asthma_chemLevels_correlation_Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "file_path1 = \"asthma_mort_states_2016.csv\"\n",
    "asthma_data = pd.read_csv(file_path1)\n",
    "\n",
    "file_path2 = \"../CSV/Chem_mean_by_State.csv\"\n",
    "chem_data = pd.read_csv(file_path2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>61</td>\n",
       "      <td>36</td>\n",
       "      <td>57</td>\n",
       "      <td>42</td>\n",
       "      <td>57</td>\n",
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       "      <td>48</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Alaska</td>\n",
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       "      <td></td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>58</td>\n",
       "      <td>92</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>86</td>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
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       "      <th>3</th>\n",
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       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
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       "      <td>California</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>384</td>\n",
       "      <td>410</td>\n",
       "      <td>407</td>\n",
       "      <td>393</td>\n",
       "      <td>391</td>\n",
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      "text/plain": [
       "   Unnamed: 0 LocationDesc 2010 2011 2012 2013 2014 2015 2016\n",
       "0           0      Alabama   61   36   57   42   57   52   48\n",
       "1           1       Alaska                           NaN  NaN\n",
       "2           2      Arizona   58   92   80   98   86   76  108\n",
       "3           3     Arkansas   37   46   30   36   33   30   45\n",
       "4           4   California  403  403  384  410  407  393  391"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/PythonData/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0        int64\n",
       "LocationDesc     object\n",
       "2010            float64\n",
       "2011            float64\n",
       "2012            float64\n",
       "2013            float64\n",
       "2014            float64\n",
       "2015            float64\n",
       "2016            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_data.dtypes\n",
    "asthma_data = asthma_data.convert_objects(convert_numeric=True)\n",
    "asthma_data.dtypes\n",
    "\n",
    "#extract cols\n",
    "#asthma_data_df = asthma_data[['State', 'Year','Asthma Mortality']]\n",
    "#dropnas\n",
    "#asthma_data_df = asthma_data_df.dropna(how = 'any')\n",
    "\n",
    "#asthma_data_df.tail()\n",
    "\n",
    "#asthma_data = asthma_data.set_index('LocationDesc')\n",
    "#asthma_data = asthma_data.groupby(\"LocationDesc\").mean()\n",
    "#asthma_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Alabama</td>\n",
       "      <td>61.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>108.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>403.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 LocationDesc   2010   2011   2012   2013   2014   2015   2016\n",
       "0           0      Alabama   61.0   36.0   57.0   42.0   57.0   52.0   48.0\n",
       "1           1       Alaska    NaN    NaN    NaN    NaN    NaN    NaN    NaN\n",
       "2           2      Arizona   58.0   92.0   80.0   98.0   86.0   76.0  108.0\n",
       "3           3     Arkansas   37.0   46.0   30.0   36.0   33.0   30.0   45.0\n",
       "4           4   California  403.0  403.0  384.0  410.0  407.0  393.0  391.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>Colorado</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Unnamed: 0  2010  2011  2012  2013  2014  2015  2016\n",
       "LocationDesc                                                              \n",
       "Alabama                        1     1     1     1     1     1     1     1\n",
       "Alaska                         1     0     0     0     0     0     0     0\n",
       "Arizona                        1     1     1     1     1     1     1     1\n",
       "Arkansas                       1     1     1     1     1     1     1     1\n",
       "California                     1     1     1     1     1     1     1     1\n",
       "Colorado                       1     1     1     1     1     1     1     1\n",
       "Connecticut                    1     1     1     1     1     1     1     1\n",
       "Delaware                       1     0     0     0     0     0     1     1\n",
       "District of Columbia           1     0     0     0     0     0     1     1\n",
       "Florida                        1     1     1     1     1     1     1     1\n",
       "Georgia                        1     1     1     1     1     1     1     1\n",
       "Hawaii                         1     0     1     0     1     1     1     1\n",
       "Idaho                          1     1     0     1     1     1     1     1\n",
       "Illinois                       1     1     1     1     1     1     1     1\n",
       "Indiana                        1     1     1     1     1     1     1     1\n",
       "Iowa                           1     1     1     1     1     1     1     1\n",
       "Kansas                         1     1     1     1     1     1     1     1\n",
       "Kentucky                       1     1     1     1     1     1     1     1\n",
       "Louisiana                      1     1     1     1     1     1     1     1\n",
       "Maine                          1     0     0     0     0     0     1     0\n",
       "Maryland                       1     1     1     1     1     1     1     1\n",
       "Massachusetts                  1     1     1     1     1     1     1     1\n",
       "Michigan                       1     1     1     1     1     1     1     1\n",
       "Minnesota                      1     1     1     1     1     1     1     1\n",
       "Mississippi                    1     1     1     1     1     1     1     1\n",
       "Missouri                       1     1     1     1     1     1     1     1\n",
       "Montana                        1     0     0     0     0     0     1     1\n",
       "Nebraska                       1     1     1     1     1     1     1     1\n",
       "Nevada                         1     1     1     1     1     1     1     1\n",
       "New Hampshire                  1     0     0     0     0     0     1     1\n",
       "New Jersey                     1     1     1     1     1     1     1     1\n",
       "New Mexico                     1     1     1     1     0     1     1     1\n",
       "New York                       1     1     1     1     1     1     1     1\n",
       "North Carolina                 1     1     1     1     1     1     1     1\n",
       "North Dakota                   1     0     0     0     0     0     0     1\n",
       "Ohio                           1     1     1     1     1     1     1     1\n",
       "Oklahoma                       1     1     1     1     1     1     1     1\n",
       "Oregon                         1     1     1     1     1     1     1     1\n",
       "Pennsylvania                   1     1     1     1     1     1     1     1\n",
       "Rhode Island                   1     0     0     0     0     0     0     0\n",
       "South Carolina                 1     1     1     1     1     1     1     1\n",
       "South Dakota                   1     0     1     0     0     0     1     1\n",
       "Tennessee                      1     1     1     1     1     1     1     1\n",
       "Texas                          1     1     1     1     1     1     1     1\n",
       "United States                  1     1     1     1     1     1     0     0\n",
       "Utah                           1     1     1     1     1     1     1     1\n",
       "Vermont                        1     0     0     0     0     0     1     1\n",
       "Virginia                       1     1     1     1     1     1     1     1\n",
       "Washington                     1     1     1     1     1     1     1     1\n",
       "West Virginia                  1     1     1     0     1     0     1     1\n",
       "Wisconsin                      1     1     1     1     1     1     1     1\n",
       "Wyoming                        1     0     0     0     0     0     0     0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_data.groupby(\"LocationDesc\").count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>LocationDesc</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>61.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>58.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>403.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>177.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>97.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>183.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>69.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>50.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>105.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>73.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>49.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>80.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>283.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>101.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>151.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>60.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>132.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>66.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>67.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>192.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>United States</td>\n",
       "      <td>3404.0</td>\n",
       "      <td>3345.0</td>\n",
       "      <td>3531.0</td>\n",
       "      <td>3630.0</td>\n",
       "      <td>3651.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Utah</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>69.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Washington</td>\n",
       "      <td>75.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LocationDesc    2010    2011    2012    2013    2014   2015   2016\n",
       "0                Alabama    61.0    36.0    57.0    42.0    57.0   52.0   48.0\n",
       "1                 Alaska     NaN     NaN     NaN     NaN     NaN    NaN    NaN\n",
       "2                Arizona    58.0    92.0    80.0    98.0    86.0   76.0  108.0\n",
