From 7f996b9109cee6b86706a475c52532aa039031a0 Mon Sep 17 00:00:00 2001
From: bobaney <42747366+bobaney@users.noreply.github.com>
Date: Mon, 29 Oct 2018 20:32:58 -0400
Subject: [PATCH 1/3] Speed learning

---
 00-Importing-Local-Files.ipynb                | 221 ++++-
 01a-Getting-data-with-Pandas.ipynb            | 895 +++++++++++++++++-
 01b-DEMO-Bulk-Download-with-Pandas.ipynb      |  23 +-
 02a-Fetching-Data-with-urllib.ipynb           |  64 +-
 02b-Extract-Statewide-HUCs-with-urllib.ipynb  | 215 ++++-
 03-Fetching-files-with-ftplib.ipynb           | 170 +++-
 04-Grabbing-HTML-tables-with-Pandas.ipynb     | 332 ++++++-
 ...Data-With-Requests-and-BeautifulSoup.ipynb |  84 +-
 ...ng-specialized-packages-to-grab-data.ipynb | 125 ++-
 9 files changed, 2006 insertions(+), 123 deletions(-)

diff --git a/00-Importing-Local-Files.ipynb b/00-Importing-Local-Files.ipynb
index 64d07eb..093e88d 100644
--- a/00-Importing-Local-Files.ipynb
+++ b/00-Importing-Local-Files.ipynb
@@ -33,9 +33,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['id', 'x', 'y'], ['1', '22', '33'], ['2', '2.4', '6.8'], ['3', '1.9', '8.0']]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "datafile = open('./data/examp_data.txt', 'r')\n",
     "data = []\n",
@@ -54,9 +65,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.9'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Get the value of the 3rd row (4th if we include the header row), and 2nd column\n",
     "data[3][1]"
@@ -110,9 +132,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['id', 'x', 'y'], ['1', '22', '33'], ['2', '2.4', '6.8'], ['3', '1.9', '8.0']]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import csv\n",
     "datafile = open('./data/examp_data.txt', 'r')\n",
@@ -125,9 +158,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.9'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#As above, print the value in the 3rd row, 2nd column\n",
     "data[3][1]"
@@ -167,9 +211,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. , 22. , 33. ],\n",
+       "       [ 2. ,  2.4,  6.8],\n",
+       "       [ 3. ,  1.9,  8. ]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy\n",
     "data = numpy.genfromtxt('./data/examp_data.txt', \n",
@@ -180,9 +237,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.9'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#As above, print the value in the 3rd row, 2nd column\n",
     "data[2,1]"
@@ -219,9 +287,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>6.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id     x     y\n",
+       "0   1  22.0  33.0\n",
+       "1   2   2.4   6.8\n",
+       "2   3   1.9   8.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "data = pd.read_csv('./data/examp_data.txt')\n",
@@ -237,9 +366,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#As above, print the value in the 3rd row, 2nd column\n",
     "data.iloc[2,1]"
@@ -254,9 +394,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data.loc[2,'x']"
    ]
@@ -270,9 +421,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. , 22. , 33. ],\n",
+       "       [ 2. ,  2.4,  6.8],\n",
+       "       [ 3. ,  1.9,  8. ]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Extract the data from a Pandas data frame into a numpy array\n",
     "data.values"
@@ -280,9 +444,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Extract a single value from a specified row/column\n",
     "data.values[2][1]"
@@ -323,7 +498,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
diff --git a/01a-Getting-data-with-Pandas.ipynb b/01a-Getting-data-with-Pandas.ipynb
index d368357..83e21d0 100644
--- a/01a-Getting-data-with-Pandas.ipynb
+++ b/01a-Getting-data-with-Pandas.ipynb
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,9 +53,300 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_cd</th>\n",
+       "      <th>state_name</th>\n",
+       "      <th>county_cd</th>\n",
+       "      <th>county_nm</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, groundwater, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, in Mgal/d</th>\n",
+       "      <th>Industrial deliveries from public supply, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals plus deliveries, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total consumptive use, in Mgal/d</th>\n",
+       "      <th>Industrial reclaimed wastewater, in Mgal/d</th>\n",
+       "      <th>Industrial number of facilities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2s</td>\n",
+       "      <td>40s</td>\n",
+       "      <td>3s</td>\n",
+       "      <td>40s</td>\n",
+       "      <td>4s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "      <td>16s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>6.71</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>6.42</td>\n",
+       "      <td>9.08</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>4.68</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>7.33</td>\n",
+       "      <td>12.58</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_cd      state_name county_cd        county_nm  year  \\\n",
+       "0       2s             40s        3s              40s    4s   \n",
+       "1       37  North Carolina       001  Alamance County  1985   \n",
+       "2       37  North Carolina       001  Alamance County  1990   \n",
+       "3       37  North Carolina       001  Alamance County  1995   \n",
+       "4       37  North Carolina       001  Alamance County  2000   \n",
+       "\n",
+       "  Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                                16s                   \n",
+       "1                                               0.19                   \n",
+       "2                                               0.06                   \n",
+       "3                                               0.57                   \n",
+       "4                                               0.00                   \n",
+       "\n",
+       "  Industrial self-supplied groundwater withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                16s                    \n",
+       "1                                               0.00                    \n",
+       "2                                               0.00                    \n",
+       "3                                               0.00                    \n",
+       "4                                               0.00                    \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, groundwater, in Mgal/d  \\\n",
+       "0                                                16s                   \n",
+       "1                                               0.19                   \n",
+       "2                                               0.06                   \n",
+       "3                                               0.57                   \n",
+       "4                                               0.00                   \n",
+       "\n",
+       "  Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                                16s                     \n",
+       "1                                               3.39                     \n",
+       "2                                               2.60                     \n",
+       "3                                               4.68                     \n",
+       "4                                               0.23                     \n",
+       "\n",
+       "  Industrial self-supplied surface-water withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                16s                      \n",
+       "1                                               0.00                      \n",
+       "2                                               0.00                      \n",
+       "3                                               0.00                      \n",
+       "4                                               0.00                      \n",
+       "\n",
+       "                ...                \\\n",
+       "0               ...                 \n",
+       "1               ...                 \n",
+       "2               ...                 \n",
+       "3               ...                 \n",
+       "4               ...                 \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                                16s             \n",
+       "1                                               3.58             \n",
+       "2                                               2.66             \n",
+       "3                                               5.25             \n",
+       "4                                               0.23             \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                16s              \n",
+       "1                                               0.00              \n",
+       "2                                               0.00              \n",
+       "3                                               0.00              \n",
+       "4                                               0.00              \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, in Mgal/d  \\\n",
+       "0                                                16s      \n",
+       "1                                               3.58      \n",
+       "2                                               2.66      \n",
+       "3                                               5.25      \n",
+       "4                                               0.23      \n",
+       "\n",
+       "  Industrial deliveries from public supply, in Mgal/d  \\\n",
+       "0                                                16s    \n",
+       "1                                               3.13    \n",
+       "2                                               6.42    \n",
+       "3                                               7.33    \n",
+       "4                                                  -    \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals plus deliveries, in Mgal/d  \\\n",
+       "0                                                16s                      \n",
+       "1                                               6.71                      \n",
+       "2                                               9.08                      \n",
+       "3                                              12.58                      \n",
+       "4                                                  -                      \n",
+       "\n",
+       "  Industrial consumptive use, fresh, in Mgal/d  \\\n",
+       "0                                          16s   \n",
+       "1                                         1.74   \n",
+       "2                                         1.00   \n",
+       "3                                         2.52   \n",
+       "4                                            -   \n",
+       "\n",
+       "  Industrial consumptive use, saline, in Mgal/d  \\\n",
+       "0                                           16s   \n",
+       "1                                          0.00   \n",
+       "2                                          0.00   \n",
+       "3                                          0.00   \n",
+       "4                                             -   \n",
+       "\n",
+       "  Industrial total consumptive use, in Mgal/d  \\\n",
+       "0                                         16s   \n",
+       "1                                        1.74   \n",
+       "2                                        1.00   \n",
+       "3                                        2.52   \n",
+       "4                                           -   \n",
+       "\n",
+       "  Industrial reclaimed wastewater, in Mgal/d Industrial number of facilities  \n",
+       "0                                        16s                             16s  \n",
+       "1                                       0.00                               -  \n",
+       "2                                       0.00                               8  \n",
+       "3                                       0.00                              83  \n",
+       "4                                          -                               -  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Read in the data as a pandas data frame and display the first 5 rows\n",
     "# -Note we need to specify that it's a tab delimited file and uses '#' to indicated commments\n",
@@ -72,9 +363,300 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_cd</th>\n",
+       "      <th>state_name</th>\n",
+       "      <th>county_cd</th>\n",
+       "      <th>county_nm</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, groundwater, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, in Mgal/d</th>\n",
+       "      <th>Industrial deliveries from public supply, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals plus deliveries, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total consumptive use, in Mgal/d</th>\n",
+       "      <th>Industrial reclaimed wastewater, in Mgal/d</th>\n",
+       "      <th>Industrial number of facilities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>6.71</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>6.42</td>\n",
+       "      <td>9.08</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>4.68</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>7.33</td>\n",
+       "      <td>12.58</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>001</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state_cd      state_name county_cd        county_nm  year  \\\n",
+       "1       37  North Carolina       001  Alamance County  1985   \n",
+       "2       37  North Carolina       001  Alamance County  1990   \n",
+       "3       37  North Carolina       001  Alamance County  1995   \n",
+       "4       37  North Carolina       001  Alamance County  2000   \n",
+       "5       37  North Carolina       001  Alamance County  2005   \n",
+       "\n",
+       "  Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d  \\\n",
+       "1                                               0.19                   \n",
+       "2                                               0.06                   \n",
+       "3                                               0.57                   \n",
+       "4                                               0.00                   \n",
+       "5                                               0.00                   \n",
+       "\n",
+       "  Industrial self-supplied groundwater withdrawals, saline, in Mgal/d  \\\n",
+       "1                                               0.00                    \n",
+       "2                                               0.00                    \n",
+       "3                                               0.00                    \n",
+       "4                                               0.00                    \n",
+       "5                                               0.00                    \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, groundwater, in Mgal/d  \\\n",
+       "1                                               0.19                   \n",
+       "2                                               0.06                   \n",
+       "3                                               0.57                   \n",
+       "4                                               0.00                   \n",
+       "5                                               0.00                   \n",
+       "\n",
+       "  Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d  \\\n",
+       "1                                               3.39                     \n",
+       "2                                               2.60                     \n",
+       "3                                               4.68                     \n",
+       "4                                               0.23                     \n",
+       "5                                               0.14                     \n",
+       "\n",
+       "  Industrial self-supplied surface-water withdrawals, saline, in Mgal/d  \\\n",
+       "1                                               0.00                      \n",
+       "2                                               0.00                      \n",
+       "3                                               0.00                      \n",
+       "4                                               0.00                      \n",
+       "5                                               0.00                      \n",
+       "\n",
+       "                ...                \\\n",
+       "1               ...                 \n",
+       "2               ...                 \n",
+       "3               ...                 \n",
+       "4               ...                 \n",
+       "5               ...                 \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, fresh, in Mgal/d  \\\n",
+       "1                                               3.58             \n",
+       "2                                               2.66             \n",
+       "3                                               5.25             \n",
+       "4                                               0.23             \n",
+       "5                                               0.14             \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, saline, in Mgal/d  \\\n",
+       "1                                               0.00              \n",
+       "2                                               0.00              \n",
+       "3                                               0.00              \n",
+       "4                                               0.00              \n",
+       "5                                               0.00              \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals, in Mgal/d  \\\n",
+       "1                                               3.58      \n",
+       "2                                               2.66      \n",
+       "3                                               5.25      \n",
+       "4                                               0.23      \n",
+       "5                                               0.14      \n",
+       "\n",
+       "  Industrial deliveries from public supply, in Mgal/d  \\\n",
+       "1                                               3.13    \n",
+       "2                                               6.42    \n",
+       "3                                               7.33    \n",
+       "4                                                  -    \n",
+       "5                                                  -    \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals plus deliveries, in Mgal/d  \\\n",
+       "1                                               6.71                      \n",
+       "2                                               9.08                      \n",
+       "3                                              12.58                      \n",
+       "4                                                  -                      \n",
+       "5                                                  -                      \n",
+       "\n",
+       "  Industrial consumptive use, fresh, in Mgal/d  \\\n",
+       "1                                         1.74   \n",
+       "2                                         1.00   \n",
+       "3                                         2.52   \n",
+       "4                                            -   \n",
+       "5                                            -   \n",
+       "\n",
+       "  Industrial consumptive use, saline, in Mgal/d  \\\n",
+       "1                                          0.00   \n",
+       "2                                          0.00   \n",
+       "3                                          0.00   \n",
+       "4                                             -   \n",
+       "5                                             -   \n",
+       "\n",
+       "  Industrial total consumptive use, in Mgal/d  \\\n",
+       "1                                        1.74   \n",
+       "2                                        1.00   \n",
+       "3                                        2.52   \n",
+       "4                                           -   \n",
+       "5                                           -   \n",
+       "\n",
+       "  Industrial reclaimed wastewater, in Mgal/d Industrial number of facilities  \n",
+       "1                                       0.00                               -  \n",
+       "2                                       0.00                               8  \n",
+       "3                                       0.00                              83  \n",
+       "4                                          -                               -  \n",
+       "5                                          -                               -  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Drop the first row, and again show the first 5 rows of data...\n",
