{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Snippets and Programs from Chapter 3: Describing Data with Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean donation over the last 12 days is 477.75\n"
     ]
    }
   ],
   "source": [
    "#P62: Calculating the mean\n",
    "'''\n",
    "Calculating the mean\n",
    "'''\n",
    "def calculate_mean(numbers):\n",
    "    s = sum(numbers)\n",
    "    N = len(numbers)\n",
    "    # calculate the mean\n",
    "    mean = s/N\n",
    "    return mean\n",
    "if __name__ == '__main__':\n",
    "    donations = [100, 60, 70, 900, 100, 200, 500, 500, 503, 600, 1000, 1200]\n",
    "    mean = calculate_mean(donations)\n",
    "    N = len(donations)\n",
    "    print('Mean donation over the last {0} days is {1}'.format(N, mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median donation over the last 12 days is 500.0\n"
     ]
    }
   ],
   "source": [
    "#P63: Calculating the median\n",
    "'''\n",
    "Calculating the median\n",
    "'''\n",
    "def calculate_median(numbers):\n",
    "    N = len(numbers)\n",
    "    numbers.sort()\n",
    "\n",
    "    # find the median\n",
    "    if N % 2 == 0:\n",
    "        # if N is even\n",
    "        m1 = N/2\n",
    "        m2 = (N/2) + 1\n",
    "        # convert to integer, match position\n",
    "        m1 = int(m1) - 1\n",
    "        m2 = int(m2) - 1\n",
    "        median = (numbers[m1] + numbers[m2])/2\n",
    "    else:\n",
    "        m = (N+1)/2\n",
    "        # convert to integer, match position\n",
    "        m = int(m) - 1\n",
    "        median = numbers[m]\n",
    "    return median\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    donations = [100, 60, 70, 900, 100, 200, 500, 500, 503, 600, 1000, 1200]\n",
    "    median = calculate_median(donations)\n",
    "    N = len(donations)\n",
    "    print('Median donation over the last {0} days is {1}'.format(N, median))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P66/67: Example of calculating the mode\n",
    ">>> simplelist = [4, 2, 1, 3, 4]\n",
    ">>> from collections import Counter\n",
    ">>> c = Counter(simplelist)\n",
    ">>> mode = c.most_common(1)\n",
    ">>> mode\n",
    ">>> mode[0]\n",
    ">>> mode[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode of the list of numbers is: 9\n"
     ]
    }
   ],
   "source": [
    "#P67: Calculating the mode\n",
    "'''\n",
    "Calculating the mode\n",
    "'''\n",
    "from collections import Counter\n",
    "def calculate_mode(numbers):\n",
    "    c = Counter(numbers)\n",
    "    mode = c.most_common(1)\n",
    "    return mode[0][0]\n",
    "\n",
    "if __name__=='__main__':\n",
    "    scores = [7,8,9,2,10,9,9,9,9,4,5,6,1,5,6,7,8,6,1,10]\n",
    "    mode = calculate_mode(scores)\n",
    "    print('The mode of the list of numbers is: {0}'.format(mode))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode(s) of the list of numbers are:\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "#P68: \n",
    "\n",
    "'''\n",
    "Calculating the mode when the list of numbers may have multiple modes\n",
    "'''\n",
    "from collections import Counter\n",
    "def calculate_mode(numbers):\n",
    "    c = Counter(numbers)\n",
    "    numbers_freq = c.most_common()\n",
    "    max_count = numbers_freq[0][1]\n",
    "    modes = []\n",
    "    for num in numbers_freq:\n",
    "        if num[1] == max_count:\n",
    "            modes.append(num[0])\n",
    "    return modes\n",
    "if __name__ == '__main__':\n",
    "    scores = [5, 5, 5, 4, 4, 4, 9, 1, 3]\n",
    "    modes = calculate_mode(scores)\n",
    "    print('The mode(s) of the list of numbers are:')\n",
    "    for mode in modes:\n",
    "        print(mode)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number\tFrequency\n",
      "9\t5\n",
      "6\t3\n",
      "1\t2\n",
      "5\t2\n",
      "7\t2\n",
      "8\t2\n",
      "10\t2\n",
      "2\t1\n",
      "4\t1\n"
     ]
    }
   ],
   "source": [
    "#P69: Frequency table\n",
    "'''\n",
    "Frequency table for a list of numbers\n",