       "3               Arkansas    37.0    46.0    30.0    36.0    33.0   30.0   45.0\n",
       "4             California   403.0   403.0   384.0   410.0   407.0  393.0  391.0\n",
       "5               Colorado    48.0    42.0    55.0    51.0    41.0   46.0   46.0\n",
       "6            Connecticut    45.0    24.0    39.0    36.0    36.0   48.0   44.0\n",
       "7               Delaware     NaN     NaN     NaN     NaN     NaN   13.0   11.0\n",
       "8   District of Columbia     NaN     NaN     NaN     NaN     NaN   13.0   10.0\n",
       "9                Florida   177.0   148.0   195.0   188.0   191.0  175.0  184.0\n",
       "10               Georgia    97.0    96.0    93.0   101.0    96.0   92.0   93.0\n",
       "11                Hawaii     NaN    26.0     NaN    24.0    24.0   17.0   15.0\n",
       "12                 Idaho    21.0     NaN    21.0    23.0    22.0   21.0   21.0\n",
       "13              Illinois   183.0   176.0   174.0   192.0   167.0  157.0  149.0\n",
       "14               Indiana    69.0    73.0    72.0    69.0    73.0   90.0   55.0\n",
       "15                  Iowa    46.0    43.0    35.0    36.0    54.0   41.0   34.0\n",
       "16                Kansas    33.0    25.0    26.0    32.0    27.0   29.0   41.0\n",
       "17              Kentucky    44.0    33.0    25.0    36.0    35.0   38.0   48.0\n",
       "18             Louisiana    36.0    37.0    40.0    56.0    61.0   47.0   42.0\n",
       "19                 Maine     NaN     NaN     NaN     NaN     NaN   11.0    NaN\n",
       "20              Maryland    71.0    71.0    91.0    71.0    87.0   79.0   79.0\n",
       "21         Massachusetts    50.0    69.0    58.0    67.0    76.0   84.0   86.0\n",
       "22              Michigan   105.0   113.0   107.0   110.0    94.0  112.0   93.0\n",
       "23             Minnesota    73.0    60.0    72.0    79.0    66.0   61.0   76.0\n",
       "24           Mississippi    49.0    54.0    45.0    61.0    48.0   52.0   65.0\n",
       "25              Missouri    76.0    48.0    75.0    53.0    89.0   72.0   73.0\n",
       "26               Montana     NaN     NaN     NaN     NaN     NaN   12.0   16.0\n",
       "27              Nebraska    26.0    32.0    26.0    25.0    30.0   36.0   28.0\n",
       "28                Nevada    27.0    20.0    20.0    27.0    25.0   24.0   37.0\n",
       "29         New Hampshire     NaN     NaN     NaN     NaN     NaN   16.0   11.0\n",
       "30            New Jersey    80.0    94.0   103.0   134.0   111.0  116.0  102.0\n",
       "31            New Mexico    22.0    26.0    27.0     NaN    28.0   26.0   27.0\n",
       "32              New York   283.0   257.0   284.0   283.0   302.0  295.0  273.0\n",
       "33        North Carolina   101.0    99.0   123.0   112.0   106.0   96.0  103.0\n",
       "34          North Dakota     NaN     NaN     NaN     NaN     NaN    NaN   11.0\n",
       "35                  Ohio   151.0   127.0   145.0   130.0   116.0  159.0  133.0\n",
       "36              Oklahoma    34.0    44.0    54.0    50.0    56.0   52.0   43.0\n",
       "37                Oregon    60.0    46.0    56.0    62.0    77.0   53.0   75.0\n",
       "38          Pennsylvania   132.0   131.0   146.0   157.0   149.0  155.0  128.0\n",
       "39          Rhode Island     NaN     NaN     NaN     NaN     NaN    NaN    NaN\n",
       "40        South Carolina    66.0    70.0    81.0    61.0    74.0   57.0   60.0\n",
       "41          South Dakota     NaN    20.0     NaN     NaN     NaN   13.0   16.0\n",
       "42             Tennessee    67.0    69.0    58.0    62.0    64.0   64.0   78.0\n",
       "43                 Texas   192.0   195.0   206.0   213.0   212.0  241.0  196.0\n",
       "44         United States  3404.0  3345.0  3531.0  3630.0  3651.0    NaN    NaN\n",
       "45                  Utah    28.0    44.0    28.0    31.0    31.0   40.0   36.0\n",
       "46               Vermont     NaN     NaN     NaN     NaN     NaN   11.0   13.0\n",
       "47              Virginia    69.0    89.0   107.0    87.0   103.0   90.0   81.0\n",
       "48            Washington    75.0    82.0    77.0    87.0    93.0   88.0   78.0\n",
       "49         West Virginia    24.0    22.0     NaN    30.0     NaN   25.0   25.0\n",
       "50             Wisconsin    63.0    64.0    78.0    75.0    77.0   66.0   66.0\n",
       "51               Wyoming     NaN     NaN     NaN     NaN     NaN    NaN    NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_data = asthma_data[['LocationDesc', '2010','2011', '2012', '2013','2014','2015', '2016']]\n",
    "asthma_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LocationDesc', '2010', '2011', '2012', '2013', '2014', '2015', '2016'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#asthma_data.groupby(\"LocationDesc\")[[2010,2011,2012, 2013,2014,2015,2016]].mean()\n",
    "asthma_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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",
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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>LocationDesc</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2010</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2010</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2010</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2010</td>\n",
       "      <td>403.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LocationDesc variable  value\n",
       "0      Alabama     2010   61.0\n",
       "1       Alaska     2010    NaN\n",
       "2      Arizona     2010   58.0\n",
       "3     Arkansas     2010   37.0\n",
       "4   California     2010  403.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melt columns\n",
    "asthma_melted_df=pd.melt(asthma_data, id_vars=['LocationDesc'], value_vars=['2010','2011','2012','2013','2014','2015','2016'])\n",
    "asthma_melted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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</th>\n",
       "      <th>Year</th>\n",
       "      <th>Asthma Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2010</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2010</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2010</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2010</td>\n",
       "      <td>403.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year  Asthma Mortality\n",
       "0     Alabama  2010              61.0\n",
       "1      Alaska  2010               NaN\n",
       "2     Arizona  2010              58.0\n",
       "3    Arkansas  2010              37.0\n",
       "4  California  2010             403.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns\n",
    "asthma_melted_df = asthma_melted_df.rename(index=str, columns={\"LocationDesc\": \"State\", \"variable\": \"Year\", \"value\":\"Asthma Mortality\"})\n",
    "asthma_melted_df.head()\n",
    "asthma_melted_df.to_csv('asthma_mort_states_melted.csv', index=True, header=True)\n",
    "asthma_melted_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>Asthma Mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Asthma Mortality\n",
       "State                       \n",
       "Alabama            50.428571\n",
       "Alaska                   NaN\n",
       "Arizona            85.428571\n",
       "Arkansas           36.714286\n",
       "California        398.714286"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Avg_asthma_byState = pd.DataFrame(asthma_melted_df.groupby(\"State\")[\"Asthma Mortality\"].mean())\n",
    "Avg_asthma_byState.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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</th>\n",
       "      <th>Asthma Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>38.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>179.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>95.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>171.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>71.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>41.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>30.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>45.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>78.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>104.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>69.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>53.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>69.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>25.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>105.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>282.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>105.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>137.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>47.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>61.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>142.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>16.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>United States</td>\n",
       "      <td>3512.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Utah</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Washington</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>25.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>69.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State  Asthma Mortality\n",
       "0                Alabama         50.428571\n",
       "1                 Alaska               NaN\n",
       "2                Arizona         85.428571\n",
       "3               Arkansas         36.714286\n",
       "4             California        398.714286\n",
       "5               Colorado         47.000000\n",
       "6            Connecticut         38.857143\n",
       "7               Delaware         12.000000\n",
       "8   District of Columbia         11.500000\n",
       "9                Florida        179.714286\n",
       "10               Georgia         95.428571\n",
       "11                Hawaii         21.200000\n",
       "12                 Idaho         21.500000\n",
       "13              Illinois        171.142857\n",
       "14               Indiana         71.571429\n",
       "15                  Iowa         41.285714\n",
       "16                Kansas         30.428571\n",
       "17              Kentucky         37.000000\n",
       "18             Louisiana         45.571429\n",
       "19                 Maine         11.000000\n",
