much out of the way to hear the Rabbit say to itself, `Oh dear! Oh dear! I shall be late!' (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when the Rabbit actually TOOK A WATCH OUT OF ITS WAISTCOAT- POCKET, and looked at it, and then hurried on, Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge. In another moment down went Alice after it, never once considering how in the world she was to get out again. The rabbit-hole went straight on like a tunnel for some way, and then dipped suddenly down, so suddenly that Alice had not a moment to think about stopping herself before she found herself falling down a very deep well. Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled `ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it. `Well!' thought Alice to herself, `after such a fall as this, I shall think nothing of tumbling down stairs! How brave they'll all think me at home! Why, I wouldn't say anything about it, even if I fell off the top of the house!' (Which was very likely true.) Down, down, down. Would the fall NEVER come to an end! `I wonder how many miles I've fallen by this time?' she said aloud. `I must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I think--' (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a VERY good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) `--yes, that's about the right distance--but then I wonder what Latitude or Longitude I've got to?' (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.) Presently she began again. `I wonder if I shall fall right THROUGH the earth! How funny it'll seem to come out among the people that walk with their heads downward! The Antipathies, I think--' (she was rather glad there WAS no one listening, this time, as it didn't sound at all the right word) `--but I shall have to ask them what the name of the country is, you know. Please, Ma'am, is this New Zealand or Australia?' (and she tried to curtsey as she spoke--fancy CURTSEYING as you're falling through the air! Do you think you could manage it?) `And what an ignorant little girl she'll think me for asking! No, it'll never do to ask: perhaps I shall see it written up somewhere.' Down, down, down. There was nothing else to do, so Alice soon began talking again. `Dinah'll miss me very much to-night, I should think!' (Dinah was the cat.) `I hope they'll remember her saucer of milk at tea-time. Dinah my dear! I wish you were down here with me! There are no mice in the air, I'm afraid, but you might catch a bat, and that's very like a mouse, you know. But do cats eat bats, I wonder?' And here Alice began to get rather sleepy, and went on saying to herself, in a dreamy sort of way, `Do cats eat bats? Do cats eat bats?' and sometimes, `Do bats eat cats?' for, you see, as she couldn't answer either question, it didn't much matter which way she put it. She felt that she was dozing off, and had just begun to dream that she was walking hand in hand with Dinah, and saying to her very earnestly, `Now, Dinah, tell me the truth: did you ever eat a bat?' when suddenly, thump! thump! down she came upon a heap of sticks and dry leaves, and the fall was over. Alice was not a bit hurt, and she jumped up on to her feet in a moment: she looked up, but it was all dark overhead; before her was another long passage, and the White Rabbit was still in sight, hurrying down it. There was not a moment to be lost: away went Alice like the wind, and was just in time to hear it say, as it turned a corner, `Oh my ears and whiskers, how late it's getting!' She was close behind it when she turned the corner, but the Rabbit was no longer to be seen: she found herself in a long, low hall, which was lit up by a row of lamps hanging from the roof. There were doors all round the hall, but they were all locked; and when Alice had been all the way down one side and up the other, trying every door, she walked sadly down the middle, wondering how she was ever to get out again. Suddenly she came upon a little three-legged table, all made of solid glass; there was nothing on it except a tiny golden key, and Alice's first thought was that it might belong to one of the doors of the hall; but, alas! either the locks were too large, or the key was too small, but at any rate it would not open any of them. However, on the second time round, she came upon a low curtain she had not noticed before, and behind it was a little door about fifteen inches high: she tried the little golden key in the lock, and to her great delight it fitted! Alice opened the door and found that it led into a small passage, not much larger than a rat-hole: she knelt down and looked along the passage into the loveliest garden you ever saw. How she longed to get out of that dark hall, and wander about among those beds of bright flowers and those cool fountains, but she could not even get her head though the doorway; `and even if my head would go through,' thought poor Alice, `it would be of very little use without my shoulders. Oh, how I wish I could shut up like a telescope! I think I could, if I only know how to begin.' For, you see, so many out-of-the-way things had happened lately, that Alice had begun to think that very few things indeed were really impossible. There seemed to be no use in waiting by the little door, so she went back to the table, half hoping she might find another key on it, or at any rate a book of rules for shutting people up like telescopes: this time she found a little bottle on it, (`which certainly was not here before,' said Alice,) and round the neck of the bottle was a paper label, with the words `DRINK ME' beautifully printed on it in large letters. It was all very well to say `Drink me,' but the wise little Alice was not going to do THAT in a hurry. `No, I'll look first,' she said, `and see whether it's marked "poison" or not'; for she had read several nice little histories about children who had got burnt, and eaten up by wild beasts and other unpleasant things, all because they WOULD not remember the simple rules their friends had taught them: such as, that a red-hot poker will burn you if you hold it too long; and that if you cut your finger VERY deeply with a knife, it usually bleeds; and she had never forgotten that, if you drink much from a bottle marked `poison,' it is almost certain to disagree with you, sooner or later. However, this bottle was NOT marked `poison,' so Alice ventured to taste it, and finding it very nice, (it had, in fact, a sort of mixed flavour of cherry-tart, custard, pine-apple, roast turkey, toffee, and hot buttered toast,) she very soon finished it off. * * * * * * * * * * * * * * * * * * * * `What a curious feeling!' said Alice; `I must be shutting up like a telescope.' And so it was indeed: she was now only ten inches high, and her face brightened up at the thought that she was now the right size for going though the little door into that lovely garden. First, however, she waited for a few minutes to see if she was going to shrink any further: she felt a little nervous about this; `for it might end, you know,' said Alice to herself, `in my going out altogether, like a candle. I wonder what I should be like then?' And she tried to fancy what the flame of a candle is like after the candle is blown out, for she could not remember ever having seen such a thing. After a while, finding that nothing more happened, she decided on going into the garden at once; but, alas for poor Alice! when she got to the door, she found he had forgotten the little golden key, and when she went back to the table for it, she found she could not possibly reach it: she could see it quite plainly through the glass, and she tried her best to climb up one of the legs of the table, but it was too slippery; and when she had tired herself out with trying, the poor little thing sat down and cried. `Come, there's no use in crying like that!' said Alice to herself, rather sharply; `I advise you to leave off this minute!' She generally gave herself very good advice, (though she very seldom followed it), and sometimes she scolded herself so severely as to bring tears into her eyes; and once she remembered trying to box her own ears for having cheated herself in a game of croquet she was playing against herself, for this curious child was very fond of pretending to be two people. `But it's no use now,' thought poor Alice, `to pretend to be two people! Why, there's hardly enough of me left to make ONE respectable person!' Soon her eye fell on a little glass box that was lying under the table: she opened it, and found in it a very small cake, on which the words `EAT ME' were beautifully marked in currants. `Well, I'll eat it,' said Alice, `and if it makes me grow larger, I can reach the key; and if it makes me grow smaller, I can creep under the door; so either way I'll get into the garden, and I don't care which happens!' She ate a little bit, and said anxiously to herself, `Which way? Which way?', holding her hand on the top of her head to feel which way it was growing, and she was quite surprised to find that she remained the same size: to be sure, this generally happens when one eats cake, but Alice had got so much into the way of expecting nothing but out-of-the-way things to happen, that it seemed quite dull and stupid for life to go on in the common way. So she set to work, and very soon finished off the cake. * * * * * * * * * * * * * * * * * * * * CHAPTER II The Pool of Tears `Curiouser and curiouser!' cried Alice (she was so much surprised, that for the moment she quite forgot how to speak good English); `now I'm opening out like the largest telescope that ever was! Good-bye, feet!' (for when she looked down at her feet, they seemed to be almost out of sight, they were getting so far off). `Oh, my poor little feet, I wonder who will put on your shoes and stockings for you now, dears? I'm sure _I_ shan't be able! I shall be a great deal too far off to trouble myself about you: you must manage the best way you can; --but I must be kind to them,' thought Alice, `or perhaps they won't walk the way I want to go! Let me see: I'll give them a new pair of boots every Christmas.' And she went on planning to herself how she would manage it. `They must go by the carrier,' she thought; `and how funny it'll seem, sending presents to one's own feet! And how odd the directions will look! ALICE'S RIGHT FOOT, ESQ. HEARTHRUG, NEAR THE FENDER, (WITH ALICE'S LOVE). Oh dear, what nonsense I'm talking!' Just then her head struck against the roof of the hall: in fact she was now more than nine feet high, and she at once took up the little golden key and hurried off to the garden door. Poor Alice! It was as much as she could do, lying down on one side, to look through into the garden with one eye; but to get through was more hopeless than ever: she sat down and began to cry again. `You ought to be ashamed of yourself,' said Alice, `a great girl like you,' (she might well say this), `to go on crying in this way! Stop this moment, I tell you!' But she went on all the same, shedding gallons of tears, until there was a large pool all round her, about four inches deep and reaching half down the hall. After a time she heard a little pattering of feet in the distance, and she hastily dried her eyes to see what was coming. It was the White Rabbit returning, splendidly dressed, with a pair of white kid gloves in one hand and a large fan in the other: he came trotting along in a great hurry, muttering to himself as he came, `Oh! the Duchess, the Duchess! Oh! won't she be savage if I've kept her waiting!' Alice felt so desperate that she was ready to ask help of any one; so, when the Rabbit came near her, she began, in a low, timid voice, `If you please, sir--' The Rabbit started violently, dropped the white kid gloves and the fan, and skurried away into the darkness as hard as he could go. Alice took up the fan and gloves, and, as the hall was very hot, she kept fanning herself all the time she went on talking: `Dear, dear! How queer everything is to-day! And yesterday things went on just as usual. I wonder if I've been changed in the night? Let me think: was I the same when I got up this morning? I almost think I can remember feeling a little different. But if I'm not the same, the next question is, Who in the world am I? Ah, THAT'S the great puzzle!' And she began thinking over all the children she knew that were of the same age as herself, to see if she could have been changed for any of them. `I'm sure I'm not Ada,' she said, `for her hair goes in such long ringlets, and mine doesn't go in ringlets at all; and I'm sure I can't be Mabel, for I know all sorts of things, and she, oh! she knows such a very little! Besides, SHE'S she, and I'm I, and--oh dear, how puzzling it all is! I'll try if I know all the things I used to know. Let me see: four times five is twelve, and four times six is thirteen, and four times seven is--oh dear! I shall never get to twenty at that rate! However, the Multiplication Table doesn't signify: let's try Geography. London is the capital of Paris, and Paris is the capital of Rome, and Rome--no, THAT'S all wrong, I'm certain! I must have been changed for Mabel! I'll try and say "How doth the little--"' and she crossed her hands on her lap as if she were saying lessons, and began to repeat it, but her voice sounded hoarse and strange, and the words did not come the same as they used to do:-- `How doth the little crocodile Improve his shining tail, And pour the waters of the Nile On every golden scale! `How cheerfully he seems to grin, How neatly spread his claws, And welcome little fishes in With gently smiling jaws!' `I'm sure those are not the right words,' said poor Alice, and her eyes filled with tears again as she went on, `I must be Mabel after all, and I shall have to go and live in that poky little house, and have next to no toys to play with, and oh! ever so many lessons to learn! No, I've made up my mind about it; if I'm Mabel, I'll stay down here! It'll be no use their putting their heads down and saying "Come up again, dear!" I shall only look up and say "Who am I then? Tell me that first, and then, if I like being that person, I'll come up: if not, I'll stay down here till I'm somebody else"--but, oh dear!' cried Alice, with a sudden burst of tears, `I do wish they WOULD put their heads down! I am so VERY tired of being all alone here!' As she said this she looked down at her hands, and was surprised to see that she had put on one of the Rabbit's little white kid gloves while she was talking. `How CAN I have done that?' she thought. `I must be growing small again.' She got up and went to the table to measure herself by it, and found that, as nearly as she could guess, she was now about two feet high, and was going on shrinking rapidly: she soon found out that the cause of this was the fan she was holding, and she dropped it hastily, just in time to avoid shrinking away altogether. `That WAS a narrow escape!' said Alice, a good deal frightened at the sudden change, but very glad to find herself still in existence; `and now for the garden!' and she ran with all speed back to the little door: but, alas! the little door was shut again, and the little golden key was lying on the glass table as before, `and things are worse than ever,' thought the poor child, `for I never was so small as this before, never! And I declare it's too bad, that it is!' As she said these words her foot slipped, and in another moment, splash! she was up to her chin in salt water. He first idea was that she had somehow fallen into the sea, `and in that case I can go back by railway,' she said to herself. (Alice had been to the seaside once in her life, and had come to the general conclusion, that wherever you go to on the English coast you find a number of bathing machines in the sea, some children digging in the sand with wooden spades, then a row of lodging houses, and behind them a railway station.) However, she soon made out that she was in the pool of tears which she had wept when she was nine feet high. `I wish I hadn't cried so much!' said Alice, as she swam about, trying to find her way out. `I shall be punished for it now, I suppose, by being drowned in my own tears! That WILL be a queer thing, to be sure! However, everything is queer to-day.' Just then she heard something splashing about in the pool a little way off, and she swam nearer to make out what it was: at first she thought it must be a walrus or hippopotamus, but then she remembered how small she was now, and she soon made out that it was only a mouse that had slipped in like herself. `Would it be of any use, now,' thought Alice, `to speak to this mouse? Everything is so out-of-the-way down here, that I should think very likely it can talk: at any rate, there's no harm in trying.' So she began: `O Mouse, do you know the way out of this pool? I am very tired of swimming about here, O Mouse!' (Alice thought this must be the right way of speaking to a mouse: she had never done such a thing before, but she remembered having seen in her brother's Latin Grammar, `A mouse--of a mouse--to a mouse--a mouse--O mouse!' The Mouse looked at her rather inquisitively, and seemed to her to wink with one of its little eyes, but it said nothing. `Perhaps it doesn't understand English,' thought Alice; `I daresay it's a French mouse, come over with William the Conqueror.' (For, with all her knowledge of history, Alice had no very clear notion how long ago anything had happened.) So she began again: `Ou est ma chatte?' which was the first sentence in her French lesson-book. The Mouse gave a sudden leap out of the water, and seemed to quiver all over with fright. `Oh, I beg your pardon!' cried Alice hastily, afraid that she had hurt the poor animal's feelings. `I quite forgot you didn't like cats.' `Not like cats!' cried the Mouse, in a shrill, passionate voice. `Would YOU like cats if you were me?' `Well, perhaps not,' said Alice in a soothing tone: `don't be angry about it. And yet I wish I could show you our cat Dinah: I think you'd take a fancy to cats if you could only see her. She is such a dear quiet thing,' Alice went on, half to herself, as she swam lazily about in the pool, `and she sits purring so nicely by the fire, licking her paws and washing her face--and she is such a nice soft thing to nurse--and she's such a capital one for catching mice--oh, I beg your pardon!' cried Alice again, for this time the Mouse was bristling all over, and she felt certain it must be really offended. `We won't talk about her any more if you'd rather not.' `We indeed!' cried the Mouse, who was trembling down to the end of his tail. `As if I would talk on such a subject! Our family always HATED cats: nasty, low, vulgar things! Don't let me hear the name again!' `I won't indeed!' said Alice, in a great hurry to change the subject of conversation. `Are you--are you fond--of--of dogs?' The Mouse did not answer, so Alice went on eagerly: `There is such a nice little dog near our house I should like to show you! A little bright-eyed terrier, you know, with oh, such long curly brown hair! And it'll fetch things when you throw them, and it'll sit up and beg for its dinner, and all sorts of things--I can't remember half of them--and it belongs to a farmer, you know, and he says it's so useful, it's worth a hundred pounds! He says it kills all the rats and--oh dear!' cried Alice in a sorrowful tone, `I'm afraid I've offended it again!' For the Mouse was swimming away from her as hard as it could go, and making quite a commotion in the pool as it went. So she called softly after it, `Mouse dear! Do come back again, and we won't talk about cats or dogs either, if you don't like them!' When the Mouse heard this, it turned round and swam slowly back to her: its face was quite pale (with passion, Alice thought), and it said in a low trembling voice, `Let us get to the shore, and then I'll tell you my history, and you'll understand why it is I hate cats and dogs.' It was high time to go, for the pool was getting quite crowded with the birds and animals that had fallen into it: there were a Duck and a Dodo, a Lory and an Eaglet, and several other curious creatures. Alice led the way, and the whole party swam to the shore. CHAPTER III A Caucus-Race and a Long Tale They were indeed a queer-looking party that assembled on the bank--the birds with draggled feathers, the animals with their fur clinging close to them, and all dripping wet, cross, and uncomfortable. The first question of course was, how to get dry again: they had a consultation about this, and after a few minutes it seemed quite natural to Alice to find herself talking familiarly with them, as if she had known them all her life. Indeed, she had quite a long argument with the Lory, who at last turned sulky, and would only say, `I am older than you, and must know better'; and this Alice would not allow without knowing how old it was, and, as the Lory positively refused to tell its age, there was no more to be said. At last the Mouse, who seemed to be a person of authority among them, called out, `Sit down, all of you, and listen to me! I'LL soon make you dry enough!' They all sat down at once, in a large ring, with the Mouse in the middle. Alice kept her eyes anxiously fixed on it, for she felt sure she would catch a bad cold if she did not get dry very soon. `Ahem!' said the Mouse with an important air, `are you all ready? This is the driest thing I know. Silence all round, if you please! "William the Conqueror, whose cause was favoured by the pope, was soon submitted to by the English, who wanted leaders, and had been of late much accustomed to usurpation and conquest. Edwin and Morcar, the earls of Mercia and Northumbria--"' `Ugh!' said the Lory, with a shiver. `I beg your pardon!' said the Mouse, frowning, but very politely: `Did you speak?' `Not I!' said the Lory hastily. `I thought you did,' said the Mouse. `--I proceed. "Edwin and Morcar, the earls of Mercia and Northumbria, declared for him: and even Stigand, the patriotic archbishop of Canterbury, found it advisable--"' `Found WHAT?' said the Duck. `Found IT,' the Mouse replied rather crossly: `of course you know what "it" means.' `I know what "it" means well enough, when I find a thing,' said the Duck: `it's generally a frog or a worm. The question is, what did the archbishop find?' The Mouse did not notice this question, but hurriedly went on, `"--found it advisable to go with Edgar Atheling to meet William and offer him the crown. William's conduct at first was moderate. But the insolence of his Normans--" How are you getting on now, my dear?' it continued, turning to Alice as it spoke. `As wet as ever,' said Alice in a melancholy tone: `it doesn't seem to dry me at all.' `In that case,' said the Dodo solemnly, rising to its feet, `I move that the meeting adjourn, for the immediate adoption of more energetic remedies--' `Speak English!' said the Eaglet. `I don't know the meaning of half those long words, and, what's more, I don't believe you do either!' And the Eaglet bent down its head to hide a smile: some of the other birds tittered audibly. `What I was going to say,' said the Dodo in an offended tone, `was, that the best thing to get us dry would be a Caucus-race.' `What IS a Caucus-race?' said Alice; not that she wanted much to know, but the Dodo had paused as if it thought that SOMEBODY ought to speak, and no one else seemed inclined to say anything. `Why,' said the Dodo, `the best way to explain it is to do it.' (And, as you might like to try the thing yourself, some winter day, I will tell you how the Dodo managed it.) First it marked out a race-course, in a sort of circle, (`the exact shape doesn't matter,' it said,) and then all the party were placed along the course, here and there. There was no `One, two, three, and away,' but they began running when they liked, and left off when they liked, so that it was not easy to know when the race was over. However, when they had been running half an hour or so, and were quite dry again, the Dodo suddenly called out `The race is over!' and they all crowded round it, panting, and asking, `But who has won?' This question the Dodo could not answer without a great deal of thought, and it sat for a long time with one finger pressed upon its forehead (the position in which you usually see Shakespeare, in the pictures of him), while the rest waited in silence. At last the Dodo said, `EVERYBODY has won, and all must have prizes.' `But who is to give the prizes?' quite a chorus of voices asked. `Why, SHE, of course,' said the Dodo, pointing to Alice with one finger; and the whole party at once crowded round her, calling out in a confused way, `Prizes! Prizes!' Alice had no idea what to do, and in despair she put her hand in her pocket, and pulled out a box of comfits, (luckily the salt water had not got into it), and handed them round as prizes. There was exactly one a-piece all round. `But she must have a prize herself, you know,' said the Mouse. `Of course,' the Dodo replied very gravely. `What else have you got in your pocket?' he went on, turning to Alice. `Only a thimble,' said Alice sadly. `Hand it over here,' said the Dodo. Then they all crowded round her once more, while the Dodo solemnly presented the thimble, saying `We beg your acceptance of this elegant thimble'; and, when it had finished this short speech, they all cheered. Alice thought the whole thing very absurd, but they all looked so grave that she did not dare to laugh; and, as she could not think of anything to say, she simply bowed, and took the thimble, looking as solemn as she could. The next thing was to eat the comfits: this caused some noise and confusion, as the large birds complained that they could not taste theirs, and the small ones choked and had to be patted on the back. However, it was over at last, and they sat down again in a ring, and begged the Mouse to tell them something more. `You promised to tell me your history, you know,' said Alice, `and why it is you hate--C and D,' she added in a whisper, half afraid that it would be offended again. `Mine is a long and a sad tale!' said the Mouse, turning to Alice, and sighing. `It IS a long tail, certainly,' said Alice, looking down with wonder at the Mouse's tail; `but why do you call it sad?' And she kept on puzzling about it while the Mouse was speaking, so that her idea of the tale was something like this:-- `Fury said to a mouse, That he met in the house, "Let us both go to law: I will prosecute YOU. --Come, I'll take no denial; We must have a trial: For really this morning I've nothing to do." Said the mouse to the cur, "Such a trial, dear Sir, With no jury or judge, would be wasting our breath." "I'll be judge, I'll be jury," Said cunning old Fury: "I'll try the whole cause, and condemn you to death."' `You are not attending!' said the Mouse to Alice severely. `What are you thinking of?' `I beg your pardon,' said Alice very humbly: `you had got to the fifth bend, I think?' `I had NOT!' cried the Mouse, sharply and very angrily. `A knot!' said Alice, always ready to make herself useful, and looking anxiously about her. `Oh, do let me help to undo it!' `I shall do nothing of the sort,' said the Mouse, getting up and walking away. `You insult me by talking such nonsense!' `I didn't mean it!' pleaded poor Alice. `But you're so easily offended, you know!' The Mouse only growled in reply. `Please come back and finish your story!' Alice called after it; and the others all joined in chorus, `Yes, please do!' but the Mouse only shook its head impatiently, and walked a little quicker. `What a pity it wouldn't stay!' sighed the Lory, as soon as it was quite out of sight; and an old Crab took the opportunity of saying to her daughter `Ah, my dear! Let this be a lesson to you never to lose YOUR temper!' `Hold your tongue, Ma!' said the young Crab, a little snappishly. `You're enough to try the patience of an oyster!' `I wish I had our Dinah here, I know I do!' said Alice aloud, addressing nobody in particular. `She'd soon fetch it back!' `And who is Dinah, if I might venture to ask the question?' said the Lory. Alice replied eagerly, for she was always ready to talk about her pet: `Dinah's our cat. And she's such a capital one for catching mice you can't think! And oh, I wish you could see her after the birds! Why, she'll eat a little bird as soon as look at it!' This speech caused a remarkable sensation among the party. Some of the birds hurried off at once: one the old Magpie began wrapping itself up very carefully, remarking, `I really must be getting home; the night-air doesn't suit my throat!' and a Canary called out in a trembling voice to its children, `Come away, my dears! It's high time you were all in bed!' On various pretexts they all moved off, and Alice was soon left alone. `I wish I hadn't mentioned Dinah!' she said to herself in a melancholy tone. `Nobody seems to like her, down here, and I'm sure she's the best cat in the world! Oh, my dear Dinah! I wonder if I shall ever see you any more!' And here poor Alice began to cry again, for she felt very lonely and low-spirited. In a little while, however, she again heard a little pattering of footsteps in the distance, and she looked up eagerly, half hoping that the Mouse had changed his mind, and was coming back to finish his story. CHAPTER IV The Rabbit Sends in a Little Bill It was the White Rabbit, trotting slowly back again, and looking anxiously about as it went, as if it had lost something; and she heard it muttering to itself `The Duchess! The Duchess! Oh my dear paws! Oh my fur and whiskers! She'll get me executed, as sure as ferrets are ferrets! Where CAN I have dropped them, I wonder?' Alice guessed in a moment that it was looking for the fan and the pair of white kid gloves, and she very good-naturedly began hunting about for them, but they were nowhere to be seen--everything seemed to have changed since her swim in the pool, and the great hall, with the glass table and the little door, had vanished completely. Very soon the Rabbit noticed Alice, as she went hunting about, and called out to her in an angry tone, `Why, Mary Ann, what ARE you doing out here? Run home this moment, and fetch me a pair of gloves and a fan! Quick, now!' And Alice was so much frightened that she ran off at once in the direction it pointed to, without trying to explain the mistake it had made. `He took me for his housemaid,' she said to herself as she ran. `How surprised he'll be when he finds out who I am! But I'd better take him his fan and gloves--that is, if I can find them.' As she said this, she came upon a neat little house, on the door of which was a bright brass plate with the name `W. RABBIT' engraved upon it. She went in without knocking, and hurried upstairs, in great fear lest she should meet the real Mary Ann, and be turned out of the house before she had found the fan and gloves. `How queer it seems,' Alice said to herself, `to be going messages for a rabbit! I suppose Dinah'll be sending me on messages next!' And she began fancying the sort of thing that would happen: `"Miss Alice! Come here directly, and get ready for your walk!" "Coming in a minute, nurse! But I've got to see that the mouse doesn't get out." Only I don't think,' Alice went on, `that they'd let Dinah stop in the house if it began ordering people about like that!' By this time she had found her way into a tidy little room with a table in the window, and on it (as she had hoped) a fan and two or three pairs of tiny white kid gloves: she took up the fan and a pair of the gloves, and was just going to leave the room, when her eye fell upon a little bottle that stood near the looking- glass. There was no label this time with the words `DRINK ME,' but nevertheless she uncorked it and put it to her lips. `I know SOMETHING interesting is sure to happen,' she said to herself, `whenever I eat or drink anything; so I'll just see what this bottle does. I do hope it'll make me grow large again, for really I'm quite tired of being such a tiny little thing!' It did so indeed, and much sooner than she had expected: before she had drunk half the bottle, she found her head pressing against the ceiling, and had to stoop to save her neck from being broken. She hastily put down the bottle, saying to herself `That's quite enough--I hope I shan't grow any more--As it is, I can't get out at the door--I do wish I hadn't drunk quite so much!' Alas! it was too late to wish that! She went on growing, and growing, and very soon had to kneel down on the floor: in another minute there was not even room for this, and she tried the effect of lying down with one elbow against the door, and the other arm curled round her head. Still she went on growing, and, as a last resource, she put one arm out of the window, and one foot up the chimney, and said to herself `Now I can do no more, whatever happens. What WILL become of me?' Luckily for Alice, the little magic bottle had now had its full effect, and she grew no larger: still it was very uncomfortable, and, as there seemed to be no sort of chance of her ever getting out of the room again, no wonder she felt unhappy. `It was much pleasanter at home,' thought poor Alice, `when one wasn't always growing larger and smaller, and being ordered about by mice and rabbits. I almost wish I hadn't gone down that rabbit-hole--and yet--and yet--it's rather curious, you know, this sort of life! I do wonder what CAN have happened to me! When I used to read fairy-tales, I fancied that kind of thing never happened, and now here I am in the middle of one! There ought to be a book written about me, that there ought! And when I grow up, I'll write one--but I'm grown up now,' she added in a sorrowful tone; `at least there's no room to grow up any more HERE.' `But then,' thought Alice, `shall I NEVER get any older than I am now? That'll be a comfort, one way--never to be an old woman- -but then--always to have lessons to learn! Oh, I shouldn't like THAT!' `Oh, you foolish Alice!' she answered herself. `How can you learn lessons in here? Why, there's hardly room for YOU, and no room at all for any lesson-books!' And so she went on, taking first one side and then the other, and making quite a conversation of it altogether; but after a few minutes she heard a voice outside, and stopped to listen. `Mary Ann! Mary Ann!' said the voice. `Fetch me my gloves this moment!' Then came a little pattering of feet on the stairs. Alice knew it was the Rabbit coming to look for her, and she trembled till she shook the house, quite forgetting that she was now about a thousand times as large as the Rabbit, and had no reason to be afraid of it. Presently the Rabbit came up to the door, and tried to open it; but, as the door opened inwards, and Alice's elbow was pressed hard against it, that attempt proved a failure. Alice heard it say to itself `Then I'll go round and get in at the window.' `THAT you won't' thought Alice, and, after waiting till she fancied she heard the Rabbit just under the window, she suddenly spread out her hand, and made a snatch in the air. She did not get hold of anything, but she heard a little shriek and a fall, and a crash of broken glass, from which she concluded that it was just possible it had fallen into a cucumber-frame, or something of the sort. Next came an angry voice--the Rabbit's--`Pat! Pat! Where are you?' And then a voice she had never heard before, `Sure then I'm here! Digging for apples, yer honour!' `Digging for apples, indeed!' said the Rabbit angrily. `Here! Come and help me out of THIS!' (Sounds of more broken glass.) `Now tell me, Pat, what's that in the window?' `Sure, it's an arm, yer honour!' (He pronounced it `arrum.') `An arm, you goose! Who ever saw one that size? Why, it fills the whole window!' `Sure, it does, yer honour: but it's an arm for all that.' `Well, it's got no business there, at any rate: go and take it away!' There was a long silence after this, and Alice could only hear whispers now and then; such as, `Sure, I don't like it, yer honour, at all, at all!' `Do as I tell you, you coward!' and at last she spread out her hand again, and made another snatch in the air. This time there were TWO little shrieks, and more sounds of broken glass. `What a number of cucumber-frames there must be!' thought Alice. `I wonder what they'll do next! As for pulling me out of the window, I only wish they COULD! I'm sure I don't want to stay in here any longer!' She waited for some time without hearing anything more: at last came a rumbling of little cartwheels, and the sound of a good many voice all talking together: she made out the words: `Where's the other ladder?--Why, I hadn't to bring but one; Bill's got the other--Bill! fetch it here, lad!--Here, put 'em up at this corner--No, tie 'em together first--they don't reach half high enough yet--Oh! they'll do well enough; don't be particular- -Here, Bill! catch hold of this rope--Will the roof bear?--Mind that loose slate--Oh, it's coming down! Heads below!' (a loud crash)--`Now, who did that?--It was Bill, I fancy--Who's to go down the chimney?--Nay, I shan't! YOU do it!--That I won't, then!--Bill's to go down--Here, Bill! the master says you're to go down the chimney!' `Oh! So Bill's got to come down the chimney, has he?' said Alice to herself. `Shy, they seem to put everything upon Bill! I wouldn't be in Bill's place for a good deal: this fireplace is narrow, to be sure; but I THINK I can kick a little!' She drew her foot as far down the chimney as she could, and waited till she heard a little animal (she couldn't guess of what sort it was) scratching and scrambling about in the chimney close above her: then, saying to herself `This is Bill,' she gave one sharp kick, and waited to see what would happen next. The first thing she heard was a general chorus of `There goes Bill!' then the Rabbit's voice along--`Catch him, you by the hedge!' then silence, and then another confusion of voices--`Hold up his head--Brandy now--Don't choke him--How was it, old fellow? What happened to you? Tell us all about it!' Last came a little feeble, squeaking voice, (`That's Bill,' thought Alice,) `Well, I hardly know--No more, thank ye; I'm better now--but I'm a deal too flustered to tell you--all I know is, something comes at me like a Jack-in-the-box, and up I goes like a sky-rocket!' `So you did, old fellow!' said the others. `We must burn the house down!' said the Rabbit's voice; and Alice called out as loud as she could, `If you do. I'll set Dinah at you!' There was a dead silence instantly, and Alice thought to herself, `I wonder what they WILL do next! If they had any sense, they'd take the roof off.' After a minute or two, they began moving about again, and Alice heard the Rabbit say, `A barrowful will do, to begin with.' `A barrowful of WHAT?' thought Alice; but she had not long to doubt, for the next moment a shower of little pebbles came rattling in at the window, and some of them hit her in the face. `I'll put a stop to this,' she said to herself, and shouted out, `You'd better not do that again!' which produced another dead silence. Alice noticed with some surprise that the pebbles were all turning into little cakes as they lay on the floor, and a bright idea came into her head. `If I eat one of these cakes,' she thought, `it's sure to make SOME change in my size; and as it can't possibly make me larger, it must make me smaller, I suppose.' So she swallowed one of the cakes, and was delighted to find that she began shrinking directly. As soon as she was small enough to get through the door, she ran out of the house, and found quite a crowd of little animals and birds waiting outside. The poor little Lizard, Bill, was in the middle, being held up by two guinea-pigs, who were giving it something out of a bottle. They all made a rush at Alice the moment she appeared; but she ran off as hard as she could, and soon found herself safe in a thick wood. `The first thing I've got to do,' said Alice to herself, as she wandered about in the wood, `is to grow to my right size again; and the second thing is to find my way into that lovely garden. I think that will be the best plan.' It sounded an excellent plan, no doubt, and very neatly and simply arranged; the only difficulty was, that she had not the smallest idea how to set about it; and while she was peering about anxiously among the trees, a little sharp bark just over her head made her look up in a great hurry. An enormous puppy was looking down at her with large round eyes, and feebly stretching out one paw, trying to touch her. `Poor little thing!' said Alice, in a coaxing tone, and she tried hard to whistle to it; but she was terribly frightened all the time at the thought that it might be hungry, in which case it would be very likely to eat her up in spite of all her coaxing. Hardly knowing what she did, she picked up a little bit of stick, and held it out to the puppy; whereupon the puppy jumped into the air off all its feet at once, with a yelp of delight, and rushed at the stick, and made believe to worry it; then Alice dodged behind a great thistle, to keep herself from being run over; and the moment she appeared on the other side, the puppy made another rush at the stick, and tumbled head over heels in its hurry to get hold of it; then Alice, thinking it was very like having a game of play with a cart-horse, and expecting every moment to be trampled under its feet, ran round the thistle again; then the puppy began a series of short charges at the stick, running a very little way forwards each time and a long way back, and barking hoarsely all the while, till at last it sat down a good way off, panting, with its tongue hanging out of its mouth, and its great eyes half shut. This seemed to Alice a good opportunity for making her escape; so she set off at once, and ran till she was quite tired and out of breath, and till the puppy's bark sounded quite faint in the distance. `And yet what a dear little puppy it was!' said Alice, as she leant against a buttercup to rest herself, and fanned herself with one of the leaves: `I should have liked teaching it tricks very much, if--if I'd only been the right size to do it! Oh dear! I'd nearly forgotten that I've got to grow up again! Let me see--how IS it to be managed? I suppose I ought to eat or drink something or other; but the great question is, what?' The great question certainly was, what? Alice looked all round her at the flowers and the blades of grass, but she did not see anything that looked like the right thing to eat or drink under the circumstances. There was a large mushroom growing near her, about the same height as herself; and when she had looked under it, and on both sides of it, and behind it, it occurred to her that she might as well look and see what was on the top of it. She stretched herself up on tiptoe, and peeped over the edge of the mushroom, and her eyes immediately met those of a large caterpillar, that was sitting on the top with its arms folded, quietly smoking a long hookah, and taking not the smallest notice of her or of anything else. CHAPTER V Advice from a Caterpillar The Caterpillar and Alice looked at each other for some time in silence: at last the Caterpillar took the hookah out of its mouth, and addressed her in a languid, sleepy voice. `Who are YOU?' said the Caterpillar. This was not an encouraging opening for a conversation. Alice replied, rather shyly, `I--I hardly know, sir, just at present-- at least I know who I WAS when I got up this morning, but I think I must have been changed several times since then.' `What do you mean by that?' said the Caterpillar sternly. `Explain yourself!' `I can't explain MYSELF, I'm afraid, sir' said Alice, `because I'm not myself, you see.' `I don't see,' said the Caterpillar. `I'm afraid I can't put it more clearly,' Alice replied very politely, `for I can't understand it myself to begin with; and being so many different sizes in a day is very confusing.' `It isn't,' said the Caterpillar. `Well, perhaps you haven't found it so yet,' said Alice; `but when you have to turn into a chrysalis--you will some day, you know--and then after that into a butterfly, I should think you'll feel it a little queer, won't you?' `Not a bit,' said the Caterpillar. `Well, perhaps your feelings may be different,' said Alice; `all I know is, it would feel very queer to ME.' `You!' said the Caterpillar contemptuously. `Who are YOU?' Which brought them back again to the beginning of the conversation. Alice felt a little irritated at the Caterpillar's making such VERY short remarks, and she drew herself up and said, very gravely, `I think, you ought to tell me who YOU are, first.' `Why?' said the Caterpillar. Here was another puzzling question; and as Alice could not think of any good reason, and as the Caterpillar seemed to be in a VERY unpleasant state of mind, she turned away. `Come back!' the Caterpillar called after her. `I've something important to say!' This sounded promising, certainly: Alice turned and came back again. `Keep your temper,' said the Caterpillar. `Is that all?' said Alice, swallowing down her anger as well as she could. `No,' said the Caterpillar. Alice thought she might as well wait, as she had nothing else to do, and perhaps after all it might tell her something worth hearing. For some minutes it puffed away without speaking, but at last it unfolded its arms, took the hookah out of its mouth again, and said, `So you think you're changed, do you?' `I'm afraid I am, sir,' said Alice; `I can't remember things as I used--and I don't keep the same size for ten minutes together!' `Can't remember WHAT things?' said the Caterpillar. `Well, I've tried to say "HOW DOTH THE LITTLE BUSY BEE," but it all came different!' Alice replied in a very melancholy voice. `Repeat, "YOU ARE OLD, FATHER WILLIAM,"' said the Caterpillar. Alice folded her hands, and began:-- `You are old, Father William,' the young man said, `And your hair has become very white; And yet you incessantly stand on your head-- Do you think, at your age, it is right?' `In my youth,' Father William replied to his son, `I feared it might injure the brain; But, now that I'm perfectly sure I have none, Why, I do it again and again.' `You are old,' said the youth, `as I mentioned before, And have grown most uncommonly fat; Yet you turned a back-somersault in at the door-- Pray, what is the reason of that?' `In my youth,' said the sage, as he shook his grey locks, `I kept all my limbs very supple By the use of this ointment--one shilling the box-- Allow me to sell you a couple?' `You are old,' said the youth, `and your jaws are too weak For anything tougher than suet; Yet you finished the goose, with the bones and the beak-- Pray how did you manage to do it?' `In my youth,' said his father, `I took to the law, And argued each case with my wife; And the muscular strength, which it gave to my jaw, Has lasted the rest of my life.' `You are old,' said the youth, `one would hardly suppose That your eye was as steady as ever; Yet you balanced an eel on the end of your nose-- What made you so awfully clever?' `I have answered three questions, and that is enough,' Said his father; `don't give yourself airs! Do you think I can listen all day to such stuff? Be off, or I'll kick you down stairs!' `That is not said right,' said the Caterpillar. `Not QUITE right, I'm afraid,' said Alice, timidly; `some of the words have got altered.' `It is wrong from beginning to end,' said the Caterpillar decidedly, and there was silence for some minutes. The Caterpillar was the first to speak. `What size do you want to be?' it asked. `Oh, I'm not particular as to size,' Alice hastily replied; `only one doesn't like changing so often, you know.' `I DON'T know,' said the Caterpillar. Alice said nothing: she had never been so much contradicted in her life before, and she felt that she was losing her temper. `Are you content now?' said the Caterpillar. `Well, I should like to be a LITTLE larger, sir, if you wouldn't mind,' said Alice: `three inches is such a wretched height to be.' `It is a very good height indeed!' said the Caterpillar angrily, rearing itself upright as it spoke (it was exactly three inches high). `But I'm not used to it!' pleaded poor Alice in a piteous tone. And she thought of herself, `I wish the creatures wouldn't be so easily offended!' `You'll get used to it in time,' said the Caterpillar; and it put the hookah into its mouth and began smoking again. This time Alice waited patiently until it chose to speak again. In a minute or two the Caterpillar took the hookah out of its mouth and yawned once or twice, and shook itself. Then it got down off the mushroom, and crawled away in the grass, merely remarking as it went, `One side will make you grow taller, and the other side will make you grow shorter.' `One side of WHAT? The other side of WHAT?' thought Alice to herself. `Of the mushroom,' said the Caterpillar, just as if she had asked it aloud; and in another moment it was out of sight. Alice remained looking thoughtfully at the mushroom for a minute, trying to make out which were the two sides of it; and as it was perfectly round, she found this a very difficult question. However, at last she stretched her arms round it as far as they would go, and broke off a bit of the edge with each hand. `And now which is which?' she said to herself, and nibbled a little of the right-hand bit to try the effect: the next moment she felt a violent blow underneath her chin: it had struck her foot! She was a good deal frightened by this very sudden change, but she felt that there was no time to be lost, as she was shrinking rapidly; so she set to work at once to eat some of the other bit. Her chin was pressed so closely against her foot, that there was hardly room to open her mouth; but she did it at last, and managed to swallow a morsel of the lefthand bit. * * * * * * * * * * * * * * * * * * * * `Come, my head's free at last!' said Alice in a tone of delight, which changed into alarm in another moment, when she found that her shoulders were nowhere to be found: all she could see, when she looked down, was an immense length of neck, which seemed to rise like a stalk out of a sea of green leaves that lay far below her. `What CAN all that green stuff be?' said Alice. `And where HAVE my shoulders got to? And oh, my poor hands, how is it I can't see you?' She was moving them about as she spoke, but no result seemed to follow, except a little shaking among the distant green leaves. As there seemed to be no chance of getting her hands up to her head, she tried to get her head down to them, and was delighted to find that her neck would bend about easily in any direction, like a serpent. She had just succeeded in curving it down into a graceful zigzag, and was going to dive in among the leaves, which she found to be nothing but the tops of the trees under which she had been wandering, when a sharp hiss made her draw back in a hurry: a large pigeon had flown into her face, and was beating her violently with its wings. `Serpent!' screamed the Pigeon. `I'm NOT a serpent!' said Alice indignantly. `Let me alone!' `Serpent, I say again!' repeated the Pigeon, but in a more subdued tone, and added with a kind of sob, `I've tried every way, and nothing seems to suit them!' `I haven't the least idea what you're talking about,' said Alice. `I've tried the roots of trees, and I've tried banks, and I've tried hedges,' the Pigeon went on, without attending to her; `but those serpents! There's no pleasing them!' Alice was more and more puzzled, but she thought there was no use in saying anything more till the Pigeon had finished. `As if it wasn't trouble enough hatching the eggs,' said the Pigeon; `but I must be on the look-out for serpents night and day! Why, I haven't had a wink of sleep these three weeks!' `I'm very sorry you've been annoyed,' said Alice, who was beginning to see its meaning. `And just as I'd taken the highest tree in the wood,' continued the Pigeon, raising its voice to a shriek, `and just as I was thinking I should be free of them at last, they must needs come wriggling down from the sky! Ugh, Serpent!' `But I'm NOT a serpent, I tell you!' said Alice. `I'm a--I'm a--' `Well! WHAT are you?' said the Pigeon. `I can see you're trying to invent something!' `I--I'm a little girl,' said Alice, rather doubtfully, as she remembered the number of changes she had gone through that day. `A likely story indeed!' said the Pigeon in a tone of the deepest contempt. `I've seen a good many little girls in my time, but never ONE with such a neck as that! No, no! You're a serpent; and there's no use denying it. I suppose you'll be telling me next that you never tasted an egg!' `I HAVE tasted eggs, certainly,' said Alice, who was a very truthful child; `but little girls eat eggs quite as much as serpents do, you know.' `I don't believe it,' said the Pigeon; `but if they do, why then they're a kind of serpent, that's all I can say.' This was such a new idea to Alice, that she was quite silent for a minute or two, which gave the Pigeon the opportunity of adding, `You're looking for eggs, I know THAT well enough; and what does it matter to me whether you're a little girl or a serpent?' `It matters a good deal to ME,' said Alice hastily; `but I'm not looking for eggs, as it happens; and if I was, I shouldn't want YOURS: I don't like them raw.' `Well, be off, then!' said the Pigeon in a sulky tone, as it settled down again into its nest. Alice crouched down among the trees as well as she could, for her neck kept getting entangled among the branches, and every now and then she had to stop and untwist it. After a while she remembered that she still held the pieces of mushroom in her hands, and she set to work very carefully, nibbling first at one and then at the other, and growing sometimes taller and sometimes shorter, until she had succeeded in bringing herself down to her usual height. It was so long since she had been anything near the right size, that it felt quite strange at first; but she got used to it in a few minutes, and began talking to herself, as usual. `Come, there's half my plan done now! How puzzling all these changes are! I'm never sure what I'm going to be, from one minute to another! However, I've got back to my right size: the next thing is, to get into that beautiful garden--how IS that to be done, I wonder?' As she said this, she came suddenly upon an open place, with a little house in it about four feet high. `Whoever lives there,' thought Alice, `it'll never do to come upon them THIS size: why, I should frighten them out of their wits!' So she began nibbling at the righthand bit again, and did not venture to go near the house till she had brought herself down to nine inches high. CHAPTER VI Pig and Pepper For a minute or two she stood looking at the house, and wondering what to do next, when suddenly a footman in livery came running out of the wood--(she considered him to be a footman because he was in livery: otherwise, judging by his face only, she would have called him a fish)--and rapped loudly at the door with his knuckles. It was opened by another footman in livery, with a round face, and large eyes like a frog; and both footmen, Alice noticed, had powdered hair that curled all over their heads. She felt very curious to know what it was all about, and crept a little way out of the wood to listen. The Fish-Footman began by producing from under his arm a great letter, nearly as large as himself, and this he handed over to the other, saying, in a solemn tone, `For the Duchess. An invitation from the Queen to play croquet.' The Frog-Footman repeated, in the same solemn tone, only changing the order of the words a little, `From the Queen. An invitation for the Duchess to play croquet.' Then they both bowed low, and their curls got entangled together. Alice laughed so much at this, that she had to run back into the wood for fear of their hearing her; and when she next peeped out the Fish-Footman was gone, and the other was sitting on the ground near the door, staring stupidly up into the sky. Alice went timidly up to the door, and knocked. `There's no sort of use in knocking,' said the Footman, `and that for two reasons. First, because I'm on the same side of the door as you are; secondly, because they're making such a noise inside, no one could possibly hear you.' And certainly there was a most extraordinary noise going on within--a constant howling and sneezing, and every now and then a great crash, as if a dish or kettle had been broken to pieces. `Please, then,' said Alice, `how am I to get in?' `There might be some sense in your knocking,' the Footman went on without attending to her, `if we had the door between us. For instance, if you were INSIDE, you might knock, and I could let you out, you know.' He was looking up into the sky all the time he was speaking, and this Alice thought decidedly uncivil. `But perhaps he can't help it,' she said to herself; `his eyes are so VERY nearly at the top of his head. But at any rate he might answer questions.--How am I to get in?' she repeated, aloud. `I shall sit here,' the Footman remarked, `till tomorrow--' At this moment the door of the house opened, and a large plate came skimming out, straight at the Footman's head: it just grazed his nose, and broke to pieces against one of the trees behind him. `--or next day, maybe,' the Footman continued in the same tone, exactly as if nothing had happened. `How am I to get in?' asked Alice again, in a louder tone. `ARE you to get in at all?' said the Footman. `That's the first question, you know.' It was, no doubt: only Alice did not like to be told so. `It's really dreadful,' she muttered to herself, `the way all the creatures argue. It's enough to drive one crazy!' The Footman seemed to think this a good opportunity for repeating his remark, with variations. `I shall sit here,' he said, `on and off, for days and days.' `But what am I to do?' said Alice. `Anything you like,' said the Footman, and began whistling. `Oh, there's no use in talking to him,' said Alice desperately: `he's perfectly idiotic!' And she opened the door and went in. The door led right into a large kitchen, which was full of smoke from one end to the other: the Duchess was sitting on a three-legged stool in the middle, nursing a baby; the cook was leaning over the fire, stirring a large cauldron which seemed to be full of soup. `There's certainly too much pepper in that soup!' Alice said to herself, as well as she could for sneezing. There was certainly too much of it in the air. Even the Duchess sneezed occasionally; and as for the baby, it was sneezing and howling alternately without a moment's pause. The only things in the kitchen that did not sneeze, were the cook, and a large cat which was sitting on the hearth and grinning from ear to ear. `Please would you tell me,' said Alice, a little timidly, for she was not quite sure whether it was good manners for her to speak first, `why your cat grins like that?' `It's a Cheshire cat,' said the Duchess, `and that's why. Pig!' She said the last word with such sudden violence that Alice quite jumped; but she saw in another moment that it was addressed to the baby, and not to her, so she took courage, and went on again:-- `I didn't know that Cheshire cats always grinned; in fact, I didn't know that cats COULD grin.' `They all can,' said the Duchess; `and most of 'em do.' `I don't know of any that do,' Alice said very politely, feeling quite pleased to have got into a conversation. `You don't know much,' said the Duchess; `and that's a fact.' Alice did not at all like the tone of this remark, and thought it would be as well to introduce some other subject of conversation. While she was trying to fix on one, the cook took the cauldron of soup off the fire, and at once set to work throwing everything within her reach at the Duchess and the baby --the fire-irons came first; then followed a shower of saucepans, plates, and dishes. The Duchess took no notice of them even when they hit her; and the baby was howling so much already, that it was quite impossible to say whether the blows hurt it or not. `Oh, PLEASE mind what you're doing!' cried Alice, jumping up and down in an agony of terror. `Oh, there goes his PRECIOUS nose'; as an unusually large saucepan flew close by it, and very nearly carried it off. `If everybody minded their own business,' the Duchess said in a hoarse growl, `the world would go round a deal faster than it does.' `Which would NOT be an advantage,' said Alice, who felt very glad to get an opportunity of showing off a little of her knowledge. `Just think of what work it would make with the day and night! You see the earth takes twenty-four hours to turn round on its axis--' `Talking of axes,' said the Duchess, `chop off her head!' Alice glanced rather anxiously at the cook, to see if she meant to take the hint; but the cook was busily stirring the soup, and seemed not to be listening, so she went on again: `Twenty-four hours, I THINK; or is it twelve? I--' `Oh, don't bother ME,' said the Duchess; `I never could abide figures!' And with that she began nursing her child again, singing a sort of lullaby to it as she did so, and giving it a violent shake at the end of every line: `Speak roughly to your little boy, And beat him when he sneezes: He only does it to annoy, Because he knows it teases.' CHORUS. (In which the cook and the baby joined):-- `Wow! wow! wow!' While the Duchess sang the second verse of the song, she kept tossing the baby violently up and down, and the poor little thing howled so, that Alice could hardly hear the words:-- `I speak severely to my boy, I beat him when he sneezes; For he can thoroughly enjoy The pepper when he pleases!' CHORUS. `Wow! wow! wow!' `Here! you may nurse it a bit, if you like!' the Duchess said to Alice, flinging the baby at her as she spoke. `I must go and get ready to play croquet with the Queen,' and she hurried out of the room. The cook threw a frying-pan after her as she went out, but it just missed her. Alice caught the baby with some difficulty, as it was a queer- shaped little creature, and held out its arms and legs in all directions, `just like a star-fish,' thought Alice. The poor little thing was snorting like a steam-engine when she caught it, and kept doubling itself up and straightening itself out again, so that altogether, for the first minute or two, it was as much as she could do to hold it. As soon as she had made out the proper way of nursing it, (which was to twist it up into a sort of knot, and then keep tight hold of its right ear and left foot, so as to prevent its undoing itself,) she carried it out into the open air. `IF I don't take this child away with me,' thought Alice, `they're sure to kill it in a day or two: wouldn't it be murder to leave it behind?' She said the last words out loud, and the little thing grunted in reply (it had left off sneezing by this time). `Don't grunt,' said Alice; `that's not at all a proper way of expressing yourself.' The baby grunted again, and Alice looked very anxiously into its face to see what was the matter with it. There could be no doubt that it had a VERY turn-up nose, much more like a snout than a real nose; also its eyes were getting extremely small for a baby: altogether Alice did not like the look of the thing at all. `But perhaps it was only sobbing,' she thought, and looked into its eyes again, to see if there were any tears. No, there were no tears. `If you're going to turn into a pig, my dear,' said Alice, seriously, `I'll have nothing more to do with you. Mind now!' The poor little thing sobbed again (or grunted, it was impossible to say which), and they went on for some while in silence. Alice was just beginning to think to herself, `Now, what am I to do with this creature when I get it home?' when it grunted again, so violently, that she looked down into its face in some alarm. This time there could be NO mistake about it: it was neither more nor less than a pig, and she felt that it would be quite absurd for her to carry it further. So she set the little creature down, and felt quite relieved to see it trot away quietly into the wood. `If it had grown up,' she said to herself, `it would have made a dreadfully ugly child: but it makes rather a handsome pig, I think.' And she began thinking over other children she knew, who might do very well as pigs, and was just saying to herself, `if one only knew the right way to change them--' when she was a little startled by seeing the Cheshire Cat sitting on a bough of a tree a few yards off. The Cat only grinned when it saw Alice. It looked good- natured, she thought: still it had VERY long claws and a great many teeth, so she felt that it ought to be treated with respect. `Cheshire Puss,' she began, rather timidly, as she did not at all know whether it would like the name: however, it only grinned a little wider. `Come, it's pleased so far,' thought Alice, and she went on. `Would you tell me, please, which way I ought to go from here?' `That depends a good deal on where you want to get to,' said the Cat. `I don't much care where--' said Alice. `Then it doesn't matter which way you go,' said the Cat. `--so long as I get SOMEWHERE,' Alice added as an explanation. `Oh, you're sure to do that,' said the Cat, `if you only walk long enough.' Alice felt that this could not be denied, so she tried another question. `What sort of people live about here?' `In THAT direction,' the Cat said, waving its right paw round, `lives a Hatter: and in THAT direction,' waving the other paw, `lives a March Hare. Visit either you like: they're both mad.' `But I don't want to go among mad people,' Alice remarked. `Oh, you can't help that,' said the Cat: `we're all mad here. I'm mad. You're mad.' `How do you know I'm mad?' said Alice. `You must be,' said the Cat, `or you wouldn't have come here.' Alice didn't think that proved it at all; however, she went on `And how do you know that you're mad?' `To begin with,' said the Cat, `a dog's not mad. You grant that?' `I suppose so,' said Alice. `Well, then,' the Cat went on, `you see, a dog growls when it's angry, and wags its tail when it's pleased. Now I growl when I'm pleased, and wag my tail when I'm angry. Therefore I'm mad.' `I call it purring, not growling,' said Alice. `Call it what you like,' said the Cat. `Do you play croquet with the Queen to-day?' `I should like it very much,' said Alice, `but I haven't been invited yet.' `You'll see me there,' said the Cat, and vanished. Alice was not much surprised at this, she was getting so used to queer things happening. While she was looking at the place where it had been, it suddenly appeared again. `By-the-bye, what became of the baby?' said the Cat. `I'd nearly forgotten to ask.' `It turned into a pig,' Alice quietly said, just as if it had come back in a natural way. `I thought it would,' said the Cat, and vanished again. Alice waited a little, half expecting to see it again, but it did not appear, and after a minute or two she walked on in the direction in which the March Hare was said to live. `I've seen hatters before,' she said to herself; `the March Hare will be much the most interesting, and perhaps as this is May it won't be raving mad--at least not so mad as it was in March.' As she said this, she looked up, and there was the Cat again, sitting on a branch of a tree. `Did you say pig, or fig?' said the Cat. `I said pig,' replied Alice; `and I wish you wouldn't keep appearing and vanishing so suddenly: you make one quite giddy.' `All right,' said the Cat; and this time it vanished quite slowly, beginning with the end of the tail, and ending with the grin, which remained some time after the rest of it had gone. `Well! I've often seen a cat without a grin,' thought Alice; `but a grin without a cat! It's the most curious thing I ever say in my life!' She had not gone much farther before she came in sight of the house of the March Hare: she thought it must be the right house, because the chimneys were shaped like ears and the roof was thatched with fur. It was so large a house, that she did not like to go nearer till she had nibbled some more of the lefthand bit of mushroom, and raised herself to about two feet high: even then she walked up towards it rather timidly, saying to herself `Suppose it should be raving mad after all! I almost wish I'd gone to see the Hatter instead!' CHAPTER VII A Mad Tea-Party There was a table set out under a tree in front of the house, and the March Hare and the Hatter were having tea at it: a Dormouse was sitting between them, fast asleep, and the other two were using it as a cushion, resting their elbows on it, and the talking over its head. `Very uncomfortable for the Dormouse,' thought Alice; `only, as it's asleep, I suppose it doesn't mind.' The table was a large one, but the three were all crowded together at one corner of it: `No room! No room!' they cried out when they saw Alice coming. `There's PLENTY of room!' said Alice indignantly, and she sat down in a large arm-chair at one end of the table. `Have some wine,' the March Hare said in an encouraging tone. Alice looked all round the table, but there was nothing on it but tea. `I don't see any wine,' she remarked. `There isn't any,' said the March Hare. `Then it wasn't very civil of you to offer it,' said Alice angrily. `It wasn't very civil of you to sit down without being invited,' said the March Hare. `I didn't know it was YOUR table,' said Alice; `it's laid for a great many more than three.' `Your hair wants cutting,' said the Hatter. He had been looking at Alice for some time with great curiosity, and this was his first speech. `You should learn not to make personal remarks,' Alice said with some severity; `it's very rude.' The Hatter opened his eyes very wide on hearing this; but all he SAID was, `Why is a raven like a writing-desk?' `Come, we shall have some fun now!' thought Alice. `I'm glad they've begun asking riddles.--I believe I can guess that,' she added aloud. `Do you mean that you think you can find out the answer to it?' said the March Hare. `Exactly so,' said Alice. `Then you should say what you mean,' the March Hare went on. `I do,' Alice hastily replied; `at least--at least I mean what I say--that's the same thing, you know.' `Not the same thing a bit!' said the Hatter. `You might just as well say that "I see what I eat" is the same thing as "I eat what I see"!' `You might just as well say,' added the March Hare, `that "I like what I get" is the same thing as "I get what I like"!' `You might just as well say,' added the Dormouse, who seemed to be talking in his sleep, `that "I breathe when I sleep" is the same thing as "I sleep when I breathe"!' `It IS the same thing with you,' said the Hatter, and here the conversation dropped, and the party sat silent for a minute, while Alice thought over all she could remember about ravens and writing-desks, which wasn't much. The Hatter was the first to break the silence. `What day of the month is it?' he said, turning to Alice: he had taken his watch out of his pocket, and was looking at it uneasily, shaking it every now and then, and holding it to his ear. Alice considered a little, and then said `The fourth.' `Two days wrong!' sighed the Hatter. `I told you butter wouldn't suit the works!' he added looking angrily at the March Hare. `It was the BEST butter,' the March Hare meekly replied. `Yes, but some crumbs must have got in as well,' the Hatter grumbled: `you shouldn't have put it in with the bread-knife.' The March Hare took the watch and looked at it gloomily: then he dipped it into his cup of tea, and looked at it again: but he could think of nothing better to say than his first remark, `It was the BEST butter, you know.' Alice had been looking over his shoulder with some curiosity. `What a funny watch!' she remarked. `It tells the day of the month, and doesn't tell what o'clock it is!' `Why should it?' muttered the Hatter. `Does YOUR watch tell you what year it is?' `Of course not,' Alice replied very readily: `but that's because it stays the same year for such a long time together.' `Which is just the case with MINE,' said the Hatter. Alice felt dreadfully puzzled. The Hatter's remark seemed to have no sort of meaning in it, and yet it was certainly English. `I don't quite understand you,' she said, as politely as she could. `The Dormouse is asleep again,' said the Hatter, and he poured a little hot tea upon its nose. The Dormouse shook its head impatiently, and said, without opening its eyes, `Of course, of course; just what I was going to remark myself.' `Have you guessed the riddle yet?' the Hatter said, turning to Alice again. `No, I give it up,' Alice replied: `what's the answer?' `I haven't the slightest idea,' said the Hatter. `Nor I,' said the March Hare. Alice sighed wearily. `I think you might do something better with the time,' she said, `than waste it in asking riddles that have no answers.' `If you knew Time as well as I do,' said the Hatter, `you wouldn't talk about wasting IT. It's HIM.' `I don't know what you mean,' said Alice. `Of course you don't!' the Hatter said, tossing his head contemptuously. `I dare say you never even spoke to Time!' `Perhaps not,' Alice cautiously replied: `but I know I have to beat time when I learn music.' `Ah! that accounts for it,' said the Hatter. `He won't stand beating. Now, if you only kept on good terms with him, he'd do almost anything you liked with the clock. For instance, suppose it were nine o'clock in the morning, just time to begin lessons: you'd only have to whisper a hint to Time, and round goes the clock in a twinkling! Half-past one, time for dinner!' (`I only wish it was,' the March Hare said to itself in a whisper.) `That would be grand, certainly,' said Alice thoughtfully: `but then--I shouldn't be hungry for it, you know.' `Not at first, perhaps,' said the Hatter: `but you could keep it to half-past one as long as you liked.' `Is that the way YOU manage?' Alice asked. The Hatter shook his head mournfully. `Not I!' he replied. `We quarrelled last March--just before HE went mad, you know--' (pointing with his tea spoon at the March Hare,) `--it was at the great concert given by the Queen of Hearts, and I had to sing "Twinkle, twinkle, little bat! How I wonder what you're at!" You know the song, perhaps?' `I've heard something like it,' said Alice. `It goes on, you know,' the Hatter continued, `in this way:-- "Up above the world you fly, Like a tea-tray in the sky. Twinkle, twinkle--"' Here the Dormouse shook itself, and began singing in its sleep `Twinkle, twinkle, twinkle, twinkle--' and went on so long that they had to pinch it to make it stop. `Well, I'd hardly finished the first verse,' said the Hatter, `when the Queen jumped up and bawled out, "He's murdering the time! Off with his head!"' `How dreadfully savage!' exclaimed Alice. `And ever since that,' the Hatter went on in a mournful tone, `he won't do a thing I ask! It's always six o'clock now.' A bright idea came into Alice's head. `Is that the reason so many tea-things are put out here?' she asked. `Yes, that's it,' said the Hatter with a sigh: `it's always tea-time, and we've no time to wash the things between whiles.' `Then you keep moving round, I suppose?' said Alice. `Exactly so,' said the Hatter: `as the things get used up.' `But what happens when you come to the beginning again?' Alice ventured to ask. `Suppose we change the subject,' the March Hare interrupted, yawning. `I'm getting tired of this. I vote the young lady tells us a story.' `I'm afraid I don't know one,' said Alice, rather alarmed at the proposal. `Then the Dormouse shall!' they both cried. `Wake up, Dormouse!' And they pinched it on both sides at once. The Dormouse slowly opened his eyes. `I wasn't asleep,' he said in a hoarse, feeble voice: `I heard every word you fellows were saying.' `Tell us a story!' said the March Hare. `Yes, please do!' pleaded Alice. `And be quick about it,' added the Hatter, `or you'll be asleep again before it's done.' `Once upon a time there were three little sisters,' the Dormouse began in a great hurry; `and their names were Elsie, Lacie, and Tillie; and they lived at the bottom of a well--' `What did they live on?' said Alice, who always took a great interest in questions of eating and drinking. `They lived on treacle,' said the Dormouse, after thinking a minute or two. `They couldn't have done that, you know,' Alice gently remarked; `they'd have been ill.' `So they were,' said the Dormouse; `VERY ill.' Alice tried to fancy to herself what such an extraordinary ways of living would be like, but it puzzled her too much, so she went on: `But why did they live at the bottom of a well?' `Take some more tea,' the March Hare said to Alice, very earnestly. `I've had nothing yet,' Alice replied in an offended tone, `so I can't take more.' `You mean you can't take LESS,' said the Hatter: `it's very easy to take MORE than nothing.' `Nobody asked YOUR opinion,' said Alice. `Who's making personal remarks now?' the Hatter asked triumphantly. Alice did not quite know what to say to this: so she helped herself to some tea and bread-and-butter, and then turned to the Dormouse, and repeated her question. `Why did they live at the bottom of a well?' The Dormouse again took a minute or two to think about it, and then said, `It was a treacle-well.' `There's no such thing!' Alice was beginning very angrily, but the Hatter and the March Hare went `Sh! sh!' and the Dormouse sulkily remarked, `If you can't be civil, you'd better finish the story for yourself.' `No, please go on!' Alice said very humbly; `I won't interrupt again. I dare say there may be ONE.' `One, indeed!' said the Dormouse indignantly. However, he consented to go on. `And so these three little sisters--they were learning to draw, you know--' `What did they draw?' said Alice, quite forgetting her promise. `Treacle,' said the Dormouse, without considering at all this time. `I want a clean cup,' interrupted the Hatter: `let's all move one place on.' He moved on as he spoke, and the Dormouse followed him: the March Hare moved into the Dormouse's place, and Alice rather unwillingly took the place of the March Hare. The Hatter was the only one who got any advantage from the change: and Alice was a good deal worse off than before, as the March Hare had just upset the milk-jug into his plate. Alice did not wish to offend the Dormouse again, so she began very cautiously: `But I don't understand. Where did they draw the treacle from?' `You can draw water out of a water-well,' said the Hatter; `so I should think you could draw treacle out of a treacle-well--eh, stupid?' `But they were IN the well,' Alice said to the Dormouse, not choosing to notice this last remark. `Of course they were', said the Dormouse; `--well in.' This answer so confused poor Alice, that she let the Dormouse go on for some time without interrupting it. `They were learning to draw,' the Dormouse went on, yawning and rubbing its eyes, for it was getting very sleepy; `and they drew all manner of things--everything that begins with an M--' `Why with an M?' said Alice. `Why not?' said the March Hare. Alice was silent. The Dormouse had closed its eyes by this time, and was going off into a doze; but, on being pinched by the Hatter, it woke up again with a little shriek, and went on: `--that begins with an M, such as mouse-traps, and the moon, and memory, and muchness-- you know you say things are "much of a muchness"--did you ever see such a thing as a drawing of a muchness?' `Really, now you ask me,' said Alice, very much confused, `I don't think--' `Then you shouldn't talk,' said the Hatter. This piece of rudeness was more than Alice could bear: she got up in great disgust, and walked off; the Dormouse fell asleep instantly, and neither of the others took the least notice of her going, though she looked back once or twice, half hoping that they would call after her: the last time she saw them, they were trying to put the Dormouse into the teapot. `At any rate I'll never go THERE again!' said Alice as she picked her way through the wood. `It's the stupidest tea-party I ever was at in all my life!' Just as she said this, she noticed that one of the trees had a door leading right into it. `That's very curious!' she thought. `But everything's curious today. I think I may as well go in at once.' And in she went. Once more she found herself in the long hall, and close to the little glass table. `Now, I'll manage better this time,' she said to herself, and began by taking the little golden key, and unlocking the door that led into the garden. Then she went to work nibbling at the mushroom (she had kept a piece of it in her pocked) till she was about a foot high: then she walked down the little passage: and THEN--she found herself at last in the beautiful garden, among the bright flower-beds and the cool fountains. CHAPTER VIII The Queen's Croquet-Ground A large rose-tree stood near the entrance of the garden: the roses growing on it were white, but there were three gardeners at it, busily painting them red. Alice thought this a very curious thing, and she went nearer to watch them, and just as she came up to them she heard one of them say, `Look out now, Five! Don't go splashing paint over me like that!' `I couldn't help it,' said Five, in a sulky tone; `Seven jogged my elbow.' On which Seven looked up and said, `That's right, Five! Always lay the blame on others!' `YOU'D better not talk!' said Five. `I heard the Queen say only yesterday you deserved to be beheaded!' `What for?' said the one who had spoken first. `That's none of YOUR business, Two!' said Seven. `Yes, it IS his business!' said Five, `and I'll tell him--it was for bringing the cook tulip-roots instead of onions.' Seven flung down his brush, and had just begun `Well, of all the unjust things--' when his eye chanced to fall upon Alice, as she stood watching them, and he checked himself suddenly: the others looked round also, and all of them bowed low. `Would you tell me,' said Alice, a little timidly, `why you are painting those roses?' Five and Seven said nothing, but looked at Two. Two began in a low voice, `Why the fact is, you see, Miss, this here ought to have been a RED rose-tree, and we put a white one in by mistake; and if the Queen was to find it out, we should all have our heads cut off, you know. So you see, Miss, we're doing our best, afore she comes, to--' At this moment Five, who had been anxiously looking across the garden, called out `The Queen! The Queen!' and the three gardeners instantly threw themselves flat upon their faces. There was a sound of many footsteps, and Alice looked round, eager to see the Queen. First came ten soldiers carrying clubs; these were all shaped like the three gardeners, oblong and flat, with their hands and feet at the corners: next the ten courtiers; these were ornamented all over with diamonds, and walked two and two, as the soldiers did. After these came the royal children; there were ten of them, and the little dears came jumping merrily along hand in hand, in couples: they were all ornamented with hearts. Next came the guests, mostly Kings and Queens, and among them Alice recognised the White Rabbit: it was talking in a hurried nervous manner, smiling at everything that was said, and went by without noticing her. Then followed the Knave of Hearts, carrying the King's crown on a crimson velvet cushion; and, last of all this grand procession, came THE KING AND QUEEN OF HEARTS. Alice was rather doubtful whether she ought not to lie down on her face like the three gardeners, but she could not remember every having heard of such a rule at processions; `and besides, what would be the use of a procession,' thought she, `if people had all to lie down upon their faces, so that they couldn't see it?' So she stood still where she was, and waited. When the procession came opposite to Alice, they all stopped and looked at her, and the Queen said severely `Who is this?' She said it to the Knave of Hearts, who only bowed and smiled in reply. `Idiot!' said the Queen, tossing her head impatiently; and, turning to Alice, she went on, `What's your name, child?' `My name is Alice, so please your Majesty,' said Alice very politely; but she added, to herself, `Why, they're only a pack of cards, after all. I needn't be afraid of them!' `And who are THESE?' said the Queen, pointing to the three gardeners who were lying round the rosetree; for, you see, as they were lying on their faces, and the pattern on their backs was the same as the rest of the pack, she could not tell whether they were gardeners, or soldiers, or courtiers, or three of her own children. `How should I know?' said Alice, surprised at her own courage. `It's no business of MINE.' The Queen turned crimson with fury, and, after glaring at her for a moment like a wild beast, screamed `Off with her head! Off--' `Nonsense!' said Alice, very loudly and decidedly, and the Queen was silent. The King laid his hand upon her arm, and timidly said `Consider, my dear: she is only a child!' The Queen turned angrily away from him, and said to the Knave `Turn them over!' The Knave did so, very carefully, with one foot. `Get up!' said the Queen, in a shrill, loud voice, and the three gardeners instantly jumped up, and began bowing to the King, the Queen, the royal children, and everybody else. `Leave off that!' screamed the Queen. `You make me giddy.' And then, turning to the rose-tree, she went on, `What HAVE you been doing here?' `May it please your Majesty,' said Two, in a very humble tone, going down on one knee as he spoke, `we were trying--' `I see!' said the Queen, who had meanwhile been examining the roses. `Off with their heads!' and the procession moved on, three of the soldiers remaining behind to execute the unfortunate gardeners, who ran to Alice for protection. `You shan't be beheaded!' said Alice, and she put them into a large flower-pot that stood near. The three soldiers wandered about for a minute or two, looking for them, and then quietly marched off after the others. `Are their heads off?' shouted the Queen. `Their heads are gone, if it please your Majesty!' the soldiers shouted in reply. `That's right!' shouted the Queen. `Can you play croquet?' The soldiers were silent, and looked at Alice, as the question was evidently meant for her. `Yes!' shouted Alice. `Come on, then!' roared the Queen, and Alice joined the procession, wondering very much what would happen next. `It's--it's a very fine day!' said a timid voice at her side. She was walking by the White Rabbit, who was peeping anxiously into her face. `Very,' said Alice: `--where's the Duchess?' `Hush! Hush!' said the Rabbit in a low, hurried tone. He looked anxiously over his shoulder as he spoke, and then raised himself upon tiptoe, put his mouth close to her ear, and whispered `She's under sentence of execution.' `What for?' said Alice. `Did you say "What a pity!"