    "'''\n",
    "from collections import Counter\n",
    "def frequency_table(numbers):\n",
    "    table = Counter(numbers)\n",
    "    print('Number\\tFrequency')\n",
    "    for number in table.most_common():\n",
    "        print('{0}\\t{1}'.format(number[0], number[1]))\n",
    "\n",
    "if __name__=='__main__':\n",
    "    scores = [7, 8, 9, 2, 10, 9, 9, 9, 9, 4, 5, 6, 1, 5, 6, 7, 8, 6, 1, 10]\n",
    "    frequency_table(scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number\tFrequency\n",
      "1\t2\n",
      "2\t1\n",
      "4\t1\n",
      "5\t2\n",
      "6\t3\n",
      "7\t2\n",
      "8\t2\n",
      "9\t5\n",
      "10\t2\n"
     ]
    }
   ],
   "source": [
    "#P70: Frequency table with the numbers sorted\n",
    "\n",
    "'''\n",
    "Frequency table for a list of numbers\n",
    "Enhanced to display the table sorted by the numbers\n",
    "'''\n",
    "from collections import Counter\n",
    "def frequency_table(numbers):\n",
    "    table = Counter(numbers)\n",
    "    numbers_freq = table.most_common()\n",
    "    numbers_freq.sort()\n",
    "\n",
    "    print('Number\\tFrequency')\n",
    "    for number in numbers_freq:\n",
    "        print('{0}\\t{1}'.format(number[0], number[1]))\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    scores = [7,8,9,2,10,9,9,9,9,4,5,6,1,5,6,7,8,6,1,10]\n",
    "    frequency_table(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest: 60 Highest: 1200 Range: 1140\n"
     ]
    }
   ],
   "source": [
    "#P72\n",
    "\n",
    "'''\n",
    "Find the range\n",
    "'''\n",
    "def find_range(numbers):\n",
    "    lowest = min(numbers)\n",
    "    highest = max(numbers)\n",
    "    # find the range\n",
    "    r = highest-lowest\n",
    "    return lowest, highest, r\n",
    "if __name__ == '__main__':\n",
    "    donations = [100, 60, 70, 900, 100, 200, 500, 500, 503, 600, 1000, 1200]\n",
    "    lowest, highest, r = find_range(donations)\n",
    "    print('Lowest: {0} Highest: {1} Range: {2}'.format(lowest, highest, r))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest: 60 Highest: 1200 Range: 1140\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "'''\n",
    "Find the range using a dictionary to return values, Appendix B\n",
    "'''\n",
    "def find_range(numbers):\n",
    "    lowest = min(numbers)\n",
    "    highest = max(numbers)\n",
    "    # find the range\n",
    "    r = highest-lowest\n",
    "    return {'lowest':lowest, 'highest':highest, 'range':r}\n",
    "if __name__ == '__main__':\n",
    "    donations = [100, 60, 70, 900, 100, 200, 500, 500, 503, 600, 1000, 1200]\n",
    "    result = find_range(donations)\n",
    "    print('Lowest: {0} Highest: {1} Range: {2}'.\n",
    "          format(result['lowest'], result['highest'], result['range']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variance of the list of numbers is 141047.35416666666\n",
      "The standard deviation of the list of numbers is 375.5627166887931\n"
     ]
    }
   ],
   "source": [
    "#P73: Find the variance and standard deviation\n",
    "\n",
    "'''\n",
    "Find the variance and standard deviation of a list of numbers\n",
    "'''\n",
    "def calculate_mean(numbers):\n",
    "    s = sum(numbers)\n",
    "    N = len(numbers)\n",
    "    # calculate the mean\n",
    "    mean = s/N\n",
    "    return mean\n",
    "\n",
    "def find_differences(numbers):\n",
    "    # find the mean\n",
    "    mean = calculate_mean(numbers)\n",
    "    # find the differences from the mean\n",
    "    diff = []\n",
    "    \n",
    "    for num in numbers:\n",
    "        diff.append(num-mean)\n",
    "    return diff\n",
    "def calculate_variance(numbers):\n",
    "    # find the list of differences\n",
    "    diff = find_differences(numbers)\n",
    "    # find the squared differences\n",
    "    squared_diff = []\n",
    "    for d in diff:\n",
    "        squared_diff.append(d**2)\n",
    "    #find the variance\n",
    "    sum_squared_diff = sum(squared_diff)\n",
    "    variance = sum_squared_diff/len(numbers)\n",