       "20              Maryland         78.428571\n",
       "21         Massachusetts         70.000000\n",
       "22              Michigan        104.857143\n",
       "23             Minnesota         69.571429\n",
       "24           Mississippi         53.428571\n",
       "25              Missouri         69.428571\n",
       "26               Montana         14.000000\n",
       "27              Nebraska         29.000000\n",
       "28                Nevada         25.714286\n",
       "29         New Hampshire         13.500000\n",
       "30            New Jersey        105.714286\n",
       "31            New Mexico         26.000000\n",
       "32              New York        282.428571\n",
       "33        North Carolina        105.714286\n",
       "34          North Dakota         11.000000\n",
       "35                  Ohio        137.285714\n",
       "36              Oklahoma         47.571429\n",
       "37                Oregon         61.285714\n",
       "38          Pennsylvania        142.571429\n",
       "39          Rhode Island               NaN\n",
       "40        South Carolina         67.000000\n",
       "41          South Dakota         16.333333\n",
       "42             Tennessee         66.000000\n",
       "43                 Texas        207.857143\n",
       "44         United States       3512.200000\n",
       "45                  Utah         34.000000\n",
       "46               Vermont         12.000000\n",
       "47              Virginia         89.428571\n",
       "48            Washington         82.857143\n",
       "49         West Virginia         25.200000\n",
       "50             Wisconsin         69.857143\n",
       "51               Wyoming               NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Avg_asthma_byState.reset_index(inplace=True)\n",
    "Avg_asthma_byState.reset_index(inplace=True)\n",
    "\n",
    "Avg_asthma_byState"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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</th>\n",
       "      <th>Asthma Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Asthma Mortality\n",
       "0     Alabama         50.428571\n",
       "1      Alaska               NaN\n",
       "2     Arizona         85.428571\n",
       "3    Arkansas         36.714286\n",
       "4  California        398.714286"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove United States row\n",
    "Avg_asthma_noUS = Avg_asthma_byState.loc[Avg_asthma_byState['State'] != 'United States']\n",
    "Avg_asthma_noUS.head()\n",
    "# plt.plot(Avg_asthma_byState['State'], Avg_asthma_byState['Asthma Mortality'], marker='o')\n",
    "# plt.title(\"Asthma vs State\")\n",
    "# plt.xlabel(\"State\")\n",
    "# plt.ylabel(\"Asthma\")\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Avg_asthma_noUS.plot(x='State', y='Asthma Mortality', kind=\"bar\")\n",
    "plt.title(\"Asthma per State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel(\"Asthma Mortality Num\")\n",
    "plt.grid()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>NO2 Mean</th>\n",
       "      <th>O3 Mean</th>\n",
       "      <th>SO2 Mean</th>\n",
       "      <th>CO Mean</th>\n",
       "      <th>State Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2010</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.015912</td>\n",
       "      <td>0.025911</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>0.367078</td>\n",
       "      <td>0.102489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2010</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.010005</td>\n",
       "      <td>0.027650</td>\n",
       "      <td>0.001304</td>\n",
       "      <td>0.456337</td>\n",
       "      <td>0.123824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2010</td>\n",
       "      <td>California</td>\n",
       "      <td>0.012243</td>\n",
       "      <td>0.025671</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.346204</td>\n",
       "      <td>0.096212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2010</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>0.016170</td>\n",
       "      <td>0.022951</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.366873</td>\n",
       "      <td>0.101752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2010</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>0.009739</td>\n",
       "      <td>0.027474</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.280659</td>\n",
       "      <td>0.079710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year        State  NO2 Mean   O3 Mean  SO2 Mean   CO Mean  State Average\n",
       "238  2010      Arizona  0.015912  0.025911  0.001057  0.367078       0.102489\n",
       "239  2010     Arkansas  0.010005  0.027650  0.001304  0.456337       0.123824\n",
       "240  2010   California  0.012243  0.025671  0.000730  0.346204       0.096212\n",
       "241  2010     Colorado  0.016170  0.022951  0.001015  0.366873       0.101752\n",
       "242  2010  Connecticut  0.009739  0.027474  0.000968  0.280659       0.079710"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter dataset by year:2010 to 2016\n",
    "chem_data.head()\n",
    "cfilt=chem_data\n",
    "cfilt=cfilt[cfilt['Year'].str.contains('2010|2011|2012|2013|2014|2015|2016')]\n",
    "\n",
    "cfilt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/PythonData/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <th>CO Mean</th>\n",
       "      <th>NO2 Mean</th>\n",
       "      <th>O3 Mean</th>\n",
       "      <th>SO2 Mean</th>\n",
       "      <th>State Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>0.212376</td>\n",
       "      <td>0.010020</td>\n",
       "      <td>0.022270</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.061408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0.431045</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>0.115193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>0.372598</td>\n",
       "      <td>0.015777</td>\n",
       "      <td>0.026297</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.103862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>0.390687</td>\n",
       "      <td>0.009498</td>\n",
       "      <td>0.026109</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.106869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.349392</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.026368</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.096824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0.364444</td>\n",
       "      <td>0.018420</td>\n",
       "      <td>0.025505</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.102372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0.223108</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.029887</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.065429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Of Mexico</th>\n",
       "      <td>0.864252</td>\n",
       "      <td>0.020489</td>\n",
       "      <td>0.019515</td>\n",
       "      <td>0.002781</td>\n",
       "      <td>0.226759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>0.265640</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.026699</td>\n",
       "      <td>0.000989</td>\n",
       "      <td>0.076244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District Of Columbia</th>\n",
       "      <td>0.482873</td>\n",
       "      <td>0.014188</td>\n",
       "      <td>0.026657</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.131396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>0.437028</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>0.027269</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.117374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0.328268</td>\n",
       "      <td>0.011850</td>\n",
       "      <td>0.020048</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.090176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>0.368768</td>\n",
       "      <td>0.003179</td>\n",
       "      <td>0.025443</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.099606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>0.226237</td>\n",
       "      <td>0.009663</td>\n",
       "      <td>0.019712</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.064035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>0.313479</td>\n",
       "      <td>0.011056</td>\n",
       "      <td>0.025347</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>0.087820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>0.231732</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>0.029609</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.068541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>0.215234</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.028443</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.062718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>0.375179</td>\n",
       "      <td>0.011963</td>\n",
       "      <td>0.024427</td>\n",
       "      <td>0.002595</td>\n",
       "      <td>0.103541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>0.294861</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>0.019821</td>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.081756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>0.230845</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>0.024697</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.066948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>0.250777</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.025725</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.070627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>0.261214</td>\n",
       "      <td>0.009933</td>\n",
       "      <td>0.027030</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.074866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>0.277181</td>\n",
       "      <td>0.016384</td>\n",
       "      <td>0.021823</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.079117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>0.229175</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.028439</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.066252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>0.276651</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.023234</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.078508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>0.289366</td>\n",