?' the Rabbit asked. `No, I didn't,' said Alice: `I don't think it's at all a pity. I said "What for?"' `She boxed the Queen's ears--' the Rabbit began. Alice gave a little scream of laughter. `Oh, hush!' the Rabbit whispered in a frightened tone. `The Queen will hear you! You see, she came rather late, and the Queen said--' `Get to your places!' shouted the Queen in a voice of thunder, and people began running about in all directions, tumbling up against each other; however, they got settled down in a minute or two, and the game began. Alice thought she had never seen such a curious croquet-ground in her life; it was all ridges and furrows; the balls were live hedgehogs, the mallets live flamingoes, and the soldiers had to double themselves up and to stand on their hands and feet, to make the arches. The chief difficulty Alice found at first was in managing her flamingo: she succeeded in getting its body tucked away, comfortably enough, under her arm, with its legs hanging down, but generally, just as she had got its neck nicely straightened out, and was going to give the hedgehog a blow with its head, it WOULD twist itself round and look up in her face, with such a puzzled expression that she could not help bursting out laughing: and when she had got its head down, and was going to begin again, it was very provoking to find that the hedgehog had unrolled itself, and was in the act of crawling away: besides all this, there was generally a ridge or furrow in the way wherever she wanted to send the hedgehog to, and, as the doubled-up soldiers were always getting up and walking off to other parts of the ground, Alice soon came to the conclusion that it was a very difficult game indeed. The players all played at once without waiting for turns, quarrelling all the while, and fighting for the hedgehogs; and in a very short time the Queen was in a furious passion, and went stamping about, and shouting `Off with his head!' or `Off with her head!' about once in a minute. Alice began to feel very uneasy: to be sure, she had not as yet had any dispute with the Queen, but she knew that it might happen any minute, `and then,' thought she, `what would become of me? They're dreadfully fond of beheading people here; the great wonder is, that there's any one left alive!' She was looking about for some way of escape, and wondering whether she could get away without being seen, when she noticed a curious appearance in the air: it puzzled her very much at first, but, after watching it a minute or two, she made it out to be a grin, and she said to herself `It's the Cheshire Cat: now I shall have somebody to talk to.' `How are you getting on?' said the Cat, as soon as there was mouth enough for it to speak with. Alice waited till the eyes appeared, and then nodded. `It's no use speaking to it,' she thought, `till its ears have come, or at least one of them.' In another minute the whole head appeared, and then Alice put down her flamingo, and began an account of the game, feeling very glad she had someone to listen to her. The Cat seemed to think that there was enough of it now in sight, and no more of it appeared. `I don't think they play at all fairly,' Alice began, in rather a complaining tone, `and they all quarrel so dreadfully one can't hear oneself speak--and they don't seem to have any rules in particular; at least, if there are, nobody attends to them--and you've no idea how confusing it is all the things being alive; for instance, there's the arch I've got to go through next walking about at the other end of the ground--and I should have croqueted the Queen's hedgehog just now, only it ran away when it saw mine coming!' `How do you like the Queen?' said the Cat in a low voice. `Not at all,' said Alice: `she's so extremely--' Just then she noticed that the Queen was close behind her, listening: so she went on, `--likely to win, that it's hardly worth while finishing the game.' The Queen smiled and passed on. `Who ARE you talking to?' said the King, going up to Alice, and looking at the Cat's head with great curiosity. `It's a friend of mine--a Cheshire Cat,' said Alice: `allow me to introduce it.' `I don't like the look of it at all,' said the King: `however, it may kiss my hand if it likes.' `I'd rather not,' the Cat remarked. `Don't be impertinent,' said the King, `and don't look at me like that!' He got behind Alice as he spoke. `A cat may look at a king,' said Alice. `I've read that in some book, but I don't remember where.' `Well, it must be removed,' said the King very decidedly, and he called the Queen, who was passing at the moment, `My dear! I wish you would have this cat removed!' The Queen had only one way of settling all difficulties, great or small. `Off with his head!' she said, without even looking round. `I'll fetch the executioner myself,' said the King eagerly, and he hurried off. Alice thought she might as well go back, and see how the game was going on, as she heard the Queen's voice in the distance, screaming with passion. She had already heard her sentence three of the players to be executed for having missed their turns, and she did not like the look of things at all, as the game was in such confusion that she never knew whether it was her turn or not. So she went in search of her hedgehog. The hedgehog was engaged in a fight with another hedgehog, which seemed to Alice an excellent opportunity for croqueting one of them with the other: the only difficulty was, that her flamingo was gone across to the other side of the garden, where Alice could see it trying in a helpless sort of way to fly up into a tree. By the time she had caught the flamingo and brought it back, the fight was over, and both the hedgehogs were out of sight: `but it doesn't matter much,' thought Alice, `as all the arches are gone from this side of the ground.' So she tucked it away under her arm, that it might not escape again, and went back for a little more conversation with her friend. When she got back to the Cheshire Cat, she was surprised to find quite a large crowd collected round it: there was a dispute going on between the executioner, the King, and the Queen, who were all talking at once, while all the rest were quite silent, and looked very uncomfortable. The moment Alice appeared, she was appealed to by all three to settle the question, and they repeated their arguments to her, though, as they all spoke at once, she found it very hard indeed to make out exactly what they said. The executioner's argument was, that you couldn't cut off a head unless there was a body to cut it off from: that he had never had to do such a thing before, and he wasn't going to begin at HIS time of life. The King's argument was, that anything that had a head could be beheaded, and that you weren't to talk nonsense. The Queen's argument was, that if something wasn't done about it in less than no time she'd have everybody executed, all round. (It was this last remark that had made the whole party look so grave and anxious.) Alice could think of nothing else to say but `It belongs to the Duchess: you'd better ask HER about it.' `She's in prison,' the Queen said to the executioner: `fetch her here.' And the executioner went off like an arrow. The Cat's head began fading away the moment he was gone, and, by the time he had come back with the Dutchess, it had entirely disappeared; so the King and the executioner ran wildly up and down looking for it, while the rest of the party went back to the game. CHAPTER IX The Mock Turtle's Story `You can't think how glad I am to see you again, you dear old thing!' said the Duchess, as she tucked her arm affectionately into Alice's, and they walked off together. Alice was very glad to find her in such a pleasant temper, and thought to herself that perhaps it was only the pepper that had made her so savage when they met in the kitchen. `When I'M a Duchess,' she said to herself, (not in a very hopeful tone though), `I won't have any pepper in my kitchen AT ALL. Soup does very well without--Maybe it's always pepper that makes people hot-tempered,' she went on, very much pleased at having found out a new kind of rule, `and vinegar that makes them sour--and camomile that makes them bitter--and--and barley-sugar and such things that make children sweet-tempered. I only wish people knew that: then they wouldn't be so stingy about it, you know--' She had quite forgotten the Duchess by this time, and was a little startled when she heard her voice close to her ear. `You're thinking about something, my dear, and that makes you forget to talk. I can't tell you just now what the moral of that is, but I shall remember it in a bit.' `Perhaps it hasn't one,' Alice ventured to remark. `Tut, tut, child!' said the Duchess. `Everything's got a moral, if only you can find it.' And she squeezed herself up closer to Alice's side as she spoke. Alice did not much like keeping so close to her: first, because the Duchess was VERY ugly; and secondly, because she was exactly the right height to rest her chin upon Alice's shoulder, and it was an uncomfortably sharp chin. However, she did not like to be rude, so she bore it as well as she could. `The game's going on rather better now,' she said, by way of keeping up the conversation a little. `'Tis so,' said the Duchess: `and the moral of that is--"Oh, 'tis love, 'tis love, that makes the world go round!"' `Somebody said,' Alice whispered, `that it's done by everybody minding their own business!' `Ah, well! It means much the same thing,' said the Duchess, digging her sharp little chin into Alice's shoulder as she added, `and the moral of THAT is--"Take care of the sense, and the sounds will take care of themselves."' `How fond she is of finding morals in things!' Alice thought to herself. `I dare say you're wondering why I don't put my arm round your waist,' the Duchess said after a pause: `the reason is, that I'm doubtful about the temper of your flamingo. Shall I try the experiment?' `HE might bite,' Alice cautiously replied, not feeling at all anxious to have the experiment tried. `Very true,' said the Duchess: `flamingoes and mustard both bite. And the moral of that is--"Birds of a feather flock together."' `Only mustard isn't a bird,' Alice remarked. `Right, as usual,' said the Duchess: `what a clear way you have of putting things!' `It's a mineral, I THINK,' said Alice. `Of course it is,' said the Duchess, who seemed ready to agree to everything that Alice said; `there's a large mustard-mine near here. And the moral of that is--"The more there is of mine, the less there is of yours."' `Oh, I know!' exclaimed Alice, who had not attended to this last remark, `it's a vegetable. It doesn't look like one, but it is.' `I quite agree with you,' said the Duchess; `and the moral of that is--"Be what you would seem to be"--or if you'd like it put more simply--"Never imagine yourself not to be otherwise than what it might appear to others that what you were or might have been was not otherwise than what you had been would have appeared to them to be otherwise."' `I think I should understand that better,' Alice said very politely, `if I had it written down: but I can't quite follow it as you say it.' `That's nothing to what I could say if I chose,' the Duchess replied, in a pleased tone. `Pray don't trouble yourself to say it any longer than that,' said Alice. `Oh, don't talk about trouble!' said the Duchess. `I make you a present of everything I've said as yet.' `A cheap sort of present!' thought Alice. `I'm glad they don't give birthday presents like that!' But she did not venture to say it out loud. `Thinking again?' the Duchess asked, with another dig of her sharp little chin. `I've a right to think,' said Alice sharply, for she was beginning to feel a little worried. `Just about as much right,' said the Duchess, `as pigs have to fly; and the m--' But here, to Alice's great surprise, the Duchess's voice died away, even in the middle of her favourite word `moral,' and the arm that was linked into hers began to tremble. Alice looked up, and there stood the Queen in front of them, with her arms folded, frowning like a thunderstorm. `A fine day, your Majesty!' the Duchess began in a low, weak voice. `Now, I give you fair warning,' shouted the Queen, stamping on the ground as she spoke; `either you or your head must be off, and that in about half no time! Take your choice!' The Duchess took her choice, and was gone in a moment. `Let's go on with the game,' the Queen said to Alice; and Alice was too much frightened to say a word, but slowly followed her back to the croquet-ground. The other guests had taken advantage of the Queen's absence, and were resting in the shade: however, the moment they saw her, they hurried back to the game, the Queen merely remarking that a moment's delay would cost them their lives. All the time they were playing the Queen never left off quarrelling with the other players, and shouting `Off with his head!' or `Off with her head!' Those whom she sentenced were taken into custody by the soldiers, who of course had to leave off being arches to do this, so that by the end of half an hour or so there were no arches left, and all the players, except the King, the Queen, and Alice, were in custody and under sentence of execution. Then the Queen left off, quite out of breath, and said to Alice, `Have you seen the Mock Turtle yet?' `No,' said Alice. `I don't even know what a Mock Turtle is.' `It's the thing Mock Turtle Soup is made from,' said the Queen. `I never saw one, or heard of one,' said Alice. `Come on, then,' said the Queen, `and he shall tell you his history,' As they walked off together, Alice heard the King say in a low voice, to the company generally, `You are all pardoned.' `Come, THAT'S a good thing!' she said to herself, for she had felt quite unhappy at the number of executions the Queen had ordered. They very soon came upon a Gryphon, lying fast asleep in the sun. (IF you don't know what a Gryphon is, look at the picture.) `Up, lazy thing!' said the Queen, `and take this young lady to see the Mock Turtle, and to hear his history. I must go back and see after some executions I have ordered'; and she walked off, leaving Alice alone with the Gryphon. Alice did not quite like the look of the creature, but on the whole she thought it would be quite as safe to stay with it as to go after that savage Queen: so she waited. The Gryphon sat up and rubbed its eyes: then it watched the Queen till she was out of sight: then it chuckled. `What fun!' said the Gryphon, half to itself, half to Alice. `What IS the fun?' said Alice. `Why, SHE,' said the Gryphon. `It's all her fancy, that: they never executes nobody, you know. Come on!' `Everybody says "come on!" here,' thought Alice, as she went slowly after it: `I never was so ordered about in all my life, never!' They had not gone far before they saw the Mock Turtle in the distance, sitting sad and lonely on a little ledge of rock, and, as they came nearer, Alice could hear him sighing as if his heart would break. She pitied him deeply. `What is his sorrow?' she asked the Gryphon, and the Gryphon answered, very nearly in the same words as before, `It's all his fancy, that: he hasn't got no sorrow, you know. Come on!' So they went up to the Mock Turtle, who looked at them with large eyes full of tears, but said nothing. `This here young lady,' said the Gryphon, `she wants for to know your history, she do.' `I'll tell it her,' said the Mock Turtle in a deep, hollow tone: `sit down, both of you, and don't speak a word till I've finished.' So they sat down, and nobody spoke for some minutes. Alice thought to herself, `I don't see how he can EVEN finish, if he doesn't begin.' But she waited patiently. `Once,' said the Mock Turtle at last, with a deep sigh, `I was a real Turtle.' These words were followed by a very long silence, broken only by an occasional exclamation of `Hjckrrh!' from the Gryphon, and the constant heavy sobbing of the Mock Turtle. Alice was very nearly getting up and saying, `Thank you, sir, for your interesting story,' but she could not help thinking there MUST be more to come, so she sat still and said nothing. `When we were little,' the Mock Turtle went on at last, more calmly, though still sobbing a little now and then, `we went to school in the sea. The master was an old Turtle--we used to call him Tortoise--' `Why did you call him Tortoise, if he wasn't one?' Alice asked. `We called him Tortoise because he taught us,' said the Mock Turtle angrily: `really you are very dull!' `You ought to be ashamed of yourself for asking such a simple question,' added the Gryphon; and then they both sat silent and looked at poor Alice, who felt ready to sink into the earth. At last the Gryphon said to the Mock Turtle, `Drive on, old fellow! Don't be all day about it!' and he went on in these words: `Yes, we went to school in the sea, though you mayn't believe it--' `I never said I didn't!' interrupted Alice. `You did,' said the Mock Turtle. `Hold your tongue!' added the Gryphon, before Alice could speak again. The Mock Turtle went on. `We had the best of educations--in fact, we went to school every day--' `I'VE been to a day-school, too,' said Alice; `you needn't be so proud as all that.' `With extras?' asked the Mock Turtle a little anxiously. `Yes,' said Alice, `we learned French and music.' `And washing?' said the Mock Turtle. `Certainly not!' said Alice indignantly. `Ah! then yours wasn't a really good school,' said the Mock Turtle in a tone of great relief. `Now at OURS they had at the end of the bill, "French, music, AND WASHING--extra."' `You couldn't have wanted it much,' said Alice; `living at the bottom of the sea.' `I couldn't afford to learn it.' said the Mock Turtle with a sigh. `I only took the regular course.' `What was that?' inquired Alice. `Reeling and Writhing, of course, to begin with,' the Mock Turtle replied; `and then the different branches of Arithmetic-- Ambition, Distraction, Uglification, and Derision.' `I never heard of "Uglification,"' Alice ventured to say. `What is it?' The Gryphon lifted up both its paws in surprise. `What! Never heard of uglifying!' it exclaimed. `You know what to beautify is, I suppose?' `Yes,' said Alice doubtfully: `it means--to--make--anything-- prettier.' `Well, then,' the Gryphon went on, `if you don't know what to uglify is, you ARE a simpleton.' Alice did not feel encouraged to ask any more questions about it, so she turned to the Mock Turtle, and said `What else had you to learn?' `Well, there was Mystery,' the Mock Turtle replied, counting off the subjects on his flappers, `--Mystery, ancient and modern, with Seaography: then Drawling--the Drawling-master was an old conger-eel, that used to come once a week: HE taught us Drawling, Stretching, and Fainting in Coils.' `What was THAT like?' said Alice. `Well, I can't show it you myself,' the Mock Turtle said: `I'm too stiff. And the Gryphon never learnt it.' `Hadn't time,' said the Gryphon: `I went to the Classics master, though. He was an old crab, HE was.' `I never went to him,' the Mock Turtle said with a sigh: `he taught Laughing and Grief, they used to say.' `So he did, so he did,' said the Gryphon, sighing in his turn; and both creatures hid their faces in their paws. `And how many hours a day did you do lessons?' said Alice, in a hurry to change the subject. `Ten hours the first day,' said the Mock Turtle: `nine the next, and so on.' `What a curious plan!' exclaimed Alice. `That's the reason they're called lessons,' the Gryphon remarked: `because they lessen from day to day.' This was quite a new idea to Alice, and she thought it over a little before she made her next remark. `Then the eleventh day must have been a holiday?' `Of course it was,' said the Mock Turtle. `And how did you manage on the twelfth?' Alice went on eagerly. `That's enough about lessons,' the Gryphon interrupted in a very decided tone: `tell her something about the games now.' CHAPTER X The Lobster Quadrille The Mock Turtle sighed deeply, and drew the back of one flapper across his eyes. He looked at Alice, and tried to speak, but for a minute or two sobs choked his voice. `Same as if he had a bone in his throat,' said the Gryphon: and it set to work shaking him and punching him in the back. At last the Mock Turtle recovered his voice, and, with tears running down his cheeks, he went on again:-- `You may not have lived much under the sea--' (`I haven't,' said Alice)--`and perhaps you were never even introduced to a lobster--' (Alice began to say `I once tasted--' but checked herself hastily, and said `No, never') `--so you can have no idea what a delightful thing a Lobster Quadrille is!' `No, indeed,' said Alice. `What sort of a dance is it?' `Why,' said the Gryphon, `you first form into a line along the sea-shore--' `Two lines!' cried the Mock Turtle. `Seals, turtles, salmon, and so on; then, when you've cleared all the jelly-fish out of the way--' `THAT generally takes some time,' interrupted the Gryphon. `--you advance twice--' `Each with a lobster as a partner!' cried the Gryphon. `Of course,' the Mock Turtle said: `advance twice, set to partners--' `--change lobsters, and retire in same order,' continued the Gryphon. `Then, you know,' the Mock Turtle went on, `you throw the--' `The lobsters!' shouted the Gryphon, with a bound into the air. `--as far out to sea as you can--' `Swim after them!' screamed the Gryphon. `Turn a somersault in the sea!' cried the Mock Turtle, capering wildly about. `Back to land again, and that's all the first figure,' said the Mock Turtle, suddenly dropping his voice; and the two creatures, who had been jumping about like mad things all this time, sat down again very sadly and quietly, and looked at Alice. `It must be a very pretty dance,' said Alice timidly. `Would you like to see a little of it?' said the Mock Turtle. `Very much indeed,' said Alice. `Come, let's try the first figure!' said the Mock Turtle to the Gryphon. `We can do without lobsters, you know. Which shall sing?' `Oh, YOU sing,' said the Gryphon. `I've forgotten the words.' So they began solemnly dancing round and round Alice, every now and then treading on her toes when they passed too close, and waving their forepaws to mark the time, while the Mock Turtle sang this, very slowly and sadly:-- `"Will you walk a little faster?" said a whiting to a snail. "There's a porpoise close behind us, and he's treading on my tail. See how eagerly the lobsters and the turtles all advance! They are waiting on the shingle--will you come and join the dance? Will you, won't you, will you, won't you, will you join the dance? Will you, won't you, will you, won't you, won't you join the dance? "You can really have no notion how delightful it will be When they take us up and throw us, with the lobsters, out to sea!" But the snail replied "Too far, too far!" and gave a look askance-- Said he thanked the whiting kindly, but he would not join the dance. Would not, could not, would not, could not, would not join the dance. Would not, could not, would not, could not, could not join the dance. `"What matters it how far we go?" his scaly friend replied. "There is another shore, you know, upon the other side. The further off from England the nearer is to France-- Then turn not pale, beloved snail, but come and join the dance. Will you, won't you, will you, won't you, will you join the dance? Will you, won't you, will you, won't you, won't you join the dance?"' `Thank you, it's a very interesting dance to watch,' said Alice, feeling very glad that it was over at last: `and I do so like that curious song about the whiting!' `Oh, as to the whiting,' said the Mock Turtle, `they--you've seen them, of course?' `Yes,' said Alice, `I've often seen them at dinn--' she checked herself hastily. `I don't know where Dinn may be,' said the Mock Turtle, `but if you've seen them so often, of course you know what they're like.' `I believe so,' Alice replied thoughtfully. `They have their tails in their mouths--and they're all over crumbs.' `You're wrong about the crumbs,' said the Mock Turtle: `crumbs would all wash off in the sea. But they HAVE their tails in their mouths; and the reason is--' here the Mock Turtle yawned and shut his eyes.--`Tell her about the reason and all that,' he said to the Gryphon. `The reason is,' said the Gryphon, `that they WOULD go with the lobsters to the dance. So they got thrown out to sea. So they had to fall a long way. So they got their tails fast in their mouths. So they couldn't get them out again. That's all.' `Thank you,' said Alice, `it's very interesting. I never knew so much about a whiting before.' `I can tell you more than that, if you like,' said the Gryphon. `Do you know why it's called a whiting?' `I never thought about it,' said Alice. `Why?' `IT DOES THE BOOTS AND SHOES.' the Gryphon replied very solemnly. Alice was thoroughly puzzled. `Does the boots and shoes!' she repeated in a wondering tone. `Why, what are YOUR shoes done with?' said the Gryphon. `I mean, what makes them so shiny?' Alice looked down at them, and considered a little before she gave her answer. `They're done with blacking, I believe.' `Boots and shoes under the sea,' the Gryphon went on in a deep voice, `are done with a whiting. Now you know.' `And what are they made of?' Alice asked in a tone of great curiosity. `Soles and eels, of course,' the Gryphon replied rather impatiently: `any shrimp could have told you that.' `If I'd been the whiting,' said Alice, whose thoughts were still running on the song, `I'd have said to the porpoise, "Keep back, please: we don't want YOU with us!"' `They were obliged to have him with them,' the Mock Turtle said: `no wise fish would go anywhere without a porpoise.' `Wouldn't it really?' said Alice in a tone of great surprise. `Of course not,' said the Mock Turtle: `why, if a fish came to ME, and told me he was going a journey, I should say "With what porpoise?"' `Don't you mean "purpose"?' said Alice. `I mean what I say,' the Mock Turtle replied in an offended tone. And the Gryphon added `Come, let's hear some of YOUR adventures.' `I could tell you my adventures--beginning from this morning,' said Alice a little timidly: `but it's no use going back to yesterday, because I was a different person then.' `Explain all that,' said the Mock Turtle. `No, no! The adventures first,' said the Gryphon in an impatient tone: `explanations take such a dreadful time.' So Alice began telling them her adventures from the time when she first saw the White Rabbit. She was a little nervous about it just at first, the two creatures got so close to her, one on each side, and opened their eyes and mouths so VERY wide, but she gained courage as she went on. Her listeners were perfectly quiet till she got to the part about her repeating `YOU ARE OLD, FATHER WILLIAM,' to the Caterpillar, and the words all coming different, and then the Mock Turtle drew a long breath, and said `That's very curious.' `It's all about as curious as it can be,' said the Gryphon. `It all came different!' the Mock Turtle repeated thoughtfully. `I should like to hear her try and repeat something now. Tell her to begin.' He looked at the Gryphon as if he thought it had some kind of authority over Alice. `Stand up and repeat "'TIS THE VOICE OF THE SLUGGARD,"' said the Gryphon. `How the creatures order one about, and make one repeat lessons!' thought Alice; `I might as well be at school at once.' However, she got up, and began to repeat it, but her head was so full of the Lobster Quadrille, that she hardly knew what she was saying, and the words came very queer indeed:-- `'Tis the voice of the Lobster; I heard him declare, "You have baked me too brown, I must sugar my hair." As a duck with its eyelids, so he with his nose Trims his belt and his buttons, and turns out his toes.' [later editions continued as follows When the sands are all dry, he is gay as a lark, And will talk in contemptuous tones of the Shark, But, when the tide rises and sharks are around, His voice has a timid and tremulous sound.] `That's different from what I used to say when I was a child,' said the Gryphon. `Well, I never heard it before,' said the Mock Turtle; `but it sounds uncommon nonsense.' Alice said nothing; she had sat down with her face in her hands, wondering if anything would EVER happen in a natural way again. `I should like to have it explained,' said the Mock Turtle. `She can't explain it,' said the Gryphon hastily. `Go on with the next verse.' `But about his toes?' the Mock Turtle persisted. `How COULD he turn them out with his nose, you know?' `It's the first position in dancing.' Alice said; but was dreadfully puzzled by the whole thing, and longed to change the subject. `Go on with the next verse,' the Gryphon repeated impatiently: `it begins "I passed by his garden."' Alice did not dare to disobey, though she felt sure it would all come wrong, and she went on in a trembling voice:-- `I passed by his garden, and marked, with one eye, How the Owl and the Panther were sharing a pie--' [later editions continued as follows The Panther took pie-crust, and gravy, and meat, While the Owl had the dish as its share of the treat. When the pie was all finished, the Owl, as a boon, Was kindly permitted to pocket the spoon: While the Panther received knife and fork with a growl, And concluded the banquet--] `What IS the use of repeating all that stuff,' the Mock Turtle interrupted, `if you don't explain it as you go on? It's by far the most confusing thing I ever heard!' `Yes, I think you'd better leave off,' said the Gryphon: and Alice was only too glad to do so. `Shall we try another figure of the Lobster Quadrille?' the Gryphon went on. `Or would you like the Mock Turtle to sing you a song?' `Oh, a song, please, if the Mock Turtle would be so kind,' Alice replied, so eagerly that the Gryphon said, in a rather offended tone, `Hm! No accounting for tastes! Sing her "Turtle Soup," will you, old fellow?' The Mock Turtle sighed deeply, and began, in a voice sometimes choked with sobs, to sing this:-- `Beautiful Soup, so rich and green, Waiting in a hot tureen! Who for such dainties would not stoop? Soup of the evening, beautiful Soup! Soup of the evening, beautiful Soup! Beau--ootiful Soo--oop! Beau--ootiful Soo--oop! Soo--oop of the e--e--evening, Beautiful, beautiful Soup! `Beautiful Soup! Who cares for fish, Game, or any other dish? Who would not give all else for two p ennyworth only of beautiful Soup? Pennyworth only of beautiful Soup? Beau--ootiful Soo--oop! Beau--ootiful Soo--oop! Soo--oop of the e--e--evening, Beautiful, beauti--FUL SOUP!' `Chorus again!' cried the Gryphon, and the Mock Turtle had just begun to repeat it, when a cry of `The trial's beginning!' was heard in the distance. `Come on!' cried the Gryphon, and, taking Alice by the hand, it hurried off, without waiting for the end of the song. `What trial is it?' Alice panted as she ran; but the Gryphon only answered `Come on!' and ran the faster, while more and more faintly came, carried on the breeze that followed them, the melancholy words:-- `Soo--oop of the e--e--evening, Beautiful, beautiful Soup!' CHAPTER XI Who Stole the Tarts? The King and Queen of Hearts were seated on their throne when they arrived, with a great crowd assembled about them--all sorts of little birds and beasts, as well as the whole pack of cards: the Knave was standing before them, in chains, with a soldier on each side to guard him; and near the King was the White Rabbit, with a trumpet in one hand, and a scroll of parchment in the other. In the very middle of the court was a table, with a large dish of tarts upon it: they looked so good, that it made Alice quite hungry to look at them--`I wish they'd get the trial done,' she thought, `and hand round the refreshments!' But there seemed to be no chance of this, so she began looking at everything about her, to pass away the time. Alice had never been in a court of justice before, but she had read about them in books, and she was quite pleased to find that she knew the name of nearly everything there. `That's the judge,' she said to herself, `because of his great wig.' The judge, by the way, was the King; and as he wore his crown over the wig, (look at the frontispiece if you want to see how he did it,) he did not look at all comfortable, and it was certainly not becoming. `And that's the jury-box,' thought Alice, `and those twelve creatures,' (she was obliged to say `creatures,' you see, because some of them were animals, and some were birds,) `I suppose they are the jurors.' She said this last word two or three times over to herself, being rather proud of it: for she thought, and rightly too, that very few little girls of her age knew the meaning of it at all. However, `jury-men' would have done just as well. The twelve jurors were all writing very busily on slates. `What are they doing?' Alice whispered to the Gryphon. `They can't have anything to put down yet, before the trial's begun.' `They're putting down their names,' the Gryphon whispered in reply, `for fear they should forget them before the end of the trial.' `Stupid things!' Alice began in a loud, indignant voice, but she stopped hastily, for the White Rabbit cried out, `Silence in the court!' and the King put on his spectacles and looked anxiously round, to make out who was talking. Alice could see, as well as if she were looking over their shoulders, that all the jurors were writing down `stupid things!' on their slates, and she could even make out that one of them didn't know how to spell `stupid,' and that he had to ask his neighbour to tell him. `A nice muddle their slates'll be in before the trial's over!' thought Alice. One of the jurors had a pencil that squeaked. This of course, Alice could not stand, and she went round the court and got behind him, and very soon found an opportunity of taking it away. She did it so quickly that the poor little juror (it was Bill, the Lizard) could not make out at all what had become of it; so, after hunting all about for it, he was obliged to write with one finger for the rest of the day; and this was of very little use, as it left no mark on the slate. `Herald, read the accusation!' said the King. On this the White Rabbit blew three blasts on the trumpet, and then unrolled the parchment scroll, and read as follows:-- `The Queen of Hearts, she made some tarts, All on a summer day: The Knave of Hearts, he stole those tarts, And took them quite away!' `Consider your verdict,' the King said to the jury. `Not yet, not yet!' the Rabbit hastily interrupted. `There's a great deal to come before that!' `Call the first witness,' said the King; and the White Rabbit blew three blasts on the trumpet, and called out, `First witness!' The first witness was the Hatter. He came in with a teacup in one hand and a piece of bread-and-butter in the other. `I beg pardon, your Majesty,' he began, `for bringing these in: but I hadn't quite finished my tea when I was sent for.' `You ought to have finished,' said the King. `When did you begin?' The Hatter looked at the March Hare, who had followed him into the court, arm-in-arm with the Dormouse. `Fourteenth of March, I think it was,' he said. `Fifteenth,' said the March Hare. `Sixteenth,' added the Dormouse. `Write that down,' the King said to the jury, and the jury eagerly wrote down all three dates on their slates, and then added them up, and reduced the answer to shillings and pence. `Take off your hat,' the King said to the Hatter. `It isn't mine,' said the Hatter. `Stolen!' the King exclaimed, turning to the jury, who instantly made a memorandum of the fact. `I keep them to sell,' the Hatter added as an explanation; `I've none of my own. I'm a hatter.' Here the Queen put on her spectacles, and began staring at the Hatter, who turned pale and fidgeted. `Give your evidence,' said the King; `and don't be nervous, or I'll have you executed on the spot.' This did not seem to encourage the witness at all: he kept shifting from one foot to the other, looking uneasily at the Queen, and in his confusion he bit a large piece out of his teacup instead of the bread-and-butter. Just at this moment Alice felt a very curious sensation, which puzzled her a good deal until she made out what it was: she was beginning to grow larger again, and she thought at first she would get up and leave the court; but on second thoughts she decided to remain where she was as long as there was room for her. `I wish you wouldn't squeeze so.' said the Dormouse, who was sitting next to her. `I can hardly breathe.' `I can't help it,' said Alice very meekly: `I'm growing.' `You've no right to grow here,' said the Dormouse. `Don't talk nonsense,' said Alice more boldly: `you know you're growing too.' `Yes, but I grow at a reasonable pace,' said the Dormouse: `not in that ridiculous fashion.' And he got up very sulkily and crossed over to the other side of the court. All this time the Queen had never left off staring at the Hatter, and, just as the Dormouse crossed the court, she said to one of the officers of the court, `Bring me the list of the singers in the last concert!' on which the wretched Hatter trembled so, that he shook both his shoes off. `Give your evidence,' the King repeated angrily, `or I'll have you executed, whether you're nervous or not.' `I'm a poor man, your Majesty,' the Hatter began, in a trembling voice, `--and I hadn't begun my tea--not above a week or so--and what with the bread-and-butter getting so thin--and the twinkling of the tea--' `The twinkling of the what?' said the King. `It began with the tea,' the Hatter replied. `Of course twinkling begins with a T!' said the King sharply. `Do you take me for a dunce? Go on!' `I'm a poor man,' the Hatter went on, `and most things twinkled after that--only the March Hare said--' `I didn't!' the March Hare interrupted in a great hurry. `You did!' said the Hatter. `I deny it!' said the March Hare. `He denies it,' said the King: `leave out that part.' `Well, at any rate, the Dormouse said--' the Hatter went on, looking anxiously round to see if he would deny it too: but the Dormouse denied nothing, being fast asleep. `After that,' continued the Hatter, `I cut some more bread- and-butter--' `But what did the Dormouse say?' one of the jury asked. `That I can't remember,' said the Hatter. `You MUST remember,' remarked the King, `or I'll have you executed.' The miserable Hatter dropped his teacup and bread-and-butter, and went down on one knee. `I'm a poor man, your Majesty,' he began. `You're a very poor speaker,' said the King. Here one of the guinea-pigs cheered, and was immediately suppressed by the officers of the court. (As that is rather a hard word, I will just explain to you how it was done. They had a large canvas bag, which tied up at the mouth with strings: into this they slipped the guinea-pig, head first, and then sat upon it.) `I'm glad I've seen that done,' thought Alice. `I've so often read in the newspapers, at the end of trials, "There was some attempts at applause, which was immediately suppressed by the officers of the court," and I never understood what it meant till now.' `If that's all you know about it, you may stand down,' continued the King. `I can't go no lower,' said the Hatter: `I'm on the floor, as it is.' `Then you may SIT down,' the King replied. Here the other guinea-pig cheered, and was suppressed. `Come, that finished the guinea-pigs!' thought Alice. `Now we shall get on better.' `I'd rather finish my tea,' said the Hatter, with an anxious look at the Queen, who was reading the list of singers. `You may go,' said the King, and the Hatter hurriedly left the court, without even waiting to put his shoes on. `--and just take his head off outside,' the Queen added to one of the officers: but the Hatter was out of sight before the officer could get to the door. `Call the next witness!' said the King. The next witness was the Duchess's cook. She carried the pepper-box in her hand, and Alice guessed who it was, even before she got into the court, by the way the people near the door began sneezing all at once. `Give your evidence,' said the King. `Shan't,' said the cook. The King looked anxiously at the White Rabbit, who said in a low voice, `Your Majesty must cross-examine THIS witness.' `Well, if I must, I must,' the King said, with a melancholy air, and, after folding his arms and frowning at the cook till his eyes were nearly out of sight, he said in a deep voice, `What are tarts made of?' `Pepper, mostly,' said the cook. `Treacle,' said a sleepy voice behind her. `Collar that Dormouse,' the Queen shrieked out. `Behead that Dormouse! Turn that Dormouse out of court! Suppress him! Pinch him! Off with his whiskers!' For some minutes the whole court was in confusion, getting the Dormouse turned out, and, by the time they had settled down again, the cook had disappeared. `Never mind!' said the King, with an air of great relief. `Call the next witness.' And he added in an undertone to the Queen, `Really, my dear, YOU must cross-examine the next witness. It quite makes my forehead ache!' Alice watched the White Rabbit as he fumbled over the list, feeling very curious to see what the next witness would be like, `--for they haven't got much evidence YET,' she said to herself. Imagine her surprise, when the White Rabbit read out, at the top of his shrill little voice, the name `Alice!' CHAPTER XII Alice's Evidence `Here!' cried Alice, quite forgetting in the flurry of the moment how large she had grown in the last few minutes, and she jumped up in such a hurry that she tipped over the jury-box with the edge of her skirt, upsetting all the jurymen on to the heads of the crowd below, and there they lay sprawling about, reminding her very much of a globe of goldfish she had accidentally upset the week before. `Oh, I BEG your pardon!' she exclaimed in a tone of great dismay, and began picking them up again as quickly as she could, for the accident of the goldfish kept running in her head, and she had a vague sort of idea that they must be collected at once and put back into the jury-box, or they would die. `The trial cannot proceed,' said the King in a very grave voice, `until all the jurymen are back in their proper places-- ALL,' he repeated with great emphasis, looking hard at Alice as he said do. Alice looked at the jury-box, and saw that, in her haste, she had put the Lizard in head downwards, and the poor little thing was waving its tail about in a melancholy way, being quite unable to move. She soon got it out again, and put it right; `not that it signifies much,' she said to herself; `I should think it would be QUITE as much use in the trial one way up as the other.' As soon as the jury had a little recovered from the shock of being upset, and their slates and pencils had been found and handed back to them, they set to work very diligently to write out a history of the accident, all except the Lizard, who seemed too much overcome to do anything but sit with its mouth open, gazing up into the roof of the court. `What do you know about this business?' the King said to Alice. `Nothing,' said Alice. `Nothing WHATEVER?' persisted the King. `Nothing whatever,' said Alice. `That's very important,' the King said, turning to the jury. They were just beginning to write this down on their slates, when the White Rabbit interrupted: `UNimportant, your Majesty means, of course,' he said in a very respectful tone, but frowning and making faces at him as he spoke. `UNimportant, of course, I meant,' the King hastily said, and went on to himself in an undertone, `important--unimportant-- unimportant--important--' as if he were trying which word sounded best. Some of the jury wrote it down `important,' and some `unimportant.' Alice could see this, as she was near enough to look over their slates; `but it doesn't matter a bit,' she thought to herself. At this moment the King, who had been for some time busily writing in his note-book, cackled out `Silence!' and read out from his book, `Rule Forty-two. ALL PERSONS MORE THAN A MILE HIGH TO LEAVE THE COURT.' Everybody looked at Alice. `I'M not a mile high,' said Alice. `You are,' said the King. `Nearly two miles high,' added the Queen. `Well, I shan't go, at any rate,' said Alice: `besides, that's not a regular rule: you invented it just now.' `It's the oldest rule in the book,' said the King. `Then it ought to be Number One,' said Alice. The King turned pale, and shut his note-book hastily. `Consider your verdict,' he said to the jury, in a low, trembling voice. `There's more evidence to come yet, please your Majesty,' said the White Rabbit, jumping up in a great hurry; `this paper has just been picked up.' `What's in it?' said the Queen. `I haven't opened it yet,' said the White Rabbit, `but it seems to be a letter, written by the prisoner to--to somebody.' `It must have been that,' said the King, `unless it was written to nobody, which isn't usual, you know.' `Who is it directed to?' said one of the jurymen. `It isn't directed at all,' said the White Rabbit; `in fact, there's nothing written on the OUTSIDE.' He unfolded the paper as he spoke, and added `It isn't a letter, after all: it's a set of verses.' `Are they in the prisoner's handwriting?' asked another of they jurymen. `No, they're not,' said the White Rabbit, `and that's the queerest thing about it.' (The jury all looked puzzled.) `He must have imitated somebody else's hand,' said the King. (The jury all brightened up again.) `Please your Majesty,' said the Knave, `I didn't write it, and they can't prove I did: there's no name signed at the end.' `If you didn't sign it,' said the King, `that only makes the matter worse. You MUST have meant some mischief, or else you'd have signed your name like an honest man.' There was a general clapping of hands at this: it was the first really clever thing the King had said that day. `That PROVES his guilt,' said the Queen. `It proves nothing of the sort!' said Alice. `Why, you don't even know what they're about!' `Read them,' said the King. The White Rabbit put on his spectacles. `Where shall I begin, please your Majesty?' he asked. `Begin at the beginning,' the King said gravely, `and go on till you come to the end: then stop.' These were the verses the White Rabbit read:-- `They told me you had been to her, And mentioned me to him: She gave me a good character, But said I could not swim. He sent them word I had not gone (We know it to be true): If she should push the matter on, What would become of you? I gave her one, they gave him two, You gave us three or more; They all returned from him to you, Though they were mine before. If I or she should chance to be Involved in this affair, He trusts to you to set them free, Exactly as we were. My notion was that you had been (Before she had this fit) An obstacle that came between Him, and ourselves, and it. Don't let him know she liked them best, For this must ever be A secret, kept from all the rest, Between yourself and me.' `That's the most important piece of evidence we've heard yet,' said the King, rubbing his hands; `so now let the jury--' `If any one of them can explain it,' said Alice, (she had grown so large in the last few minutes that she wasn't a bit afraid of interrupting him,) `I'll give him sixpence. _I_ don't believe there's an atom of meaning in it.' The jury all wrote down on their slates, `SHE doesn't believe there's an atom of meaning in it,' but none of them attempted to explain the paper. `If there's no meaning in it,' said the King, `that saves a world of trouble, you know, as we needn't try to find any. And yet I don't know,' he went on, spreading out the verses on his knee, and looking at them with one eye; `I seem to see some meaning in them, after all. "--SAID I COULD NOT SWIM--" you can't swim, can you?' he added, turning to the Knave. The Knave shook his head sadly. `Do I look like it?' he said. (Which he certainly did NOT, being made entirely of cardboard.) `All right, so far,' said the King, and he went on muttering over the verses to himself: `"WE KNOW IT TO BE TRUE--" that's the jury, of course-- "I GAVE HER ONE, THEY GAVE HIM TWO--" why, that must be what he did with the tarts, you know--' `But, it goes on "THEY ALL RETURNED FROM HIM TO YOU,"' said Alice. `Why, there they are!' said the King triumphantly, pointing to the tarts on the table. `Nothing can be clearer than THAT. Then again--"BEFORE SHE HAD THIS FIT--" you never had fits, my dear, I think?' he said to the Queen. `Never!' said the Queen furiously, throwing an inkstand at the Lizard as she spoke. (The unfortunate little Bill had left off writing on his slate with one finger, as he found it made no mark; but he now hastily began again, using the ink, that was trickling down his face, as long as it lasted.) `Then the words don't FIT you,' said the King, looking round the court with a smile. There was a dead silence. `It's a pun!' the King added in an offended tone, and everybody laughed, `Let the jury consider their verdict,' the King said, for about the twentieth time that day. `No, no!' said the Queen. `Sentence first--verdict afterwards.' `Stuff and nonsense!' said Alice loudly. `The idea of having the sentence first!' `Hold your tongue!' said the Queen, turning purple. `I won't!' said Alice. `Off with her head!' the Queen shouted at the top of her voice. Nobody moved. `Who cares for you?' said Alice, (she had grown to her full size by this time.) `You're nothing but a pack of cards!' At this the whole pack rose up into the air, and came flying down upon her: she gave a little scream, half of fright and half of anger, and tried to beat them off, and found herself lying on the bank, with her head in the lap of her sister, who was gently brushing away some dead leaves that had fluttered down from the trees upon her face. `Wake up, Alice dear!' said her sister; `Why, what a long sleep you've had!' `Oh, I've had such a curious dream!' said Alice, and she told her sister, as well as she could remember them, all these strange Adventures of hers that you have just been reading about; and when she had finished, her sister kissed her, and said, `It WAS a curious dream, dear, certainly: but now run in to your tea; it's getting late.' So Alice got up and ran off, thinking while she ran, as well she might, what a wonderful dream it had been. But her sister sat still just as she left her, leaning her head on her hand, watching the setting sun, and thinking of little Alice and all her wonderful Adventures, till she too began dreaming after a fashion, and this was her dream:-- First, she dreamed of little Alice herself, and once again the tiny hands were clasped upon her knee, and the bright eager eyes were looking up into hers--she could hear the very tones of her voice, and see that queer little toss of her head to keep back the wandering hair that WOULD always get into her eyes--and still as she listened, or seemed to listen, the whole place around her became alive the strange creatures of her little sister's dream. The long grass rustled at her feet as the White Rabbit hurried by--the frightened Mouse splashed his way through the neighbouring pool--she could hear the rattle of the teacups as the March Hare and his friends shared their never-ending meal, and the shrill voice of the Queen ordering off her unfortunate guests to execution--once more the pig-baby was sneezing on the Duchess's knee, while plates and dishes crashed around it--once more the shriek of the Gryphon, the squeaking of the Lizard's slate-pencil, and the choking of the suppressed guinea-pigs, filled the air, mixed up with the distant sobs of the miserable Mock Turtle. So she sat on, with closed eyes, and half believed herself in Wonderland, though she knew she had but to open them again, and all would change to dull reality--the grass would be only rustling in the wind, and the pool rippling to the waving of the reeds--the rattling teacups would change to tinkling sheep- bells, and the Queen's shrill cries to the voice of the shepherd boy--and the sneeze of the baby, the shriek of the Gryphon, and all thy other queer noises, would change (she knew) to the confused clamour of the busy farm-yard--while the lowing of the cattle in the distance would take the place of the Mock Turtle's heavy sobs. Lastly, she pictured to herself how this same little sister of hers would, in the after-time, be herself a grown woman; and how she would keep, through all her riper years, the simple and loving heart of her childhood: and how she would gather about her other little children, and make THEIR eyes bright and eager with many a strange tale, perhaps even with the dream of Wonderland of long ago: and how she would feel with all their simple sorrows, and find a pleasure in all their simple joys, remembering her own child-life, and the happy summer days. THE END  \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/data/numbers.txt b/Lectures/Week 5 - MapReduce - MRJob/data/numbers.txt new file mode 100644 index 0000000..b4f229a --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/data/numbers.txt @@ -0,0 +1,11 @@ +1 10 +2 11 +3 3 +4 12 +5 4 +6 1 +7 1 +8 41 +9 532 +10 2 +11 0 \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/data/sortdata.txt b/Lectures/Week 5 - MapReduce - MRJob/data/sortdata.txt new file mode 100644 index 0000000..0ba6fc2 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/data/sortdata.txt @@ -0,0 +1,8 @@ +1 1 +2 4 +3 8 +4 2 +4 7 +5 5 +6 10 +7 11 \ No newline at end of file diff --git a/Lectures/Week 5 - MapReduce - MRJob/data/vocab.txt b/Lectures/Week 5 - MapReduce - MRJob/data/vocab.txt new file mode 100644 index 0000000..27f64a3 --- /dev/null +++ b/Lectures/Week 5 - MapReduce - MRJob/data/vocab.txt @@ -0,0 +1,1899 @@ +1 aa +2 ab +3 abil +4 abl +5 about +6 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+1887 ye +1888 yeah +1889 year +1890 yesterdai +1891 yet +1892 york +1893 you +1894 young +1895 your +1896 yourself +1897 zdnet +1898 zero +1899 zip diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb new file mode 100644 index 0000000..be7e6e5 --- /dev/null +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Algebra Tutorial\n", + "\n", + "Based on Chapter 4 for Data Science from Scratch Book by Joel Grus with code from https://github.com/joelgrus/data-science-from-scratch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# resources for the rest of the page\n", + "from __future__ import division # want 3 / 2 == 1.5\n", + "import re, math, random # regexes, math functions, random numbers\n", + "import matplotlib.pyplot as plt # pyplot\n", + "from collections import defaultdict, Counter\n", + "from functools import partial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple vector operations\n", + "\n", + "Vectors can be thought of a as representation of a single point in multi-dimensional space.\n", + "\n", + "Vectors are objects that can be added together to form new vectors or multiplied by scalars to form new vectors. \n", + "\n", + "If we have the height, weight, and age data for a large number of people then we can treat the data as a 3-d vector using [height, weight, age]." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "john = [72, #inches\n", + " 195, #pound\n", + " 32] #years\n", + "mary = [53, #inches\n", + " 105, #pounds\n", + " 28] #years" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vector addition requires that the dimensionality of the vectors is the same other wise it fails.\n", + "So if we add v + w then each element v(x) + v(x) with x=0,1,...N-1 must have a value in both the vectors for the addition to be successful.\n", + "Lists are not really vectors so we need to create them our selves." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions for working with vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def vector_add(v, w):\n", + " \"\"\"adds two vectors componentwise\"\"\"\n", + " return [v_i + w_i for v_i, w_i in zip(v,w)]\n", + "\n", + "def vector_subtract(v, w):\n", + " \"\"\"subtracts two vectors componentwise\"\"\"\n", + " return [v_i - w_i for v_i, w_i in zip(v,w)]\n", + "\n", + "def vector_sum(vectors):\n", + " return reduce(vector_add, vectors)\n", + "\n", + "def scalar_multiply(c, v):\n", + " return [c * v_i for v_i in v]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 3]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x=[1,2]\n", + "y=[2,1]\n", + "vector_add(x,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[-1, 1]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vector_subtract(x,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[6, 6]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vlist=[x,y,x,y]\n", + "vector_sum(vlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 20]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scalar_multiply(10,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### this isn't right if you don't -- from \\_\\_future\\_ import division" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.5, 1.5]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def vector_mean(vectors):\n", + " \"\"\"compute the vector whose i-th element is the mean of the\n", + " i-th elements of the input vectors\"\"\"\n", + " n = len(vectors)\n", + " return scalar_multiply(1/n, vector_sum(vectors))\n", + "\n", + "vector_mean(vlist)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def dot(v, w):\n", + " \"\"\"v_1 * w_1 + ... + v_n * w_n\"\"\"\n", + " return sum(v_i * w_i for v_i, w_i in zip(v, w))\n", + "\n", + "dot(x,x)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sum_of_squares(v):\n", + " \"\"\"v_1 * v_1 + ... + v_n * v_n\"\"\"\n", + " return dot(v, v)\n", + "\n", + "sum_of_squares(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.23606797749979" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def magnitude(v):\n", + " return math.sqrt(sum_of_squares(v))\n", + "\n", + "magnitude(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def squared_distance(v, w):\n", + " return sum_of_squares(vector_subtract(v, w))\n", + "\n", + "squared_distance(x,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def distance(v, w):\n", + " return math.sqrt(squared_distance(v, w))\n", + "\n", + "distance(x,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions for working with matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def shape(A):\n", + " num_rows = len(A)\n", + " num_cols = len(A[0]) if A else 0\n", + " return num_rows, num_cols\n", + "\n", + "def get_row(A, i):\n", + " return A[i]\n", + " \n", + "def get_column(A, j):\n", + " return [A_i[j] for A_i in A]\n", + "\n", + "def make_matrix(num_rows, num_cols, entry_fn):\n", + " \"\"\"returns a num_rows x num_cols matrix \n", + " whose (i,j)-th entry is entry_fn(i, j)\"\"\"\n", + " return [[entry_fn(i, j) for j in range(num_cols)]\n", + " for i in range(num_rows)] \n", + "\n", + "def is_diagonal(i, j):\n", + " \"\"\"1's on the 'diagonal', 0's everywhere else\"\"\"\n", + " return 1 if i == j else 0" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "identity_matrix =[[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]\n" + ] + } + ], + "source": [ + "identity_matrix = make_matrix(5, 5, is_diagonal)\n", + "print \"identity_matrix =\" + str(identity_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using matrix manipulation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "friendships = [[0, 1, 1, 0, 0, 0, 0, 0, 0, 0], # user 0\n", + " [1, 0, 1, 1, 0, 0, 0, 0, 0, 0], # user 1\n", + " [1, 1, 0, 1, 0, 0, 0, 0, 0, 0], # user 2\n", + " [0, 1, 1, 0, 1, 0, 0, 0, 0, 0], # user 3\n", + " [0, 0, 0, 1, 0, 1, 0, 0, 0, 0], # user 4\n", + " [0, 0, 0, 0, 1, 0, 1, 1, 0, 0], # user 5\n", + " [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 6\n", + " [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 7\n", + " [0, 0, 0, 0, 0, 0, 1, 1, 0, 1], # user 8\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]] # user 9\n", + "identity_matrix_10 = make_matrix(10, 10, is_diagonal)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n", + " [0, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 1, 1, 1, 1, 0, 0],\n", + " [0, 0, 0, 0, 0, 1, 1, 0, 1, 0],\n", + " [0, 0, 0, 0, 0, 1, 0, 1, 1, 0],\n", + " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def matrix_add(A, B):\n", + " if shape(A) != shape(B):\n", + " raise ArithmeticError(\"cannot add matrices with different shapes\")\n", + " \n", + " num_rows, num_cols = shape(A)\n", + " def entry_fn(i, j): return A[i][j] + B[i][j]\n", + " \n", + " return make_matrix(num_rows, num_cols, entry_fn)\n", + "\n", + "matrix_add(friendships,identity_matrix_10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_graph_dot_product_as_vector_projection(plt):\n", + "\n", + " v = [2, 1]\n", + " w = [math.sqrt(.25), math.sqrt(.75)]\n", + " c = dot(v, w)\n", + " vonw = scalar_multiply(c, w)\n", + " o = [0,0]\n", + "\n", + " plt.arrow(0, 0, v[0], v[1], \n", + " width=0.002, head_width=.1, length_includes_head=True)\n", + " plt.annotate(\"v\", v, xytext=[v[0] + 0.1, v[1]])\n", + " plt.arrow(0 ,0, w[0], w[1], \n", + " width=0.002, head_width=.1, length_includes_head=True)\n", + " plt.annotate(\"w\", w, xytext=[w[0] - 0.1, w[1]])\n", + " plt.arrow(0, 0, vonw[0], vonw[1], length_includes_head=True)\n", + " plt.annotate(u\"(v•w)w\", vonw, xytext=[vonw[0] - 0.1, vonw[1] + 0.1])\n", + " plt.arrow(v[0], v[1], vonw[0] - v[0], vonw[1] - v[1], \n", + " linestyle='dotted', length_includes_head=True)\n", + " plt.scatter(*zip(v,w,o),marker='.')\n", + " plt.axis('equal')\n", + " plt.show()\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "make_graph_dot_product_as_vector_projection(plt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb new file mode 100644 index 0000000..8fe2f9d --- /dev/null +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PROBABILITY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Concrete Introduction to Probability (using Python 3)\n", + "http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P(both | older): 0.514228456914\n", + "P(both | either): 0.341541328364\n" + ] + } + ], + "source": [ + "# code from: https://github.com/joelgrus/data-science-from-scratch/blob/master/code/probability.py\n", + "from __future__ import division\n", + "from collections import Counter\n", + "import math, random\n", + "\n", + "def random_kid():\n", + " return random.choice([\"boy\", \"girl\"])\n", + "\n", + "def uniform_pdf(x):\n", + " return 1 if x >= 0 and x < 1 else 0\n", + "\n", + "def uniform_cdf(x):\n", + " \"returns the probability that a uniform random variable is less than x\"\n", + " if x < 0: return 0 # uniform random is never less than 0\n", + " elif x < 1: return x # e.g. P(X < 0.4) = 0.4\n", + " else: return 1 # uniform random is always less than 1\n", + "\n", + "def normal_pdf(x, mu=0, sigma=1):\n", + " sqrt_two_pi = math.sqrt(2 * math.pi)\n", + " return (math.exp(-(x-mu) ** 2 / 2 / sigma ** 2) / (sqrt_two_pi * sigma))\n", + "\n", + "def plot_normal_pdfs(plt):\n", + " xs = [x / 10.0 for x in range(-50, 50)]\n", + " plt.plot(xs,[normal_pdf(x,sigma=1) for x in xs],'-',label='mu=0,sigma=1')\n", + " plt.plot(xs,[normal_pdf(x,sigma=2) for x in xs],'--',label='mu=0,sigma=2')\n", + " plt.plot(xs,[normal_pdf(x,sigma=0.5) for x in xs],':',label='mu=0,sigma=0.5')\n", + " plt.plot(xs,[normal_pdf(x,mu=-1) for x in xs],'-.',label='mu=-1,sigma=1')\n", + " plt.legend()\n", + " plt.show() \n", + "\n", + "def normal_cdf(x, mu=0,sigma=1):\n", + " return (1 + math.erf((x - mu) / math.sqrt(2) / sigma)) / 2 \n", + "\n", + "def plot_normal_cdfs(plt):\n", + " xs = [x / 10.0 for x in range(-50, 50)]\n", + " plt.plot(xs,[normal_cdf(x,sigma=1) for x in xs],'-',label='mu=0,sigma=1')\n", + " plt.plot(xs,[normal_cdf(x,sigma=2) for x in xs],'--',label='mu=0,sigma=2')\n", + " plt.plot(xs,[normal_cdf(x,sigma=0.5) for x in xs],':',label='mu=0,sigma=0.5')\n", + " plt.plot(xs,[normal_cdf(x,mu=-1) for x in xs],'-.',label='mu=-1,sigma=1')\n", + " plt.legend(loc=4) # bottom right\n", + " plt.show()\n", + "\n", + "def inverse_normal_cdf(p, mu=0, sigma=1, tolerance=0.00001):\n", + " \"\"\"find approximate inverse using binary search\"\"\"\n", + "\n", + " # if not standard, compute standard and rescale\n", + " if mu != 0 or sigma != 1:\n", + " return mu + sigma * inverse_normal_cdf(p, tolerance=tolerance)\n", + " \n", + " low_z, low_p = -10.0, 0 # normal_cdf(-10) is (very close to) 0\n", + " hi_z, hi_p = 10.0, 1 # normal_cdf(10) is (very close to) 1\n", + " while hi_z - low_z > tolerance:\n", + " mid_z = (low_z + hi_z) / 2 # consider the midpoint\n", + " mid_p = normal_cdf(mid_z) # and the cdf's value there\n", + " if mid_p < p:\n", + " # midpoint is still too low, search above it\n", + " low_z, low_p = mid_z, mid_p\n", + " elif mid_p > p:\n", + " # midpoint is still too high, search below it\n", + " hi_z, hi_p = mid_z, mid_p\n", + " else:\n", + " break\n", + "\n", + " return mid_z\n", + "\n", + "def bernoulli_trial(p):\n", + " return 1 if random.random() < p else 0\n", + "\n", + "def binomial(p, n):\n", + " return sum(bernoulli_trial(p) for _ in range(n))\n", + "\n", + "def make_hist(p, n, num_points):\n", + " \n", + " data = [binomial(p, n) for _ in range(num_points)]\n", + " \n", + " # use a bar chart to show the actual binomial samples\n", + " histogram = Counter(data)\n", + " plt.bar([x - 0.4 for x in histogram.keys()],\n", + " [v / num_points for v in histogram.values()],\n", + " 0.8,\n", + " color='0.75')\n", + " \n", + " mu = p * n\n", + " sigma = math.sqrt(n * p * (1 - p))\n", + "\n", + " # use a line chart to show the normal approximation\n", + " xs = range(min(data), max(data) + 1)\n", + " ys = [normal_cdf(i + 0.5, mu, sigma) - normal_cdf(i - 0.5, mu, sigma) \n", + " for i in xs]\n", + " plt.plot(xs,ys)\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " #\n", + " # CONDITIONAL PROBABILITY\n", + " #\n", + "\n", + " both_girls = 0\n", + " older_girl = 0\n", + " either_girl = 0\n", + "\n", + " random.seed(0)\n", + " for _ in range(10000):\n", + " younger = random_kid()\n", + " older = random_kid()\n", + " if older == \"girl\":\n", + " older_girl += 1\n", + " if older == \"girl\" and younger == \"girl\":\n", + " both_girls += 1\n", + " if older == \"girl\" or younger == \"girl\":\n", + " either_girl += 1\n", + "\n", + " print \"P(both | older):\", both_girls / older_girl # 0.514 ~ 1/2\n", + " print \"P(both | either): \", both_girls / either_girl # 0.342 ~ 1/3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + " \n", + "Bayesian Data Analysis tutorials:\n", + " https://github.com/JWarmenhoven/DBDA-python\n", + "\n", + "Class central:\n", + "https://www.class-central.com/course/edx-probability-and-statistics-in-data-science-using-python-8213" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Statistics.ipynb b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Statistics.ipynb new file mode 100644 index 0000000..9f6d9d9 --- /dev/null +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Statistics.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Statistics tutorial\n", + "\n", + "Based on Chapter 5 for Data Science from Scratch Book by Joel Grus with code from https://github.com/joelgrus/data-science-from-scratch" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "num_points 204\n", + "largest value 100\n", + "smallest value 1\n", + "second_smallest_value 1\n", + "second_largest_value 49\n", + "mean(num_friends) 7.33333333333\n", + "median(num_friends) 6.0\n", + "quantile(num_friends, 0.10) 1\n", + "quantile(num_friends, 0.25) 3\n", + "quantile(num_friends, 0.75) 9\n", + "quantile(num_friends, 0.90) 13\n", + "mode(num_friends) [1, 6]\n", + "data_range(num_friends) 99\n", + "variance(num_friends) 81.5435139573\n", + "standard_deviation(num_friends) 9.03014473623\n", + "interquartile_range(num_friends) 6\n", + "covariance(num_friends, daily_minutes) 22.4254351396\n", + "correlation(num_friends, daily_minutes) 0.247369573665\n", + "correlation(num_friends_good, daily_minutes_good) 0.573679211567\n" + ] + } + ], + "source": [ + "from __future__ import division\n", + "from collections import Counter\n", + "import sys\n", + "sys.path.append('./code')\n", + "from linear_algebra import sum_of_squares, dot\n", + "import math\n", + "\n", + "num_friends = [100,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n", + "\n", + "def make_friend_counts_histogram(plt):\n", + " friend_counts = Counter(num_friends)\n", + " xs = range(101)\n", + " ys = [friend_counts[x] for x in xs]\n", + " plt.bar(xs, ys)\n", + " plt.axis([0,101,0,25])\n", + " plt.title(\"Histogram of Friend Counts\")\n", + " plt.xlabel(\"# of friends\")\n", + " plt.ylabel(\"# of people\")\n", + " plt.show()\n", + "\n", + "num_points = len(num_friends) # 204\n", + "\n", + "largest_value = max(num_friends) # 100\n", + "smallest_value = min(num_friends) # 1\n", + "\n", + "sorted_values = sorted(num_friends)\n", + "smallest_value = sorted_values[0] # 1\n", + "second_smallest_value = sorted_values[1] # 1\n", + "second_largest_value = sorted_values[-2] # 49\n", + "\n", + "# this isn't right if you don't from __future__ import division\n", + "def mean(x): \n", + " return sum(x) / len(x)\n", + "\n", + "def median(v):\n", + " \"\"\"finds the 'middle-most' value of v\"\"\"\n", + " n = len(v)\n", + " sorted_v = sorted(v)\n", + " midpoint = n // 2\n", + " \n", + " if n % 2 == 1:\n", + " # if odd, return the middle value\n", + " return sorted_v[midpoint]\n", + " else:\n", + " # if even, return the average of the middle values\n", + " lo = midpoint - 1\n", + " hi = midpoint\n", + " return (sorted_v[lo] + sorted_v[hi]) / 2\n", + " \n", + "def quantile(x, p):\n", + " \"\"\"returns the pth-percentile value in x\"\"\"\n", + " p_index = int(p * len(x))\n", + " return sorted(x)[p_index]\n", + "\n", + "def mode(x):\n", + " \"\"\"returns a list, might be more than one mode\"\"\"\n", + " counts = Counter(x)\n", + " max_count = max(counts.values())\n", + " return [x_i for x_i, count in counts.iteritems()\n", + " if count == max_count]\n", + "\n", + "# \"range\" already means something in Python, so we'll use a different name\n", + "def data_range(x):\n", + " return max(x) - min(x)\n", + "\n", + "def de_mean(x):\n", + " \"\"\"translate x by subtracting its mean (so the result has mean 0)\"\"\"\n", + " x_bar = mean(x)\n", + " return [x_i - x_bar for x_i in x]\n", + "\n", + "def variance(x):\n", + " \"\"\"assumes x has at least two elements\"\"\"\n", + " n = len(x)\n", + " deviations = de_mean(x)\n", + " return sum_of_squares(deviations) / (n - 1)\n", + " \n", + "def standard_deviation(x):\n", + " return math.sqrt(variance(x))\n", + "\n", + "def interquartile_range(x):\n", + " return quantile(x, 0.75) - quantile(x, 0.25)\n", + "\n", + "####\n", + "#\n", + "# CORRELATION\n", + "#\n", + "#####\n", + "\n", + "daily_minutes = [1,68.77,51.25,52.08,38.36,44.54,57.13,51.4,41.42,31.22,34.76,54.01,38.79,47.59,49.1,27.66,41.03,36.73,48.65,28.12,46.62,35.57,32.98,35,26.07,23.77,39.73,40.57,31.65,31.21,36.32,20.45,21.93,26.02,27.34,23.49,46.94,30.5,33.8,24.23,21.4,27.94,32.24,40.57,25.07,19.42,22.39,18.42,46.96,23.72,26.41,26.97,36.76,40.32,35.02,29.47,30.2,31,38.11,38.18,36.31,21.03,30.86,36.07,28.66,29.08,37.28,15.28,24.17,22.31,30.17,25.53,19.85,35.37,44.6,17.23,13.47,26.33,35.02,32.09,24.81,19.33,28.77,24.26,31.98,25.73,24.86,16.28,34.51,15.23,39.72,40.8,26.06,35.76,34.76,16.13,44.04,18.03,19.65,32.62,35.59,39.43,14.18,35.24,40.13,41.82,35.45,36.07,43.67,24.61,20.9,21.9,18.79,27.61,27.21,26.61,29.77,20.59,27.53,13.82,33.2,25,33.1,36.65,18.63,14.87,22.2,36.81,25.53,24.62,26.25,18.21,28.08,19.42,29.79,32.8,35.99,28.32,27.79,35.88,29.06,36.28,14.1,36.63,37.49,26.9,18.58,38.48,24.48,18.95,33.55,14.24,29.04,32.51,25.63,22.22,19,32.73,15.16,13.9,27.2,32.01,29.27,33,13.74,20.42,27.32,18.23,35.35,28.48,9.08,24.62,20.12,35.26,19.92,31.02,16.49,12.16,30.7,31.22,34.65,13.13,27.51,33.2,31.57,14.1,33.42,17.44,10.12,24.42,9.82,23.39,30.93,15.03,21.67,31.09,33.29,22.61,26.89,23.48,8.38,27.81,32.35,23.84]\n", + "\n", + "def covariance(x, y):\n", + " n = len(x)\n", + " return dot(de_mean(x), de_mean(y)) / (n - 1)\n", + "\n", + "def correlation(x, y):\n", + " stdev_x = standard_deviation(x)\n", + " stdev_y = standard_deviation(y)\n", + " if stdev_x > 0 and stdev_y > 0:\n", + " return covariance(x, y) / stdev_x / stdev_y\n", + " else:\n", + " return 0 # if no variation, correlation is zero\n", + "\n", + "outlier = num_friends.index(100) # index of outlier\n", + "\n", + "num_friends_good = [x \n", + " for i, x in enumerate(num_friends) \n", + " if i != outlier]\n", + "\n", + "daily_minutes_good = [x \n", + " for i, x in enumerate(daily_minutes) \n", + " if i != outlier]\n", + "\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " print \"num_points\", len(num_friends)\n", + " print \"largest value\", max(num_friends)\n", + " print \"smallest value\", min(num_friends)\n", + " print \"second_smallest_value\", sorted_values[1]\n", + " print \"second_largest_value\", sorted_values[-2] \n", + " print \"mean(num_friends)\", mean(num_friends)\n", + " print \"median(num_friends)\", median(num_friends)\n", + " print \"quantile(num_friends, 0.10)\", quantile(num_friends, 0.10)\n", + " print \"quantile(num_friends, 0.25)\", quantile(num_friends, 0.25)\n", + " print \"quantile(num_friends, 0.75)\", quantile(num_friends, 0.75)\n", + " print \"quantile(num_friends, 0.90)\", quantile(num_friends, 0.90)\n", + " print \"mode(num_friends)\", mode(num_friends)\n", + " print \"data_range(num_friends)\", data_range(num_friends)\n", + " print \"variance(num_friends)\", variance(num_friends)\n", + " print \"standard_deviation(num_friends)\", standard_deviation(num_friends)\n", + " print \"interquartile_range(num_friends)\", interquartile_range(num_friends)\n", + "\n", + " print \"covariance(num_friends, daily_minutes)\", covariance(num_friends, daily_minutes)\n", + " print \"correlation(num_friends, daily_minutes)\", correlation(num_friends, daily_minutes)\n", + " print \"correlation(num_friends_good, daily_minutes_good)\", correlation(num_friends_good, daily_minutes_good)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Let's see how numpy handles statistics:\n", + "https://github.com/tuanavu/edX-course/blob/master/Microsoft/introduction_to_python_for_data_science/Basic%20Statistics%20with%20Numpy.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using SciPy.stats \n", + "https://github.com/normonics/Intro-to-Data-Science-in-Python/blob/master/003b_Intro%20to%20scipy.stats.ipynb" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/code/linear_algebra.py b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/code/linear_algebra.py new file mode 100644 index 0000000..6d964ab --- /dev/null +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/code/linear_algebra.py @@ -0,0 +1,126 @@ +# -*- coding: iso-8859-15 -*- +# From https://github.com/joelgrus/data-science-from-scratch/blob/master/code/linear_algebra.py +from __future__ import division # want 3 / 2 == 1.5 +import re, math, random # regexes, math functions, random numbers +import matplotlib.pyplot as plt # pyplot +from collections import defaultdict, Counter +from functools import partial + +# +# functions for working with vectors +# + +def vector_add(v, w): + """adds two vectors componentwise""" + return [v_i + w_i for v_i, w_i in zip(v,w)] + +def vector_subtract(v, w): + """subtracts two vectors componentwise""" + return [v_i - w_i for v_i, w_i in zip(v,w)] + +def vector_sum(vectors): + return reduce(vector_add, vectors) + +def scalar_multiply(c, v): + return [c * v_i for v_i in v] + +# this isn't right if you don't from __future__ import division +def vector_mean(vectors): + """compute the vector whose i-th element is the mean of the + i-th elements of the input vectors""" + n = len(vectors) + return scalar_multiply(1/n, vector_sum(vectors)) + +def dot(v, w): + """v_1 * w_1 + ... + v_n * w_n""" + return sum(v_i * w_i for v_i, w_i in zip(v, w)) + +def sum_of_squares(v): + """v_1 * v_1 + ... + v_n * v_n""" + return dot(v, v) + +def magnitude(v): + return math.sqrt(sum_of_squares(v)) + +def squared_distance(v, w): + return sum_of_squares(vector_subtract(v, w)) + +def distance(v, w): + return math.sqrt(squared_distance(v, w)) + +# +# functions for working with matrices +# + +def shape(A): + num_rows = len(A) + num_cols = len(A[0]) if A else 0 + return num_rows, num_cols + +def get_row(A, i): + return A[i] + +def get_column(A, j): + return [A_i[j] for A_i in A] + +def make_matrix(num_rows, num_cols, entry_fn): + """returns a num_rows x num_cols matrix + whose (i,j)-th entry is entry_fn(i, j)""" + return [[entry_fn(i, j) for j in range(num_cols)] + for i in range(num_rows)] + +def is_diagonal(i, j): + """1's on the 'diagonal', 0's everywhere else""" + return 1 if i == j else 0 + +identity_matrix = make_matrix(5, 5, is_diagonal) + +# user 0 1 2 3 4 5 6 7 8 9 +# +friendships = [[0, 1, 1, 0, 0, 0, 0, 0, 0, 0], # user 0 + [1, 0, 1, 1, 0, 0, 0, 0, 0, 0], # user 1 + [1, 1, 0, 1, 0, 0, 0, 0, 0, 0], # user 2 + [0, 1, 1, 0, 1, 0, 0, 0, 0, 0], # user 3 + [0, 0, 0, 1, 0, 1, 0, 0, 0, 0], # user 4 + [0, 0, 0, 0, 1, 0, 1, 1, 0, 0], # user 5 + [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 6 + [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 7 + [0, 0, 0, 0, 0, 0, 1, 1, 0, 1], # user 8 + [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]] # user 9 + +##### +# DELETE DOWN +# + + +def matrix_add(A, B): + if shape(A) != shape(B): + raise ArithmeticError("cannot add matrices with different shapes") + + num_rows, num_cols = shape(A) + def entry_fn(i, j): return A[i][j] + B[i][j] + + return make_matrix(num_rows, num_cols, entry_fn) + + +def make_graph_dot_product_as_vector_projection(plt): + + v = [2, 1] + w = [math.sqrt(.25), math.sqrt(.75)] + c = dot(v, w) + vonw = scalar_multiply(c, w) + o = [0,0] + + plt.arrow(0, 0, v[0], v[1], + width=0.002, head_width=.1, length_includes_head=True) + plt.annotate("v", v, xytext=[v[0] + 0.1, v[1]]) + plt.arrow(0 ,0, w[0], w[1], + width=0.002, head_width=.1, length_includes_head=True) + plt.annotate("w", w, xytext=[w[0] - 0.1, w[1]]) + plt.arrow(0, 0, vonw[0], vonw[1], length_includes_head=True) + plt.annotate(u"(v•w)w", vonw, xytext=[vonw[0] - 0.1, vonw[1] + 0.1]) + plt.arrow(v[0], v[1], vonw[0] - v[0], vonw[1] - v[1], + linestyle='dotted', length_includes_head=True) + plt.scatter(*zip(v,w,o),marker='.') + plt.axis('equal') + plt.show() \ No newline at end of file From 8effaf96f6c018f9eae319fb5ccd90e751234504 Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 7 Oct 2018 12:45:15 -0400 Subject: [PATCH 08/10] Week6 lecture update --- .../Statistics and Probablity Homework.ipynb | 6 +- .../Linear Algebra Tutorial.ipynb | 171 ++++++++++++++---- .../Probabilty Tutorial.ipynb | 20 +- 3 files changed, 154 insertions(+), 43 deletions(-) diff --git a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb index 7ae361c..70a2eb1 100644 --- a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb +++ b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb @@ -79,7 +79,7 @@ "\n", "Write a function, estimate_prob, that uses flip_sum to estimate the following probability:\n", "\n", - "$P( $k_1$ <=$number of heads in $n$ flips$ < $k_2$ ) $\n", + "$P( k_1 <= $ number of heads in $n$ flips $< k_2 ) $\n", "\n", "The function should estimate the probability by running $m$ different trials of flip_sum(n), probably using a for loop.\n", "\n", @@ -87,12 +87,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "https://github.com/VSerpak/DSE210x-Statistics-and-Probability-in-Data-Science-using-Python/blob/master/Week%201%20Introduction%20to%20Statistics%20and%20Probability/1.8%20HW_1.ipynb" ] diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb index be7e6e5..e5728df 100644 --- a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Linear Algebra Tutorial.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "[3, 3]" ] }, - "execution_count": 11, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -126,7 +126,7 @@ "[-1, 1]" ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -146,7 +146,7 @@ "[6, 6]" ] }, - "execution_count": 18, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -167,7 +167,7 @@ "[10, 20]" ] }, - "execution_count": 19, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -194,7 +194,7 @@ "[1.5, 1.5]" ] }, - "execution_count": 20, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ "5" ] }, - "execution_count": 21, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -244,7 +244,7 @@ "5" ] }, - "execution_count": 22, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -268,7 +268,7 @@ "2.23606797749979" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -291,7 +291,7 @@ "2" ] }, - "execution_count": 25, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -314,7 +314,7 @@ "0.0" ] }, - "execution_count": 26, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -330,12 +330,90 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Functions for working with matrices" + "## Demonstration of show 2d vectors on a graph" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "def plot_2D_vectors(tupleof2dvectors,xlim_in,ylim_in):\n", + " \"\"\"plot_2d_vectors pass in the 2d vectors and axes object and x and y limits \"\"\"\n", + " fig, ax = plt.subplots(figsize=(10, 8)) \n", + "# Set the axes through the origin\n", + " for spine in ['left', 'bottom']:\n", + " ax.spines[spine].set_position('zero')\n", + " for spine in ['right', 'top']:\n", + " ax.spines[spine].set_color('none')\n", + " vecs = tupleof2dvectors\n", + " \n", + " ax.set(xlim=xlim_in, ylim=ylim_in)\n", + " ax.grid()\n", + " \n", + " for v in vecs:\n", + " ax.annotate('', xy=v, xytext=(0, 0),\n", + " arrowprops=dict(facecolor='blue',\n", + " shrink=0,\n", + " alpha=0.7,\n", + " width=0.5))\n", + " ax.text(1.1 * v[0], 1.1 * v[1], str(v))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "x=[-1,4]\n", + "y=[2,4]\n", + "z=[1,-3]\n", + "vt=(x,y,z)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ht+/MoK/BKdSaGQwahi1evNh2BHgEtRafCgsLlZEhRSIH1djYaDsOXMQLOzPo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\n5+dr8eI7dfKkFI3ucB0HWcBcgy10zQwWDcPq6+t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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "plot_2D_vectors(vt,(-5,5),(-5,5))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions for working with matrices\n", + "\n", + "Definition of terms for matrix\n", + "https://en.wikipedia.org/wiki/Matrix_(mathematics)\n", + "\n", + "A matrix (plural: matrices) is a rectangular array of numbers, symbols, or expressions, arranged in rows and columns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 94, "metadata": { "collapsed": true }, @@ -365,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -390,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 96, "metadata": { "collapsed": true }, @@ -411,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -429,7 +507,7 @@ " [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]" ] }, - "execution_count": 34, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +538,7 @@ " c = dot(v, w)\n", " vonw = scalar_multiply(c, w)\n", " o = [0,0]\n", - "\n", + " fig, ax = plt.subplots(figsize=(10, 8)) \n", " plt.arrow(0, 0, v[0], v[1], \n", " width=0.002, head_width=.1, length_includes_head=True)\n", " plt.annotate(\"v\", v, xytext=[v[0] + 0.1, v[1]])\n", @@ -479,12 +557,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scalar multiplication of V=[2,1] * scalar 1.86602540378 is [0.9330127018922193, 1.6160254037844386]\n" + ] + }, + { + "data": { + "image/png": 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DhgOZODGLW265lfXr10cdTdIesiRJUoTeeeedvKk5/fv3jziNpIKqVq0a999/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\nQ3+W+49gG8eAATPa/cBxYLCUUk4spXy7lPJEKeWRUsqlpZQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "v = [2, 1]\n", + "w = [math.sqrt(.25), math.sqrt(.75)]\n", + "c = dot(v, w)\n", + "print \"Scalar multiplication of V=[2,1] * scalar \"+ str(c) + \" is \" + str(scalar_multiply(c, w))\n", + " \n", "make_graph_dot_product_as_vector_projection(plt)" ] }, diff --git a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb index 8fe2f9d..5f14b26 100644 --- a/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb +++ b/Lectures/Week 6 - Linear Algebra, Statistics, and Probability/Probabilty Tutorial.ipynb @@ -12,12 +12,27 @@ "metadata": {}, "source": [ "Concrete Introduction to Probability (using Python 3)\n", - "http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb" + "http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb\n", + "Great intoduction: http://astronomy.swin.edu.au/~cblake/StatsLecture4.pdf\n", + "Bayes' theorem: https://en.wikipedia.org/wiki/Bayes%27_theorem\n", + "Good intro: http://faculty.washington.edu/tamre/BayesTheorem.pdf\n", + "Naive Bayes Classifier: https://en.wikipedia.org/wiki/Naive_Bayes_classifier" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "The following code from: https://github.com/joelgrus/data-science-from-scratch/blob/master/code/probability.py" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -30,7 +45,6 @@ } ], "source": [ - "# code from: https://github.com/joelgrus/data-science-from-scratch/blob/master/code/probability.py\n", "from __future__ import division\n", "from collections import Counter\n", "import math, random\n", From ef8f330f64371d0c838b05f23ca58f472ad79153 Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 7 Oct 2018 23:43:11 -0400 Subject: [PATCH 09/10] Adding homework6 --- .../Statistics and Probablity Homework.ipynb | 278 +- ...Leading_Causes_of_Death__United_States.csv | 10297 ++++++++++++++++ Homeworks/Homework6/data/heart.csv | 295 + 3 files changed, 10866 insertions(+), 4 deletions(-) create mode 100644 Homeworks/Homework6/data/NCHS_-_Leading_Causes_of_Death__United_States.csv create mode 100644 Homeworks/Homework6/data/heart.csv diff --git a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb index 70a2eb1..252bf0d 100644 --- a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb +++ b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb @@ -4,7 +4,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# HW 6.1" + "# HW 6 Statistics and probability homework\n", + "\n", + "Complete homework notebook in a homework directory with your name and zip up the homework directory and submit it to our class blackboard/elearn site.\n", + "Complete all the parts 6.1 to 6.5 for score of 3. \n", + "\n", + "Investigate plotting, linearegression, or complex matrix manipulation to get a score of 4 or cover two additional investigations for a score of 5. \n", + "\n", + "## 6.1 Coin flipping " ] }, { @@ -75,8 +82,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 6.2\n", - "\n", + "## 6.1.4\n", "Write a function, estimate_prob, that uses flip_sum to estimate the following probability:\n", "\n", "$P( k_1 <= $ number of heads in $n$ flips $< k_2 ) $\n", @@ -86,13 +92,277 @@ "In order to receive full credit estimate_prob call flip_sum (aka: flip_sum is located inside the estimate_prob function)" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def estimate_prob(n,k1,k2,m):\n", + " \"\"\"Estimate the probability that n flips of a fair coin result in k1 to k2 heads\n", + " n: the number of coin flips (length of the sequence)\n", + " k1,k2: the trial is successful if the number of heads is \n", + " between k1 and k2-1\n", + " m: the number of trials (number of sequences of length n)\n", + " \n", + " output: the estimated probability \n", + " \"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "does x==0.687?\n" + ] + } + ], + "source": [ + "# this is a small sanity check\n", + "\n", + "\n", + "x = estimate_prob(100,45,55,1000)\n", + "print x\n", + "assert 'float' in str(type(x))\n", + "print \"does x==0.687?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.2.2 Calculate the actual probablities and compare it to your estimates for:\n", + "n= number of coins\n", + "k1 = min number of heads\n", + "k2 = upper limit of number of heads\n", + "m = the number of experiments\n", + "### 6.2.2.a n=100, k1 = 40, k2=60 m=100\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2.2.b n=100, k1 = 40, k2=60 m=1000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.3 Conditional probablity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a recent study, the following data were obtained in response to the question\"\n", + " \"Do you favor the proposal of the school’s combining the elementary and middle school students in one building?\"\n", + " \n", + "Answers = [Yes, No, No opinion]\n", + "Males = [75, 89, 10]\n", + "Females = [105, 56, 6]\n", + "\n", + "If a person is selected at random, find these probabilities solving using python.\n", + "1. The person has no opinion\n", + "2. The person is a male or is against the issue.\n", + "3. The person is a female, given that the person opposes the issue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.4 Matrix creation\n", + "Write a 12 by 12 times table matrix shown below.\n", + "Do this\n", + "6.4.1 using nested for loops\n", + "6.4.2 using numpy fromfunction array constructor\n", + "6.4.3 using numpy broadcasting" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n", + " [ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],\n", + " [ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36],\n", + " [ 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48],\n", + " [ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],\n", + " [ 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72],\n", + " [ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84],\n", + " [ 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96],\n", + " [ 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 99, 108],\n", + " [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120],\n", + " [ 11, 22, 33, 44, 55, 66, 77, 88, 99, 110, 121, 132],\n", + " [ 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import array \n", + "array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n", + " [ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],\n", + " [ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36],\n", + " [ 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48],\n", + " [ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],\n", + " [ 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72],\n", + " [ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84],\n", + " [ 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96],\n", + " [ 9, 18, 27, 36, 45, 54, 63, 72, 81, 90, 99, 108],\n", + " [ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120],\n", + " [ 11, 22, 33, 44, 55, 66, 77, 88, 99, 110, 121, 132],\n", + " [ 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144]])" + ] + }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ - "https://github.com/VSerpak/DSE210x-Statistics-and-Probability-in-Data-Science-using-Python/blob/master/Week%201%20Introduction%20to%20Statistics%20and%20Probability/1.8%20HW_1.ipynb" + "## 6.5 \n", + "Answer the following questions with respect to the\n", + "https://data.cdc.gov/NCHS/NCHS-Leading-Causes-of-Death-United-States/bi63-dtpu\n", + " \n", + " \n", + "How many patients were censored?\n", + "What is the correlation coefficient between state and Suicide for deaths above 100 ?\n", + "What is the average deaths for each state and type of cause ?\n", + "What is the year was the most deadly for each cause name ?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Year113 Cause NameCause NameStateDeathsAge-adjusted Death Rate
02016Accidents (unintentional injuries) (V01-X59,Y8...Unintentional injuriesAlabama275555.5
12016Accidents (unintentional injuries) (V01-X59,Y8...Unintentional injuriesAlaska43963.1
22016Accidents (unintentional injuries) (V01-X59,Y8...Unintentional injuriesArizona401054.2
32016Accidents (unintentional injuries) (V01-X59,Y8...Unintentional injuriesArkansas160451.8
42016Accidents (unintentional injuries) (V01-X59,Y8...Unintentional injuriesCalifornia1321332.0
\n", + "
" + ], + "text/plain": [ + " Year 113 Cause Name \\\n", + "0 2016 Accidents (unintentional injuries) (V01-X59,Y8... \n", + "1 2016 Accidents (unintentional injuries) (V01-X59,Y8... \n", + "2 2016 Accidents (unintentional injuries) (V01-X59,Y8... \n", + "3 2016 Accidents (unintentional injuries) (V01-X59,Y8... \n", + "4 2016 Accidents (unintentional injuries) (V01-X59,Y8... \n", + "\n", + " Cause Name State Deaths Age-adjusted Death Rate \n", + "0 Unintentional injuries Alabama 2755 55.5 \n", + "1 Unintentional injuries Alaska 439 63.1 \n", + "2 Unintentional injuries Arizona 4010 54.2 \n", + "3 Unintentional injuries Arkansas 1604 51.8 \n", + "4 Unintentional injuries California 13213 32.0 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "dfh = pd.read_csv(\".\\data\\NCHS_-_Leading_Causes_of_Death__United_States.csv\")\n", + "dfh.head()" ] }, { diff --git a/Homeworks/Homework6/data/NCHS_-_Leading_Causes_of_Death__United_States.csv b/Homeworks/Homework6/data/NCHS_-_Leading_Causes_of_Death__United_States.csv new file mode 100644 index 0000000..3b172aa --- /dev/null +++ b/Homeworks/Homework6/data/NCHS_-_Leading_Causes_of_Death__United_States.csv @@ -0,0 +1,10297 @@ +Year,113 Cause Name,Cause Name,State,Deaths,Age-adjusted Death Rate +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2755,55.50 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,439,63.10 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,4010,54.20 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1604,51.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,13213,32.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2880,51.20 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1978,50.30 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,516,52.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,401,58.30 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,12561,54.90 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,4701,45.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,577,35.30 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,849,49.50 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,5508,41.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,3496,51.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1608,45.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1444,45.70 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,3194,71.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2710,57.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,909,62.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,2271,35.70 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,3831,52.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,5313,50.50 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2697,43.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1803,59.20 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3625,57.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,626,54.10 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,772,37.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1395,46.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,924,66.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,3839,40.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1487,69.50 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,7354,34.20 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,5476,52.20 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,371,45.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,7999,66.60 +2006,All Causes,All causes,Alaska,3354,787.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2592,64.10 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,2105,46.00 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,8410,61.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,675,56.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,3012,58.90 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,505,53.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,4238,61.10 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,10536,38.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1211,43.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,372,54.80 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,3710,42.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,3221,41.40 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1705,89.70 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,3575,55.