     "dfNWIS.drop(0,axis='rows',inplace=True)\n",
@@ -90,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,9 +684,300 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state_cd</th>\n",
+       "      <th>state_name</th>\n",
+       "      <th>county_cd</th>\n",
+       "      <th>county_nm</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied groundwater withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, groundwater, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial self-supplied surface-water withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals, in Mgal/d</th>\n",
+       "      <th>Industrial deliveries from public supply, in Mgal/d</th>\n",
+       "      <th>Industrial total self-supplied withdrawals plus deliveries, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, fresh, in Mgal/d</th>\n",
+       "      <th>Industrial consumptive use, saline, in Mgal/d</th>\n",
+       "      <th>Industrial total consumptive use, in Mgal/d</th>\n",
+       "      <th>Industrial reclaimed wastewater, in Mgal/d</th>\n",
+       "      <th>Industrial number of facilities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>6.71</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>6.42</td>\n",
+       "      <td>9.08</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>4.68</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>7.33</td>\n",
+       "      <td>12.58</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.52</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Alamance County</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   state_cd      state_name  county_cd        county_nm  year  \\\n",
+       "0        37  North Carolina          1  Alamance County  1985   \n",
+       "1        37  North Carolina          1  Alamance County  1990   \n",
+       "2        37  North Carolina          1  Alamance County  1995   \n",
+       "3        37  North Carolina          1  Alamance County  2000   \n",
+       "4        37  North Carolina          1  Alamance County  2005   \n",
+       "\n",
+       "   Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                               0.19                    \n",
+       "1                                               0.06                    \n",
+       "2                                               0.57                    \n",
+       "3                                               0.00                    \n",
+       "4                                               0.00                    \n",
+       "\n",
+       "   Industrial self-supplied groundwater withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                0.0                     \n",
+       "1                                                0.0                     \n",
+       "2                                                0.0                     \n",
+       "3                                                0.0                     \n",
+       "4                                                0.0                     \n",
+       "\n",
+       "   Industrial total self-supplied withdrawals, groundwater, in Mgal/d  \\\n",
+       "0                                               0.19                    \n",
+       "1                                               0.06                    \n",
+       "2                                               0.57                    \n",
+       "3                                               0.00                    \n",
+       "4                                               0.00                    \n",
+       "\n",
+       "   Industrial self-supplied surface-water withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                               3.39                      \n",
+       "1                                               2.60                      \n",
+       "2                                               4.68                      \n",
+       "3                                               0.23                      \n",
+       "4                                               0.14                      \n",
+       "\n",
+       "   Industrial self-supplied surface-water withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                0.0                       \n",
+       "1                                                0.0                       \n",
+       "2                                                0.0                       \n",
+       "3                                                0.0                       \n",
+       "4                                                0.0                       \n",
+       "\n",
+       "                ...                \\\n",
+       "0               ...                 \n",
+       "1               ...                 \n",
+       "2               ...                 \n",
+       "3               ...                 \n",
+       "4               ...                 \n",
+       "\n",
+       "   Industrial total self-supplied withdrawals, fresh, in Mgal/d  \\\n",
+       "0                                               3.58              \n",
+       "1                                               2.66              \n",
+       "2                                               5.25              \n",
+       "3                                               0.23              \n",
+       "4                                               0.14              \n",
+       "\n",
+       "   Industrial total self-supplied withdrawals, saline, in Mgal/d  \\\n",
+       "0                                                0.0               \n",
+       "1                                                0.0               \n",
+       "2                                                0.0               \n",
+       "3                                                0.0               \n",
+       "4                                                0.0               \n",
+       "\n",
+       "   Industrial total self-supplied withdrawals, in Mgal/d  \\\n",
+       "0                                               3.58       \n",
+       "1                                               2.66       \n",
+       "2                                               5.25       \n",
+       "3                                               0.23       \n",
+       "4                                               0.14       \n",
+       "\n",
+       "   Industrial deliveries from public supply, in Mgal/d  \\\n",
+       "0                                               3.13     \n",
+       "1                                               6.42     \n",
+       "2                                               7.33     \n",
+       "3                                                  -     \n",
+       "4                                                  -     \n",
+       "\n",
+       "  Industrial total self-supplied withdrawals plus deliveries, in Mgal/d  \\\n",
+       "0                                               6.71                      \n",
+       "1                                               9.08                      \n",
+       "2                                              12.58                      \n",
+       "3                                                  -                      \n",
+       "4                                                  -                      \n",
+       "\n",
+       "  Industrial consumptive use, fresh, in Mgal/d  \\\n",
+       "0                                         1.74   \n",
+       "1                                         1.00   \n",
+       "2                                         2.52   \n",
+       "3                                            -   \n",
+       "4                                            -   \n",
+       "\n",
+       "  Industrial consumptive use, saline, in Mgal/d  \\\n",
+       "0                                          0.00   \n",
+       "1                                          0.00   \n",
+       "2                                          0.00   \n",
+       "3                                             -   \n",
+       "4                                             -   \n",
+       "\n",
+       "  Industrial total consumptive use, in Mgal/d  \\\n",
+       "0                                        1.74   \n",
+       "1                                        1.00   \n",
+       "2                                        2.52   \n",
+       "3                                           -   \n",
+       "4                                           -   \n",
+       "\n",
+       "  Industrial reclaimed wastewater, in Mgal/d Industrial number of facilities  \n",
+       "0                                       0.00                               -  \n",
+       "1                                       0.00                               8  \n",
+       "2                                       0.00                              83  \n",
+       "3                                          -                               -  \n",
+       "4                                          -                               -  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Use the read_csv function as before, but skip the rows we want to skip\n",
     "dfNWIS = pd.read_csv(theURL, delimiter='\\t', skiprows=rowsToSkip)\n",
@@ -120,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +1044,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
diff --git a/01b-DEMO-Bulk-Download-with-Pandas.ipynb b/01b-DEMO-Bulk-Download-with-Pandas.ipynb
index dcee792..f17bd84 100644
--- a/01b-DEMO-Bulk-Download-with-Pandas.ipynb
+++ b/01b-DEMO-Bulk-Download-with-Pandas.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,9 +34,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "al>ak>az>ar>ca>co>ct>de>dc>fl>ga>hi>id>il>in>ia>ks>ky>la>me>md>ma>mi>mn>ms>mo>mt>ne>nv>nh>nj>nm>ny>nc>nd>oh>ok>or>pa>ri>sc>sd>tn>tx>ut>vt>va>wa>wv>wi>wy>"
+     ]
+    }
+   ],
    "source": [
     "#Loop through each state, download it's data, and save to a local file\n",
     "for state in us.STATES:\n",
@@ -53,6 +61,13 @@
     "    #write df to csv in the WaterData folder\n",
     "    dfState.to_csv(\"WaterData/{}.csv\".format(stateAbbr),index=False)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/02a-Fetching-Data-with-urllib.ipynb b/02a-Fetching-Data-with-urllib.ipynb
index 2858af0..01b6fe7 100644
--- a/02a-Fetching-Data-with-urllib.ipynb
+++ b/02a-Fetching-Data-with-urllib.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,9 +48,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('tl_2017_38_tract.zip', <http.client.HTTPMessage at 0x19bf99fa5c0>)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#The urlretrieve function downloads a file, saving it as the file name we specify\n",
     "request.urlretrieve(url=theURL,filename=localFile)"
@@ -65,9 +76,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Volume in drive C is Windows\n",
+      " Volume Serial Number is 8292-14BA\n",
+      "\n",
+      " Directory of C:\\Users\\rb312\\Documents\\GitHub\\GettingData\n",
+      "\n",
+      "10/29/2018  12:36 PM         1,734,270 tl_2017_38_tract.zip\n",
+      "               1 File(s)      1,734,270 bytes\n",
+      "               0 Dir(s)  61,388,533,760 bytes free\n"
+     ]
+    }
+   ],
    "source": [
     "!dir *.zip"
    ]
@@ -81,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,11 +117,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Display the file in our notebook\n",
     "from IPython.display import Image\n",
@@ -111,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/02b-Extract-Statewide-HUCs-with-urllib.ipynb b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
index b8366ad..4e05eeb 100644
--- a/02b-Extract-Statewide-HUCs-with-urllib.ipynb
+++ b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,9 +72,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading NHD_H_Maryland_State_Shape.zip\n"
+     ]
+    }
+   ],
    "source": [
     "#Get the file (this can take a few minutes...)\n",
     "url = ftpFolder + \"/\" + stateFile\n",
@@ -94,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,9 +123,183 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>TNMID</th>\n",
+       "      <th>MetaSource</th>\n",
+       "      <th>SourceData</th>\n",
+       "      <th>SourceOrig</th>\n",
+       "      <th>SourceFeat</th>\n",
+       "      <th>LoadDate</th>\n",
+       "      <th>GNIS_ID</th>\n",
+       "      <th>AreaAcres</th>\n",
+       "      <th>AreaSqKm</th>\n",
+       "      <th>States</th>\n",
+       "      <th>HUC8</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>{FC8C752C-8677-495A-BA68-4781601761B2}</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2016-05-10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1457399.01</td>\n",
+       "      <td>5897.89</td>\n",
+       "      <td>MD,PA,VA,WV</td>\n",
+       "      <td>02070004</td>\n",
+       "      <td>Conococheague-Opequon</td>\n",
+       "      <td>5.544624</td>\n",
+       "      <td>0.618756</td>\n",
+       "      <td>POLYGON ((-77.60122679102938 40.2384876719168,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>{D93EE135-0A9B-424D-A41D-C5F4B2C90E92}</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2012-06-11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>483698.94</td>\n",
+       "      <td>1957.46</td>\n",
+       "      <td>DE,MD,PA</td>\n",
+       "      <td>02040205</td>\n",
+       "      <td>Brandywine-Christina</td>\n",
+       "      <td>3.253687</td>\n",
+       "      <td>0.205834</td>\n",
+       "      <td>POLYGON ((-75.79380026570988 40.1428963585235,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>{57DA0D48-3009-46C6-8CE5-8ED60DEEBA2C}</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2012-06-11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>859591.63</td>\n",
+       "      <td>3478.65</td>\n",
+       "      <td>MD,PA,WV</td>\n",
+       "      <td>02070002</td>\n",
+       "      <td>North Branch Potomac</td>\n",
+       "      <td>4.101937</td>\n",
+       "      <td>0.364322</td>\n",
+       "      <td>POLYGON ((-78.70751961222891 39.96501682338294...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>{E9CF0528-8AB4-4603-A2E9-7383F6374BEF}</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2016-07-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1591179.52</td>\n",
+       "      <td>6439.28</td>\n",
+       "      <td>MD,PA</td>\n",
+       "      <td>02050306</td>\n",
+       "      <td>Lower Susquehanna</td>\n",
+       "      <td>5.356391</td>\n",
+       "      <td>0.678530</td>\n",
+       "      <td>POLYGON ((-76.19345075571454 40.30860870722461...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>{387093E8-0163-40B6-B314-E36F7E100A3B}</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2016-08-30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>671505.39</td>\n",
+       "      <td>2717.49</td>\n",
+       "      <td>DE,MD,NJ,VA</td>\n",
+       "      <td>02040303</td>\n",
+       "      <td>Chincoteague</td>\n",
+       "      <td>3.805717</td>\n",
+       "      <td>0.280041</td>\n",
+       "      <td>POLYGON ((-74.99871150652734 38.8055048012244,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID                                   TNMID MetaSource SourceData  \\\n",
+       "0         1  {FC8C752C-8677-495A-BA68-4781601761B2}       None       None   \n",
+       "1         2  {D93EE135-0A9B-424D-A41D-C5F4B2C90E92}       None       None   \n",
+       "2         3  {57DA0D48-3009-46C6-8CE5-8ED60DEEBA2C}       None       None   \n",
+       "3         4  {E9CF0528-8AB4-4603-A2E9-7383F6374BEF}       None       None   \n",
+       "4         5  {387093E8-0163-40B6-B314-E36F7E100A3B}       None       None   \n",
+       "\n",
+       "  SourceOrig SourceFeat    LoadDate  GNIS_ID   AreaAcres  AreaSqKm  \\\n",
+       "0       None       None  2016-05-10        0  1457399.01   5897.89   \n",
+       "1       None       None  2012-06-11        0   483698.94   1957.46   \n",
+       "2       None       None  2012-06-11        0   859591.63   3478.65   \n",
+       "3       None       None  2016-07-02        0  1591179.52   6439.28   \n",
+       "4       None       None  2016-08-30        0   671505.39   2717.49   \n",
+       "\n",
+       "        States      HUC8                   Name  Shape_Leng  Shape_Area  \\\n",
+       "0  MD,PA,VA,WV  02070004  Conococheague-Opequon    5.544624    0.618756   \n",
+       "1     DE,MD,PA  02040205   Brandywine-Christina    3.253687    0.205834   \n",
+       "2     MD,PA,WV  02070002   North Branch Potomac    4.101937    0.364322   \n",
+       "3        MD,PA  02050306      Lower Susquehanna    5.356391    0.678530   \n",
+       "4  DE,MD,NJ,VA  02040303           Chincoteague    3.805717    0.280041   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((-77.60122679102938 40.2384876719168,...  \n",
+       "1  POLYGON ((-75.79380026570988 40.1428963585235,...  \n",
+       "2  POLYGON ((-78.70751961222891 39.96501682338294...  \n",
+       "3  POLYGON ((-76.19345075571454 40.30860870722461...  \n",
+       "4  POLYGON ((-74.99871150652734 38.8055048012244,...  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#HUC8\n",
     "shp = outFolder + os.sep + 'Shape' + os.sep + 'WBDHU8.shp'\n",
@@ -136,9 +318,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Plot the geodataframe\n",
     "gdf.plot(figsize=(20,10));"
diff --git a/03-Fetching-files-with-ftplib.ipynb b/03-Fetching-files-with-ftplib.ipynb
index ba5dbf0..3d90077 100644
--- a/03-Fetching-files-with-ftplib.ipynb
+++ b/03-Fetching-files-with-ftplib.ipynb
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,9 +106,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'230 User logged in, proceed.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ftp = ftplib.FTP(ftpURL)\n",
     "ftp.login(\"anonymous\",\"user@duke.edu\")"
@@ -123,9 +134,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "220-*******  WARNING NOTICE *******\n",
+      "**** \n",
+      "**** In proceeding and accessing U.S. Government information and information systems,\n",
+      "**** you acknowledge that you fully understand and consent\n",
+      "**** to all of the following:\n",
+      "**** 1) you are accessing U.S. Government information and information systems\n",
+      "**** that are provided for official U.S. Government purposes only;\n",
+      "**** 2) unauthorized access to or unauthorized use of U.S. Government\n",
+      "**** information or information systems is subject to criminal, civil, \n",
+      "**** administrative, or other lawful action;\n",
+      "**** 3) the term U.S. Government information system includes systems operated\n",
+      "**** on behalf of the U.S. Government;\n",
+      "**** 4) you have no reasonable expectation of privacy regarding any \n",
+      "**** communications or information used, transmitted, or stored on U.S. Government\n",
+      "**** information systems;\n",
+      "**** 5) at any time, the U.S. Government may for any lawful government purpose, \n",
+      "**** without notice, monitor, intercept, search, and seize any authorized or \n",
+      "**** unauthorized communication to or from U.S. Government information systems or\n",
+      "**** information used or stored on U.S. Government information systems;\n",
+      "**** 6) at any time, the U.S. Government may for any lawful government purpose, \n",
+      "**** search and seize any authorized or unauthorized device, to include non-U.S. \n",
+      "**** Government owned devices, that stores U.S. Government information;\n",
+      "**** 7) any communications or information used, transmitted, or stored on U.S. \n",
+      "**** Government information systems may be used or disclosed for any lawful\n",
+      "**** government purpose, including but not limited to, administrative purposes,\n",
+      "**** penetration testing, communication security monitoring, personnel misconduct\n",
+      "**** measures, law enforcement, and counterintelligence inquiries; and\n",
+      "**** 8) you may not process or store classified national security information on \n",
+      "**** this computer system.\n",
+      "**** \n",
+      "**** By continuing to access this information system, you acknowledge, you understand and you accept the above terms.\n",
+      "**** \n",
+      "220 *******  WARNING NOTICE ******* \n"
+     ]
+    }
+   ],
    "source": [
     "print(ftp.welcome)"
    ]
@@ -141,9 +192,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'250 Directory changed to /EPADataCommons/ORD/NHDPlusLandscapeAttributes/StreamCat/States'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ftp.cwd(ftpDirectory)"
    ]
@@ -157,9 +219,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2449"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "files = ftp.nlst()\n",
     "len(files) #Over 2000 files here!"