       "      <td>0.016240</td>\n",
       "      <td>0.027198</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.083309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0.258519</td>\n",
       "      <td>0.004931</td>\n",
       "      <td>0.029212</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.073390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>0.297169</td>\n",
       "      <td>0.016597</td>\n",
       "      <td>0.024081</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>0.084693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>0.211213</td>\n",
       "      <td>0.012335</td>\n",
       "      <td>0.031701</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.063969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>0.295603</td>\n",
       "      <td>0.016289</td>\n",
       "      <td>0.025002</td>\n",
       "      <td>0.001922</td>\n",
       "      <td>0.084704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>0.288254</td>\n",
       "      <td>0.008716</td>\n",
       "      <td>0.027723</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.081276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0.171340</td>\n",
       "      <td>0.004670</td>\n",
       "      <td>0.026292</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.050632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.275638</td>\n",
       "      <td>0.011503</td>\n",
       "      <td>0.026141</td>\n",
       "      <td>0.002516</td>\n",
       "      <td>0.078949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>0.209420</td>\n",
       "      <td>0.006907</td>\n",
       "      <td>0.029935</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.061739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.294798</td>\n",
       "      <td>0.009119</td>\n",
       "      <td>0.019478</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.081064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>0.193798</td>\n",
       "      <td>0.009523</td>\n",
       "      <td>0.026269</td>\n",
       "      <td>0.002010</td>\n",
       "      <td>0.057900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>0.222838</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.030458</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.065191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>0.123180</td>\n",
       "      <td>0.005369</td>\n",
       "      <td>0.031170</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.040043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>0.187796</td>\n",
       "      <td>0.005257</td>\n",
       "      <td>0.029653</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.055800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>0.180008</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.039955</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.055310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.199889</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>0.026160</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.059141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.377364</td>\n",
       "      <td>0.017066</td>\n",
       "      <td>0.027837</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.105689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0.249607</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>0.029793</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.071951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>0.218829</td>\n",
       "      <td>0.011286</td>\n",
       "      <td>0.020511</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>0.062806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>0.091880</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.037879</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.033389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         value                                            \n",
       "variable               CO Mean  NO2 Mean   O3 Mean  SO2 Mean State Average\n",
       "State                                                                     \n",
       "Alabama               0.212376  0.010020  0.022270  0.000964      0.061408\n",
       "Alaska                0.431045  0.011403  0.012254  0.006070      0.115193\n",
       "Arizona               0.372598  0.015777  0.026297  0.000777      0.103862\n",
       "Arkansas              0.390687  0.009498  0.026109  0.001183      0.106869\n",
       "California            0.349392  0.010945  0.026368  0.000589      0.096824\n",
       "Colorado              0.364444  0.018420  0.025505  0.001121      0.102372\n",
       "Connecticut           0.223108  0.008041  0.029887  0.000678      0.065429\n",
       "Country Of Mexico     0.864252  0.020489  0.019515  0.002781      0.226759\n",
       "Delaware              0.265640  0.011649  0.026699  0.000989      0.076244\n",
       "District Of Columbia  0.482873  0.014188  0.026657  0.001867      0.131396\n",
       "Florida               0.437028  0.005081  0.027269  0.000119      0.117374\n",
       "Georgia               0.328268  0.011850  0.020048  0.000539      0.090176\n",
       "Hawaii                0.368768  0.003179  0.025443  0.001033      0.099606\n",
       "Idaho                 0.226237  0.009663  0.019712  0.000525      0.064035\n",
       "Illinois              0.313479  0.011056  0.025347  0.001398      0.087820\n",
       "Indiana               0.231732  0.011169  0.029609  0.001654      0.068541\n",
       "Iowa                  0.215234  0.006803  0.028443  0.000394      0.062718\n",
       "Kansas                0.375179  0.011963  0.024427  0.002595      0.103541\n",
       "Kentucky              0.294861  0.011156  0.019821  0.001185      0.081756\n",
       "Louisiana             0.230845  0.011169  0.024697  0.001080      0.066948\n",
       "Maine                 0.250777  0.005272  0.025725  0.000736      0.070627\n",
       "Maryland              0.261214  0.009933  0.027030  0.001289      0.074866\n",
       "Massachusetts         0.277181  0.016384  0.021823  0.001078      0.079117\n",
       "Minnesota             0.229175  0.006779  0.028439  0.000613      0.066252\n",
       "Missouri              0.276651  0.012362  0.023234  0.001783      0.078508\n",
       "Nevada                0.289366  0.016240  0.027198  0.000434      0.083309\n",
       "New Hampshire         0.258519  0.004931  0.029212  0.000896      0.073390\n",
       "New Jersey            0.297169  0.016597  0.024081  0.000925      0.084693\n",
       "New Mexico            0.211213  0.012335  0.031701  0.000628      0.063969\n",
       "New York              0.295603  0.016289  0.025002  0.001922      0.084704\n",
       "North Carolina        0.288254  0.008716  0.027723  0.000412      0.081276\n",
       "North Dakota          0.171340  0.004670  0.026292  0.000227      0.050632\n",
       "Ohio                  0.275638  0.011503  0.026141  0.002516      0.078949\n",
       "Oklahoma              0.209420  0.006907  0.029935  0.000695      0.061739\n",
       "Oregon                0.294798  0.009119  0.019478  0.000862      0.081064\n",
       "Pennsylvania          0.193798  0.009523  0.026269  0.002010      0.057900\n",
       "Rhode Island          0.222838  0.006987  0.030458  0.000482      0.065191\n",
       "South Carolina        0.123180  0.005369  0.031170  0.000454      0.040043\n",
       "South Dakota          0.187796  0.005257  0.029653  0.000493      0.055800\n",
       "Tennessee             0.180008  0.000894  0.039955  0.000384      0.055310\n",
       "Texas                 0.199889  0.010033  0.026160  0.000483      0.059141\n",
       "Utah                  0.377364  0.017066  0.027837  0.000489      0.105689\n",
       "Virginia              0.249607  0.007714  0.029793  0.000689      0.071951\n",
       "Washington            0.218829  0.011286  0.020511  0.000597      0.062806\n",
       "Wyoming               0.091880  0.003611  0.037879  0.000186      0.033389"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfilt['Year']=cfilt['Year'].astype('str')\n",
    "cfilt.drop(['State Average'], axis=1)\n",
    "statesummary=pd.DataFrame(cfilt.melt(id_vars=['Year','State']).groupby(['State','variable'])['value'].mean())\n",
    "statesummary.head()\n",
    "statesummary=statesummary.reset_index()\n",
    "\n",
    "statesummary.head()\n",
    "statesummary.pivot(index='State',columns='variable')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Asthma Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.212376</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.010020</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.022270</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.061408</td>\n",
       "      <td>50.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.431045</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.012254</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.115193</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.372598</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.015777</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.026297</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.103862</td>\n",
       "      <td>85.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.390687</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.009498</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.026109</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.106869</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>California</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.349392</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>California</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>California</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.026368</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>California</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>California</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.096824</td>\n",