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,371,61.90 +2016,All Causes,All causes,Alabama,52466,920.40 +2015,All Causes,All causes,Alabama,51909,924.50 +2014,All Causes,All causes,Alabama,50215,909.10 +2013,All Causes,All causes,Alabama,50189,925.20 +2012,All Causes,All causes,Alabama,49301,926.70 +2011,All Causes,All causes,Alabama,48681,933.60 +2010,All Causes,All causes,Alabama,48038,939.70 +2009,All Causes,All causes,Alabama,47470,940.20 +2008,All Causes,All causes,Alabama,47707,959.70 +2007,All Causes,All causes,Alabama,46696,957.20 +2006,All Causes,All causes,Alabama,46977,979.50 +2005,All Causes,All causes,Alabama,47090,1001.30 +2004,All Causes,All causes,Alabama,46121,998.30 +2003,All Causes,All causes,Alabama,46716,1020.20 +2002,All Causes,All causes,Alabama,46069,1013.40 +2001,All Causes,All causes,Alabama,45316,1002.10 +2000,All Causes,All causes,Alabama,45062,1004.80 +1999,All Causes,All causes,Alabama,44806,1009.30 +2016,All Causes,All causes,Alaska,4494,745.60 +2015,All Causes,All causes,Alaska,4316,747.40 +2014,All Causes,All causes,Alaska,4128,736.80 +2013,All Causes,All causes,Alaska,3997,724.40 +2012,All Causes,All causes,Alaska,3912,731.40 +2011,All Causes,All causes,Alaska,3849,747.80 +2010,All Causes,All causes,Alaska,3728,771.50 +2009,All Causes,All causes,Alaska,3618,763.80 +2008,All Causes,All causes,Alaska,3494,761.30 +2007,All Causes,All causes,Alaska,3463,780.60 +2005,All Causes,All causes,Alaska,3168,762.30 +2004,All Causes,All causes,Alaska,3051,756.70 +2003,All Causes,All causes,Alaska,3180,843.50 +2002,All Causes,All causes,Alaska,3030,802.40 +2001,All Causes,All causes,Alaska,2974,831.30 +2000,All Causes,All causes,Alaska,2914,869.10 +1999,All Causes,All causes,Alaska,2708,838.90 +2016,All Causes,All causes,Arizona,56645,675.80 +2015,All Causes,All causes,Arizona,54299,671.80 +2014,All Causes,All causes,Arizona,51538,661.70 +2013,All Causes,All causes,Arizona,50534,674.20 +2012,All Causes,All causes,Arizona,49549,682.90 +2011,All Causes,All causes,Arizona,48381,688.90 +2010,All Causes,All causes,Arizona,46762,693.10 +2009,All Causes,All causes,Arizona,45816,693.80 +2008,All Causes,All causes,Arizona,45823,714.50 +2007,All Causes,All causes,Arizona,45554,731.00 +2006,All Causes,All causes,Arizona,46365,767.10 +2005,All Causes,All causes,Arizona,45827,786.70 +2004,All Causes,All causes,Arizona,43198,771.10 +2003,All Causes,All causes,Arizona,43392,798.40 +2002,All Causes,All causes,Arizona,42816,811.20 +2001,All Causes,All causes,Arizona,41058,795.90 +2000,All Causes,All causes,Arizona,40500,810.40 +1999,All Causes,All causes,Arizona,40050,818.40 +2016,All Causes,All causes,Arkansas,31756,893.20 +2015,All Causes,All causes,Arkansas,31617,901.80 +2014,All Causes,All causes,Arkansas,30467,883.70 +2013,All Causes,All causes,Arkansas,30437,893.80 +2012,All Causes,All causes,Arkansas,30117,897.50 +2011,All Causes,All causes,Arkansas,29653,895.30 +2010,All Causes,All causes,Arkansas,28916,892.70 +2009,All Causes,All causes,Arkansas,28673,896.40 +2008,All Causes,All causes,Arkansas,29322,927.30 +2007,All Causes,All causes,Arkansas,28191,906.20 +2006,All Causes,All causes,Arkansas,27901,909.50 +2005,All Causes,All causes,Arkansas,28055,931.50 +2004,All Causes,All causes,Arkansas,27528,927.40 +2003,All Causes,All causes,Arkansas,27918,948.20 +2002,All Causes,All causes,Arkansas,28513,975.20 +2001,All Causes,All causes,Arkansas,27759,955.40 +2000,All Causes,All causes,Arkansas,28217,977.10 +1999,All Causes,All causes,Arkansas,27925,975.30 +2016,All Causes,All causes,California,262240,616.90 +2015,All Causes,All causes,California,259206,621.60 +2014,All Causes,All causes,California,245929,605.70 +2013,All Causes,All causes,California,248359,630.10 +2012,All Causes,All causes,California,242554,630.40 +2011,All Causes,All causes,California,239942,641.30 +2010,All Causes,All causes,California,234012,646.70 +2009,All Causes,All causes,California,232736,655.50 +2008,All Causes,All causes,California,234766,677.90 +2007,All Causes,All causes,California,233720,691.70 +2006,All Causes,All causes,California,237126,718.20 +2005,All Causes,All causes,California,237037,730.70 +2004,All Causes,All causes,California,232525,734.30 +2003,All Causes,All causes,California,239371,768.40 +2002,All Causes,All causes,California,234565,770.00 +2001,All Causes,All causes,California,234044,783.20 +2000,All Causes,All causes,California,229551,787.90 +1999,All Causes,All causes,California,229380,802.30 +2016,All Causes,All causes,Colorado,37530,669.50 +2015,All Causes,All causes,Colorado,36349,665.00 +2014,All Causes,All causes,Colorado,35237,664.40 +2013,All Causes,All causes,Colorado,33712,655.40 +2012,All Causes,All causes,Colorado,33133,665.60 +2011,All Causes,All causes,Colorado,32563,677.80 +2010,All Causes,All causes,Colorado,31465,682.70 +2009,All Causes,All causes,Colorado,31173,689.70 +2008,All Causes,All causes,Colorado,31274,715.80 +2007,All Causes,All causes,Colorado,29993,708.00 +2006,All Causes,All causes,Colorado,29521,720.90 +2005,All Causes,All causes,Colorado,29627,752.10 +2004,All Causes,All causes,Colorado,28309,738.50 +2003,All Causes,All causes,Colorado,29506,792.00 +2002,All Causes,All causes,Colorado,29210,804.40 +2001,All Causes,All causes,Colorado,28294,796.10 +2000,All Causes,All causes,Colorado,27288,792.10 +1999,All Causes,All causes,Colorado,27114,801.80 +2016,All Causes,All causes,Connecticut,30543,654.00 +2015,All Causes,All causes,Connecticut,30535,656.10 +2014,All Causes,All causes,Connecticut,29860,646.50 +2013,All Causes,All causes,Connecticut,29632,646.30 +2012,All Causes,All causes,Connecticut,29316,648.20 +2011,All Causes,All causes,Connecticut,29526,660.60 +2010,All Causes,All causes,Connecticut,28692,652.90 +2009,All Causes,All causes,Connecticut,28585,659.30 +2008,All Causes,All causes,Connecticut,28794,671.90 +2007,All Causes,All causes,Connecticut,28651,680.50 +2006,All Causes,All causes,Connecticut,29260,704.60 +2005,All Causes,All causes,Connecticut,29467,717.70 +2004,All Causes,All causes,Connecticut,29314,725.10 +2003,All Causes,All causes,Connecticut,29627,739.70 +2002,All Causes,All causes,Connecticut,30122,766.10 +2001,All Causes,All causes,Connecticut,29827,772.10 +2000,All Causes,All causes,Connecticut,30129,793.00 +1999,All Causes,All causes,Connecticut,29446,783.20 +2016,All Causes,All causes,Delaware,8874,746.20 +2015,All Causes,All causes,Delaware,8582,741.50 +2014,All Causes,All causes,Delaware,8260,734.00 +2013,All Causes,All causes,Delaware,7967,726.80 +2012,All Causes,All causes,Delaware,7875,745.40 +2011,All Causes,All causes,Delaware,7845,764.20 +2010,All Causes,All causes,Delaware,7706,769.90 +2009,All Causes,All causes,Delaware,7534,768.00 +2008,All Causes,All causes,Delaware,7622,793.50 +2007,All Causes,All causes,Delaware,7327,786.10 +2006,All Causes,All causes,Delaware,7204,795.70 +2005,All Causes,All causes,Delaware,7472,851.40 +2004,All Causes,All causes,Delaware,7143,838.20 +2003,All Causes,All causes,Delaware,7070,852.90 +2002,All Causes,All causes,Delaware,6861,849.30 +2001,All Causes,All causes,Delaware,7112,903.10 +2000,All Causes,All causes,Delaware,6875,897.10 +1999,All Causes,All causes,Delaware,6666,881.60 +2016,All Causes,All causes,District of Columbia,5037,766.00 +2015,All Causes,All causes,District of Columbia,4871,748.60 +2014,All Causes,All causes,District of Columbia,4723,743.80 +2013,All Causes,All causes,District of Columbia,4719,752.00 +2012,All Causes,All causes,District of Columbia,4650,757.20 +2011,All Causes,All causes,District of Columbia,4589,755.90 +2010,All Causes,All causes,District of Columbia,4672,792.40 +2009,All Causes,All causes,District of Columbia,4834,833.30 +2008,All Causes,All causes,District of Columbia,5140,897.60 +2007,All Causes,All causes,District of Columbia,5188,911.20 +2006,All Causes,All causes,District of Columbia,5344,951.70 +2005,All Causes,All causes,District of Columbia,5483,970.60 +2004,All Causes,All causes,District of Columbia,5454,972.10 +2003,All Causes,All causes,District of Columbia,5573,993.00 +2002,All Causes,All causes,District of Columbia,5851,1034.10 +2001,All Causes,All causes,District of Columbia,5951,1049.90 +2000,All Causes,All causes,District of Columbia,6001,1061.20 +1999,All Causes,All causes,District of Columbia,6076,1087.30 +2016,All Causes,All causes,Florida,197313,666.60 +2015,All Causes,All causes,Florida,191737,662.90 +2014,All Causes,All causes,Florida,185956,662.00 +2013,All Causes,All causes,Florida,181112,663.40 +2012,All Causes,All causes,Florida,177291,669.90 +2011,All Causes,All causes,Florida,173976,677.10 +2010,All Causes,All causes,Florida,173791,701.10 +2009,All Causes,All causes,Florida,169924,700.10 +2008,All Causes,All causes,Florida,170703,718.10 +2007,All Causes,All causes,Florida,168096,723.30 +2006,All Causes,All causes,Florida,170066,745.60 +2005,All Causes,All causes,Florida,170791,761.90 +2004,All Causes,All causes,Florida,169008,771.60 +2003,All Causes,All causes,Florida,168657,786.70 +2002,All Causes,All causes,Florida,167814,796.20 +2001,All Causes,All causes,Florida,167269,806.10 +2000,All Causes,All causes,Florida,164395,807.80 +1999,All Causes,All causes,Florida,163224,812.80 +2016,All Causes,All causes,Georgia,81428,800.40 +2015,All Causes,All causes,Georgia,79942,808.10 +2014,All Causes,All causes,Georgia,76887,801.90 +2013,All Causes,All causes,Georgia,75088,806.20 +2012,All Causes,All causes,Georgia,72847,808.60 +2011,All Causes,All causes,Georgia,71248,815.70 +2010,All Causes,All causes,Georgia,71263,845.40 +2009,All Causes,All causes,Georgia,69712,842.30 +2008,All Causes,All causes,Georgia,69640,864.10 +2007,All Causes,All causes,Georgia,68331,872.00 +2006,All Causes,All causes,Georgia,67808,890.20 +2005,All Causes,All causes,Georgia,66736,910.50 +2004,All Causes,All causes,Georgia,65818,926.40 +2003,All Causes,All causes,Georgia,66478,956.80 +2002,All Causes,All causes,Georgia,65449,961.60 +2001,All Causes,All causes,Georgia,64485,966.40 +2000,All Causes,All causes,Georgia,63870,981.00 +1999,All Causes,All causes,Georgia,62028,964.90 +2016,All Causes,All causes,Hawaii,10913,572.00 +2015,All Causes,All causes,Hawaii,11053,588.20 +2014,All Causes,All causes,Hawaii,10767,588.70 +2013,All Causes,All causes,Hawaii,10505,590.80 +2012,All Causes,All causes,Hawaii,10274,586.50 +2011,All Causes,All causes,Hawaii,9923,584.90 +2010,All Causes,All causes,Hawaii,9617,589.60 +2009,All Causes,All causes,Hawaii,9914,621.90 +2008,All Causes,All causes,Hawaii,9501,613.60 +2007,All Causes,All causes,Hawaii,9495,631.00 +2006,All Causes,All causes,Hawaii,9432,643.40 +2005,All Causes,All causes,Hawaii,9136,638.30 +2004,All Causes,All causes,Hawaii,9030,652.90 +2003,All Causes,All causes,Hawaii,8978,667.10 +2002,All Causes,All causes,Hawaii,8801,673.40 +2001,All Causes,All causes,Hawaii,8394,660.40 +2000,All Causes,All causes,Hawaii,8290,675.90 +1999,All Causes,All causes,Hawaii,8270,688.80 +2016,All Causes,All causes,Idaho,13366,725.00 +2015,All Causes,All causes,Idaho,13026,727.80 +2014,All Causes,All causes,Idaho,12613,723.80 +2013,All Causes,All causes,Idaho,12434,730.60 +2012,All Causes,All causes,Idaho,11998,726.60 +2011,All Causes,All causes,Idaho,12027,745.00 +2010,All Causes,All causes,Idaho,11429,731.60 +2009,All Causes,All causes,Idaho,11098,725.10 +2008,All Causes,All causes,Idaho,10962,736.80 +2007,All Causes,All causes,Idaho,10822,747.00 +2006,All Causes,All causes,Idaho,10613,756.00 +2005,All Causes,All causes,Idaho,10556,781.20 +2004,All Causes,All causes,Idaho,10028,766.20 +2003,All Causes,All causes,Idaho,10380,812.80 +2002,All Causes,All causes,Idaho,9923,798.80 +2001,All Causes,All causes,Idaho,9753,803.50 +2000,All Causes,All causes,Idaho,9563,809.40 +1999,All Causes,All causes,Idaho,9579,825.20 +2016,All Causes,All causes,Illinois,107020,724.30 +2015,All Causes,All causes,Illinois,106872,728.30 +2014,All Causes,All causes,Illinois,105293,726.00 +2013,All Causes,All causes,Illinois,103401,724.00 +2012,All Causes,All causes,Illinois,102433,728.70 +2011,All Causes,All causes,Illinois,101906,737.40 +2010,All Causes,All causes,Illinois,99931,736.90 +2009,All Causes,All causes,Illinois,100056,746.90 +2008,All Causes,All causes,Illinois,103471,782.00 +2007,All Causes,All causes,Illinois,100503,770.30 +2006,All Causes,All causes,Illinois,102171,794.40 +2005,All Causes,All causes,Illinois,103974,817.50 +2004,All Causes,All causes,Illinois,102670,816.80 +2003,All Causes,All causes,Illinois,105325,846.60 +2002,All Causes,All causes,Illinois,106667,866.70 +2001,All Causes,All causes,Illinois,105430,863.50 +2000,All Causes,All causes,Illinois,106634,883.00 +1999,All Causes,All causes,Illinois,108436,905.10 +2016,All Causes,All causes,Indiana,63473,835.00 +2015,All Causes,All causes,Indiana,62713,833.90 +2014,All Causes,All causes,Indiana,60940,822.30 +2013,All Causes,All causes,Indiana,60716,832.20 +2012,All Causes,All causes,Indiana,59332,827.50 +2011,All Causes,All causes,Indiana,58202,825.10 +2010,All Causes,All causes,Indiana,56743,820.60 +2009,All Causes,All causes,Indiana,55973,820.60 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causes,Louisiana,41238,1021.60 +2016,All Causes,All causes,Maine,14182,759.00 +2015,All Causes,All causes,Maine,14479,783.50 +2014,All Causes,All causes,Maine,13510,739.00 +2013,All Causes,All causes,Maine,13547,754.20 +2012,All Causes,All causes,Maine,12870,730.40 +2011,All Causes,All causes,Maine,13001,752.80 +2010,All Causes,All causes,Maine,12750,749.60 +2009,All Causes,All causes,Maine,12594,752.60 +2008,All Causes,All causes,Maine,12541,759.40 +2007,All Causes,All causes,Maine,12493,771.60 +2006,All Causes,All causes,Maine,12294,775.10 +2005,All Causes,All causes,Maine,12868,828.20 +2004,All Causes,All causes,Maine,12443,813.00 +2003,All Causes,All causes,Maine,12540,829.80 +2002,All Causes,All causes,Maine,12694,853.70 +2001,All Causes,All causes,Maine,12421,848.70 +2000,All Causes,All causes,Maine,12354,859.40 +1999,All Causes,All causes,Maine,12261,865.90 +2016,All Causes,All causes,Maryland,48824,717.60 +2015,All Causes,All causes,Maryland,47247,705.70 +2014,All Causes,All causes,Maryland,45867,699.50 +2013,All Causes,All causes,Maryland,45689,710.40 +2012,All Causes,All causes,Maryland,44477,709.10 +2011,All Causes,All causes,Maryland,43745,715.80 +2010,All Causes,All causes,Maryland,43325,728.60 +2009,All Causes,All causes,Maryland,43843,750.20 +2008,All Causes,All causes,Maryland,43892,769.60 +2007,All Causes,All causes,Maryland,43757,784.00 +2006,All Causes,All causes,Maryland,43582,795.90 +2005,All Causes,All causes,Maryland,43892,818.10 +2004,All Causes,All causes,Maryland,43232,823.70 +2003,All Causes,All causes,Maryland,44499,864.50 +2002,All Causes,All causes,Maryland,43970,872.30 +2001,All Causes,All causes,Maryland,43839,888.00 +2000,All Causes,All causes,Maryland,43753,908.90 +1999,All Causes,All causes,Maryland,43089,907.00 +2016,All Causes,All causes,Massachusetts,56961,669.00 +2015,All Causes,All causes,Massachusetts,57806,684.80 +2014,All Causes,All causes,Massachusetts,55200,663.00 +2013,All Causes,All causes,Massachusetts,54567,663.50 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causes,Michigan,89901,774.20 +2011,All Causes,All causes,Michigan,89508,784.20 +2010,All Causes,All causes,Michigan,88021,786.20 +2009,All Causes,All causes,Michigan,86455,783.10 +2008,All Causes,All causes,Michigan,88445,812.00 +2007,All Causes,All causes,Michigan,86721,808.10 +2006,All Causes,All causes,Michigan,86042,816.00 +2005,All Causes,All causes,Michigan,86867,838.60 +2004,All Causes,All causes,Michigan,85169,834.10 +2003,All Causes,All causes,Michigan,86728,860.50 +2002,All Causes,All causes,Michigan,87795,885.70 +2001,All Causes,All causes,Michigan,86424,884.00 +2000,All Causes,All causes,Michigan,86953,904.20 +1999,All Causes,All causes,Michigan,87232,916.40 +2016,All Causes,All causes,Minnesota,43078,648.10 +2015,All Causes,All causes,Minnesota,42800,653.80 +2014,All Causes,All causes,Minnesota,41445,647.00 +2013,All Causes,All causes,Minnesota,40987,651.00 +2012,All Causes,All causes,Minnesota,40016,649.50 +2011,All Causes,All causes,Minnesota,39820,659.20 +2010,All Causes,All causes,Minnesota,38972,661.50 +2009,All Causes,All causes,Minnesota,37851,652.10 +2008,All Causes,All causes,Minnesota,38499,674.00 +2007,All Causes,All causes,Minnesota,37138,663.10 +2006,All Causes,All causes,Minnesota,37028,675.60 +2005,All Causes,All causes,Minnesota,37535,698.60 +2004,All Causes,All causes,Minnesota,37034,699.70 +2003,All Causes,All causes,Minnesota,37620,720.90 +2002,All Causes,All causes,Minnesota,38510,751.30 +2001,All Causes,All causes,Minnesota,37735,748.20 +2000,All Causes,All causes,Minnesota,37690,760.70 +1999,All Causes,All causes,Minnesota,38537,784.50 +2016,All Causes,All causes,Mississippi,31741,948.90 +2015,All Causes,All causes,Mississippi,31783,963.70 +2014,All Causes,All causes,Mississippi,30557,937.60 +2013,All Causes,All causes,Mississippi,30703,959.60 +2012,All Causes,All causes,Mississippi,29544,942.90 +2011,All Causes,All causes,Mississippi,29278,956.10 +2010,All Causes,All causes,Mississippi,28965,962.00 +2009,All Causes,All causes,Mississippi,28275,950.50 +2008,All Causes,All causes,Mississippi,28984,982.80 +2007,All Causes,All causes,Mississippi,28255,973.30 +2006,All Causes,All causes,Mississippi,28564,999.10 +2005,All Causes,All causes,Mississippi,29196,1028.70 +2004,All Causes,All causes,Mississippi,27871,996.60 +2003,All Causes,All causes,Mississippi,28489,1031.60 +2002,All Causes,All causes,Mississippi,28853,1051.60 +2001,All Causes,All causes,Mississippi,28259,1034.30 +2000,All Causes,All causes,Mississippi,28654,1051.90 +1999,All Causes,All causes,Mississippi,28185,1043.40 +2016,All Causes,All causes,Missouri,59873,808.20 +2015,All Causes,All causes,Missouri,59871,816.90 +2014,All Causes,All causes,Missouri,58320,807.00 +2013,All Causes,All causes,Missouri,57444,807.70 +2012,All Causes,All causes,Missouri,56094,803.00 +2011,All Causes,All causes,Missouri,55848,812.00 +2010,All Causes,All causes,Missouri,55281,819.50 +2009,All Causes,All causes,Missouri,54263,815.30 +2008,All Causes,All causes,Missouri,56578,861.30 +2007,All Causes,All causes,Missouri,54166,837.70 +2006,All Causes,All causes,Missouri,54681,859.00 +2005,All Causes,All causes,Missouri,54656,873.30 +2004,All Causes,All causes,Missouri,53950,872.70 +2003,All Causes,All causes,Missouri,55582,907.00 +2002,All Causes,All causes,Missouri,55940,922.30 +2001,All Causes,All causes,Missouri,54982,913.40 +2000,All Causes,All causes,Missouri,54865,920.80 +1999,All Causes,All causes,Missouri,55931,945.00 +2016,All Causes,All causes,Montana,9905,743.20 +2015,All Causes,All causes,Montana,9942,762.70 +2014,All Causes,All causes,Montana,9381,732.10 +2013,All Causes,All causes,Montana,9511,761.30 +2012,All Causes,All causes,Montana,8976,732.40 +2011,All Causes,All causes,Montana,9115,760.60 +2010,All Causes,All causes,Montana,8827,754.70 +2009,All Causes,All causes,Montana,8730,758.00 +2008,All Causes,All causes,Montana,8913,788.70 +2007,All Causes,All causes,Montana,8624,775.40 +2006,All Causes,All causes,Montana,8472,782.20 +2005,All Causes,All causes,Montana,8528,807.10 +2004,All Causes,All causes,Montana,8094,783.00 +2003,All Causes,All causes,Montana,8467,837.60 +2002,All Causes,All causes,Montana,8506,854.40 +2001,All Causes,All causes,Montana,8265,844.10 +2000,All Causes,All causes,Montana,8096,843.30 +1999,All Causes,All causes,Montana,8128,857.60 +2016,All Causes,All causes,Nebraska,16217,707.00 +2015,All Causes,All causes,Nebraska,16740,739.20 +2014,All Causes,All causes,Nebraska,15978,718.20 +2013,All Causes,All causes,Nebraska,15754,714.70 +2012,All Causes,All causes,Nebraska,15659,719.00 +2011,All Causes,All causes,Nebraska,15476,719.80 +2010,All Causes,All causes,Nebraska,15171,717.80 +2009,All Causes,All causes,Nebraska,14810,710.90 +2008,All Causes,All causes,Nebraska,15461,751.30 +2007,All Causes,All causes,Nebraska,15263,750.40 +2006,All Causes,All causes,Nebraska,14899,743.70 +2005,All Causes,All causes,Nebraska,14963,757.80 +2004,All Causes,All causes,Nebraska,14657,751.90 +2003,All Causes,All causes,Nebraska,15465,798.20 +2002,All Causes,All causes,Nebraska,15738,820.20 +2001,All Causes,All causes,Nebraska,15174,796.60 +2000,All Causes,All causes,Nebraska,14992,794.60 +1999,All Causes,All causes,Nebraska,15579,830.10 +2016,All Causes,All causes,Nevada,23902,762.60 +2015,All Causes,All causes,Nevada,22879,757.20 +2014,All Causes,All causes,Nevada,21793,749.20 +2013,All Causes,All causes,Nevada,21468,769.80 +2012,All Causes,All causes,Nevada,20785,774.60 +2011,All Causes,All causes,Nevada,20343,789.70 +2010,All Causes,All causes,Nevada,19623,795.40 +2009,All Causes,All causes,Nevada,19224,794.60 +2008,All Causes,All causes,Nevada,19335,828.70 +2007,All Causes,All causes,Nevada,18687,822.00 +2006,All Causes,All causes,Nevada,18872,864.70 +2005,All Causes,All causes,Nevada,19029,904.80 +2004,All Causes,All causes,Nevada,17929,887.60 +2003,All Causes,All causes,Nevada,17858,927.30 +2002,All Causes,All causes,Nevada,16927,922.30 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Hampshire,9815,809.30 +2000,All Causes,All causes,New Hampshire,9697,815.50 +1999,All Causes,All causes,New Hampshire,9537,815.50 +2016,All Causes,All causes,New Jersey,73155,668.50 +2015,All Causes,All causes,New Jersey,72271,666.00 +2014,All Causes,All causes,New Jersey,71316,665.70 +2013,All Causes,All causes,New Jersey,71403,676.40 +2012,All Causes,All causes,New Jersey,70534,677.60 +2011,All Causes,All causes,New Jersey,70558,690.60 +2010,All Causes,All causes,New Jersey,69495,691.10 +2009,All Causes,All causes,New Jersey,68277,689.30 +2008,All Causes,All causes,New Jersey,70026,718.30 +2007,All Causes,All causes,New Jersey,69662,726.90 +2006,All Causes,All causes,New Jersey,70356,744.90 +2005,All Causes,All causes,New Jersey,71963,772.40 +2004,All Causes,All causes,New Jersey,71371,775.80 +2003,All Causes,All causes,New Jersey,73689,807.80 +2002,All Causes,All causes,New Jersey,74009,821.70 +2001,All Causes,All causes,New Jersey,74710,840.30 +2000,All Causes,All causes,New Jersey,74800,852.60 +1999,All Causes,All causes,New Jersey,73981,853.70 +2016,All Causes,All causes,New Mexico,18365,753.40 +2015,All Causes,All causes,New Mexico,17685,741.50 +2014,All Causes,All causes,New Mexico,17579,749.60 +2013,All Causes,All causes,New Mexico,16805,731.80 +2012,All Causes,All causes,New Mexico,16710,744.60 +2011,All Causes,All causes,New Mexico,16452,748.90 +2010,All Causes,All causes,New Mexico,15931,749.00 +2009,All Causes,All causes,New Mexico,15643,749.80 +2008,All Causes,All causes,New Mexico,16005,788.60 +2007,All Causes,All causes,New Mexico,15482,778.60 +2006,All Causes,All causes,New Mexico,15296,793.10 +2005,All Causes,All causes,New Mexico,14983,800.10 +2004,All Causes,All causes,New Mexico,14298,786.70 +2003,All Causes,All causes,New Mexico,14805,832.10 +2002,All Causes,All causes,New Mexico,14344,823.80 +2001,All Causes,All causes,New Mexico,14129,829.50 +2000,All Causes,All causes,New Mexico,13425,808.60 +1999,All Causes,All causes,New Mexico,13676,842.20 +2016,All Causes,All causes,New York,154358,640.70 +2015,All Causes,All causes,New York,153628,644.00 +2014,All Causes,All causes,New York,149944,636.50 +2013,All Causes,All causes,New York,150919,649.30 +2012,All Causes,All causes,New York,148991,652.10 +2011,All Causes,All causes,New York,149174,665.40 +2010,All Causes,All causes,New York,146432,665.50 +2009,All Causes,All causes,New York,146475,674.60 +2008,All Causes,All causes,New York,148698,693.70 +2007,All Causes,All causes,New York,147680,698.90 +2006,All Causes,All causes,New York,148806,713.70 +2005,All Causes,All causes,New York,152427,738.00 +2004,All Causes,All causes,New York,152681,748.40 +2003,All Causes,All causes,New York,155877,771.10 +2002,All Causes,All causes,New York,158118,791.40 +2001,All Causes,All causes,New York,159240,806.60 +2000,All Causes,All causes,New York,158203,813.60 +1999,All Causes,All causes,New York,159927,833.40 +2016,All Causes,All causes,North Carolina,90465,782.40 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Dakota,6242,688.40 +2015,All Causes,All causes,North Dakota,6223,696.80 +2014,All Causes,All causes,North Dakota,6184,692.70 +2013,All Causes,All causes,North Dakota,6233,709.70 +2012,All Causes,All causes,North Dakota,6038,701.20 +2011,All Causes,All causes,North Dakota,5965,697.40 +2010,All Causes,All causes,North Dakota,5944,704.30 +2009,All Causes,All causes,North Dakota,5914,712.70 +2008,All Causes,All causes,North Dakota,5871,713.30 +2007,All Causes,All causes,North Dakota,5561,680.40 +2006,All Causes,All causes,North Dakota,5868,724.40 +2005,All Causes,All causes,North Dakota,5744,716.30 +2004,All Causes,All causes,North Dakota,5601,702.20 +2003,All Causes,All causes,North Dakota,6090,776.10 +2002,All Causes,All causes,North Dakota,5892,755.50 +2001,All Causes,All causes,North Dakota,6048,780.20 +2000,All Causes,All causes,North Dakota,5856,761.80 +1999,All Causes,All causes,North Dakota,6103,796.80 +2016,All Causes,All causes,Ohio,119572,832.30 +2015,All Causes,All causes,Ohio,118188,828.40 +2014,All Causes,All causes,Ohio,114509,810.00 +2013,All Causes,All causes,Ohio,113258,811.20 +2012,All Causes,All causes,Ohio,112498,817.90 +2011,All Causes,All causes,Ohio,111427,821.80 +2010,All Causes,All causes,Ohio,108711,815.70 +2009,All Causes,All causes,Ohio,107156,814.50 +2008,All Causes,All causes,Ohio,109767,844.30 +2007,All Causes,All causes,Ohio,106534,831.10 +2006,All Causes,All causes,Ohio,106825,846.60 +2005,All Causes,All causes,Ohio,109031,879.10 +2004,All Causes,All causes,Ohio,106288,867.10 +2003,All Causes,All causes,Ohio,109110,898.50 +2002,All Causes,All causes,Ohio,109766,916.70 +2001,All Causes,All causes,Ohio,108027,912.40 +2000,All Causes,All causes,Ohio,108125,923.50 +1999,All Causes,All causes,Ohio,108517,934.00 +2016,All Causes,All causes,Oklahoma,39276,888.40 +2015,All Causes,All causes,Oklahoma,39422,904.30 +2014,All Causes,All causes,Oklahoma,38464,897.50 +2013,All Causes,All causes,Oklahoma,38384,910.70 +2012,All Causes,All causes,Oklahoma,36870,891.50 +2011,All Causes,All causes,Oklahoma,37175,910.90 +2010,All Causes,All causes,Oklahoma,36529,915.50 +2009,All Causes,All causes,Oklahoma,35601,903.80 +2008,All Causes,All causes,Oklahoma,37014,956.50 +2007,All Causes,All causes,Oklahoma,36032,945.10 +2006,All Causes,All causes,Oklahoma,35427,942.50 +2005,All Causes,All causes,Oklahoma,36180,981.40 +2004,All Causes,All causes,Oklahoma,34483,945.60 +2003,All Causes,All causes,Oklahoma,35721,986.50 +2002,All Causes,All causes,Oklahoma,35502,986.40 +2001,All Causes,All causes,Oklahoma,34682,968.50 +2000,All Causes,All causes,Oklahoma,35079,983.00 +1999,All Causes,All causes,Oklahoma,34700,979.00 +2016,All Causes,All causes,Oregon,35778,705.90 +2015,All Causes,All causes,Oregon,35705,722.30 +2014,All Causes,All causes,Oregon,34151,706.70 +2013,All Causes,All causes,Oregon,33939,717.50 +2012,All Causes,All causes,Oregon,32759,706.60 +2011,All Causes,All causes,Oregon,32788,724.10 +2010,All Causes,All causes,Oregon,31890,723.10 +2009,All Causes,All causes,Oregon,31636,728.40 +2008,All Causes,All causes,Oregon,31967,753.20 +2007,All Causes,All causes,Oregon,31403,756.00 +2006,All Causes,All causes,Oregon,31380,773.30 +2005,All Causes,All causes,Oregon,31091,787.60 +2004,All Causes,All causes,Oregon,30313,786.40 +2003,All Causes,All causes,Oregon,30912,816.90 +2002,All Causes,All causes,Oregon,31119,839.60 +2001,All Causes,All causes,Oregon,30158,830.50 +2000,All Causes,All causes,Oregon,29552,830.90 +1999,All Causes,All causes,Oregon,29422,837.60 +2016,All Causes,All causes,Pennsylvania,133040,770.10 +2015,All Causes,All causes,Pennsylvania,132598,768.30 +2014,All Causes,All causes,Pennsylvania,128434,750.20 +2013,All Causes,All causes,Pennsylvania,129123,761.30 +2012,All Causes,All causes,Pennsylvania,126981,759.20 +2011,All Causes,All causes,Pennsylvania,128237,776.00 +2010,All Causes,All causes,Pennsylvania,124596,765.90 +2009,All Causes,All causes,Pennsylvania,124780,775.30 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causes,Rhode Island,9738,742.90 +2007,All Causes,All causes,Rhode Island,9723,747.20 +2006,All Causes,All causes,Rhode Island,9690,749.10 +2005,All Causes,All causes,Rhode Island,10007,781.20 +2004,All Causes,All causes,Rhode Island,9769,766.00 +2003,All Causes,All causes,Rhode Island,10039,798.70 +2002,All Causes,All causes,Rhode Island,10246,823.00 +2001,All Causes,All causes,Rhode Island,10021,816.90 +2000,All Causes,All causes,Rhode Island,10027,823.70 +1999,All Causes,All causes,Rhode Island,9708,807.50 +2016,All Causes,All causes,South Carolina,48130,829.80 +2015,All Causes,All causes,South Carolina,47198,840.00 +2014,All Causes,All causes,South Carolina,45454,829.10 +2013,All Causes,All causes,South Carolina,44582,837.80 +2012,All Causes,All causes,South Carolina,43198,835.20 +2011,All Causes,All causes,South Carolina,42072,839.50 +2010,All Causes,All causes,South Carolina,41614,854.80 +2009,All Causes,All causes,South Carolina,40449,846.90 +2008,All Causes,All causes,South Carolina,40289,867.00 +2007,All Causes,All causes,South Carolina,39439,873.90 +2006,All Causes,All causes,South Carolina,38761,886.80 +2005,All Causes,All causes,South Carolina,38707,920.90 +2004,All Causes,All causes,South Carolina,37276,909.50 +2003,All Causes,All causes,South Carolina,38112,952.70 +2002,All Causes,All causes,South Carolina,37736,964.30 +2001,All Causes,All causes,South Carolina,36612,949.60 +2000,All Causes,All causes,South Carolina,36948,980.20 +1999,All Causes,All causes,South Carolina,36053,973.50 +2016,All Causes,All causes,South Dakota,7845,719.50 +2015,All Causes,All causes,South Dakota,7731,715.40 +2014,All Causes,All causes,South Dakota,7507,710.40 +2013,All Causes,All causes,South Dakota,7099,679.30 +2012,All Causes,All causes,South Dakota,7333,712.30 +2011,All Causes,All causes,South Dakota,7314,720.60 +2010,All Causes,All causes,South Dakota,7100,715.10 +2009,All Causes,All causes,South Dakota,6923,706.20 +2008,All Causes,All causes,South Dakota,7083,731.90 +2007,All Causes,All causes,South Dakota,6826,712.90 +2006,All Causes,All causes,South Dakota,7084,752.50 +2005,All Causes,All causes,South Dakota,7086,770.70 +2004,All Causes,All causes,South Dakota,6833,752.40 +2003,All Causes,All causes,South Dakota,7132,796.60 +2002,All Causes,All causes,South Dakota,6898,778.60 +2001,All Causes,All causes,South Dakota,6923,790.00 +2000,All Causes,All causes,South Dakota,7021,805.70 +1999,All Causes,All causes,South Dakota,6953,808.40 +2016,All Causes,All causes,Tennessee,67857,886.30 +2015,All Causes,All causes,Tennessee,66570,886.40 +2014,All Causes,All causes,Tennessee,64661,880.00 +2013,All Causes,All causes,Tennessee,63406,881.10 +2012,All Causes,All causes,Tennessee,61956,880.60 +2011,All Causes,All causes,Tennessee,60541,879.00 +2010,All Causes,All causes,Tennessee,59578,890.80 +2009,All Causes,All causes,Tennessee,58288,885.60 +2008,All Causes,All causes,Tennessee,58820,913.40 +2007,All Causes,All causes,Tennessee,57087,905.40 +2006,All Causes,All causes,Tennessee,56838,921.20 +2005,All Causes,All causes,Tennessee,57260,956.60 +2004,All Causes,All causes,Tennessee,55829,952.80 +2003,All Causes,All causes,Tennessee,57313,994.00 +2002,All Causes,All causes,Tennessee,56606,995.30 +2001,All Causes,All causes,Tennessee,55151,979.50 +2000,All Causes,All causes,Tennessee,55246,996.90 +1999,All Causes,All causes,Tennessee,53765,980.40 +2016,All Causes,All causes,Texas,191966,730.60 +2015,All Causes,All causes,Texas,189654,745.00 +2014,All Causes,All causes,Texas,183912,745.30 +2013,All Causes,All causes,Texas,179183,751.60 +2012,All Causes,All causes,Texas,174187,753.30 +2011,All Causes,All causes,Texas,168640,751.60 +2010,All Causes,All causes,Texas,166527,772.30 +2009,All Causes,All causes,Texas,163249,772.10 +2008,All Causes,All causes,Texas,164914,803.60 +2007,All Causes,All causes,Texas,160548,803.10 +2006,All Causes,All causes,Texas,157150,807.70 +2005,All Causes,All causes,Texas,156457,832.10 +2004,All Causes,All causes,Texas,152870,835.90 +2003,All Causes,All causes,Texas,154870,865.10 +2002,All Causes,All causes,Texas,155524,888.40 +2001,All Causes,All causes,Texas,152779,888.90 +2000,All Causes,All causes,Texas,149939,891.00 +1999,All Causes,All causes,Texas,146858,886.60 +2016,All Causes,All causes,United States,2319475,616.00 +2015,All Causes,All causes,United States,2712630,733.10 +2014,All Causes,All causes,United States,2626418,724.60 +2013,All Causes,All causes,United States,2596993,731.90 +2012,All Causes,All causes,United States,2543279,732.80 +2011,All Causes,All causes,United States,2515458,741.30 +2010,All Causes,All causes,United States,2468435,747.00 +2009,All Causes,All causes,United States,2437163,749.60 +2008,All Causes,All causes,United States,2471984,774.90 +2007,All Causes,All causes,United States,2423712,775.30 +2006,All Causes,All causes,United States,2426264,791.80 +2005,All Causes,All causes,United States,2448017,815.00 +2004,All Causes,All causes,United States,2397615,813.70 +2003,All Causes,All causes,United States,2448288,843.50 +2002,All Causes,All causes,United States,2443387,855.90 +2001,All Causes,All causes,United States,2416425,858.80 +2000,All Causes,All causes,United States,2403351,869.00 +1999,All Causes,All causes,United States,2391399,875.60 +2016,All Causes,All causes,Utah,17913,714.70 +2015,All Causes,All causes,Utah,17334,712.10 +2014,All Causes,All causes,Utah,16719,709.60 +2013,All Causes,All causes,Utah,16366,710.40 +2012,All Causes,All causes,Utah,15676,700.00 +2011,All Causes,All causes,Utah,15266,699.10 +2010,All Causes,All causes,Utah,14776,703.20 +2009,All Causes,All causes,Utah,14138,686.50 +2008,All Causes,All causes,Utah,14040,703.80 +2007,All Causes,All causes,Utah,14143,729.40 +2006,All Causes,All causes,Utah,13764,733.70 +2005,All Causes,All causes,Utah,13432,749.40 +2004,All Causes,All causes,Utah,13331,768.80 +2003,All Causes,All causes,Utah,13412,793.60 +2002,All Causes,All causes,Utah,13116,794.00 +2001,All Causes,All causes,Utah,12662,785.20 +2000,All Causes,All causes,Utah,12364,788.60 +1999,All Causes,All causes,Utah,12058,781.60 +2016,All Causes,All causes,Vermont,5909,712.80 +2015,All Causes,All causes,Vermont,5919,714.70 +2014,All Causes,All causes,Vermont,5623,694.80 +2013,All Causes,All causes,Vermont,5639,710.60 +2012,All Causes,All causes,Vermont,5491,700.10 +2011,All Causes,All causes,Vermont,5433,711.00 +2010,All Causes,All causes,Vermont,5380,718.70 +2009,All Causes,All causes,Vermont,5034,683.20 +2008,All Causes,All causes,Vermont,5211,722.50 +2007,All Causes,All causes,Vermont,5179,733.00 +2006,All Causes,All causes,Vermont,5048,728.00 +2005,All Causes,All causes,Vermont,5066,751.60 +2004,All Causes,All causes,Vermont,4995,749.40 +2003,All Causes,All causes,Vermont,5120,779.70 +2002,All Causes,All causes,Vermont,5075,786.80 +2001,All Causes,All causes,Vermont,5201,819.60 +2000,All Causes,All causes,Vermont,5127,821.80 +1999,All Causes,All causes,Vermont,4993,812.90 +2016,All Causes,All causes,Virginia,66473,715.50 +2015,All Causes,All causes,Virginia,65577,721.60 +2014,All Causes,All causes,Virginia,63598,717.50 +2013,All Causes,All causes,Virginia,62716,724.80 +2012,All Causes,All causes,Virginia,61564,730.20 +2011,All Causes,All causes,Virginia,60804,741.60 +2010,All Causes,All causes,Virginia,59032,741.60 +2009,All Causes,All causes,Virginia,58653,750.80 +2008,All Causes,All causes,Virginia,59100,776.70 +2007,All Causes,All causes,Virginia,58225,784.00 +2006,All Causes,All causes,Virginia,57690,795.00 +2005,All Causes,All causes,Virginia,57855,817.40 +2004,All Causes,All causes,Virginia,56550,820.50 +2003,All Causes,All causes,Virginia,58282,863.30 +2002,All Causes,All causes,Virginia,57196,865.90 +2001,All Causes,All causes,Virginia,56280,869.90 +2000,All Causes,All causes,Virginia,56282,890.40 +1999,All Causes,All causes,Virginia,55320,889.60 +2016,All Causes,All causes,Washington,54769,672.00 +2015,All Causes,All causes,Washington,54595,687.40 +2014,All Causes,All causes,Washington,52099,672.90 +2013,All Causes,All causes,Washington,51264,679.30 +2012,All Causes,All causes,Washington,50105,681.50 +2011,All Causes,All causes,Washington,49691,690.40 +2010,All Causes,All causes,Washington,48146,692.30 +2009,All Causes,All causes,Washington,48270,708.40 +2008,All Causes,All causes,Washington,48627,734.30 +2007,All Causes,All causes,Washington,47323,731.90 +2006,All Causes,All causes,Washington,46120,731.20 +2005,All Causes,All causes,Washington,46203,757.60 +2004,All Causes,All causes,Washington,44770,751.90 +2003,All Causes,All causes,Washington,45920,789.20 +2002,All Causes,All causes,Washington,45338,795.00 +2001,All Causes,All causes,Washington,44642,799.40 +2000,All Causes,All causes,Washington,43941,804.60 +1999,All Causes,All causes,Washington,43865,815.10 +2016,All Causes,All causes,West Virginia,22732,943.30 +2015,All Causes,All causes,West Virginia,22752,943.40 +2014,All Causes,All causes,West Virginia,22186,929.10 +2013,All Causes,All causes,West Virginia,21843,923.80 +2012,All Causes,All causes,West Virginia,21915,939.30 +2011,All Causes,All causes,West Virginia,21867,953.20 +2010,All Causes,All causes,West Virginia,21275,933.60 +2009,All Causes,All causes,West Virginia,21386,947.70 +2008,All Causes,All causes,West Virginia,21557,964.30 +2007,All Causes,All causes,West Virginia,21086,954.20 +2006,All Causes,All causes,West Virginia,20672,947.50 +2005,All Causes,All causes,West Virginia,20780,965.10 +2004,All Causes,All causes,West Virginia,20793,973.00 +2003,All Causes,All causes,West Virginia,21306,1003.10 +2002,All Causes,All causes,West Virginia,21016,999.00 +2001,All Causes,All causes,West Virginia,20967,1000.90 +2000,All Causes,All causes,West Virginia,21114,1011.10 +1999,All Causes,All causes,West Virginia,21049,1012.30 +2016,All Causes,All causes,Wisconsin,51815,717.90 +2015,All Causes,All causes,Wisconsin,51264,715.90 +2014,All Causes,All causes,Wisconsin,50291,712.10 +2013,All Causes,All causes,Wisconsin,50026,720.10 +2012,All Causes,All causes,Wisconsin,48384,707.80 +2011,All Causes,All causes,Wisconsin,48410,721.10 +2010,All Causes,All causes,Wisconsin,47308,719.00 +2009,All Causes,All causes,Wisconsin,45697,703.00 +2008,All Causes,All causes,Wisconsin,46815,730.30 +2007,All Causes,All causes,Wisconsin,46241,734.20 +2006,All Causes,All causes,Wisconsin,46153,745.30 +2005,All Causes,All causes,Wisconsin,46709,765.90 +2004,All Causes,All causes,Wisconsin,45600,760.00 +2003,All Causes,All causes,Wisconsin,46177,780.10 +2002,All Causes,All causes,Wisconsin,46981,806.10 +2001,All Causes,All causes,Wisconsin,46628,812.50 +2000,All Causes,All causes,Wisconsin,46461,820.10 +1999,All Causes,All causes,Wisconsin,46672,831.50 +2016,All Causes,All causes,Wyoming,4722,720.90 +2015,All Causes,All causes,Wyoming,4778,748.30 +2014,All Causes,All causes,Wyoming,4666,742.40 +2013,All Causes,All causes,Wyoming,4516,731.70 +2012,All Causes,All causes,Wyoming,4481,748.30 +2011,All Causes,All causes,Wyoming,4387,754.60 +2010,All Causes,All causes,Wyoming,4438,778.80 +2009,All Causes,All causes,Wyoming,4283,769.60 +2008,All Causes,All causes,Wyoming,4227,778.90 +2007,All Causes,All causes,Wyoming,4266,806.50 +2006,All Causes,All causes,Wyoming,4311,831.10 +2005,All Causes,All causes,Wyoming,4099,806.50 +2004,All Causes,All causes,Wyoming,3955,794.80 +2003,All Causes,All causes,Wyoming,4172,857.70 +2002,All Causes,All causes,Wyoming,4174,878.20 +2001,All Causes,All causes,Wyoming,4029,858.80 +2000,All Causes,All causes,Wyoming,3920,851.80 +1999,All Causes,All causes,Wyoming,4042,895.40 +2016,Alzheimer's disease (G30),Alzheimer's disease,Alabama,2507,45.00 +2015,Alzheimer's disease (G30),Alzheimer's disease,Alabama,2282,41.80 +2014,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1885,35.30 +2013,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1398,26.70 +2012,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1390,27.20 +2011,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1486,29.70 +2010,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1523,31.20 +2009,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1521,31.60 +2008,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1518,32.10 +2007,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1517,32.60 +2006,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1497,32.70 +2005,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1501,33.40 +2004,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1385,31.40 +2003,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1268,28.80 +2002,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1189,27.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,Alabama,1103,25.10 +2000,Alzheimer's disease (G30),Alzheimer's disease,Alabama,895,20.40 +1999,Alzheimer's disease (G30),Alzheimer's disease,Alabama,772,17.80 +2016,Alzheimer's disease (G30),Alzheimer's disease,Alaska,111,25.80 +2015,Alzheimer's disease (G30),Alzheimer's disease,Alaska,68,16.70 +2014,Alzheimer's disease (G30),Alzheimer's disease,Alaska,68,17.20 +2013,Alzheimer's disease (G30),Alzheimer's disease,Alaska,72,18.90 +2012,Alzheimer's disease (G30),Alzheimer's disease,Alaska,102,27.20 +2011,Alzheimer's disease (G30),Alzheimer's disease,Alaska,72,20.50 +2010,Alzheimer's disease (G30),Alzheimer's disease,Alaska,85,25.90 +2009,Alzheimer's disease (G30),Alzheimer's disease,Alaska,67,21.50 +2008,Alzheimer's disease (G30),Alzheimer's disease,Alaska,80,27.00 +2007,Alzheimer's disease (G30),Alzheimer's disease,Alaska,65,22.40 +2006,Alzheimer's disease (G30),Alzheimer's disease,Alaska,73,25.90 +2005,Alzheimer's disease (G30),Alzheimer's disease,Alaska,61,22.20 +2004,Alzheimer's disease (G30),Alzheimer's disease,Alaska,48,18.00 +2003,Alzheimer's disease (G30),Alzheimer's disease,Alaska,56,22.90 +2002,Alzheimer's disease (G30),Alzheimer's disease,Alaska,61,25.80 +2001,Alzheimer's disease (G30),Alzheimer's disease,Alaska,44,18.90 +2000,Alzheimer's disease (G30),Alzheimer's disease,Alaska,47,21.50 +1999,Alzheimer's disease (G30),Alzheimer's disease,Alaska,24,11.90 +2016,Alzheimer's disease (G30),Alzheimer's disease,Arizona,3082,35.80 +2015,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2943,35.80 +2014,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2485,31.60 +2013,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2383,31.70 +2012,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2160,30.00 +2011,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2348,33.70 +2010,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2327,35.30 +2009,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2097,32.70 +2008,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2099,34.00 +2007,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2051,34.20 +2006,Alzheimer's disease (G30),Alzheimer's disease,Arizona,2066,35.80 +2005,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1831,33.00 +2004,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1675,31.60 +2003,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1703,32.90 +2002,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1433,28.70 +2001,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1108,22.70 +2000,Alzheimer's disease (G30),Alzheimer's disease,Arizona,1045,22.00 +1999,Alzheimer's disease (G30),Alzheimer's disease,Arizona,963,20.80 +2016,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,1475,41.30 +2015,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,1457,41.50 +2014,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,1193,34.80 +2013,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,918,27.20 +2012,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,931,27.90 +2011,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,1002,30.40 +2010,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,955,29.60 +2009,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,885,27.80 +2008,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,893,28.40 +2007,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,824,26.50 +2006,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,783,25.50 +2005,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,686,22.80 +2004,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,622,20.90 +2003,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,552,18.60 +2002,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,551,18.60 +2001,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,452,15.30 +2000,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,430,14.60 +1999,Alzheimer's disease (G30),Alzheimer's disease,Arkansas,434,14.80 +2016,Alzheimer's disease (G30),Alzheimer's disease,California,15570,36.10 +2015,Alzheimer's disease (G30),Alzheimer's disease,California,15065,35.70 +2014,Alzheimer's disease (G30),Alzheimer's disease,California,12644,30.90 +2013,Alzheimer's disease (G30),Alzheimer's disease,California,11891,30.00 +2012,Alzheimer's disease (G30),Alzheimer's disease,California,11649,30.00 +2011,Alzheimer's disease (G30),Alzheimer's disease,California,11555,30.70 +2010,Alzheimer's disease (G30),Alzheimer's disease,California,10856,30.10 +2009,Alzheimer's disease (G30),Alzheimer's disease,California,9905,28.10 +2008,Alzheimer's disease (G30),Alzheimer's disease,California,10098,29.50 +2007,Alzheimer's disease (G30),Alzheimer's disease,California,8497,25.60 +2006,Alzheimer's disease (G30),Alzheimer's disease,California,8146,25.30 +2005,Alzheimer's disease (G30),Alzheimer's disease,California,7706,24.60 +2004,Alzheimer's disease (G30),Alzheimer's disease,California,6964,22.90 +2003,Alzheimer's disease (G30),Alzheimer's disease,California,6585,22.00 +2002,Alzheimer's disease (G30),Alzheimer's disease,California,5421,18.60 +2001,Alzheimer's disease (G30),Alzheimer's disease,California,4935,17.20 +2000,Alzheimer's disease (G30),Alzheimer's disease,California,4419,15.80 +1999,Alzheimer's disease (G30),Alzheimer's disease,California,4532,16.60 +2016,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1835,34.70 +2015,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1612,31.30 +2014,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1364,27.40 +2013,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1316,27.20 +2012,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1322,28.40 +2011,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1307,29.00 +2010,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1334,31.10 +2009,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1319,31.40 +2008,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1353,33.40 +2007,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1109,28.30 +2006,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1058,28.10 +2005,Alzheimer's disease (G30),Alzheimer's disease,Colorado,1064,29.50 +2004,Alzheimer's disease (G30),Alzheimer's disease,Colorado,912,26.10 +2003,Alzheimer's disease (G30),Alzheimer's disease,Colorado,899,26.50 +2002,Alzheimer's disease (G30),Alzheimer's disease,Colorado,954,29.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,Colorado,852,26.40 +2000,Alzheimer's disease (G30),Alzheimer's disease,Colorado,712,22.60 +1999,Alzheimer's disease (G30),Alzheimer's disease,Colorado,756,24.50 +2016,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,1035,20.30 +2015,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,966,19.00 +2014,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,923,18.40 +2013,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,824,16.20 +2012,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,842,16.80 +2011,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,813,16.40 +2010,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,820,17.10 +2009,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,778,16.30 +2008,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,839,17.80 +2007,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,764,16.60 +2006,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,728,16.00 +2005,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,777,17.40 +2004,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,684,15.70 +2003,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,612,14.20 +2002,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,570,13.60 +2001,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,573,14.00 +2000,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,526,13.10 +1999,Alzheimer's disease (G30),Alzheimer's disease,Connecticut,449,11.40 +2016,Alzheimer's disease (G30),Alzheimer's disease,Delaware,329,27.40 +2015,Alzheimer's disease (G30),Alzheimer's disease,Delaware,264,22.60 +2014,Alzheimer's disease (G30),Alzheimer's disease,Delaware,188,16.60 +2013,Alzheimer's disease (G30),Alzheimer's disease,Delaware,192,17.60 +2012,Alzheimer's disease (G30),Alzheimer's disease,Delaware,213,20.20 +2011,Alzheimer's disease (G30),Alzheimer's disease,Delaware,203,20.00 +2010,Alzheimer's disease (G30),Alzheimer's disease,Delaware,215,21.90 +2009,Alzheimer's disease (G30),Alzheimer's disease,Delaware,163,16.90 +2008,Alzheimer's disease (G30),Alzheimer's disease,Delaware,204,21.70 +2007,Alzheimer's disease (G30),Alzheimer's disease,Delaware,201,22.10 +2006,Alzheimer's disease (G30),Alzheimer's disease,Delaware,189,21.60 +2005,Alzheimer's disease (G30),Alzheimer's disease,Delaware,180,21.20 +2004,Alzheimer's disease (G30),Alzheimer's disease,Delaware,155,19.00 +2003,Alzheimer's disease (G30),Alzheimer's disease,Delaware,147,18.60 +2002,Alzheimer's disease (G30),Alzheimer's disease,Delaware,128,16.70 +2001,Alzheimer's disease (G30),Alzheimer's disease,Delaware,135,18.00 +2000,Alzheimer's disease (G30),Alzheimer's disease,Delaware,110,15.10 +1999,Alzheimer's disease (G30),Alzheimer's disease,Delaware,107,15.00 +2016,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,120,18.30 +2015,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,129,19.20 +2014,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,119,18.30 +2013,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,130,19.80 +2012,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,129,20.50 +2011,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,120,19.60 +2010,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,114,18.70 +2009,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,94,15.70 +2008,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,132,22.50 +2007,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,140,24.00 +2006,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,117,20.70 +2005,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,112,19.70 +2004,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,119,21.20 +2003,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,95,17.00 +2002,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,107,19.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,99,17.50 +2000,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,75,13.10 +1999,Alzheimer's disease (G30),Alzheimer's disease,District of Columbia,53,9.50 +2016,Alzheimer's disease (G30),Alzheimer's disease,Florida,7155,21.50 +2015,Alzheimer's disease (G30),Alzheimer's disease,Florida,7031,21.80 +2014,Alzheimer's disease (G30),Alzheimer's disease,Florida,5874,18.80 +2013,Alzheimer's disease (G30),Alzheimer's disease,Florida,5093,16.90 +2012,Alzheimer's disease (G30),Alzheimer's disease,Florida,4422,15.20 +2011,Alzheimer's disease (G30),Alzheimer's disease,Florida,4507,16.00 +2010,Alzheimer's disease (G30),Alzheimer's 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disease,Georgia,1235,21.30 +1999,Alzheimer's disease (G30),Alzheimer's disease,Georgia,1080,18.80 +2016,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,445,20.10 +2015,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,422,19.50 +2014,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,326,15.00 +2013,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,260,12.60 +2012,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,249,12.40 +2011,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,197,10.40 +2010,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,189,10.50 +2009,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,253,14.80 +2008,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,218,13.30 +2007,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,247,15.70 +2006,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,201,13.20 +2005,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,192,13.10 +2004,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,171,12.20 +2003,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,161,11.90 +2002,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,141,10.90 +2001,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,122,9.80 +2000,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,118,10.00 +1999,Alzheimer's disease (G30),Alzheimer's disease,Hawaii,109,9.40 +2016,Alzheimer's disease (G30),Alzheimer's disease,Idaho,607,33.90 +2015,Alzheimer's disease (G30),Alzheimer's disease,Idaho,552,31.60 +2014,Alzheimer's disease (G30),Alzheimer's disease,Idaho,376,22.40 +2013,Alzheimer's disease (G30),Alzheimer's disease,Idaho,347,21.00 +2012,Alzheimer's disease (G30),Alzheimer's disease,Idaho,354,21.90 +2011,Alzheimer's disease (G30),Alzheimer's disease,Idaho,409,25.70 +2010,Alzheimer's disease (G30),Alzheimer's disease,Idaho,410,26.80 +2009,Alzheimer's disease (G30),Alzheimer's disease,Idaho,369,24.70 +2008,Alzheimer's disease (G30),Alzheimer's disease,Idaho,393,27.20 +2007,Alzheimer's disease (G30),Alzheimer's 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(G30),Alzheimer's disease,Virginia,1550,23.60 +2004,Alzheimer's disease (G30),Alzheimer's disease,Virginia,1480,23.20 +2003,Alzheimer's disease (G30),Alzheimer's disease,Virginia,1466,23.40 +2002,Alzheimer's disease (G30),Alzheimer's disease,Virginia,1368,22.40 +2001,Alzheimer's disease (G30),Alzheimer's disease,Virginia,1184,19.80 +2000,Alzheimer's disease (G30),Alzheimer's disease,Virginia,1094,18.70 +1999,Alzheimer's disease (G30),Alzheimer's disease,Virginia,917,15.90 +2016,Alzheimer's disease (G30),Alzheimer's disease,Washington,3269,40.70 +2015,Alzheimer's disease (G30),Alzheimer's disease,Washington,3490,44.40 +2014,Alzheimer's disease (G30),Alzheimer's disease,Washington,3344,43.60 +2013,Alzheimer's disease (G30),Alzheimer's disease,Washington,3277,43.60 +2012,Alzheimer's disease (G30),Alzheimer's disease,Washington,3219,43.80 +2011,Alzheimer's disease (G30),Alzheimer's disease,Washington,3138,43.40 +2010,Alzheimer's disease (G30),Alzheimer's disease,Washington,3025,43.60 +2009,Alzheimer's disease (G30),Alzheimer's disease,Washington,3010,44.60 +2008,Alzheimer's disease (G30),Alzheimer's disease,Washington,3105,47.10 +2007,Alzheimer's disease (G30),Alzheimer's disease,Washington,2689,41.80 +2006,Alzheimer's disease (G30),Alzheimer's disease,Washington,2470,39.30 +2005,Alzheimer's disease (G30),Alzheimer's disease,Washington,2309,38.20 +2004,Alzheimer's disease (G30),Alzheimer's disease,Washington,2233,37.90 +2003,Alzheimer's disease (G30),Alzheimer's disease,Washington,2380,41.40 +2002,Alzheimer's disease (G30),Alzheimer's disease,Washington,2195,39.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,Washington,2050,37.10 +2000,Alzheimer's disease (G30),Alzheimer's disease,Washington,1799,33.40 +1999,Alzheimer's disease (G30),Alzheimer's disease,Washington,1577,29.80 +2016,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,786,31.70 +2015,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,738,30.00 +2014,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,620,25.50 +2013,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,590,24.60 +2012,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,598,25.30 +2011,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,610,26.20 +2010,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,594,26.00 +2009,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,563,24.90 +2008,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,662,29.70 +2007,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,534,24.20 +2006,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,496,22.80 +2005,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,504,23.60 +2004,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,493,23.30 +2003,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,470,22.20 +2002,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,405,19.20 +2001,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,398,19.00 +2000,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,361,17.20 +1999,Alzheimer's disease (G30),Alzheimer's disease,West Virginia,314,15.00 +2016,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,2256,29.60 +2015,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,2087,27.50 +2014,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1876,25.00 +2013,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1671,22.70 +2012,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1646,22.60 +2011,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1807,25.30 +2010,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1762,25.30 +2009,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1611,23.30 +2008,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1655,24.40 +2007,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1658,24.70 +2006,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1596,24.40 +2005,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1512,23.50 +2004,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1419,22.50 +2003,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1411,22.60 +2002,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1344,22.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1269,21.10 +2000,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1177,19.90 +1999,Alzheimer's disease (G30),Alzheimer's disease,Wisconsin,1170,19.90 +2016,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,138,21.20 +2015,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,151,24.20 +2014,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,162,26.60 +2013,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,126,21.00 +2012,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,123,21.50 +2011,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,153,27.70 +2010,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,146,27.20 +2009,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,136,25.80 +2008,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,125,24.30 +2007,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,110,22.00 +2006,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,112,22.70 +2005,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,110,22.80 +2004,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,105,22.40 +2003,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,142,30.90 +2002,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,122,27.00 +2001,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,114,25.40 +2000,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,102,23.10 +1999,Alzheimer's disease (G30),Alzheimer's disease,Wyoming,103,23.90 +2016,Malignant neoplasms (C00-C97),Cancer,Alabama,10419,174.00 +2015,Malignant neoplasms (C00-C97),Cancer,Alabama,10354,175.60 +2014,Malignant neoplasms (C00-C97),Cancer,Alabama,10286,177.60 +2013,Malignant neoplasms (C00-C97),Cancer,Alabama,10328,182.20 +2012,Malignant neoplasms (C00-C97),Cancer,Alabama,10276,185.20 +2011,Malignant neoplasms (C00-C97),Cancer,Alabama,10233,187.80 +2010,Malignant neoplasms (C00-C97),Cancer,Alabama,10196,191.70 +2009,Malignant neoplasms (C00-C97),Cancer,Alabama,10289,196.30 +2008,Malignant neoplasms (C00-C97),Cancer,Alabama,10182,197.50 +2007,Malignant neoplasms (C00-C97),Cancer,Alabama,10025,198.70 +2006,Malignant neoplasms (C00-C97),Cancer,Alabama,9899,199.40 +2005,Malignant neoplasms (C00-C97),Cancer,Alabama,9913,204.40 +2004,Malignant neoplasms (C00-C97),Cancer,Alabama,9756,204.50 +2003,Malignant neoplasms (C00-C97),Cancer,Alabama,9812,208.20 +2002,Malignant neoplasms (C00-C97),Cancer,Alabama,9698,208.30 +2001,Malignant neoplasms (C00-C97),Cancer,Alabama,9801,213.00 +2000,Malignant neoplasms (C00-C97),Cancer,Alabama,9807,215.40 +1999,Malignant neoplasms (C00-C97),Cancer,Alabama,9506,210.90 +2016,Malignant neoplasms (C00-C97),Cancer,Alaska,995,158.70 +2015,Malignant neoplasms (C00-C97),Cancer,Alaska,978,159.80 +2014,Malignant neoplasms (C00-C97),Cancer,Alaska,972,164.20 +2013,Malignant neoplasms (C00-C97),Cancer,Alaska,1016,172.00 +2012,Malignant neoplasms (C00-C97),Cancer,Alaska,925,166.50 +2011,Malignant neoplasms (C00-C97),Cancer,Alaska,935,175.50 +2010,Malignant neoplasms (C00-C97),Cancer,Alaska,884,176.90 +2009,Malignant neoplasms (C00-C97),Cancer,Alaska,895,183.00 +2008,Malignant neoplasms (C00-C97),Cancer,Alaska,867,181.10 +2007,Malignant neoplasms (C00-C97),Cancer,Alaska,839,182.60 +2006,Malignant neoplasms (C00-C97),Cancer,Alaska,790,177.80 +2005,Malignant neoplasms (C00-C97),Cancer,Alaska,732,171.00 +2004,Malignant neoplasms (C00-C97),Cancer,Alaska,728,184.20 +2003,Malignant neoplasms (C00-C97),Cancer,Alaska,735,188.40 +2002,Malignant neoplasms (C00-C97),Cancer,Alaska,715,189.80 +2001,Malignant neoplasms (C00-C97),Cancer,Alaska,689,193.70 +2000,Malignant neoplasms (C00-C97),Cancer,Alaska,704,208.20 +1999,Malignant neoplasms (C00-C97),Cancer,Alaska,633,190.50 +2016,Malignant neoplasms (C00-C97),Cancer,Arizona,11876,136.80 +2015,Malignant neoplasms (C00-C97),Cancer,Arizona,11776,141.30 +2014,Malignant neoplasms (C00-C97),Cancer,Arizona,11455,142.70 +2013,Malignant neoplasms (C00-C97),Cancer,Arizona,11347,147.00 +2012,Malignant neoplasms (C00-C97),Cancer,Arizona,11085,148.60 +2011,Malignant neoplasms (C00-C97),Cancer,Arizona,10690,148.50 +2010,Malignant neoplasms (C00-C97),Cancer,Arizona,10678,154.20 +2009,Malignant neoplasms (C00-C97),Cancer,Arizona,10271,152.00 +2008,Malignant neoplasms (C00-C97),Cancer,Arizona,10081,153.20 +2007,Malignant neoplasms (C00-C97),Cancer,Arizona,10134,158.90 +2006,Malignant neoplasms (C00-C97),Cancer,Arizona,9919,160.00 +2005,Malignant neoplasms (C00-C97),Cancer,Arizona,9820,164.20 +2004,Malignant neoplasms (C00-C97),Cancer,Arizona,9618,167.50 +2003,Malignant neoplasms (C00-C97),Cancer,Arizona,9627,173.00 +2002,Malignant neoplasms (C00-C97),Cancer,Arizona,9359,173.20 +2001,Malignant neoplasms (C00-C97),Cancer,Arizona,9143,173.80 +2000,Malignant neoplasms (C00-C97),Cancer,Arizona,9073,177.90 +1999,Malignant neoplasms (C00-C97),Cancer,Arizona,9006,179.90 +2016,Malignant neoplasms (C00-C97),Cancer,Arkansas,6612,178.80 +2015,Malignant neoplasms (C00-C97),Cancer,Arkansas,6727,185.40 +2014,Malignant neoplasms (C00-C97),Cancer,Arkansas,6546,183.10 +2013,Malignant neoplasms (C00-C97),Cancer,Arkansas,6688,190.00 +2012,Malignant neoplasms (C00-C97),Cancer,Arkansas,6540,189.10 +2011,Malignant neoplasms (C00-C97),Cancer,Arkansas,6497,191.10 +2010,Malignant neoplasms (C00-C97),Cancer,Arkansas,6475,194.70 +2009,Malignant neoplasms (C00-C97),Cancer,Arkansas,6513,198.30 +2008,Malignant neoplasms (C00-C97),Cancer,Arkansas,6526,201.40 +2007,Malignant neoplasms (C00-C97),Cancer,Arkansas,6388,201.30 +2006,Malignant neoplasms (C00-C97),Cancer,Arkansas,6177,196.70 +2005,Malignant neoplasms (C00-C97),Cancer,Arkansas,6361,207.70 +2004,Malignant neoplasms (C00-C97),Cancer,Arkansas,6304,208.60 +2003,Malignant neoplasms (C00-C97),Cancer,Arkansas,6119,205.10 +2002,Malignant neoplasms (C00-C97),Cancer,Arkansas,6282,212.80 +2001,Malignant neoplasms (C00-C97),Cancer,Arkansas,6092,208.80 +2000,Malignant neoplasms (C00-C97),Cancer,Arkansas,6090,210.50 +1999,Malignant neoplasms (C00-C97),Cancer,Arkansas,6137,214.10 +2016,Malignant neoplasms (C00-C97),Cancer,California,59515,139.70 +2015,Malignant neoplasms (C00-C97),Cancer,California,59629,142.80 +2014,Malignant neoplasms (C00-C97),Cancer,California,58412,144.10 +2013,Malignant neoplasms (C00-C97),Cancer,California,57714,147.00 +2012,Malignant neoplasms (C00-C97),Cancer,California,57676,150.90 +2011,Malignant neoplasms (C00-C97),Cancer,California,56449,152.00 +2010,Malignant neoplasms (C00-C97),Cancer,California,56453,156.90 +2009,Malignant neoplasms (C00-C97),Cancer,California,55991,158.80 +2008,Malignant neoplasms (C00-C97),Cancer,California,54686,158.80 +2007,Malignant neoplasms (C00-C97),Cancer,California,55011,163.80 +2006,Malignant neoplasms (C00-C97),Cancer,California,54140,164.50 +2005,Malignant neoplasms (C00-C97),Cancer,California,54732,168.90 +2004,Malignant neoplasms (C00-C97),Cancer,California,53700,169.20 +2003,Malignant neoplasms (C00-C97),Cancer,California,54319,174.00 +2002,Malignant neoplasms (C00-C97),Cancer,California,54143,177.30 +2001,Malignant neoplasms (C00-C97),Cancer,California,53924,180.20 +2000,Malignant neoplasms (C00-C97),Cancer,California,53158,182.10 +1999,Malignant neoplasms (C00-C97),Cancer,California,53067,184.90 +2016,Malignant neoplasms (C00-C97),Cancer,Colorado,7928,137.10 +2015,Malignant neoplasms (C00-C97),Cancer,Colorado,7604,134.40 +2014,Malignant neoplasms (C00-C97),Cancer,Colorado,7405,136.00 +2013,Malignant neoplasms (C00-C97),Cancer,Colorado,7357,139.20 +2012,Malignant neoplasms (C00-C97),Cancer,Colorado,7306,143.10 +2011,Malignant neoplasms (C00-C97),Cancer,Colorado,7051,143.90 +2010,Malignant neoplasms (C00-C97),Cancer,Colorado,7035,149.50 +2009,Malignant neoplasms (C00-C97),Cancer,Colorado,6950,151.40 +2008,Malignant neoplasms (C00-C97),Cancer,Colorado,6719,150.90 +2007,Malignant neoplasms (C00-C97),Cancer,Colorado,6617,154.10 +2006,Malignant neoplasms (C00-C97),Cancer,Colorado,6550,157.50 +2005,Malignant neoplasms (C00-C97),Cancer,Colorado,6395,159.70 +2004,Malignant neoplasms (C00-C97),Cancer,Colorado,6196,159.40 +2003,Malignant neoplasms (C00-C97),Cancer,Colorado,6417,169.40 +2002,Malignant neoplasms (C00-C97),Cancer,Colorado,6384,173.70 +2001,Malignant neoplasms (C00-C97),Cancer,Colorado,6145,170.90 +2000,Malignant neoplasms (C00-C97),Cancer,Colorado,5922,170.00 +1999,Malignant neoplasms (C00-C97),Cancer,Colorado,5863,171.10 +2016,Malignant neoplasms (C00-C97),Cancer,Connecticut,6696,144.90 +2015,Malignant neoplasms (C00-C97),Cancer,Connecticut,6666,146.20 +2014,Malignant neoplasms (C00-C97),Cancer,Connecticut,6621,146.70 +2013,Malignant neoplasms (C00-C97),Cancer,Connecticut,6619,148.40 +2012,Malignant neoplasms (C00-C97),Cancer,Connecticut,6681,152.00 +2011,Malignant neoplasms (C00-C97),Cancer,Connecticut,6837,158.40 +2010,Malignant neoplasms (C00-C97),Cancer,Connecticut,6954,163.40 +2009,Malignant neoplasms (C00-C97),Cancer,Connecticut,6819,162.80 +2008,Malignant neoplasms (C00-C97),Cancer,Connecticut,6830,164.80 +2007,Malignant neoplasms (C00-C97),Cancer,Connecticut,6827,167.20 +2006,Malignant neoplasms (C00-C97),Cancer,Connecticut,7044,175.50 +2005,Malignant neoplasms (C00-C97),Cancer,Connecticut,7052,176.80 +2004,Malignant neoplasms (C00-C97),Cancer,Connecticut,7175,182.40 +2003,Malignant neoplasms (C00-C97),Cancer,Connecticut,7131,183.00 +2002,Malignant neoplasms (C00-C97),Cancer,Connecticut,7163,186.30 +2001,Malignant neoplasms (C00-C97),Cancer,Connecticut,7093,187.60 +2000,Malignant neoplasms (C00-C97),Cancer,Connecticut,7065,189.50 +1999,Malignant neoplasms (C00-C97),Cancer,Connecticut,7054,191.00 +2016,Malignant neoplasms (C00-C97),Cancer,Delaware,2124,170.80 +2015,Malignant neoplasms (C00-C97),Cancer,Delaware,2010,165.60 +2014,Malignant neoplasms (C00-C97),Cancer,Delaware,1972,167.30 +2013,Malignant neoplasms (C00-C97),Cancer,Delaware,1905,167.60 +2012,Malignant neoplasms (C00-C97),Cancer,Delaware,1935,177.00 +2011,Malignant neoplasms (C00-C97),Cancer,Delaware,1906,179.80 +2010,Malignant neoplasms (C00-C97),Cancer,Delaware,1909,185.70 +2009,Malignant neoplasms (C00-C97),Cancer,Delaware,1813,178.70 +2008,Malignant neoplasms (C00-C97),Cancer,Delaware,1912,193.30 +2007,Malignant neoplasms (C00-C97),Cancer,Delaware,1853,193.50 +2006,Malignant neoplasms (C00-C97),Cancer,Delaware,1780,190.70 +2005,Malignant neoplasms (C00-C97),Cancer,Delaware,1799,199.10 +2004,Malignant neoplasms (C00-C97),Cancer,Delaware,1827,208.30 +2003,Malignant neoplasms (C00-C97),Cancer,Delaware,1719,201.50 +2002,Malignant neoplasms (C00-C97),Cancer,Delaware,1621,194.80 +2001,Malignant neoplasms (C00-C97),Cancer,Delaware,1786,221.00 +2000,Malignant neoplasms (C00-C97),Cancer,Delaware,1609,203.30 +1999,Malignant neoplasms (C00-C97),Cancer,Delaware,1737,223.30 +2016,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1044,160.10 +2015,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1072,167.50 +2014,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1116,178.60 +2013,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1095,178.20 +2012,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1081,178.80 +2011,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1070,180.70 +2010,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1041,178.30 +2009,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1131,197.40 +2008,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1143,201.60 +2007,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1169,208.20 +2006,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1178,210.50 +2005,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1151,205.30 +2004,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1152,206.40 +2003,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1122,200.90 +2002,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1298,231.80 +2001,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1327,237.50 +2000,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1335,238.30 +1999,Malignant neoplasms (C00-C97),Cancer,District of Columbia,1340,241.40 +2016,Malignant neoplasms (C00-C97),Cancer,Florida,44266,146.90 +2015,Malignant neoplasms (C00-C97),Cancer,Florida,44027,150.60 +2014,Malignant neoplasms (C00-C97),Cancer,Florida,43212,152.90 +2013,Malignant neoplasms (C00-C97),Cancer,Florida,42735,155.90 +2012,Malignant neoplasms (C00-C97),Cancer,Florida,42188,158.80 +2011,Malignant neoplasms (C00-C97),Cancer,Florida,41681,161.20 +2010,Malignant neoplasms (C00-C97),Cancer,Florida,41467,165.60 +2009,Malignant neoplasms (C00-C97),Cancer,Florida,40932,166.60 +2008,Malignant neoplasms (C00-C97),Cancer,Florida,40814,169.50 +2007,Malignant neoplasms (C00-C97),Cancer,Florida,40088,169.80 +2006,Malignant neoplasms (C00-C97),Cancer,Florida,40415,174.60 +2005,Malignant neoplasms (C00-C97),Cancer,Florida,40592,178.40 +2004,Malignant neoplasms (C00-C97),Cancer,Florida,39840,179.40 +2003,Malignant neoplasms (C00-C97),Cancer,Florida,39404,181.70 +2002,Malignant neoplasms (C00-C97),Cancer,Florida,39140,183.80 +2001,Malignant neoplasms (C00-C97),Cancer,Florida,39090,187.40 +2000,Malignant neoplasms (C00-C97),Cancer,Florida,39183,191.60 +1999,Malignant neoplasms (C00-C97),Cancer,Florida,38478,190.60 +2016,Malignant neoplasms (C00-C97),Cancer,Georgia,17184,160.20 +2015,Malignant neoplasms (C00-C97),Cancer,Georgia,16945,163.00 +2014,Malignant neoplasms (C00-C97),Cancer,Georgia,16684,165.50 +2013,Malignant neoplasms (C00-C97),Cancer,Georgia,16417,167.70 +2012,Malignant neoplasms (C00-C97),Cancer,Georgia,16021,169.10 +2011,Malignant neoplasms (C00-C97),Cancer,Georgia,15602,170.30 +2010,Malignant neoplasms (C00-C97),Cancer,Georgia,15435,174.80 +2009,Malignant neoplasms (C00-C97),Cancer,Georgia,15143,175.60 +2008,Malignant neoplasms (C00-C97),Cancer,Georgia,14621,175.10 +2007,Malignant neoplasms (C00-C97),Cancer,Georgia,14983,185.50 +2006,Malignant neoplasms (C00-C97),Cancer,Georgia,14474,184.00 +2005,Malignant neoplasms (C00-C97),Cancer,Georgia,14358,189.90 +2004,Malignant neoplasms (C00-C97),Cancer,Georgia,14313,195.50 +2003,Malignant neoplasms (C00-C97),Cancer,Georgia,14032,196.30 +2002,Malignant neoplasms (C00-C97),Cancer,Georgia,13975,200.00 +2001,Malignant neoplasms (C00-C97),Cancer,Georgia,13818,202.80 +2000,Malignant neoplasms (C00-C97),Cancer,Georgia,13690,206.70 +1999,Malignant neoplasms (C00-C97),Cancer,Georgia,13225,202.30 +2016,Malignant neoplasms (C00-C97),Cancer,Hawaii,2401,128.70 +2015,Malignant neoplasms (C00-C97),Cancer,Hawaii,2462,135.30 +2014,Malignant neoplasms (C00-C97),Cancer,Hawaii,2493,140.00 +2013,Malignant neoplasms (C00-C97),Cancer,Hawaii,2332,135.50 +2012,Malignant neoplasms (C00-C97),Cancer,Hawaii,2284,134.50 +2011,Malignant neoplasms (C00-C97),Cancer,Hawaii,2278,138.10 +2010,Malignant neoplasms (C00-C97),Cancer,Hawaii,2266,140.90 +2009,Malignant neoplasms (C00-C97),Cancer,Hawaii,2244,141.30 +2008,Malignant neoplasms (C00-C97),Cancer,Hawaii,2194,142.50 +2007,Malignant neoplasms (C00-C97),Cancer,Hawaii,2214,148.00 +2006,Malignant neoplasms (C00-C97),Cancer,Hawaii,2171,147.80 +2005,Malignant neoplasms (C00-C97),Cancer,Hawaii,2169,151.30 +2004,Malignant neoplasms (C00-C97),Cancer,Hawaii,2088,150.50 +2003,Malignant neoplasms (C00-C97),Cancer,Hawaii,2133,157.40 +2002,Malignant neoplasms (C00-C97),Cancer,Hawaii,1945,147.20 +2001,Malignant neoplasms (C00-C97),Cancer,Hawaii,2024,157.20 +2000,Malignant neoplasms (C00-C97),Cancer,Hawaii,1943,155.70 +1999,Malignant neoplasms (C00-C97),Cancer,Hawaii,1916,156.60 +2016,Malignant neoplasms (C00-C97),Cancer,Idaho,2884,150.90 +2015,Malignant neoplasms (C00-C97),Cancer,Idaho,2849,153.60 +2014,Malignant neoplasms (C00-C97),Cancer,Idaho,2795,155.40 +2013,Malignant neoplasms (C00-C97),Cancer,Idaho,2707,156.20 +2012,Malignant neoplasms (C00-C97),Cancer,Idaho,2572,152.00 +2011,Malignant neoplasms (C00-C97),Cancer,Idaho,2573,157.40 +2010,Malignant neoplasms (C00-C97),Cancer,Idaho,2530,159.90 +2009,Malignant neoplasms (C00-C97),Cancer,Idaho,2458,158.90 +2008,Malignant neoplasms (C00-C97),Cancer,Idaho,2511,166.70 +2007,Malignant neoplasms (C00-C97),Cancer,Idaho,2405,166.30 +2006,Malignant neoplasms (C00-C97),Cancer,Idaho,2306,163.20 +2005,Malignant neoplasms (C00-C97),Cancer,Idaho,2368,174.60 +2004,Malignant neoplasms (C00-C97),Cancer,Idaho,2227,169.60 +2003,Malignant neoplasms (C00-C97),Cancer,Idaho,2321,181.80 +2002,Malignant neoplasms (C00-C97),Cancer,Idaho,2138,172.30 +2001,Malignant neoplasms (C00-C97),Cancer,Idaho,2093,172.80 +2000,Malignant neoplasms (C00-C97),Cancer,Idaho,2128,180.60 +1999,Malignant neoplasms (C00-C97),Cancer,Idaho,2162,186.40 +2016,Malignant neoplasms (C00-C97),Cancer,Illinois,24389,163.50 +2015,Malignant neoplasms (C00-C97),Cancer,Illinois,24713,167.60 +2014,Malignant neoplasms (C00-C97),Cancer,Illinois,24501,168.90 +2013,Malignant neoplasms (C00-C97),Cancer,Illinois,24491,171.90 +2012,Malignant neoplasms (C00-C97),Cancer,Illinois,24562,175.60 +2011,Malignant neoplasms (C00-C97),Cancer,Illinois,24007,175.00 +2010,Malignant neoplasms (C00-C97),Cancer,Illinois,24070,178.60 +2009,Malignant neoplasms (C00-C97),Cancer,Illinois,24185,181.80 +2008,Malignant neoplasms (C00-C97),Cancer,Illinois,24300,185.00 +2007,Malignant neoplasms (C00-C97),Cancer,Illinois,24115,186.60 +2006,Malignant neoplasms (C00-C97),Cancer,Illinois,24084,188.70 +2005,Malignant neoplasms (C00-C97),Cancer,Illinois,24250,192.10 +2004,Malignant neoplasms (C00-C97),Cancer,Illinois,24289,194.80 +2003,Malignant neoplasms (C00-C97),Cancer,Illinois,24464,198.20 +2002,Malignant neoplasms (C00-C97),Cancer,Illinois,24737,202.80 +2001,Malignant neoplasms (C00-C97),Cancer,Illinois,24778,205.10 +2000,Malignant neoplasms (C00-C97),Cancer,Illinois,25365,212.00 +1999,Malignant neoplasms (C00-C97),Cancer,Illinois,25024,210.40 +2016,Malignant neoplasms (C00-C97),Cancer,Indiana,13424,172.50 +2015,Malignant neoplasms (C00-C97),Cancer,Indiana,13511,176.30 +2014,Malignant neoplasms (C00-C97),Cancer,Indiana,13519,179.70 +2013,Malignant neoplasms (C00-C97),Cancer,Indiana,13258,179.50 +2012,Malignant neoplasms (C00-C97),Cancer,Indiana,13368,184.40 +2011,Malignant neoplasms (C00-C97),Cancer,Indiana,13180,185.40 +2010,Malignant neoplasms (C00-C97),Cancer,Indiana,13164,188.60 +2009,Malignant neoplasms (C00-C97),Cancer,Indiana,13093,190.40 +2008,Malignant neoplasms (C00-C97),Cancer,Indiana,13137,194.30 +2007,Malignant neoplasms (C00-C97),Cancer,Indiana,12778,192.60 +2006,Malignant neoplasms (C00-C97),Cancer,Indiana,12903,197.50 +2005,Malignant neoplasms (C00-C97),Cancer,Indiana,12796,199.80 +2004,Malignant neoplasms (C00-C97),Cancer,Indiana,12552,199.10 +2003,Malignant neoplasms (C00-C97),Cancer,Indiana,12933,207.50 +2002,Malignant neoplasms (C00-C97),Cancer,Indiana,12865,209.70 +2001,Malignant neoplasms (C00-C97),Cancer,Indiana,12831,211.90 +2000,Malignant neoplasms (C00-C97),Cancer,Indiana,12842,214.70 +1999,Malignant neoplasms (C00-C97),Cancer,Indiana,12898,217.40 +2016,Malignant neoplasms (C00-C97),Cancer,Iowa,6432,159.80 +2015,Malignant neoplasms (C00-C97),Cancer,Iowa,6513,164.10 +2014,Malignant neoplasms (C00-C97),Cancer,Iowa,6504,166.00 +2013,Malignant neoplasms (C00-C97),Cancer,Iowa,6509,168.40 +2012,Malignant neoplasms (C00-C97),Cancer,Iowa,6438,168.60 +2011,Malignant neoplasms (C00-C97),Cancer,Iowa,6481,173.10 +2010,Malignant neoplasms (C00-C97),Cancer,Iowa,6358,171.90 +2009,Malignant neoplasms (C00-C97),Cancer,Iowa,6249,170.40 +2008,Malignant neoplasms (C00-C97),Cancer,Iowa,6424,177.10 +2007,Malignant neoplasms (C00-C97),Cancer,Iowa,6376,176.80 +2006,Malignant neoplasms (C00-C97),Cancer,Iowa,6359,179.00 +2005,Malignant neoplasms (C00-C97),Cancer,Iowa,6453,183.70 +2004,Malignant neoplasms (C00-C97),Cancer,Iowa,6340,182.30 +2003,Malignant neoplasms (C00-C97),Cancer,Iowa,6469,187.80 +2002,Malignant neoplasms (C00-C97),Cancer,Iowa,6473,189.30 +2001,Malignant neoplasms (C00-C97),Cancer,Iowa,6404,188.90 +2000,Malignant neoplasms (C00-C97),Cancer,Iowa,6448,191.70 +1999,Malignant neoplasms (C00-C97),Cancer,Iowa,6346,189.10 +2016,Malignant neoplasms (C00-C97),Cancer,Kansas,5484,158.60 +2015,Malignant neoplasms (C00-C97),Cancer,Kansas,5604,164.60 +2014,Malignant neoplasms (C00-C97),Cancer,Kansas,5587,166.80 +2013,Malignant neoplasms (C00-C97),Cancer,Kansas,5379,163.10 +2012,Malignant neoplasms (C00-C97),Cancer,Kansas,5429,168.20 +2011,Malignant neoplasms (C00-C97),Cancer,Kansas,5440,170.30 +2010,Malignant neoplasms (C00-C97),Cancer,Kansas,5377,171.30 +2009,Malignant neoplasms (C00-C97),Cancer,Kansas,5319,171.90 +2008,Malignant neoplasms (C00-C97),Cancer,Kansas,5294,173.10 +2007,Malignant neoplasms (C00-C97),Cancer,Kansas,5406,179.20 +2006,Malignant neoplasms (C00-C97),Cancer,Kansas,5343,180.10 +2005,Malignant neoplasms (C00-C97),Cancer,Kansas,5428,185.60 +2004,Malignant neoplasms (C00-C97),Cancer,Kansas,5312,183.10 +2003,Malignant neoplasms (C00-C97),Cancer,Kansas,5332,185.90 +2002,Malignant neoplasms (C00-C97),Cancer,Kansas,5362,188.70 +2001,Malignant neoplasms (C00-C97),Cancer,Kansas,5448,193.80 +2000,Malignant neoplasms (C00-C97),Cancer,Kansas,5223,187.00 +1999,Malignant neoplasms (C00-C97),Cancer,Kansas,5334,192.40 +2016,Malignant neoplasms (C00-C97),Cancer,Kentucky,10363,193.80 +2015,Malignant neoplasms (C00-C97),Cancer,Kentucky,10312,195.90 +2014,Malignant neoplasms (C00-C97),Cancer,Kentucky,10263,198.80 +2013,Malignant neoplasms (C00-C97),Cancer,Kentucky,10085,199.30 +2012,Malignant neoplasms (C00-C97),Cancer,Kentucky,10012,201.50 +2011,Malignant neoplasms (C00-C97),Cancer,Kentucky,9733,200.80 +2010,Malignant neoplasms (C00-C97),Cancer,Kentucky,9930,208.30 +2009,Malignant neoplasms (C00-C97),Cancer,Kentucky,9634,204.90 +2008,Malignant neoplasms (C00-C97),Cancer,Kentucky,9589,207.80 +2007,Malignant neoplasms (C00-C97),Cancer,Kentucky,9692,214.70 +2006,Malignant neoplasms (C00-C97),Cancer,Kentucky,9394,212.00 +2005,Malignant neoplasms (C00-C97),Cancer,Kentucky,9505,219.10 +2004,Malignant neoplasms (C00-C97),Cancer,Kentucky,9159,215.50 +2003,Malignant neoplasms (C00-C97),Cancer,Kentucky,9378,223.70 +2002,Malignant neoplasms (C00-C97),Cancer,Kentucky,9438,228.60 +2001,Malignant neoplasms (C00-C97),Cancer,Kentucky,9358,229.70 +2000,Malignant neoplasms (C00-C97),Cancer,Kentucky,9207,228.70 +1999,Malignant neoplasms (C00-C97),Cancer,Kentucky,8925,224.20 +2016,Malignant neoplasms (C00-C97),Cancer,Louisiana,9149,171.90 +2015,Malignant neoplasms (C00-C97),Cancer,Louisiana,9397,180.20 +2014,Malignant neoplasms (C00-C97),Cancer,Louisiana,9455,186.10 +2013,Malignant neoplasms (C00-C97),Cancer,Louisiana,9419,188.50 +2012,Malignant neoplasms (C00-C97),Cancer,Louisiana,9308,190.70 +2011,Malignant neoplasms (C00-C97),Cancer,Louisiana,9233,193.70 +2010,Malignant neoplasms (C00-C97),Cancer,Louisiana,9203,197.60 +2009,Malignant neoplasms (C00-C97),Cancer,Louisiana,9098,198.60 +2008,Malignant neoplasms (C00-C97),Cancer,Louisiana,9197,205.70 +2007,Malignant neoplasms (C00-C97),Cancer,Louisiana,8736,199.30 +2006,Malignant neoplasms (C00-C97),Cancer,Louisiana,8853,206.10 +2005,Malignant neoplasms (C00-C97),Cancer,Louisiana,9249,208.60 +2004,Malignant neoplasms (C00-C97),Cancer,Louisiana,9434,216.30 +2003,Malignant neoplasms (C00-C97),Cancer,Louisiana,9533,221.80 +2002,Malignant neoplasms (C00-C97),Cancer,Louisiana,9441,222.80 +2001,Malignant neoplasms (C00-C97),Cancer,Louisiana,9517,228.00 +2000,Malignant neoplasms (C00-C97),Cancer,Louisiana,9400,227.90 +1999,Malignant neoplasms (C00-C97),Cancer,Louisiana,9412,230.50 +2016,Malignant neoplasms (C00-C97),Cancer,Maine,3275,168.90 +2015,Malignant neoplasms (C00-C97),Cancer,Maine,3398,178.00 +2014,Malignant neoplasms (C00-C97),Cancer,Maine,3209,170.30 +2013,Malignant neoplasms (C00-C97),Cancer,Maine,3227,175.20 +2012,Malignant neoplasms (C00-C97),Cancer,Maine,3226,179.30 +2011,Malignant neoplasms (C00-C97),Cancer,Maine,3201,182.20 +2010,Malignant neoplasms (C00-C97),Cancer,Maine,3248,187.90 +2009,Malignant neoplasms (C00-C97),Cancer,Maine,3133,185.20 +2008,Malignant neoplasms (C00-C97),Cancer,Maine,3093,185.00 +2007,Malignant neoplasms (C00-C97),Cancer,Maine,3112,189.90 +2006,Malignant neoplasms (C00-C97),Cancer,Maine,3089,192.70 +2005,Malignant neoplasms (C00-C97),Cancer,Maine,3218,204.80 +2004,Malignant neoplasms (C00-C97),Cancer,Maine,3124,202.20 +2003,Malignant neoplasms (C00-C97),Cancer,Maine,3120,205.40 +2002,Malignant neoplasms (C00-C97),Cancer,Maine,3206,214.90 +2001,Malignant neoplasms (C00-C97),Cancer,Maine,3045,208.30 +2000,Malignant neoplasms (C00-C97),Cancer,Maine,3070,213.60 +1999,Malignant neoplasms (C00-C97),Cancer,Maine,3035,214.60 +2016,Malignant neoplasms (C00-C97),Cancer,Maryland,10911,156.50 +2015,Malignant neoplasms (C00-C97),Cancer,Maryland,10568,155.00 +2014,Malignant neoplasms (C00-C97),Cancer,Maryland,10759,161.70 +2013,Malignant neoplasms (C00-C97),Cancer,Maryland,10609,162.90 +2012,Malignant neoplasms (C00-C97),Cancer,Maryland,10525,165.60 +2011,Malignant neoplasms (C00-C97),Cancer,Maryland,10249,166.10 +2010,Malignant neoplasms (C00-C97),Cancer,Maryland,10269,171.20 +2009,Malignant neoplasms (C00-C97),Cancer,Maryland,10412,176.70 +2008,Malignant neoplasms (C00-C97),Cancer,Maryland,10360,180.10 +2007,Malignant neoplasms (C00-C97),Cancer,Maryland,10179,180.50 +2006,Malignant neoplasms (C00-C97),Cancer,Maryland,10350,187.00 +2005,Malignant neoplasms (C00-C97),Cancer,Maryland,10371,190.40 +2004,Malignant neoplasms (C00-C97),Cancer,Maryland,10168,190.50 +2003,Malignant neoplasms (C00-C97),Cancer,Maryland,10292,196.40 +2002,Malignant neoplasms (C00-C97),Cancer,Maryland,10395,202.20 +2001,Malignant neoplasms (C00-C97),Cancer,Maryland,10322,205.40 +2000,Malignant neoplasms (C00-C97),Cancer,Maryland,10290,209.70 +1999,Malignant neoplasms (C00-C97),Cancer,Maryland,10143,209.50 +2016,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12717,150.20 +2015,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12750,152.90 +2014,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12787,155.50 +2013,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12858,159.60 +2012,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12864,163.30 +2011,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12895,166.90 +2010,Malignant neoplasms (C00-C97),Cancer,Massachusetts,12993,171.30 +2009,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13112,175.50 +2008,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13031,177.20 +2007,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13003,179.50 +2006,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13407,187.50 +2005,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13182,186.30 +2004,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13337,190.10 +2003,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13551,194.60 +2002,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13914,201.40 +2001,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13750,201.20 +2000,Malignant neoplasms (C00-C97),Cancer,Massachusetts,14027,207.20 +1999,Malignant neoplasms (C00-C97),Cancer,Massachusetts,13853,206.40 +2016,Malignant neoplasms (C00-C97),Cancer,Michigan,20870,166.40 +2015,Malignant neoplasms (C00-C97),Cancer,Michigan,20732,168.00 +2014,Malignant neoplasms (C00-C97),Cancer,Michigan,21169,174.10 +2013,Malignant neoplasms (C00-C97),Cancer,Michigan,20367,170.50 +2012,Malignant neoplasms (C00-C97),Cancer,Michigan,20496,174.80 +2011,Malignant neoplasms (C00-C97),Cancer,Michigan,20420,177.80 +2010,Malignant neoplasms (C00-C97),Cancer,Michigan,20620,182.90 +2009,Malignant neoplasms (C00-C97),Cancer,Michigan,20257,182.20 +2008,Malignant neoplasms (C00-C97),Cancer,Michigan,20211,184.40 +2007,Malignant neoplasms (C00-C97),Cancer,Michigan,20087,186.40 +2006,Malignant neoplasms (C00-C97),Cancer,Michigan,20192,190.50 +2005,Malignant neoplasms (C00-C97),Cancer,Michigan,20094,192.60 +2004,Malignant neoplasms (C00-C97),Cancer,Michigan,19653,190.90 +2003,Malignant neoplasms (C00-C97),Cancer,Michigan,19713,194.10 +2002,Malignant neoplasms (C00-C97),Cancer,Michigan,19985,199.90 +2001,Malignant neoplasms (C00-C97),Cancer,Michigan,19689,199.90 +2000,Malignant neoplasms (C00-C97),Cancer,Michigan,19798,204.00 +1999,Malignant neoplasms (C00-C97),Cancer,Michigan,19744,205.20 +2016,Malignant neoplasms (C00-C97),Cancer,Minnesota,9857,148.60 +2015,Malignant neoplasms (C00-C97),Cancer,Minnesota,9925,153.00 +2014,Malignant neoplasms (C00-C97),Cancer,Minnesota,9649,152.60 +2013,Malignant neoplasms (C00-C97),Cancer,Minnesota,9601,155.40 +2012,Malignant neoplasms (C00-C97),Cancer,Minnesota,9424,155.90 +2011,Malignant neoplasms (C00-C97),Cancer,Minnesota,9489,160.80 +2010,Malignant neoplasms (C00-C97),Cancer,Minnesota,9612,167.20 +2009,Malignant neoplasms (C00-C97),Cancer,Minnesota,9580,168.70 +2008,Malignant neoplasms (C00-C97),Cancer,Minnesota,9446,170.20 +2007,Malignant neoplasms (C00-C97),Cancer,Minnesota,9176,168.70 +2006,Malignant neoplasms (C00-C97),Cancer,Minnesota,9079,170.70 +2005,Malignant neoplasms (C00-C97),Cancer,Minnesota,8823,169.00 +2004,Malignant neoplasms (C00-C97),Cancer,Minnesota,9093,177.00 +2003,Malignant neoplasms (C00-C97),Cancer,Minnesota,9183,181.70 +2002,Malignant neoplasms (C00-C97),Cancer,Minnesota,9210,185.30 +2001,Malignant neoplasms (C00-C97),Cancer,Minnesota,8967,183.80 +2000,Malignant neoplasms (C00-C97),Cancer,Minnesota,9215,191.80 +1999,Malignant neoplasms (C00-C97),Cancer,Minnesota,8892,187.00 +2016,Malignant neoplasms (C00-C97),Cancer,Mississippi,6568,187.70 +2015,Malignant neoplasms (C00-C97),Cancer,Mississippi,6485,188.40 +2014,Malignant neoplasms (C00-C97),Cancer,Mississippi,6534,193.10 +2013,Malignant neoplasms (C00-C97),Cancer,Mississippi,6527,197.00 +2012,Malignant neoplasms (C00-C97),Cancer,Mississippi,6499,200.40 +2011,Malignant neoplasms (C00-C97),Cancer,Mississippi,6278,197.40 +2010,Malignant neoplasms (C00-C97),Cancer,Mississippi,6271,201.40 +2009,Malignant neoplasms (C00-C97),Cancer,Mississippi,6131,199.80 +2008,Malignant neoplasms (C00-C97),Cancer,Mississippi,6166,202.90 +2007,Malignant neoplasms (C00-C97),Cancer,Mississippi,6002,201.80 +2006,Malignant neoplasms (C00-C97),Cancer,Mississippi,6236,212.70 +2005,Malignant neoplasms (C00-C97),Cancer,Mississippi,6065,208.90 +2004,Malignant neoplasms (C00-C97),Cancer,Mississippi,5983,209.80 +2003,Malignant neoplasms (C00-C97),Cancer,Mississippi,5960,212.20 +2002,Malignant neoplasms (C00-C97),Cancer,Mississippi,6069,218.80 +2001,Malignant neoplasms (C00-C97),Cancer,Mississippi,5958,217.10 +2000,Malignant neoplasms (C00-C97),Cancer,Mississippi,6075,223.60 +1999,Malignant neoplasms (C00-C97),Cancer,Mississippi,6143,227.90 +2016,Malignant neoplasms (C00-C97),Cancer,Missouri,12696,167.00 +2015,Malignant neoplasms (C00-C97),Cancer,Missouri,12965,173.40 +2014,Malignant neoplasms (C00-C97),Cancer,Missouri,13067,177.70 +2013,Malignant neoplasms (C00-C97),Cancer,Missouri,12955,179.40 +2012,Malignant neoplasms (C00-C97),Cancer,Missouri,12919,182.30 +2011,Malignant neoplasms (C00-C97),Cancer,Missouri,12473,179.40 +2010,Malignant neoplasms (C00-C97),Cancer,Missouri,12626,185.60 +2009,Malignant neoplasms (C00-C97),Cancer,Missouri,12472,185.70 +2008,Malignant neoplasms (C00-C97),Cancer,Missouri,12523,189.50 +2007,Malignant neoplasms (C00-C97),Cancer,Missouri,12380,190.90 +2006,Malignant neoplasms (C00-C97),Cancer,Missouri,12519,195.80 +2005,Malignant neoplasms (C00-C97),Cancer,Missouri,12419,198.00 +2004,Malignant neoplasms (C00-C97),Cancer,Missouri,12450,201.10 +2003,Malignant neoplasms (C00-C97),Cancer,Missouri,12354,202.10 +2002,Malignant neoplasms (C00-C97),Cancer,Missouri,12322,204.10 +2001,Malignant neoplasms (C00-C97),Cancer,Missouri,12365,207.10 +2000,Malignant neoplasms (C00-C97),Cancer,Missouri,12144,205.90 +1999,Malignant neoplasms (C00-C97),Cancer,Missouri,12186,207.90 +2016,Malignant neoplasms (C00-C97),Cancer,Montana,2031,145.90 +2015,Malignant neoplasms (C00-C97),Cancer,Montana,2130,156.90 +2014,Malignant neoplasms (C00-C97),Cancer,Montana,2066,156.30 +2013,Malignant neoplasms (C00-C97),Cancer,Montana,1997,154.10 +2012,Malignant neoplasms (C00-C97),Cancer,Montana,1954,155.10 +2011,Malignant neoplasms (C00-C97),Cancer,Montana,2022,164.80 +2010,Malignant neoplasms (C00-C97),Cancer,Montana,1923,161.00 +2009,Malignant neoplasms (C00-C97),Cancer,Montana,1914,163.00 +2008,Malignant neoplasms (C00-C97),Cancer,Montana,1862,161.30 +2007,Malignant neoplasms (C00-C97),Cancer,Montana,1921,170.70 +2006,Malignant neoplasms (C00-C97),Cancer,Montana,1943,177.90 +2005,Malignant neoplasms (C00-C97),Cancer,Montana,1956,184.20 +2004,Malignant neoplasms (C00-C97),Cancer,Montana,1867,179.60 +2003,Malignant neoplasms (C00-C97),Cancer,Montana,1847,181.70 +2002,Malignant neoplasms (C00-C97),Cancer,Montana,1911,191.10 +2001,Malignant neoplasms (C00-C97),Cancer,Montana,1956,199.40 +2000,Malignant neoplasms (C00-C97),Cancer,Montana,1867,194.00 +1999,Malignant neoplasms (C00-C97),Cancer,Montana,1854,195.10 +2016,Malignant neoplasms (C00-C97),Cancer,Nebraska,3477,153.60 +2015,Malignant neoplasms (C00-C97),Cancer,Nebraska,3514,157.80 +2014,Malignant neoplasms (C00-C97),Cancer,Nebraska,3459,159.60 +2013,Malignant neoplasms (C00-C97),Cancer,Nebraska,3459,161.30 +2012,Malignant neoplasms (C00-C97),Cancer,Nebraska,3479,164.70 +2011,Malignant neoplasms (C00-C97),Cancer,Nebraska,3410,164.10 +2010,Malignant neoplasms (C00-C97),Cancer,Nebraska,3438,167.40 +2009,Malignant neoplasms (C00-C97),Cancer,Nebraska,3336,165.10 +2008,Malignant neoplasms (C00-C97),Cancer,Nebraska,3376,169.70 +2007,Malignant neoplasms (C00-C97),Cancer,Nebraska,3479,176.60 +2006,Malignant neoplasms (C00-C97),Cancer,Nebraska,3430,176.60 +2005,Malignant neoplasms (C00-C97),Cancer,Nebraska,3355,175.50 +2004,Malignant neoplasms (C00-C97),Cancer,Nebraska,3270,173.70 +2003,Malignant neoplasms (C00-C97),Cancer,Nebraska,3339,179.00 +2002,Malignant neoplasms (C00-C97),Cancer,Nebraska,3433,186.10 +2001,Malignant neoplasms (C00-C97),Cancer,Nebraska,3389,185.50 +2000,Malignant neoplasms (C00-C97),Cancer,Nebraska,3382,186.20 +1999,Malignant neoplasms (C00-C97),Cancer,Nebraska,3410,189.00 +2016,Malignant neoplasms (C00-C97),Cancer,Nevada,5214,157.30 +2015,Malignant neoplasms (C00-C97),Cancer,Nevada,5015,157.20 +2014,Malignant neoplasms (C00-C97),Cancer,Nevada,5015,164.50 +2013,Malignant neoplasms (C00-C97),Cancer,Nevada,4817,164.80 +2012,Malignant neoplasms (C00-C97),Cancer,Nevada,4610,163.60 +2011,Malignant neoplasms (C00-C97),Cancer,Nevada,4606,170.20 +2010,Malignant neoplasms (C00-C97),Cancer,Nevada,4529,174.20 +2009,Malignant neoplasms (C00-C97),Cancer,Nevada,4461,175.30 +2008,Malignant neoplasms (C00-C97),Cancer,Nevada,4404,179.40 +2007,Malignant neoplasms (C00-C97),Cancer,Nevada,4331,180.80 +2006,Malignant neoplasms (C00-C97),Cancer,Nevada,4226,184.50 +2005,Malignant neoplasms (C00-C97),Cancer,Nevada,4238,191.00 +2004,Malignant neoplasms (C00-C97),Cancer,Nevada,4119,191.90 +2003,Malignant neoplasms (C00-C97),Cancer,Nevada,4138,200.20 +2002,Malignant neoplasms (C00-C97),Cancer,Nevada,3937,201.30 +2001,Malignant neoplasms (C00-C97),Cancer,Nevada,3815,205.10 +2000,Malignant neoplasms (C00-C97),Cancer,Nevada,3763,213.60 +1999,Malignant neoplasms (C00-C97),Cancer,Nevada,3556,206.70 +2016,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2875,164.10 +2015,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2773,161.30 +2014,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2698,160.40 +2013,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2584,158.00 +2012,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2660,167.80 +2011,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2740,177.50 +2010,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2525,167.90 +2009,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2562,173.30 +2008,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2576,177.30 +2007,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2609,184.70 +2006,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2534,183.90 +2005,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2549,189.80 +2004,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2554,194.80 +2003,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2485,193.70 +2002,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2529,201.80 +2001,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2398,195.90 +2000,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2484,206.90 +1999,Malignant neoplasms (C00-C97),Cancer,New Hampshire,2408,203.60 +2016,Malignant neoplasms (C00-C97),Cancer,New Jersey,16377,149.70 +2015,Malignant neoplasms (C00-C97),Cancer,New Jersey,16270,150.80 +2014,Malignant neoplasms (C00-C97),Cancer,New Jersey,16591,156.10 +2013,Malignant neoplasms (C00-C97),Cancer,New Jersey,16315,156.20 +2012,Malignant neoplasms (C00-C97),Cancer,New Jersey,16485,160.30 +2011,Malignant neoplasms (C00-C97),Cancer,New Jersey,16708,166.00 +2010,Malignant neoplasms (C00-C97),Cancer,New Jersey,16815,169.50 +2009,Malignant neoplasms (C00-C97),Cancer,New Jersey,16541,169.00 +2008,Malignant neoplasms (C00-C97),Cancer,New Jersey,16876,175.00 +2007,Malignant neoplasms (C00-C97),Cancer,New Jersey,17096,180.10 +2006,Malignant neoplasms (C00-C97),Cancer,New Jersey,17180,183.00 +2005,Malignant neoplasms (C00-C97),Cancer,New Jersey,17171,185.00 +2004,Malignant neoplasms (C00-C97),Cancer,New Jersey,17208,187.10 +2003,Malignant neoplasms (C00-C97),Cancer,New Jersey,17957,197.30 +2002,Malignant neoplasms (C00-C97),Cancer,New Jersey,17827,198.10 +2001,Malignant neoplasms (C00-C97),Cancer,New Jersey,18165,204.40 +2000,Malignant neoplasms (C00-C97),Cancer,New Jersey,18073,205.90 +1999,Malignant neoplasms (C00-C97),Cancer,New Jersey,18178,209.30 +2016,Malignant neoplasms (C00-C97),Cancer,New Mexico,3560,138.80 +2015,Malignant neoplasms (C00-C97),Cancer,New Mexico,3591,143.30 +2014,Malignant neoplasms (C00-C97),Cancer,New Mexico,3478,142.40 +2013,Malignant neoplasms (C00-C97),Cancer,New Mexico,3482,145.40 +2012,Malignant neoplasms (C00-C97),Cancer,New Mexico,3462,148.10 +2011,Malignant neoplasms (C00-C97),Cancer,New Mexico,3328,146.90 +2010,Malignant neoplasms (C00-C97),Cancer,New Mexico,3358,152.40 +2009,Malignant neoplasms (C00-C97),Cancer,New Mexico,3202,149.00 +2008,Malignant neoplasms (C00-C97),Cancer,New Mexico,3355,160.30 +2007,Malignant neoplasms (C00-C97),Cancer,New Mexico,3238,158.10 +2006,Malignant neoplasms (C00-C97),Cancer,New Mexico,3147,158.50 +2005,Malignant neoplasms (C00-C97),Cancer,New Mexico,3141,162.90 +2004,Malignant neoplasms (C00-C97),Cancer,New Mexico,3036,162.50 +2003,Malignant neoplasms (C00-C97),Cancer,New Mexico,3103,170.10 +2002,Malignant neoplasms (C00-C97),Cancer,New Mexico,3067,173.20 +2001,Malignant neoplasms (C00-C97),Cancer,New Mexico,2900,167.30 +2000,Malignant neoplasms (C00-C97),Cancer,New Mexico,2906,172.30 +1999,Malignant neoplasms (C00-C97),Cancer,New Mexico,2857,173.30 +2016,Malignant neoplasms (C00-C97),Cancer,New York,35368,147.50 +2015,Malignant neoplasms (C00-C97),Cancer,New York,35089,148.40 +2014,Malignant neoplasms (C00-C97),Cancer,New York,35392,151.80 +2013,Malignant neoplasms (C00-C97),Cancer,New York,35738,155.90 +2012,Malignant neoplasms (C00-C97),Cancer,New York,35882,159.40 +2011,Malignant neoplasms (C00-C97),Cancer,New York,35469,160.60 +2010,Malignant neoplasms (C00-C97),Cancer,New York,35431,163.10 +2009,Malignant neoplasms (C00-C97),Cancer,New York,35216,164.20 +2008,Malignant neoplasms (C00-C97),Cancer,New York,35351,166.90 +2007,Malignant neoplasms (C00-C97),Cancer,New York,35485,169.80 +2006,Malignant neoplasms (C00-C97),Cancer,New York,35284,170.90 +2005,Malignant neoplasms (C00-C97),Cancer,New York,35556,173.40 +2004,Malignant neoplasms (C00-C97),Cancer,New York,36100,177.80 +2003,Malignant neoplasms (C00-C97),Cancer,New York,36238,180.20 +2002,Malignant neoplasms (C00-C97),Cancer,New York,36661,184.40 +2001,Malignant neoplasms (C00-C97),Cancer,New York,36975,188.40 +2000,Malignant neoplasms (C00-C97),Cancer,New York,37198,192.20 +1999,Malignant neoplasms (C00-C97),Cancer,New York,37609,196.60 +2016,Malignant neoplasms (C00-C97),Cancer,North Carolina,19523,161.60 +2015,Malignant neoplasms (C00-C97),Cancer,North Carolina,19322,164.70 +2014,Malignant neoplasms (C00-C97),Cancer,North Carolina,19342,169.30 +2013,Malignant neoplasms (C00-C97),Cancer,North Carolina,18589,167.60 +2012,Malignant neoplasms (C00-C97),Cancer,North Carolina,18405,170.20 +2011,Malignant neoplasms (C00-C97),Cancer,North Carolina,18284,175.00 +2010,Malignant neoplasms (C00-C97),Cancer,North Carolina,18061,179.00 +2009,Malignant neoplasms (C00-C97),Cancer,North Carolina,17513,176.90 +2008,Malignant neoplasms (C00-C97),Cancer,North Carolina,17453,181.30 +2007,Malignant neoplasms (C00-C97),Cancer,North Carolina,17478,187.10 +2006,Malignant neoplasms (C00-C97),Cancer,North Carolina,17318,191.40 +2005,Malignant neoplasms (C00-C97),Cancer,North Carolina,16724,191.20 +2004,Malignant neoplasms (C00-C97),Cancer,North Carolina,16477,193.70 +2003,Malignant neoplasms (C00-C97),Cancer,North Carolina,16145,193.90 +2002,Malignant neoplasms (C00-C97),Cancer,North Carolina,16210,199.90 +2001,Malignant neoplasms (C00-C97),Cancer,North Carolina,16065,202.70 +2000,Malignant neoplasms (C00-C97),Cancer,North Carolina,15786,204.20 +1999,Malignant neoplasms (C00-C97),Cancer,North Carolina,15815,207.10 +2016,Malignant neoplasms (C00-C97),Cancer,North Dakota,1253,142.70 +2015,Malignant neoplasms (C00-C97),Cancer,North Dakota,1320,152.90 +2014,Malignant neoplasms (C00-C97),Cancer,North Dakota,1304,152.30 +2013,Malignant neoplasms (C00-C97),Cancer,North Dakota,1286,151.80 +2012,Malignant neoplasms (C00-C97),Cancer,North Dakota,1253,150.70 +2011,Malignant neoplasms (C00-C97),Cancer,North Dakota,1321,161.00 +2010,Malignant neoplasms (C00-C97),Cancer,North Dakota,1269,157.10 +2009,Malignant neoplasms (C00-C97),Cancer,North Dakota,1243,156.30 +2008,Malignant neoplasms (C00-C97),Cancer,North Dakota,1354,171.40 +2007,Malignant neoplasms (C00-C97),Cancer,North Dakota,1264,162.60 +2006,Malignant neoplasms (C00-C97),Cancer,North Dakota,1387,180.20 +2005,Malignant neoplasms (C00-C97),Cancer,North Dakota,1302,170.50 +2004,Malignant neoplasms (C00-C97),Cancer,North Dakota,1265,166.80 +2003,Malignant neoplasms (C00-C97),Cancer,North Dakota,1334,179.20 +2002,Malignant neoplasms (C00-C97),Cancer,North Dakota,1293,175.50 +2001,Malignant neoplasms (C00-C97),Cancer,North Dakota,1391,189.20 +2000,Malignant neoplasms (C00-C97),Cancer,North Dakota,1346,183.70 +1999,Malignant neoplasms (C00-C97),Cancer,North Dakota,1366,186.70 +2016,Malignant neoplasms (C00-C97),Cancer,Ohio,25509,173.40 +2015,Malignant neoplasms (C00-C97),Cancer,Ohio,25396,175.10 +2014,Malignant neoplasms (C00-C97),Cancer,Ohio,25433,177.80 +2013,Malignant neoplasms (C00-C97),Cancer,Ohio,24986,177.60 +2012,Malignant neoplasms (C00-C97),Cancer,Ohio,25261,182.70 +2011,Malignant neoplasms (C00-C97),Cancer,Ohio,25140,184.90 +2010,Malignant neoplasms (C00-C97),Cancer,Ohio,25083,187.70 +2009,Malignant neoplasms (C00-C97),Cancer,Ohio,25149,190.50 +2008,Malignant neoplasms (C00-C97),Cancer,Ohio,24998,191.90 +2007,Malignant neoplasms (C00-C97),Cancer,Ohio,25230,196.30 +2006,Malignant neoplasms (C00-C97),Cancer,Ohio,24975,197.30 +2005,Malignant neoplasms (C00-C97),Cancer,Ohio,24702,198.10 +2004,Malignant neoplasms (C00-C97),Cancer,Ohio,24940,202.10 +2003,Malignant neoplasms (C00-C97),Cancer,Ohio,25076,205.30 +2002,Malignant neoplasms (C00-C97),Cancer,Ohio,25173,209.00 +2001,Malignant neoplasms (C00-C97),Cancer,Ohio,24804,208.40 +2000,Malignant neoplasms (C00-C97),Cancer,Ohio,24988,212.00 +1999,Malignant neoplasms (C00-C97),Cancer,Ohio,25233,215.40 +2016,Malignant neoplasms (C00-C97),Cancer,Oklahoma,8115,177.80 +2015,Malignant neoplasms (C00-C97),Cancer,Oklahoma,8280,184.30 +2014,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7934,179.90 +2013,Malignant neoplasms (C00-C97),Cancer,Oklahoma,8039,185.70 +2012,Malignant neoplasms (C00-C97),Cancer,Oklahoma,8040,189.90 +2011,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7997,191.10 +2010,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7831,191.30 +2009,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7639,189.20 +2008,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7657,193.10 +2007,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7727,198.50 +2006,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7491,195.20 +2005,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7446,198.00 +2004,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7269,196.00 +2003,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7338,199.80 +2002,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7474,205.70 +2001,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7411,206.30 +2000,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7402,207.40 +1999,Malignant neoplasms (C00-C97),Cancer,Oklahoma,7312,206.70 +2016,Malignant neoplasms (C00-C97),Cancer,Oregon,8078,155.90 +2015,Malignant neoplasms (C00-C97),Cancer,Oregon,8093,160.20 +2014,Malignant neoplasms (C00-C97),Cancer,Oregon,7863,160.20 +2013,Malignant neoplasms (C00-C97),Cancer,Oregon,7799,163.40 +2012,Malignant neoplasms (C00-C97),Cancer,Oregon,7832,168.00 +2011,Malignant neoplasms (C00-C97),Cancer,Oregon,7802,172.60 +2010,Malignant neoplasms (C00-C97),Cancer,Oregon,7638,173.90 +2009,Malignant neoplasms (C00-C97),Cancer,Oregon,7487,172.70 +2008,Malignant neoplasms (C00-C97),Cancer,Oregon,7479,177.00 +2007,Malignant neoplasms (C00-C97),Cancer,Oregon,7393,178.80 +2006,Malignant neoplasms (C00-C97),Cancer,Oregon,7309,180.90 +2005,Malignant neoplasms (C00-C97),Cancer,Oregon,7326,186.50 +2004,Malignant neoplasms (C00-C97),Cancer,Oregon,7236,188.80 +2003,Malignant neoplasms (C00-C97),Cancer,Oregon,7232,192.50 +2002,Malignant neoplasms (C00-C97),Cancer,Oregon,7249,197.30 +2001,Malignant neoplasms (C00-C97),Cancer,Oregon,7057,196.50 +2000,Malignant neoplasms (C00-C97),Cancer,Oregon,6951,197.30 +1999,Malignant neoplasms (C00-C97),Cancer,Oregon,6905,198.20 +2016,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28492,164.70 +2015,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28697,167.20 +2014,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28692,169.60 +2013,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28512,170.60 +2012,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28909,175.50 +2011,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28895,178.00 +2010,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29055,181.60 +2009,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28881,182.50 +2008,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,28964,184.70 +2007,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29014,186.90 +2006,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29172,190.50 +2005,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29616,195.60 +2004,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29424,196.10 +2003,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29841,200.00 +2002,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29849,201.90 +2001,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,29914,204.40 +2000,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,30161,207.00 +1999,Malignant neoplasms (C00-C97),Cancer,Pennsylvania,30312,209.50 +2016,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2171,158.00 +2015,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2226,163.10 +2014,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2242,167.00 +2013,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2326,174.60 +2012,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2148,163.50 +2011,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2170,167.60 +2010,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2266,178.30 +2009,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2220,174.90 +2008,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2227,178.10 +2007,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2213,178.40 +2006,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2250,181.40 +2005,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2292,186.80 +2004,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2418,196.80 +2003,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2328,192.10 +2002,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2404,199.30 +2001,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2398,202.50 +2000,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2433,206.40 +1999,Malignant neoplasms (C00-C97),Cancer,Rhode Island,2463,210.30 +2016,Malignant neoplasms (C00-C97),Cancer,South Carolina,10356,167.70 +2015,Malignant neoplasms (C00-C97),Cancer,South Carolina,9950,166.60 +2014,Malignant neoplasms (C00-C97),Cancer,South Carolina,9930,171.40 +2013,Malignant neoplasms (C00-C97),Cancer,South Carolina,9745,174.00 +2012,Malignant neoplasms (C00-C97),Cancer,South Carolina,9728,178.80 +2011,Malignant neoplasms (C00-C97),Cancer,South Carolina,9543,182.00 +2010,Malignant neoplasms (C00-C97),Cancer,South Carolina,9356,183.60 +2009,Malignant neoplasms (C00-C97),Cancer,South Carolina,9123,182.80 +2008,Malignant neoplasms (C00-C97),Cancer,South Carolina,9199,190.00 +2007,Malignant neoplasms (C00-C97),Cancer,South Carolina,8867,188.50 +2006,Malignant neoplasms (C00-C97),Cancer,South Carolina,8853,194.70 +2005,Malignant neoplasms (C00-C97),Cancer,South Carolina,8652,198.20 +2004,Malignant neoplasms (C00-C97),Cancer,South Carolina,8348,195.80 +2003,Malignant neoplasms (C00-C97),Cancer,South Carolina,8512,204.40 +2002,Malignant neoplasms (C00-C97),Cancer,South Carolina,8333,205.80 +2001,Malignant neoplasms (C00-C97),Cancer,South Carolina,8272,208.30 +2000,Malignant neoplasms (C00-C97),Cancer,South Carolina,8250,212.60 +1999,Malignant neoplasms (C00-C97),Cancer,South Carolina,8089,211.70 +2016,Malignant neoplasms (C00-C97),Cancer,South Dakota,1694,156.70 +2015,Malignant neoplasms (C00-C97),Cancer,South Dakota,1640,154.00 +2014,Malignant neoplasms (C00-C97),Cancer,South Dakota,1698,163.40 +2013,Malignant neoplasms (C00-C97),Cancer,South Dakota,1577,154.50 +2012,Malignant neoplasms (C00-C97),Cancer,South Dakota,1630,163.00 +2011,Malignant neoplasms (C00-C97),Cancer,South Dakota,1665,169.70 +2010,Malignant neoplasms (C00-C97),Cancer,South Dakota,1655,171.00 +2009,Malignant neoplasms (C00-C97),Cancer,South Dakota,1503,158.70 +2008,Malignant neoplasms (C00-C97),Cancer,South Dakota,1570,168.40 +2007,Malignant neoplasms (C00-C97),Cancer,South Dakota,1612,174.30 +2006,Malignant neoplasms (C00-C97),Cancer,South Dakota,1570,173.10 +2005,Malignant neoplasms (C00-C97),Cancer,South Dakota,1612,182.60 +2004,Malignant neoplasms (C00-C97),Cancer,South Dakota,1555,177.50 +2003,Malignant neoplasms (C00-C97),Cancer,South Dakota,1636,190.30 +2002,Malignant neoplasms (C00-C97),Cancer,South Dakota,1562,183.30 +2001,Malignant neoplasms (C00-C97),Cancer,South Dakota,1590,188.50 +2000,Malignant neoplasms (C00-C97),Cancer,South Dakota,1597,190.50 +1999,Malignant neoplasms (C00-C97),Cancer,South Dakota,1632,197.40 +2016,Malignant neoplasms (C00-C97),Cancer,Tennessee,14450,179.90 +2015,Malignant neoplasms (C00-C97),Cancer,Tennessee,14214,180.50 +2014,Malignant neoplasms (C00-C97),Cancer,Tennessee,14173,184.20 +2013,Malignant neoplasms (C00-C97),Cancer,Tennessee,13953,185.50 +2012,Malignant neoplasms (C00-C97),Cancer,Tennessee,13765,187.60 +2011,Malignant neoplasms (C00-C97),Cancer,Tennessee,13562,189.00 +2010,Malignant neoplasms (C00-C97),Cancer,Tennessee,13593,195.70 +2009,Malignant neoplasms (C00-C97),Cancer,Tennessee,13482,197.50 +2008,Malignant neoplasms (C00-C97),Cancer,Tennessee,13162,197.20 +2007,Malignant neoplasms (C00-C97),Cancer,Tennessee,13161,201.60 +2006,Malignant neoplasms (C00-C97),Cancer,Tennessee,13051,204.60 +2005,Malignant neoplasms (C00-C97),Cancer,Tennessee,12995,210.30 +2004,Malignant neoplasms (C00-C97),Cancer,Tennessee,12586,208.10 +2003,Malignant neoplasms (C00-C97),Cancer,Tennessee,12613,212.70 +2002,Malignant neoplasms (C00-C97),Cancer,Tennessee,12518,214.60 +2001,Malignant neoplasms (C00-C97),Cancer,Tennessee,12229,213.10 +2000,Malignant neoplasms (C00-C97),Cancer,Tennessee,12339,218.80 +1999,Malignant neoplasms (C00-C97),Cancer,Tennessee,11943,213.90 +2016,Malignant neoplasms (C00-C97),Cancer,Texas,40195,148.50 +2015,Malignant neoplasms (C00-C97),Cancer,Texas,39121,149.20 +2014,Malignant neoplasms (C00-C97),Cancer,Texas,38847,152.90 +2013,Malignant neoplasms (C00-C97),Cancer,Texas,38412,156.90 +2012,Malignant neoplasms (C00-C97),Cancer,Texas,38142,160.30 +2011,Malignant neoplasms (C00-C97),Cancer,Texas,37351,162.30 +2010,Malignant neoplasms (C00-C97),Cancer,Texas,36717,165.90 +2009,Malignant neoplasms (C00-C97),Cancer,Texas,35591,164.40 +2008,Malignant neoplasms (C00-C97),Cancer,Texas,35713,169.90 +2007,Malignant neoplasms (C00-C97),Cancer,Texas,35074,171.90 +2006,Malignant neoplasms (C00-C97),Cancer,Texas,34939,176.10 +2005,Malignant neoplasms (C00-C97),Cancer,Texas,34291,178.90 +2004,Malignant neoplasms (C00-C97),Cancer,Texas,33937,182.10 +2003,Malignant neoplasms (C00-C97),Cancer,Texas,33867,186.10 +2002,Malignant neoplasms (C00-C97),Cancer,Texas,34164,192.20 +2001,Malignant neoplasms (C00-C97),Cancer,Texas,33488,192.60 +2000,Malignant neoplasms (C00-C97),Cancer,Texas,33300,196.30 +1999,Malignant neoplasms (C00-C97),Cancer,Texas,32755,196.10 +2016,Malignant neoplasms (C00-C97),Cancer,United States,598038,155.80 +2015,Malignant neoplasms (C00-C97),Cancer,United States,595930,158.50 +2014,Malignant neoplasms (C00-C97),Cancer,United States,591700,161.20 +2013,Malignant neoplasms (C00-C97),Cancer,United States,584881,163.20 +2012,Malignant neoplasms (C00-C97),Cancer,United States,582623,166.50 +2011,Malignant neoplasms (C00-C97),Cancer,United States,576691,169.00 +2010,Malignant neoplasms (C00-C97),Cancer,United States,574743,172.80 +2009,Malignant neoplasms (C00-C97),Cancer,United States,567628,173.50 +2008,Malignant neoplasms (C00-C97),Cancer,United States,565469,176.40 +2007,Malignant neoplasms (C00-C97),Cancer,United States,562875,179.30 +2006,Malignant neoplasms (C00-C97),Cancer,United States,559888,181.80 +2005,Malignant neoplasms (C00-C97),Cancer,United States,559312,185.10 +2004,Malignant neoplasms (C00-C97),Cancer,United States,553888,186.80 +2003,Malignant neoplasms (C00-C97),Cancer,United States,556902,190.90 +2002,Malignant neoplasms (C00-C97),Cancer,United States,557271,194.30 +2001,Malignant neoplasms (C00-C97),Cancer,United States,553768,196.50 +2000,Malignant neoplasms (C00-C97),Cancer,United States,553091,199.60 +1999,Malignant neoplasms (C00-C97),Cancer,United States,549838,200.80 +2016,Malignant neoplasms (C00-C97),Cancer,Utah,3125,122.40 +2015,Malignant neoplasms (C00-C97),Cancer,Utah,3091,125.20 +2014,Malignant neoplasms (C00-C97),Cancer,Utah,3043,127.40 +2013,Malignant neoplasms (C00-C97),Cancer,Utah,2971,127.60 +2012,Malignant neoplasms (C00-C97),Cancer,Utah,2876,128.20 +2011,Malignant neoplasms (C00-C97),Cancer,Utah,2746,125.70 +2010,Malignant neoplasms (C00-C97),Cancer,Utah,2810,133.70 +2009,Malignant neoplasms (C00-C97),Cancer,Utah,2555,124.70 +2008,Malignant neoplasms (C00-C97),Cancer,Utah,2492,125.40 +2007,Malignant neoplasms (C00-C97),Cancer,Utah,2572,133.20 +2006,Malignant neoplasms (C00-C97),Cancer,Utah,2615,139.90 +2005,Malignant neoplasms (C00-C97),Cancer,Utah,2520,140.90 +2004,Malignant neoplasms (C00-C97),Cancer,Utah,2445,141.10 +2003,Malignant neoplasms (C00-C97),Cancer,Utah,2444,144.60 +2002,Malignant neoplasms (C00-C97),Cancer,Utah,2376,144.40 +2001,Malignant neoplasms (C00-C97),Cancer,Utah,2313,144.30 +2000,Malignant neoplasms (C00-C97),Cancer,Utah,2359,151.20 +1999,Malignant neoplasms (C00-C97),Cancer,Utah,2393,155.70 +2016,Malignant neoplasms (C00-C97),Cancer,Vermont,1356,158.40 +2015,Malignant neoplasms (C00-C97),Cancer,Vermont,1399,165.30 +2014,Malignant neoplasms (C00-C97),Cancer,Vermont,1379,167.90 +2013,Malignant neoplasms (C00-C97),Cancer,Vermont,1318,163.40 +2012,Malignant neoplasms (C00-C97),Cancer,Vermont,1325,165.00 +2011,Malignant neoplasms (C00-C97),Cancer,Vermont,1347,175.90 +2010,Malignant neoplasms (C00-C97),Cancer,Vermont,1392,183.30 +2009,Malignant neoplasms (C00-C97),Cancer,Vermont,1254,169.20 +2008,Malignant neoplasms (C00-C97),Cancer,Vermont,1279,176.30 +2007,Malignant neoplasms (C00-C97),Cancer,Vermont,1346,188.60 +2006,Malignant neoplasms (C00-C97),Cancer,Vermont,1214,172.60 +2005,Malignant neoplasms (C00-C97),Cancer,Vermont,1202,175.90 +2004,Malignant neoplasms (C00-C97),Cancer,Vermont,1212,179.50 +2003,Malignant neoplasms (C00-C97),Cancer,Vermont,1210,183.60 +2002,Malignant neoplasms (C00-C97),Cancer,Vermont,1224,188.30 +2001,Malignant neoplasms (C00-C97),Cancer,Vermont,1249,196.40 +2000,Malignant neoplasms (C00-C97),Cancer,Vermont,1240,198.00 +1999,Malignant neoplasms (C00-C97),Cancer,Vermont,1255,203.70 +2016,Malignant neoplasms (C00-C97),Cancer,Virginia,15027,156.10 +2015,Malignant neoplasms (C00-C97),Cancer,Virginia,14947,159.50 +2014,Malignant neoplasms (C00-C97),Cancer,Virginia,14749,161.50 +2013,Malignant neoplasms (C00-C97),Cancer,Virginia,14414,162.00 +2012,Malignant neoplasms (C00-C97),Cancer,Virginia,14294,165.00 +2011,Malignant neoplasms (C00-C97),Cancer,Virginia,14376,170.80 +2010,Malignant neoplasms (C00-C97),Cancer,Virginia,14080,172.40 +2009,Malignant neoplasms (C00-C97),Cancer,Virginia,14122,176.20 +2008,Malignant neoplasms (C00-C97),Cancer,Virginia,13983,179.10 +2007,Malignant neoplasms (C00-C97),Cancer,Virginia,14009,184.00 +2006,Malignant neoplasms (C00-C97),Cancer,Virginia,13829,185.30 +2005,Malignant neoplasms (C00-C97),Cancer,Virginia,13877,190.00 +2004,Malignant neoplasms (C00-C97),Cancer,Virginia,13385,188.50 +2003,Malignant neoplasms (C00-C97),Cancer,Virginia,13781,198.10 +2002,Malignant neoplasms (C00-C97),Cancer,Virginia,13602,200.00 +2001,Malignant neoplasms (C00-C97),Cancer,Virginia,13345,200.50 +2000,Malignant neoplasms (C00-C97),Cancer,Virginia,13528,208.60 +1999,Malignant neoplasms (C00-C97),Cancer,Virginia,13365,209.10 +2016,Malignant neoplasms (C00-C97),Cancer,Washington,12594,150.90 +2015,Malignant neoplasms (C00-C97),Cancer,Washington,12687,156.40 +2014,Malignant neoplasms (C00-C97),Cancer,Washington,12205,155.50 +2013,Malignant neoplasms (C00-C97),Cancer,Washington,11928,156.10 +2012,Malignant neoplasms (C00-C97),Cancer,Washington,11952,161.40 +2011,Malignant neoplasms (C00-C97),Cancer,Washington,12002,166.10 +2010,Malignant neoplasms (C00-C97),Cancer,Washington,11874,170.50 +2009,Malignant neoplasms (C00-C97),Cancer,Washington,11922,174.90 +2008,Malignant neoplasms (C00-C97),Cancer,Washington,11618,175.50 +2007,Malignant neoplasms (C00-C97),Cancer,Washington,11568,179.00 +2006,Malignant neoplasms (C00-C97),Cancer,Washington,11055,175.30 +2005,Malignant neoplasms (C00-C97),Cancer,Washington,11048,181.00 +2004,Malignant neoplasms (C00-C97),Cancer,Washington,10989,184.60 +2003,Malignant neoplasms (C00-C97),Cancer,Washington,11064,190.40 +2002,Malignant neoplasms (C00-C97),Cancer,Washington,10858,190.60 +2001,Malignant neoplasms (C00-C97),Cancer,Washington,10802,194.20 +2000,Malignant neoplasms (C00-C97),Cancer,Washington,10668,195.80 +1999,Malignant neoplasms (C00-C97),Cancer,Washington,10653,198.30 +2016,Malignant neoplasms (C00-C97),Cancer,West Virginia,4659,182.20 +2015,Malignant neoplasms (C00-C97),Cancer,West Virginia,4839,190.40 +2014,Malignant neoplasms (C00-C97),Cancer,West Virginia,4880,195.10 +2013,Malignant neoplasms (C00-C97),Cancer,West Virginia,4718,190.80 +2012,Malignant neoplasms (C00-C97),Cancer,West Virginia,4684,191.80 +2011,Malignant neoplasms (C00-C97),Cancer,West Virginia,4782,200.10 +2010,Malignant neoplasms (C00-C97),Cancer,West Virginia,4685,198.00 +2009,Malignant neoplasms (C00-C97),Cancer,West Virginia,4786,204.20 +2008,Malignant neoplasms (C00-C97),Cancer,West Virginia,4605,198.50 +2007,Malignant neoplasms (C00-C97),Cancer,West Virginia,4690,204.30 +2006,Malignant neoplasms (C00-C97),Cancer,West Virginia,4613,203.60 +2005,Malignant neoplasms (C00-C97),Cancer,West Virginia,4617,207.40 +2004,Malignant neoplasms (C00-C97),Cancer,West Virginia,4694,212.20 +2003,Malignant neoplasms (C00-C97),Cancer,West Virginia,4610,211.60 +2002,Malignant neoplasms (C00-C97),Cancer,West Virginia,4652,215.20 +2001,Malignant neoplasms (C00-C97),Cancer,West Virginia,4685,219.20 +2000,Malignant neoplasms (C00-C97),Cancer,West Virginia,4753,223.90 +1999,Malignant neoplasms (C00-C97),Cancer,West Virginia,4762,225.10 +2016,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11498,158.00 +2015,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11423,159.30 +2014,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11393,161.80 +2013,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11425,165.20 +2012,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11252,166.30 +2011,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11608,175.50 +2010,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11279,174.50 +2009,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10866,169.50 +2008,Malignant neoplasms (C00-C97),Cancer,Wisconsin,11185,177.80 +2007,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10963,177.20 +2006,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10925,179.90 +2005,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10943,182.80 +2004,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10861,184.70 +2003,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10648,183.40 +2002,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10828,189.60 +2001,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10777,191.60 +2000,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10638,191.70 +1999,Malignant neoplasms (C00-C97),Cancer,Wisconsin,10755,195.50 +2016,Malignant neoplasms (C00-C97),Cancer,Wyoming,962,140.90 +2015,Malignant neoplasms (C00-C97),Cancer,Wyoming,931,139.40 +2014,Malignant neoplasms (C00-C97),Cancer,Wyoming,922,140.70 +2013,Malignant neoplasms (C00-C97),Cancer,Wyoming,946,148.20 +2012,Malignant neoplasms (C00-C97),Cancer,Wyoming,955,154.20 +2011,Malignant neoplasms (C00-C97),Cancer,Wyoming,936,156.00 +2010,Malignant neoplasms (C00-C97),Cancer,Wyoming,1016,172.60 +2009,Malignant neoplasms (C00-C97),Cancer,Wyoming,936,164.70 +2008,Malignant neoplasms (C00-C97),Cancer,Wyoming,874,156.70 +2007,Malignant neoplasms (C00-C97),Cancer,Wyoming,940,172.90 +2006,Malignant neoplasms (C00-C97),Cancer,Wyoming,927,174.60 +2005,Malignant neoplasms (C00-C97),Cancer,Wyoming,886,169.20 +2004,Malignant neoplasms (C00-C97),Cancer,Wyoming,875,171.70 +2003,Malignant neoplasms (C00-C97),Cancer,Wyoming,943,188.90 +2002,Malignant neoplasms (C00-C97),Cancer,Wyoming,859,177.10 +2001,Malignant neoplasms (C00-C97),Cancer,Wyoming,923,194.00 +2000,Malignant neoplasms (C00-C97),Cancer,Wyoming,868,185.50 +1999,Malignant neoplasms (C00-C97),Cancer,Wyoming,899,195.10 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,3326,56.20 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,3279,56.