    "    return variance\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    donations = [100, 60, 70, 900, 100, 200, 500, 500, 503, 600, 1000, 1200]\n",
    "    variance = calculate_variance(donations)\n",
    "    print('The variance of the list of numbers is {0}'.format(variance))\n",
    "    std = variance**0.5\n",
    "    print('The standard deviation of the list of numbers is {0}'.format(std))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4\n",
      "2 5\n",
      "3 6\n"
     ]
    }
   ],
   "source": [
    "#P77: How the zip() function works\n",
    ">>> simple_list1 = [1, 2, 3]\n",
    ">>> simple_list2 = [4, 5, 6]\n",
    ">>> for x,y in zip(simple_list1, simple_list2):\n",
    "        print(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mc3k5yfLH8yRvS14A/GPDmDLMZ5qcucxnN8mnd3pJ1rmavRB5FiefSeaRrH8XoZd0L0Re\nRrEvTPQyes6yzGWF5Bfs7jHGlGFO0+Qsek7PJFlTBXgL8F3gqoYxZZjLNDmLnstG82n+rowyzOdI\no+XMNJ9pPxF5DLgVWEfywsSXSV7subm+/x+ADwMfr48dBK5Led+d9DDJxJxJspa1nORZazjjkyQ/\nyF3AQYr7oFCrnGWYS4ArgN8Dvk/y5x3AXwLn1bfLMqdpchY9p+8geVGsq375KvAUP/l/qAxzmSZn\n0XPZzPCyR9nms1GznGWcT0mSJEmSJEmSJEmSJEmSJEmSJKl8/h/PDzr/hXAlIQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f6ce9d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# P81: Example of creating a scatter plot\n",
    ">>> x = [1, 2, 3, 4]\n",
    ">>> y = [2, 4, 6, 8]\n",
    ">>> import matplotlib.pyplot as plt\n",
    ">>> plt.scatter(x, y)\n",
    ">>> plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the numbers: 5733.0\n"
     ]
    }
   ],
   "source": [
    "#P84: Sum of the numbers read from a file\n",
    "\n",
    "# Find the sum of numbers stored in a file\n",
    "def sum_data(filename):\n",
    "    s = 0\n",
    "    with open(filename) as f:\n",
    "        for line in f:\n",
    "            s = s + float(line)\n",
    "    print('Sum of the numbers: {0}'.format(s))\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    sum_data('mydata.txt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 477.75\n"
     ]
    }
   ],
   "source": [
    "#P85: Calculate the mean of numbers stored in a file\n",
    "\n",
    "'''\n",
    "Calculating the mean of numbers stored in a file\n",
    "'''\n",
    "def read_data(filename):\n",
    "    numbers = []\n",
    "    with open(filename) as f:\n",
    "        for line in f:\n",
    "            numbers.append(float(line))\n",
    "    return numbers\n",
    "\n",
    "def calculate_mean(numbers):\n",
    "    s = sum(numbers)\n",
    "    N = len(numbers)\n",
    "    mean = s/N\n",
    "    \n",
    "    return mean\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    data = read_data('mydata.txt')\n",
    "    mean = calculate_mean(data)\n",
    "    print('Mean: {0}'.format(mean))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f6c7471d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P86: Read a CSV file and create a scatter plot\n",
    "\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def scatter_plot(x, y):\n",
    "    plt.scatter(x, y)\n",
    "    plt.xlabel('Number')\n",
    "    plt.ylabel('Square')\n",
    "    plt.show()\n",
    "    \n",
    "def read_csv(filename):\n",
    "    numbers = []\n",
    "    squared = []\n",
    "    with open(filename) as f:\n",
    "        reader = csv.reader(f)\n",
    "        next(reader)\n",
    "        for row in reader:\n",
    "            numbers.append(int(row[0]))\n",
    "            squared.append(int(row[1]))\n",
    "    return numbers, squared\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    numbers, squared = read_csv('numbers.csv')\n",
    "    scatter_plot(numbers, squared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#P77: Function to calculate the linear correlation\n",
    "def find_corr_x_y(x,y):    \n",
    "    n = len(x)\n",
    "    # find the sum of the products\n",
    "    prod = []\n",
    "    for xi,yi in zip(x,y):\n",
    "        prod.append(xi*yi)\n",
    "    sum_prod_x_y = sum(prod)\n",
    "    sum_x = sum(x)\n",
    "    sum_y = sum(y)\n",
    "    squared_sum_x = sum_x**2\n",