       "      <td>398.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.364444</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.018420</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.025505</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.102372</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.180008</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.039955</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Texas</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.199889</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Texas</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Texas</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.026160</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Texas</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Texas</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.059141</td>\n",
       "      <td>207.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Utah</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.377364</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Utah</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.017066</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Utah</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.027837</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Utah</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Utah</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.105689</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.249607</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.029793</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.071951</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Washington</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.218829</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Washington</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.011286</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Washington</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.020511</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Washington</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Washington</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.062806</td>\n",
       "      <td>82.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>CO Mean</td>\n",
       "      <td>0.091880</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NO2 Mean</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>O3 Mean</td>\n",
       "      <td>0.037879</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>SO2 Mean</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>State Average</td>\n",
       "      <td>0.033389</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>215 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          State       variable     value  Asthma Mortality\n",
       "0       Alabama        CO Mean  0.212376         50.428571\n",
       "1       Alabama       NO2 Mean  0.010020         50.428571\n",
       "2       Alabama        O3 Mean  0.022270         50.428571\n",
       "3       Alabama       SO2 Mean  0.000964         50.428571\n",
       "4       Alabama  State Average  0.061408         50.428571\n",
       "5        Alaska        CO Mean  0.431045               NaN\n",
       "6        Alaska       NO2 Mean  0.011403               NaN\n",
       "7        Alaska        O3 Mean  0.012254               NaN\n",
       "8        Alaska       SO2 Mean  0.006070               NaN\n",
       "9        Alaska  State Average  0.115193               NaN\n",
       "10      Arizona        CO Mean  0.372598         85.428571\n",
       "11      Arizona       NO2 Mean  0.015777         85.428571\n",
       "12      Arizona        O3 Mean  0.026297         85.428571\n",
       "13      Arizona       SO2 Mean  0.000777         85.428571\n",
       "14      Arizona  State Average  0.103862         85.428571\n",
       "15     Arkansas        CO Mean  0.390687         36.714286\n",
       "16     Arkansas       NO2 Mean  0.009498         36.714286\n",
       "17     Arkansas        O3 Mean  0.026109         36.714286\n",
       "18     Arkansas       SO2 Mean  0.001183         36.714286\n",
       "19     Arkansas  State Average  0.106869         36.714286\n",
       "20   California        CO Mean  0.349392        398.714286\n",
       "21   California       NO2 Mean  0.010945        398.714286\n",
       "22   California        O3 Mean  0.026368        398.714286\n",
       "23   California       SO2 Mean  0.000589        398.714286\n",
       "24   California  State Average  0.096824        398.714286\n",
       "25     Colorado        CO Mean  0.364444         47.000000\n",
       "26     Colorado       NO2 Mean  0.018420         47.000000\n",
       "27     Colorado        O3 Mean  0.025505         47.000000\n",
       "28     Colorado       SO2 Mean  0.001121         47.000000\n",
       "29     Colorado  State Average  0.102372         47.000000\n",
       "..          ...            ...       ...               ...\n",
       "185   Tennessee        CO Mean  0.180008         66.000000\n",
       "186   Tennessee       NO2 Mean  0.000894         66.000000\n",
       "187   Tennessee        O3 Mean  0.039955         66.000000\n",
       "188   Tennessee       SO2 Mean  0.000384         66.000000\n",
       "189   Tennessee  State Average  0.055310         66.000000\n",
       "190       Texas        CO Mean  0.199889        207.857143\n",
       "191       Texas       NO2 Mean  0.010033        207.857143\n",
       "192       Texas        O3 Mean  0.026160        207.857143\n",
       "193       Texas       SO2 Mean  0.000483        207.857143\n",
       "194       Texas  State Average  0.059141        207.857143\n",
       "195        Utah        CO Mean  0.377364         34.000000\n",
       "196        Utah       NO2 Mean  0.017066         34.000000\n",
       "197        Utah        O3 Mean  0.027837         34.000000\n",
       "198        Utah       SO2 Mean  0.000489         34.000000\n",
       "199        Utah  State Average  0.105689         34.000000\n",
       "200    Virginia        CO Mean  0.249607         89.428571\n",
       "201    Virginia       NO2 Mean  0.007714         89.428571\n",
       "202    Virginia        O3 Mean  0.029793         89.428571\n",
       "203    Virginia       SO2 Mean  0.000689         89.428571\n",
       "204    Virginia  State Average  0.071951         89.428571\n",
       "205  Washington        CO Mean  0.218829         82.857143\n",
       "206  Washington       NO2 Mean  0.011286         82.857143\n",
       "207  Washington        O3 Mean  0.020511         82.857143\n",
       "208  Washington       SO2 Mean  0.000597         82.857143\n",
       "209  Washington  State Average  0.062806         82.857143\n",
       "210     Wyoming        CO Mean  0.091880               NaN\n",
       "211     Wyoming       NO2 Mean  0.003611               NaN\n",
       "212     Wyoming        O3 Mean  0.037879               NaN\n",
       "213     Wyoming       SO2 Mean  0.000186               NaN\n",
       "214     Wyoming  State Average  0.033389               NaN\n",
       "\n",
       "[215 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_final_df = pd.merge(statesummary, Avg_asthma_noUS, on =['State'])\n",
    "merge_final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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</th>\n",
       "      <th>Asthma Mortality</th>\n",
       "      <th>value_CO Mean</th>\n",
       "      <th>value_NO2 Mean</th>\n",
       "      <th>value_O3 Mean</th>\n",
       "      <th>value_SO2 Mean</th>\n",
       "      <th>value_State Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>50.428571</td>\n",
       "      <td>0.212376</td>\n",
       "      <td>0.010020</td>\n",
       "      <td>0.022270</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.061408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>85.428571</td>\n",
       "      <td>0.372598</td>\n",
       "      <td>0.015777</td>\n",
       "      <td>0.026297</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.103862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36.714286</td>\n",
       "      <td>0.390687</td>\n",
       "      <td>0.009498</td>\n",
       "      <td>0.026109</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.106869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>398.714286</td>\n",
       "      <td>0.349392</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.026368</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.096824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.364444</td>\n",
       "      <td>0.018420</td>\n",
       "      <td>0.025505</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.102372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Asthma Mortality  value_CO Mean  value_NO2 Mean  value_O3 Mean  \\\n",
       "0     Alabama         50.428571       0.212376        0.010020       0.022270   \n",
       "1     Arizona         85.428571       0.372598        0.015777       0.026297   \n",
       "2    Arkansas         36.714286       0.390687        0.009498       0.026109   \n",
       "3  California        398.714286       0.349392        0.010945       0.026368   \n",
       "4    Colorado         47.000000       0.364444        0.018420       0.025505   \n",
       "\n",
       "   value_SO2 Mean  value_State Average  \n",
       "0        0.000964             0.061408  \n",
       "1        0.000777             0.103862  \n",
       "2        0.001183             0.106869  \n",
       "3        0.000589             0.096824  \n",
       "4        0.001121             0.102372  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvt_merged=pd.DataFrame(merge_final_df.pivot_table(index=['State','Asthma Mortality'], columns='variable'))\n",
    "pvt_merged.columns = ['_'.join(tup).rstrip('_') for tup in pvt_merged.columns.values]\n",
    "pvt_merged.reset_index(inplace=True)\n",
    "pvt_merged.to_csv('asthma_polluts_avg_2010_16.csv', index=True, header=True)\n",
    "\n",
    "pvt_merged.head()\n",
    "#merge_final_df.columns = merge_final_df.columns.droplevel().rename(None)\n",
    "#pvt_merged.reset_index(inplace=True)\n",
    "# pvt_merged.reset_index(inplace=True)\n",
    "# pvt_merged.reset_index(inplace=True)\n",
    "\n",
    "#pvt_merged.reset_index(drop=True)\n",
    "\n",
    "#merge_final_df.reset_index().fillna(\"null\").to_csv(\"test.csv\", sep=\"\\t\", index=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['value_CO Mean',\n",
       " 'value_NO2 Mean',\n",
       " 'value_O3 Mean',\n",
       " 'value_SO2 Mean',\n",
       " 'value_State Average']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pollutants=list(pvt_merged.columns[2:])\n",
    "\n",
    "pollutants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for pol in pollutants:    \n",