@@ -167,9 +240,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Create a list of just Rhode Island files, that is one's that end in \"_RI.zip\"\n",
     "RI_files = ftp.nlst(\"*_RI.zip\")\n",
@@ -185,9 +269,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AgMidHiSlopes_RI.zip\n"
+     ]
+    }
+   ],
    "source": [
     "#First, let's get the file we want to download. We'll just get the first item in the list\n",
     "f = RI_files[0] \n",
@@ -205,9 +297,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./StreamCat\\RI\\AgMidHiSlopes_RI.zip\n"
+     ]
+    }
+   ],
    "source": [
     "#First we need to create a filename to which the remote contents will be written\n",
     "outFilename = stateFolder + os.sep + f\n",
@@ -216,9 +316,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'226 Transfer complete.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Ok, here's the command that actually grabs the file and writes it locally\n",
     "ftp.retrbinary(\"RETR \" + f,open(outFilename,'wb').write)"
@@ -226,9 +337,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AgMidHiSlopes_RI.zip']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Did it work?\n",
     "os.listdir(stateFolder)"
@@ -236,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/04-Grabbing-HTML-tables-with-Pandas.ipynb b/04-Grabbing-HTML-tables-with-Pandas.ipynb
index dc47384..410b04b 100644
--- a/04-Grabbing-HTML-tables-with-Pandas.ipynb
+++ b/04-Grabbing-HTML-tables-with-Pandas.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,9 +24,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 tables were found\n"
+     ]
+    }
+   ],
    "source": [
     "#Here, the read_html function pulls into a list object any table in the URL we provide.\n",
     "tableList = pandas.read_html('https://en.wikipedia.org/wiki/World_Happiness_Report',header=0)\n",
@@ -35,9 +43,152 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Overall Rank</th>\n",
+       "      <th>Change in rank</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Change in score</th>\n",
+       "      <th>GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Trust</th>\n",
+       "      <th>Residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.616</td>\n",
+       "      <td>1.534</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.362</td>\n",
+       "      <td>0.316</td>\n",
+       "      <td>2.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1.482</td>\n",
+       "      <td>1.551</td>\n",
+       "      <td>0.793</td>\n",
+       "      <td>0.626</td>\n",
+       "      <td>0.355</td>\n",
+       "      <td>0.401</td>\n",
+       "      <td>2.314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1.481</td>\n",
+       "      <td>1.611</td>\n",
+       "      <td>0.834</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>0.476</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>2.323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1.565</td>\n",
+       "      <td>1.517</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.367</td>\n",
+       "      <td>2.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>1.444</td>\n",
+       "      <td>1.540</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.618</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>2.430</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Overall Rank Change in rank      Country  Score  Change in score  \\\n",
+       "0            1              3       Norway  7.537            0.039   \n",
+       "1            2              1      Denmark  7.522            0.004   \n",
+       "2            3            NaN      Iceland  7.504            0.003   \n",
+       "3            4              2  Switzerland  7.494            0.015   \n",
+       "4            5            NaN      Finland  7.469            0.056   \n",
+       "\n",
+       "   GDP per capita  Social support  Healthy life expectancy  \\\n",
+       "0           1.616           1.534                    0.797   \n",
+       "1           1.482           1.551                    0.793   \n",
+       "2           1.481           1.611                    0.834   \n",
+       "3           1.565           1.517                    0.858   \n",
+       "4           1.444           1.540                    0.809   \n",
+       "\n",
+       "   Freedom to make life choices  Generosity  Trust  Residual  \n",
+       "0                         0.635       0.362  0.316     2.277  \n",
+       "1                         0.626       0.355  0.401     2.314  \n",
+       "2                         0.627       0.476  0.154     2.323  \n",
+       "3                         0.620       0.291  0.367     2.277  \n",
+       "4                         0.618       0.245  0.383     2.430  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Let's grab the 6th table one and display it's firt five rows\n",
     "df = tableList[5]\n",
@@ -46,9 +197,152 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Overall Rank</th>\n",
+       "      <th>Change in rank</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Change in score</th>\n",
+       "      <th>GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Trust</th>\n",
+       "      <th>Residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.616</td>\n",
+       "      <td>1.534</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.362</td>\n",
+       "      <td>0.316</td>\n",
+       "      <td>2.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1.482</td>\n",
+       "      <td>1.551</td>\n",
+       "      <td>0.793</td>\n",
+       "      <td>0.626</td>\n",
+       "      <td>0.355</td>\n",
+       "      <td>0.401</td>\n",
+       "      <td>2.314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1.481</td>\n",
+       "      <td>1.611</td>\n",
+       "      <td>0.834</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>0.476</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>2.323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1.565</td>\n",
+       "      <td>1.517</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.367</td>\n",
+       "      <td>2.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>1.444</td>\n",
+       "      <td>1.540</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.618</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>2.430</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Overall Rank Change in rank      Country  Score  Change in score  \\\n",
+       "0            1              3       Norway  7.537            0.039   \n",
+       "1            2              1      Denmark  7.522            0.004   \n",
+       "2            3            NaN      Iceland  7.504            0.003   \n",
+       "3            4              2  Switzerland  7.494            0.015   \n",
+       "4            5            NaN      Finland  7.469            0.056   \n",
+       "\n",
+       "   GDP per capita  Social support  Healthy life expectancy  \\\n",
+       "0           1.616           1.534                    0.797   \n",
+       "1           1.482           1.551                    0.793   \n",
+       "2           1.481           1.611                    0.834   \n",
+       "3           1.565           1.517                    0.858   \n",
+       "4           1.444           1.540                    0.809   \n",
+       "\n",
+       "   Freedom to make life choices  Generosity  Trust  Residual  \n",
+       "0                         0.635       0.362  0.316     2.277  \n",
+       "1                         0.626       0.355  0.401     2.314  \n",
+       "2                         0.627       0.476  0.154     2.323  \n",
+       "3                         0.620       0.291  0.367     2.277  \n",
+       "4                         0.618       0.245  0.383     2.430  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Looks like the first row should be a header, we can fix this by adding the 'header=0' option when reading int the tables\n",
     "df = pandas.read_html('https://en.wikipedia.org/wiki/World_Happiness_Report',header=0)[5]\n",