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,3050,53.60 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,3043,54.80 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,3015,55.20 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2912,55.20 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2866,55.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2770,54.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2733,54.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2530,51.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2309,47.60 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2382,50.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2361,50.20 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2434,52.40 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2328,50.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2204,48.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2057,45.50 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Alabama,2179,48.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,238,41.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,204,37.80 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,192,38.80 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,197,37.90 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,189,40.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,194,41.80 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,176,41.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,196,49.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,181,44.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,175,45.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,140,37.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,158,42.70 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,139,39.50 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,147,46.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,142,47.50 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,147,49.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,132,47.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Alaska,146,56.40 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3800,43.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3681,43.80 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3396,42.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3345,43.50 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3221,43.60 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,3166,44.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2926,43.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2839,42.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2939,45.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2686,42.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2799,46.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2821,48.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2416,42.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2560,46.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2575,48.40 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2499,47.70 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2508,49.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Arizona,2553,51.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,2169,59.10 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,2270,62.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,2092,58.90 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,2090,60.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1910,55.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1985,58.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1773,53.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1831,56.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1887,58.70 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1656,52.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1498,48.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1559,51.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1428,47.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1500,50.10 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1441,48.50 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1368,46.50 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1405,48.00 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Arkansas,1358,46.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,California,13710,32.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,California,13621,33.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,California,12780,32.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,California,13598,35.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,California,12963,34.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,California,13357,36.70 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,California,12987,37.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,California,12936,37.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,California,13426,39.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,California,12532,38.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,California,12829,40.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,California,13188,41.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,California,12522,40.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,California,13448,44.20 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,California,12684,42.60 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,California,12965,44.40 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,California,12756,44.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,California,13165,46.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2575,46.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2577,47.80 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2451,47.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2297,45.90 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2239,46.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2151,46.40 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2199,49.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2073,47.80 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2188,52.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,2002,49.40 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1932,49.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1914,50.90 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1899,51.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1931,54.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1848,53.00 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1836,53.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1787,53.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Colorado,1894,58.10 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1425,30.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1369,29.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1368,30.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1348,29.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1387,30.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1415,32.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1289,29.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1448,33.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1505,35.30 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1353,32.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1458,35.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1471,36.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1432,35.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1445,36.10 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1453,36.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1486,38.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1533,40.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Connecticut,1435,37.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,544,43.70 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,510,42.50 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,458,40.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,482,43.50 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,420,39.10 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,460,44.80 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,441,44.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,428,43.40 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,472,48.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,379,40.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,344,37.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,411,46.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,345,40.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,339,40.60 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,350,43.30 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,306,38.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,341,44.00 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Delaware,327,42.70 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,152,24.00 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,147,23.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,136,21.90 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,150,25.00 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,139,23.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,151,25.40 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,147,25.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,136,24.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,139,25.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,129,23.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,126,23.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,132,24.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,165,29.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,137,24.70 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,133,23.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,150,26.80 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,175,31.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,District of Columbia,166,29.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,11970,38.00 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,11705,38.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,11178,38.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,11379,39.90 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,10585,38.40 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,10279,38.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,10337,40.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,10182,40.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,10198,41.10 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,9357,38.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,8912,37.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,9482,40.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,8971,39.10 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,9087,40.40 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,9062,40.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,8954,41.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,8645,40.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Florida,9131,43.10 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,4805,47.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,4607,46.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,4332,45.60 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,4172,45.40 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3977,45.00 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3837,45.30 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3816,46.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3736,46.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3546,45.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3384,45.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3387,46.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3411,48.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3125,45.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3254,48.70 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3163,48.10 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3097,48.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3067,48.70 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Georgia,3056,49.10 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,342,17.70 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,332,17.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,313,17.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,279,15.60 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,312,17.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,318,18.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,296,18.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,303,19.10 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,293,19.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,299,19.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,293,20.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,287,19.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,307,22.00 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,287,21.10 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,265,20.00 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,280,21.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,278,22.40 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Hawaii,290,24.00 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,865,45.70 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,843,46.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,819,45.80 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,808,46.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,754,46.00 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,824,51.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,727,47.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,725,48.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,703,48.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,666,47.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,644,46.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,717,54.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,572,44.50 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,601,47.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,595,48.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,581,48.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,569,48.80 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Idaho,568,49.50 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5632,38.10 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5544,38.20 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5631,39.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5532,39.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5313,38.60 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5390,39.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5231,39.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5299,40.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5602,43.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4742,37.00 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4731,37.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5067,40.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4723,38.10 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4858,39.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4827,39.60 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4776,39.50 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,4768,39.70 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Illinois,5155,43.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,4214,54.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,4212,55.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,4029,54.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,4266,58.40 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3962,55.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,4064,57.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3794,55.30 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3745,55.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3878,58.10 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3227,49.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3291,51.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3471,54.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3145,50.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3264,52.80 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3138,51.50 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3136,52.10 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3052,51.20 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Indiana,3053,51.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,2006,48.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,2011,49.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1915,47.70 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1892,47.80 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1824,46.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1833,47.60 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1698,44.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1832,48.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1848,49.40 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1660,44.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1655,45.20 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1703,47.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1547,42.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1674,46.60 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1580,44.40 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1552,43.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1512,42.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Iowa,1643,46.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1654,48.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1704,49.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1673,49.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1673,50.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1687,51.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1647,51.30 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1580,50.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1580,50.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1626,52.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1476,48.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1501,49.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1567,52.70 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1316,44.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1443,49.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1367,47.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1444,50.10 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1411,49.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Kansas,1386,48.70 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3486,66.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3331,64.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3214,63.80 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3187,64.60 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3100,64.20 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,3020,64.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2779,60.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2854,62.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2926,65.20 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2629,59.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2407,55.60 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2578,61.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2265,54.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2391,58.40 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2401,59.30 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2265,56.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2170,54.80 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Kentucky,2327,59.00 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,2221,43.00 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,2178,43.20 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,2237,45.50 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,2274,47.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,2074,44.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1904,41.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1939,43.60 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1878,42.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1896,44.00 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1685,40.00 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1687,40.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1906,44.60 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1615,38.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1731,41.40 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1696,41.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1763,43.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1684,41.70 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Louisiana,1610,40.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,928,47.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,1025,53.60 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,896,48.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,902,49.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,817,45.40 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,842,48.40 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,809,47.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,821,48.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,794,47.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,728,44.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,783,49.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,830,53.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,766,49.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,782,51.30 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,791,52.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,800,54.40 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,768,53.00 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Maine,751,52.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2072,30.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2040,30.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1909,29.40 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2051,32.50 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1981,32.30 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2050,34.30 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2039,35.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,2064,36.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1982,35.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1901,35.20 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1832,34.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1908,36.50 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1910,37.30 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1986,39.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1944,39.40 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1907,39.50 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1927,40.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Maryland,1943,41.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2674,31.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2784,33.00 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2592,31.40 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2572,31.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2520,31.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2651,33.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2383,31.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2559,33.80 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2587,34.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2332,31.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2535,34.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2647,36.60 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2574,35.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2765,38.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2745,38.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2810,40.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2925,42.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Massachusetts,2862,41.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5572,44.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5848,47.50 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5345,44.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5539,46.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5251,45.20 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5192,45.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5079,45.60 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4961,45.00 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,5185,47.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4624,43.40 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4474,42.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4466,43.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4252,41.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4472,44.60 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4431,44.80 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4148,42.50 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4352,45.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Michigan,4316,45.30 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2369,35.80 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2351,36.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2277,36.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2287,37.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2124,35.30 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2170,37.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2016,35.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1961,34.80 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,2096,37.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1758,32.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1770,33.20 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1965,37.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1839,35.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1832,35.80 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1971,39.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1909,38.80 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1902,39.00 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Minnesota,1994,41.30 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,2116,62.00 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1924,57.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1738,52.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1755,54.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1729,54.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1694,55.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1661,55.20 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1556,52.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1520,51.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1408,48.70 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1378,48.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1473,52.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1350,48.30 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1394,50.70 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1378,50.30 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1333,49.10 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1257,46.50 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Mississippi,1267,47.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3961,52.10 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3935,52.80 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3762,51.40 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3798,52.90 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3650,51.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3492,50.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3557,52.60 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3456,51.90 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3765,57.40 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3081,47.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3017,47.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3085,49.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,2731,44.10 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,2939,47.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,2867,47.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,2882,47.80 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,2802,46.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Missouri,3068,51.70 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,722,51.90 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,679,50.20 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,668,50.30 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,647,50.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,605,48.60 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,637,52.90 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,602,51.30 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,598,51.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,691,61.00 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,604,54.70 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,579,53.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,580,54.90 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,578,55.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,588,58.30 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,576,57.80 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,583,59.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,517,53.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Montana,566,59.40 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1117,48.90 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1174,52.20 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1123,50.60 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1032,47.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1052,48.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1068,50.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1008,48.80 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,987,48.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,1047,51.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,919,45.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,878,44.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,949,48.70 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,815,42.20 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,894,47.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,934,49.10 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,882,46.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,838,45.00 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Nebraska,945,50.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1778,56.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1617,54.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1522,52.90 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1482,54.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1370,52.00 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1243,49.80 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1186,49.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1244,53.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1257,56.40 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1050,48.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1066,50.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1227,60.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1124,57.20 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1173,62.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1174,65.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1136,65.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,989,61.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Nevada,1029,64.70 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,684,39.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,705,41.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,680,41.30 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,668,42.00 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,674,43.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,650,43.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,609,41.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,652,45.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,688,48.80 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,611,44.20 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,603,44.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,630,48.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,600,46.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,531,42.80 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,577,47.40 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,616,51.40 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,581,49.40 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,New Hampshire,592,51.00 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3064,27.90 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3202,29.60 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3046,28.50 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3245,31.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3271,31.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3157,31.20 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3106,31.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3125,31.80 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3280,33.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,2991,31.40 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,2862,30.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3148,34.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3031,33.00 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,2910,31.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,2885,32.00 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,2911,32.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3007,34.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,New Jersey,3130,35.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1131,44.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1109,44.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1127,46.40 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1052,44.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,965,42.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1024,46.20 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1022,47.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,984,47.10 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,1001,49.70 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,884,45.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,888,46.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,855,46.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,753,42.00 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,936,53.30 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,857,49.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,775,45.80 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,767,46.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,New Mexico,848,52.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6860,28.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,New York,7109,29.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6806,29.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,New York,7110,30.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,New York,7034,31.10 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6963,31.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6847,31.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6740,31.40 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6919,32.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6561,31.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6329,30.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6818,33.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6786,33.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6709,33.30 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6966,34.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6906,35.00 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,New York,6784,34.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,New York,7086,36.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,5311,44.80 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,5221,45.50 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,5023,45.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4987,46.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4844,46.40 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4708,46.70 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4495,46.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4329,45.40 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4572,49.30 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4231,46.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4017,45.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,4149,49.20 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3625,43.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3887,48.20 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3674,46.60 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3514,45.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3699,48.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,North Carolina,3591,48.00 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,314,34.70 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,351,40.00 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,317,36.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,331,38.20 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,306,35.30 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,342,39.60 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,356,43.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,341,41.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,355,43.40 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,265,32.70 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,283,34.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,272,34.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,273,34.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,302,39.10 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,322,41.80 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,308,39.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,295,38.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,North Dakota,273,35.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,7015,47.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,7211,49.60 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6765,47.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,7007,49.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,7070,51.20 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6996,51.70 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6717,50.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6642,50.40 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6928,53.30 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6454,50.40 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6054,47.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6580,52.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,5896,47.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,5927,48.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,6063,50.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,5881,49.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,5983,50.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Ohio,5858,49.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2794,61.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2924,65.80 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2772,63.30 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2680,62.40 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2585,61.40 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2637,63.80 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2720,67.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2595,65.10 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2694,68.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2386,62.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2192,57.60 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2368,63.60 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,1985,53.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,2160,59.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,1988,54.80 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,1922,53.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,1980,55.20 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Oklahoma,1751,49.30 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,2080,40.40 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,2119,42.40 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1955,40.10 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,2029,43.00 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1915,42.20 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,2023,45.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1971,45.30 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1943,45.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1951,46.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1892,46.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1828,45.60 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1837,47.00 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1778,46.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1819,48.50 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1845,50.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1726,47.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1677,47.50 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Oregon,1765,50.40 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6523,36.80 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6664,38.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6422,37.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6716,39.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6550,38.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6627,39.70 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6202,37.90 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6432,39.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6767,42.00 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6077,38.10 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,5621,35.60 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6149,39.30 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,5978,38.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6046,39.20 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6017,39.30 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,5845,38.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6079,40.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Pennsylvania,6141,41.10 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,445,31.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,510,36.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,505,36.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,476,34.70 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,504,36.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,526,39.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,507,38.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,511,39.80 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,478,37.30 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,421,32.50 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,485,38.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,523,40.60 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,463,36.30 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,495,39.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,521,41.50 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,511,41.30 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,500,40.40 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Rhode Island,495,40.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2870,47.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2908,49.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2715,48.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2756,50.80 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2467,46.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2402,47.60 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2264,46.30 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2317,48.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2266,48.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,2036,44.90 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1935,44.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1977,46.70 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1792,43.50 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1913,47.40 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1889,48.10 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1731,44.80 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1730,45.80 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,South Carolina,1752,47.00 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,426,38.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,502,45.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,441,40.80 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,416,39.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,481,45.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,486,47.40 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,451,46.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,440,44.90 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,491,50.70 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,457,48.00 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,376,40.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,440,48.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,391,42.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,379,42.90 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,383,43.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,362,41.70 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,383,43.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,South Dakota,335,38.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,4318,54.70 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,4239,54.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3969,52.50 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3904,53.20 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3695,51.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3658,52.70 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3551,52.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3503,52.90 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3545,54.70 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3167,50.00 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,2980,48.10 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3185,52.90 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,2987,50.70 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3067,52.80 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,3011,52.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,2941,52.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,2887,52.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Tennessee,2748,49.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,10107,39.50 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,10231,41.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,9668,40.50 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,9800,42.40 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,9524,42.70 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,9097,42.30 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,8921,43.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,8649,42.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,8874,45.10 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,8107,42.20 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7639,40.80 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7988,44.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7402,41.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7567,43.80 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7720,45.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7739,46.70 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7286,44.70 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Texas,7518,46.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,United States,154596,40.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,United States,155041,41.60 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,United States,147101,40.50 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,United States,149205,42.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,United States,143489,41.50 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,United States,142943,42.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,United States,138080,42.20 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,United States,137353,42.70 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,United States,141090,44.70 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,United States,127924,41.40 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,United States,124583,41.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,United States,130933,43.90 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,United States,121987,41.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,United States,126382,43.70 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,United States,124816,43.90 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,United States,123013,43.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,United States,122009,44.20 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,United States,124181,45.40 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,875,35.80 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,811,33.90 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,757,32.60 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,718,32.00 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,663,30.60 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,647,30.20 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,671,33.20 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,581,29.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,638,33.30 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,617,33.20 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,591,32.90 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,592,34.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,595,35.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,569,35.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,603,38.10 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,525,33.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,524,34.60 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Utah,559,37.60 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,355,41.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,357,42.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,333,41.20 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,353,44.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,364,46.30 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,349,45.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,335,45.30 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,364,49.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,341,47.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,316,45.00 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,320,46.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,381,57.10 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,296,45.00 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,301,46.20 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,276,43.10 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,305,48.50 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,312,50.50 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Vermont,300,49.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3233,34.60 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3370,37.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3108,35.30 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3181,37.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3070,36.90 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3112,38.60 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2969,38.10 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3013,39.60 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,3023,40.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2770,38.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2697,38.00 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2897,41.80 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2730,40.40 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2983,45.00 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2752,42.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2744,43.10 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2820,45.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Virginia,2699,43.80 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,3036,37.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,3154,39.70 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2916,37.90 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2933,39.40 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2955,41.00 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,3080,44.00 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2737,40.40 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2933,44.30 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2930,45.60 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2684,42.80 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2662,43.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2699,45.40 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2549,43.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2652,46.60 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2721,48.60 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2634,48.10 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2644,49.30 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Washington,2713,51.20 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1599,62.10 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1628,64.60 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1578,63.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1590,64.60 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1524,62.40 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1571,66.10 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1482,62.70 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1490,63.90 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1590,68.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1331,58.30 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1267,56.30 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1350,60.50 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1225,55.60 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1296,59.20 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1228,56.60 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1273,59.20 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1345,62.90 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,West Virginia,1236,57.90 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2786,38.30 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2846,39.30 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2759,39.30 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2789,40.30 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2524,37.10 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2615,39.50 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2474,38.00 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2461,38.50 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2538,39.90 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2399,38.60 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2354,38.50 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2449,40.70 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2312,38.90 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2299,39.10 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2335,40.20 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2375,41.60 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2316,41.10 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Wisconsin,2270,40.50 +2016,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,327,48.90 +2015,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,368,57.10 +2014,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,343,55.00 +2013,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,387,63.10 +2012,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,334,55.80 +2011,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,327,57.00 +2010,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,332,59.50 +2009,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,310,57.20 +2008,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,311,58.50 +2007,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,295,56.70 +2006,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,345,67.40 +2005,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,291,57.60 +2004,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,308,62.80 +2003,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,278,57.60 +2002,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,324,68.70 +2001,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,270,57.90 +2000,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,283,62.40 +1999,Chronic lower respiratory diseases (J40-J47),CLRD,Wyoming,338,75.60 +2016,Diabetes mellitus (E10-E14),Diabetes,Alabama,1183,20.10 +2015,Diabetes mellitus (E10-E14),Diabetes,Alabama,1255,21.70 +2014,Diabetes mellitus (E10-E14),Diabetes,Alabama,1281,22.80 +2013,Diabetes mellitus (E10-E14),Diabetes,Alabama,1349,24.30 +2012,Diabetes mellitus (E10-E14),Diabetes,Alabama,1300,23.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Alabama,1278,23.90 +2010,Diabetes mellitus (E10-E14),Diabetes,Alabama,1302,25.00 +2009,Diabetes mellitus (E10-E14),Diabetes,Alabama,1259,24.40 +2008,Diabetes mellitus (E10-E14),Diabetes,Alabama,1386,27.20 +2007,Diabetes mellitus (E10-E14),Diabetes,Alabama,1313,26.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Alabama,1453,29.80 +2005,Diabetes mellitus (E10-E14),Diabetes,Alabama,1429,29.90 +2004,Diabetes mellitus (E10-E14),Diabetes,Alabama,1449,30.80 +2003,Diabetes mellitus (E10-E14),Diabetes,Alabama,1414,30.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Alabama,1486,32.30 +2001,Diabetes mellitus (E10-E14),Diabetes,Alabama,1344,29.60 +2000,Diabetes mellitus (E10-E14),Diabetes,Alabama,1321,29.30 +1999,Diabetes mellitus (E10-E14),Diabetes,Alabama,1341,30.00 +2016,Diabetes mellitus (E10-E14),Diabetes,Alaska,124,19.30 +2015,Diabetes mellitus (E10-E14),Diabetes,Alaska,142,23.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Alaska,113,19.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Alaska,112,20.10 +2012,Diabetes mellitus (E10-E14),Diabetes,Alaska,107,18.80 +2011,Diabetes mellitus (E10-E14),Diabetes,Alaska,107,20.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Alaska,86,19.60 +2009,Diabetes mellitus (E10-E14),Diabetes,Alaska,84,18.10 +2008,Diabetes mellitus (E10-E14),Diabetes,Alaska,93,22.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Alaska,105,24.10 +2006,Diabetes mellitus (E10-E14),Diabetes,Alaska,109,25.90 +2005,Diabetes mellitus (E10-E14),Diabetes,Alaska,93,22.40 +2004,Diabetes mellitus (E10-E14),Diabetes,Alaska,93,22.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Alaska,102,27.70 +2002,Diabetes mellitus (E10-E14),Diabetes,Alaska,86,22.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Alaska,80,22.80 +2000,Diabetes mellitus (E10-E14),Diabetes,Alaska,87,27.30 +1999,Diabetes mellitus (E10-E14),Diabetes,Alaska,67,23.80 +2016,Diabetes mellitus (E10-E14),Diabetes,Arizona,2025,23.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Arizona,2081,25.30 +2014,Diabetes mellitus (E10-E14),Diabetes,Arizona,1936,24.30 +2013,Diabetes mellitus (E10-E14),Diabetes,Arizona,1786,23.50 +2012,Diabetes mellitus (E10-E14),Diabetes,Arizona,1736,23.40 +2011,Diabetes mellitus (E10-E14),Diabetes,Arizona,1740,24.30 +2010,Diabetes mellitus (E10-E14),Diabetes,Arizona,1389,20.30 +2009,Diabetes mellitus (E10-E14),Diabetes,Arizona,1083,16.20 +2008,Diabetes mellitus (E10-E14),Diabetes,Arizona,1173,18.00 +2007,Diabetes mellitus (E10-E14),Diabetes,Arizona,1159,18.30 +2006,Diabetes mellitus (E10-E14),Diabetes,Arizona,1206,19.60 +2005,Diabetes mellitus (E10-E14),Diabetes,Arizona,1208,20.40 +2004,Diabetes mellitus (E10-E14),Diabetes,Arizona,1196,21.00 +2003,Diabetes mellitus (E10-E14),Diabetes,Arizona,1153,20.90 +2002,Diabetes mellitus (E10-E14),Diabetes,Arizona,1231,23.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Arizona,1058,20.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Arizona,1012,20.00 +1999,Diabetes mellitus (E10-E14),Diabetes,Arizona,1063,21.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Arkansas,920,25.40 +2015,Diabetes mellitus (E10-E14),Diabetes,Arkansas,886,24.70 +2014,Diabetes mellitus (E10-E14),Diabetes,Arkansas,828,24.00 +2013,Diabetes mellitus (E10-E14),Diabetes,Arkansas,835,24.20 +2012,Diabetes mellitus (E10-E14),Diabetes,Arkansas,833,24.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Arkansas,908,27.00 +2010,Diabetes mellitus (E10-E14),Diabetes,Arkansas,850,25.80 +2009,Diabetes mellitus (E10-E14),Diabetes,Arkansas,890,27.40 +2008,Diabetes mellitus (E10-E14),Diabetes,Arkansas,901,28.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Arkansas,838,26.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Arkansas,839,27.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Arkansas,824,27.10 +2004,Diabetes mellitus (E10-E14),Diabetes,Arkansas,838,28.00 +2003,Diabetes mellitus (E10-E14),Diabetes,Arkansas,884,29.90 +2002,Diabetes mellitus (E10-E14),Diabetes,Arkansas,793,27.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Arkansas,756,26.00 +2000,Diabetes mellitus (E10-E14),Diabetes,Arkansas,714,24.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Arkansas,691,24.20 +2016,Diabetes mellitus (E10-E14),Diabetes,California,9124,21.40 +2015,Diabetes mellitus (E10-E14),Diabetes,California,8845,21.20 +2014,Diabetes mellitus (E10-E14),Diabetes,California,8249,20.40 +2013,Diabetes mellitus (E10-E14),Diabetes,California,8057,20.60 +2012,Diabetes mellitus (E10-E14),Diabetes,California,7895,20.70 +2011,Diabetes mellitus (E10-E14),Diabetes,California,7695,20.70 +2010,Diabetes mellitus (E10-E14),Diabetes,California,7061,19.70 +2009,Diabetes mellitus (E10-E14),Diabetes,California,6979,19.80 +2008,Diabetes mellitus (E10-E14),Diabetes,California,7375,21.50 +2007,Diabetes mellitus (E10-E14),Diabetes,California,7413,22.20 +2006,Diabetes mellitus (E10-E14),Diabetes,California,7376,22.50 +2005,Diabetes mellitus (E10-E14),Diabetes,California,7697,23.80 +2004,Diabetes mellitus (E10-E14),Diabetes,California,7117,22.60 +2003,Diabetes mellitus (E10-E14),Diabetes,California,7093,22.80 +2002,Diabetes mellitus (E10-E14),Diabetes,California,6807,22.50 +2001,Diabetes mellitus (E10-E14),Diabetes,California,6395,21.50 +2000,Diabetes mellitus (E10-E14),Diabetes,California,6190,21.30 +1999,Diabetes mellitus (E10-E14),Diabetes,California,6401,22.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Colorado,938,16.20 +2015,Diabetes mellitus (E10-E14),Diabetes,Colorado,887,15.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Colorado,835,15.50 +2013,Diabetes mellitus (E10-E14),Diabetes,Colorado,788,15.00 +2012,Diabetes mellitus (E10-E14),Diabetes,Colorado,797,15.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Colorado,790,16.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Colorado,721,15.20 +2009,Diabetes mellitus (E10-E14),Diabetes,Colorado,779,17.20 +2008,Diabetes mellitus (E10-E14),Diabetes,Colorado,767,17.60 +2007,Diabetes mellitus (E10-E14),Diabetes,Colorado,710,16.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Colorado,677,16.70 +2005,Diabetes mellitus (E10-E14),Diabetes,Colorado,753,19.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Colorado,696,18.20 +2003,Diabetes mellitus (E10-E14),Diabetes,Colorado,708,19.10 +2002,Diabetes mellitus (E10-E14),Diabetes,Colorado,659,18.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Colorado,667,18.80 +2000,Diabetes mellitus (E10-E14),Diabetes,Colorado,635,18.50 +1999,Diabetes mellitus (E10-E14),Diabetes,Colorado,639,19.20 +2016,Diabetes mellitus (E10-E14),Diabetes,Connecticut,699,14.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Connecticut,653,13.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Connecticut,685,14.90 +2013,Diabetes mellitus (E10-E14),Diabetes,Connecticut,664,14.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Connecticut,631,14.20 +2011,Diabetes mellitus (E10-E14),Diabetes,Connecticut,692,15.50 +2010,Diabetes mellitus (E10-E14),Diabetes,Connecticut,662,15.40 +2009,Diabetes mellitus (E10-E14),Diabetes,Connecticut,624,14.50 +2008,Diabetes mellitus (E10-E14),Diabetes,Connecticut,624,14.70 +2007,Diabetes mellitus (E10-E14),Diabetes,Connecticut,646,15.50 +2006,Diabetes mellitus (E10-E14),Diabetes,Connecticut,782,19.00 +2005,Diabetes mellitus (E10-E14),Diabetes,Connecticut,811,20.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Connecticut,767,19.10 +2003,Diabetes mellitus (E10-E14),Diabetes,Connecticut,665,16.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Connecticut,675,17.30 +2001,Diabetes mellitus (E10-E14),Diabetes,Connecticut,759,19.70 +2000,Diabetes mellitus (E10-E14),Diabetes,Connecticut,683,18.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Connecticut,691,18.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Delaware,203,17.00 +2015,Diabetes mellitus (E10-E14),Diabetes,Delaware,215,18.10 +2014,Diabetes mellitus (E10-E14),Diabetes,Delaware,226,19.80 +2013,Diabetes mellitus (E10-E14),Diabetes,Delaware,218,19.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Delaware,241,22.30 +2011,Diabetes mellitus (E10-E14),Diabetes,Delaware,224,21.50 +2010,Diabetes mellitus (E10-E14),Diabetes,Delaware,196,19.20 +2009,Diabetes mellitus (E10-E14),Diabetes,Delaware,206,20.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Delaware,217,22.50 +2007,Diabetes mellitus (E10-E14),Diabetes,Delaware,223,23.60 +2006,Diabetes mellitus (E10-E14),Diabetes,Delaware,191,20.60 +2005,Diabetes mellitus (E10-E14),Diabetes,Delaware,233,26.50 +2004,Diabetes mellitus (E10-E14),Diabetes,Delaware,210,24.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Delaware,239,28.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Delaware,215,26.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Delaware,218,27.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Delaware,193,24.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Delaware,179,23.50 +2016,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,127,19.80 +2015,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,162,25.70 +2014,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,119,18.80 +2013,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,108,17.90 +2012,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,145,24.10 +2011,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,152,26.00 +2010,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,145,24.90 +2009,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,129,22.50 +2008,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,156,27.50 +2007,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,152,26.60 +2006,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,184,33.00 +2005,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,192,34.40 +2004,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,224,40.20 +2003,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,179,32.40 +2002,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,191,34.10 +2001,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,209,37.40 +2000,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,221,39.30 +1999,Diabetes mellitus (E10-E14),Diabetes,District of Columbia,221,39.60 +2016,Diabetes mellitus (E10-E14),Diabetes,Florida,5782,19.50 +2015,Diabetes mellitus (E10-E14),Diabetes,Florida,5403,18.60 +2014,Diabetes mellitus (E10-E14),Diabetes,Florida,5371,19.20 +2013,Diabetes mellitus (E10-E14),Diabetes,Florida,5238,19.20 +2012,Diabetes mellitus (E10-E14),Diabetes,Florida,5092,19.20 +2011,Diabetes mellitus (E10-E14),Diabetes,Florida,5093,19.70 +2010,Diabetes mellitus (E10-E14),Diabetes,Florida,5024,20.10 +2009,Diabetes mellitus (E10-E14),Diabetes,Florida,4901,20.00 +2008,Diabetes mellitus (E10-E14),Diabetes,Florida,5161,21.40 +2007,Diabetes mellitus (E10-E14),Diabetes,Florida,5110,21.70 +2006,Diabetes mellitus (E10-E14),Diabetes,Florida,5172,22.30 +2005,Diabetes mellitus (E10-E14),Diabetes,Florida,5193,22.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Florida,4809,21.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Florida,4762,21.90 +2002,Diabetes mellitus (E10-E14),Diabetes,Florida,4583,21.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Florida,4631,22.10 +2000,Diabetes mellitus (E10-E14),Diabetes,Florida,4449,21.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Florida,4357,21.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Georgia,2238,21.20 +2015,Diabetes mellitus (E10-E14),Diabetes,Georgia,2210,21.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Georgia,2230,22.30 +2013,Diabetes mellitus (E10-E14),Diabetes,Georgia,2199,23.00 +2012,Diabetes mellitus (E10-E14),Diabetes,Georgia,2088,22.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Georgia,2121,23.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Georgia,1996,23.10 +2009,Diabetes mellitus (E10-E14),Diabetes,Georgia,1582,18.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Georgia,1496,18.00 +2007,Diabetes mellitus (E10-E14),Diabetes,Georgia,1604,20.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Georgia,1657,21.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Georgia,1742,23.20 +2004,Diabetes mellitus (E10-E14),Diabetes,Georgia,1623,22.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Georgia,1717,24.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Georgia,1576,23.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Georgia,1475,22.10 +2000,Diabetes mellitus (E10-E14),Diabetes,Georgia,1461,22.40 +1999,Diabetes mellitus (E10-E14),Diabetes,Georgia,1448,22.60 +2016,Diabetes mellitus (E10-E14),Diabetes,Hawaii,285,15.10 +2015,Diabetes mellitus (E10-E14),Diabetes,Hawaii,263,14.50 +2014,Diabetes mellitus (E10-E14),Diabetes,Hawaii,276,15.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Hawaii,271,15.50 +2012,Diabetes mellitus (E10-E14),Diabetes,Hawaii,277,16.20 +2011,Diabetes mellitus (E10-E14),Diabetes,Hawaii,264,15.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Hawaii,272,16.60 +2009,Diabetes mellitus (E10-E14),Diabetes,Hawaii,308,19.50 +2008,Diabetes mellitus (E10-E14),Diabetes,Hawaii,286,18.60 +2007,Diabetes mellitus (E10-E14),Diabetes,Hawaii,291,19.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Hawaii,271,18.40 +2005,Diabetes mellitus (E10-E14),Diabetes,Hawaii,218,15.10 +2004,Diabetes mellitus (E10-E14),Diabetes,Hawaii,194,13.80 +2003,Diabetes mellitus (E10-E14),Diabetes,Hawaii,202,14.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Hawaii,204,15.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Hawaii,173,13.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Hawaii,203,16.30 +1999,Diabetes mellitus (E10-E14),Diabetes,Hawaii,211,17.30 +2016,Diabetes mellitus (E10-E14),Diabetes,Idaho,375,19.80 +2015,Diabetes mellitus (E10-E14),Diabetes,Idaho,403,22.10 +2014,Diabetes mellitus (E10-E14),Diabetes,Idaho,409,22.90 +2013,Diabetes mellitus (E10-E14),Diabetes,Idaho,404,23.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Idaho,371,22.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Idaho,393,24.20 +2010,Diabetes mellitus (E10-E14),Diabetes,Idaho,353,22.50 +2009,Diabetes mellitus (E10-E14),Diabetes,Idaho,372,24.30 +2008,Diabetes mellitus (E10-E14),Diabetes,Idaho,356,23.90 +2007,Diabetes mellitus (E10-E14),Diabetes,Idaho,331,22.90 +2006,Diabetes mellitus (E10-E14),Diabetes,Idaho,320,23.10 +2005,Diabetes mellitus (E10-E14),Diabetes,Idaho,299,22.40 +2004,Diabetes mellitus (E10-E14),Diabetes,Idaho,344,26.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Idaho,358,28.30 +2002,Diabetes mellitus (E10-E14),Diabetes,Idaho,322,26.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Idaho,318,26.40 +2000,Diabetes mellitus (E10-E14),Diabetes,Idaho,304,25.90 +1999,Diabetes mellitus (E10-E14),Diabetes,Idaho,267,23.10 +2016,Diabetes mellitus (E10-E14),Diabetes,Illinois,2781,18.70 +2015,Diabetes mellitus (E10-E14),Diabetes,Illinois,2817,19.20 +2014,Diabetes mellitus (E10-E14),Diabetes,Illinois,2712,18.70 +2013,Diabetes mellitus (E10-E14),Diabetes,Illinois,2798,19.70 +2012,Diabetes mellitus (E10-E14),Diabetes,Illinois,2706,19.20 +2011,Diabetes mellitus (E10-E14),Diabetes,Illinois,2693,19.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Illinois,2507,18.50 +2009,Diabetes mellitus (E10-E14),Diabetes,Illinois,2741,20.60 +2008,Diabetes mellitus (E10-E14),Diabetes,Illinois,2846,21.60 +2007,Diabetes mellitus (E10-E14),Diabetes,Illinois,2851,22.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Illinois,2795,21.90 +2005,Diabetes mellitus (E10-E14),Diabetes,Illinois,3034,24.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Illinois,3069,24.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Illinois,3044,24.60 +2002,Diabetes mellitus (E10-E14),Diabetes,Illinois,3011,24.60 +2001,Diabetes mellitus (E10-E14),Diabetes,Illinois,3092,25.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Illinois,2995,25.00 +1999,Diabetes mellitus (E10-E14),Diabetes,Illinois,3004,25.20 +2016,Diabetes mellitus (E10-E14),Diabetes,Indiana,1992,26.00 +2015,Diabetes mellitus (E10-E14),Diabetes,Indiana,2030,26.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Indiana,1819,24.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Indiana,1943,26.30 +2012,Diabetes mellitus (E10-E14),Diabetes,Indiana,1860,25.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Indiana,1808,25.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Indiana,1587,22.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Indiana,1646,24.10 +2008,Diabetes mellitus (E10-E14),Diabetes,Indiana,1683,24.90 +2007,Diabetes mellitus (E10-E14),Diabetes,Indiana,1564,23.50 +2006,Diabetes mellitus (E10-E14),Diabetes,Indiana,1682,25.90 +2005,Diabetes mellitus (E10-E14),Diabetes,Indiana,1719,27.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Indiana,1673,26.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Indiana,1731,27.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Indiana,1688,27.60 +2001,Diabetes mellitus (E10-E14),Diabetes,Indiana,1677,27.70 +2000,Diabetes mellitus (E10-E14),Diabetes,Indiana,1673,28.00 +1999,Diabetes mellitus (E10-E14),Diabetes,Indiana,1591,26.90 +2016,Diabetes mellitus (E10-E14),Diabetes,Iowa,847,20.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Iowa,1077,26.70 +2014,Diabetes mellitus (E10-E14),Diabetes,Iowa,1019,25.60 +2013,Diabetes mellitus (E10-E14),Diabetes,Iowa,747,19.00 +2012,Diabetes mellitus (E10-E14),Diabetes,Iowa,692,17.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Iowa,770,19.90 +2010,Diabetes mellitus (E10-E14),Diabetes,Iowa,733,19.30 +2009,Diabetes mellitus (E10-E14),Diabetes,Iowa,702,18.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Iowa,764,20.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Iowa,767,20.60 +2006,Diabetes mellitus (E10-E14),Diabetes,Iowa,804,22.00 +2005,Diabetes mellitus (E10-E14),Diabetes,Iowa,727,20.10 +2004,Diabetes mellitus (E10-E14),Diabetes,Iowa,700,19.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Iowa,727,20.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Iowa,734,20.80 +2001,Diabetes mellitus (E10-E14),Diabetes,Iowa,709,20.10 +2000,Diabetes mellitus (E10-E14),Diabetes,Iowa,630,18.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Iowa,684,19.70 +2016,Diabetes mellitus (E10-E14),Diabetes,Kansas,727,21.20 +2015,Diabetes mellitus (E10-E14),Diabetes,Kansas,684,20.20 +2014,Diabetes mellitus (E10-E14),Diabetes,Kansas,643,19.20 +2013,Diabetes mellitus (E10-E14),Diabetes,Kansas,654,19.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Kansas,639,19.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Kansas,711,22.10 +2010,Diabetes mellitus (E10-E14),Diabetes,Kansas,655,20.60 +2009,Diabetes mellitus (E10-E14),Diabetes,Kansas,635,20.30 +2008,Diabetes mellitus (E10-E14),Diabetes,Kansas,706,22.70 +2007,Diabetes mellitus (E10-E14),Diabetes,Kansas,702,22.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Kansas,749,24.80 +2005,Diabetes mellitus (E10-E14),Diabetes,Kansas,710,23.80 +2004,Diabetes mellitus (E10-E14),Diabetes,Kansas,690,23.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Kansas,676,23.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Kansas,765,26.40 +2001,Diabetes mellitus (E10-E14),Diabetes,Kansas,721,25.20 +2000,Diabetes mellitus (E10-E14),Diabetes,Kansas,668,23.50 +1999,Diabetes mellitus (E10-E14),Diabetes,Kansas,650,23.00 +2016,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1479,28.40 +2015,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1458,28.20 +2014,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1175,23.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1187,24.10 +2012,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1256,25.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1268,26.50 +2010,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1212,26.00 +2009,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1348,29.30 +2008,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1214,26.70 +2007,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1091,24.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1141,26.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1187,27.80 +2004,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1195,28.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1296,31.60 +2002,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1265,31.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1099,27.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1107,27.90 +1999,Diabetes mellitus (E10-E14),Diabetes,Kentucky,1133,28.80 +2016,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1324,25.50 +2015,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1199,23.50 +2014,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1238,24.80 +2013,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1329,26.90 +2012,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1290,26.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1265,27.10 +2010,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1205,26.40 +2009,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1249,27.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1332,30.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1437,33.50 +2006,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1536,36.30 +2005,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1695,38.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1717,39.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1740,40.90 +2002,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1774,42.30 +2001,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1734,41.90 +2000,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1694,41.40 +1999,Diabetes mellitus (E10-E14),Diabetes,Louisiana,1687,41.80 +2016,Diabetes mellitus (E10-E14),Diabetes,Maine,463,23.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Maine,407,21.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Maine,414,22.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Maine,373,20.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Maine,380,21.30 +2011,Diabetes mellitus (E10-E14),Diabetes,Maine,353,20.00 +2010,Diabetes mellitus (E10-E14),Diabetes,Maine,376,21.80 +2009,Diabetes mellitus (E10-E14),Diabetes,Maine,350,20.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Maine,344,20.80 +2007,Diabetes mellitus (E10-E14),Diabetes,Maine,355,21.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Maine,343,21.40 +2005,Diabetes mellitus (E10-E14),Diabetes,Maine,385,24.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Maine,382,24.70 +2003,Diabetes mellitus (E10-E14),Diabetes,Maine,398,26.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Maine,404,27.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Maine,398,27.10 +2000,Diabetes mellitus (E10-E14),Diabetes,Maine,357,24.80 +1999,Diabetes mellitus (E10-E14),Diabetes,Maine,348,24.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Maryland,1358,19.60 +2015,Diabetes mellitus (E10-E14),Diabetes,Maryland,1243,18.30 +2014,Diabetes mellitus (E10-E14),Diabetes,Maryland,1305,19.80 +2013,Diabetes mellitus (E10-E14),Diabetes,Maryland,1249,19.10 +2012,Diabetes mellitus (E10-E14),Diabetes,Maryland,1218,19.20 +2011,Diabetes mellitus (E10-E14),Diabetes,Maryland,1297,21.20 +2010,Diabetes mellitus (E10-E14),Diabetes,Maryland,1191,19.90 +2009,Diabetes mellitus (E10-E14),Diabetes,Maryland,1207,20.60 +2008,Diabetes mellitus (E10-E14),Diabetes,Maryland,1239,21.60 +2007,Diabetes mellitus (E10-E14),Diabetes,Maryland,1301,23.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Maryland,1237,22.50 +2005,Diabetes mellitus (E10-E14),Diabetes,Maryland,1388,26.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Maryland,1417,26.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Maryland,1454,28.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Maryland,1519,30.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Maryland,1458,29.40 +2000,Diabetes mellitus (E10-E14),Diabetes,Maryland,1515,31.30 +1999,Diabetes mellitus (E10-E14),Diabetes,Maryland,1422,29.80 +2016,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1268,14.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1398,16.70 +2014,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1202,14.50 +2013,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1142,14.10 +2012,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1099,13.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1140,14.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1028,13.30 +2009,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1007,13.30 +2008,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1079,14.50 +2007,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1222,16.60 +2006,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1132,15.50 +2005,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1271,17.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1329,18.70 +2003,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1424,20.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1423,20.40 +2001,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1422,20.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1361,19.80 +1999,Diabetes mellitus (E10-E14),Diabetes,Massachusetts,1354,19.90 +2016,Diabetes mellitus (E10-E14),Diabetes,Michigan,2672,21.50 +2015,Diabetes mellitus (E10-E14),Diabetes,Michigan,2751,22.50 +2014,Diabetes mellitus (E10-E14),Diabetes,Michigan,2844,23.70 +2013,Diabetes mellitus (E10-E14),Diabetes,Michigan,2825,23.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Michigan,2685,23.00 +2011,Diabetes mellitus (E10-E14),Diabetes,Michigan,2811,24.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Michigan,2697,24.00 +2009,Diabetes mellitus (E10-E14),Diabetes,Michigan,2705,24.40 +2008,Diabetes mellitus (E10-E14),Diabetes,Michigan,2752,25.20 +2007,Diabetes mellitus (E10-E14),Diabetes,Michigan,2826,26.30 +2006,Diabetes mellitus (E10-E14),Diabetes,Michigan,2830,26.80 +2005,Diabetes mellitus (E10-E14),Diabetes,Michigan,2842,27.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Michigan,2953,28.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Michigan,2640,26.20 +2002,Diabetes mellitus (E10-E14),Diabetes,Michigan,2785,28.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Michigan,2655,27.10 +2000,Diabetes mellitus (E10-E14),Diabetes,Michigan,2603,27.00 +1999,Diabetes mellitus (E10-E14),Diabetes,Michigan,2587,27.10 +2016,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1265,19.20 +2015,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1221,18.60 +2014,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1193,18.70 +2013,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1163,18.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1165,19.10 +2011,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1189,20.10 +2010,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1037,17.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1021,17.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1087,19.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1084,19.60 +2006,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1158,21.40 +2005,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1258,23.60 +2004,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1130,21.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1276,24.90 +2002,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1317,26.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1213,24.40 +2000,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1209,24.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Minnesota,1249,25.70 +2016,Diabetes mellitus (E10-E14),Diabetes,Mississippi,1084,31.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Mississippi,1092,32.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Mississippi,1015,30.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Mississippi,1071,32.90 +2012,Diabetes mellitus (E10-E14),Diabetes,Mississippi,1050,32.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Mississippi,996,32.00 +2010,Diabetes mellitus (E10-E14),Diabetes,Mississippi,926,30.30 +2009,Diabetes mellitus (E10-E14),Diabetes,Mississippi,900,29.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Mississippi,758,25.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Mississippi,654,22.10 +2006,Diabetes mellitus (E10-E14),Diabetes,Mississippi,707,24.50 +2005,Diabetes mellitus (E10-E14),Diabetes,Mississippi,677,23.50 +2004,Diabetes mellitus (E10-E14),Diabetes,Mississippi,665,23.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Mississippi,680,24.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Mississippi,671,24.40 +2001,Diabetes mellitus (E10-E14),Diabetes,Mississippi,661,24.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Mississippi,676,24.90 +1999,Diabetes mellitus (E10-E14),Diabetes,Mississippi,593,22.00 +2016,Diabetes mellitus (E10-E14),Diabetes,Missouri,1508,20.10 +2015,Diabetes mellitus (E10-E14),Diabetes,Missouri,1468,19.70 +2014,Diabetes mellitus (E10-E14),Diabetes,Missouri,1423,19.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Missouri,1477,20.50 +2012,Diabetes mellitus (E10-E14),Diabetes,Missouri,1377,19.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Missouri,1438,20.80 +2010,Diabetes mellitus (E10-E14),Diabetes,Missouri,1425,21.20 +2009,Diabetes mellitus (E10-E14),Diabetes,Missouri,1335,19.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Missouri,1348,20.40 +2007,Diabetes mellitus (E10-E14),Diabetes,Missouri,1444,22.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Missouri,1493,23.50 +2005,Diabetes mellitus (E10-E14),Diabetes,Missouri,1549,24.80 +2004,Diabetes mellitus (E10-E14),Diabetes,Missouri,1461,23.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Missouri,1666,27.10 +2002,Diabetes mellitus (E10-E14),Diabetes,Missouri,1625,26.80 +2001,Diabetes mellitus (E10-E14),Diabetes,Missouri,1535,25.60 +2000,Diabetes mellitus (E10-E14),Diabetes,Missouri,1458,24.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Missouri,1554,26.30 +2016,Diabetes mellitus (E10-E14),Diabetes,Montana,311,23.60 +2015,Diabetes mellitus (E10-E14),Diabetes,Montana,321,24.50 +2014,Diabetes mellitus (E10-E14),Diabetes,Montana,250,19.20 +2013,Diabetes mellitus (E10-E14),Diabetes,Montana,250,19.70 +2012,Diabetes mellitus (E10-E14),Diabetes,Montana,233,19.10 +2011,Diabetes mellitus (E10-E14),Diabetes,Montana,252,20.90 +2010,Diabetes mellitus (E10-E14),Diabetes,Montana,227,19.10 +2009,Diabetes mellitus (E10-E14),Diabetes,Montana,228,19.40 +2008,Diabetes mellitus (E10-E14),Diabetes,Montana,254,22.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Montana,258,23.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Montana,249,22.80 +2005,Diabetes mellitus (E10-E14),Diabetes,Montana,285,26.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Montana,237,22.70 +2003,Diabetes mellitus (E10-E14),Diabetes,Montana,261,25.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Montana,210,21.10 +2001,Diabetes mellitus (E10-E14),Diabetes,Montana,229,23.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Montana,227,23.60 +1999,Diabetes mellitus (E10-E14),Diabetes,Montana,244,25.70 +2016,Diabetes mellitus (E10-E14),Diabetes,Nebraska,501,21.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Nebraska,553,24.80 +2014,Diabetes mellitus (E10-E14),Diabetes,Nebraska,473,21.50 +2013,Diabetes mellitus (E10-E14),Diabetes,Nebraska,471,21.70 +2012,Diabetes mellitus (E10-E14),Diabetes,Nebraska,442,20.70 +2011,Diabetes mellitus (E10-E14),Diabetes,Nebraska,460,21.70 +2010,Diabetes mellitus (E10-E14),Diabetes,Nebraska,452,21.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Nebraska,445,21.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Nebraska,470,23.20 +2007,Diabetes mellitus (E10-E14),Diabetes,Nebraska,472,23.