    "    squared_sum_y = sum_y**2\n",
    "    x_square = []\n",
    "    for xi in x:\n",
    "        x_square.append(xi**2)\n",
    "    # find the sum\n",
    "    x_square_sum = sum(x_square)\n",
    "    y_square=[]\n",
    "    for yi in y:\n",
    "        y_square.append(yi**2)\n",
    "    # find the sum\n",
    "    y_square_sum = sum(y_square)\n",
    "    \n",
    "    # use formula to calculate correlation\n",
    "    numerator = n*sum_prod_x_y - sum_x*sum_y\n",
    "    denominator_term1 = n*x_square_sum - squared_sum_x\n",
    "    denominator_term2 = n*y_square_sum - squared_sum_y\n",
    "    denominator = (denominator_term1*denominator_term2)**0.5\n",
    "    correlation = numerator/denominator\n",
    "    \n",
    "    return correlation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest correlation: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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iROR1c/91eL2JwC7S0kqRnj6eO+7owr59m3P6EjXtX8tO8O8B9EH9Zv4JaAis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\nREQknvw//ef3H/RM6h0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f6c7474a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P86/88: Calculate correlation and create a scatter plot similar to that \n",
    "# on Google correlate\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "\n",
    "def read_csv(filename):\n",
    "    with open(filename) as f:\n",
    "        reader = csv.reader(f)\n",
    "        next(reader)\n",
    "        summer = []\n",
    "        highest_correlated = []\n",
    "        for row in reader:\n",
    "            summer.append(float(row[1]))\n",
    "            highest_correlated.append(float(row[2]))\n",
    "    return summer, highest_correlated\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    summer, highest_correlated = read_csv('correlate-summer.csv')\n",
    "    corr = find_corr_x_y(summer, highest_correlated)\n",
    "    print('Highest correlation: {0}'.format(corr))\n",
    "    scatter_plot(summer, highest_correlated)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.3183785775683751\n"
     ]
    }
   ],
   "source": [
    "#P79/80: Correlation\n",
    "\n",
    "# Find the correlation for high school grades and college admission scores\n",
    "if __name__=='__main__':\n",
    "    high_school_math = [90, 92, 95, 96, 87, 87, 90, 95, 98, 96]\n",
    "    college_admission = [85, 87, 86, 97, 96, 88, 89, 98, 98, 87]\n",
    "    corr = find_corr_x_y(high_school_math, college_admission)\n",
    "    print('Correlation coefficient: {0}'.format(corr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.9989633063220916\n"
     ]
    }
   ],
   "source": [
    "#P79/80: Correlation\n",
    "\n",
    "# Find the correlation for high school math grades and college admission scores\n",
    "if __name__=='__main__':\n",
    "    high_school_math = [83, 85, 84, 96, 94, 86, 87, 97, 97, 85]\n",
    "    college_admission = [85, 87, 86, 97, 96, 88, 89, 98, 98, 87]\n",
    "    corr = find_corr_x_y(high_school_math, college_admission)\n",
    "    print('Correlation coefficient: {0}'.format(corr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 3.25), (3.25, 5.5), (5.5, 7.75), (7.75, 11)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P91: Create classes for grouped frequency table\n",
    "def create_classes(numbers, n):\n",
    "    low = min(numbers)\n",
    "    high = max(numbers)\n",
    "    # Width of each class\n",
    "    width = (high - low)/n\n",
    "    classes = []\n",
    "    a = low\n",
    "    b = low + width\n",
    "    classes = []\n",
    "    while a < (high-width):\n",
    "        classes.append((a, b)) \n",
    "        a= b\n",
    "        b = a + width\n",
    "    # The last class may be of a size that is less than width\n",
    "    classes.append((a, high+1))\n",
    "    return classes\n",
    "\n",
    "create_classes([7, 8, 9, 2, 10, 9, 9, 9, 9, 4, 5, 6, 1, 5, 6, 7, 8, 6, 1, 10], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
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