@@ -57,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,15 +363,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Eg80vJhuW62RpFhVr5yvCLL63qBIcuRys37yNow4MTuDHvHIfPnXbuojvHmt3LsfiOdMiS2xf9IOg6ynO/tGFn9XAcA4VTcAZGDLO+eavyZll4nNMUpwqqQ/kvzCzATPbVnifc41WbX2cWqW9s1easvre9hzfPWqGEcDgcI73Xr9qpPpo/uc/rlujjhtXULm0N/z6sjcdxLx9J4/8vhQOQm8fGB553+s3bytZJ6r4sxocyoVls0fbOTg85nNs5R3WSinZUpD0MmAWMFHSIUD+JzSFYDaSc4lJY2FQO9S+LyWr723H4DC93Rpz0h0cHn3lvmTeDHJF1VFzOeNr7+xj5tSJY3ZDK+x6uugH69k+8FLXU5AMVLL1GfVZTRjXRS5n9HR1sXP36G6s/OdYqkpqqyvXUvg74HPAbODzBf8+DPyf5ENznS7pPQraofZ9KWm/t7hXzLP3moi6FPlYYYXX9Zufp/hifdjg+V1DJbfHzHc9RZXaXjhzSsnWZ9RnBXDnh/6GC056FT1F8e7O5UaNO2SpJdYIJVsKZnY9cL2kN5nZrSnG5FwqmrmVZtIDwGm+t2rGLvJxnX/LWgaGyiWt6EW1H755DRN6uku+Ti37R5f6nhse+OOoWU89XdDTHSSTHYPDmWyJNULFFc2S/pVgsPkv4e29gI+a2adSiG8MX9HsGi3tGTppDgAn/d6qWYlc/H3ffugpvnLP44zvfukkX7jJzRuv+HlYpjpaudeJ876Ljym8vXXHIMd/4b4x33PL+15L3wHTa37fzdSwFc3AG81spLvIzLZKOgGomBQkLQW+BHQD15jZZyOOeTNwEcGlwaNmdnqMmJxrmDRXvqa9BiPp91br2MX0Sb2cd9x8Tj987siJ+Bcbn2PJpStHkuVbD5vDTav66e4SQ8M5BKPGIsq9TqX3XSox57/nnt89E/l9f9yyk74Dpje1lZm0OEmhW1JvOC0VSROBiu9cUjdwJfAGoB9YJWmFmW0oOGY+8C/AkjDZ7FPLm3AuTfVcfWd1ALhWccYuyn1e+ZN3VLK8eXU/d5x3JDsGh1/aHrMgKdRTibVSYl48Z1rk9xbe365VUuNMSf0mcLek90h6N/AT4PoY33cYsNHMnjCzQeBG4JSiY84CrjSzrQBmFp2encuI29ZsYsmlKznjmodYcunKUZu4x9Fug9uVpg/H/bxKbS26Y3B4ZHvMRk1TjnqtfJ2l/EDxvH0n844j5o465h1HzGXevpPHvP8kJ0M0Q9wqqUuB4wmmpf7YzO6K8T2nAkvN7L3h7bcDh5vZuQXHfB94DFhC0MV0kZn9KOK5zgbOBpg7d+6rn3zyyRhvzbnGalQ/8oo1m8Z0O7T6VMao1kC1lU+Ljx3f08Wd5x056kS8ZfsA6zdvA8TCmVNqOhlHvRYEayiGixanbfzzC6x5+i8sDhNTK2vkmALAb4EhM/uppD0kTTazFyrFEHFfcQbqAeYDRxNMff25pEX5Qe2RbzK7GrgagoHmmDE711CN6vpp126HYtV8XoV99BAUvZMZJ33lF6NO0o1YGzBqR7WIOkuFXUnz9p3c8smgWhW7jySdBdwC/Ed41yzg+zGeux+YU3B7NrA54pjbzGy3mf0B+D1BknAucxrZ9dNO3Q6luoiq/byWLZ7F7eceObJobWDYRs3/b+Qq7WWLZ3H/8mP5zMkL2WNc9ErnerTySuc4YwofJOjeeR7AzB4H4gwIrwLmSzpA0njgNGBF0THfJ9inAUkzgAOBJ+KF7ly60iq/0UrKnahr+bx2DA7T2zN6v4T8SbrUuEOtJ/Dpk3oZGMqxc3djx3jqHXdqtjjdRwNmNigFvUGSeii1sqSAmQ1JOhe4i2C84FozWy/pYmC1ma0IH/tbSRuAYeB8M9tS43txLnGd0vUTV1QXURdi/ebnOerAvav+vCq1Lho5SL9l+wCX3LFhzP3LDt6v5p9rO5R9j9NS+Jmk/0NQA+kNwHeBH8R5cjO708wONLNXmNn/De/7dJgQsMBHzGyBmf1vM7ux1jfiXFraqeunXlEn8Z27hznrhtWjCtzF/bzKtS4a3VLr37prTAkLgO8/srnmbp9Gt2aaIU5L4RPAe4DfAO8D7sT3U3DOUVi24lEGhl7qQBgYytV8hVyuddHIltrsvSYyGLFielx3betGtmwfYNuuQQaHRxfQa7UpxxWTgpnlJF0PPETQbfR7izOP1TnXEZYtnsW0PcZxzjd/zc7B0dVJa12UV25FcqNWaU+f1MuFJy/gk/81eu+GYbOqT+KFK6RzFtRJmjiupyVXOldMCpJOBK4C/ptgmukBkt5nZj9MOjjnXGtYOHMqORtbnTTrV8iTenvo6YJ8bb6eLmKfxMvt1Nbb08WVbzu05rUUzRSn++jzwDFmthFA0iuAOwBPCs45oLkVZ2uVHxQuLNba3dXFknkzKn7vqJ3ahobpKhqbGN/dxdSJ4zL9/kuJkxSeySeE0BOAl6NwzgEvXTEv2G8KV7+9D7CG7GWcdIXXqJlT42OMJ0TNMCre/KEVWkmlxEkK6yXdCdxMMKbwjwTF7f4BwMy+l2B8zqUua5vdZ1n+itlyxsCwjWyXWW/pjjTKi9e6GDEqmfR2C5Po7W6NVlI5cZLCBODPwOvD288C/ws4mSBJeFJwbSOLm91nVeEVc17+63rm5kddiZ9/y6NM22NcQ1ogebV2eUUlE3WJO849suSucK0kzuyjM4vvkzQ+rHzqXNtoh4VHaYq6Ys6rZ+ZR1PMODBnnfPPX5IoK1tWrlimupZJJu9RIijP76F7gXWb2x/D2awjWKRycaGTOpayd9jpIowus1N7GUF+feqnn3RlRsC6ucrus1TLFtZ1XtsfpPvo34EeSriAohncCMKb14Fyra5e9DtLqAiu8Yo4aU6j1RFn4vF2InbtHLwarNlEXfx5v7pvNzav76/580tyxL01x91M4mmBzneeAQ8zsTwnHVZLv0eyS1Op7HTRj7+DC+fqN7FMP9k54nrNuWM3AUG3vp9TeCYWyvrdyozRsPwVJFwBvBo4CDgLulfRRM7uj/jCdy5ZW7xZoRhdYuSvmerqxpk/q5agD9+byU2tf/1Bu3COvVbsIkxKn+2gGcJiZ7QIekPQjgjEFTwptrJOnZbZqt0DWau80qhurnkRdbtwjrxmfT5b/vuLMPvonAEl7mtkOM3sSeEPikbmm8WmZrSdrtXcaPZOrlkSdP/FecOICLrljQ9kxhTQ/n6z/fcXpPjoC+BowCZgr6WDgfWb2gaSDc+nzaZmtJ+pn1uzaO43uxqr2yrr4xHvBSQtYNHPqyPf/03EHNuVKvRX+vuLsp/BF4O+ALQBm9ijB+IJrQ+1QD77TRP3Mml17p5EzuardySxqN7hLbt8wKgE0a0+MVvj7ipMUMLOni+4ajjzQtbx2mZbZSbL4M2vUhji17Muc5RNvFn9WxeIkhaclvQ4wSeMlfQz4bcJxuSbxfYhbT1Z/ZssWz+L+5cfyzfcezv3Ljy3bb15qo/taTvBZPvFm9WdVKM7so3OALxEsXOsHfgx8MMmgXHO1+rTMTpTVn1mcAeJSA6/BbKrdDA5Xd4LPehnvrP6s8mItXssSX7zmXPsotdiucMbQrt1DSGJCT3dVs3WyPO2zGRq2eM0555ISNUupW+Izt29gcKhwNhVc+bZDqqqS2qrrTZot1kCzc52sVH+3q19k//9wjvHdxTuZdTN14ng/yafAk4JzZVQ7HbJdpJUIowZeLzx5IUO59tnJrNXEWby2L/CvwEwze6OkBcARZva1xKNzrolaYaFREtJecRs18Dp5Qk9mB4rbXZwxheuArwOfDG8/BtxEsMrZubbVTvsrxNWsRFjc/5/1GTrtLE730QwzuxmC3xAzG8IXr7kOkOX57knJ0sKvZq06rlW7jD3FSQo7JE0n2I8ZSa8FtiUalXMZ0AoLjSqp9kTViYmwEdpp7ClO99FHgBXAKyTdD+wNnJpoVM5lRCt3Y9QyNpD1hV9Z1G5jTyWTgqR/NLPvAluB1wN/DQj4vZntTik+55quFee713OiauVE2AztNvZUrvvoX8L/bzWzITNbb2brPCE4l331jg20Wn9+M7Vbl1u57qMtku4BDpC0ovhBM1uWXFjOuXpk6UTV7uUm2q3LrVxSOBE4FPgG8Pl0wnHONUJWTlRZ32WsUdqpy61iQTxJe5vZszU9ubSUoMJqN3CNmX22xHGnAt8FXmNmZavdeUE8V4t2v1otpZnvu1Sxu/uXH9tRP4OsqLsgnqQvmtk/A9dKGpM5KnUfSeoGriTYz7kfWCVphZltKDpuMvAh4KFKwTpXi065Wo2S1CB5nGTTbgOwnaJc99E3wv8/V+NzHwZsNLMnACTdCJwCbCg67hLgMuBjNb6OcyW123TBLIibZLM0ruHiKzn7yMweDv//WdS/GM89CyjcxrM/vG+EpEOAOWZ2e7knknS2pNWSVj/7bE09Wa5DZWmFbjuoZnvMdlj814nKdR/9hnAVcxQzO6jCcyvivpHnk9QFfAF4V4XnwcyuBq6GYEyh0vHO5fnVamNV2yXUTgOwnaJc99FJdT53PzCn4PZsYHPB7cnAIuBeSQAvA1ZIWlZpsNm5uLIyC6cWWRwcryXJtuLiv05WMimY2ZN1PvcqYL6kA4BNwGnA6QXPvw2Ykb8t6V7gY54QXKO14tVqVgfHWznJungS247TzIYknQvcRTAl9VozWy/pYmC1mY1ZEOdcUlrpajWtwfFaWyKtmGRdfInu0WxmdwJ3Ft336RLHHp1kLM61ijSmctbbEmmlJOuqU7F0tqSTwkFh51wKkh4cr2YGUS3P3Q57CnSyOCf704DHJV0m6VVJB+RcJyh38kx6KmdS03TbaU+BTlax+8jMzpA0BXgr8PVwdfPXge+Y2QtJB+hcu4nTdZNkv30SLRFfJNg+YnULmdnzwK3AjcB+wN8Dv5Z0XoKxOdd2ql38lUT56iRaIvW0PrzLKVsqthQknQy8G3gFQemLw8zsGUl7AL8FvpxsiM61j6zUA6qmJRJnllKtrY+sTr3tZHFmH/0j8AUzu6/wTjPbKendyYTlXHvK0grrODOI4p60a1m/4F1O2RRnTOEdZR67u7HhONfeGrH4K62VztWetKsdB8lKq8mNFqf76B+AS4F9COoZCTAzm5JwbM61pXoGkdPsbqnlpF3N+oUstZrcS+IMNF8GLDOzqWY2xcwme0Jwrj61DCInub4gStInba+imk1xxhT+bGa/TTwS1xGyWOStVaTd3ZJGnSMvmZE95Upn/0P45WpJNwHfB0YuSczsewnH5tqMzzSpz57juxkYTra7pThpp3HS9pIZ2VKupXBywdc7gb8tuG2AJwUXm880qU8+oSrcU33CuKDnt5FX7qWStp+0O0u50tlnAkhaYmb3Fz4maUnSgbn24jNNxorblVaYUPNyOePOD/0N8/ad3LBYPGk7iDem8GXg0Bj3OVeSzzQZrZqutKiE2tvTzY7B4YbF04yk7eNL2VRuTOEI4HXA3pI+UvDQFIL9EZyLrRM3Zyl10qv2qjyNhJp20vbxpewq11IYD0wKjylsoz4PnJpkUK49ddJMk3InvWqvytNIqGkm7Vq6qrxVkZ5yYwo/A34m6boGbM3pHJDsTJOsnDgqnfRquSpPI6GmlbSrTYreqkhXue6jHxDMMkLSmMfNbFlyYTlXnSydOCqd9Gq9Kk9jFlAar1FNUvQB8PSV6z76XGpROFeHrJ044pz0kr4qz0qrKUo1SdFnraWvUveRc5mXtRNH3JNeUlflWWo1lRI3KfqstfTFKYg3H/g3YAEwIX+/mf1VgnE5F1sWTxzNGlSPajWdf0s2u1viJMVOnLXWbHHWKXwduBD4AnAMcCZBpVTnMiGrJ45mrASOajUNDOX49kNPcd5x81ONpVE6adZaFsRJChPN7G5JCmchXSTp5wSJwnWYrPZV+4kjMHuviQwW1UcC+Mo9j3P64XNb9nPxUhvpiZMUXpTUBTwu6VxgE8HeCq7DZL2v2k8cwWdw7jHz+PxPHht1//jubh+cdbHE2U/hn4E9gA8BrwbOAN6ZZFAue9Ku5e9qd/rhc+ntGd3D2+wxFtc6KiYFM1tlZtuBrWZ2ppm9ycweTCHjIrXUAAAPCUlEQVQ2lyH5vupC+Rk+LlumT+rl8lMP9s1rXE3izD46AvgaQcmLuZIOBt5nZh9IOjiXHVmc4dPqkhyf8TEWV6s4YwpfBP4OWAFgZo9KOirRqFzmZHWGT6tKY3zGx1hcLeIkBczs6aJSF42r2etahl99NkbWVmDXKqsz0Vx94iSFpyW9DjBJ4wkGnH3P5g7lV5/1y8oK7HpO6lmfieZqFycpnAN8CZgF9AM/Bj6YZFAu2/wKsT5ZGJ+p56TeLi0dFy3O7KPnzOxtZravme1jZmeY2ZY4Ty5pqaTfS9oo6RMRj39E0gZJayXdLenltbwJl57b1mxiyaUrOeOah1hy6UpWrNnU7JBaTn58plmzg+qdXuwz0dpbudLZXyYsnR3FzD5U7okldQNXAm8gaGGskrTCzDYUHPYI0GdmOyW9H7gMeEsV8bsU+RVi4zRzfKbe7qsstHRccsq1FFYDD4f/lhV8nf9XyWHARjN7wswGgRuBUwoPMLN7zGxnePNBYHZ14bs0+RXiWFu2D/Do03+paRHf9Em9HDxnWuoJNc5Jvdz7anZLxyWrXOns6/NfS/rnwtsxzQKeLrjdDxxe5vj3AD+s8jVcivwKcbRWHWytNL04zvtqdEvHx6myI9aUVMp0I5URVUk18nkknQH0Aa8v8fjZwNkAc+fOrSEU1wi+VuElrd6VVuqkXs37atRMtGYlV09E0eImhVr0A3MKbs8GNhcfJOl44JPA680ssg1uZlcDVwP09fXVkqBcgzSjLzyLf7xZmVZaj6iTetrvq1nJtVVbeWkoN9D8Ai9d2e8h6fn8Q4CZ2ZQKz70KmC/pAILKqqcBpxe9xiHAfwBLzeyZGuJ3TZDmWoWs/vG2a1da2u+rGcm11Vt5SSs50Gxmk81sSvivp+DryTESAmY2BJwL3EWw2O1mM1sv6WJJy8LDLieoqfRdSWskrWjAe3JtIsuVWdt1sDXt91VtEqpnYD/PJ0yUl2T3EWZ2J3Bn0X2fLvj6+CRf37W2rHfRtGvZjzTfVzXjVI1qNbZrK69REk0KztUj6o93cHiYbbsG2bJ9IBMn4XYt+5Hm+4qThBrZ5eMTJsrzpOAyq/iPd9fuIXIGH/zWI5kaX3D1q5SEGt1qbNdWXiN4UnCZlv/jXb/5ec66YTUDQzl2Dw8Bo68UszhDyTVOEl0+7drKq5cnBZd50yf1MnXiOMZ3dzEwNPZK8Rcbn8vkDCXXON7lkx5PCq4llLpS3HN8t08v7BDe5ZOOilVSncuCUlMldwwO+/TCDtKselGdxFsKrmVEXSlu2T7QcdMLffzEJcmTgmspxYODndbXnNUV3q59eFJwLa9T+pq9PINLgycF1xY6YXph1ld4u/bgA83OhRpRVydJXp7BpcGTgnM0Z+/papNQuxbhc9ni3Ueu4zWjr77WAeNOGT9xzeMtBdfx0i6lXG9JcJ+r75LkScF1vGZtLFPIF9y5rPCk4Dpe1jeWcS5NPqbgHNndWMa5tHlScC6UtY1lnGsGTwrONUknLLhzrcfHFJxzzo3wpOCcc26EJwXnXFNkvaxIp/IxBedc6rwEeHZ5S8G5jMniFXQjY6p3RbdLlrcUnMuQuFfQae6+1uirei8Bnm2eFJzLiLiF+dLsekmiWKCv6M427z5yLiPi1ERKu+sliTpNXgI827yl4FxGxLmCTrvrJamrel/RnV3eUnAuI+JcQafd9ZLkVb2XAM8mmVmzY6hKX1+frV69uubvT3OAzrlaVPodXbFm05hieklP5/S/m9Yn6WEz66t0XEd1H/ncaNcKKtVEakbXi9dp6hwdkxSaseWic0nxk7RLSqJjCpKWSvq9pI2SPhHxeK+km8LHH5K0f1Kx+G5XrhNkceGbay2JtRQkdQNXAm8A+oFVklaY2YaCw94DbDWzeZJOAy4F3pJEPD432rW7rHeP+rhEa0iypXAYsNHMnjCzQeBG4JSiY04Brg+/vgU4TpKSCMbnRrt2lvXSEbet2cSSS1dyxjUPseTSlaxYs6nZIbkSkhxTmAU8XXC7Hzi81DFmNiRpGzAdeC6JgHxutGtXWS4d4eN5rSXJpBB1xV88/zXOMUg6GzgbYO7cuXUF5QN0rh1luXs0ywnLjZVk91E/MKfg9mxgc6ljJPUAU4H/V/xEZna1mfWZWd/ee++dULjOta4sd49mOWG5sZJsKawC5ks6ANgEnAacXnTMCuCdwAPAqcBKa7XVdM5lRFa7R/MJq3jBXVbic6MllhTCMYJzgbuAbuBaM1sv6WJgtZmtAL4GfEPSRoIWwmlJxeNcJ8hq92hWE5Ybq+PKXDjnXCeKW+bCC+I555wb4UnBOefcCE8KzjnnRnhScM45N8KTgnPOuRGeFJxzzo3wpOCcc25Ey61TkPQs8GSdTzODhIru1cFjii+LcWUxJshmXFmMCbIZVyNjermZVawT1HJJoREkrY6ziCNNHlN8WYwrizFBNuPKYkyQzbiaEZN3HznnnBvhScE559yITk0KVzc7gAgeU3xZjCuLMUE248piTJDNuFKPqSPHFJxzzkXr1JaCc865CG2bFCQtlfR7SRslfSLi8V5JN4WPPyRp/4zE9RFJGyStlXS3pJc3O6aC406VZJJSmQ0RJy5Jbw4/r/WSvt3smCTNlXSPpEfCn+EJKcR0raRnJK0r8bgkXRHGvFbSoUnHFDOut4XxrJX0S0kHNzumguNeI2lY0qlZiEnS0ZLWhL/nP0s0IDNru38Em/r8N/BXwHjgUWBB0TEfAK4Kvz4NuCkjcR0D7BF+/f6k44oTU3jcZOA+4EGgLyOf1XzgEWCv8PY+GYjpauD94dcLgD+m8FkdBRwKrCvx+AnADwn2RH8t8FDSMcWM63UFP7s3phFXpZgKfs4rgTuBU5sdEzAN2ADMDW8n+nveri2Fw4CNZvaEmQ0CNwKnFB1zCnB9+PUtwHGS1Oy4zOweM9sZ3nyQYG/rpsYUugS4DHgx4Xiqiess4Eoz2wpgZs9kICYDpoRfT2XsvuQNZ2b3EbG3eYFTgBss8CAwTdJ+zY7LzH6Z/9mRzu96nM8K4DzgViDp3ycgVkynA98zs6fC4xONq12Twizg6YLb/eF9kceY2RCwDZiegbgKvYfgCi9JFWOSdAgwx8xuTziWquICDgQOlHS/pAclLc1ATBcBZ0jqJ7jSPC/hmOKo9veuGdL4Xa9I0izg74Grmh1LgQOBvSTdK+lhSe9I8sUS26O5yaKu+IunWcU5ptFiv6akM4A+4PWJRlQhJkldwBeAdyUcR7E4n1UPQRfS0QRXmT+XtMjM/tLEmN4KXGdmn5d0BMEe5IvMLJdQTHE043c9NknHECSFI5sdC/BFYLmZDSffcRBbD/Bq4DhgIvCApAfN7LGkXqwd9QNzCm7PZmwzPn9Mv6QegqZ+pWZlGnEh6Xjgk8DrzWygyTFNBhYB94Z/JC8DVkhaZmZJbpYd92f4oJntBv4g6fcESWJVE2N6D7AUwMwekDSBoH5NKl0RJcT6vWsGSQcB1wBvNLMtzY6H4ELsxvB3fQZwgqQhM/t+E2PqB54zsx3ADkn3AQcDiSSFxAebmvGPINk9ARzASwOCC4uO+SCjB5pvzkhchxAMZs7PymdVdPy9pDPQHOezWgpcH349g6CLZHqTY/oh8K7w61cRnHyVwue1P6UHKk9k9EDzr9L43YoR11xgI/C6tOKpFFPRcdeRwkBzjM/pVcDd4e/fHsA6YFFSsbRlS8HMhiSdC9xFMJPgWjNbL+liYLWZrQC+RtC030jQQjgtI3FdDkwCvhterTxlZsuaHFPqYsZ1F/C3kjYAw8D5luDVZsyYPgr8p6QPE3TRvMvCv+ykSPoOQRfajHAs40JgXBjzVQRjGycQnIB3AmcmGU8VcX2aYBzvq+Hv+pAlXPwtRkypqxSTmf1W0o+AtUAOuMbMyk6prSuehH9fnXPOtZB2nX3knHOuBp4UnHPOjfCk4JxzboQnBeeccyM8KTjnnBvhScG1PEmfDKtHrg0rSR5ew3P0SbqiwjFHS0qz1EdVJE2T9IFmx+FaW1uuU3CdIywlcRJwqJkNSJpBsLCsKhaszk5yhXaiJHUTVNP8APDVJofjWpi3FFyr24+gBMAAgJk9Z2abASQdF+5r8JuwZn1veP9rwvr9j0r6laTJha0ASYeFjz8S/v/X5QKQtDB8njVha2W+pP0L6+NL+piki8Kv75X0xfC510k6LLz/IknfkLRS0uOSzgrvl6TLw2N/I+kt4f1HK9i74dvAb4DPAq8I47i8oZ+y6xjeUnCt7sfApyU9BvyUYP+Jn4U1h64DjjOzxyTdALxf0leBm4C3mNkqSVOAXUXP+TvgqHAF8/HAvwJvKhPDOcCXzOxbksYTrHbet0Lce5rZ6yQdBVxLUF8K4CCCUhR7Ao9IugM4AlhMUO9mBrAqrH8DQTnvRWb2BwUbRS0ys8UVXtu5kjwpuJZmZtslvRr4G4INim5SsCPaI8Af7KVKktcT1Lu6G/gfM1sVfv/zAEUVMacC10uaT1CqYlyFMB4APilpNkHd+8djVNj8Tvj690maImlaeP9tZrYL2CXpHoKT/pHAd8xsGPizgp23XgM8T1DH6A+VXsy5uLz7yLU8Mxs2s3vN7ELgXIKr+lJnZVG5bPQlwD1mtgg4GZhQ4fW/DSwjaHHcJelYYIjRf1/Fz1Ecg5W5v1yG2VEuNueq5UnBtTRJfx1e0ectBp4k6ALaX9K88P63Az8L758p6TXh908OS6cXmgpsCr9+V4wY/gp4wsyuAFYQdAH9GdhH0vRwLOOkom/LjwscCWwzs23h/adImiBpOkGRtFUE26C+RVK3pL0Jtm/8VUQoLxCUOneuZt595FrdJODLYffLEEEl0LPN7EVJZxJUm+0hOLleZWaD4UDtlyVNJLi6P77oOS8j6D76CMFevZW8hWC3td3An4CLzWx3WD31IeAPBMmo0FZJvyTYuvPdBff/CriDoKz0JWa2WdJ/EYwrPErQcvi4mf1J0isLn9DMtijYhW4d8EMzOz9G7M6N4lVSnUuZpHuBj1nRJkXh7KTtZva5ZsTlHHj3kXPOuQLeUnDOOTfCWwrOOedGeFJwzjk3wpOCc865EZ4UnHPOjfCk4JxzboQnBeeccyP+Px5ubZcmTdnpAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#...or we can examine it\n",
     "#Here is as quick preview of pandas' plotting capability\n",
     "%matplotlib inline\n",
     "df.plot.scatter(x='Social support',y='Healthy life expectancy');"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb b/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
index e18b2b6..05a5566 100644
--- a/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
+++ b/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,22 +45,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'response' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-90976483410b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# The response object simply has the contents of the web page at the address provided\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'response' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "# Send a request to a web page\n",
-    "response = requests.get('https://xkcd.com/869')"
+    "# The response object simply has the contents of the web page at the address provided\n",
+    "print(response.text)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# The response object simply has the contents of the web page at the address provided\n",
-    "print(response.text)"
+    "# Send a request to a web page\n",
+    "response = requests.get('https://xkcd.com/869')"
    ]
   },
   {
@@ -74,9 +86,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bs4.BeautifulSoup"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# BeautifulSoup \n",
     "soup = BeautifulSoup(response.text, 'lxml')\n",
@@ -94,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,9 +127,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://imgs.xkcd.com/comics/server_attention_span.png\n"
+     ]
+    }
+   ],
    "source": [
     "#What was found in the search\n",
     "print(match.group())"
@@ -114,14 +145,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"https://imgs.xkcd.com/comics/server_attention_span.png\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#And here is some Juptyer code to display the picture resulting from it\n",
     "from IPython.display import Image\n",
     "Image(url=match.group())"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/06-Using-specialized-packages-to-grab-data.ipynb b/06-Using-specialized-packages-to-grab-data.ipynb
index f498bb1..77c441b 100644
--- a/06-Using-specialized-packages-to-grab-data.ipynb
+++ b/06-Using-specialized-packages-to-grab-data.ipynb
@@ -18,9 +18,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting census\n",
+      "  Downloading https://files.pythonhosted.org/packages/52/af/ad681b4a1b903d96569a4295305ef3bb17d99959a15a4d7cd4f6f65e8f3a/census-0.8.7-py2.py3-none-any.whl\n",
+      "Requirement already satisfied: requests>=1.1.0 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from census)\n",
+      "Requirement already satisfied: future in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages\\future-0.16.0-py3.6.egg (from census)\n",
+      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from requests>=1.1.0->census)\n",
+      "Requirement already satisfied: idna<2.7,>=2.5 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from requests>=1.1.0->census)\n",
+      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from requests>=1.1.0->census)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from requests>=1.1.0->census)\n",
+      "Installing collected packages: census\n",
+      "Successfully installed census-0.8.7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using pip version 9.0.3, however version 18.1 is available.\n",
+      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
+     ]
+    }
+   ],
    "source": [
     "#Import the 'census' package; install if needed\n",
     "try:\n",
@@ -33,9 +58,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting ggplot\n",
+      "  Downloading https://files.pythonhosted.org/packages/48/04/5c88cc51c6713583f2dc78a5296adb9741505348c323d5875bc976143db2/ggplot-0.11.5-py2.py3-none-any.whl (2.2MB)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Collecting patsy>=0.4 (from ggplot)\n",
+      "  Downloading https://files.pythonhosted.org/packages/ea/0c/5f61f1a3d4385d6bf83b83ea495068857ff8dfb89e74824c6e9eb63286d8/patsy-0.5.1-py2.py3-none-any.whl (231kB)\n",
+      "Collecting brewer2mpl (from ggplot)\n",
+      "  Downloading https://files.pythonhosted.org/packages/84/57/00c45a199719e617db0875181134fcb3aeef701deae346547ac722eaaf5e/brewer2mpl-1.4.1-py2.py3-none-any.whl\n",
+      "Requirement already satisfied: numpy in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Requirement already satisfied: six in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Requirement already satisfied: scipy in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Requirement already satisfied: pandas in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Requirement already satisfied: cycler in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from ggplot)\n",
+      "Collecting statsmodels (from ggplot)\n",
+      "  Downloading https://files.pythonhosted.org/packages/77/2b/8ba61399b31f984c263b177c2e2547a34f0d4d972a24a51fc77c376079b0/statsmodels-0.9.0-cp36-cp36m-win_amd64.whl (7.0MB)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from matplotlib->ggplot)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from matplotlib->ggplot)\n",
+      "Requirement already satisfied: pytz in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages\\pytz-2018.3-py3.6.egg (from matplotlib->ggplot)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from matplotlib->ggplot)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\rb312\\appdata\\local\\esri\\conda\\envs\\my_env\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib->ggplot)\n",
+      "Installing collected packages: patsy, brewer2mpl, statsmodels, ggplot\n",
+      "Successfully installed brewer2mpl-1.4.1 ggplot-0.11.5 patsy-0.5.1 statsmodels-0.9.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using pip version 9.0.3, however version 18.1 is available.\n",
+      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n",
+      "C:\\Users\\rb312\\AppData\\Local\\ESRI\\conda\\envs\\my_env\\lib\\site-packages\\ggplot\\utils.py:81: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
+      "You can access Timestamp as pandas.Timestamp\n",
+      "  pd.tslib.Timestamp,\n",
+      "C:\\Users\\rb312\\AppData\\Local\\ESRI\\conda\\envs\\my_env\\lib\\site-packages\\ggplot\\stats\\smoothers.py:4: FutureWarning: The pandas.lib module is deprecated and will be removed in a future version. These are private functions and can be accessed from pandas._libs.lib instead\n",
+      "  from pandas.lib import Timestamp\n"
+     ]
+    }
+   ],
    "source": [
     "#And we'll preview 'ggplot', import/install it...\n",
     "try:\n",
@@ -48,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,9 +144,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "B19001_001E    float64\n",
+       "NAME            object\n",
+       "county          object\n",
+       "state           object\n",
+       "tract           object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "variables = ('NAME', 'B19001_001E')\n",
     "params = {'for':'tract:*', 'in':'state:24'}\n",
@@ -91,9 +173,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<ggplot: (141083224282)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import ggplot as gg\n",
     "\n",

From b3074e648b9df701eebcd9984e293a17a6210ac5 Mon Sep 17 00:00:00 2001
From: bobaney <42747366+bobaney@users.noreply.github.com>
Date: Wed, 31 Oct 2018 11:43:31 -0400
Subject: [PATCH 2/3] redos!