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Nebraska,437,22.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Nebraska,449,23.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Nebraska,395,20.50 +2003,Diabetes mellitus (E10-E14),Diabetes,Nebraska,407,21.10 +2002,Diabetes mellitus (E10-E14),Diabetes,Nebraska,393,20.80 +2001,Diabetes mellitus (E10-E14),Diabetes,Nebraska,400,21.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Nebraska,411,22.20 +1999,Diabetes mellitus (E10-E14),Diabetes,Nebraska,372,20.20 +2016,Diabetes mellitus (E10-E14),Diabetes,Nevada,580,17.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Nevada,420,13.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Nevada,350,11.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Nevada,424,14.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Nevada,438,15.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Nevada,400,15.10 +2010,Diabetes mellitus (E10-E14),Diabetes,Nevada,350,13.80 +2009,Diabetes mellitus (E10-E14),Diabetes,Nevada,377,15.50 +2008,Diabetes mellitus (E10-E14),Diabetes,Nevada,373,15.60 +2007,Diabetes mellitus (E10-E14),Diabetes,Nevada,312,13.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Nevada,294,13.00 +2005,Diabetes mellitus (E10-E14),Diabetes,Nevada,336,15.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Nevada,290,13.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Nevada,301,15.00 +2002,Diabetes mellitus (E10-E14),Diabetes,Nevada,343,17.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Nevada,322,17.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Nevada,276,15.40 +1999,Diabetes mellitus (E10-E14),Diabetes,Nevada,286,17.20 +2016,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,316,17.90 +2015,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,308,17.90 +2014,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,300,18.00 +2013,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,310,18.70 +2012,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,275,17.50 +2011,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,280,18.30 +2010,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,236,16.10 +2009,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,276,18.70 +2008,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,297,20.60 +2007,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,280,20.20 +2006,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,296,21.40 +2005,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,310,23.40 +2004,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,313,24.00 +2003,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,300,23.60 +2002,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,311,25.00 +2001,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,291,23.90 +2000,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,297,25.10 +1999,Diabetes mellitus (E10-E14),Diabetes,New Hampshire,294,25.30 +2016,Diabetes mellitus (E10-E14),Diabetes,New Jersey,1949,17.80 +2015,Diabetes mellitus (E10-E14),Diabetes,New Jersey,1933,17.90 +2014,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2062,19.30 +2013,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2043,19.40 +2012,Diabetes mellitus (E10-E14),Diabetes,New Jersey,1988,19.30 +2011,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2222,22.00 +2010,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2098,21.00 +2009,Diabetes mellitus (E10-E14),Diabetes,New Jersey,1959,19.90 +2008,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2242,23.10 +2007,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2329,24.40 +2006,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2454,26.10 +2005,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2540,27.30 +2004,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2595,28.20 +2003,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2484,27.20 +2002,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2532,28.10 +2001,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2556,28.70 +2000,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2483,28.20 +1999,Diabetes mellitus (E10-E14),Diabetes,New Jersey,2436,28.00 +2016,Diabetes mellitus (E10-E14),Diabetes,New Mexico,678,27.20 +2015,Diabetes mellitus (E10-E14),Diabetes,New Mexico,608,24.90 +2014,Diabetes mellitus (E10-E14),Diabetes,New Mexico,671,27.50 +2013,Diabetes mellitus (E10-E14),Diabetes,New Mexico,646,27.60 +2012,Diabetes mellitus (E10-E14),Diabetes,New Mexico,652,28.30 +2011,Diabetes mellitus (E10-E14),Diabetes,New Mexico,612,27.30 +2010,Diabetes mellitus (E10-E14),Diabetes,New Mexico,643,29.80 +2009,Diabetes mellitus (E10-E14),Diabetes,New Mexico,597,28.30 +2008,Diabetes mellitus (E10-E14),Diabetes,New Mexico,582,28.30 +2007,Diabetes mellitus (E10-E14),Diabetes,New Mexico,673,33.20 +2006,Diabetes mellitus (E10-E14),Diabetes,New Mexico,606,30.60 +2005,Diabetes mellitus (E10-E14),Diabetes,New Mexico,595,31.40 +2004,Diabetes mellitus (E10-E14),Diabetes,New Mexico,590,31.90 +2003,Diabetes mellitus (E10-E14),Diabetes,New Mexico,599,33.20 +2002,Diabetes mellitus (E10-E14),Diabetes,New Mexico,582,33.00 +2001,Diabetes mellitus (E10-E14),Diabetes,New Mexico,538,31.50 +2000,Diabetes mellitus (E10-E14),Diabetes,New Mexico,485,29.10 +1999,Diabetes mellitus (E10-E14),Diabetes,New Mexico,517,31.40 +2016,Diabetes mellitus (E10-E14),Diabetes,New York,4038,16.90 +2015,Diabetes mellitus (E10-E14),Diabetes,New York,4045,17.10 +2014,Diabetes mellitus (E10-E14),Diabetes,New York,4064,17.40 +2013,Diabetes mellitus (E10-E14),Diabetes,New York,4094,17.80 +2012,Diabetes mellitus (E10-E14),Diabetes,New York,4010,17.70 +2011,Diabetes mellitus (E10-E14),Diabetes,New York,3988,18.00 +2010,Diabetes mellitus (E10-E14),Diabetes,New York,3642,16.70 +2009,Diabetes mellitus (E10-E14),Diabetes,New York,3723,17.30 +2008,Diabetes mellitus (E10-E14),Diabetes,New York,3605,16.90 +2007,Diabetes mellitus (E10-E14),Diabetes,New York,3715,17.70 +2006,Diabetes mellitus (E10-E14),Diabetes,New York,3848,18.60 +2005,Diabetes mellitus (E10-E14),Diabetes,New York,4051,19.70 +2004,Diabetes mellitus (E10-E14),Diabetes,New York,3913,19.20 +2003,Diabetes mellitus (E10-E14),Diabetes,New York,4227,21.00 +2002,Diabetes mellitus (E10-E14),Diabetes,New York,3934,19.70 +2001,Diabetes mellitus (E10-E14),Diabetes,New York,3844,19.50 +2000,Diabetes mellitus (E10-E14),Diabetes,New York,3877,20.00 +1999,Diabetes mellitus (E10-E14),Diabetes,New York,3799,19.80 +2016,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2811,23.50 +2015,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2746,23.60 +2014,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2687,23.70 +2013,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2402,21.80 +2012,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2409,22.60 +2011,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2278,22.10 +2010,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2042,20.50 +2009,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2114,21.50 +2008,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2170,22.70 +2007,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2156,23.30 +2006,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2234,24.90 +2005,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2261,26.20 +2004,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2253,26.90 +2003,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2388,29.10 +2002,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2205,27.60 +2001,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2181,27.90 +2000,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2084,27.30 +1999,Diabetes mellitus (E10-E14),Diabetes,North Carolina,2050,27.00 +2016,Diabetes mellitus (E10-E14),Diabetes,North Dakota,171,19.40 +2015,Diabetes mellitus (E10-E14),Diabetes,North Dakota,195,22.60 +2014,Diabetes mellitus (E10-E14),Diabetes,North Dakota,176,19.90 +2013,Diabetes mellitus (E10-E14),Diabetes,North Dakota,197,22.30 +2012,Diabetes mellitus (E10-E14),Diabetes,North Dakota,185,22.00 +2011,Diabetes mellitus (E10-E14),Diabetes,North Dakota,207,24.50 +2010,Diabetes mellitus (E10-E14),Diabetes,North Dakota,192,22.20 +2009,Diabetes mellitus (E10-E14),Diabetes,North Dakota,214,25.40 +2008,Diabetes mellitus (E10-E14),Diabetes,North Dakota,201,25.00 +2007,Diabetes mellitus (E10-E14),Diabetes,North Dakota,226,28.10 +2006,Diabetes mellitus (E10-E14),Diabetes,North Dakota,208,26.10 +2005,Diabetes mellitus (E10-E14),Diabetes,North Dakota,204,26.20 +2004,Diabetes mellitus (E10-E14),Diabetes,North Dakota,209,26.60 +2003,Diabetes mellitus (E10-E14),Diabetes,North Dakota,211,27.00 +2002,Diabetes mellitus (E10-E14),Diabetes,North Dakota,214,27.10 +2001,Diabetes mellitus (E10-E14),Diabetes,North Dakota,196,25.80 +2000,Diabetes mellitus (E10-E14),Diabetes,North Dakota,203,26.60 +1999,Diabetes mellitus (E10-E14),Diabetes,North Dakota,203,26.70 +2016,Diabetes mellitus (E10-E14),Diabetes,Ohio,3568,24.60 +2015,Diabetes mellitus (E10-E14),Diabetes,Ohio,3645,25.30 +2014,Diabetes mellitus (E10-E14),Diabetes,Ohio,3641,25.70 +2013,Diabetes mellitus (E10-E14),Diabetes,Ohio,3563,25.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Ohio,3618,26.10 +2011,Diabetes mellitus (E10-E14),Diabetes,Ohio,3668,26.80 +2010,Diabetes mellitus (E10-E14),Diabetes,Ohio,3470,25.80 +2009,Diabetes mellitus (E10-E14),Diabetes,Ohio,3401,25.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Ohio,3565,27.20 +2007,Diabetes mellitus (E10-E14),Diabetes,Ohio,3722,29.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Ohio,3761,29.60 +2005,Diabetes mellitus (E10-E14),Diabetes,Ohio,3794,30.50 +2004,Diabetes mellitus (E10-E14),Diabetes,Ohio,3615,29.30 +2003,Diabetes mellitus (E10-E14),Diabetes,Ohio,3731,30.60 +2002,Diabetes mellitus (E10-E14),Diabetes,Ohio,3846,32.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Ohio,3750,31.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Ohio,3691,31.40 +1999,Diabetes mellitus (E10-E14),Diabetes,Ohio,3675,31.40 +2016,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1393,30.80 +2015,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1442,32.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1261,29.10 +2013,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1269,29.90 +2012,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1201,28.40 +2011,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1221,29.70 +2010,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1089,26.90 +2009,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1146,28.70 +2008,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1103,28.30 +2007,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1148,29.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1166,30.60 +2005,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1217,32.60 +2004,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1139,30.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1104,30.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1064,29.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,1065,29.70 +2000,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,978,27.50 +1999,Diabetes mellitus (E10-E14),Diabetes,Oklahoma,986,27.90 +2016,Diabetes mellitus (E10-E14),Diabetes,Oregon,1240,24.00 +2015,Diabetes mellitus (E10-E14),Diabetes,Oregon,1148,22.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Oregon,1083,22.40 +2013,Diabetes mellitus (E10-E14),Diabetes,Oregon,1109,23.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Oregon,1126,24.30 +2011,Diabetes mellitus (E10-E14),Diabetes,Oregon,1113,24.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Oregon,1052,23.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Oregon,1073,24.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Oregon,1025,24.00 +2007,Diabetes mellitus (E10-E14),Diabetes,Oregon,1113,27.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Oregon,1139,28.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Oregon,1149,29.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Oregon,1073,27.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Oregon,1032,27.30 +2002,Diabetes mellitus (E10-E14),Diabetes,Oregon,1041,28.20 +2001,Diabetes mellitus (E10-E14),Diabetes,Oregon,1011,28.00 +2000,Diabetes mellitus (E10-E14),Diabetes,Oregon,844,23.80 +1999,Diabetes mellitus (E10-E14),Diabetes,Oregon,860,24.60 +2016,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3560,20.30 +2015,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3777,22.10 +2014,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3765,22.00 +2013,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3804,22.60 +2012,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3735,22.30 +2011,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3462,21.10 +2010,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3242,20.00 +2009,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3254,20.30 +2008,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3313,20.80 +2007,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3442,21.90 +2006,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3466,22.30 +2005,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3553,23.20 +2004,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3579,23.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3732,24.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3708,24.80 +2001,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3826,25.90 +2000,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3794,25.80 +1999,Diabetes mellitus (E10-E14),Diabetes,Pennsylvania,3742,25.50 +2016,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,239,16.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,276,20.20 +2014,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,252,18.30 +2013,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,258,18.90 +2012,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,198,14.70 +2011,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,180,13.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,211,16.00 +2009,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,213,16.60 +2008,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,195,15.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,248,19.10 +2006,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,206,16.20 +2005,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,282,22.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,280,22.40 +2003,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,252,20.40 +2002,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,263,21.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,265,21.90 +2000,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,292,24.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Rhode Island,236,20.10 +2016,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1369,22.30 +2015,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1347,23.40 +2014,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1239,21.80 +2013,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1240,22.50 +2012,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1197,22.30 +2011,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1090,21.00 +2010,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1128,22.60 +2009,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1128,23.10 +2008,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1133,23.90 +2007,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1231,26.70 +2006,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1136,25.50 +2005,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1187,27.60 +2004,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1165,27.80 +2003,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1161,28.30 +2002,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1112,27.80 +2001,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1088,27.70 +2000,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1206,31.50 +1999,Diabetes mellitus (E10-E14),Diabetes,South Carolina,1118,29.60 +2016,Diabetes mellitus (E10-E14),Diabetes,South Dakota,253,23.70 +2015,Diabetes mellitus (E10-E14),Diabetes,South Dakota,282,26.30 +2014,Diabetes mellitus (E10-E14),Diabetes,South Dakota,224,21.30 +2013,Diabetes mellitus (E10-E14),Diabetes,South Dakota,237,22.70 +2012,Diabetes mellitus (E10-E14),Diabetes,South Dakota,218,21.50 +2011,Diabetes mellitus (E10-E14),Diabetes,South Dakota,263,26.40 +2010,Diabetes mellitus (E10-E14),Diabetes,South Dakota,240,24.50 +2009,Diabetes mellitus (E10-E14),Diabetes,South Dakota,202,21.10 +2008,Diabetes mellitus (E10-E14),Diabetes,South Dakota,216,22.30 +2007,Diabetes mellitus (E10-E14),Diabetes,South Dakota,247,26.10 +2006,Diabetes mellitus (E10-E14),Diabetes,South Dakota,262,27.60 +2005,Diabetes mellitus (E10-E14),Diabetes,South Dakota,241,26.20 +2004,Diabetes mellitus (E10-E14),Diabetes,South Dakota,229,25.40 +2003,Diabetes mellitus (E10-E14),Diabetes,South Dakota,203,23.00 +2002,Diabetes mellitus (E10-E14),Diabetes,South Dakota,195,22.80 +2001,Diabetes mellitus (E10-E14),Diabetes,South Dakota,212,24.40 +2000,Diabetes mellitus (E10-E14),Diabetes,South Dakota,180,20.70 +1999,Diabetes mellitus (E10-E14),Diabetes,South Dakota,196,23.10 +2016,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1883,24.00 +2015,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1798,23.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1727,23.20 +2013,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1827,24.80 +2012,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1852,25.80 +2011,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1748,25.00 +2010,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1687,24.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1759,26.20 +2008,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1733,26.40 +2007,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1700,26.70 +2006,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1715,27.40 +2005,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1844,30.30 +2004,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1882,31.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1855,31.70 +2002,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1749,30.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1746,30.70 +2000,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1591,28.50 +1999,Diabetes mellitus (E10-E14),Diabetes,Tennessee,1436,26.10 +2016,Diabetes mellitus (E10-E14),Diabetes,Texas,5470,20.30 +2015,Diabetes mellitus (E10-E14),Diabetes,Texas,5521,21.20 +2014,Diabetes mellitus (E10-E14),Diabetes,Texas,5348,21.30 +2013,Diabetes mellitus (E10-E14),Diabetes,Texas,5273,21.60 +2012,Diabetes mellitus (E10-E14),Diabetes,Texas,5136,21.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Texas,5091,22.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Texas,4744,21.60 +2009,Diabetes mellitus (E10-E14),Diabetes,Texas,4873,22.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Texas,5155,24.90 +2007,Diabetes mellitus (E10-E14),Diabetes,Texas,5109,25.30 +2006,Diabetes mellitus (E10-E14),Diabetes,Texas,5201,26.50 +2005,Diabetes mellitus (E10-E14),Diabetes,Texas,5605,29.70 +2004,Diabetes mellitus (E10-E14),Diabetes,Texas,5435,29.70 +2003,Diabetes mellitus (E10-E14),Diabetes,Texas,5668,31.60 +2002,Diabetes mellitus (E10-E14),Diabetes,Texas,5654,32.30 +2001,Diabetes mellitus (E10-E14),Diabetes,Texas,5456,31.90 +2000,Diabetes mellitus (E10-E14),Diabetes,Texas,5196,31.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Texas,4931,29.90 +2016,Diabetes mellitus (E10-E14),Diabetes,United States,80058,21.00 +2015,Diabetes mellitus (E10-E14),Diabetes,United States,79535,21.30 +2014,Diabetes mellitus (E10-E14),Diabetes,United States,76488,20.90 +2013,Diabetes mellitus (E10-E14),Diabetes,United States,75578,21.20 +2012,Diabetes mellitus (E10-E14),Diabetes,United States,73932,21.20 +2011,Diabetes mellitus (E10-E14),Diabetes,United States,73831,21.70 +2010,Diabetes mellitus (E10-E14),Diabetes,United States,69071,20.80 +2009,Diabetes mellitus (E10-E14),Diabetes,United States,68705,21.10 +2008,Diabetes mellitus (E10-E14),Diabetes,United States,70553,22.00 +2007,Diabetes mellitus (E10-E14),Diabetes,United States,71382,22.80 +2006,Diabetes mellitus (E10-E14),Diabetes,United States,72449,23.60 +2005,Diabetes mellitus (E10-E14),Diabetes,United States,75119,24.90 +2004,Diabetes mellitus (E10-E14),Diabetes,United States,73138,24.80 +2003,Diabetes mellitus (E10-E14),Diabetes,United States,74219,25.50 +2002,Diabetes mellitus (E10-E14),Diabetes,United States,73249,25.60 +2001,Diabetes mellitus (E10-E14),Diabetes,United States,71372,25.40 +2000,Diabetes mellitus (E10-E14),Diabetes,United States,69301,25.10 +1999,Diabetes mellitus (E10-E14),Diabetes,United States,68399,25.00 +2016,Diabetes mellitus (E10-E14),Diabetes,Utah,624,24.60 +2015,Diabetes mellitus (E10-E14),Diabetes,Utah,604,24.60 +2014,Diabetes mellitus (E10-E14),Diabetes,Utah,570,24.30 +2013,Diabetes mellitus (E10-E14),Diabetes,Utah,578,25.30 +2012,Diabetes mellitus (E10-E14),Diabetes,Utah,541,24.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Utah,561,26.00 +2010,Diabetes mellitus (E10-E14),Diabetes,Utah,471,22.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Utah,463,22.60 +2008,Diabetes mellitus (E10-E14),Diabetes,Utah,468,23.70 +2007,Diabetes mellitus (E10-E14),Diabetes,Utah,548,28.80 +2006,Diabetes mellitus (E10-E14),Diabetes,Utah,496,27.00 +2005,Diabetes mellitus (E10-E14),Diabetes,Utah,541,31.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Utah,485,28.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Utah,520,31.50 +2002,Diabetes mellitus (E10-E14),Diabetes,Utah,514,31.80 +2001,Diabetes mellitus (E10-E14),Diabetes,Utah,509,32.20 +2000,Diabetes mellitus (E10-E14),Diabetes,Utah,530,34.80 +1999,Diabetes mellitus (E10-E14),Diabetes,Utah,472,31.40 +2016,Diabetes mellitus (E10-E14),Diabetes,Vermont,166,20.50 +2015,Diabetes mellitus (E10-E14),Diabetes,Vermont,159,19.80 +2014,Diabetes mellitus (E10-E14),Diabetes,Vermont,154,18.70 +2013,Diabetes mellitus (E10-E14),Diabetes,Vermont,139,17.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Vermont,168,21.40 +2011,Diabetes mellitus (E10-E14),Diabetes,Vermont,168,21.50 +2010,Diabetes mellitus (E10-E14),Diabetes,Vermont,151,20.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Vermont,143,19.20 +2008,Diabetes mellitus (E10-E14),Diabetes,Vermont,151,21.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Vermont,170,24.40 +2006,Diabetes mellitus (E10-E14),Diabetes,Vermont,175,25.00 +2005,Diabetes mellitus (E10-E14),Diabetes,Vermont,173,25.80 +2004,Diabetes mellitus (E10-E14),Diabetes,Vermont,150,22.60 +2003,Diabetes mellitus (E10-E14),Diabetes,Vermont,183,27.70 +2002,Diabetes mellitus (E10-E14),Diabetes,Vermont,174,27.00 +2001,Diabetes mellitus (E10-E14),Diabetes,Vermont,155,24.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Vermont,163,26.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Vermont,179,29.30 +2016,Diabetes mellitus (E10-E14),Diabetes,Virginia,2062,21.70 +2015,Diabetes mellitus (E10-E14),Diabetes,Virginia,2044,21.90 +2014,Diabetes mellitus (E10-E14),Diabetes,Virginia,1683,18.50 +2013,Diabetes mellitus (E10-E14),Diabetes,Virginia,1626,18.40 +2012,Diabetes mellitus (E10-E14),Diabetes,Virginia,1603,18.60 +2011,Diabetes mellitus (E10-E14),Diabetes,Virginia,1643,19.50 +2010,Diabetes mellitus (E10-E14),Diabetes,Virginia,1530,18.80 +2009,Diabetes mellitus (E10-E14),Diabetes,Virginia,1560,19.50 +2008,Diabetes mellitus (E10-E14),Diabetes,Virginia,1534,19.80 +2007,Diabetes mellitus (E10-E14),Diabetes,Virginia,1507,20.00 +2006,Diabetes mellitus (E10-E14),Diabetes,Virginia,1632,22.10 +2005,Diabetes mellitus (E10-E14),Diabetes,Virginia,1642,22.90 +2004,Diabetes mellitus (E10-E14),Diabetes,Virginia,1602,22.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Virginia,1587,23.00 +2002,Diabetes mellitus (E10-E14),Diabetes,Virginia,1558,23.20 +2001,Diabetes mellitus (E10-E14),Diabetes,Virginia,1613,24.60 +2000,Diabetes mellitus (E10-E14),Diabetes,Virginia,1564,24.40 +1999,Diabetes mellitus (E10-E14),Diabetes,Virginia,1486,23.60 +2016,Diabetes mellitus (E10-E14),Diabetes,Washington,1673,20.30 +2015,Diabetes mellitus (E10-E14),Diabetes,Washington,1811,22.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Washington,1668,21.20 +2013,Diabetes mellitus (E10-E14),Diabetes,Washington,1616,21.30 +2012,Diabetes mellitus (E10-E14),Diabetes,Washington,1650,22.40 +2011,Diabetes mellitus (E10-E14),Diabetes,Washington,1633,22.60 +2010,Diabetes mellitus (E10-E14),Diabetes,Washington,1499,21.60 +2009,Diabetes mellitus (E10-E14),Diabetes,Washington,1554,22.80 +2008,Diabetes mellitus (E10-E14),Diabetes,Washington,1590,24.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Washington,1508,23.50 +2006,Diabetes mellitus (E10-E14),Diabetes,Washington,1543,24.70 +2005,Diabetes mellitus (E10-E14),Diabetes,Washington,1554,25.50 +2004,Diabetes mellitus (E10-E14),Diabetes,Washington,1508,25.30 +2003,Diabetes mellitus (E10-E14),Diabetes,Washington,1509,26.10 +2002,Diabetes mellitus (E10-E14),Diabetes,Washington,1494,26.30 +2001,Diabetes mellitus (E10-E14),Diabetes,Washington,1403,25.30 +2000,Diabetes mellitus (E10-E14),Diabetes,Washington,1331,24.50 +1999,Diabetes mellitus (E10-E14),Diabetes,Washington,1307,24.50 +2016,Diabetes mellitus (E10-E14),Diabetes,West Virginia,860,34.80 +2015,Diabetes mellitus (E10-E14),Diabetes,West Virginia,784,31.70 +2014,Diabetes mellitus (E10-E14),Diabetes,West Virginia,818,33.30 +2013,Diabetes mellitus (E10-E14),Diabetes,West Virginia,841,34.10 +2012,Diabetes mellitus (E10-E14),Diabetes,West Virginia,741,30.50 +2011,Diabetes mellitus (E10-E14),Diabetes,West Virginia,790,33.30 +2010,Diabetes mellitus (E10-E14),Diabetes,West Virginia,776,32.90 +2009,Diabetes mellitus (E10-E14),Diabetes,West Virginia,746,32.00 +2008,Diabetes mellitus (E10-E14),Diabetes,West Virginia,753,32.70 +2007,Diabetes mellitus (E10-E14),Diabetes,West Virginia,800,35.20 +2006,Diabetes mellitus (E10-E14),Diabetes,West Virginia,743,33.00 +2005,Diabetes mellitus (E10-E14),Diabetes,West Virginia,766,34.50 +2004,Diabetes mellitus (E10-E14),Diabetes,West Virginia,840,38.20 +2003,Diabetes mellitus (E10-E14),Diabetes,West Virginia,807,37.10 +2002,Diabetes mellitus (E10-E14),Diabetes,West Virginia,846,39.50 +2001,Diabetes mellitus (E10-E14),Diabetes,West Virginia,802,37.70 +2000,Diabetes mellitus (E10-E14),Diabetes,West Virginia,759,35.90 +1999,Diabetes mellitus (E10-E14),Diabetes,West Virginia,734,34.80 +2016,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1440,19.90 +2015,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1382,19.40 +2014,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1352,19.10 +2013,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1285,18.50 +2012,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1289,19.00 +2011,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1197,17.90 +2010,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1158,17.70 +2009,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1110,17.20 +2008,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1152,18.20 +2007,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1136,18.30 +2006,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1212,19.70 +2005,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1276,21.00 +2004,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1310,21.90 +2003,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1331,22.70 +2002,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1353,23.40 +2001,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1337,23.50 +2000,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1309,23.30 +1999,Diabetes mellitus (E10-E14),Diabetes,Wisconsin,1273,22.90 +2016,Diabetes mellitus (E10-E14),Diabetes,Wyoming,112,16.60 +2015,Diabetes mellitus (E10-E14),Diabetes,Wyoming,136,20.80 +2014,Diabetes mellitus (E10-E14),Diabetes,Wyoming,110,17.60 +2013,Diabetes mellitus (E10-E14),Diabetes,Wyoming,89,14.20 +2012,Diabetes mellitus (E10-E14),Diabetes,Wyoming,97,15.90 +2011,Diabetes mellitus (E10-E14),Diabetes,Wyoming,108,18.40 +2010,Diabetes mellitus (E10-E14),Diabetes,Wyoming,105,18.50 +2009,Diabetes mellitus (E10-E14),Diabetes,Wyoming,105,18.10 +2008,Diabetes mellitus (E10-E14),Diabetes,Wyoming,130,24.10 +2007,Diabetes mellitus (E10-E14),Diabetes,Wyoming,139,26.30 +2006,Diabetes mellitus (E10-E14),Diabetes,Wyoming,126,23.90 +2005,Diabetes mellitus (E10-E14),Diabetes,Wyoming,130,25.80 +2004,Diabetes mellitus (E10-E14),Diabetes,Wyoming,110,22.20 +2003,Diabetes mellitus (E10-E14),Diabetes,Wyoming,138,27.80 +2002,Diabetes mellitus (E10-E14),Diabetes,Wyoming,145,30.50 +2001,Diabetes mellitus (E10-E14),Diabetes,Wyoming,120,25.70 +2000,Diabetes mellitus (E10-E14),Diabetes,Wyoming,111,24.10 +1999,Diabetes mellitus (E10-E14),Diabetes,Wyoming,135,29.80 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12832,222.50 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12981,229.70 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12461,224.00 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12472,228.40 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12036,225.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,11942,228.70 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12083,236.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12021,238.00 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12074,243.30 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,11926,245.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12583,263.00 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12869,274.50 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,12774,278.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,13150,288.50 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,13197,291.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,13207,292.70 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,13406,299.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alabama,13419,303.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,831,141.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,846,154.10 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,782,146.60 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,706,135.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,708,136.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,739,149.60 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,707,151.50 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,714,158.90 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,634,150.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,613,154.20 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,645,170.60 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,627,166.10 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,589,160.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,626,185.50 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,567,169.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,603,189.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,607,211.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Alaska,563,198.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,11957,138.90 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,11458,138.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10805,136.40 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10755,141.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10689,145.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10662,150.30 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,9954,146.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10273,155.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10385,162.30 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10302,166.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10607,176.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10966,189.80 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10539,190.00 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10887,202.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10852,207.90 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10588,207.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10584,214.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arizona,10800,223.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,8090,223.70 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7938,223.20 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7581,217.50 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7377,214.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7407,218.60 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7168,213.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7274,222.50 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7295,226.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7516,236.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7214,230.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7431,240.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7575,250.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7534,252.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,7786,262.50 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,8330,282.80 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,8263,281.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,8278,283.80 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Arkansas,8315,287.80 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,61573,143.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,61289,145.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,58189,142.20 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,60299,151.80 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,59371,153.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,59772,159.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,58641,161.90 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,59206,166.90 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,60709,176.20 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,61690,183.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,64871,198.80 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,64916,203.00 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,64999,209.00 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,68864,225.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,68797,230.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,68234,233.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,68426,239.90 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,California,71930,257.30 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,7277,129.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,7009,128.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6900,130.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6456,125.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6311,127.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6177,129.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6038,132.80 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6093,136.50 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6118,142.50 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6106,147.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6124,152.70 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6307,164.80 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6079,163.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6499,180.30 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6425,182.70 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6293,183.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6184,185.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Colorado,6420,196.30 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7051,144.30 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7205,147.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7018,145.60 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7090,148.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7282,155.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7206,155.20 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7127,155.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7105,157.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7355,165.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7289,167.40 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7490,174.70 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7650,180.70 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,7868,189.50 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,8389,204.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,8815,219.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,8582,218.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,8993,232.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Connecticut,9127,238.80 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1974,163.20 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1940,165.20 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1921,168.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1861,168.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1790,168.80 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1809,175.20 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1776,175.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1796,182.30 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1777,184.30 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1914,205.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1866,206.00 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,2031,232.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,2015,238.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,2027,247.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1918,239.90 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,2033,261.20 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,1983,262.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Delaware,2011,269.20 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1375,211.70 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1217,187.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1324,207.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1340,213.50 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1298,213.00 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1171,194.40 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1306,222.40 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1288,224.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1389,244.30 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1434,253.60 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1566,280.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1518,270.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1544,276.40 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1630,291.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1666,296.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1761,312.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1756,310.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,District of Columbia,1652,296.20 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,45659,146.20 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,45441,149.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,44511,151.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,42656,149.80 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,42125,153.00 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,40919,153.10 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,41737,162.30 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,41274,163.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,42067,170.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,42254,175.80 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,44305,187.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,46279,200.00 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,47160,208.70 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,48141,217.30 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,49235,226.10 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,50629,236.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,50336,238.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Florida,51434,247.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,18143,179.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17769,180.20 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17107,179.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16550,178.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,15825,177.60 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,15773,182.50 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,15987,192.60 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16165,198.50 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,15721,199.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16184,212.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16478,222.20 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16781,236.00 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,16557,240.30 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17188,255.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17529,265.80 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17478,271.40 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17406,276.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Georgia,17597,282.80 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2488,127.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2605,135.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2528,136.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2521,139.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2430,135.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2312,132.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2239,134.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2363,146.60 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2313,147.70 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2227,146.70 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2244,151.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2319,160.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2457,176.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2461,182.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2512,191.80 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2310,181.70 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2390,195.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Hawaii,2410,201.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2969,160.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2825,156.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2676,152.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2495,145.40 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2515,150.90 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2522,155.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2495,159.30 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2398,155.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2330,157.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2433,167.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2399,171.30 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2450,182.60 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2450,188.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2566,202.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2532,205.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2489,207.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2519,215.20 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Idaho,2532,220.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,25013,165.70 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,25652,171.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,25024,169.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,24839,170.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,24667,172.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,24987,178.10 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,24959,181.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,24931,184.00 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,26078,195.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,25813,196.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,27007,208.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,28226,220.70 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,28284,224.30 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,29816,239.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,30821,250.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,30990,253.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,31844,263.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Illinois,33387,279.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13952,180.60 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13948,182.30 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13764,182.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13773,186.20 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13675,188.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13372,187.20 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13388,191.80 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13509,196.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13663,201.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,13682,205.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,14375,220.80 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,14542,227.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,14636,233.20 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,15467,249.40 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,15321,250.90 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,15682,259.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,16210,272.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Indiana,16661,282.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6937,162.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6813,160.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6615,157.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6995,168.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7005,170.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6736,166.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6880,173.30 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6938,176.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7284,185.50 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,6880,177.20 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7172,187.10 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7437,196.60 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7299,195.00 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,7840,210.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,8181,221.50 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,8250,225.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,8559,236.10 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Iowa,8699,240.30 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5672,159.20 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5624,158.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5479,157.40 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5364,156.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5366,158.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5327,158.30 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5433,164.90 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5294,163.00 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5643,175.40 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5749,180.80 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5849,186.30 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,5960,192.60 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6048,198.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6491,214.70 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6680,223.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6716,225.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6931,234.20 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kansas,6975,237.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10519,203.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10077,197.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10013,200.50 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,9971,203.40 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10024,208.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,9905,210.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,9662,210.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,9728,214.30 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10061,225.50 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,9916,227.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10353,241.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10782,257.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,10465,254.80 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,11319,279.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,11696,292.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,11808,297.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,11936,303.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Kentucky,12098,310.90 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10943,213.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10665,212.10 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10647,216.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10346,214.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10074,213.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,9781,212.40 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10282,229.40 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10169,229.90 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10347,238.50 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,9947,234.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10026,241.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,11008,256.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,10852,257.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,11540,277.50 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,11185,272.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,11474,282.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,11629,288.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Louisiana,12007,302.30 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2907,149.50 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3009,157.30 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2776,147.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2807,152.30 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2623,145.90 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2648,149.50 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2628,151.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2674,156.40 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2785,164.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2852,173.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2815,174.30 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2941,186.50 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,2948,189.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3106,203.00 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3170,210.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3272,220.70 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3400,233.90 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maine,3418,238.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11390,164.30 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11481,169.30 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11135,167.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11244,172.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11023,174.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,10557,171.20 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,10915,182.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11210,190.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11157,195.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11314,203.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11268,206.60 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11594,217.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,11346,218.50 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,12164,239.80 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,12008,242.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,12310,253.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,12348,261.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Maryland,12080,259.20 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,11921,134.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12130,138.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,11817,137.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3091,142.40 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12023,141.50 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,11602,139.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,11765,143.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12043,150.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12322,156.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12776,164.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12710,166.40 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,12947,171.70 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,13280,179.00 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,13824,188.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,14634,201.50 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,14736,205.50 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,15144,213.50 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,15314,217.90 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Massachusetts,15871,228.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,25304,200.60 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24794,198.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24692,200.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24156,199.80 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,23520,197.90 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,23358,200.10 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,23326,204.30 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,23099,205.50 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24344,220.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24149,222.50 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24255,228.10 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,25128,241.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,24825,242.70 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,26010,258.00 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,26659,269.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,26896,276.40 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,27325,285.90 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Michigan,27693,292.90 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7825,114.90 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7844,116.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7659,116.50 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7714,119.60 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7496,119.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7240,117.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7185,119.40 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7238,122.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7367,126.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7477,130.70 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7525,134.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7926,145.40 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,7891,146.70 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,8144,154.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,8602,165.90 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,8760,172.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,8885,177.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Minnesota,9533,192.40 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7865,233.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7969,240.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7538,229.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7715,240.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7269,231.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7335,240.10 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7542,251.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7530,253.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,7997,273.20 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,8037,278.50 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,8097,285.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,8637,306.40 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,8282,298.80 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,8683,317.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,9061,332.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,9050,333.50 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,9256,341.20 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Mississippi,9336,347.40 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14579,192.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14808,197.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14338,194.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14095,194.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,13742,193.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,13810,197.40 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,13840,201.80 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,13916,206.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14644,219.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14338,218.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14749,228.80 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,14974,236.80 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,15500,248.50 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,16410,264.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,16708,272.70 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,16633,273.40 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,17235,286.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Missouri,17974,300.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,2138,154.40 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,2104,155.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1957,147.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,2004,154.30 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1903,151.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1921,156.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1849,154.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1832,154.30 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1975,170.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1870,164.50 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1869,169.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1855,172.20 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1838,175.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1984,193.30 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1944,192.80 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1970,199.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,1995,205.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Montana,2049,214.60 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3322,140.30 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3591,154.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3296,143.00 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3381,147.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3307,146.90 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3265,146.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3355,154.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3277,153.20 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3459,163.20 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3520,168.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3445,168.00 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3640,179.40 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3738,187.20 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,3954,199.60 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,4242,215.50 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,4150,212.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,4197,217.10 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nebraska,4497,235.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,6457,205.90 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,6114,200.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5761,197.20 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5434,195.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5166,192.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5043,196.30 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4811,197.30 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4687,194.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4646,202.00 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4591,205.60 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5013,233.70 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,5094,246.70 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4693,240.00 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4599,244.00 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4421,247.50 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4393,257.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4089,255.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Nevada,4231,274.30 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2631,151.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2571,149.00 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2464,147.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2434,148.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2324,146.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2333,151.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2290,152.70 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2268,153.40 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2409,166.00 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2511,176.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2501,181.10 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2530,189.80 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2639,203.40 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2725,216.70 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2776,225.00 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2835,234.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2811,237.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Hampshire,2751,236.20 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18597,164.70 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18647,166.70 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18319,166.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18460,170.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18340,171.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18330,174.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18730,182.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18086,178.60 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,19056,191.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,18831,193.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,19548,203.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,20655,219.10 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,20560,221.80 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,22043,239.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,22510,248.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,22704,254.00 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,23724,269.40 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Jersey,23493,270.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3800,150.60 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3508,142.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3424,143.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3425,147.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3352,147.80 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3333,150.30 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3224,151.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3214,153.60 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3275,161.30 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3305,167.20 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3411,179.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3435,186.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3264,183.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3402,194.80 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3360,196.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3423,204.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3191,196.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New Mexico,3452,218.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,44076,177.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,44450,181.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,43116,178.30 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,44039,184.80 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,43795,186.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,44501,193.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,44981,200.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,47283,213.20 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,49324,225.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,49528,230.40 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,50470,238.20 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,51985,248.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,52480,254.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,55276,270.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,56672,281.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,56643,284.90 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,57474,293.80 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,New York,58987,306.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,18266,155.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,18474,162.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17592,158.70 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17830,165.30 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17348,166.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17003,167.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17154,174.90 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17203,179.70 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17335,186.40 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17395,193.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17271,197.60 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17765,211.70 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,17607,215.20 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,18674,233.30 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,18524,237.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,18792,246.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,19723,264.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Carolina,19191,260.90 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1338,140.90 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1323,142.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1381,149.20 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1382,150.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1368,151.60 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1290,144.60 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1395,158.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1431,165.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1391,160.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1414,165.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1527,182.00 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1512,181.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1470,178.00 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1632,201.80 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1623,203.10 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1700,212.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1679,212.80 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,North Dakota,1833,234.20 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,27410,185.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,28069,191.70 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,27000,186.40 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,26878,187.90 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,26426,187.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,26298,190.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,26164,192.40 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,25453,190.00 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,27324,206.90 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,26757,205.70 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,27886,218.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,29003,231.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,29078,235.80 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,30667,251.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,31388,261.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,32453,273.50 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,32722,279.20 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Ohio,33192,285.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,10209,228.20 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,10310,234.00 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9868,228.10 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9721,228.50 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9198,220.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9399,228.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9426,235.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9202,232.60 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9767,252.00 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9602,251.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,9798,260.10 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,10043,272.40 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,10335,283.20 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,11061,304.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,11230,311.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,10840,301.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,11297,315.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oklahoma,11263,316.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6968,135.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6859,136.10 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6524,132.10 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6523,135.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6180,130.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6255,135.30 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6198,137.90 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6262,141.20 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6519,150.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6655,157.60 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6620,160.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6791,169.70 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,6725,172.40 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,7049,184.40 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,7262,194.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,7075,193.20 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,7094,198.10 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Oregon,7263,205.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,31990,176.20 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,32042,177.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,31353,175.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,31629,179.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,30952,177.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,31934,185.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,31556,187.00 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,32297,193.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,33308,201.80 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,32862,201.30 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,33744,210.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,36207,229.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,36434,233.80 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,38075,246.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,38852,254.70 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,39438,261.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,40708,271.90 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Pennsylvania,41707,281.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2256,152.40 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2371,160.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2341,160.80 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2364,163.40 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2360,165.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2390,167.70 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2322,167.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2411,173.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2657,193.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2751,203.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2718,201.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3005,226.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,2969,225.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3011,231.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3109,243.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3076,244.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3115,249.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Rhode Island,3008,244.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,10195,173.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,10092,177.80 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9964,181.10 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9648,180.00 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9364,180.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9295,184.50 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9295,189.90 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9068,189.50 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,8996,193.40 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,8992,199.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9030,206.80 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9359,223.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9182,225.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9510,240.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9659,249.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9471,248.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9893,265.80 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Carolina,9981,273.40 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1729,153.40 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1711,150.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1704,154.60 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1630,150.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1668,155.40 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1625,154.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1616,155.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1778,173.30 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1681,167.40 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1633,164.90 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1757,180.60 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1776,186.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1783,189.20 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1943,210.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1937,212.10 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,1985,219.90 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,2105,234.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,South Dakota,2024,228.60 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15429,198.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15730,207.30 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15223,205.60 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14803,204.10 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14454,203.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14235,205.40 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14582,217.40 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14257,216.50 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14661,228.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14280,227.20 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14642,238.00 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,14946,251.20 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15038,258.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15919,277.60 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,16226,287.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,15688,280.60 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,16174,293.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Tennessee,16280,299.00 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43772,167.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3036,144.40 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43298,171.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,41479,169.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,40203,170.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,39061,171.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38094,172.50 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38253,181.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38077,184.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38384,191.70 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38912,200.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,38782,205.20 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,40152,220.40 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,40196,226.90 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,41779,241.20 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43452,256.30 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43199,259.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43020,263.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Texas,43418,270.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,635260,165.50 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,633842,168.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,614348,167.00 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,611105,169.80 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,599711,170.50 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,596577,173.70 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,597689,179.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,599413,182.80 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,616828,192.10 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,616067,196.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,631636,205.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,652091,216.80 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,652486,221.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,685089,236.30 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,696947,244.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,700142,249.50 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,710760,257.60 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,United States,725192,266.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3636,150.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3598,152.90 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3431,151.00 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3323,149.00 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2889,143.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2813,142.20 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2844,148.70 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2980,161.00 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2932,163.70 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2872,167.90 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2942,178.10 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,3018,187.40 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2977,188.60 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2896,187.70 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2909,194.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Utah,2786,190.10 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1366,158.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1311,152.50 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1311,156.60 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1220,149.60 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1206,150.60 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1187,151.70 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1172,153.60 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1171,156.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1216,166.20 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1166,162.40 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1244,177.20 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1234,180.50 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1289,192.30 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1349,204.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1370,212.00 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1429,224.10 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1438,229.40 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Vermont,1342,217.40 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14124,150.70 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14077,154.20 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13874,156.10 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13663,157.30 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13386,158.60 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13272,162.20 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13404,168.50 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13397,172.00 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13675,180.60 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,13750,186.70 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14021,195.20 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14192,203.00 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14284,210.40 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14756,222.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14952,230.00 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,14913,234.50 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,15264,245.50 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Virginia,15329,250.90 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11161,136.10 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11025,137.60 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10710,137.20 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10524,138.20 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10360,139.30 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10439,143.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10602,151.50 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10561,153.90 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10916,164.00 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11037,170.10 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10604,168.40 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10985,180.50 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,10644,179.30 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11185,192.80 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11141,196.40 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11281,203.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11370,209.70 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Washington,11515,215.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4767,191.00 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4727,191.30 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4692,192.90 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4666,193.70 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4942,208.10 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4914,210.10 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,4897,211.20 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5038,219.60 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5264,232.50 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5208,232.50 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5311,240.50 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5538,253.60 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,5674,263.60 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,6186,288.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,6189,291.20 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,6325,298.80 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,6435,305.00 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,West Virginia,6822,325.50 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11526,154.90 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11473,156.00 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11229,155.10 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11362,159.30 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11282,160.70 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11266,163.80 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11115,165.10 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,10834,163.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11275,172.20 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11110,172.70 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11451,181.10 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11842,190.60 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,11909,195.40 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,12479,207.90 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,12923,218.80 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,13023,224.30 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,13601,237.30 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wisconsin,13827,243.70 +2016,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1051,157.80 +2015,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1030,159.40 +2014,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1035,162.20 +2013,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,939,152.40 +2012,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1005,167.20 +2011,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,926,158.90 +2010,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,962,169.80 +2009,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,964,172.10 +2008,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,937,171.90 +2007,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,957,180.40 +2006,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,994,192.90 +2005,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,952,188.30 +2004,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,950,192.50 +2003,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,975,202.10 +2002,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1005,214.50 +2001,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,985,211.90 +2000,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,992,217.40 +1999,"Diseases of heart (I00-I09,I11,I13,I20-I51)",Heart disease,Wyoming,1009,227.80 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,987,17.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1097,19.50 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1031,18.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1035,19.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,942,17.90 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,951,18.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,942,18.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,952,19.20 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,912,18.70 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,898,18.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,918,19.50 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1011,22.00 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,994,22.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1157,25.90 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1218,27.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1105,24.90 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1138,25.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alabama,1228,28.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,61,12.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,41,8.40 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,68,14.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,66,14.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,52,10.00 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,58,12.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,64,15.80 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,51,12.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,51,13.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,48,13.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,49,14.00 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,44,12.70 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,42,15.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,60,21.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,51,19.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,36,13.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,45,16.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Alaska,46,19.60 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,885,10.40 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,775,9.50 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,779,10.00 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,776,10.40 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,683,9.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,690,10.00 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,768,11.40 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1058,16.20 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1068,16.80 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,905,14.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1166,19.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1297,22.60 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1111,20.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1286,24.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1319,25.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1133,22.60 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1222,25.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arizona,1287,27.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,623,17.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,700,20.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,710,20.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,794,23.30 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,734,21.90 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,727,21.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,647,19.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,705,22.10 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,846,26.80 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,734,23.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,772,25.10 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,886,29.40 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,759,25.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,921,31.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,776,26.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,735,25.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,798,27.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Arkansas,741,25.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,5981,14.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6188,14.80 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,5970,14.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6551,16.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,5849,15.20 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6200,16.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,5882,16.40 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6394,18.10 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6560,19.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,6546,19.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,7338,22.70 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,7553,23.80 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,7323,23.80 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,8185,27.00 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,8128,27.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,8129,28.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,8324,29.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,California,4560,16.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,533,9.60 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,659,12.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,701,13.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,606,12.00 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,525,11.00 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,618,13.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,550,12.30 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,658,14.80 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,678,15.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,592,14.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,614,15.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,666,17.60 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,637,17.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,810,23.00 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,752,21.90 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,650,19.30 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,607,18.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Colorado,807,25.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,572,11.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,667,13.60 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,644,13.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,604,12.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,558,11.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,696,14.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,568,12.10 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,696,15.40 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,694,15.10 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,776,17.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,800,18.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,956,21.90 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,868,20.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,860,20.50 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,888,21.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,854,21.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,890,22.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Connecticut,885,22.70 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,132,10.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,189,16.10 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,156,13.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,150,13.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,121,11.30 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,133,13.00 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,138,13.80 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,132,13.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,135,14.40 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,117,12.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,138,15.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,162,18.80 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,145,17.40 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,127,15.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,168,21.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,181,23.80 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,229,31.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Delaware,155,21.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,78,11.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,104,16.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,74,11.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,83,13.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,76,12.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,96,15.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,80,13.50 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,73,12.40 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,78,13.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,82,14.30 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,76,13.70 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,98,17.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,91,16.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,87,15.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,81,14.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,86,15.30 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,111,19.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,District of Columbia,116,20.60 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2808,9.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2676,8.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2719,9.60 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2662,9.50 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2338,8.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2453,9.30 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2259,8.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2410,9.90 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2300,9.40 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2246,9.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2454,10.50 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2802,12.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,3044,13.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,2996,13.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,3290,15.10 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,3317,15.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,3365,16.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Florida,3328,16.10 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1423,14.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1467,15.50 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1510,16.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1494,16.80 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1390,16.40 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1457,17.50 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1456,18.30 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1500,19.20 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1498,19.80 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1407,19.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1463,20.80 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1596,23.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1547,23.80 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1687,26.30 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1791,28.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1515,24.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1792,29.40 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Georgia,1703,28.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,507,24.40 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,557,27.40 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,438,22.60 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,457,24.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,394,20.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,325,18.00 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,289,17.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,291,17.80 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,270,17.10 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,199,12.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,257,17.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,241,16.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,236,16.90 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,237,17.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,246,18.90 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,207,16.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,221,18.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Hawaii,229,19.60 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,207,11.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,218,12.40 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,200,11.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,259,15.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,189,11.30 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,217,13.50 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,208,13.50 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,207,13.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,206,14.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,228,15.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,225,16.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,289,21.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,219,17.00 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,334,26.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,265,21.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,239,19.90 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,288,24.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Idaho,289,25.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2178,14.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2343,15.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2485,16.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2441,16.80 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2323,16.30 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2424,17.30 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2212,16.10 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2416,17.80 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2672,19.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2550,19.30 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2668,20.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2949,23.00 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2779,21.90 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2875,22.90 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2940,23.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2659,21.60 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,2917,24.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Illinois,3121,25.90 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,962,12.60 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1044,13.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1063,14.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1132,15.30 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,973,13.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1031,14.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1175,16.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1191,17.40 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1318,19.50 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1098,16.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1129,17.40 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1317,20.70 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1136,18.20 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1365,22.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1360,22.30 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1184,19.70 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1270,21.40 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Indiana,1362,23.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,504,11.60 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,618,14.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,582,13.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,761,17.80 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,664,15.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,660,15.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,560,13.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,661,16.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,829,20.30 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,749,18.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,770,19.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,896,22.40 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,890,22.70 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,1032,26.30 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,942,24.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,877,22.90 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,937,24.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Iowa,1082,28.40 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,522,14.40 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,682,18.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,637,18.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,715,20.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,624,18.00 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,647,18.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,550,16.40 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,594,18.00 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,743,22.50 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,665,20.30 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,647,20.00 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,730,23.10 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,562,17.90 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,694,22.30 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,698,22.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,561,18.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,743,24.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kansas,676,22.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,888,17.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,967,19.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1017,20.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,919,19.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,879,18.70 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,961,20.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,943,21.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1018,22.90 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,943,21.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,897,21.00 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,927,22.10 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1021,24.90 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,965,24.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1033,26.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1236,31.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1114,28.60 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1157,29.90 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Kentucky,1198,31.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,720,14.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,753,15.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,854,17.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,892,18.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,808,17.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,851,18.90 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,878,20.20 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,891,20.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,894,21.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,870,21.10 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,833,20.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,996,24.00 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,913,22.20 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,915,22.70 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,956,24.00 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,941,23.70 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,972,24.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Louisiana,1008,26.00 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,231,12.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,333,17.60 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,258,13.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,258,14.00 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,205,11.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,302,16.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,234,13.40 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,251,14.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,263,15.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,236,14.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,269,16.80 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,352,22.30 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,308,20.00 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,327,21.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,317,20.90 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,269,18.00 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,336,22.90 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maine,287,19.90 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1025,15.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1176,17.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1019,15.60 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1103,17.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,986,15.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1036,17.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,925,15.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,976,16.90 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1008,17.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,994,18.00 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1082,20.10 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1194,22.80 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1111,21.90 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1175,23.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1121,23.10 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,928,19.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1116,24.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Maryland,1140,25.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1251,14.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1512,17.10 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1370,15.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1551,18.00 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1356,16.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1418,16.90 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1291,16.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1348,17.00 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1598,20.10 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1538,19.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1750,22.80 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1935,25.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1959,26.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,2019,27.00 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,2087,28.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1770,24.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,2078,28.90 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Massachusetts,1904,26.80 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1672,13.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1894,15.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1875,15.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1896,15.80 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1565,13.30 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1750,15.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1529,13.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1613,14.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1853,16.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1637,15.10 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1679,15.90 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1950,18.90 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1957,19.20 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,1941,19.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,2029,20.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,2076,21.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,2052,21.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Michigan,2307,24.70 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,529,7.80 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,738,10.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,638,9.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,751,11.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,674,10.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,680,10.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,595,9.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,618,10.30 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,745,12.50 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,603,10.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,630,11.00 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,846,15.10 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,750,13.60 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,858,15.70 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,900,16.90 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,828,15.70 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,845,16.40 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Minnesota,1077,21.10 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,785,23.40 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,791,24.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,763,23.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,769,24.30 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,565,18.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,603,20.20 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,567,19.20 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,578,19.90 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,626,21.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,554,19.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,621,22.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,645,23.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,637,23.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,758,28.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,801,29.80 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,754,28.00 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,809,29.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Mississippi,801,29.80 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1150,15.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1337,17.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1321,18.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1352,18.70 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1213,17.00 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1205,17.20 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1188,17.40 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1330,19.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1414,21.30 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1289,19.60 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1285,19.90 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1527,24.00 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1389,22.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1604,25.70 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1619,26.20 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1604,26.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1629,26.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Missouri,1716,28.30 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,147,11.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,183,13.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,179,13.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,210,16.40 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,155,12.20 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,156,12.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,168,13.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,176,14.80 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,168,14.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,183,16.10 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,169,15.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,213,19.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,165,15.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,256,24.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,255,25.20 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,193,19.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,202,20.90 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Montana,252,26.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,342,14.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,397,16.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,351,15.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,343,14.50 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,291,12.70 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,325,14.20 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,266,11.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,266,12.30 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,366,17.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,331,15.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,345,16.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,370,17.70 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,347,17.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,435,21.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,418,20.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,296,14.60 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,433,21.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nebraska,459,23.00 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,567,18.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,630,21.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,687,23.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,513,18.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,496,19.00 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,499,19.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,471,19.80 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,542,23.30 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,496,22.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,408,19.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,426,20.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,454,23.10 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,402,21.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,410,23.30 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,368,22.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,352,22.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,320,21.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Nevada,319,22.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,203,11.80 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,275,16.10 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,194,11.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,226,13.90 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,204,12.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,220,14.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,191,12.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,194,13.10 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,211,14.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,207,14.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,212,15.40 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,273,20.80 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,271,21.00 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,204,16.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,237,19.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,216,18.00 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,194,16.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Hampshire,187,16.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1208,10.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1402,12.50 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1234,11.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1356,12.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1131,10.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1257,12.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1128,11.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1309,13.00 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1413,14.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1343,13.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1315,13.70 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1637,17.30 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1586,17.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1823,19.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1973,21.80 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,1893,21.20 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,2044,23.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Jersey,2075,23.90 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,353,14.60 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,322,13.60 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,376,16.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,337,14.80 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,281,12.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,355,16.50 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,294,14.20 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,344,16.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,351,17.50 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,298,15.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,353,18.90 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,353,19.40 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,308,17.70 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,369,21.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,372,22.30 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,398,24.30 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,290,18.20 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New Mexico,374,24.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4513,18.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4881,20.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4702,19.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4892,20.50 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4404,18.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4915,21.40 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4642,20.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4527,20.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4609,21.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4431,20.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,4892,23.00 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5521,26.30 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5499,26.60 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5335,26.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5368,26.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5085,25.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5201,26.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,New York,5482,28.40 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1896,16.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,2115,18.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1874,17.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1931,18.30 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1899,18.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1619,16.30 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1700,17.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1752,18.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1737,19.10 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1645,18.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1705,20.10 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1830,22.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1686,21.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1990,25.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1898,25.10 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1780,24.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1945,26.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Carolina,1891,26.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,134,14.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,172,17.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,174,18.50 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,134,14.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,133,14.30 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,120,13.00 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,128,13.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,138,15.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,148,17.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,133,14.90 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,138,15.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,172,19.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,148,17.00 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,189,21.90 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,162,18.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,162,18.90 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,166,19.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,North Dakota,157,19.40 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2187,15.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2445,16.60 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2443,16.90 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2374,16.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2195,15.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2291,16.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,1975,14.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2040,15.40 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2085,15.80 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,1743,13.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,1947,15.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2416,19.40 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2204,17.90 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2330,19.10 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2487,20.80 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2401,20.30 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2533,21.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Ohio,2641,22.90 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,546,12.40 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,717,16.50 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,723,16.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,759,18.00 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,573,13.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,863,21.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,778,19.70 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,879,22.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,938,24.30 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,801,21.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,879,23.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,948,26.10 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,810,22.50 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,949,26.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,914,25.50 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,913,25.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,885,24.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oklahoma,1007,28.10 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,452,8.90 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,453,9.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,450,9.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,496,10.40 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,382,8.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,401,8.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,417,9.20 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,507,11.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,517,12.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,477,11.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,522,12.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,614,15.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,558,14.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,632,16.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,666,17.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,575,15.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,642,17.80 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Oregon,695,19.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2491,13.90 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2899,15.90 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2541,14.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2884,16.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2360,13.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2771,16.00 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2324,13.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2601,15.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2705,16.30 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2555,15.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2716,16.50 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,3068,19.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2938,18.60 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,3010,19.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2957,19.10 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,2735,17.90 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,3069,20.30 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Pennsylvania,3116,20.90 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,165,11.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,246,16.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,176,11.70 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,195,12.90 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,161,11.20 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,226,15.30 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,197,13.80 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,222,16.10 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,257,18.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,224,16.10 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,268,19.60 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,253,18.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,260,19.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,267,20.00 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,319,24.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,295,22.80 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,342,26.60 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Rhode Island,300,23.60 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,690,12.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,848,15.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,751,13.90 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,747,14.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,726,14.40 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,768,15.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,747,15.90 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,759,16.40 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,727,16.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,723,16.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,724,17.30 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,771,19.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,770,19.70 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,907,23.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,910,24.60 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,754,20.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,699,19.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Carolina,883,25.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,195,16.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,213,18.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,181,16.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,184,16.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,185,16.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,178,16.30 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,169,15.80 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,134,12.90 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,177,17.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,189,18.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,173,17.00 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,241,24.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,179,18.20 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,227,23.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,240,25.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,188,19.80 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,212,22.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,South Dakota,264,28.20 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1533,20.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1723,23.30 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1602,22.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1564,22.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1435,20.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1480,21.90 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1352,20.50 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1390,21.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1424,22.70 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1438,23.50 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1535,25.70 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1588,27.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1571,27.80 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1814,32.50 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1710,31.00 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1617,29.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1672,30.90 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Tennessee,1588,29.70 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,2860,11.10 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3214,13.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3452,14.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3339,14.40 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,2974,13.40 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3055,14.20 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3022,14.60 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3387,16.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3554,18.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3230,17.00 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3342,18.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3654,20.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3209,18.60 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3613,21.40 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3673,22.30 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3608,22.20 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3706,23.10 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Texas,3534,22.40 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,51537,13.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,57062,15.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,55227,15.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,56979,15.90 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,50636,14.50 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,53826,15.70 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,50097,15.10 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,53692,16.50 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,56284,17.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,52717,16.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,56326,18.40 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,63001,21.00 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,59664,20.40 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,65163,22.60 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,65681,23.20 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,62034,22.20 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,65313,23.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,United States,63730,23.50 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,380,15.50 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,372,15.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,366,16.20 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,413,18.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,337,15.60 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,358,17.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,348,17.10 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,332,16.30 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,343,18.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,313,17.10 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,343,19.20 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,333,19.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,384,23.30 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,453,28.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,424,27.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,411,27.20 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,388,26.10 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Utah,391,27.10 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,59,7.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,85,10.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,74,9.30 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,77,9.30 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,81,9.90 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,68,8.40 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,60,8.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,52,7.00 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,74,10.20 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,70,9.90 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,77,10.90 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,97,14.40 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,86,12.70 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,116,17.70 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,111,17.10 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,133,20.70 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,119,19.00 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Vermont,134,21.70 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1177,12.70 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1416,15.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1498,17.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1442,17.00 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1307,15.80 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1421,17.60 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1195,15.50 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1237,16.30 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1324,17.90 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1231,17.20 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1281,18.40 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1464,21.60 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1449,22.10 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1559,24.20 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1480,23.70 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1481,24.10 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1510,25.10 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Virginia,1594,27.10 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,815,10.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,851,10.70 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,729,9.40 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,768,10.10 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,728,9.90 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,734,10.10 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,578,8.30 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,728,10.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,771,11.60 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,743,11.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,814,12.90 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,927,15.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,738,12.40 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,1086,18.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,907,16.00 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,965,17.40 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,1010,18.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Washington,1259,23.70 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,423,17.30 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,526,21.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,473,19.60 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,481,20.20 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,425,18.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,455,19.80 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,436,19.00 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,443,19.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,448,20.00 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,408,18.40 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,459,21.10 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,458,21.20 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,474,22.20 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,536,25.30 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,427,20.30 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,483,23.00 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,456,21.70 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,West Virginia,520,25.00 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,888,11.90 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1052,14.20 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1002,13.80 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1125,15.60 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,996,14.10 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,986,14.20 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,904,13.30 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,979,14.60 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1116,16.70 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1022,15.70 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1014,15.80 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1268,19.90 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1141,18.40 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1165,19.00 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1291,21.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1236,20.80 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1258,21.50 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wisconsin,1425,24.60 +2016,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,99,15.00 +2015,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,99,16.00 +2014,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,113,18.10 +2013,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,112,18.40 +2012,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,91,15.70 +2011,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,116,20.40 +2010,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,109,19.50 +2009,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,142,26.70 +2008,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,123,23.40 +2007,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,113,21.80 +2006,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,117,23.50 +2005,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,119,24.50 +2004,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,109,22.70 +2003,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,145,30.80 +2002,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,135,29.40 +2001,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,112,24.50 +2000,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,126,28.30 +1999,Influenza and pneumonia (J09-J18),Influenza and pneumonia,Wyoming,130,30.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1012,17.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1040,18.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1011,18.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1057,19.30 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1035,19.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1047,20.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1184,23.10 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1180,23.40 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1107,22.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1051,21.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1104,23.00 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1036,22.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1049,22.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1062,23.20 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,1032,22.80 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,974,21.70 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,939,21.10 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alabama,979,22.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,53,10.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,50,10.00 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,45,10.10 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,49,10.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,44,9.50 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,53,12.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,56,13.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,47,12.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,50,13.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,40,11.10 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,44,12.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,38,10.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,23,7.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,28,8.40 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,21,6.80 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,24,7.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,30,11.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Alaska,34,13.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,487,5.70 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,458,5.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,325,4.10 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,374,4.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,414,5.70 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,395,5.60 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,537,7.90 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,546,8.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,503,7.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,528,8.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,565,9.30 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,608,10.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,634,11.20 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,563,10.40 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,622,11.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,644,12.50 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,614,12.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arizona,544,11.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,722,20.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,707,19.80 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,672,19.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,762,22.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,674,19.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,787,23.60 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,737,22.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,680,21.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,638,20.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,666,21.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,616,20.00 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,624,20.60 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,562,18.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,526,17.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,601,20.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,521,17.70 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,500,17.30 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Arkansas,458,15.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,3640,8.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,3557,8.60 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,3119,7.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2840,7.20 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2728,7.20 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2599,7.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,3112,8.70 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,3033,8.60 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2854,8.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2835,8.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2676,8.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2482,7.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2373,7.60 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2334,7.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2164,7.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,1994,6.80 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,1785,6.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,California,2261,8.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,491,8.90 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,458,8.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,448,8.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,450,9.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,428,8.90 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,450,9.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,426,9.60 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,447,10.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,483,11.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,444,11.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,472,12.10 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,470,12.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,406,11.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,409,11.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,417,11.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,391,11.40 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,357,10.80 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Colorado,312,9.60 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,570,12.10 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,584,12.10 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,605,12.90 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,565,11.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,569,12.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,602,13.10 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,599,13.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,585,13.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,589,13.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,566,13.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,567,13.50 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,580,13.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,604,14.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,567,14.00 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,554,14.00 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,585,15.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,524,13.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Connecticut,457,12.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,208,17.10 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,174,14.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,173,14.80 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,178,16.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,139,12.70 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,149,14.20 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,158,15.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,176,17.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,189,19.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,163,17.40 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,152,16.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,128,14.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,128,15.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,130,15.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,117,14.50 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,108,13.90 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,111,14.40 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Delaware,100,13.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,57,9.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,65,9.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,49,8.00 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,54,8.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,44,7.20 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,60,10.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,84,14.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,57,10.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1866,23.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,78,13.90 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,67,11.90 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,68,12.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,68,12.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,79,14.40 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,81,14.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,70,12.50 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,79,13.90 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,84,14.80 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,District of Columbia,91,16.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3144,10.20 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3203,10.80 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3076,10.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3130,11.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2912,10.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3012,11.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3297,12.90 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,3050,12.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2946,12.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2923,12.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2620,11.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2416,10.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2252,10.00 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2265,10.20 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2201,10.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,2172,10.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,1888,8.90 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Florida,1846,8.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1864,18.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1881,19.20 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1742,18.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1652,18.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1612,18.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1571,18.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1753,21.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1668,21.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1689,22.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1668,22.50 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1520,21.20 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1458,21.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1471,21.90 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1335,20.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1362,21.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1263,20.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Georgia,1142,18.30 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,215,11.20 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,202,10.80 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,220,11.80 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,209,11.30 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,199,11.20 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,201,11.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,208,12.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,225,14.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,197,12.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,176,11.70 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,179,12.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,150,10.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,164,11.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,123,9.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,136,10.30 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,123,9.60 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,113,9.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Hawaii,133,11.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,190,10.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,154,8.70 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,137,8.00 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,148,8.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,148,8.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,154,9.70 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,186,12.10 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,186,12.40 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,173,11.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,134,9.40 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,130,9.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,110,8.20 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,109,8.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,97,7.70 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,86,7.00 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,95,8.00 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,96,8.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Idaho,99,8.60 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2518,17.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2543,17.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2517,17.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2444,17.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2392,17.00 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2386,17.20 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2612,19.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2695,20.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2576,19.60 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2536,19.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2502,19.50 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2402,18.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2339,18.70 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2297,18.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2328,19.00 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2178,17.90 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,2128,17.70 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Illinois,1965,16.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1411,18.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1450,18.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1392,18.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1352,18.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1221,16.90 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1367,19.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1516,22.00 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1442,21.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1363,20.20 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1293,19.60 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1372,21.10 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1284,20.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1244,19.90 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1235,20.00 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1222,20.10 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1149,19.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1084,18.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Indiana,1022,17.30 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,352,8.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,336,8.00 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,313,7.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,369,8.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,322,7.90 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,316,7.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,323,8.10 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,302,7.60 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,304,7.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,272,6.90 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,225,5.80 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,250,6.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,266,7.20 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,241,6.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,261,7.10 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,220,6.00 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,313,8.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Iowa,226,6.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,533,15.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,582,16.80 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,569,16.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,594,17.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,600,17.70 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,574,17.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,577,17.70 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,556,17.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,620,19.60 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,554,17.60 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,594,19.00 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,517,16.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,554,18.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,531,17.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,517,17.30 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,498,16.60 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,478,16.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kansas,390,13.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,1034,20.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,998,19.70 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,968,19.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,991,20.50 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,933,19.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,971,20.90 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,1079,23.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,945,21.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,993,22.60 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,994,23.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,922,21.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,912,21.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,827,20.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,838,20.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,813,20.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,807,20.40 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,756,19.30 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Kentucky,685,17.70 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1153,22.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1160,23.20 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1217,24.90 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1108,23.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1138,24.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1169,25.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1218,27.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1160,26.60 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1195,27.70 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1152,27.40 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1074,26.10 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1187,27.80 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1110,26.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,1065,25.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,983,23.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,993,24.30 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,920,22.80 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Louisiana,911,22.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,239,12.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,211,11.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,223,11.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,252,13.60 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,257,14.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,257,14.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,272,15.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,272,16.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,263,15.60 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,269,16.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,252,15.70 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,250,15.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,264,17.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,265,17.40 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,232,15.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,208,14.00 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,175,12.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maine,191,13.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,823,12.10 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,834,12.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,751,11.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,737,11.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,704,11.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,713,11.90 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,803,13.60 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,803,13.90 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,775,13.70 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,731,13.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,767,14.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,752,14.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,699,13.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,583,11.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,630,12.60 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,593,12.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,594,12.40 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Maryland,563,12.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1139,13.20 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1228,14.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1228,14.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1261,15.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1271,15.50 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1234,15.20 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1381,17.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1269,16.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1377,17.90 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1361,18.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1406,18.70 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1410,19.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1250,17.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1286,17.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1297,18.10 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1207,17.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1231,17.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Massachusetts,1127,16.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1791,14.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1921,15.60 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1853,15.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1660,13.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1586,13.50 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1642,14.30 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1720,15.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1650,14.90 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1663,15.20 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1610,15.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1673,15.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1681,16.20 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1507,14.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1665,16.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1618,16.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1533,15.80 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1433,15.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Michigan,1417,15.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,588,8.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,639,9.70 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,676,10.40 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,653,10.20 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,669,10.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,696,11.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,897,15.00 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,807,13.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,813,14.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,780,13.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,711,12.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,669,12.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,669,12.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,654,12.30 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,649,12.60 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,729,14.30 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,661,13.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Minnesota,605,12.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,763,22.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,731,22.00 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,701,21.30 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,731,22.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,692,22.20 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,664,21.70 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,738,24.70 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,700,23.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,723,24.60 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,697,24.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,673,23.70 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,667,23.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,662,23.90 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,676,24.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,580,21.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,602,22.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,612,22.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Mississippi,623,23.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1483,19.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1483,19.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1452,19.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1305,18.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1259,17.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1245,17.90 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1305,19.20 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1223,18.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1274,19.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1184,18.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1118,17.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1156,18.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1083,17.40 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1099,17.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,1076,17.60 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,991,16.30 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,907,15.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Missouri,907,15.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,155,11.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,121,9.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,125,9.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,130,10.30 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,100,8.00 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,104,8.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,133,11.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,129,11.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,151,13.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,110,9.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,132,12.00 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,109,10.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,108,10.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,101,9.90 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,105,10.50 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,128,12.90 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,116,12.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Montana,92,9.60 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,220,9.50 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,264,11.20 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,265,11.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,222,10.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,217,9.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,214,9.70 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,290,13.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,258,12.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,270,12.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,269,12.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,249,12.30 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,245,12.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,283,14.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,302,15.30 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,277,14.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,289,14.90 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,254,13.30 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nebraska,232,12.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,274,8.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,316,10.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,365,12.80 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,370,13.30 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,385,14.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,412,16.60 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,471,19.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,446,19.00 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,456,20.00 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,461,21.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,475,22.70 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,438,22.00 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,425,21.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,439,23.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,372,20.70 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,376,21.60 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,329,21.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Nevada,298,19.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,161,9.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,194,11.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,167,10.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,175,11.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,161,10.20 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,172,11.30 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,206,14.20 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,165,11.40 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,174,12.40 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,149,10.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,160,11.80 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,173,13.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,163,12.80 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,150,12.00 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,141,11.60 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,136,11.40 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,106,9.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Hampshire,93,8.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1535,13.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1582,14.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1502,13.80 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1400,13.20 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1431,13.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1490,14.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1580,15.60 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1709,17.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1715,17.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1690,17.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1645,17.30 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1597,17.10 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1624,17.60 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1696,18.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1662,18.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1495,16.80 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1495,17.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Jersey,1380,15.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,293,11.70 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,316,12.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,267,11.30 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,302,13.20 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,285,12.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,268,12.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,276,13.10 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,274,13.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,287,14.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,252,12.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,262,13.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,239,13.00 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,227,12.60 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,217,12.30 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,220,12.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,172,10.30 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,152,9.30 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New Mexico,162,10.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2385,9.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2245,9.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2207,9.30 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2285,9.70 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2240,9.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2197,9.70 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2439,11.00 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2426,11.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2394,11.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2387,11.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2351,11.20 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2360,11.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2383,11.60 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2415,11.90 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2465,12.30 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2421,12.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2440,12.50 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,New York,2302,12.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,2002,17.10 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1821,16.00 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1791,16.30 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1780,16.60 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1656,15.90 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1705,17.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1892,19.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1828,19.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1729,18.70 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1723,19.10 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1661,19.10 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1561,18.60 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1470,18.00 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1387,17.30 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1437,18.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1332,17.40 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1315,17.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Carolina,1138,15.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,106,11.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,118,12.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,104,11.00 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,117,12.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,117,13.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,110,12.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,104,11.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,118,13.30 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,98,11.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,56,6.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,59,6.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,67,7.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,55,6.40 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,56,6.60 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,57,6.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,50,6.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,67,8.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,North Dakota,85,10.50 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2262,15.40 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2100,14.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2002,14.10 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1985,14.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2056,14.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1943,14.20 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2066,15.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1909,14.40 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1865,14.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1747,13.50 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1758,13.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1902,15.20 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1896,15.40 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2093,17.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,2027,16.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1912,16.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1829,15.60 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Ohio,1748,15.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,577,12.90 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,616,14.20 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,604,14.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,606,14.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,554,13.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,550,13.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,595,15.00 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,637,16.20 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,664,17.20 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,623,16.40 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,594,15.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,564,15.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,565,15.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,566,15.70 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,500,13.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,567,15.80 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,536,15.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oklahoma,425,11.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,398,7.70 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,409,8.20 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,378,7.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,327,6.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,320,6.90 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,316,6.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,400,8.90 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,387,8.90 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,397,9.40 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,426,10.20 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,353,8.80 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,295,7.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,308,8.00 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,306,8.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,266,7.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,331,9.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,295,8.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Oregon,260,7.30 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2813,15.70 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3021,16.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2798,15.70 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2767,15.70 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2736,15.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2873,16.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2982,17.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3054,18.40 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3138,19.20 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2965,18.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3084,19.30 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3108,19.80 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3067,19.70 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,3013,19.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2944,19.30 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2838,18.70 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2677,17.80 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Pennsylvania,2680,18.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,141,9.90 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,142,9.80 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,135,9.40 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,149,10.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,135,9.70 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,174,12.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,199,14.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,190,13.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,163,12.00 +2010,Cerebrovascular diseases (I60-I69),Stroke,Alaska,167,40.90 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,167,12.60 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,167,12.90 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,156,11.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,131,9.90 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,149,11.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,142,11.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,118,9.40 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,123,9.90 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Rhode Island,111,9.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,897,15.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,886,15.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,837,15.10 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,869,16.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,860,16.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,802,16.10 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,963,19.90 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,893,18.80 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,945,20.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,806,17.90 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,846,19.50 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,823,19.60 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,818,20.10 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,797,20.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,781,20.10 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,632,16.50 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,539,14.50 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Carolina,434,11.80 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,90,7.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,79,7.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,72,6.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,64,5.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,58,5.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,49,4.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,74,7.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,102,10.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,89,9.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,77,7.70 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,62,6.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,55,5.80 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,92,9.90 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,124,13.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,128,13.90 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,103,11.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,130,14.70 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,South Dakota,97,10.80 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,1150,14.90 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,1090,14.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,1036,14.10 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,1068,14.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,933,13.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,810,11.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,983,14.80 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,949,14.50 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,908,14.30 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,831,13.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,768,12.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,719,12.00 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,680,11.70 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,677,11.80 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,586,10.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,624,11.20 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,565,10.20 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Tennessee,675,12.30 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,4125,15.80 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,4052,16.10 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,4008,16.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3722,15.90 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3525,15.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3397,15.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3878,18.40 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3694,17.90 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3536,17.70 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,3291,17.