---
 00-Importing-Local-Files.ipynb               | 140 +++++++++++++++----
 01a-Getting-data-with-Pandas.ipynb           |  54 +++++--
 02a-Fetching-Data-with-urllib.ipynb          |  12 +-
 02b-Extract-Statewide-HUCs-with-urllib.ipynb |  14 +-
 data/NWISDischarge.csv                       | 140 +++++++++++++++++++
 5 files changed, 304 insertions(+), 56 deletions(-)
 create mode 100644 data/NWISDischarge.csv

diff --git a/00-Importing-Local-Files.ipynb b/00-Importing-Local-Files.ipynb
index 093e88d..9a96aa9 100644
--- a/00-Importing-Local-Files.ipynb
+++ b/00-Importing-Local-Files.ipynb
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +74,7 @@
        "'1.9'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -105,11 +105,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'4.56'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Exercise 1:"
+    "# Exercise 1:\n",
+    "dataFile2 = open('./data/example_data2.txt', 'r')\n",
+    "data = []\n",
+    "for row in dataFile2:\n",
+    "    data.append(row.strip().split(','))\n",
+    "dataFile2.close()\n",
+    "data[4][2]"
    ]
   },
   {
@@ -132,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +158,7 @@
        "[['id', 'x', 'y'], ['1', '22', '33'], ['2', '2.4', '6.8'], ['3', '1.9', '8.0']]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,16 +175,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.9'"
+       "'8.03'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,11 +205,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'4.56'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#Exercise 2:"
+    "#Exercise 2:\n",
+    "dataFile2 = open('./data/example_data2.txt', 'r')\n",
+    "datareader2 = csv.reader(dataFile2, delimiter = ',')\n",
+    "data2 = []\n",
+    "for row in datareader2:\n",
+    "    data2.append(row)\n",
+    "data2\n",
+    "data[4][2]"
    ]
   },
   {
@@ -211,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +257,7 @@
        "       [ 3. ,  1.9,  8. ]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,16 +272,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.9'"
+       "1.9"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -267,11 +302,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.72 3.84 4.59 1.36]\n",
+      " [5.15 6.43 7.92 6.26]\n",
+      " [1.56 8.03 4.36 5.1 ]\n",
+      " [7.38 1.2  4.56 3.49]\n",
+      " [4.24 7.69 6.49 5.28]\n",
+      " [1.25 9.64 1.83 6.84]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4.56"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#Exercise 3:"
+    "#Exercise 3:\n",
+    "import numpy\n",
+    "dataF3 = numpy.genfromtxt('./data/example_data2.txt',\n",
+    "                          delimiter = ',', skip_header = True)\n",
+    "print(dataF3)\n",
+    "dataF3[3,2]"
    ]
   },
   {
@@ -287,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -346,7 +409,7 @@
        "2   3   1.9   8.0"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -366,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -375,7 +438,7 @@
        "1.9"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -474,12 +537,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.56"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#Exercise 4:"
+    "#Exercise 4:\n",
+    "import pandas as pa\n",
+    "data4 = pa.read_csv('./data/example_data2.txt')\n",
+    "data4.values[3][2]"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/01a-Getting-data-with-Pandas.ipynb b/01a-Getting-data-with-Pandas.ipynb
index 83e21d0..5b3ca71 100644
--- a/01a-Getting-data-with-Pandas.ipynb
+++ b/01a-Getting-data-with-Pandas.ipynb
@@ -7,7 +7,7 @@
     "## ♦ Grabbing static text files with `Pandas`\n",
     "While once much more common, a number of agencies still provide access to data as raw text files served up on a web server. Some examples are [USGS Water Use Data](https://water.usgs.gov/watuse/data/2010/index.html), and [NWIS Stream Flow data](https://waterdata.usgs.gov/nc/nwis/water_use?format=rdb&rdb_compression=value&wu_area=County&wu_year=ALL&wu_county=ALL&wu_category=IN&wu_county_nms=--ALL%2BCounties--&wu_category_nms=Industrial). If you've ever used these data in your projects, you know it's fairly easy to manage: just download the link to the file (if a download link is provided), or justcopy and paste. \n",
     "\n",
-    "But if you have a lot of files to download, or if, like the stream gage data, you need to download it in real-time for your reseach, you can write a script to do this. Turns out it's note even that hard, thanks to the `pandas` package. \n",
+    "But if you have a lot of files to download, or if, like the stream gage data, you need to download it in real-time for your reseach, you can write a script to do this. Turns out it's not even that hard, thanks to the `pandas` package. \n",
     "\n",
     "This notebook demonstrates how `pandas` can read a text file from the web just as easily as a text file located on our local machine. We just substitute the data's web address (i.e., its URL) where we'd put the filename in using the `read_csv` function. It's that easy, but it only works if the data being served remotely is a raw text file...\n",
     "\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +342,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -652,7 +652,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -973,7 +973,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -993,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1009,9 +1009,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "col =  'Industrial self-supplied groundwater withdrawals, fresh, in Mgal/d'\n",
     "dfNWIS.groupby('year')[col].mean().plot();"
@@ -1026,6 +1037,27 @@
     "► See if you can import NWIS discharge data located at this web address:<br>\n",
     "http://waterdata.usgs.gov/nwis/uv?cb_00060=on&cb_00065=on&format=rdb&period=1&begin_date=&end_date=&site_no=02085070<br> and save it to a file named `NWISDischarge.csv`."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pa\n",
+    "myURL = 'http://waterdata.usgs.gov/nwis/uv?cb_00060=on&cb_00065=on&format=rdb&period=1&begin_date=&end_date=&site_no=02085070'\n",
+    "badrows = list(range(26))\n",
+    "badrows.append(28)\n",
+    "pract = pa.read_csv(myURL, delimiter = '\\t', skiprows = badrows)\n",
+    "pract.to_csv('Data/NWISDischarge.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/02a-Fetching-Data-with-urllib.ipynb b/02a-Fetching-Data-with-urllib.ipynb
index 01b6fe7..e8434f6 100644
--- a/02a-Fetching-Data-with-urllib.ipynb
+++ b/02a-Fetching-Data-with-urllib.ipynb
@@ -54,7 +54,7 @@
     {
      "data": {
       "text/plain": [
-       "('tl_2017_38_tract.zip', <http.client.HTTPMessage at 0x19bf99fa5c0>)"
+       "('tl_2017_38_tract.zip', <http.client.HTTPMessage at 0x1b25d94c5c0>)"
       ]
      },
      "execution_count": 4,
@@ -83,14 +83,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Volume in drive C is Windows\n",
-      " Volume Serial Number is 8292-14BA\n",
+      " Volume in drive V is Data\n",
+      " Volume Serial Number is B61D-F46F\n",
       "\n",
-      " Directory of C:\\Users\\rb312\\Documents\\GitHub\\GettingData\n",
+      " Directory of V:\\GettingData\n",
       "\n",
-      "10/29/2018  12:36 PM         1,734,270 tl_2017_38_tract.zip\n",
+      "10/31/2018  11:24 AM         1,734,270 tl_2017_38_tract.zip\n",
       "               1 File(s)      1,734,270 bytes\n",
-      "               0 Dir(s)  61,388,533,760 bytes free\n"
+      "               0 Dir(s)  1,713,750,867,968 bytes free\n"
      ]
     }
    ],
diff --git a/02b-Extract-Statewide-HUCs-with-urllib.ipynb b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
index 4e05eeb..d97b4ee 100644
--- a/02b-Extract-Statewide-HUCs-with-urllib.ipynb
+++ b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
@@ -72,17 +72,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading NHD_H_Maryland_State_Shape.zip\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Get the file (this can take a few minutes...)\n",
     "url = ftpFolder + \"/\" + stateFile\n",
@@ -102,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/data/NWISDischarge.csv b/data/NWISDischarge.csv
new file mode 100644
index 0000000..7b38ae0
--- /dev/null
+++ b/data/NWISDischarge.csv
@@ -0,0 +1,140 @@
+,,,,,,,# 
+agency_cd,site_no,datetime,tz_cd,89062_00060,89062_00060_cd,89063_00065,89063_00065_cd
+USGS,02085070,2018-10-30 00:00,EDT,106,P,2.22,P
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+USGS,02085070,2018-10-30 05:30,EDT,96.0,P,2.16,P
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+USGS,02085070,2018-10-30 06:30,EDT,96.0,P,2.16,P
+USGS,02085070,2018-10-30 06:45,EDT,94.2,P,2.15,P
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+USGS,02085070,2018-10-30 07:30,EDT,94.2,P,2.15,P
+USGS,02085070,2018-10-30 07:45,EDT,94.2,P,2.15,P
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+USGS,02085070,2018-10-30 08:30,EDT,92.4,P,2.14,P
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+USGS,02085070,2018-10-30 10:30,EDT,92.4,P,2.14,P
+USGS,02085070,2018-10-30 10:45,EDT,92.4,P,2.14,P
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+USGS,02085070,2018-10-30 11:45,EDT,92.4,P,2.14,P
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+USGS,02085070,2018-10-30 12:30,EDT,90.6,P,2.13,P
+USGS,02085070,2018-10-30 12:45,EDT,90.6,P,2.13,P
+USGS,02085070,2018-10-30 13:00,EDT,90.6,P,2.13,P
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+USGS,02085070,2018-10-30 13:30,EDT,90.6,P,2.13,P
+USGS,02085070,2018-10-30 13:45,EDT,88.9,P,2.12,P
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+USGS,02085070,2018-10-30 14:15,EDT,90.6,P,2.13,P
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+USGS,02085070,2018-10-30 14:45,EDT,88.9,P,2.12,P
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+USGS,02085070,2018-10-30 15:45,EDT,88.9,P,2.12,P
+USGS,02085070,2018-10-30 16:00,EDT,88.9,P,2.12,P
+USGS,02085070,2018-10-30 16:15,EDT,87.2,P,2.11,P
+USGS,02085070,2018-10-30 16:30,EDT,87.2,P,2.11,P
+USGS,02085070,2018-10-30 16:45,EDT,87.2,P,2.11,P
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+USGS,02085070,2018-10-30 17:45,EDT,85.5,P,2.10,P
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+USGS,02085070,2018-10-30 18:45,EDT,85.5,P,2.10,P
+USGS,02085070,2018-10-30 19:00,EDT,85.5,P,2.10,P
+USGS,02085070,2018-10-30 19:15,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 19:30,EDT,85.5,P,2.10,P
+USGS,02085070,2018-10-30 19:45,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 20:00,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 20:15,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 20:30,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 20:45,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 21:00,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 21:15,EDT,83.8,P,2.09,P
+USGS,02085070,2018-10-30 21:30,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 21:45,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 22:00,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 22:15,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 22:30,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 22:45,EDT,82.2,P,2.08,P
+USGS,02085070,2018-10-30 23:00,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-30 23:15,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-30 23:30,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-30 23:45,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-31 00:00,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-31 00:15,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-31 00:30,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-31 00:45,EDT,80.6,P,2.07,P
+USGS,02085070,2018-10-31 01:00,EDT,79.0,P,2.06,P
+USGS,02085070,2018-10-31 01:15,EDT,79.0,P,2.06,P
+USGS,02085070,2018-10-31 01:30,EDT,79.0,P,2.06,P
+USGS,02085070,2018-10-31 01:45,EDT,79.0,P,2.06,P
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+USGS,02085070,2018-10-31 02:15,EDT,79.0,P,2.06,P
+USGS,02085070,2018-10-31 02:30,EDT,77.5,P,2.05,P
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+USGS,02085070,2018-10-31 03:30,EDT,77.5,P,2.05,P
+USGS,02085070,2018-10-31 03:45,EDT,77.5,P,2.05,P
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+USGS,02085070,2018-10-31 04:15,EDT,77.5,P,2.05,P
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+USGS,02085070,2018-10-31 06:15,EDT,75.9,P,2.04,P
+USGS,02085070,2018-10-31 06:30,EDT,75.9,P,2.04,P
+USGS,02085070,2018-10-31 06:45,EDT,75.9,P,2.04,P
+USGS,02085070,2018-10-31 07:00,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 07:15,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 07:30,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 07:45,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 08:00,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 08:15,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 08:30,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 08:45,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 09:00,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 09:15,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 09:30,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 09:45,EDT,74.4,P,2.03,P
+USGS,02085070,2018-10-31 10:00,EDT,73.0,P,2.02,P
+USGS,02085070,2018-10-31 10:15,EDT,73.0,P,2.02,P

From 3b215a0b3933da424bd56c623b74c037b405b8d7 Mon Sep 17 00:00:00 2001
From: bobaney <42747366+bobaney@users.noreply.github.com>
Date: Wed, 7 Nov 2018 12:10:50 -0500
Subject: [PATCH 3/3] azz

---
 01a-Getting-data-with-Pandas.ipynb            |   2 +-
 02b-Extract-Statewide-HUCs-with-urllib.ipynb  | 255 ++++++++++++++--
 03-Fetching-files-with-ftplib.ipynb           | 156 +++++++++-
 04-Grabbing-HTML-tables-with-Pandas.ipynb     |  14 +-
 ...Data-With-Requests-and-BeautifulSoup.ipynb |  22 +-
 ...ing-eBird-data-with-Requests-and-bs4.ipynb |  86 +++++-
 data/NWISDischarge.csv                        | 272 +++++++++---------
 7 files changed, 622 insertions(+), 185 deletions(-)

diff --git a/01a-Getting-data-with-Pandas.ipynb b/01a-Getting-data-with-Pandas.ipynb
index 5b3ca71..5e96a79 100644
--- a/01a-Getting-data-with-Pandas.ipynb
+++ b/01a-Getting-data-with-Pandas.ipynb
@@ -1040,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/02b-Extract-Statewide-HUCs-with-urllib.ipynb b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
index d97b4ee..2fb82ed 100644
--- a/02b-Extract-Statewide-HUCs-with-urllib.ipynb
+++ b/02b-Extract-Statewide-HUCs-with-urllib.ipynb
@@ -72,9 +72,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading NHD_H_Maryland_State_Shape.zip\n"
+     ]
+    }
+   ],
    "source": [
     "#Get the file (this can take a few minutes...)\n",
     "url = ftpFolder + \"/\" + stateFile\n",
@@ -94,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,8 +123,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -287,7 +297,7 @@
        "4  POLYGON ((-74.99871150652734 38.8055048012244,...  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,8 +320,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "data": {
@@ -339,38 +351,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "state = 'Delaware'"
+    "state = 'Hawaii'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Set the URLs\n",
-    "ftpFolder = \n",
-    "stateFile = "
+    "ftpFolder = 'ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/LndCvr/Shape/'\n",
+    "stateFile = 'LNDCVR_{}_State_Shape.zip'.format(state)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading LNDCVR_Hawaii_State_Shape.zip\n"
+     ]
+    }
+   ],
    "source": [
     "#Get the file (this can take a few minutes...)\n",
-    "url = \n",
-    "data = "
+    "url = ftpFolder + \"/\" + stateFile\n",
+    "if os.path.exists(stateFile):\n",
+    "    print(\"{} already downloaded\".format(stateFile))\n",
+    "else:\n",
+    "    print(\"Downloading {}\".format(stateFile))\n",
+    "data = urllib.request.urlretrieve(url,stateFile)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,9 +408,178 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>PERMANENT_</th>\n",
+       "      <th>SOURCE_FEA</th>\n",
+       "      <th>SOURCE_DAT</th>\n",
+       "      <th>SOURCE_D_1</th>\n",
+       "      <th>SOURCE_ORI</th>\n",
+       "      <th>DATA_SECUR</th>\n",
+       "      <th>DISTRIBUTI</th>\n",
+       "      <th>LOADDATE</th>\n",
+       "      <th>FCODE</th>\n",
+       "      <th>GLOBALID</th>\n",
+       "      <th>SHAPE_Leng</th>\n",
+       "      <th>SHAPE_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ec138b23-24da-42a1-aa3e-9043674bdffe</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2479fac2-9dfc-47f2-8882-db86c5eadfdb</td>\n",
+       "      <td>21160-G2</td>\n",
+       "      <td>U.S. Geological Survey</td>\n",
+       "      <td>5</td>\n",
+       "      <td>E4</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>{ACBD4216-80BA-4629-BF67-15890B28F633}</td>\n",
+       "      <td>0.019465</td>\n",
+       "      <td>1.130435e-05</td>\n",
+       "      <td>POLYGON ((-160.2437310700663 21.84114844625987...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>708fd120-1af6-43a7-8787-5a12fa911fc6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2479fac2-9dfc-47f2-8882-db86c5eadfdb</td>\n",
+       "      <td>21160-G2</td>\n",
+       "      <td>U.S. Geological Survey</td>\n",
+       "      <td>5</td>\n",
+       "      <td>E4</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>{8A288E76-A9A2-40BC-AA28-E3F9CB9C1E66}</td>\n",
+       "      <td>0.007791</td>\n",
+       "      <td>1.707514e-06</td>\n",
+       "      <td>POLYGON ((-160.2316734711267 21.86506426393106...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>424372f8-9b34-45c1-8e09-d56ecb3471b3</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2479fac2-9dfc-47f2-8882-db86c5eadfdb</td>\n",
+       "      <td>21160-G2</td>\n",
+       "      <td>U.S. Geological Survey</td>\n",
+       "      <td>5</td>\n",
+       "      <td>E4</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>{5C33A856-4AFD-4758-872C-371AC813082B}</td>\n",
+       "      <td>0.004497</td>\n",
+       "      <td>6.445749e-07</td>\n",
+       "      <td>POLYGON ((-160.193917944102 21.80772857652005,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>9191892c-0b73-49fc-8ea6-9da6422307fb</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2479fac2-9dfc-47f2-8882-db86c5eadfdb</td>\n",
+       "      <td>21160-G2</td>\n",
+       "      <td>U.S. Geological Survey</td>\n",
+       "      <td>5</td>\n",
+       "      <td>E4</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>{0C82A245-2938-492E-B375-1403943362B7}</td>\n",
+       "      <td>0.003049</td>\n",
+       "      <td>3.601925e-07</td>\n",
+       "      <td>POLYGON ((-160.1925687097291 21.84650897958488...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>e678bdc8-bf05-4dbb-b851-b259b1e49983</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2479fac2-9dfc-47f2-8882-db86c5eadfdb</td>\n",
+       "      <td>21160-H2</td>\n",
+       "      <td>U.S. Geological Survey</td>\n",
+       "      <td>5</td>\n",
+       "      <td>E4</td>\n",
+       "      <td>2016-08-15</td>\n",
+       "      <td>10600</td>\n",
+       "      <td>{523DCA8A-90BA-4DE6-909E-8BF243577332}</td>\n",
+       "      <td>0.542289</td>\n",
+       "      <td>6.538356e-04</td>\n",
+       "      <td>(POLYGON ((-160.1794233587078 21.8748177712075...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID                            PERMANENT_ SOURCE_FEA  \\\n",
+       "0         1  ec138b23-24da-42a1-aa3e-9043674bdffe       None   \n",
+       "1         2  708fd120-1af6-43a7-8787-5a12fa911fc6       None   \n",
+       "2         3  424372f8-9b34-45c1-8e09-d56ecb3471b3       None   \n",
+       "3         4  9191892c-0b73-49fc-8ea6-9da6422307fb       None   \n",
+       "4         5  e678bdc8-bf05-4dbb-b851-b259b1e49983       None   \n",
+       "\n",
+       "                             SOURCE_DAT SOURCE_D_1              SOURCE_ORI  \\\n",
+       "0  2479fac2-9dfc-47f2-8882-db86c5eadfdb   21160-G2  U.S. Geological Survey   \n",
+       "1  2479fac2-9dfc-47f2-8882-db86c5eadfdb   21160-G2  U.S. Geological Survey   \n",
+       "2  2479fac2-9dfc-47f2-8882-db86c5eadfdb   21160-G2  U.S. Geological Survey   \n",
+       "3  2479fac2-9dfc-47f2-8882-db86c5eadfdb   21160-G2  U.S. Geological Survey   \n",
+       "4  2479fac2-9dfc-47f2-8882-db86c5eadfdb   21160-H2  U.S. Geological Survey   \n",
+       "\n",
+       "   DATA_SECUR DISTRIBUTI    LOADDATE  FCODE  \\\n",
+       "0           5         E4  2016-08-15  10600   \n",
+       "1           5         E4  2016-08-15  10600   \n",
+       "2           5         E4  2016-08-15  10600   \n",
+       "3           5         E4  2016-08-15  10600   \n",
+       "4           5         E4  2016-08-15  10600   \n",
+       "\n",
+       "                                 GLOBALID  SHAPE_Leng    SHAPE_Area  \\\n",
+       "0  {ACBD4216-80BA-4629-BF67-15890B28F633}    0.019465  1.130435e-05   \n",
+       "1  {8A288E76-A9A2-40BC-AA28-E3F9CB9C1E66}    0.007791  1.707514e-06   \n",
+       "2  {5C33A856-4AFD-4758-872C-371AC813082B}    0.004497  6.445749e-07   \n",
+       "3  {0C82A245-2938-492E-B375-1403943362B7}    0.003049  3.601925e-07   \n",
+       "4  {523DCA8A-90BA-4DE6-909E-8BF243577332}    0.542289  6.538356e-04   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((-160.2437310700663 21.84114844625987...  \n",
+       "1  POLYGON ((-160.2316734711267 21.86506426393106...  \n",
+       "2  POLYGON ((-160.193917944102 21.80772857652005,...  \n",
+       "3  POLYGON ((-160.1925687097291 21.84650897958488...  \n",
+       "4  (POLYGON ((-160.1794233587078 21.8748177712075...  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#LandCover\n",
     "shp = outFolder + os.sep + 'Shape' + os.sep + 'LandCover_Woodland.shp'\n",
@@ -396,13 +589,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Plot\n",
     "gdf.plot(figsize=(20,10));"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/03-Fetching-files-with-ftplib.ipynb b/03-Fetching-files-with-ftplib.ipynb
index 3d90077..cbf6d84 100644
--- a/03-Fetching-files-with-ftplib.ipynb
+++ b/03-Fetching-files-with-ftplib.ipynb
@@ -135,7 +135,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -373,12 +375,164 @@
     "► ** So what would it take to download the same file for, say, North Carolina? Not much! **"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set the state as a variable, so it's easily changed\n",
+    "state = 'NC'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make a subfolder for the state in the outFolder, to facilitate file management\n",
+    "stateFolder = outFolder + os.sep + state\n",
+    "if not os.path.exists(stateFolder): os.mkdir(stateFolder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'230 User logged in, proceed.'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ftp = ftplib.FTP(ftpURL)\n",
+    "ftp.login(\"anonymous\",\"user@duke.edu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create a list of just North Carolina files, that is one's that end in \"_NC.zip\"\n",
+    "NC_files = ftp.nlst(\"*_NC.zip\")\n",
+    "len(NC_files) #49 files; that's better..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AgMidHiSlopes_NC.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "#First, let's get the file we want to download. We'll just get the first item in the list\n",
+    "h = NC_files[0] \n",
+    "print(h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./StreamCat\\NC\\AgMidHiSlopes_NC.zip\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'226 Transfer complete.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First we need to create a filename to which the remote contents will be written\n",
+    "outFilename = stateFolder + os.sep + h\n",
+    "print (outFilename)\n",
+    "\n",
+    "#Ok, here's the command that actually grabs the file and writes it locally\n",
+    "ftp.retrbinary(\"RETR \" + h,open(outFilename,'wb').write)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AgMidHiSlopes_NC.zip']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Did it work?\n",
+    "os.listdir(stateFolder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ftp.close()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "► ** How Might you tweak this script to grab all the RI files? **"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/04-Grabbing-HTML-tables-with-Pandas.ipynb b/04-Grabbing-HTML-tables-with-Pandas.ipynb
index 410b04b..d97aba6 100644
--- a/04-Grabbing-HTML-tables-with-Pandas.ipynb
+++ b/04-Grabbing-HTML-tables-with-Pandas.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +184,7 @@
        "4                         0.618       0.245  0.383     2.430  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -338,7 +338,7 @@
        "4                         0.618       0.245  0.383     2.430  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb b/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
index 05a5566..778ec4a 100644
--- a/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
+++ b/05a-Scraping-Data-With-Requests-and-BeautifulSoup.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +55,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-3-90976483410b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# The response object simply has the contents of the web page at the address provided\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-6-90976483410b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# The response object simply has the contents of the web page at the address provided\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;31mNameError\u001b[0m: name 'response' is not defined"
      ]
     }
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +95,7 @@
        "bs4.BeautifulSoup"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -157,7 +157,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/05b-Demo-Grabbing-eBird-data-with-Requests-and-bs4.ipynb b/05b-Demo-Grabbing-eBird-data-with-Requests-and-bs4.ipynb
index 6489ef7..e8cb816 100644
--- a/05b-Demo-Grabbing-eBird-data-with-Requests-and-bs4.ipynb
+++ b/05b-Demo-Grabbing-eBird-data-with-Requests-and-bs4.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,9 +38,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41.7411322 -111.815114 Corvus brachyrhynchos\n",
+      "41.7411322 -111.815114 Haemorhous mexicanus\n",
+      "41.7411322 -111.815114 Sturnus vulgaris\n",
+      "41.7411322 -111.815114 Junco hyemalis\n",
+      "41.7411322 -111.815114 Poecile atricapillus\n",
+      "41.7456052 -111.7483377 Cinclus mexicanus\n",
+      "41.7456052 -111.7483377 Myadestes townsendi\n",
+      "41.7456052 -111.7483377 Passeriformes sp.\n",
+      "41.7456052 -111.7483377 Anas platyrhynchos (Domestic type)\n",
+      "41.7456052 -111.7483377 Megaceryle alcyon\n",
+      "41.7391769 -111.8024755 Turdus migratorius\n",
+      "41.7391769 -111.8024755 Pipilo maculatus\n",
+      "41.7065615 -111.8743372 Spinus tristis\n",
+      "41.7065615 -111.8743372 Melospiza melodia\n",
+      "41.7065615 -111.8743372 Regulus calendula\n",
+      "41.7065615 -111.8743372 Cistothorus palustris\n",
+      "41.7065615 -111.8743372 Anas platyrhynchos\n",
+      "41.7065615 -111.8743372 Spinus psaltria\n",
+      "41.7065615 -111.8743372 Streptopelia decaocto\n",
+      "41.7065615 -111.8743372 Bombycilla cedrorum\n",
+      "41.7065615 -111.8743372 Euphagus cyanocephalus\n",
+      "41.7456052 -111.7483377 Corvus corax\n",
+      "41.7625926 -111.8646137 Falco sparverius\n",
+      "41.7625926 -111.8646137 Zonotrichia leucophrys\n",
+      "41.7625926 -111.8646137 Buteo jamaicensis\n",
+      "41.7625926 -111.8646137 Falco mexicanus\n",
+      "41.7625926 -111.8646137 Phalacrocorax auritus\n",
+      "41.7625926 -111.8646137 Haliaeetus leucocephalus\n",
+      "41.7411322 -111.815114 Colaptes auratus\n",
+      "41.7411322 -111.815114 Branta canadensis\n",
+      "41.7422935 -111.7898228 Lophodytes cucullatus\n",
+      "41.7422935 -111.7898228 Larus delawarensis\n",
+      "41.7456052 -111.7483377 Nucifraga columbiana\n",
+      "41.72127 -111.822214 Pica hudsonia\n",
+      "41.836329 -111.89354 Larus californicus\n",
+      "41.836329 -111.89354 Sturnella neglecta\n",
+      "41.836329 -111.89354 Accipiter striatus\n",
+      "41.836329 -111.89354 Columba livia\n",
+      "41.836329 -111.89354 Phasianus colchicus\n",
+      "41.836329 -111.89354 Agelaius phoeniceus\n",
+      "41.836329 -111.89354 Spatula clypeata\n",
+      "41.836329 -111.89354 Circus hudsonius\n",
+      "41.836329 -111.89354 Buteo regalis\n",
+      "41.7617487 -111.7138828 Spinus pinus\n",
+      "41.7617487 -111.7138828 Sitta canadensis\n",
+      "41.72127 -111.822214 Passer domesticus\n",
+      "41.740738 -111.8050504 Callipepla californica\n",
+      "41.740738 -111.8050504 Callipepla gambelii\n",
+      "41.7529928 -111.7253052 Aves sp.\n",
+      "41.7651148 -111.9116323 Bucephala albeola\n",
+      "41.7651148 -111.9116323 Fulica americana\n",
+      "41.7651148 -111.9116323 Oxyura jamaicensis\n",
+      "41.7651148 -111.9116323 Aythya americana\n",
+      "41.7651148 -111.9116323 Aythya affinis\n",
+      "41.781617 -111.9431734 Mareca americana\n",
+      "41.781617 -111.9431734 Aechmophorus occidentalis\n",
+      "41.781617 -111.9431734 Podilymbus podiceps\n",
+      "41.781617 -111.9431734 Anas crecca\n",
+      "41.781617 -111.9431734 Podiceps nigricollis\n",
+      "41.7625926 -111.8646137 Icteridae sp.\n",
+      "41.7625926 -111.8646137 Gallinago delicata\n",
+      "41.7625926 -111.8646137 Charadrius vociferus\n",
+      "41.79525 -112.040306 Meleagris gallopavo\n",
+      "41.729113 -111.9305992 Spatula cyanoptera\n",
+      "41.729113 -111.9305992 Mareca strepera\n",
+      "41.7062251 -111.8640697 Melospiza lincolnii\n",
+      "41.7999833 -111.8215942 Catharus guttatus\n",