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2995,15.80 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2729,14.90 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2561,14.40 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2678,15.40 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2166,12.70 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,2269,13.60 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,1870,11.40 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Texas,1669,10.30 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,50046,13.10 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,49959,13.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,48146,13.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,47112,13.20 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,45622,13.10 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,45591,13.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,50476,15.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,48935,15.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,48237,15.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,46448,14.90 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,45344,14.80 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,43901,14.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,42480,14.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,42453,14.70 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,40974,14.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,39480,14.10 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,37251,13.50 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,United States,35525,13.00 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,370,15.20 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,363,15.60 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,393,17.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,312,14.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,270,12.40 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,228,11.00 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,263,13.10 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,212,10.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,212,11.00 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,220,12.00 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,210,11.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,177,10.30 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,235,14.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,207,12.70 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,184,11.70 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,178,11.50 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,170,11.30 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Utah,135,9.10 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,30,3.70 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,39,4.50 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,37,4.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,30,3.80 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,21,2.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,43,5.40 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,50,6.70 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,49,6.80 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,39,5.40 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,54,7.60 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,52,7.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,52,7.70 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,57,8.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,53,8.10 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,53,8.30 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,86,13.50 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,62,10.00 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Vermont,56,9.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1543,16.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1470,16.10 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1553,17.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1547,18.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1523,18.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1419,17.50 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1595,20.20 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1521,19.70 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1537,20.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1439,19.70 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1400,19.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1277,18.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1249,18.50 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1244,18.70 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1236,19.00 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1128,17.80 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1118,17.90 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Virginia,1035,16.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,532,6.60 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,474,5.90 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,481,6.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,461,6.10 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,482,6.60 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,492,6.80 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,552,8.00 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,509,7.50 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,471,7.10 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,440,6.80 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,423,6.70 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,450,7.50 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,348,5.90 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,304,5.30 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,306,5.40 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,279,5.00 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,293,5.40 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Washington,278,5.20 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,481,19.30 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,510,20.40 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,479,19.60 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,450,18.60 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,471,19.70 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,445,19.10 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,475,20.30 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,504,21.90 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,522,23.00 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,480,21.30 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,468,21.00 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,442,20.40 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,450,20.70 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,458,21.20 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,462,21.60 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,391,18.30 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,378,17.90 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,West Virginia,345,16.40 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,960,13.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,996,13.70 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,980,13.50 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,993,14.00 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,1024,14.80 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,979,14.30 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,1163,17.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,994,15.10 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,1007,15.50 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,1002,15.60 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,962,15.40 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,930,15.00 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,925,15.20 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,873,14.50 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,852,14.50 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,791,13.50 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,751,13.10 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wisconsin,677,11.90 +2016,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,66,10.00 +2015,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,86,13.30 +2014,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,78,12.20 +2013,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,56,9.40 +2012,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,72,12.30 +2011,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,85,14.90 +2010,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,69,12.50 +2009,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,76,14.00 +2008,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,56,10.80 +2007,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,68,13.40 +2006,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,58,11.60 +2005,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,66,13.60 +2004,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,45,9.30 +2003,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,57,11.90 +2002,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,43,9.20 +2001,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,54,11.70 +2000,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,45,9.90 +1999,"Nephritis, nephrotic syndrome and nephrosis (N00-N07,N17-N19,N25-N27)",Kidney disease,Wyoming,30,6.80 +2016,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2967,51.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2937,52.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2663,48.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2604,48.10 +2012,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2628,49.50 +2011,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2568,49.40 +2010,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2619,51.60 +2009,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2675,53.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2863,58.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2747,56.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2740,57.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2952,63.40 +2004,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2986,65.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Alabama,3028,67.00 +2002,Cerebrovascular diseases (I60-I69),Stroke,Alabama,3201,71.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,Alabama,2998,66.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,Alabama,3183,71.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Alabama,3148,71.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Alaska,196,39.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Alaska,182,36.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Alaska,157,32.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,Alaska,189,40.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Alaska,188,42.50 +2011,Cerebrovascular diseases (I60-I69),Stroke,Alaska,168,39.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,Alaska,162,41.80 +2008,Cerebrovascular diseases (I60-I69),Stroke,Alaska,172,44.50 +2007,Cerebrovascular diseases (I60-I69),Stroke,Alaska,157,47.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,Alaska,177,48.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Alaska,178,54.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Alaska,172,52.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Alaska,185,62.30 +2002,Cerebrovascular diseases (I60-I69),Stroke,Alaska,158,56.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Alaska,158,57.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,Alaska,170,66.10 +1999,Cerebrovascular diseases (I60-I69),Stroke,Alaska,171,71.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2556,29.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2522,30.70 +2014,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2235,28.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2162,28.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2164,29.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2148,30.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2138,31.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2072,31.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2147,33.90 +2007,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2207,35.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2226,37.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2364,41.40 +2004,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2446,44.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2428,45.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2535,49.10 +2001,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2480,49.00 +2000,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2648,53.80 +1999,Cerebrovascular diseases (I60-I69),Stroke,Arizona,2600,54.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1643,45.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1653,46.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1583,45.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1637,47.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1664,49.10 +2011,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1692,50.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1741,53.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1694,52.90 +2008,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1722,54.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1873,60.10 +2006,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1884,61.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1847,61.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,1948,65.30 +2003,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,2107,71.10 +2002,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,2232,75.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,2256,76.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,2255,77.20 +1999,Cerebrovascular diseases (I60-I69),Stroke,Arkansas,2255,77.70 +2016,Cerebrovascular diseases (I60-I69),Stroke,California,15680,36.90 +2015,Cerebrovascular diseases (I60-I69),Stroke,California,15065,36.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,California,13731,33.90 +2013,Cerebrovascular diseases (I60-I69),Stroke,California,13698,34.90 +2012,Cerebrovascular diseases (I60-I69),Stroke,California,13542,35.40 +2011,Cerebrovascular diseases (I60-I69),Stroke,California,13503,36.40 +2010,Cerebrovascular diseases (I60-I69),Stroke,California,13662,38.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,California,13506,38.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,California,14048,41.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,California,14557,43.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,California,15039,46.40 +2005,Cerebrovascular diseases (I60-I69),Stroke,California,15585,49.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,California,16882,54.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,California,17692,58.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,California,17626,59.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,California,18088,62.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,California,18185,64.00 +1999,Cerebrovascular diseases (I60-I69),Stroke,California,17962,64.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1927,35.20 +2015,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1856,34.90 +2014,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1714,33.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1588,32.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1576,32.60 +2011,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1606,34.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1607,36.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1532,35.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1565,37.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1600,39.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1532,39.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1599,42.60 +2004,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1638,44.90 +2003,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1813,51.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1915,55.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1825,54.70 +2000,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1907,58.70 +1999,Cerebrovascular diseases (I60-I69),Stroke,Colorado,1834,57.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1269,26.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1388,28.50 +2014,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1266,26.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1348,28.30 +2012,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1273,27.00 +2011,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1315,28.30 +2010,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1349,29.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1460,32.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1433,32.00 +2007,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1463,33.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1547,35.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1528,36.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1635,39.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1828,44.00 +2002,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1861,46.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,2003,50.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,2011,51.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,Connecticut,1933,50.00 +2016,Cerebrovascular diseases (I60-I69),Stroke,Delaware,506,41.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Delaware,466,39.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Delaware,439,38.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Delaware,409,37.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,Delaware,404,38.20 +2011,Cerebrovascular diseases (I60-I69),Stroke,Delaware,418,40.50 +2010,Cerebrovascular diseases (I60-I69),Stroke,Delaware,407,40.70 +2009,Cerebrovascular diseases (I60-I69),Stroke,Delaware,415,42.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,Delaware,365,37.90 +2007,Cerebrovascular diseases (I60-I69),Stroke,Delaware,374,40.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Delaware,384,42.90 +2005,Cerebrovascular diseases (I60-I69),Stroke,Delaware,384,44.30 +2004,Cerebrovascular diseases (I60-I69),Stroke,Delaware,350,41.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Delaware,407,49.90 +2002,Cerebrovascular diseases (I60-I69),Stroke,Delaware,405,51.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,Delaware,383,50.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Delaware,430,57.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Delaware,365,49.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,252,38.40 +2015,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,242,38.30 +2014,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,214,33.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,187,30.10 +2012,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,207,33.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,209,34.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,196,32.70 +2009,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,199,34.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,222,38.50 +2007,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,220,39.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,221,40.00 +2005,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,231,41.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,218,39.30 +2003,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,257,46.00 +2002,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,279,49.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,265,47.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,252,44.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,District of Columbia,297,53.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Florida,11854,37.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Florida,11433,37.10 +2014,Cerebrovascular diseases (I60-I69),Stroke,Florida,9770,33.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Florida,8698,30.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Florida,8456,30.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,Florida,8411,31.50 +2010,Cerebrovascular diseases (I60-I69),Stroke,Florida,8432,32.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Florida,8395,33.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,Florida,8589,34.80 +2007,Cerebrovascular diseases (I60-I69),Stroke,Florida,8781,36.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Florida,8925,37.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,Florida,9361,40.30 +2004,Cerebrovascular diseases (I60-I69),Stroke,Florida,9715,42.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Florida,9899,44.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,Florida,10269,46.90 +2001,Cerebrovascular diseases (I60-I69),Stroke,Florida,10414,48.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Florida,10532,49.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Florida,10560,50.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4349,44.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4335,45.30 +2014,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3948,42.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3694,41.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3636,41.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3564,42.70 +2010,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3762,46.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3732,46.90 +2008,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3805,49.50 +2007,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3894,52.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3889,53.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,Georgia,3854,55.60 +2004,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4063,60.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4301,65.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4261,66.50 +2001,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4312,68.30 +2000,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4625,74.80 +1999,Cerebrovascular diseases (I60-I69),Stroke,Georgia,4416,72.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,675,34.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,735,38.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,655,34.10 +2013,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,634,34.90 +2012,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,634,35.20 +2011,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,621,35.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,605,35.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,671,41.00 +2008,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,642,40.70 +2007,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,643,41.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,665,44.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,688,47.60 +2004,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,714,51.00 +2003,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,752,55.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,812,62.10 +2001,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,766,60.60 +2000,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,747,61.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,Hawaii,762,64.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Idaho,682,37.20 +2015,Cerebrovascular diseases (I60-I69),Stroke,Idaho,641,36.30 +2014,Cerebrovascular diseases (I60-I69),Stroke,Idaho,640,36.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Idaho,598,35.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,Idaho,610,37.20 +2011,Cerebrovascular diseases (I60-I69),Stroke,Idaho,623,38.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Idaho,642,42.00 +2009,Cerebrovascular diseases (I60-I69),Stroke,Idaho,627,41.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,Idaho,609,41.60 +2007,Cerebrovascular diseases (I60-I69),Stroke,Idaho,640,44.60 +2006,Cerebrovascular diseases (I60-I69),Stroke,Idaho,725,52.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Idaho,718,53.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Idaho,712,55.30 +2003,Cerebrovascular diseases (I60-I69),Stroke,Idaho,762,60.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Idaho,736,60.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,Idaho,781,65.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Idaho,720,61.80 +1999,Cerebrovascular diseases (I60-I69),Stroke,Idaho,771,67.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5658,37.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5709,38.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5489,37.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5294,36.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5337,37.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5372,38.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5349,39.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5258,39.00 +2008,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5788,43.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5864,44.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Illinois,5989,46.30 +2005,Cerebrovascular diseases (I60-I69),Stroke,Illinois,6252,49.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,Illinois,6489,51.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Illinois,6909,55.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,Illinois,7183,58.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,Illinois,7230,59.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Illinois,7429,61.30 +1999,Cerebrovascular diseases (I60-I69),Stroke,Illinois,7714,64.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3040,39.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,Indiana,2959,39.10 +2014,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3107,41.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,Indiana,2996,40.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3073,42.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3127,44.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3082,44.50 +2009,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3004,44.00 +2008,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3114,46.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3083,46.60 +2006,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3238,49.90 +2005,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3296,51.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3454,55.30 +2003,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3627,58.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3717,61.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Indiana,3877,64.30 +2000,Cerebrovascular diseases (I60-I69),Stroke,Indiana,4247,71.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Indiana,4057,69.00 +2016,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1388,32.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1418,33.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1433,34.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1409,33.90 +2012,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1422,34.10 +2011,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1432,34.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1537,38.00 +2009,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1635,41.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1732,43.60 +2007,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1686,42.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1718,43.60 +2005,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1902,49.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Iowa,1960,50.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Iowa,2081,54.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Iowa,2226,58.50 +2001,Cerebrovascular diseases (I60-I69),Stroke,Iowa,2218,59.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Iowa,2187,58.30 +1999,Cerebrovascular diseases (I60-I69),Stroke,Iowa,2317,62.10 +2016,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1363,38.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1364,38.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1363,39.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1320,38.10 +2012,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1343,39.40 +2011,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1338,39.70 +2010,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1370,41.20 +2009,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1416,43.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1574,48.60 +2007,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1498,46.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1489,47.30 +2005,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1571,50.30 +2004,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1611,52.20 +2003,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1758,57.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1845,60.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1848,60.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1830,60.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Kansas,1841,61.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2057,40.40 +2015,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2050,40.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2050,41.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,1990,41.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2073,44.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2064,44.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,1992,44.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2064,46.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2098,47.90 +2007,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2144,49.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2197,51.90 +2005,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2168,52.50 +2004,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2339,58.00 +2003,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2442,61.30 +2002,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2554,64.90 +2001,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2557,65.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2637,67.70 +1999,Cerebrovascular diseases (I60-I69),Stroke,Kentucky,2710,70.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2322,46.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2280,46.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2230,45.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2098,44.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2039,43.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2071,45.70 +2010,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,1977,44.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2061,47.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2084,48.60 +2007,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2147,51.30 +2006,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2195,53.90 +2005,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2469,58.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2489,59.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2526,61.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2595,63.80 +2001,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2638,65.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2533,63.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Louisiana,2684,68.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Maine,663,34.40 +2015,Cerebrovascular diseases (I60-I69),Stroke,Maine,616,32.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,Maine,628,33.20 +2013,Cerebrovascular diseases (I60-I69),Stroke,Maine,620,33.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,Maine,619,34.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Maine,630,35.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Maine,602,34.50 +2009,Cerebrovascular diseases (I60-I69),Stroke,Maine,647,38.00 +2008,Cerebrovascular diseases (I60-I69),Stroke,Maine,671,40.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Maine,664,40.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Maine,670,41.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,Maine,693,43.80 +2004,Cerebrovascular diseases (I60-I69),Stroke,Maine,801,51.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Maine,802,52.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,Maine,823,54.50 +2001,Cerebrovascular diseases (I60-I69),Stroke,Maine,822,55.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Maine,833,57.00 +1999,Cerebrovascular diseases (I60-I69),Stroke,Maine,879,61.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2709,39.70 +2015,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2540,37.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2469,38.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2315,36.10 +2012,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2280,36.50 +2011,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2306,37.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2279,38.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2288,39.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2330,41.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2364,42.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2365,43.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2476,47.10 +2004,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2718,53.20 +2003,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2734,54.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2811,57.50 +2001,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2882,60.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2955,63.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,Maryland,2892,63.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2468,27.90 +2015,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2475,28.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2460,28.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2354,27.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2371,28.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2466,30.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2516,31.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2579,32.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2747,35.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2832,36.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2880,38.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,2977,39.80 +2004,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3254,44.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3407,46.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3559,49.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3535,49.30 +2000,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3669,51.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Massachusetts,3548,50.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4874,39.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4666,37.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4596,37.90 +2013,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4373,36.30 +2012,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4414,37.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4442,38.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4474,39.50 +2009,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4435,39.80 +2008,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4769,43.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4798,44.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Michigan,4752,44.90 +2005,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5057,48.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5290,52.00 +2003,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5470,54.50 +2002,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5814,59.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5701,58.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,Michigan,5864,61.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Michigan,6041,64.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2197,32.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2238,33.50 +2014,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2202,34.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2073,32.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2081,33.00 +2011,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2154,35.00 +2010,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2167,36.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2033,34.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2199,37.70 +2007,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2193,38.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2219,39.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2379,43.50 +2004,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2542,47.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2550,47.90 +2002,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2706,51.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2727,52.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2820,55.90 +1999,Cerebrovascular diseases (I60-I69),Stroke,Minnesota,2997,59.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1705,50.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1734,52.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1584,48.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1500,47.20 +2012,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1502,48.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1526,50.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1520,51.20 +2009,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1436,48.80 +2008,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1592,54.70 +2007,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1589,55.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1585,56.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1622,58.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1651,59.90 +2003,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1736,63.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1926,70.90 +2001,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1935,71.50 +2000,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1999,74.10 +1999,Cerebrovascular diseases (I60-I69),Stroke,Mississippi,1854,69.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3069,40.40 +2015,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3037,40.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3030,41.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Missouri,2935,40.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Missouri,2989,42.20 +2011,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3033,43.40 +2010,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3001,44.00 +2009,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3017,44.90 +2008,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3261,49.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3229,49.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3247,50.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3347,52.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3503,56.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3580,57.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3885,63.10 +2001,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3796,62.00 +2000,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3940,65.10 +1999,Cerebrovascular diseases (I60-I69),Stroke,Missouri,3950,65.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Montana,440,32.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,Montana,458,33.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Montana,480,36.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,Montana,480,37.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Montana,415,33.10 +2011,Cerebrovascular diseases (I60-I69),Stroke,Montana,460,37.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,Montana,494,41.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Montana,462,39.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,Montana,466,40.30 +2007,Cerebrovascular diseases (I60-I69),Stroke,Montana,443,39.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,Montana,461,41.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Montana,522,48.60 +2004,Cerebrovascular diseases (I60-I69),Stroke,Montana,484,46.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Montana,576,56.10 +2002,Cerebrovascular diseases (I60-I69),Stroke,Montana,639,63.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Montana,578,58.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Montana,583,60.10 +1999,Cerebrovascular diseases (I60-I69),Stroke,Montana,595,62.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,787,33.20 +2015,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,776,33.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,798,34.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,820,36.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,776,34.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,810,36.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,876,40.50 +2009,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,857,40.10 +2008,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,863,41.00 +2007,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,921,43.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,922,44.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,986,48.40 +2004,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,978,48.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,1094,54.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,1103,55.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,1130,57.50 +2000,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,1091,55.80 +1999,Cerebrovascular diseases (I60-I69),Stroke,Nebraska,1176,60.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Nevada,1096,35.90 +2015,Cerebrovascular diseases (I60-I69),Stroke,Nevada,1078,37.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,Nevada,948,33.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Nevada,905,33.30 +2012,Cerebrovascular diseases (I60-I69),Stroke,Nevada,897,34.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Nevada,889,36.00 +2010,Cerebrovascular diseases (I60-I69),Stroke,Nevada,796,33.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Nevada,859,36.90 +2008,Cerebrovascular diseases (I60-I69),Stroke,Nevada,906,40.80 +2007,Cerebrovascular diseases (I60-I69),Stroke,Nevada,850,39.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,Nevada,847,41.30 +2005,Cerebrovascular diseases (I60-I69),Stroke,Nevada,945,48.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Nevada,1030,54.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,Nevada,1028,57.80 +2002,Cerebrovascular diseases (I60-I69),Stroke,Nevada,976,57.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,Nevada,913,55.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Nevada,872,56.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Nevada,882,59.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,490,28.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,457,26.90 +2014,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,474,28.90 +2013,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,441,27.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,457,29.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,490,32.30 +2010,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,500,33.50 +2009,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,499,34.10 +2008,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,484,33.90 +2007,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,489,34.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,494,36.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,497,37.70 +2004,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,586,45.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,536,43.00 +2002,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,627,51.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,633,52.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,662,56.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,New Hampshire,669,57.90 +2016,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3401,30.40 +2015,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3413,31.10 +2014,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3419,31.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3456,32.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3440,32.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3418,32.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3402,33.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3265,32.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3264,33.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3492,36.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3468,36.30 +2005,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3614,38.50 +2004,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3781,40.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,3966,43.10 +2002,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,4016,44.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,4007,44.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,4316,49.00 +1999,Cerebrovascular diseases (I60-I69),Stroke,New Jersey,4122,47.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,885,35.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,786,32.50 +2014,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,822,34.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,689,30.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,677,30.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,727,33.50 +2010,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,806,38.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,726,35.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,760,38.30 +2007,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,804,41.30 +2006,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,739,39.30 +2005,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,730,40.10 +2004,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,721,41.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,770,44.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,715,42.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,824,50.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,813,50.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,New Mexico,817,52.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,New York,6258,25.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,New York,6292,26.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,New York,6212,26.10 +2013,Cerebrovascular diseases (I60-I69),Stroke,New York,6176,26.30 +2012,Cerebrovascular diseases (I60-I69),Stroke,New York,6139,26.60 +2011,Cerebrovascular diseases (I60-I69),Stroke,New York,6254,27.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,New York,6213,27.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,New York,5950,27.00 +2008,Cerebrovascular diseases (I60-I69),Stroke,New York,6123,28.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,New York,6160,28.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,New York,6398,30.40 +2005,Cerebrovascular diseases (I60-I69),Stroke,New York,6622,31.70 +2004,Cerebrovascular diseases (I60-I69),Stroke,New York,6927,33.70 +2003,Cerebrovascular diseases (I60-I69),Stroke,New York,7281,35.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,New York,7625,37.90 +2001,Cerebrovascular diseases (I60-I69),Stroke,New York,7706,38.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,New York,8006,40.90 +1999,Cerebrovascular diseases (I60-I69),Stroke,New York,8124,42.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4940,43.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5033,44.70 +2014,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4702,43.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4503,42.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4410,42.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4299,43.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4298,44.70 +2009,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4442,47.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4637,50.80 +2007,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4530,51.10 +2006,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4572,53.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4861,59.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,4955,61.70 +2003,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5203,66.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5259,68.80 +2001,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5401,72.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5749,78.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,North Carolina,5626,77.90 +2016,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,311,32.70 +2015,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,308,33.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,325,35.50 +2013,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,306,32.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,343,37.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,337,37.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,382,42.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,337,38.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,325,37.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,330,37.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,428,49.60 +2005,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,368,43.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,475,55.40 +2003,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,476,56.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,469,55.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,507,60.80 +2000,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,456,55.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,North Dakota,513,63.00 +2016,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5987,40.60 +2015,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5945,40.70 +2014,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5791,40.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5690,39.90 +2012,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5788,41.20 +2011,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5691,41.30 +2010,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5755,42.60 +2009,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5576,41.80 +2008,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5951,45.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5905,45.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Ohio,5828,45.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Ohio,6279,50.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Ohio,6501,52.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Ohio,6910,56.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,Ohio,7252,60.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,Ohio,6891,58.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Ohio,7175,61.20 +1999,Cerebrovascular diseases (I60-I69),Stroke,Ohio,7235,62.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1859,41.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1881,43.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1847,43.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1880,44.50 +2012,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1893,45.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1864,45.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1980,50.00 +2009,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,1961,50.10 +2008,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2079,54.00 +2007,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2126,56.20 +2006,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2085,55.70 +2005,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2235,60.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2183,60.30 +2003,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2485,68.90 +2002,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2427,67.50 +2001,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2384,66.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2493,69.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Oklahoma,2481,69.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1943,37.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1873,37.50 +2014,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1821,37.40 +2013,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1775,37.30 +2012,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1763,37.50 +2011,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1913,41.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1793,40.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1912,43.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1899,44.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1835,43.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Oregon,1978,48.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2289,57.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2328,59.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2554,66.70 +2002,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2645,70.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2588,70.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2585,71.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Oregon,2799,78.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6730,37.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6987,38.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6576,36.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6604,37.20 +2012,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6546,37.40 +2011,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6825,39.60 +2010,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6701,39.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6842,40.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,6942,41.90 +2007,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,7152,43.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,7151,44.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,7650,48.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,7792,49.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,8261,52.80 +2002,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,8579,55.60 +2001,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,8619,56.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,8919,59.00 +1999,Cerebrovascular diseases (I60-I69),Stroke,Pennsylvania,8600,57.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,399,26.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,394,27.10 +2014,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,373,25.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,397,27.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,433,30.60 +2011,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,404,29.40 +2010,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,431,31.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,429,31.40 +2008,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,462,34.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,457,33.40 +2006,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,421,31.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,533,39.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,522,39.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,565,42.90 +2002,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,605,46.70 +2001,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,614,48.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,577,45.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,Rhode Island,633,50.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2627,45.50 +2015,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2600,46.70 +2014,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2393,44.20 +2013,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2508,47.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2349,45.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2287,46.30 +2010,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2293,47.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2400,51.30 +2008,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2394,52.60 +2007,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2466,55.80 +2006,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2291,53.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2458,60.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2643,66.50 +2003,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2748,71.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2822,74.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2832,76.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2956,81.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,South Carolina,2974,83.40 +2016,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,420,35.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,383,33.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,439,38.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,418,38.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,408,37.40 +2011,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,444,42.30 +2010,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,416,39.90 +2009,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,418,40.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,396,39.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,410,40.20 +2006,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,442,44.00 +2005,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,511,52.70 +2004,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,469,48.70 +2003,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,476,50.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,518,54.90 +2001,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,490,52.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,560,61.60 +1999,Cerebrovascular diseases (I60-I69),Stroke,South Dakota,547,60.00 +2016,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3508,46.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3447,46.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3326,45.80 +2013,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3157,44.40 +2012,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3120,44.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3218,47.50 +2010,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3205,48.70 +2009,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3156,48.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3356,53.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3450,55.90 +2006,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3407,56.40 +2005,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3659,62.70 +2004,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3680,64.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3883,69.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,3980,71.60 +2001,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,4037,73.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,4266,78.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Tennessee,4103,76.30 +2016,Cerebrovascular diseases (I60-I69),Stroke,Texas,10673,42.00 +2015,Cerebrovascular diseases (I60-I69),Stroke,Texas,10485,42.70 +2014,Cerebrovascular diseases (I60-I69),Stroke,Texas,9898,41.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,Texas,9283,40.20 +2012,Cerebrovascular diseases (I60-I69),Stroke,Texas,9305,41.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,Texas,9065,41.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,Texas,9180,44.40 +2009,Cerebrovascular diseases (I60-I69),Stroke,Texas,9137,45.20 +2008,Cerebrovascular diseases (I60-I69),Stroke,Texas,9629,49.20 +2007,Cerebrovascular diseases (I60-I69),Stroke,Texas,9796,51.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Texas,9366,50.40 +2005,Cerebrovascular diseases (I60-I69),Stroke,Texas,9366,52.40 +2004,Cerebrovascular diseases (I60-I69),Stroke,Texas,9853,56.90 +2003,Cerebrovascular diseases (I60-I69),Stroke,Texas,10303,60.80 +2002,Cerebrovascular diseases (I60-I69),Stroke,Texas,10548,63.60 +2001,Cerebrovascular diseases (I60-I69),Stroke,Texas,10612,65.00 +2000,Cerebrovascular diseases (I60-I69),Stroke,Texas,10684,66.50 +1999,Cerebrovascular diseases (I60-I69),Stroke,Texas,10414,65.90 +2016,Cerebrovascular diseases (I60-I69),Stroke,United States,142142,37.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,United States,140323,37.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,United States,133103,36.50 +2013,Cerebrovascular diseases (I60-I69),Stroke,United States,128978,36.20 +2012,Cerebrovascular diseases (I60-I69),Stroke,United States,128546,36.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,United States,128932,37.90 +2010,Cerebrovascular diseases (I60-I69),Stroke,United States,129476,39.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,United States,128842,39.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,United States,134148,42.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,United States,135952,43.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,United States,137119,44.80 +2005,Cerebrovascular diseases (I60-I69),Stroke,United States,143579,48.00 +2004,Cerebrovascular diseases (I60-I69),Stroke,United States,150074,51.20 +2003,Cerebrovascular diseases (I60-I69),Stroke,United States,157689,54.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,United States,162672,57.20 +2001,Cerebrovascular diseases (I60-I69),Stroke,United States,163538,58.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,United States,167661,60.90 +1999,Cerebrovascular diseases (I60-I69),Stroke,United States,167366,61.60 +2016,Cerebrovascular diseases (I60-I69),Stroke,Utah,932,38.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Utah,888,38.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Utah,856,37.90 +2013,Cerebrovascular diseases (I60-I69),Stroke,Utah,836,38.20 +2012,Cerebrovascular diseases (I60-I69),Stroke,Utah,809,37.80 +2011,Cerebrovascular diseases (I60-I69),Stroke,Utah,782,37.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Utah,739,37.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Utah,738,37.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,Utah,748,39.70 +2007,Cerebrovascular diseases (I60-I69),Stroke,Utah,755,41.30 +2006,Cerebrovascular diseases (I60-I69),Stroke,Utah,674,38.20 +2005,Cerebrovascular diseases (I60-I69),Stroke,Utah,794,46.90 +2004,Cerebrovascular diseases (I60-I69),Stroke,Utah,790,48.60 +2003,Cerebrovascular diseases (I60-I69),Stroke,Utah,876,55.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,Utah,903,58.10 +2001,Cerebrovascular diseases (I60-I69),Stroke,Utah,870,57.20 +2000,Cerebrovascular diseases (I60-I69),Stroke,Utah,975,66.00 +1999,Cerebrovascular diseases (I60-I69),Stroke,Utah,869,60.20 +2016,Cerebrovascular diseases (I60-I69),Stroke,Vermont,247,29.20 +2015,Cerebrovascular diseases (I60-I69),Stroke,Vermont,307,36.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Vermont,266,31.70 +2013,Cerebrovascular diseases (I60-I69),Stroke,Vermont,260,31.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,Vermont,281,35.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,Vermont,227,29.40 +2010,Cerebrovascular diseases (I60-I69),Stroke,Vermont,265,35.30 +2009,Cerebrovascular diseases (I60-I69),Stroke,Vermont,221,29.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,Vermont,281,38.30 +2007,Cerebrovascular diseases (I60-I69),Stroke,Vermont,269,38.00 +2006,Cerebrovascular diseases (I60-I69),Stroke,Vermont,264,38.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,Vermont,260,38.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,Vermont,302,45.00 +2003,Cerebrovascular diseases (I60-I69),Stroke,Vermont,306,46.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,Vermont,335,52.00 +2001,Cerebrovascular diseases (I60-I69),Stroke,Vermont,323,50.90 +2000,Cerebrovascular diseases (I60-I69),Stroke,Vermont,343,54.90 +1999,Cerebrovascular diseases (I60-I69),Stroke,Vermont,344,55.80 +2016,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3502,38.20 +2015,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3393,38.00 +2014,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3229,37.00 +2013,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3287,38.60 +2012,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3394,40.70 +2011,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3347,41.70 +2010,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3293,42.10 +2009,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3244,42.50 +2008,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3301,44.50 +2007,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3313,45.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3523,50.10 +2005,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3675,53.70 +2004,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3788,56.70 +2003,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3938,60.60 +2002,Cerebrovascular diseases (I60-I69),Stroke,Virginia,3960,62.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,Virginia,4129,66.30 +2000,Cerebrovascular diseases (I60-I69),Stroke,Virginia,4104,67.40 +1999,Cerebrovascular diseases (I60-I69),Stroke,Virginia,4110,68.70 +2016,Cerebrovascular diseases (I60-I69),Stroke,Washington,2910,35.80 +2015,Cerebrovascular diseases (I60-I69),Stroke,Washington,2703,34.20 +2014,Cerebrovascular diseases (I60-I69),Stroke,Washington,2649,34.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,Washington,2652,35.50 +2012,Cerebrovascular diseases (I60-I69),Stroke,Washington,2513,34.30 +2011,Cerebrovascular diseases (I60-I69),Stroke,Washington,2557,35.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,Washington,2548,37.00 +2009,Cerebrovascular diseases (I60-I69),Stroke,Washington,2591,38.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,Washington,2773,42.30 +2007,Cerebrovascular diseases (I60-I69),Stroke,Washington,2692,42.10 +2006,Cerebrovascular diseases (I60-I69),Stroke,Washington,2725,43.60 +2005,Cerebrovascular diseases (I60-I69),Stroke,Washington,2895,48.10 +2004,Cerebrovascular diseases (I60-I69),Stroke,Washington,3243,55.10 +2003,Cerebrovascular diseases (I60-I69),Stroke,Washington,3595,62.40 +2002,Cerebrovascular diseases (I60-I69),Stroke,Washington,3753,66.60 +2001,Cerebrovascular diseases (I60-I69),Stroke,Washington,3765,68.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,Washington,3714,68.70 +1999,Cerebrovascular diseases (I60-I69),Stroke,Washington,3718,69.90 +2016,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1039,41.70 +2015,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1079,43.80 +2014,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1103,45.30 +2013,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,983,40.70 +2012,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1140,47.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1049,44.80 +2010,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1104,47.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1073,46.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1096,48.30 +2007,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1113,49.70 +2006,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1072,48.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1151,53.20 +2004,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1178,54.80 +2003,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1309,61.20 +2002,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1260,59.30 +2001,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1272,60.10 +2000,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1296,61.30 +1999,Cerebrovascular diseases (I60-I69),Stroke,West Virginia,1323,63.10 +2016,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2481,33.30 +2015,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2618,35.60 +2014,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2511,34.60 +2013,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2536,36.00 +2012,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2520,36.00 +2011,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2554,37.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2609,38.80 +2009,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2501,37.70 +2008,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2638,40.10 +2007,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2738,42.60 +2006,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2829,44.60 +2005,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,2960,47.40 +2004,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3071,50.00 +2003,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3216,53.10 +2002,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3479,58.40 +2001,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3658,62.40 +2000,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3598,62.20 +1999,Cerebrovascular diseases (I60-I69),Stroke,Wisconsin,3869,67.50 +2016,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,208,31.70 +2015,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,198,31.40 +2014,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,189,30.20 +2013,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,213,35.10 +2012,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,205,34.90 +2011,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,214,37.20 +2010,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,204,37.20 +2009,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,233,42.60 +2008,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,234,44.40 +2007,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,209,40.50 +2006,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,236,46.50 +2005,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,221,45.10 +2004,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,214,44.20 +2003,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,253,53.90 +2002,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,243,52.80 +2001,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,260,56.70 +2000,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,260,58.30 +1999,Cerebrovascular diseases (I60-I69),Stroke,Wyoming,265,60.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,788,15.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,750,14.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,715,14.50 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,721,14.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,724,14.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,654,13.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,679,14.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,673,13.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,604,12.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,592,12.40 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,580,12.40 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,535,11.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,541,11.70 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,521,11.40 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,514,11.50 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,512,11.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,583,13.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alabama,555,12.50 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,193,25.80 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,201,26.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,167,22.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,171,23.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,168,23.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,143,20.00 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,164,22.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,143,20.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,169,24.40 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,149,22.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,135,19.90 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,131,19.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,155,23.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,124,20.30 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,132,20.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,102,15.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,137,21.50 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Alaska,96,16.80 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1271,17.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1276,18.20 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1244,18.00 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1163,17.50 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1156,17.30 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1160,17.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1093,17.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1060,16.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,972,15.50 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,1016,16.50 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,979,16.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,945,16.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,880,15.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,840,15.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,886,16.60 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,767,14.80 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,784,15.60 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arizona,766,15.50 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,555,18.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,577,19.10 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,515,17.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,516,17.30 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,485,16.30 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,462,16.00 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,447,15.50 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,422,14.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,447,15.50 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,402,14.20 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,376,13.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,400,14.30 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,361,13.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,374,13.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,377,14.00 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,382,14.30 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,349,13.00 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Arkansas,336,12.60 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,4294,10.50 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,4167,10.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,4214,10.50 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,4025,10.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3893,10.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3996,10.40 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3913,10.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3823,10.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3775,10.30 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3602,9.90 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3334,9.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3206,9.10 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3368,9.70 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3397,9.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3228,9.60 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,2831,8.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,2969,9.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,California,3077,9.60 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,1168,20.50 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,1093,19.50 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,1083,19.90 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,1007,18.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,1052,19.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,913,17.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,865,16.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,941,18.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,803,16.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,811,16.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,730,15.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,800,17.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,797,17.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,728,16.10 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,727,16.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,722,16.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,613,14.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Colorado,574,13.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,397,10.10 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,384,9.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,379,9.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,330,8.70 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,368,9.90 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,370,9.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,353,9.40 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,316,8.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,315,8.50 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,271,7.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,292,8.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,295,8.10 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,294,8.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,272,7.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,260,7.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,283,8.10 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,304,8.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Connecticut,274,7.90 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,119,11.50 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,122,12.60 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,126,13.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,122,12.50 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,125,13.20 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,105,11.00 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,106,11.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,107,11.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,109,11.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,95,10.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,91,10.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,83,9.70 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,93,11.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,94,11.40 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,74,9.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,108,13.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,82,10.40 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Delaware,86,11.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,40,5.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,34,4.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,52,7.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,38,5.70 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,37,5.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,37,5.60 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,41,6.90 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,29,4.40 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,43,7.30 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,36,6.00 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,30,5.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,33,5.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,33,5.50 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,36,6.10 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,31,5.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,40,6.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,23,3.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,District of Columbia,30,5.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,3143,14.00 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,3205,14.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,3035,13.90 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2928,13.80 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,3002,14.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2880,13.90 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2789,13.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2858,14.40 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2740,13.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2587,13.20 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2440,12.60 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2347,12.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2389,13.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2297,12.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2338,13.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2314,13.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2086,12.40 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Florida,2029,12.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1409,13.30 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1317,12.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1295,12.60 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1212,12.00 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1168,11.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1157,11.70 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1133,11.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,1134,11.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,981,10.40 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,997,10.90 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,923,10.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,924,10.60 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,973,11.30 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,972,11.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,909,11.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,935,11.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,847,10.70 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Georgia,873,11.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,174,12.10 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,201,13.50 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,204,13.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,171,11.80 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,190,13.10 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,181,12.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,207,15.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,175,12.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,133,9.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,133,9.50 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,120,9.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,107,8.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,116,8.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,131,10.20 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,120,9.50 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,136,10.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,137,11.20 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Hawaii,136,11.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,351,21.40 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,359,22.10 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,320,20.00 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,308,19.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,297,19.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,281,18.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,290,18.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,304,20.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,252,16.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,223,15.00 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,222,15.80 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,228,16.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,236,17.60 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,217,16.40 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,202,15.50 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,210,16.30 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,167,13.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Idaho,181,14.80 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1415,10.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1363,10.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1398,10.50 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1321,9.90 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1292,9.80 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1226,9.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1178,9.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1177,9.10 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1198,9.30 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1108,8.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1010,7.90 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1086,8.60 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1028,8.10 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1011,8.00 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1145,9.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1139,9.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1003,8.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Illinois,1020,8.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,1034,15.40 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,960,14.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,948,14.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,944,14.30 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,940,14.30 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,881,13.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,864,13.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,828,12.80 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,809,12.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,790,12.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,824,13.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,745,11.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,704,11.30 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,736,11.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,743,12.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,715,11.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,683,11.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Indiana,628,10.40 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,451,14.60 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,433,13.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,407,12.90 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,447,14.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,383,12.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,422,13.90 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,372,12.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,361,11.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,380,12.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,322,10.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,334,11.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,333,11.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,343,11.50 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,352,11.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,314,10.70 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,304,10.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,289,9.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Iowa,305,10.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,514,17.90 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,477,16.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,455,15.70 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,425,14.70 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,502,17.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,394,13.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,401,14.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,382,13.60 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,337,12.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,382,13.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,379,13.90 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,362,13.30 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,370,13.70 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,347,12.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,345,12.70 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,293,10.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,325,12.20 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kansas,299,11.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,756,16.80 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,776,17.10 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,728,16.00 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,701,15.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,724,16.20 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,675,15.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,631,14.20 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,592,13.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,612,14.00 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,649,15.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,622,14.60 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,566,13.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,560,13.30 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,567,13.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,540,13.00 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,495,12.10 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,521,12.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Kentucky,470,11.60 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,677,14.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,722,15.20 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,679,14.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,583,12.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,567,12.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,573,12.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,557,12.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,490,10.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,532,12.00 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,522,12.00 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,492,11.50 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,505,11.00 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,537,11.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,461,10.30 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,499,11.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,493,11.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,468,10.70 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Louisiana,518,11.90 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,226,15.90 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,235,16.00 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,220,15.70 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,245,17.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,209,14.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,235,16.60 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,186,13.20 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,197,14.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,181,12.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,191,13.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,158,11.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,175,12.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,171,12.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,137,10.00 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,166,12.50 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,161,12.10 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,154,11.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maine,175,13.40 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,586,9.40 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,553,8.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,606,9.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,569,9.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,583,9.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,558,9.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,502,8.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,551,9.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,507,8.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,518,9.00 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,495,8.60 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,472,8.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,500,8.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,491,8.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,477,8.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,454,8.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,474,9.00 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Maryland,435,8.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,631,8.80 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,642,8.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,596,8.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,572,8.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,604,8.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,585,8.40 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,598,8.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,530,7.80 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,509,7.50 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,516,7.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,450,6.70 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,480,7.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,425,6.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,433,6.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,436,6.60 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,426,6.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,387,6.00 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Massachusetts,431,6.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1364,13.30 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1410,13.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1354,13.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1295,12.90 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1261,12.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1221,12.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1263,12.50 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1169,11.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1180,11.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1131,11.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1139,11.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1108,11.00 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1098,10.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1029,10.20 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1106,11.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,1051,10.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,974,9.90 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Michigan,974,9.90 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,745,13.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,730,13.20 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,686,12.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,678,12.10 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,656,12.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,683,12.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,606,11.20 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,584,10.80 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,596,11.20 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,572,10.80 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,554,10.60 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,547,10.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,524,10.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,497,9.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,497,9.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,480,9.60 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,440,8.90 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Minnesota,437,9.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,383,12.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,431,14.00 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,380,12.50 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,388,13.00 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,410,14.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,389,13.10 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,388,13.