+      "41.7999833 -111.8215942 Setophaga coronata\n",
+      "41.7065615 -111.8743372 Mimus polyglottos\n"
+     ]
+    }
+   ],
    "source": [
     "for sighting in soup.find_all('sighting'):\n",
     "    print (sighting.lat.text, sighting.lng.text, sighting.find('sci-name').text)"
diff --git a/data/NWISDischarge.csv b/data/NWISDischarge.csv
index 7b38ae0..63df188 100644
--- a/data/NWISDischarge.csv
+++ b/data/NWISDischarge.csv
@@ -1,140 +1,136 @@
 ,,,,,,,# 
 agency_cd,site_no,datetime,tz_cd,89062_00060,89062_00060_cd,89063_00065,89063_00065_cd
-USGS,02085070,2018-10-30 00:00,EDT,106,P,2.22,P
-USGS,02085070,2018-10-30 00:15,EDT,106,P,2.22,P
-USGS,02085070,2018-10-30 00:30,EDT,104,P,2.21,P
-USGS,02085070,2018-10-30 00:45,EDT,104,P,2.21,P
-USGS,02085070,2018-10-30 01:00,EDT,104,P,2.21,P
-USGS,02085070,2018-10-30 01:15,EDT,104,P,2.21,P
-USGS,02085070,2018-10-30 01:30,EDT,103,P,2.20,P
-USGS,02085070,2018-10-30 01:45,EDT,103,P,2.20,P
-USGS,02085070,2018-10-30 02:00,EDT,103,P,2.20,P
-USGS,02085070,2018-10-30 02:15,EDT,103,P,2.20,P
-USGS,02085070,2018-10-30 02:30,EDT,101,P,2.19,P
-USGS,02085070,2018-10-30 02:45,EDT,101,P,2.19,P
-USGS,02085070,2018-10-30 03:00,EDT,101,P,2.19,P
-USGS,02085070,2018-10-30 03:15,EDT,101,P,2.19,P
-USGS,02085070,2018-10-30 03:30,EDT,99.8,P,2.18,P
-USGS,02085070,2018-10-30 03:45,EDT,99.8,P,2.18,P
-USGS,02085070,2018-10-30 04:00,EDT,99.8,P,2.18,P
-USGS,02085070,2018-10-30 04:15,EDT,99.8,P,2.18,P
-USGS,02085070,2018-10-30 04:30,EDT,97.9,P,2.17,P
-USGS,02085070,2018-10-30 04:45,EDT,97.9,P,2.17,P
-USGS,02085070,2018-10-30 05:00,EDT,97.9,P,2.17,P
-USGS,02085070,2018-10-30 05:15,EDT,97.9,P,2.17,P
-USGS,02085070,2018-10-30 05:30,EDT,96.0,P,2.16,P
-USGS,02085070,2018-10-30 05:45,EDT,96.0,P,2.16,P
-USGS,02085070,2018-10-30 06:00,EDT,96.0,P,2.16,P
-USGS,02085070,2018-10-30 06:15,EDT,96.0,P,2.16,P
-USGS,02085070,2018-10-30 06:30,EDT,96.0,P,2.16,P
-USGS,02085070,2018-10-30 06:45,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 07:00,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 07:15,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 07:30,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 07:45,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 08:00,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 08:15,EDT,94.2,P,2.15,P
-USGS,02085070,2018-10-30 08:30,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 08:45,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 09:00,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 09:15,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 09:30,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 09:45,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 10:00,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 10:15,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 10:30,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 10:45,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 11:00,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 11:15,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 11:30,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 11:45,EDT,92.4,P,2.14,P
-USGS,02085070,2018-10-30 12:00,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 12:15,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 12:30,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 12:45,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 13:00,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 13:15,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 13:30,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 13:45,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 14:00,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 14:15,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 14:30,EDT,90.6,P,2.13,P
-USGS,02085070,2018-10-30 14:45,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 15:00,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 15:15,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 15:30,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 15:45,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 16:00,EDT,88.9,P,2.12,P
-USGS,02085070,2018-10-30 16:15,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 16:30,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 16:45,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 17:00,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 17:15,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 17:30,EDT,87.2,P,2.11,P
-USGS,02085070,2018-10-30 17:45,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 18:00,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 18:15,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 18:30,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 18:45,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 19:00,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 19:15,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 19:30,EDT,85.5,P,2.10,P
-USGS,02085070,2018-10-30 19:45,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 20:00,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 20:15,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 20:30,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 20:45,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 21:00,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 21:15,EDT,83.8,P,2.09,P
-USGS,02085070,2018-10-30 21:30,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 21:45,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 22:00,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 22:15,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 22:30,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 22:45,EDT,82.2,P,2.08,P
-USGS,02085070,2018-10-30 23:00,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-30 23:15,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-30 23:30,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-30 23:45,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-31 00:00,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-31 00:15,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-31 00:30,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-31 00:45,EDT,80.6,P,2.07,P
-USGS,02085070,2018-10-31 01:00,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 01:15,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 01:30,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 01:45,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 02:00,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 02:15,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 02:30,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 02:45,EDT,79.0,P,2.06,P
-USGS,02085070,2018-10-31 03:00,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 03:15,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 03:30,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 03:45,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 04:00,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 04:15,EDT,77.5,P,2.05,P
-USGS,02085070,2018-10-31 04:30,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 04:45,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 05:00,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 05:15,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 05:30,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 05:45,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 06:00,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 06:15,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 06:30,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 06:45,EDT,75.9,P,2.04,P
-USGS,02085070,2018-10-31 07:00,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 07:15,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 07:30,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 07:45,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 08:00,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 08:15,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 08:30,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 08:45,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 09:00,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 09:15,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 09:30,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 09:45,EDT,74.4,P,2.03,P
-USGS,02085070,2018-10-31 10:00,EDT,73.0,P,2.02,P
-USGS,02085070,2018-10-31 10:15,EDT,73.0,P,2.02,P
+USGS,02085070,2018-11-06 00:00,EST,808,P,4.32,P
+USGS,02085070,2018-11-06 00:15,EST,804,P,4.31,P
+USGS,02085070,2018-11-06 00:30,EST,790,P,4.27,P
+USGS,02085070,2018-11-06 00:45,EST,779,P,4.24,P
+USGS,02085070,2018-11-06 01:00,EST,772,P,4.22,P
+USGS,02085070,2018-11-06 01:15,EST,768,P,4.21,P
+USGS,02085070,2018-11-06 01:30,EST,755,P,4.18,P
+USGS,02085070,2018-11-06 01:45,EST,750,P,4.17,P
+USGS,02085070,2018-11-06 02:00,EST,732,P,4.13,P
+USGS,02085070,2018-11-06 02:15,EST,728,P,4.12,P
+USGS,02085070,2018-11-06 02:30,EST,719,P,4.10,P
+USGS,02085070,2018-11-06 02:45,EST,711,P,4.08,P
+USGS,02085070,2018-11-06 03:00,EST,702,P,4.06,P
+USGS,02085070,2018-11-06 03:15,EST,694,P,4.04,P
+USGS,02085070,2018-11-06 03:30,EST,685,P,4.02,P
+USGS,02085070,2018-11-06 03:45,EST,685,P,4.02,P
+USGS,02085070,2018-11-06 04:00,EST,673,P,3.99,P
+USGS,02085070,2018-11-06 04:15,EST,669,P,3.98,P
+USGS,02085070,2018-11-06 04:30,EST,656,P,3.95,P
+USGS,02085070,2018-11-06 04:45,EST,660,P,3.96,P
+USGS,02085070,2018-11-06 05:00,EST,652,P,3.94,P
+USGS,02085070,2018-11-06 05:15,EST,644,P,3.92,P
+USGS,02085070,2018-11-06 05:30,EST,640,P,3.91,P
+USGS,02085070,2018-11-06 05:45,EST,632,P,3.89,P
+USGS,02085070,2018-11-06 06:00,EST,624,P,3.87,P
+USGS,02085070,2018-11-06 06:15,EST,620,P,3.86,P
+USGS,02085070,2018-11-06 06:30,EST,613,P,3.84,P
+USGS,02085070,2018-11-06 06:45,EST,609,P,3.83,P
+USGS,02085070,2018-11-06 07:00,EST,601,P,3.81,P
+USGS,02085070,2018-11-06 07:15,EST,601,P,3.81,P
+USGS,02085070,2018-11-06 07:30,EST,594,P,3.79,P
+USGS,02085070,2018-11-06 07:45,EST,582,P,3.76,P
+USGS,02085070,2018-11-06 08:00,EST,582,P,3.76,P
+USGS,02085070,2018-11-06 08:15,EST,575,P,3.74,P
+USGS,02085070,2018-11-06 08:30,EST,571,P,3.73,P
+USGS,02085070,2018-11-06 08:45,EST,560,P,3.70,P
+USGS,02085070,2018-11-06 09:00,EST,560,P,3.70,P
+USGS,02085070,2018-11-06 09:15,EST,553,P,3.68,P
+USGS,02085070,2018-11-06 09:30,EST,549,P,3.67,P
+USGS,02085070,2018-11-06 09:45,EST,546,P,3.66,P
+USGS,02085070,2018-11-06 10:00,EST,535,P,3.63,P
+USGS,02085070,2018-11-06 10:15,EST,532,P,3.62,P
+USGS,02085070,2018-11-06 10:30,EST,525,P,3.60,P
+USGS,02085070,2018-11-06 10:45,EST,521,P,3.59,P
+USGS,02085070,2018-11-06 11:00,EST,513,P,3.57,P
+USGS,02085070,2018-11-06 11:15,EST,505,P,3.55,P
+USGS,02085070,2018-11-06 11:30,EST,505,P,3.55,P
+USGS,02085070,2018-11-06 11:45,EST,497,P,3.53,P
+USGS,02085070,2018-11-06 12:00,EST,493,P,3.52,P
+USGS,02085070,2018-11-06 12:15,EST,481,P,3.49,P
+USGS,02085070,2018-11-06 12:30,EST,485,P,3.50,P
+USGS,02085070,2018-11-06 12:45,EST,473,P,3.47,P
+USGS,02085070,2018-11-06 13:00,EST,473,P,3.47,P
+USGS,02085070,2018-11-06 13:15,EST,465,P,3.45,P
+USGS,02085070,2018-11-06 13:30,EST,461,P,3.44,P
+USGS,02085070,2018-11-06 13:45,EST,458,P,3.43,P
+USGS,02085070,2018-11-06 14:00,EST,450,P,3.41,P
+USGS,02085070,2018-11-06 14:15,EST,446,P,3.40,P
+USGS,02085070,2018-11-06 14:30,EST,446,P,3.40,P
+USGS,02085070,2018-11-06 14:45,EST,435,P,3.37,P
+USGS,02085070,2018-11-06 15:00,EST,439,P,3.38,P
+USGS,02085070,2018-11-06 15:15,EST,435,P,3.37,P
+USGS,02085070,2018-11-06 15:30,EST,432,P,3.36,P
+USGS,02085070,2018-11-06 15:45,EST,428,P,3.35,P
+USGS,02085070,2018-11-06 16:00,EST,417,P,3.32,P
+USGS,02085070,2018-11-06 16:15,EST,417,P,3.32,P
+USGS,02085070,2018-11-06 16:30,EST,417,P,3.32,P
+USGS,02085070,2018-11-06 16:45,EST,414,P,3.31,P
+USGS,02085070,2018-11-06 17:00,EST,407,P,3.29,P
+USGS,02085070,2018-11-06 17:15,EST,407,P,3.29,P
+USGS,02085070,2018-11-06 17:30,EST,400,P,3.27,P
+USGS,02085070,2018-11-06 17:45,EST,400,P,3.27,P
+USGS,02085070,2018-11-06 18:00,EST,396,P,3.26,P
+USGS,02085070,2018-11-06 18:15,EST,393,P,3.25,P
+USGS,02085070,2018-11-06 18:30,EST,390,P,3.24,P
+USGS,02085070,2018-11-06 18:45,EST,390,P,3.24,P
+USGS,02085070,2018-11-06 19:00,EST,379,P,3.21,P
+USGS,02085070,2018-11-06 19:15,EST,383,P,3.22,P
+USGS,02085070,2018-11-06 19:30,EST,375,P,3.20,P
+USGS,02085070,2018-11-06 19:45,EST,375,P,3.20,P
+USGS,02085070,2018-11-06 20:00,EST,371,P,3.19,P
+USGS,02085070,2018-11-06 20:15,EST,367,P,3.18,P
+USGS,02085070,2018-11-06 20:30,EST,364,P,3.17,P
+USGS,02085070,2018-11-06 20:45,EST,360,P,3.16,P
+USGS,02085070,2018-11-06 21:00,EST,356,P,3.15,P
+USGS,02085070,2018-11-06 21:15,EST,352,P,3.14,P
+USGS,02085070,2018-11-06 21:30,EST,352,P,3.14,P
+USGS,02085070,2018-11-06 21:45,EST,348,P,3.13,P
+USGS,02085070,2018-11-06 22:00,EST,345,P,3.12,P
+USGS,02085070,2018-11-06 22:15,EST,345,P,3.12,P
+USGS,02085070,2018-11-06 22:30,EST,337,P,3.10,P
+USGS,02085070,2018-11-06 22:45,EST,337,P,3.10,P
+USGS,02085070,2018-11-06 23:00,EST,337,P,3.10,P
+USGS,02085070,2018-11-06 23:15,EST,334,P,3.09,P
+USGS,02085070,2018-11-06 23:30,EST,330,P,3.08,P
+USGS,02085070,2018-11-06 23:45,EST,330,P,3.08,P
+USGS,02085070,2018-11-07 00:00,EST,330,P,3.08,P
+USGS,02085070,2018-11-07 00:15,EST,323,P,3.06,P
+USGS,02085070,2018-11-07 00:30,EST,326,P,3.07,P
+USGS,02085070,2018-11-07 00:45,EST,316,P,3.04,P
+USGS,02085070,2018-11-07 01:00,EST,316,P,3.04,P
+USGS,02085070,2018-11-07 01:15,EST,316,P,3.04,P
+USGS,02085070,2018-11-07 01:30,EST,316,P,3.04,P
+USGS,02085070,2018-11-07 01:45,EST,309,P,3.02,P
+USGS,02085070,2018-11-07 02:00,EST,312,P,3.03,P
+USGS,02085070,2018-11-07 02:15,EST,309,P,3.02,P
+USGS,02085070,2018-11-07 02:30,EST,309,P,3.02,P
+USGS,02085070,2018-11-07 02:45,EST,306,P,3.01,P
+USGS,02085070,2018-11-07 03:00,EST,302,P,3.00,P
+USGS,02085070,2018-11-07 03:15,EST,302,P,3.00,P
+USGS,02085070,2018-11-07 03:30,EST,302,P,3.00,P
+USGS,02085070,2018-11-07 03:45,EST,299,P,2.99,P
+USGS,02085070,2018-11-07 04:00,EST,299,P,2.99,P
+USGS,02085070,2018-11-07 04:15,EST,292,P,2.97,P
+USGS,02085070,2018-11-07 04:30,EST,292,P,2.97,P
+USGS,02085070,2018-11-07 04:45,EST,292,P,2.97,P
+USGS,02085070,2018-11-07 05:00,EST,289,P,2.96,P
+USGS,02085070,2018-11-07 05:15,EST,285,P,2.95,P
+USGS,02085070,2018-11-07 05:30,EST,285,P,2.95,P
+USGS,02085070,2018-11-07 05:45,EST,285,P,2.95,P
+USGS,02085070,2018-11-07 06:00,EST,282,P,2.94,P
+USGS,02085070,2018-11-07 06:15,EST,282,P,2.94,P
+USGS,02085070,2018-11-07 06:30,EST,282,P,2.94,P
+USGS,02085070,2018-11-07 06:45,EST,276,P,2.92,P
+USGS,02085070,2018-11-07 07:00,EST,276,P,2.92,P
+USGS,02085070,2018-11-07 07:15,EST,272,P,2.91,P
+USGS,02085070,2018-11-07 07:30,EST,269,P,2.90,P
+USGS,02085070,2018-11-07 07:45,EST,269,P,2.90,P
+USGS,02085070,2018-11-07 08:00,EST,266,P,2.89,P
+USGS,02085070,2018-11-07 08:15,EST,269,P,2.90,P
+USGS,02085070,2018-11-07 08:30,EST,266,P,2.89,P
+USGS,02085070,2018-11-07 08:45,EST,263,P,2.88,P
+USGS,02085070,2018-11-07 09:00,EST,263,P,2.88,P
+USGS,02085070,2018-11-07 09:15,EST,257,P,2.86,P