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,381,13.10 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,409,13.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,396,13.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,325,11.40 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,363,12.70 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,350,12.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,336,11.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,343,12.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,328,11.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,294,10.50 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Mississippi,304,11.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,1133,18.40 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,1052,17.10 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,1017,16.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,960,15.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,914,14.90 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,933,15.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,856,14.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,860,14.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,779,13.00 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,808,13.50 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,799,13.50 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,727,12.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,715,12.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,679,11.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,693,12.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,725,12.80 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,699,12.50 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Missouri,700,12.60 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,267,25.90 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,272,25.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,251,23.90 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,243,23.70 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,233,22.60 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,232,22.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,227,21.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,219,21.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,203,20.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,196,19.40 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,189,19.70 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,206,21.70 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,175,18.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,180,19.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,184,20.00 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,175,19.30 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,158,17.50 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Montana,162,17.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,246,13.10 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,223,11.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,251,13.40 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,220,11.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,232,12.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,193,10.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,193,10.40 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,170,9.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,191,10.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,181,10.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,202,11.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,187,10.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,166,9.50 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,176,10.20 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,201,11.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,187,10.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,193,11.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nebraska,177,10.50 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,650,21.40 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,558,18.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,573,19.60 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,541,18.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,524,18.20 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,516,18.40 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,547,19.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,505,18.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,528,19.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,471,18.00 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,486,19.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,480,19.80 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,440,18.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,434,19.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,423,19.70 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,387,18.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,400,20.20 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Nevada,404,21.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,244,17.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,228,16.50 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,247,17.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,185,12.80 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,202,14.10 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,198,13.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,196,14.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,166,11.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,179,13.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,158,11.20 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,151,11.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,162,12.00 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,133,9.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,158,12.00 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,132,10.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,167,13.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,131,10.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Hampshire,137,11.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,687,7.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,789,8.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,786,8.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,757,8.00 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,683,7.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,689,7.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,719,7.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,557,6.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,615,6.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,596,6.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,585,6.50 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,536,6.10 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,597,6.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,588,6.70 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,553,6.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,588,6.80 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,560,6.60 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Jersey,563,6.60 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,471,22.50 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,500,23.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,449,21.00 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,431,20.30 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,442,21.30 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,420,20.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,413,20.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,376,18.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,419,20.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,401,20.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,352,17.90 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,342,17.80 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,356,18.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,343,18.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,349,19.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,362,20.30 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,327,18.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New Mexico,318,18.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1679,8.10 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1652,7.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1700,8.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1687,8.10 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1708,8.30 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1658,8.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1547,7.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1417,7.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1409,7.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1396,7.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1326,6.70 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1189,6.00 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1187,6.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1169,5.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1228,6.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1253,6.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1132,5.90 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,New York,1196,6.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1373,13.00 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1406,13.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1352,13.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1284,12.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1286,12.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1213,12.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1174,12.00 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1174,12.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1162,12.30 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1077,11.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1106,12.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1009,11.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,1027,11.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,955,11.20 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,986,11.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,997,12.00 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,971,12.00 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Carolina,884,11.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,140,19.00 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,124,17.50 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,137,17.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,128,17.30 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,105,15.20 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,106,14.90 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,106,15.60 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,90,13.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,86,13.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,95,14.20 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,90,13.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,92,13.70 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,73,11.10 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,81,12.30 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,91,14.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,79,12.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,68,10.40 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,North Dakota,73,11.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1707,14.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1650,13.90 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1491,12.60 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1526,12.90 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1542,13.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1465,12.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1439,12.20 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1176,10.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1412,12.20 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1295,11.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1325,11.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1341,11.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1319,11.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1074,9.30 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1287,11.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1219,10.60 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1088,9.50 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Ohio,1102,9.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,822,21.00 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,790,20.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,736,19.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,665,17.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,670,17.60 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,693,18.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,618,16.50 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,567,15.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,575,15.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,531,14.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,537,15.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,522,14.80 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,506,14.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,476,13.70 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,501,14.40 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,515,14.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,497,14.60 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oklahoma,492,14.40 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,772,17.80 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,762,17.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,782,18.60 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,698,16.80 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,724,17.80 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,656,16.60 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,685,17.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,644,16.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,572,14.50 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,594,15.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,579,15.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,560,14.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,555,15.10 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,592,16.20 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,518,14.40 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,505,14.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,493,14.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Oregon,478,13.90 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1970,14.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1894,14.00 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1817,13.30 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1788,13.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1647,12.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1747,13.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1576,11.90 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1631,12.20 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1539,11.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1441,11.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1396,10.80 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1430,11.10 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1410,11.10 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1340,10.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1341,10.70 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1276,10.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1356,10.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Pennsylvania,1284,10.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,126,11.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,127,11.20 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,113,10.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,132,12.20 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,105,9.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,101,8.90 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,129,12.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,118,10.80 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,110,9.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,96,8.80 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,90,8.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,71,6.30 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,85,7.60 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,84,7.70 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,86,7.90 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,88,8.10 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,75,7.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Rhode Island,96,9.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,815,15.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,742,14.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,753,15.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,696,14.00 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,673,13.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,658,13.60 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,637,13.50 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,619,13.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,565,12.00 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,530,11.60 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,524,11.80 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,510,11.80 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,482,11.30 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,476,11.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,440,10.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,467,11.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,443,11.10 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Carolina,418,10.50 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,163,20.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,173,20.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,141,17.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,147,18.00 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,141,16.80 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,128,15.70 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,140,17.50 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,129,16.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,124,15.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,102,12.50 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,125,16.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,121,15.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,112,14.80 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,102,13.50 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,94,12.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,105,14.00 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,95,12.70 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,South Dakota,103,13.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,1111,16.30 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,1068,15.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,997,14.80 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,1030,15.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,978,14.60 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,955,14.40 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,943,14.60 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,947,14.60 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,973,15.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,844,13.40 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,874,14.10 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,856,14.00 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,792,13.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,762,12.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,778,13.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,711,12.20 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,730,12.70 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Tennessee,726,12.70 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,3488,12.60 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,3403,12.50 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,3254,12.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,3059,11.70 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,3037,11.90 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2896,11.50 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2891,11.80 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2809,11.60 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2552,10.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2433,10.50 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2347,10.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2418,10.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2300,10.70 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2363,11.10 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2311,11.10 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2225,10.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2053,10.20 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Texas,2005,10.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,44965,13.50 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,44193,13.30 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,42826,13.00 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,41149,12.60 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,40600,12.60 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,39518,12.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,38364,12.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,36909,11.80 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,36035,11.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,34598,11.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,33300,11.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,32637,10.90 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,32439,11.00 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,31484,10.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,31655,11.00 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,30622,10.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,29350,10.40 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,United States,29199,10.50 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,620,21.80 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,630,22.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,559,20.50 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,579,21.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,550,21.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,502,19.40 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,473,18.30 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,449,17.90 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,390,15.90 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,378,15.80 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,362,16.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,348,15.40 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,377,17.20 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,336,15.40 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,340,16.20 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,321,15.50 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,298,14.70 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Utah,282,14.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,118,17.30 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,103,14.80 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,124,18.70 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,112,16.80 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,87,13.00 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,120,17.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,106,15.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,87,13.00 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,94,14.10 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,89,13.90 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,81,12.20 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,78,12.50 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,93,14.50 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,83,13.00 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,92,14.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,72,11.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,77,12.30 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Vermont,63,10.30 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1166,13.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1118,12.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1123,12.90 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1072,12.50 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1063,12.70 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,1054,12.60 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,963,11.70 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,963,11.70 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,948,11.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,880,11.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,876,11.10 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,866,11.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,828,10.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,808,10.90 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,799,10.90 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,797,11.00 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,768,10.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Virginia,791,11.20 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1141,14.90 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1137,15.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1119,15.20 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1027,14.10 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1038,14.50 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,1021,14.30 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,957,13.90 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,921,13.30 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,889,13.20 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,865,13.10 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,809,12.30 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,822,12.80 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,830,13.30 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,803,13.10 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,811,13.40 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,712,11.90 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,727,12.40 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Washington,816,14.00 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,362,19.30 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,340,17.40 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,359,18.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,323,16.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,326,17.10 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,306,15.90 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,279,14.10 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,253,13.10 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,261,13.70 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,300,15.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,269,14.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,255,13.20 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,285,15.40 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,266,14.00 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,276,14.90 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,286,15.00 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,245,13.20 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,West Virginia,229,12.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,866,14.70 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,877,14.70 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,769,13.10 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,850,14.40 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,723,12.40 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,745,12.80 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,793,13.40 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,724,12.50 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,743,12.80 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,729,12.70 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,670,12.00 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,643,11.60 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,662,11.90 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,647,11.60 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,627,11.30 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,639,11.70 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,590,10.90 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wisconsin,593,11.10 +2016,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,144,25.20 +2015,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,157,28.00 +2014,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,120,20.60 +2013,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,129,21.50 +2012,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,171,29.60 +2011,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,132,23.20 +2010,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,131,22.40 +2009,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,111,20.40 +2008,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,124,22.60 +2007,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,101,19.30 +2006,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,116,21.70 +2005,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,90,17.30 +2004,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,88,17.50 +2003,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,109,21.80 +2002,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,105,20.80 +2001,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,83,16.40 +2000,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,83,16.80 +1999,"Intentional self-harm (suicide) (*U03,X60-X84,Y87.0)",Suicide,Wyoming,98,20.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2552,50.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2463,49.30 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2329,47.20 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2283,46.60 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2662,54.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2394,49.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2351,49.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2509,52.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2542,53.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2506,54.00 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2395,51.90 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2403,53.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2179,48.30 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2228,49.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2211,49.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2093,47.00 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alabama,2313,52.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,388,57.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,379,55.00 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,353,52.50 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,368,54.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,385,57.30 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,366,58.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,340,53.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,332,54.10 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,354,56.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,316,52.30 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,313,50.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,326,56.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,321,55.40 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,346,59.20 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,345,60.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,343,64.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Alaska,294,55.90 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3539,49.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3322,47.00 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3349,48.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3029,45.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3096,46.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3018,46.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2919,45.50 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2956,47.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3161,51.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3352,56.00 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,3150,54.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2770,49.30 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2697,49.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2577,48.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2475,47.60 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2326,46.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arizona,2214,44.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1538,49.60 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1458,47.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1373,44.80 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1521,50.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1510,50.20 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1461,49.40 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1473,50.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1477,50.60 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1391,47.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1415,49.40 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1329,47.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1406,50.60 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1304,46.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1311,47.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1277,46.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1267,46.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Arkansas,1287,47.60 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,12544,30.60 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,11804,29.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,11538,29.20 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10906,28.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10824,28.30 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10435,27.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10860,29.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10761,29.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,11614,32.30 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,11375,32.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,11129,31.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10633,30.80 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10471,30.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,10107,30.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,8132,24.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,8577,26.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,California,9198,28.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2725,49.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2517,47.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2422,46.40 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2403,47.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2330,46.50 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2106,43.50 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2144,44.50 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2172,46.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,2056,44.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1917,42.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1947,44.10 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1810,42.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1806,42.40 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1812,43.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1720,41.60 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1702,42.60 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Colorado,1519,39.00 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1799,44.80 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1641,40.90 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1582,39.30 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1392,34.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1339,33.30 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1337,33.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1293,33.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1386,35.70 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1343,35.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1302,34.60 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1134,30.30 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1261,34.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1112,30.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1182,32.50 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1056,29.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1175,32.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Connecticut,1034,29.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,449,46.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,425,43.70 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,412,42.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,374,38.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,338,36.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,357,39.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,333,36.80 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,352,39.60 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,309,34.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,329,38.00 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,293,34.50 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,294,35.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,287,34.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,292,36.40 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,290,36.60 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,290,37.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Delaware,267,35.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,265,40.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,217,33.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,216,33.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,194,31.30 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,185,29.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,212,35.10 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,145,24.60 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,160,27.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,193,33.70 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,224,39.40 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,207,35.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,219,38.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,233,40.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,200,34.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,223,38.40 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,204,35.70 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,District of Columbia,161,28.40 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,10578,46.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,9432,41.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8736,39.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8770,39.90 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8901,42.00 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8875,43.10 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8746,43.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8939,45.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,9113,46.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8917,46.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8868,47.10 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,8229,44.70 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,7919,44.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,7396,42.00 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,6969,40.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,6267,37.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Florida,5961,35.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,4344,43.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3963,40.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3727,38.30 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3731,39.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3785,40.20 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3745,40.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3812,42.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3774,42.00 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,4012,45.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3879,45.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3762,45.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3667,45.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3528,44.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3333,42.30 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3396,43.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3103,40.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Georgia,3078,41.50 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,536,32.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,476,30.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,467,30.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,458,30.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,517,34.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,432,29.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,436,30.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,406,28.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,470,33.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,441,31.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,436,32.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,390,29.60 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,415,31.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,393,30.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,372,29.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,343,28.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Hawaii,293,24.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,746,44.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,765,46.50 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,776,47.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,699,43.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,710,44.70 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,654,42.10 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,670,44.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,649,43.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,641,43.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,682,47.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,606,43.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,593,43.60 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,605,45.60 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,611,47.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,568,44.20 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,518,41.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Idaho,597,48.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4850,35.80 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4644,34.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4511,33.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4488,33.60 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4166,31.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,3997,30.40 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,3961,30.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4218,32.60 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4367,33.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4451,34.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4182,33.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4133,32.80 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,3942,31.50 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4222,33.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4077,32.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4041,32.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Illinois,4125,33.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,3258,47.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2974,43.90 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2898,43.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2776,41.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2699,40.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2534,38.50 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2577,39.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2558,39.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2499,38.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2480,38.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2480,39.30 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2395,38.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2196,35.40 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2148,34.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2185,35.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2139,35.40 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Indiana,2309,38.40 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1537,42.10 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1517,42.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1422,40.00 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1369,39.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1369,39.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1273,37.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1255,37.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1266,37.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1252,37.50 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1188,35.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1202,36.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1108,33.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1164,35.40 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1093,33.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1051,32.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1070,33.40 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Iowa,1123,35.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1475,47.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1377,44.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1359,44.40 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1319,43.60 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1333,43.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1317,44.00 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1273,42.90 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1174,40.00 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1205,41.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1198,41.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1149,40.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1144,40.60 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1089,38.60 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1139,40.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1156,41.40 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1054,38.00 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kansas,1126,40.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2962,66.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2621,58.30 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2513,55.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2741,62.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2608,59.50 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2632,60.50 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2394,55.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2379,55.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2372,55.60 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2446,57.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2405,57.40 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2260,54.80 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2270,55.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,2090,51.30 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,1990,49.10 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,1838,45.70 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Kentucky,1730,43.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2578,54.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2344,49.80 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2333,50.50 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2362,51.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2091,45.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,1999,44.40 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2142,48.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2409,54.90 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2466,56.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2422,56.80 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,3072,68.50 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2300,51.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2208,49.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2115,48.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2029,46.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,2006,45.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Louisiana,1940,44.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,802,53.80 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,690,45.80 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,644,42.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,602,40.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,574,38.30 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,540,36.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,602,40.90 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,628,43.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,584,41.60 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,572,40.80 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,579,41.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,480,35.50 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,516,38.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,511,38.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,490,36.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,407,30.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maine,458,34.90 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1903,29.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1674,26.60 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1732,27.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1696,27.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1569,26.10 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1446,24.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1415,24.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1465,25.60 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1480,26.20 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1456,26.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1376,25.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1412,26.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1432,26.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1332,25.20 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1339,25.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1182,23.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Maryland,1296,25.90 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,3229,44.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2668,36.60 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2393,32.50 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2195,29.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2224,30.70 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2060,28.50 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2076,29.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2040,29.00 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2139,31.00 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,2214,32.30 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1907,28.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1366,19.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1421,20.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1413,20.70 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1506,22.20 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1357,20.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Massachusetts,1303,19.60 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,4647,43.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,4422,41.70 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,4225,40.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3805,36.60 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3951,38.00 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3770,36.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3682,35.80 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3685,35.70 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3764,36.50 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3590,35.20 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3451,34.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3312,32.80 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3324,33.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3285,33.00 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3291,33.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3220,32.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Michigan,3188,32.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2574,42.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2385,39.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2405,40.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2301,39.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2319,39.90 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2103,36.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2037,36.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2010,36.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,2066,37.50 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1919,35.40 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1922,36.20 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1867,35.50 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1912,36.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1928,37.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1797,35.40 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1711,34.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Minnesota,1772,35.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1814,59.80 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1712,56.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1692,55.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1627,54.50 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1724,57.90 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1685,56.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1658,56.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1693,57.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1808,62.20 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1856,64.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1936,67.10 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1703,59.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1656,58.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1642,58.50 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1571,56.00 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1653,58.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Mississippi,1642,58.90 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3309,50.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3110,48.70 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2981,47.00 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3002,47.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3169,50.50 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2975,47.90 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2939,47.50 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2997,48.90 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2975,48.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,3009,50.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2848,47.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2728,46.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2786,47.60 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2641,45.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2455,42.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2398,41.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Missouri,2465,43.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,637,56.30 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,581,52.60 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,623,57.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,572,52.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,609,56.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,548,53.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,619,60.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,592,59.10 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,614,60.30 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,558,55.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,524,53.20 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,535,55.30 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,518,54.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,524,55.60 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,468,50.10 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,495,53.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Montana,461,50.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,799,38.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,781,38.60 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,703,34.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,792,39.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,678,33.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,700,35.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,683,35.60 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,714,37.10 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,674,35.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,683,36.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,704,37.40 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,743,39.70 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,696,38.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,762,41.70 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,633,34.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,640,35.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nebraska,668,37.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1340,45.40 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1166,40.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1184,41.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1177,42.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1155,42.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1088,41.30 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1025,39.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1134,44.00 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1212,48.10 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1091,44.30 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1104,47.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,1021,45.20 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,911,41.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,860,41.70 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,739,37.00 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,730,38.70 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Nevada,710,38.50 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,815,59.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,716,50.50 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,619,42.50 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,573,40.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,553,38.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,517,37.30 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,482,34.80 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,488,35.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,527,39.00 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,460,34.10 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,477,36.30 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,445,34.30 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,405,31.50 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,357,28.50 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,374,30.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,321,26.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Hampshire,329,27.50 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,3218,33.70 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2970,30.90 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,3028,31.80 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2990,31.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2685,28.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2486,26.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,1875,20.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2436,26.70 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2425,26.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2590,29.00 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2561,28.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2324,26.30 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2371,27.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2599,29.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2405,27.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2284,26.70 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Jersey,2227,26.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1430,67.30 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1534,72.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1245,59.00 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1351,65.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1336,64.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1233,60.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1281,63.80 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1366,69.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1329,67.70 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1297,67.60 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1267,67.00 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1223,66.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1227,67.30 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1105,61.40 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,1020,57.70 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,995,56.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New Mexico,969,55.90 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,6515,30.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5945,27.60 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5927,27.80 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5786,27.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5537,26.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5004,24.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4891,23.90 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5042,24.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5160,25.70 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,5235,26.20 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4645,23.30 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4530,22.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4708,23.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4663,23.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4982,25.70 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4244,22.00 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,New York,4797,25.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4991,47.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4557,44.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4324,42.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4281,43.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4297,43.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4144,43.20 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4136,43.60 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4313,46.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4389,48.30 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4156,46.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4123,47.90 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,4019,47.70 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,3835,46.30 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,3700,45.30 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,3438,42.60 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,3525,44.70 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Carolina,3290,42.10 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,368,44.10 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,349,43.30 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,326,41.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,318,41.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,298,39.10 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,285,38.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,324,44.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,342,47.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,279,38.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,275,38.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,287,39.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,274,38.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,282,39.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,246,35.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,238,33.90 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,248,35.30 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,North Dakota,267,38.40 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,6756,55.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,6178,50.80 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,5497,45.00 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,5420,44.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,5275,43.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,5124,42.40 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4012,33.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,5093,42.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4922,41.30 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4821,40.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4438,37.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4234,36.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,3757,32.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,4146,35.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,3857,33.40 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,3492,30.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Ohio,3630,31.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2422,60.10 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2421,60.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2474,62.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2388,61.60 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2261,58.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2288,60.30 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2284,61.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2119,57.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2149,58.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2039,56.60 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,2005,56.10 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1947,55.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1748,49.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1580,44.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1700,48.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1566,44.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oklahoma,1609,46.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1999,44.50 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1803,41.00 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1755,40.10 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1709,39.90 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1722,40.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1566,37.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1598,39.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1674,41.70 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1646,41.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1586,40.80 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1469,38.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1444,38.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1401,38.20 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1397,38.40 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1313,36.90 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1248,35.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Oregon,1199,34.70 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,7324,52.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,6640,46.80 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,6359,44.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,6334,45.00 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,6216,44.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5751,41.10 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5477,39.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5787,42.10 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5568,40.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5299,39.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5446,40.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5200,39.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,5014,37.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,4728,35.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,4552,34.60 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,4583,35.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Pennsylvania,4614,35.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,649,53.10 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,592,49.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,537,45.30 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,525,44.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,541,44.60 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,475,40.10 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,428,36.10 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,480,40.60 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,416,34.70 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,434,36.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,334,27.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,298,24.80 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,393,33.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,277,23.40 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,293,25.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,235,20.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Rhode Island,243,21.50 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2737,54.00 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2436,48.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2287,46.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2336,48.30 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2285,48.20 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2274,48.90 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2229,48.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2285,50.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2364,53.20 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2315,53.30 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2272,53.40 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,2094,50.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,1941,47.50 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,1972,48.70 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,1960,49.00 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,1974,50.00 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Carolina,1901,49.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,469,49.50 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,462,49.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,425,46.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,426,47.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,411,45.00 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,393,44.50 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,349,40.70 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,381,43.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,366,42.50 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,452,53.40 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,402,48.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,416,50.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,406,50.50 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,348,43.50 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,382,47.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,321,41.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,South Dakota,351,44.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3873,56.40 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3686,54.20 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3540,52.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3513,53.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3485,53.30 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3539,54.90 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3231,50.60 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3250,51.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3257,52.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3307,54.30 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3147,52.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3156,53.60 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,3004,51.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,2744,47.70 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,2709,47.30 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,2743,48.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Tennessee,2677,47.80 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9976,37.40 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9723,37.30 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9395,37.00 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9313,37.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9410,38.70 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9212,39.00 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9349,40.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9189,40.40 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9392,42.00 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,9140,41.60 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,8598,40.40 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,8323,39.70 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,8425,40.80 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,8232,40.40 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,7920,39.70 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,7407,37.60 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Texas,7227,37.60 +2016,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,161374,47.40 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,146571,43.20 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,135928,40.50 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,130557,39.40 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,127792,39.10 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,126438,39.10 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,120859,38.00 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,118021,37.50 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,121902,39.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,123706,40.40 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,121599,40.20 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,117809,39.50 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,112012,38.10 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,109277,37.60 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,106742,37.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,101537,35.70 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,97900,34.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,United States,97860,35.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1223,45.60 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1167,45.50 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1103,43.40 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1097,44.50 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,1058,42.90 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,970,40.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,899,38.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,881,37.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,811,35.60 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,715,31.60 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,743,34.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,697,33.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,702,34.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,714,35.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,643,32.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,665,34.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Utah,650,33.40 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,346,48.40 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,322,44.40 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,352,49.60 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,333,47.40 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,301,42.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,298,42.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,308,43.50 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,306,44.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,303,44.90 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,301,45.80 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,272,42.10 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,253,39.40 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,235,36.30 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,240,37.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,230,36.80 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,234,38.10 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Vermont,209,34.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,3429,39.60 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,3146,36.80 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2951,34.90 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2883,34.50 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2809,34.40 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2527,31.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2622,33.30 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2820,36.20 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2931,38.30 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2703,35.80 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2638,35.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2630,36.20 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2644,37.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2479,35.20 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2432,35.20 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2396,35.40 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Virginia,2214,33.20 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,3192,41.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2997,40.10 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2826,38.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2760,38.30 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2709,38.10 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2609,37.60 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2696,39.40 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2727,40.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2637,40.20 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2679,41.50 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2543,40.40 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2341,37.90 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2239,36.90 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2203,36.80 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2072,35.20 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,2049,35.50 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Washington,1914,33.30 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1516,77.90 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1380,71.00 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1395,71.70 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1371,70.80 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1432,74.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1234,63.70 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,942,48.20 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1253,65.50 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1241,65.80 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1177,62.40 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,940,49.60 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1110,59.00 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,1004,53.70 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,956,50.90 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,834,44.70 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,817,43.20 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,West Virginia,798,42.00 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,3206,49.30 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,3015,46.90 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2969,46.80 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2825,44.70 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2680,42.80 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2525,40.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2449,40.00 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2484,40.80 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2619,44.00 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2524,42.70 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2490,42.80 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2303,39.70 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2345,41.00 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2274,40.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2100,37.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,2159,38.90 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wisconsin,1955,35.60 +2015,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,400,65.80 +2014,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,361,60.90 +2013,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,325,55.20 +2012,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,308,52.90 +2011,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,317,56.20 +2010,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,346,59.80 +2009,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,299,53.70 +2008,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,351,64.30 +2007,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,299,56.50 +2006,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,306,58.90 +2005,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,302,58.70 +2004,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,243,47.20 +2003,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,273,55.10 +2002,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,289,58.10 +2001,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,272,55.50 +2000,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,245,50.80 +1999,"Accidents (unintentional injuries) (V01-X59,Y85-Y86)",Unintentional injuries,Wyoming,258,52.80 diff --git a/Homeworks/Homework6/data/heart.csv b/Homeworks/Homework6/data/heart.csv new file mode 100644 index 0000000..6067c7f --- /dev/null +++ b/Homeworks/Homework6/data/heart.csv @@ -0,0 +1,295 @@ +age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,num +28,1,2,130,132,0,2,185,0,0,?,?,?,0 +29,1,2,120,243,0,0,160,0,0,?,?,?,0 +29,1,2,140,?,0,0,170,0,0,?,?,?,0 +30,0,1,170,237,0,1,170,0,0,?,?,6,0 +31,0,2,100,219,0,1,150,0,0,?,?,?,0 +32,0,2,105,198,0,0,165,0,0,?,?,?,0 +32,1,2,110,225,0,0,184,0,0,?,?,?,0 +32,1,2,125,254,0,0,155,0,0,?,?,?,0 +33,1,3,120,298,0,0,185,0,0,?,?,?,0 +34,0,2,130,161,0,0,190,0,0,?,?,?,0 +34,1,2,150,214,0,1,168,0,0,?,?,?,0 +34,1,2,98,220,0,0,150,0,0,?,?,?,0 +35,0,1,120,160,0,1,185,0,0,?,?,?,0 +35,0,4,140,167,0,0,150,0,0,?,?,?,0 +35,1,2,120,308,0,2,180,0,0,?,?,?,0 +35,1,2,150,264,0,0,168,0,0,?,?,?,0 +36,1,2,120,166,0,0,180,0,0,?,?,?,0 +36,1,3,112,340,0,0,184,0,1,2,?,3,0 +36,1,3,130,209,0,0,178,0,0,?,?,?,0 +36,1,3,150,160,0,0,172,0,0,?,?,?,0 +37,0,2,120,260,0,0,130,0,0,?,?,?,0 +37,0,3,130,211,0,0,142,0,0,?,?,?,0 +37,0,4,130,173,0,1,184,0,0,?,?,?,0 +37,1,2,130,283,0,1,98,0,0,?,?,?,0 +37,1,3,130,194,0,0,150,0,0,?,?,?,0 +37,1,4,120,223,0,0,168,0,0,?,?,3,0 +37,1,4,130,315,0,0,158,0,0,?,?,?,0 +38,0,2,120,275,?,0,129,0,0,?,?,?,0 +38,1,2,140,297,0,0,150,0,0,?,?,?,0 +38,1,3,145,292,0,0,130,0,0,?,?,?,0 +39,0,3,110,182,0,1,180,0,0,?,?,?,0 +39,1,2,120,?,0,1,146,0,2,1,?,?,0 +39,1,2,120,200,0,0,160,1,1,2,?,?,0 +39,1,2,120,204,0,0,145,0,0,?,?,?,0 +39,1,2,130,?,0,0,120,0,0,?,?,?,0 +39,1,2,190,241,0,0,106,0,0,?,?,?,0 +39,1,3,120,339,0,0,170,0,0,?,?,?,0 +39,1,3,160,147,1,0,160,0,0,?,?,?,0 +39,1,4,110,273,0,0,132,0,0,?,?,?,0 +39,1,4,130,307,0,0,140,0,0,?,?,?,0 +40,1,2,130,275,0,0,150,0,0,?,?,?,0 +40,1,2,140,289,0,0,172,0,0,?,?,?,0 +40,1,3,130,215,0,0,138,0,0,?,?,?,0 +40,1,3,130,281,0,0,167,0,0,?,?,?,0 +40,1,3,140,?,0,0,188,0,0,?,?,?,0 +41,0,2,110,250,0,1,142,0,0,?,?,?,0 +41,0,2,125,184,0,0,180,0,0,?,?,?,0 +41,0,2,130,245,0,0,150,0,0,?,?,?,0 +41,1,2,120,291,0,1,160,0,0,?,?,?,0 +41,1,2,120,295,0,0,170,0,0,?,?,?,0 +41,1,2,125,269,0,0,144,0,0,?,?,?,0 +41,1,4,112,250,0,0,142,0,0,?,?,?,0 +42,0,3,115,211,0,1,137,0,0,?,?,?,0 +42,1,2,120,196,0,0,150,0,0,?,?,?,0 +42,1,2,120,198,0,0,155,0,0,?,?,?,0 +42,1,2,150,268,0,0,136,0,0,?,?,?,0 +42,1,3,120,228,0,0,152,1,1.5,2,?,?,0 +42,1,3,160,147,0,0,146,0,0,?,?,?,0 +42,1,4,140,358,0,0,170,0,0,?,?,?,0 +43,0,1,100,223,0,0,142,0,0,?,?,?,0 +43,0,2,120,201,0,0,165,0,0,?,?,?,0 +43,0,2,120,215,0,1,175,0,0,?,?,?,0 +43,0,2,120,249,0,1,176,0,0,?,?,?,0 +43,0,2,120,266,0,0,118,0,0,?,?,?,0 +43,0,2,150,186,0,0,154,0,0,?,?,?,0 +43,0,3,150,?,0,0,175,0,0,?,?,3,0 +43,1,2,142,207,0,0,138,0,0,?,?,?,0 +44,0,4,120,218,0,1,115,0,0,?,?,?,0 +44,1,2,120,184,0,0,142,0,1,2,?,?,0 +44,1,2,130,215,0,0,135,0,0,?,?,?,0 +44,1,4,150,412,0,0,170,0,0,?,?,?,0 +45,0,2,130,237,0,0,170,0,0,?,?,?,0 +45,0,2,180,?,0,0,180,0,0,?,?,?,0 +45,0,4,132,297,0,0,144,0,0,?,?,?,0 +45,1,2,140,224,1,0,122,0,0,?,?,?,0 +45,1,3,135,?,0,0,110,0,0,?,?,?,0 +45,1,4,120,225,0,0,140,0,0,?,?,?,0 +45,1,4,140,224,0,0,144,0,0,?,?,?,0 +46,0,4,130,238,0,0,90,0,0,?,?,?,0 +46,1,2,140,275,0,0,165,1,0,?,?,?,0 +46,1,3,120,230,0,0,150,0,0,?,?,?,0 +46,1,3,150,163,?,0,116,0,0,?,?,?,0 +46,1,4,110,238,0,1,140,1,1,2,?,3,0 +46,1,4,110,240,0,1,140,0,0,?,?,3,0 +46,1,4,180,280,0,1,120,0,0,?,?,?,0 +47,0,2,140,257,0,0,135,0,1,1,?,?,0 +47,0,3,130,?,0,0,145,0,2,2,?,?,0 +47,1,1,110,249,0,0,150,0,0,?,?,?,0 +47,1,2,160,263,0,0,174,0,0,?,?,?,0 +47,1,4,140,276,1,0,125,1,0,?,?,?,0 +48,0,2,?,308,0,1,?,?,2,1,?,?,0 +48,0,2,120,?,1,1,148,0,0,?,?,?,0 +48,0,2,120,284,0,0,120,0,0,?,?,?,0 +48,0,3,120,195,0,0,125,0,0,?,?,?,0 +48,0,4,108,163,0,0,175,0,2,1,?,?,0 +48,0,4,120,254,0,1,110,0,0,?,?,?,0 +48,0,4,150,227,0,0,130,1,1,2,?,?,0 +48,1,2,100,?,0,0,100,0,0,?,?,?,0 +48,1,2,130,245,0,0,160,0,0,?,?,?,0 +48,1,2,140,238,0,0,118,0,0,?,?,?,0 +48,1,3,110,211,0,0,138,0,0,?,?,6,0 +49,0,2,110,?,0,0,160,0,0,?,?,?,0 +49,0,2,110,?,0,0,160,0,0,?,?,?,0 +49,0,2,124,201,0,0,164,0,0,?,?,?,0 +49,0,3,130,207,0,1,135,0,0,?,?,?,0 +49,1,2,100,253,0,0,174,0,0,?,?,?,0 +49,1,3,140,187,0,0,172,0,0,?,?,?,0 +49,1,4,120,297,?,0,132,0,1,2,?,?,0 +49,1,4,140,?,0,0,130,0,0,?,?,?,0 +50,0,2,110,202,0,0,145,0,0,?,?,?,0 +50,0,4,120,328,0,0,110,1,1,2,?,?,0 +50,1,2,120,168,0,0,160,0,0,?,0,?,0 +50,1,2,140,216,0,0,170,0,0,?,?,3,0 +50,1,2,170,209,0,1,116,0,0,?,?,?,0 +50,1,4,140,129,0,0,135,0,0,?,?,?,0 +50,1,4,150,215,0,0,140,1,0,?,?,?,0 +51,0,2,160,194,0,0,170,0,0,?,?,?,0 +51,0,3,110,190,0,0,120,0,0,?,?,?,0 +51,0,3,130,220,0,0,160,1,2,1,?,?,0 +51,0,3,150,200,0,0,120,0,0.5,1,?,?,0 +51,1,2,125,188,0,0,145,0,0,?,?,?,0 +51,1,2,130,224,0,0,150,0,0,?,?,?,0 +51,1,4,130,179,0,0,100,0,0,?,?,7,0 +52,0,2,120,210,0,0,148,0,0,?,?,?,0 +52,0,2,140,?,0,0,140,0,0,?,?,?,0 +52,0,3,125,272,0,0,139,0,0,?,?,?,0 +52,0,4,130,180,0,0,140,1,1.5,2,?,?,0 +52,1,2,120,284,0,0,118,0,0,?,?,?,0 +52,1,2,140,100,0,0,138,1,0,?,?,?,0 +52,1,2,160,196,0,0,165,0,0,?,?,?,0 +52,1,3,140,259,0,1,170,0,0,?,?,?,0 +53,0,2,113,468,?,0,127,0,0,?,?,?,0 +53,0,2,140,216,0,0,142,1,2,2,?,?,0 +53,0,3,120,274,0,0,130,0,0,?,?,?,0 +53,1,2,120,?,0,0,132,0,0,?,?,?,0 +53,1,2,140,320,0,0,162,0,0,?,?,?,0 +53,1,3,120,195,0,0,140,0,0,?,?,?,0 +53,1,4,124,260,0,1,112,1,3,2,?,?,0 +53,1,4,130,182,0,0,148,0,0,?,?,?,0 +53,1,4,140,243,0,0,155,0,0,?,?,?,0 +54,0,2,120,221,0,0,138,0,1,1,?,?,0 +54,0,2,120,230,1,0,140,0,0,?,?,?,0 +54,0,2,120,273,0,0,150,0,1.5,2,?,?,0 +54,0,2,130,253,0,1,155,0,0,?,?,?,0 +54,0,2,140,309,?,1,140,0,0,?,?,?,0 +54,0,2,150,230,0,0,130,0,0,?,?,?,0 +54,0,2,160,312,0,0,130,0,0,?,?,?,0 +54,1,1,120,171,0,0,137,0,2,1,?,?,0 +54,1,2,110,208,0,0,142,0,0,?,?,?,0 +54,1,2,120,238,0,0,154,0,0,?,?,?,0 +54,1,2,120,246,0,0,110,0,0,?,?,?,0 +54,1,2,160,195,0,1,130,0,1,1,?,?,0 +54,1,2,160,305,0,0,175,0,0,?,?,?,0 +54,1,3,120,217,0,0,137,0,0,?,?,?,0 +54,1,3,150,?,0,0,122,0,0,?,?,?,0 +54,1,4,150,365,0,1,134,0,1,1,?,?,0 +55,0,2,110,344,0,1,160,0,0,?,?,?,0 +55,0,2,122,320,0,0,155,0,0,?,?,?,0 +55,0,2,130,394,0,2,150,0,0,?,?,?,0 +55,1,2,120,256,1,0,137,0,0,?,?,7,0 +55,1,2,140,196,0,0,150,0,0,?,?,7,0 +55,1,2,145,326,0,0,155,0,0,?,?,?,0 +55,1,3,110,277,0,0,160,0,0,?,?,?,0 +55,1,3,120,220,0,2,134,0,0,?,?,?,0 +55,1,4,120,270,0,0,140,0,0,?,?,?,0 +55,1,4,140,229,0,0,110,1,0.5,2,?,?,0 +56,0,3,130,219,?,1,164,0,0,?,?,7,0 +56,1,2,130,184,0,0,100,0,0,?,?,?,0 +56,1,3,130,?,0,0,114,0,0,?,?,?,0 +56,1,3,130,276,0,0,128,1,1,1,?,6,0 +56,1,4,120,85,0,0,140,0,0,?,?,?,0 +57,0,1,130,308,0,0,98,0,1,2,?,?,0 +57,0,4,180,347,0,1,126,1,0.8,2,?,?,0 +57,1,2,140,260,1,0,140,0,0,?,?,6,0 +58,1,2,130,230,0,0,150,0,0,?,?,?,0 +58,1,2,130,251,0,0,110,0,0,?,?,?,0 +58,1,3,140,179,0,0,160,0,0,?,?,?,0 +58,1,4,135,222,0,0,100,0,0,?,?,?,0 +59,0,2,130,188,0,0,124,0,1,2,?,?,0 +59,1,2,140,287,0,0,150,0,0,?,?,?,0 +59,1,3,130,318,0,0,120,1,1,2,?,3,0 +59,1,3,180,213,0,0,100,0,0,?,?,?,0 +59,1,4,140,?,0,0,140,0,0,?,0,?,0 +60,1,3,120,246,0,2,135,0,0,?,?,?,0 +61,0,4,130,294,0,1,120,1,1,2,?,?,0 +61,1,4,125,292,0,1,115,1,0,?,?,?,0 +62,0,1,160,193,0,0,116,0,0,?,?,?,0 +62,1,2,140,271,0,0,152,0,1,1,?,?,0 +31,1,4,120,270,0,0,153,1,1.5,2,?,?,1 +33,0,4,100,246,0,0,150,1,1,2,?,?,1 +34,1,1,140,156,0,0,180,0,0,?,?,?,1 +35,1,2,110,257,0,0,140,0,0,?,?,?,1 +36,1,2,120,267,0,0,160,0,3,2,?,?,1 +37,1,4,140,207,0,0,130,1,1.5,2,?,?,1 +38,1,4,110,196,0,0,166,0,0,?,?,?,1 +38,1,4,120,282,0,0,170,0,0,?,?,?,1 +38,1,4,92,117,0,0,134,1,2.5,2,?,?,1 +40,1,4,120,466,?,0,152,1,1,2,?,6,1 +41,1,4,110,289,0,0,170,0,0,?,?,6,1 +41,1,4,120,237,?,0,138,1,1,2,?,?,1 +43,1,4,150,247,0,0,130,1,2,2,?,?,1 +46,1,4,110,202,0,0,150,1,0,?,?,?,1 +46,1,4,118,186,0,0,124,0,0,?,?,7,1 +46,1,4,120,277,0,0,125,1,1,2,?,?,1 +47,1,3,140,193,0,0,145,1,1,2,?,?,1 +47,1,4,150,226,0,0,98,1,1.5,2,0,7,1 +48,1,4,106,263,1,0,110,0,0,?,?,?,1 +48,1,4,120,260,0,0,115,0,2,2,?,?,1 +48,1,4,160,268,0,0,103,1,1,2,?,?,1 +49,0,3,160,180,0,0,156,0,1,2,?,?,1 +49,1,3,115,265,0,0,175,0,0,?,?,?,1 +49,1,4,130,206,0,0,170,0,0,?,?,?,1 +50,0,3,140,288,0,0,140,1,0,?,?,7,1 +50,1,4,145,264,0,0,150,0,0,?,?,?,1 +51,0,4,160,303,0,0,150,1,1,2,?,?,1 +52,1,4,130,225,0,0,120,1,2,2,?,?,1 +54,1,4,125,216,0,0,140,0,0,?,?,?,1 +54,1,4,125,224,0,0,122,0,2,2,?,?,1 +55,1,4,140,201,0,0,130,1,3,2,?,?,1 +57,1,2,140,265,0,1,145,1,1,2,?,?,1 +58,1,3,130,213,0,1,140,0,0,?,?,6,1 +59,0,4,130,338,1,1,130,1,1.5,2,?,?,1 +60,1,4,100,248,0,0,125,0,1,2,?,?,1 +63,1,4,150,223,0,0,115,0,0,?,?,?,1 +65,1,4,140,306,1,0,87,1,1.5,2,?,?,1 +32,1,4,118,529,0,0,130,0,0,?,?,?,1 +38,1,4,110,?,0,0,150,1,1,2,?,?,1 +39,1,4,110,280,0,0,150,0,0,?,?,6,1 +40,0,4,150,392,0,0,130,0,2,2,?,6,1 +43,1,1,120,291,0,1,155,0,0,?,?,?,1 +45,1,4,130,219,0,1,130,1,1,2,?,?,1 +46,1,4,120,231,0,0,115,1,0,?,?,?,1 +46,1,4,130,222,0,0,112,0,0,?,?,?,1 +48,1,4,122,275,1,1,150,1,2,3,?,?,1 +48,1,4,160,193,0,0,102,1,3,2,?,?,1 +48,1,4,160,329,0,0,92,1,1.5,2,?,?,1 +48,1,4,160,355,0,0,99,1,2,2,?,?,1 +50,1,4,130,233,0,0,121,1,2,2,?,7,1 +52,1,4,120,182,0,0,150,0,0,?,?,?,1 +52,1,4,170,?,0,0,126,1,1.5,2,?,?,1 +53,1,4,120,246,0,0,116,1,0,?,?,?,1 +54,1,3,120,237,0,0,150,1,1.5,?,?,7,1 +54,1,4,130,242,0,0,91,1,1,2,?,?,1 +54,1,4,130,603,1,0,125,1,1,2,?,?,1 +54,1,4,140,?,0,0,118,1,0,?,?,?,1 +54,1,4,200,198,0,0,142,1,2,2,?,?,1 +55,1,4,140,268,0,0,128,1,1.5,2,?,?,1 +56,1,4,150,213,1,0,125,1,1,2,?,?,1 +57,1,4,150,255,0,0,92,1,3,2,?,?,1 +58,1,3,160,211,1,1,92,0,0,?,?,?,1 +58,1,4,130,263,0,0,140,1,2,2,?,?,1 +41,1,4,130,172,0,1,130,0,2,2,?,?,1 +43,1,4,120,175,0,0,120,1,1,2,?,7,1 +44,1,2,150,288,0,0,150,1,3,2,?,?,1 +44,1,4,130,290,0,0,100,1,2,2,?,?,1 +46,1,1,140,272,1,0,175,0,2,2,?,?,1 +47,0,3,135,248,1,0,170,0,0,?,?,?,1 +48,0,4,138,214,0,0,108,1,1.5,2,?,?,1 +49,1,4,130,341,0,0,120,1,1,2,?,?,1 +49,1,4,140,234,0,0,140,1,1,2,?,?,1 +51,1,3,135,160,0,0,150,0,2,2,?,?,1 +52,1,4,112,342,0,1,96,1,1,2,?,?,1 +52,1,4,130,298,0,0,110,1,1,2,?,?,1 +52,1,4,140,404,0,0,124,1,2,2,?,?,1 +52,1,4,160,246,0,1,82,1,4,2,?,?,1 +53,1,3,145,518,0,0,130,0,0,?,?,?,1 +53,1,4,180,285,0,1,120,1,1.5,2,?,?,1 +54,1,4,140,216,0,0,105,0,1.5,2,?,?,1 +55,1,1,140,295,0,?,136,0,0,?,?,?,1 +55,1,2,160,292,1,0,143,1,2,2,?,?,1 +55,1,4,145,248,0,0,96,1,2,2,?,?,1 +56,0,2,120,279,0,0,150,0,1,2,?,?,1 +56,1,4,150,230,0,1,124,1,1.5,2,?,?,1 +56,1,4,170,388,0,1,122,1,2,2,?,?,1 +58,1,2,136,164,0,1,99,1,2,2,?,?,1 +59,1,4,130,?,0,0,125,0,0,?,?,?,1 +59,1,4,140,264,1,2,119,1,0,?,?,?,1 +65,1,4,170,263,1,0,112,1,2,2,?,?,1 +66,1,4,140,?,0,0,94,1,1,2,?,?,1 +41,1,4,120,336,0,0,118,1,3,2,?,?,1 +43,1,4,140,288,0,0,135,1,2,2,?,?,1 +44,1,4,135,491,0,0,135,0,0,?,?,?,1 +47,0,4,120,205,0,0,98,1,2,2,?,6,1 +47,1,4,160,291,0,1,158,1,3,2,?,?,1 +49,1,4,128,212,0,0,96,1,0,?,?,?,1 +49,1,4,150,222,0,0,122,0,2,2,?,?,1 +50,1,4,140,231,0,1,140,1,5,2,?,?,1 +50,1,4,140,341,0,1,125,1,2.5,2,?,?,1 +52,1,4,140,266,0,0,134,1,2,2,?,?,1 +52,1,4,160,331,0,0,94,1,2.5,?,?,?,1 +54,0,3,130,294,0,1,100,1,0,2,?,?,1 +56,1,4,155,342,1,0,150,1,3,2,?,?,1 +58,0,2,180,393,0,0,110,1,1,2,?,7,1 +65,1,4,130,275,0,1,115,1,1,2,?,?,1 From 434ec4c48407abeaa0224f68893ad39d7a49c0df Mon Sep 17 00:00:00 2001 From: Paul Schragger Date: Sun, 7 Oct 2018 23:49:23 -0400 Subject: [PATCH 10/10] Update Statistics and Probablity Homework.ipynb --- Homeworks/Homework6/Statistics and Probablity Homework.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb index 252bf0d..b0a99a0 100644 --- a/Homeworks/Homework6/Statistics and Probablity Homework.ipynb +++ b/Homeworks/Homework6/Statistics and Probablity Homework.ipynb @@ -264,7 +264,7 @@ "How many patients were censored?\n", "What is the correlation coefficient between state and Suicide for deaths above 100 ?\n", "What is the average deaths for each state and type of cause ?\n", - "What is the year was the most deadly for each cause name ?" + "What is the year that was the most deadly for each cause name ?" ] }, {