    "    pvt_merged.plot(x='State', y=pol, kind=\"bar\", figsize=(20,3))\n",
    "    plt.title(pol)\n",
    "    plt.xlabel(\"State\")\n",
    "    plt.ylabel(\"Pollutant\")\n",
    "    plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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taakkSZIkSZK0MM455xz23beMkLPvvvtyzjnnALD55pvzk5/8hEmTJnHXXXcxYsSILp8/duxYxo8fD8Cmm27K1KlT55Y1uomNGzeO9ddfn9VXX51ll12WN77xjTzyyCMAnHLKKWy00UZsueWWPPLIIzzwwANdxhkyZAjbbLMN5513Hi+88AJjxoyZW3bdddex3377MWTIEEaNGsXb3/52/vznPwOwxRZbMHbs2C63mZl85jOfYcMNN+Qd73gH//jHP3jsscd6+crBgQceyM9+9jMAzjjjDD7wgQ/0+rm9YUslSZIkSZI0ID311FNceeWV3H333UQEc+bMISL4+te/znbbbcc111zDxRdfzIEHHshxxx3HQQcdtMA2ll122bl/DxkyZG73NyitewCWWmqp+dZbaqmlmD17NpMnT+byyy/nxhtv5DWveQ0TJkzgxRdfbFrffffdlz322GOBcZ+6m4dsueWWa1p21lln8cQTTzBlyhSWWWYZxowZ0238zkaPHs2oUaO48sorufnmmznrrLN6/dzesKWSJEmSJEkakM4//3wOOuggpk2bxtSpU3nkkUcYO3Ys1113HdOmTWPkyJEcdthhHHroodx2220ALLPMMrzyyistiT9jxgxWWmklXvOa13D//fdz0003dbv+tttuy6c//Wn222+/+ZZvt912nHfeecyZM4cnnniCa665hi222GKB548YMYKZM2fOF3/kyJEss8wyXHXVVUybNq3b+J2fD/DBD36QAw44gL333pshQ4b0tMt9YlJJkiRJkiQNSOeccw577LHHfMv23HNPzj77bCZPnsz48ePZeOONueCCCzjqqKMAOPzww9lwww3nDtS9KN71rncxe/ZsNtxwQz73uc+x5ZZbdrt+RHDssccuMG7RHnvswYYbbsjWW2/N9ttvz9e//nVe+9rXLvD8VVZZhbe97W1ssMEGHHfccey///7ceuutbLbZZpx11lm85S1v6Tb+xIkTuffee+cO1A2w6667MmvWrJZ3fQO7v0mSJEmSpF6aetLObY03efLkBZZ97GMfm/v3wQcfvED51772Nb72ta/NfXz33XfP/fvYY4+db9uNVj0TJkxgwoQJXca99NJLF6qeUGaug5Js+sY3vsEJJ5ww39hPneMCnH322fM9vvHGG7vd9pgxY+bu48orrzx3rCaAmTNncuedd7LRRhv1mJBaGCaVJEmSJEmSBqFvfetbnHHGGS0fS6nB7m+SJEmSJEmD0Cc+8QmmTZvGNttsU8v2TSpJkiRJkiSpz0wqSZIkSZKkpjKzv6ugmizq/9akkiRJkiRJ6tKwYcOYMWOGiaVBKDN56qmnGDZs2EJvw4G6JUmSJElSl0aPHs2dd945d6axur344ouLlOQwXt/iDRs2jNGjRy/09k0qSZIkSZKkLi2zzDLMmjWLzTbbrC3xJk+ezMYbb9yWWMZbdHZ/kyRJkiRJUp+ZVJIkSZIkSVKfmVSSJEmSJElSn5lUkiRJkiRJUp+ZVJIkSZIkSVKfmVSSJEmSJElSn5lUkiRJkiRJUp+ZVJIkSZIkSVKfmVSSJEmSJElSn5lUkiRJkiRJUp+ZVJIkSZIkSVKfmVSSJEmSJElSn5lUkiRJkiRJUp+ZVJIkSZIkSVKf9ZhUioiV21ERSZIkSZIkLT5601Lp5oj4VUTsFBFRe40kSZIkSZI04PUmqfRm4DTgQODBiPhKRLy53mpJkiRJkiRpIOsxqZTFnzJzP+CDwMHALRFxdURs1ex5ETEsIm6JiDsj4p6IOLFaPjYibo6IByLivIgYWi1ftnr8YFU+piV7KEmSJEmSpJbrzZhKq0TEURFxK3AscCSwKnAMcHY3T30J2D4zNwLGA++KiC2BrwHfzsy1gWeAQ6v1DwWeycy1gG9X60mSJEmSJGkA6k33txuB5YHdM3PnzPx1Zs7OzFuBHzZ7UtXCaVb1cJnqJ4HtgfOr5WcCu1d/71Y9pirfwTGcJEmSJEmSBqbeJJX+OzO/mJnTGwsiYi+AzOy2NVFEDImIO4DHgT8BDwHPZubsapXpwBrV32sAj1TbnQ3MAFbpw75IkiRJkiSpTSIzu18h4rbM3KSnZT1sY0XgQuAE4CdVFzci4vXAJZk5LiLuAXZsJK8i4iFgi8x8qtO2DgcOBxg1atSm5557bm+rMdesWbMYPnx4n5+3sAZDvBfvuadp2csjRzL08ce7LBu2/votrQcMjtdzIMQynvGMt+TEG8z7ZjzjGa//4g3mfTOe8YzXf/EG874tTvEmTpw4JTM363HFzOzyB3g38D3gMeCUDj8/BW5p9rxutvd54DjgSWDpatlWwGXV35cBW1V/L12tF91tc9NNN82FcdVVVy3U8xbWYIh37zpvafpz6amnNi2rw2B4PQdCLOMZz3hLTrzBvG/GM57x+i/eYN434xnPeP0XbzDv2+IUD7g1e5Hr6a772z+BW4EXgSkdfn4L7NhTsioiVqtaKBER/wG8A7gPuAp4X7XawcBF1d+/rR5TlV9Z7YgkSZIkSZIGmKWbFWTmncCdEXFWzhsDqS9WB86MiCGUsZt+mZm/j4h7gXMj4kvA7cDp1fqnAz+PiAeBp4F9FyKmJEmSJEmS2qBpUikifpmZewO3R8QCLYYyc8PuNpyZfwE27mL5w8AWXSx/EdirN5WWJEmSJElS/2qaVAKOqn7v0o6KSJIkSZIkafHRXfe3R6vf09pXHUmSJEmSJC0Ouuv+NhPoaqDsADIzl6+tVpIkSZL63fePuLJp2citn2ta/pEfbl9XlSRJA0h3LZVGtLMikiRJkiRJWnx0N6bSfCJiJDCs8Tgz/15LjSRJkiRJkjTgLdXTChGxa0Q8APwNuBqYClxac70kSZIkSZI0gPWYVAK+CGwJ/F9mjgV2AK6vtVaSJEmSJEka0HqTVHolM58CloqIpTLzKmB8zfWSJEmSJEnSANabMZWejYjhwDXAWRHxODC73mpJkiRJkiRpIOtNS6XdgOeBo4E/AA8Bu9RZKUmSJEmSJA1svUkqnZCZr2bm7Mw8MzNPAT5Vd8UkSZIkSZI0cPUmqfSfXSx7d6srIkmSJEmSpMVH0zGVIuJDwIeBN0XEXzoUjcDZ3yRJkiRJkpZo3Q3UfTZwKfBV4PgOy2dm5tO11kqSJEmSJEkDWtOkUmbOiIiZwLjMnNbGOkmSJEmSJGmA63ZMpcx8FbgzItZsU30kSZIkSZK0GOiu+1vD6sA9EXEL8FxjYWbuWlutJEmSJEmSNKD1Jql0Yu21kCRJkiRJ0mKlx6RSZl4dEaOAzatFt2Tm4/VWS5IkSZIkSQNZt2MqAUTE3sAtwF7A3sDNEfG+uismSZIkSZKkgas33d8+C2zeaJ0UEasBlwPn11kxSZIkSZIkDVw9tlQClurU3e2pXj5PkiRJkiRJg1RvWir9ISIuA86pHu8DXFJflSRJkiRJkjTQ9Wag7uMiYk/gbUAAp2XmhbXXTJIkSZIkSQNWb1oqkZkXABfUXBdJkiRJkiQtJpomlSJiJpBdFQGZmcvXVitJkiRJkiQNaN21VLoCeC3wa+C8zJzWnipJkiRJkiRpoGs6i1tm7g7sCDwBnBYRV0fEhyNi5bbVTpIkSZIkSQNS06QSQGbOyMyfAO8Gfgh8ATikDfWSJEmSJEnSANbtQN0RsTWwH7AtcB2wR2Ze246KSZIkSZIkaeBq2lIpIqYC/wP8AzgcOAN4LiI2iYhNetpwRLw+Iq6KiPsi4p6IOKpavnJE/CkiHqh+r1Qtj4g4JSIejIi/9CaGJEmSJEmS+kd3LZWmUmZ/2xF4J2XWt4YEtu9h27OBYzLztogYAUyJiD9Rus9dkZknRcTxwPHApyhd7Nauft4K/KD6LUmSJEmSBon73rJu07IXj/wo9x3xoS7L1r3/vrqqpIXUNKmUmRMWZcOZ+SjwaPX3zIi4D1gD2A1obPtMYDIlqbQb8LPMTOCmiFgxIlavtiNJkiRJkqQBpNuBulslIsYAGwM3A6MaiaLq98hqtTWARzo8bXq1TJIkSZIkSQNMlIZBNQaIGA5cDXw5M38dEc9m5oodyp/JzJUi4mLgq5l5XbX8CuCTmTml0/YOp4zxxKhRozY999xz+1ynWbNmMXz48IXfqSUw3ov33NO07OWRIxn6+ONdlg1bf/2W1gMGx+s5EGIZz3jGW3LiDeZ9M57xjFdvvCf+PrNp2dLLvcrs57q+R73amiNaVoeGxf21NJ7xjDeP15cDP97EiROnZOZmPa3X7exviyoilgEuAM7KzF9Xix9rdGuLiNWBxrtlOvD6Dk8fDfyz8zYz8zTgNIDNNtssJ0yY0Od6TZ48mYV53sIaDPGa9WkFmHbkR3nD907tsqyOPq+D4fUcCLGMZzzjLTnxBvO+Gc94xqs33vePuLJp2citn+PxG5brsmyvg1pXh4bF/bU0nvGMN4/Xl4MnXq+6v0XEShGxRURs1/jpxXMCOB24LzO/1aHot8DB1d8HAxd1WH5QNQvclsAMx1OSJEmSJEkamHpsqRQRHwSOorQcugPYEriRnmd/extwIHBXRNxRLfsMcBLwy4g4FPg7sFdVdgmwE/Ag8DzwgT7tiSRJkiRJktqmN93fjgI2B27KzIkR8RbgxJ6eVI2NFE2Kd+hi/QQ+0ov6SJIkSZIkqZ/1pvvbi5n5IkBELJuZ9wPr1FstSZIkSZIkDWS9aak0PSJWBH4D/CkinqGLAbQlSZIkSZK05OgxqZSZe1R/ToqIq4AVgD/UWitJkiRJkiQNaL1pqURErAS8HphZ/WwA3FZjvSS1yKRJk5qWrbPOOk3Lu3ueJEmSJEm9mf3ti8AhwMPAq9XipOfZ3yRJkiRJkjRI9aal0t7AmzLz5borI0mSJEmSpMVDb5JKdwMrAo/XXBdJUidjjr+4adkx42ZzSJPyqSftXFeVJEmSJAnoXVLpq8DtEXE38FJjYWbuWlutJEmSJEmSNKD1Jql0JvA14C7mjakkSZIkSZKkJVhvkkpPZuYptddEkiRJkiRJi43eJJWmRMRXgd8yf/e322qrlSRJkiRJkga03iSVNq5+b9lhWQLbt746kiRJkiRJWhz0mFTKzIntqIgkSZIkSZIWHz0mlSJiWWBPYEzH9TPzC/VVS5IkSZIkSQNZb7q/XQTMAKbQYUwlSZIkSZIkLbl6k1QanZnvqr0mkiRJkiRJWmws1Yt1boiIcbXXRJIkSZIkSYuNpi2VIuIuyixvSwMfiIiHKd3fAsjM3LA9VZQkSZIkaX5jjr+4adkx42ZzSJPyqSftXFeVpCVOd93fdmlbLSRJkiRJkrRYaZpUysxpABHx88w8sGNZRPwcOLDLJ0qSJEmSFjBp0qSmZeuss07T8u6eJ0n9qTdjKq3f8UFEDAE2rac6kiRJkiRJWhw0TSpFxKcjYiawYUT8u/qZCTwOXNS2GkqSJEmSJGnAaZpUysyvZuYI4BuZuXz1MyIzV8nMT7exjpIkSZIkSRpgetP97fcRsRxARBwQEd+KiDfUXC9JkiRJkiQNYL1JKv0AeD4iNgI+CUwDflZrrSRJkiRJkjSg9SapNDszE9gN+G5mfhcYUW+1JEmSJEmSNJAt3Yt1ZkbEp4EDgO2q2d+WqbdakiRJkiRJGsh601JpH+Al4NDM/BewBvCNWmslSZIkSZKkAa3HpFJm/iszv5WZ11aL1gTeWm+1JEmSJEmSNJD1pvsbETEeeD+wN/A34II6KyVJkiRJkqSBrWlLpYh4c0ScEBH3AacCjwCRmRMz89SeNhwRZ0TE4xFxd4dlK0fEnyLiger3StXyiIhTIuLBiPhLRGzSgn2TJEmSJElSTbprqXQ/cC3wnsx8ECAiju7Dtn9KSUb9rMOy44ErMvOkiDi+evwp4N3A2tXPW4EfYBc7SZIkLQbGnTmuadmHhn+II888ssuyuw6+q64qLbZO3meXpmWjd9ydk3/wzS7Ljjnv93VVSZLUje7GVNoT+BdwVUT8OCJ2AKK3G87Ma4CnOy3eDTiz+vtMYPcOy3+WxU3AihGxem9jSZIkSZIkqb2aJpUy88LM3Ad4CzAZOBoYFRE/iIh3LmS8UZn5aLX9R4GR1fI1KN3rGqZXyyRJkiRJkjQARWb2fuWIlYEoFCDWAAAgAElEQVS9gH0yc/terD8G+H1mblA9fjYzV+xQ/kxmrhQRFwNfzczrquVXAJ/MzCldbPNw4HCAUaNGbXruuef2uv4Ns2bNYvjw4X1+3sIaDPFevOeepmUvjxzJ0Mcf77Js2Prrt7QeMDhez3bGevTRR5uWLbvssrz00ktdlq2+eusbCw7m/11d8e76x4ymZaP+Ax57oeuycWus0NJ6wOB4PZfUeIN534xnvIEQ796n7m1attqQ1XhizhNdlq23ynotrQe0fv+e+PvMpmVLL/cqs5/r+h71amuOWKh4jz38YNOyoSusyMsznu2ybNQb11qoeN3xvGzgx/M8afGN5/XlwI83ceLEKZm5WU/r9Wr2t4bMfBr4UfWzMB6LiNUz89Gqe1vjnTIdeH2H9UYD/2xSh9OA0wA222yznDBhQp8rMXnyZBbmeQtrMMS774gPNS2bduRHecP3uh67fd3772tpPWBwvJ7tjDVp0qSmZeussw5//etfuyzbb7/9WloPGNz/u7riHXL8xU3Ljhk3m5Pv6vprfOr+ra0HDI7Xc0mNN5j3zXjGGwjxmo2ZBGVMpR/M+kGXZXft2foxlVq9f98/4sqmZSO3fo7Hb1iuy7K9Dlq4OjQbMwnKmErTL/tNl2X71DCmkudlAz+e50mLbzyvLwdPvD4llVrgt8DBwEnV74s6LP9oRJxLGaB7RqObnKTFyxVXvqlp2fPPHc0VVx7aZdkO2z9UV5UkSZIkSTWoLakUEecAE4BVI2I68HlKMumXEXEo8HdKVzqAS4CdgAeB54EP1FUvSZIkSZIkLbrakkqZ2ayN5g5drJvAR+qqiyRJkiRJUjucvM8uTctG77h7066+x9TQlbdu7e7+NnhN6mawt3VOhEm7NXle88HlJEmSJEmSBiqTSpIkSZIkSYup6cdf27TslXGzmpaPPmnbRY7d9RygkiRJkiRJUjdsqSRJkga8nqbhblbe3fMkSZK0aGypJEmSJEmSpD4zqSRJkiRJkqQ+s/ubJGkeZ7KUALjiyjc1LXv+uaO54spDuyzbYfuH6qqSJEnSgGNSSZIkqZ+99qo7mpZ9ZtYL7Nuk/F8Txy9UvDHHX9y07JhxszmkSfnUk3ZeqHiSJGlwMqkkSZIkSVIPBntCftyZ45qWfWj4hzjyzCO7LLvr4LvqqpIWA46pJEmSJEmSpD6zpZIkSZIkaZG1uyuvpP5nSyVJkiRJkiT1mUklSZIkSZIk9ZlJJUmSJEmSJPXZoB1TabCPzC9JkiRJktSfBm1SSYu37x9xZdOykVs/17T8Iz/cvq4qSZIkSYuVK658U9Oy5587miuuPLTLsh22f6iuKkkaZEwqSZI0CJy8zy5Ny0bvuDsn/+CbXZYdc97v66qSJEmSBjnHVJIkSZIkSVKf2VJJktRvxp05rmnZh4Z/iCPPPLLLsrsOvquuKkmSJEnqJZNKiykvxFrLbiOS6uD4cJIkSRrMTCpJkiRJkqRByxt99XFMJUmSJEmSJPWZLZUkSUuM+96ybtOyF4/8KPcd8aEuy9a9/766qiRJkiQttmypJEmSJEmSpD6zpZIkSZJqNeb4i5uWHTNuNoc0KZ960s51VWmxZqtLSdJAYVJJ0mLttVfd0bTsM7NeYN8m5f+aOH6h4nlhJEmSJEmF3d8kSZIkSZLUZ7ZUkiRJkiRpoJm0QvOydU6ESbs1ed6MeuojdcGkkiRJkgYXL8QkSWoLk0pSm00//tqmZa+Mm9W0fPRJ29ZVJUnqM7/LJA0GfpdJ0qIZUGMqRcS7IuKvEfFgRBzf3/WRJEmSJElS1wZMUikihgDfB94NrAfsFxHr9W+tJEmSJEmS1JUBk1QCtgAezMyHM/Nl4FygSYd3SZIkSZIk9aeBlFRaA3ikw+Pp1TJJkiRJkiQNMJGZ/V0HACJiL2DHzPxg9fhAYIvMPLLTeocDh1cP1wH+uhDhVgWeXITqGs94gyGW8YxnvCUn3mDeN+MZz3j9F28w75vxjGe8/os3mPdtcYr3hsxcraeVBtLsb9OB13d4PBr4Z+eVMvM04LRFCRQRt2bmZouyDeMZb3GPZTzjGW/JiTeY9814xjNe/8UbzPtmPOMZr//iDeZ9G4zxBlL3tz8Da0fE2IgYCuwL/Laf6yRJkiRJkqQuDJiWSpk5OyI+ClwGDAHOyMx7+rlakiRJkiRJ6sKASSoBZOYlwCVtCLVI3eeMZ7xBEst4xjPekhNvMO+b8YxnvP6LN5j3zXjGM17/xRvM+zbo4g2YgbolSZIkSZK0+BhIYypJkiRJkiRpMWFSSZIkSZIkSX1mUkmSJEmDVhQHRMQJ1eM1I2KL/q5XK0XEqIjYpfoZWWOc8V0se3dd8ST1n4gYGhFr9Xc9NPAtUWMqVQfZYY3Hmfn3fqxOy0TEcsALmflqRLwZeAtwaWa+0s9VWyxFxFGZ+d2eli3OImIlYG3m/zxc0381WrxFxFDgzdXDv9b52YuINwHTM/OliJgAbAj8LDOfrTFmW98v/fX+jIilgOGZ+e8aYwzJzDl1bb8/RUQA+wNvzMwvRMSawGsz85Z+rlpL9Mdnb7Br12c9In4AvApsn5nrVnH/mJmbtzpWFe9W4CfA2Zn5TB0xOsXbG/gGMBkIYFvguMw8v4ZYtwEHZOa91eO9gE/W9VpWMXYG1mf+98kXaoq1GvApYL1O8bavI16HuBtR/m8A12bmnXXGU+sM1vdntV/fAoZm5tgqofz5zNyj1bGqeB+lHFP/HRE/AjYGPp2ZV9QRT621RCSVImJX4GTgdcDjwBuA+zJz/ZriDQMOZcEvmP+qKd4UyoFoJeAm4Fbg+czcv6Z4WwLfA9YFhgJDgOcyc/ma4q0ATGLewfZq4AuZOaOmeLdl5iadlt2emRvXFG8U8BXgdZn57ohYD9gqM0+vKd4HgaOA0cAdwJbAjXWeMLXjgBsR38nMj0fE74AFvtgyc9dWxusQdwJwJjCVcjL/euDgupIgEXEHsBkwBrgM+C2wTmbuVFO8tr5f+iHe2cARwBxgCrAC8K3M/EZN8f4GnA/8pHFRVpd+OBa15cI9Ir5HF5/xhsz8WCvjdYjb7s9eWy4eIuKAzPxFRHyiq/LM/FYr43WI27bPeuO43vFYHhF3ZuZGrY5VbXst4APAPpRzsp9QPgu1nHRHxJ3Af2bm49Xj1YDL69i/at9+CewLbEP5jtmlruRZRPwQeA0wEfhf4H3ALZl5aE3x/gicBxxLOTYcDDyRmZ+qI14V8yjgMODX1aI9gNMy83s1xWtnYuJtlHP4N1BmHY8SKt/Y6lid4m7Agvv3sxriDNr3Z3V9uQNwVYfvzbsyc1yrY1Xb/ktmbhgR7wQ+Bnye8jnYtI54Vcy2vT/74ZxsNcr3yhjKvtUab0np/vZFysnK/2XmWMoH5Poa4/0ceC2wIyUBMhqYWWO8yMzngfcC36syyOvVGO9UYD/gAeA/gA9Skkx1OQP4N7B39fNvyglaS0XEflVCYmxE/LbDz1XAU62O18FPKRcor6se/x/w8RrjHQVsDkzLzImUOwFP1BWsOuDuAxxJ+bLei/Ll3Wo/r35/k5JE7vxTl5OBd2bm2zNzO8rn/ts1xns1M2dTTjq/k5lHA6vXGK+t75d+iLde1TJpd+ASYE3gwBrjbUj5jP9vRNwUEYdHRC0Jedp/LHprZn4EeBGgusgcWkOcWykJwGHAJpRj0QPAeEpysC7t/uydBdwHjAVOpCSu/1xDnOWq3yOa/NSlnZ/1VyJiCFUysjrZfrWmWGTmg5n5WUoL1rMp5zF/j4gTI2LlGkIu1UgoVZ6ipnP8zHwQeD/wG8q54H/W3Bpr68w8CHgmM08EtqLcvKnLKtVNvVcy8+rqAmzLGuNBudB8a2aekJknVPEOqzFeu75bAE6ntHbZhvJ536z6XZuI+DzluuR7lGTP14FabiwyuN+fr3TRErfO1iiNbb+bcuNtCvXnKtr5/mz3OdlFlBullwMXd/ipxdI9rzIovJKZT0XEUhGxVGZeFRFfqzHeWpm5V0TslplnVnfCL6sxXkTEVpRuB43MeK3/28x8sEM3jp9ExA01hntTZu7Z4fGJ1R3jVrsBeBRYlfmTEDOBv9QQr2HVzPxlRHwaIDNnR0SdF0YvZuaLEUFELJuZ90fEOjXG27q68/CXzDwxIk5m3t24lqkOPmTm1a3edg+Wycy/dqjH/0XEMjXGeyUi9qPcnXpPow41xmv3+6Xd8Zap/l+7A6dm5isRUdtJU2bOBH4M/DgitgPOAb4dEecDX6wu2Fql3ceitly4Z+aZ1fYPASZm1d20SmD/sdXxOmj3Z2+VzDw9Svfrq4GrI6Ll32+Z+aPq//bvzKwzId5ZOz/rpwAXAiMj4suU1gT/XVMsACJiQ0prpZ2ACygX8tsAV1ISoK30h4i4jPJ9AuVGzqWtDBARtzP/BeWK1e/rIoLOLbxb6IXq9/MR8TpKwmxsTbEAGt3XH61aWf+TcvFXp2D+hPicalld2vLdUpmRmS19L/bC+4CNgNsz8wNVj4D/rSnWYH5/3hela+1SETGWciPgpppiAdwZEZdQkvGfjYjh1JvEgva+P9t9TvaaOltYdrakJJWerd6Y1wBnRcTjwOwa4zU+8M9WzS//RWl6VpejgE8DF2bmPRHxRuCqGuM9H2UMmTsi4uuURMxyPTxnUbwQEdtk5nUwt6niCz08p88ycxowjXKXoZ2ei4hVmHchtiVQS9e+yvSIWJFyl/FPEfEM5aBUl7YccCPil5m5d0TcRdfd3zZsdczKrRFxOvNaSu1PaUVRlw9Qmjx/OTP/Vh3of1FjvHa/X9od70eUu7R3AtdExBsorSFrUV2870z5P46hJLDPonTvbZxMtUq7j0XtvnB/HaUlzdPV4+HMa/FZh3Z/9tp28ZCZc6IMFdDOpFLbPuuZeVaHrhwB7J6Z99URC+Z2G3mWchf8+Mx8qSq6uTqHaanMPC4i3ktJWgWly8iFLQ7zvhZvr7d+X71PvgHcRjm+15UgAPhSlGEXjqG0dFmeeluPQ2l9f3NENP5nu1PeO3VpZ2Liqoj4BuVmYuNzQGbeVlM8mDfO7OyqJfDjQF3d7Qbz+/OjwAmUm0O/piRAPlNTLCjH2E2BBzPz+YhYlXmNJVoqIhpJ8Ha+P9t9Tvb7iNgpMy+pMcZcS8qYSstRmuM3BhFdATgrM2vp0hRlnIALKN0cfkI50T0hM39YR7x2qy66HqfcoT2a8nr+T4vvsHeMN54yZs0KlP/h08AhWdMghhExk3lJiaGU/axzzKhNKAeGDYC7gdWA92Vmna2jGrHfTnld/5CZL9cU43OU/dsB+D7VATczP9fiOKtn5qPV+3MBVdKw5SJiWeAjzDuZv4byeXip2ycuhtrxfunPeB3iLl11c6pj2w9Tkv6nZ+YNncpOyRaOB9Qfx6KIeAvzLtyvqPnC/QOUsRAaN1HeDkxqtGSqIV5bJ3GIiF2AayldKRoXD5My83c1xfsy5fN2HvBcY3nNF3+N2LV91qMMwP+XzNygldvtIeYbM/PhNsYbCzyamS9Wj/8DGJWZU1scZwhwW9Y0FlUv4i8LDMuaxtSsYrwtM6/vaVkNcTehw3lEZt5eY6yuvltOzMzf1hCrq5vcmfWO4/k/lOTHvpTkyyzgjsz8QF0xq7iD8v1ZtSRtyzltlTB7E/OPOdTy3jBN3pcdQtYyvlhbz8mq69nlgJeZl9DK2q5nl4SkUkOVre44UNXT3ay+2Ki6GHySBQf+qnWminar/n9kjTMzNYm7O7BFZrY8O1+d7G4J3AKsQzmZqGX2sOhhHId2fB7accAdrLppidUYVLCulliNC4lRzP/9WcvsmRHxBcrJ7g2Z+VxP67coZjtnbhmdmdM7LRubmX+rI167tfO9UsV7LfDW6uHNmfmvGmO1exKHtl48tOvirz+ORRFxFmUWobbM+hvtn4DjVkpX85erx0OB67OGGdki4hzg2Mz8R6u33U3MrVlwsNmWD7pcxerqc77AshbFWj7LTFddfiYGy3VKf4qIMcDydd6oHcTvz7dSWl2tkJlrRpmh8IOZeWSrY1Xx/ouSBFwDuIsyttFNmTmhjnhqrSWi+1tE/D/gC5RuOK9SXYRRU1PI6sJ5Txb8gqnlIoXSdeI8YBc6zATQ6iDt7l4UTWaiiYhGvFpmpOksM38TEcfXtO1XI+LkzNwKuKeOGB1MofzfgjIY8TPV3ysCf6fFXdIiYvvMvLJqkt+5jMxs+bhK1bbbPTth55kjAMjWzxxxVPV7lxZvt1sRcSRlBo7HmDc+TlLutNRhKmXw11OquyzXUu7aXlRHsGgyc0sdsSrnRcS7G8nx6mLzl5SWii3V7mNRp/dKY0yQOt8rUD7fT1D2780R8eZs8cyLUcZRej/VJA4dikZQ7yQO36MMRN7TspbIMlh2O7T1WFRZHbgnIm5h/lZYdQ3e+1PKnejPVo//j3KeVleXpqU7tvDKzJerxFIdVqWMtXIj87+WCxzrWyEifk5puXAH88YdSqClF+1RxibdGlit0/nn8pTvmTqcTTmmNz4Tc6tDvdcpYymTp4xh/mNDyz8PVcuTzwPbVYtqm8E5It6SZWy2Bb4jI2KTOlpdDvL353cp78/fAGTmnRFR53HiaMpA2Tdm5rYRsT41j30H9d9YjH6aYbWKvSvzPnuTM/P3dcVaIpJKlGkX18/MJ9sU7yLKmDhT6NA/s0btGnCv3Re1jVln1qFkqxsn8++hdDGqRackyFKUL7g6m/T9MSL2BH6dNTYdzDLzYeMi+reNPrYR8W7gHTWEfDtlQNL3dFGW1DBYd+VUSpPnX1H+dwcBa9UUC8pFwtGUz3ttA6xn5qPV72nVXfDGHehbcv5Zf1rtKMq06XVePM+VmWcAZ1QtUPamfH8fTn2zULVlIPkOvgL8rjqJWYdy4rl/TbHafSxq63slyoQb+1AS8h0Tnq0+PrR1Eod+unggIk7oanmrk5D9cCyCMsNVO7V7Ao4nImLXRveliNgNqOuc96SattvMZpRZOuvuWjGU0h1laeY/3vybmsaTysxdqt91Duzcld9Qzl1+R42zIFbOoAztsHf1+EBKwrWOJOQnKOcLXc34m0AdPTgG7fuTMqvktMbN/ErdEwm9EGXyhqFZxgl+S43x2nVjseMMq20TESdRrhXOqhYdFWWM4loaSiwpSaWHgOfbGG90Zr6rjfHaMuBex4vaVm+7SbwTASLij8AmWWZNIiImURIGdemYBJlNaTmxW43xPkH5wpkdEY2xv2rr8wpsnplHNB5k5qUR8cVWB8nMz1e/a+3D3iR2O2cnbOvMJlFm4vgGMJnyXvleRByXmefXFPIR6h04fj4R8b/AepTWLtdSDvC1DuhZ/W7LzC2ZeXGU2eb+SDnB2D0zH6gpXLuPRW19r1AGs10nax7roTrmTYuIa7LT7JJVYqvVs6v0x8UDdGh1Qrljuwtl2vG6tOVYVG273bOCtnsCjiMoE9GcSjkuPEK5odJymXlFlAF0N6sW3VrzTdu7KdNwP1pjjMZ75OqI+Gl1IT2iLM5ZdcZtiIg1WLDFc103UF/MzFNq2nZn7ZrBmcw8vPrdrlaXMLjfn49ExBZARunafiSl1WVdHo0y6PnvgMsi4mnKuWCdar+xmJk/qn63++bGTsD4zHwVICLOBG4HTCotgk8DN0TEzcw/snvLBkTt5IaIGJeZd9W0/c66mgng6LqCVS15vgaMpJy81J0EWZMyyFjDy9Q4Wn67kyCZ2dbMNfBkRPw3ZdaiBA6gxi4cTZp7zgCmZGYdJxZtmZ0w+mfmCCjdKTZvtE6KMqba5UBdSaWHgckRcTHz719dzXVXobTGeJYyKP+TWdOg2ZW2zNwSEd9j/haPy1Ne2yOr7qB1HI/afSxq93vlYcpECu0aFP8/WTCB9O4uli2S/rq4zcz57u5HxDeZ10K4DrUfiyLiuszcJuafgAPqP2/5BOW1e1NEXE81AUdNscjMh4Ato8x0HI2bcHWoWlZ/m5L0D+CHEXF0tn62uYZVgXurrosdv1fq6ro4IiJuB1YGiIgngYMz8+6a4nVsdXkv83ehqiup9N2I+Dzl5kbd5y1tmcG5s2jfOEeD+f35IcqsrmtSkjuXV8tq0eE1+1xE7ECZvOHiuuJV2nZjsTpfP4wF35f/VUe8yorMmyF3hRrjLDFJpR9RuuHcRf3NPKHM3nBIRPyN8gVT60C6HfpHzqA036vb14H3ZI2z+nTyc+CWKFOtJrAHLe6r3FGVyT0qM5+tHq8EnFznh76KsTbz9+et62RiP0r/9sYJ4DXVsrpsVv00ZizaGfgzcERE/Cozv97ieAdSui1+lJJcfT1lXJlW69y8erMOf9fVzBpKc+SO3d2eouxvXf5e/QytfmqVmXsARMS6wI6UpN2QzKxrKvVGy4gLIuL31DeQ/K2dHk+pIUZnbT0W0eb3CqUF8h0RcQU13jCKiA8BH6YkCDp2dxsB1DkjVNsvbjt5DfVNww1tOBZl5jbV77bevMnM26LMaFf3BBxdjtUR9Y49eQLlxsZjVaxRlOREXUmlSTVtt5nTgE9k5lUAETGhWrZ1jTHb0uqyg3GUc6Xtmb/rcB3nLR8Czqxufs+dwbmGOHNFm8Y5qkyqYZvdadv7szrX3LfV220mIiZk5uQq9hXVsv2Z132rDm25sVi5iJKMv5x6uxE2fBW4PcokHEEZW6nlk041LBGzv0XEDZlZ58Ggc7x2T2n+deBLlGzrH4CNgI9n5i9qind9Zr6tjm13E3NTygUS1D/V6gKz+XS1rIXxPkgZi2Q05QC4JWWQukExe19EXAbs2bjLXt1JPZ+SHJySmeu1ON72lNki2tnltW2qVlEbAudUi/ahTJfd6i44/SLKVMfbUg5+KwE3AtdmGWupjnh7UaYxn1m1mtgE+GJd3zERsRyl68Gc6vEQYNk63q/tPha1W0Qc3NXyzDyzxXFWoLwXv8r8zcZnZo2zM1Xddj/b6eLhK3Wdz8T8k3AMobSu+UJmnlpHvHaL9s5i2dX3ypda3RIkIv5fZv6oanWygDq6W0TEXZk5rsPjpYA7Oy5bnEXEnZm5UU/LWhzzUmCvNna1ux/YMDsM7t6GmG2bwTki7qM94xy1XTvfnxHxVcpx73ng98DGwNGZeXarY1Xxrqckdj5J6QJ+GiVXsXsd8aqYyzaSuVHNUE05R2t5gjci7sjM8a3ebg8xV6eMqxTUPUPuIPy8LSAivgxMo7SU6Hg3s86TwY0oF0ZQLojurDHWHZk5PiL2oNztOBq4qq4DYER8l9J/+DfM/3rWObgtETGS+Vvy1HUyeCcwITOfqR6vDFxd1wlTdSLfmDZzfJRB6U7MzH1aHOc7mfnxiPgdXc/eV0tT3ergvlHOm+p4WeCOzFy3jmRdRPyMkph7inJH4Frgusb/sw7Rxinpq3h7Am+jHCSuqbHbQaO57idZcP9qSXpGxPcpLRauzcx/1hGjU7y/ZOlPvw3l5OmbwGcy8609PHVh490EvKNTkvWPdd74qPu7s7++W/pD9T5ZOzN/EmVcmRGZ+beaYrX14rZTEnI28FjW2PW0nd8t0WQWy7pa7bX7e6VJHYbWkTSIMubIupSZy6C0ZLg/M49tcZx+6bpYtYq/jdJKHkq3zM3quLCNed2i16DcEK611WWHuOcBR2a9k3w0YrV7CAQi4lfAx7IaC7amGEvC+7Nxfbk7pcX/J4DLazwGLUU5JhxCubHxhcz8ebdPWvSYt2XmJj0ta1GsLwE3ZDU5Rd0i4orM3KGnZa2ypHR/e3/1+9MdltU5VedRlD6TjSTLLyLitMz8Xh3xKGNKQBmQ65zMfDrmH6m/1ZanZK3f2WFZbbN5RZkO8WTgdcDjlL6991NOROtwMmUsksYYNXsBX64pFpSM+ItRZjtYNst0qOvUEKfxxfzNGrbdnbOBmyKiMSX8e4BzqhYb97Y6WGYeBBClb/T7gO9T3ju1fN9F+6ekJzMvAC6oM0YHZ1Gmwt6FMhjswZQp3GuRmR+pulNsHmXcqrpnt2s0Qd4Z+EFmXhRlMoC6DOt4NzozZ0XEa+oI1MV35xsoAy+3+ruzX75bImJtygX7esyflKjr2P55SjfXdSizFw2ljAdUV8vdhyPic8x/8VBLAgvmzizZ8YbYNdQwu10H7fxuaevMhLT5eyUiJgOHZObU6vHmlONRHRd/x1LOi7ahXECfSQ1j+mU/dV0E/osyW2DjnPYaoK6xNhvdou+lTL7xKuW9U/eYQ6OA+yPiz9Q/DlC7h0CANoxztIS8PxvnzY3ryycios7WKMtTvrOmA6sDoyIi6mhxFmWG4TWA/4h5Y6Q26lDLORnlOPSZiHiJMslWLQnIiBhG2YdVowyv0kgKLE85H6zFEtFSqd2ijLmwVWY+Vz1ejtKdqa47YidRWii9AGxBGZTr93XcEauaj38sM7/d6m13E/NOSj/vyzNz44iYCOyX1SwPNcVcr4oZwBWZ2fLkR4dYF1IOCB+vYj4DLJOZO9UQawhwZmYe0Opt9xC30X0xKK2GOo8v08pYB1AuisZRplS+jtLq5caa4jXuSDd+Dwd+nZnv7PHJCxevrQPlR8SUzNy0sX/Vsqsz8+01xduLkpyYTNm3bYHaZreLMo7SPyhTmW9K+R69pcY7cddT7hDfVj3eFDg1M7eqIVbbvzvbKSKuo7Q++TYlWf3/2zvvcMmqKn2/Xzc5KkEElQyO5CFIEBBlUFExMEoYEEUUHAMgwQA6ohgJCoKKSBBmEERQfyiCiHQ3OWcEBEUERmFAkaRI+H5/rF3ddavrduLsU33rrvd57tO3ToXv1O0T9l57rW/tToxr+pYCNaB3I5H+f30nw7L7vKig92Ji8tC5dk4hslirZF32WRB7J1BtQazNa4vCU2KbmplXPXptX1feCBxNmOq+jDCQ/4AbLLeT9G3gU26hfKnoLTGj52tVG0hasWpoM0YAACAASURBVBOc69q2ke1rKmjNSyxavp/woxPhA3kykdnWuA9X0e17jrlCl0S1bIFQNKp/v3FyfB5OXEueIwKDiwPnVszk/i1whO3jy9z5cKJMc/OZvHVOtN5LZERtSAQ5OzxOzJOqVt/UpNzL9yUCSN0Z/48B33OlkvZxE1SStBbTr2ZWMXtWKWey/Y/yeAHgGlesNy+Dz8dsP1dWvRdzpbpJSZPcYrtOSdfa3rBMkP7V9vOSrrb96oZ1FrP92Gg3ilo3iJ59eC1x0T6/Rtp60fglYbRevZa+pLLebHut2lpdmg8DvwOOI8pA/1BZ7yrbGyvKmrYnyu5utb1aJb27adEoX9KVtjcpx803iRvUWbZXqaR3EzH5G9HdruJkbCHgTcAttu9S1J+vbfuCSnobAWcw7Ua/LLCj7caNu9u6dnbptZ051AlKTPV4kXSJ7S1m9t451Lva9qs7qfG1F4x6tCcCC9ec0A9gQaz6tUXTSm/WJDLMWulM2PZ1pWhuBfyKWEz516bHgJI+QQQdP+dKnio9evcQWfD9Uu9d8bpyPXGPfaA83hL4Vo0xvKRvEN4x+7l07FN4Dx0BPGV736Y1u7SXIawXoGJGsFq2QGiL8XB8ls9/CfAX28+We8KLOtoVtFZyTzm5pNfbvqiC1v49m0xkyl7auw8NaP2Lowqlb0ldk8H/Ht2P1VoU6se4KH9TpKxvRQx0f0FEXS+lXgexk4GrSgYKRBZRFZPZLl4FrCip+/+01ve7XNKxRNr6k52NtU4K4NGysnExcJqkhwi/h6b5AZGGfx196qOpVy75BcL35/Iaq0R9+ANwmaRzGPn/1/jgukxib5K0vCt5YPXRXErSmoTR85fKRPdO2++pJNlm5wgIn5O2Oi8CfFFhVLw/cAyRPvvxinqtdrez/ZSiNHMZScuXzXdU1LtG4ZvW6Qp1R63VaNq7dnY4mWmZQ6+jZA5V1PtHCVzfJemjRGbISyrqnSnpu8CLJH2QyC74Xi0xST8gysKeI+5Li0v6uu3Da0kysiPNc9T9/2vj2tIpTWmtM2E5Jq/uXkxxeLvU9Hf5LLADcd9bB5gsaX/bjbXjtn2YpNOAr0vaA/gOXR2Vm17Zt12lrfcssBfwU0nbEQbrXybKf2rwVmB1d63wl8XN/yTuQ1WCSpJ2IMYsk4lz/BhJtTKCW7VAAJC0CXFNeRVxvk8EnnSDGd3j4fhUZMb/qgSUPtWlVyWoZPseSTsBq9j+kqRXEP5bNVikz7YVgIMlHWL7jAa19gP2ZPqu0VC3W/QD5f+wm78Rix2NB5HHRaZSyRxaF7jB9rolOn+C7e0qaq7PtJT12t3K+rbOdD2Dv0l9Ntv1jHsXJlLHJwC7EJk8p7k9b4SqSHo/caxsSqRdXkIcM/9vhm+cc73WusQUvYuI1bCrGRnEqmUMvhjhcfJaonRqKcIEvW+nqIa156deS/qOxkCM8ttCLXe3U/sGvrv1214jc7ZcO/9B3IeqXzsHkDm0EeER9SLgUOL7HWb7yhp6RXMbwk9QwC9t/6qiVsckdReihOqTRLlIrWNzP8LX6CfE93s78H3bR1XSW9p2NX+2GehOABapnPV1GvDpthZTyn3hU7b/Xh6vQIxzt6mgtRtRsnURI6+Z729aq0vzbUTADGCy7Z/X0ip6mwLfJa6fb6l1nEr6re3VZ/e5BnTbzghuzQKh6F1LGMj/iChv2o1osFClnfoQH58dW4fNiCDk1wk7gk0q6R1L+ARvWTLZliDusxvN5K1N7sMSxLnQuFF320g6l5hbdubtWwFXAqtTwQR9vASVOinr1xGrp48T5SlVjJ4lnQgc466uBiXqeUglvWFunTmRuKD8W4uarbrld2m8lFhpPAB4sSub/0lalBgIVm1hqxZr94vezUQm4qVEcO7+Sjq90f8R1ArySDq5v1yzA3pJnygr053uNL2CVYLWRbvN7nZ3Axu3FaQuf88OCwBbEx4972pDvyYKv6gtCL+Mi4jVzK/artF4oHU6QTpHmfkriWyz82plmkm6DViPWOk/1vYU1W9t3uaC2F2E8fgPCR+6mh06p8v6AqplfbW9mNKlu7BL+WKFz16TyE76X6KteLXMqx7drxJ/y9PKpp2Ba21/evR3zZFOb/fKNYjssr9Cnf87ST8ljv1Te7bvCuxQcfFtauC/PJ4A3OR6JVTdXTOXJoK61ZoOaFrpd7df2+Wu0GV1yI/PGxx+jF8GbrN9miqWLGpaaflUjdr3vFH2o8p3HGXeUC9zKI6ZD9h+sDxehriGf4C4vzdqTTIuyt+AaxXlKd8jBhNPULc70xuBDUqaeudG8TbgkEp6txKZC23d4BcnVvY7UfkpRMSz8eyMMnh/StLiNbM/ADQot3zpBOLm8CCRpfQuooyqlt5aRDehJcrjh4HdbN9WQ69MhFqp3S961f1NCp1Mx5cAmxGTaIjA9WQqdUO0XavLRy+dEruqK4r9cLvd7e6jXnr1dNj+WPfjcj1tdrVoQK2OiVKNhYC9icyh1xOZL43SZ3A9gooT94uBLcr94ULi3NiRyAKrwXeJcuWbgItL9knN7JpViInD9Qp/ni0k3WP70Rp6tleT9Goio+BgSb8BzrD9PxXk1nCUFe1C2CB8khgP1iolrJL5Oxolc+FEoqRjeUUXv71sf7hBmbOAfVzRF2oU3gysZ/t5AEmnADcwsqNzE7TdGRfgI8CPS8Z6x3phI2BBwsi6FucrvMy6M4KrtDnX9F0z56Vu10yApyTNB9wo6TBifrRwJa1hPj7/JOlbhD/chuVvWs2OAHimBDgNIGlJukps20BSp2FSDfZglMwhSY1nDgErdgJKhYeIctu/SGp8MWxcZCp1I2lFwsS6WptchYnaVkTU+o9EC8FrKkZ2JxGrmdVaZ/bonU0Esk4pm95DmPDNMHPjBeidCWxCGFB2r/g1mimhkW75DzAtqFTXLT+8t5YjasunENHj39fQKnqXAwfbnlQebwV8ucYKTvn83tr92t28lgY+QRizdpsF1yrP/Dnwwc6qrcKQ9VtNnw+DzBxqgz4BkKlPUbe73Ym0aODbR39eorzvVW3oDQOjZT92qJgF2VlF/RiwYDkfWzWalTSPK3UwU3S32xBYETifaAH+SlfoRNpHeymitGIX2xMrfP4gsr5WILIzLlQYd090MWOuoHUVsSB1TtcK/61NrkRLmt/20zN/ZbOU7OOtXJqlKMpTJre4gFSdMpFdk7jf3Wb71y1otpIRrJa7ZpbPX4FYqJ2P8GlbHPi27bsraA3t8anwZHwzMUa5Q9JyxHzvvIZ15nH4Nu1GBFM3JLyIdyA6njbpb9TRvIXpx5xLEJmYu9lu3Fuz7cwhRcfO5YkyUIB/B+4HDiS6xDfadGu8ZCp1Us42Jw6gS4FqQSUiWPcYsJ2kQ4hAweIV9Q6p+Nn9WMX2v3c9/ny5adTi3PJTFdtHA0erZbd82+8EkPQqIsttkqSJtl9eSXLhTkCp6E9WlHXU4mCiG+KI2n1i1bMGpxHlFG8lyh3eS3R0qMWKPWUADxL1yk3TauZQ29kgrlzuOQNaM/CF6f6uE4gsxR+N/o4XpLUKcL/tp0vweB3g1KYzTxSm/6NS4ViZUnTfCvyis0LcAioZIbsQK45QcRxVBpxfBpazva2kNYhVzhMrST5fBvbbA0fbPkZSzfK3xYgJxE6EL+RPgCqdCWk/6+uDhDHrEsR3exnRkbRaGb3t+6QRvurPjfbaOfz81gNKha8AN5QFVBFZ8k1ngUxFLZg89+LobtV4h6uZaLaVEfxP25bUyT6pOd7ssD5xb3iM+lmDQ3t82n5C0h+J6/IdxMJbjaqGq4H1bZ+qsKr5N+Jv+W7bt1bQg5gjdGPgEVcqHy60mjlEZEJ2B49PBc52ZBQ13sV9XASVSqRuVaalee4l6d9sf6SS5NQBtu1DFIZx+83g9S+IWquyM+Dvkja3fSmApNcQRtpVsH1KSbnsTNTvdL1uSQDPS3pRZ+JVSh12tv3tGmJlYrQFcSN6MTGwuKSGVuH3ik4xnTTLXQlfi1q02s0LWNL2iZL2KefGFEk1z5HJXWnkJiZI/czsXxC2f1b+PWVmr22ITqr19kR5backZWdicjYUuJJB/QzoTmF/FrjXlXy/iAnDhpJWJQIR5xCZGk1nnmxKlBGeDlwFVTuGdbMTsRBwNnCy63dF3JeYLPzE9m2SVqbCud7F94lykYPL498SAfNaQaVnJO1MmNp2ynvnraQFEeD5KVE+f0VFHWx/E/hm16Z7JTU+qO7iI8RE7Kqif5eiNXct7lOY6bqMl/Zm2kLEmEURJbuUyFbfiLi2fNL2nyvKHksfk+eKeq1TAsdfI8r3Rd2M4Fa7ZhbeBhwl6WLgDMKbtfEMz2E/PiV9hghIrEIEJBYgxhCbNy3V+cVhxVHFjqMb2/fW1ujDJaW6oTtz6OISaG28zLwEj86i3iL+CMZF+VtJe16r/HE7hnS3uJJRd9u0vaqiqNU/lWnZV38F3lurpLCssJ9CTGQFvKLoXVxJ70bb6/Vsq2lM9y3Cq+MS2/9bQ6NH78XEys1UM1bgEFcySVX/bl632P5EJb0rbW9SAj3fJFJZz7K9Sg29ork9ERiESmnkbWcOdelebHvLmW0bqwygXPJr7ulk129bQ1qdcq0DCYPpY2pcyxQNFbYhAo7rEJmlp7uST1uP9mJFd3fi/Di5aFcpM2oTSdfY3kgjTUunuz81qLcGkd15he3TJa0E7Gj7q5X0VDIYqplLd2ktSXhBdmesf8H1OiFeZXtjTTO6nYco/6nVuW8p4GimrfBfQPgfNf79SqnWlbafavqzR9G7zvYGbWgVvdZMngeFokHFdi0E4jt6rXXN7NKcF9iWGHNuDvzK9gcq6Azt8amWShcl3U+UP/fFLdkR1KYEITuVU52AZCdzqIZem8Hj8ZGpBNxJ1BR2opKvoEL5mwZnjtpK1LpkfhxNdG1YtwzmccW2vIUjgTfYvrPsx+pEgKLWRXxCZ7Bb9CZSsSymYsbcaHp/JVYx29I7UNPKTwUcXyPo0sUXFebH+xPB1sWImvpqODq9VTHm7mIQJo0AS0ta2cXnq0w0lx7QvtSg7XLJbQiT4G627bOtCTqZJ++lYuaJ7ecID57zJc1PBHkmK4wnq5YSO8yXzyaMbfclyqkOlPTNprVLeUM/P7MqAUjgyRIM6dyLNqGiqbzt31DuDWXxYdFaAaXCJgpPs5rm0h3OIBZQOqX7uxDnfa3OslMkHQQsWCbUHyY8qqpg+2HqGcb38j7gOEmPEFnVlxBt4muZ214paSPb11T6/F7aNHkeFA+2EVDSyA7O1QNJ3dh+RtJ5xPVzQeDthHdN0wzz8fl0Cfx37kELVdKZSNwH2spybp2ec6GtRjSH0WbweJgzlbpW9hdnWmtXAxsDl7vFNvU1aStq3Vkh7ax+N/nZM9GdLipeI1Le9dmHE0alxxHHy4eA+2zvX0mvlUyzQWW6FO3WDEvbok/weOpT1A0it4qkNwHHAx3z+BWJid8vB7ZTDdJZZey5fk6xPUMz6DnQ+U9iYrky8LuupxYFLrO9a5N6RbO1zJMSTHoLEVBakSi1O8n2A01rdWm+jchQWoUo5z3F9kPlGnO77RUa1uteyFiACFA8WzHrcn3i3rAW0RxjaeBdFbOCJxNlI/MANxLB1Sm2q5TvqwVz6S6t6bIJOmOnprXKZ08gfLemZmcAJzS9Ii3pmzN63hUbOChMe98FHED4flVZqFZ0BVydWBh+kmn32FpjwNZMnttG01qav5Yoa/8pIxtUNL44pvDce48rd3Du0XwTsdje6cb7Q+AC1ymBG9rjU9IniaSMNwFfJK5pZ9k+qmGdVueVg6Ltc0HSZbZrdlkcqTfkQaXWO8SUgcTNNQZFM9C8mFhtOwH4MxG1fp8b7moi6XTCO2NpRk6Kal9ATyIm7x0PoF2AeVyptXr5P9yLMNTspJGfUFbja+hdy/SZZqvaPniGb5x9nUF1TJpqWGp7FUmrAcfZbtSwVKN0RetQc3DdBurfqWIqtc6/oj0/8C/l4R0enGFr46ilcsmSPfdiwtTzU11PPe7SNWasomihvBZwHtESvpaxZj/dE92nFFrS1m6ng1LjAciez5+H6E4oKvsJdpVqfQB4he3PVV7AGVEiVrZV6cgm6QiiycGZZdO7gDVtf65prTaR9E8i4Hgmce0asdLvCh58knYlyr3XBh4mSjgucSVfrDKJng5X9ERRlEVju2bWautIOnkGT9v2+ytottLBuUfzDCI78bza45VhPz4lbcvI0sVGO78VjVa7qA6Kts8FSUfTUvAYhjyoNCgknQZ82vYfW9JrM2r9UmLFbbqslloX0DKh/QgjPYC+XfNGIWlBYPlOyV1NWsw0W76tY7JH90aKYWnXxOEW22s3rPPeroefJ/wzplJjcN0mow1cOlQewGxGZJ9MXYm2fWotvTZRGOVfQpRFd8olD3ExRq+kORFYhpF/z8bOTUln2t5htEBk00ECSc8zbYDUSul3Typ5KyhaRXeYQJRgf9P2KytqtnbulePlDYSH4cG2r6kcVDqL8NE4lhho7w1saHunClqPEyUinU6BE+g6Zps6RmcS/H+aWJD7iu2bGtJbEng34RvzLJGRcbbrlaIh6WHiexwHTLL9h1paPbovYaTvXaPjGUkixg0fJa5dE4i/6TG2v9Ck1niiZOnOQ5x7z1Ea+4z1MVkveXzOOZKWGOuLa7NCzzxlKrXOhVGCyFWCxzBOgkptlRd16V3EtHK77kHL2yvpbWD7up5t29WcFA0zpaTicGA+2ytJWo8w9KxmhEw7mWZT00slnW3732f2noZ0WzUsLZrjYtWjDST9N1FedCPTWlR7CDK/Xu5Ruq7VvH5K+ihwCLEQ0JngNprpKWlZ238axApqmwwglfweImAgYjB/D3FvuLSSXqvnnqR3A58l/HE+rOhud3ite4VaNJdui5kE/+chMvoOqXF/kvQyovx0P6ID1X/P5C0vRGtNomPt5oSH552231NJ622Et+ZyRAvuFYjy1kab7Uj6ONEZc0/b95RtKwPfAc63/Y0m9QaJpAWIUqbeBhWNTTbLWO/LRLe3e4kgyCuIZgoH1ci61ABsCYb5+JT0duCrxHerbvScjG3GS1CpX3nRarYPqqTXnQov4qa7c9MXmC6964luaLeUxzsBH7e9ccM6o61+Vyl/G1S5j6TrgNcDk12x20GX3grEjWheKmaa9ZQYtBZ0URgJPkqcdx8jfGV+44bL+3o0h64+exCDpaJ7O7CGh+xmIelO4I29q+ySdgc+40rdAhVddzYeyxPnuYVBlFW0ybCee4NC0jpMn/VVu8FCv/34vBsuu1P4b+1MNAK4DjjSYbzeOIomLa8hfHm2AJYiusH1XYVvQO8mYkx2YVmYeh0xpt6zYZ0bgG0cpufd25cm/HiGZqFK0o+AO4D/AL5A2ErcbnufBjW+QXgGftzFQ7McO0cAT9netymtQTLMx2cZr7yzM79M5owBZI9/wvZhGsUWpNYYabx0f8P23ZImOnxxTpZ0eUWtKSW75T+AHYjVzONq6RHeAGdJ2oUIYO1GpLA3Tedm89YKn92PtnR6edb23yLTtD5dWQN/J8q2qkmN8nttPkWsiN1CeCuda/uEFvWHAtuLDkj6VqIm+08D0q/Fx4FfSXqz7bsAJH2auG5X88gB7qNiB69uegKR8xGB62pZugPg3PLTGi2XgrZy7rU9AJX0XzN42rYPbVKvaJ4ErAPcRleGIPW7dk5HkwElSZ8nxkq3Ex4yn3YFM+IeLu36OXa0jM8Gecb2I5ImSJpge5Kkr1XQmbd3wg7hW6NoTz9MrGr73ZLebvsUST8grC2a5K3A6t1BcUe3zv8kAlrVgkoKD7WTagVWexjm4/PBDCg1wuOSXkN04W1j/tXp9nZtC1pTGS9BpVbaLypa3e9ErBY9QtS2y/brmtbqxvbvS3bST4kJyxts/72Czp/Kv62UTnTrSFqGKCkEuNr2QxWlb5X0H8BEhan03kDjQcgBZGKtK+kxIrNlwfI7VMp0KWmzL7f9LeB7CsPupYENJD1q+6yG9bon0AvV/n7jiKWA30i6mpFGf9W6BbaB7V9Ieho4T9I7iFbDGwFbuqIfCdFFb7Kkcxn59/x600K9gcjyPV/dtM6gcMueHKOVowG1gkptnXttD0Cf7LNtYWLxYUmg8aASsIntNSp87qD5LHFNWbf8fLksiFVroOJp3o8L2+73f9k0j0pahPDTPE3SQ0T5adP8cw6fG4t0Ss8elbQWYb2wYsMa7pdlafs5lRb1FbmDGHfOQ5Tbne56ZdLDfHxeo/AJ7jV6PqeS3rByM5GhtywRGzjd9o21xFzsG1ofI42HrOoWy4ueJwxf9+h8tqTf2165SZ0uvd6gxEuIFfCnoUo63aDKb3YgPI4mF60tgAObDkp06S0EHMzIVsCH2v5HwzoDM15uA0mXATvZvq88vpFIEV4EONkNd39L6qBRuga6UrfAtpG0OTFguhzYoenzvI9e3ywF2zWzFLv1r7S9SRtatSlB/68AazDSF6TWPbfVcrRhP/cAJC1KZEHvQXQwO7LGopGkE6lYEtZH7/VESdhTlXVaH0dI2hQ4EVjE9vKS1gX2sv3hprWK3sJEJvcEokxrceC0pkuIJT1H/4CngAVsD022kqLD49lE9t7JxLjsv2w3VlUh6afAj3szORXdA3doY2FK0iuB3YnF/suA79me1LDG0B6fZSGlF9verWmt8UC5Xu9UfhYATic65v62kt7qwAFMn139+ip64yGo1BaS3kkcKJsB5xOpyCfYXqmS3lAHJTqUeuVtOgPNUj98oSu0HR4U5f9yNdsXKjrPzdOpQR+rSLrG9kZdj4+1/dHy+9BMbJOxSVeQXMD8xMrtc7SU2dbGKr+k7bseTiA8BV9re9Oaum0h6VKiG843iLTy3YlxTZU28cWHZO9O1u6woDA8H5Uakz9FJ739iEnYKcDRNTMEJW0J/IzIyHiaipk8Re9Uwu/rEWKx8RLCAL1mFmQrSLqKsF04x9N8Gm+1vVbDOqsCy9i+rGf7lsADtn/XpF7SLArj+B8TAZfriPvtRsCChE/PA5X1JxIleLsTBuFnEhYhT7qBDpN5fCYvBEn/CpwErGN7YiWNmwj7neuYll2Ne5p7NcVQl7/NpLzITQclbP8E+EmJWr+DyIpaRtJ3gJ/YvqBhvXslTQBubvpmPiuocvvMLib0rFw+QkyQqtB6ZDfKwvYEliBKK15OXATGeibPi7sfdAJKhaVb3pdkNhlUZmJb9JaGtUX3Kj9Qe5V/u67fnwX+AFTpQjogFrT9a0kqiyiHSLqECDQ1hqSfEefCorRYClqCgl8jspBrdt7ZlCidPx24quhUQ9LhwPbA8cDatp+oqVc4CXgP4e33/Exe+4LprORLWo4IwHyL6KA0FONu2/dppO/kc6O99gVwFNCvoc5T5bnt+jyXzABJ+83o+SbLsEvQaOOStbcmcV05z/avm9IYDUlfJ46Pi4Av2766PPU1RZOOJhj641PRofP9TD8fatSEfLxQvK/eRCSgbA1Moa6X7rO2v1Px80cwFDe3GdDP6FnEpL1K5zeAsvp8GlFbuwTwbsKsuNGgUtF6XtJNkpavGNQZgUZpn0ncNGpwvqRfEgNegB2BX1TSgugSeBxwAnUGSr18hPA5uQrA9l0lYDfWuUrSB21/r3ujpL2Aq0d5TzKXMKigyzjgKOCNwDkAtm8qK5uNY3v3Gp87F/GPsrByl6SPAg8QAZimOaLCZ84KhwHb2b59pq98YbyU6Bq2M2FUfy7h+XBbJb39iaDcZ4CDu4ITNQPWf2zTB6SU+GwBrA08DBxLZCsNA/cpDOut8Cvdm2m+XE2you2bezfavlbSihX0xgOt39dtX0QEd9rkVqKLa7/y06Z8BcfD8fn/gCsJU/425kNDiaTO/fUtxPznDGDPWtnqJfYA8DNJHwZ+wsiFsL/U0B3qoJJHGj33dmM7u6V9+Avw3fJTi2WB28rqaecAte1aK9KHEmndI9pnNi3SlVp6YFmx3ZwYdF5BBO1q0WpkF3ja9j87A+tiLDgMdakfB36qMD2/vmzbgCg1esfA9ipJBkxLq/xIejlwDNH+28TAcB/X79bUFvsCCxGT2kMJz7bG25p3PIwkrQT8qeO7VUqVl2lar4sHWwgo4eiKez6xgDM/cT+fLOkLto+poFct03gG3KHocPUzRg6ua3V/Owr4HbFANcn2HyrpAO15OBU+BBwNvAy4n1gw/UgFnQVm8NyCFfSGno53n6QleieW5fo2LNxJybgsAd71iRLbe92cYfd4OD4Xtr3/oHdiCDgI+AFwQK2ATg+dctPOQPPArucMVPGdHOqgkgbUjW0AdKfOiQi+NB7k6aKt9plTU0vLwO/HAJI2pG5qaauRXWCKpIOIjmzbAB8mBr5jmlKyuFlX6jPAuWXVKknGK22t8kMYsP6AyJYF2LVs26aSXqvYvqb8+gThm1GbHxGeiR2eK9s26v/yF8y1kn7I9J13Gg+ElGDSW4ixw4rANyn33CFhQeJv+IaubabSd7S9lKQ1gS2BLxVT+Tttv6eGHvA+4DhJ1T2cHG3Nd2n6c/twzSjZznsQk6ZkzvmZpG1tPwYg6VXEtax1K41KfIfoeLwu8Ami5PxUoG/zgzlkPByf50l6Q9P2LeONtuMOtbycZ8ZQG3Wr5W5sg6RPJtaPa6wwFq0LiUyTrxAtjx8CNrK92QzfOPs6oxo/SrrF9tpN6nV99j19NrvWcVPKN/ZgZLe5EzzMJ2eSjFOKR8HRwL8R5/sFhPlz40FrSTfaXm9m28YagzCWLrr9/p43Ne3P2PXZJ/fZbNvvb1jnFGIyeR7RiebWJj9/PCJpMSJD8LVEGdxSRCZR45l0PbodD6cDgOVsN7Z4LOm/ZvC0bR/alFbR1NBmTwAAFEpJREFUW4ZY3Psn0ybpGwLzEUbPf25Sr0u3LS+zgSHpLUSw5S3AK4mAyy6u2Oa8TSRdb3v9csw+YPvEzrYGNYb++JT0V6Kb3VPE9+xoLTHDNyZzBZLeDZxv+3FJnyEy9g61fUMVvWGet6rlbmxtM0om1gG2Z9gV7gXorUqk+t/IyPaZKxAZKI1G5iXdbXvV2X1uLKLoaIft/xv0viRJ0jySXj5a2Zmk7Ww3np1YFgC+zzQ/up2B3W2P6SYAkv6PGRhLd8rVKuj+Cjim480j6e1EQHCs/z2fp6t0vvsphmQyLWkBYvFmTUY2GGk0QNeldzNRbnopcHHtktM+Hk6XApfYvqJBjX5lMAsTf9clbS/SlFaP7uuYlkFzW+1sZ0l3046X2UCR9A4isLQosL3tuwa8S40haQox79udyBb8P+DGGovRw3x8KjroTUcpmU7mciTdbHsdSZsTiSBHAAfZ3riK3jAHlTpoWje2nQnPhVOo0I2tbdrOxJL0c+JgvLln+4bA52w3Wo4m6XTgolFSS99ge8cm9Xo0NmP6bgenNqwhokvRR5m22vAcMWn5QpNaSZIMFkXHmTf2eqtI2p0wFF2lgubyhEHwpkSw4HIiCNJKU4dalIFux/hyHeobS3d0VyH8/JYjrtf3Abt17r8N6nzC9mGSjqGPv57tvZvUGw9I+hFwB5HR/QViQex22/tU1l3YlcxYe3Qepl0Pp0WBfYiA0pnAkR7ZpXfMIuky268Z9H7UoOeaIuB1wO+JzqBDc22R9FLiXL/G9iXlXrhV0+P4QdD28SlpJ2Bl218uPo3LNJ1EkNRB0g0O7+OvALfY/kFnWxW98RBU6kbTurHt6Eot4tui7UystsvRBpha+t/AKkRGVica76ZvtpI+DryZ6ABwT9m2MlELfr7tbzSplyTJ4JD0ZqLs7c2dFWFJnyYGvtvWzmTo2o99bR/VhlYbaJqx9OFAFWPpPpqLEOOnxyt9/na2fyapb6mU7VNq6A4zXYPrzsrtvMAva40DJW1K+LgsYnv54u2yl+0P19Armh0Pp82BKh5OZQy9HxGUO4UwP27ct2kQlLIiiJLFl9KCl1nbdF1TFiQWTU2Mc/8OeW2ZmxnE8SnpWGBeYEvbryrn/y9t1/IRTBqkJIM8QNgtbECc51dXK9kfb0GlYaStTKxBlaMNILX0dmCN2p5Gkm4AtnGYXnZvXxq4oFYkOUmSwSBpa6IT6DuADxAGz29tc1Im6Y+2l29Lrxaa3lj6HOAk2w9U1n0L05dQZWbpXI6kq22/WtLFRDOMPxOD61qZ3VcR3kbndO7lM1qYa0CvuoeTpMOB7YHjgW/ZfqKpz54bGMXDrINrlUq2SQmmfgl4P/BHIlvpFUQDh4NsPzPA3WuMYfTFGsTx2eVNdUPXdayaj2DSLJIWAt5EZCndJWlZYO1alVpD3f1tvFBSq08DTuvKxPoUYQDbJAPpdGB7EjCp1uf34VZiFeBPlXXm7Q0oQfgqlRt/kiRDhO1fS3ofMJkoRdvapT19i2jmL5m70Uhj6c+7JWNpSccBCxElIycQQYOrK+gMxIh8yDle0ouBzxIByEWAGRlPv2Bs3xdV7lOp6UNyadfPsZUyH/cnMiM+Axzc9d3G/IQdwPbuAJJeY/uy7uckDUs53GHEsb9SJ9OyBCSPILI99x3gvjXJYQyZL9aAjs9nFM2EXHSWBJ6vpJU0jO2ngB9LekkpAYUoA69CZiols8ygytHaRtIkYD1istCdWtroQF4z6EQxo+eSJBl7SHqcGJgJmB94hphktjohG4ZMpUEZS3eVTnX+XYTotPqGmb559nQGYkSeNIeks4CvE55mmwB7Axva3qmybiseTm3SdtZJv/HXsIzJJN0FrN6biV986u6wvdpg9qxZ2vQdGsbjU9I8tp+VtBvwTmKudxLRYfzzts9oSiuph6S3AUcSPpAPAcsT5/maNfQyUymZZWw/CGzWU452bu1ytAFwSEs660p6rM920VVakSTJ2Mf2om1pdQWwpnuK8NIY09ieMCDpv5d/n1K0bn8EqOFh+FKmGZH/By0ZkQ8jkvab0fO2v15J+kOEh9rLgPuJzPGPVNIa4eEEtOLh1CKtZJ2Uv+FmwNI9x81iQN8uWGMQ97N2sP2cpGHKMrhW0g9pxxdrGI/Pq4H1bZ8q6TrCk0fAu9vKDE4a4VBiUePC4in4OmJcUYUMKiWzzQDK0VrF9pSSldUxorvaFbqa2B6WQUqSJHMRbQawxhk/l/QiokzkeiJwd0LTIo52zecD53cZkU+W1IoR+ZDRfS7sRXiaVaeUtu/ShlbhKOCNRGkftm+StGWL+jV5sKUypvmIoNw8jDxuHiNKXYeB30jazT1d0CTtSsWymAGwGPAU0J1FaqBGUGkYj8+p2bFlMSMXNMYmz9h+RNIESRNsT5L0tVpiWf6WJD1I2oGYNEwmLqxbAAfaPmuQ+5UkSZLMHZRgzwK2/1bx81s3Ih9mVLGVcpfGjHyabPvQSrpX2d54mAx1B9TtaiLwQ9vDEkQagaSXEYGVvxM2FiYWUBckbCzy+jKLDKpboKQVbN9b47O7NO4nynf7UjHDM2kQSRcSjWG+CixJlMBtZHuzGnqZqZQk03MwcdI9BFO7sV0IZFApSZJknCLp3cD5xeD2QGB9SYfavqFhnYEYkY8D2lhF7edntDCwBzGorxJUAu6TtBlgSfMRHk5j3aR4u67fW8k6KWVgSzT9uXMLJWi0saTXE10sBZxn+9eD3bNmKeP2DxIB+alz3YY7pLV+fBa+369U0fbrG9SYSGRFjfnGHuMRSfsClxEBpacIA/5dgMWBat1qM1MpSXqQdIvttbseTwBu6t6WJEmSjC+6DLo3B75CdEw6yPbGDesMxIh82GnbbFnSosA+REDpTODIGqX0RWspwsOp431yAbCP7Udq6LXJaN2uerc1qHcksBrwI7qChLUyT5LmkXQ5cAmRjTW166LtsytotX18btD1cAHg34FnbX+iQY2hMKYfr0g6gvDf+hfgZqLb8GXAFbb/Uk03g0pJMhJJhwPrEJ13AHYEbrb9ycHtVZIkSTJIOqVFkr4C3GL7B22UVCVzjqRbmBaYWxW4u/MUEaBbp4LmEsB+xMrwKcDRtv/atM54oe1ubJJO7rPZDWe5JBWRdKPt9VrSGni3QElTbL+2wc/L+9oQULJWNyQCTJuWn0dtr1FDL8vfkqQgaVVgGdsHllrpzYmB5xXAaQPduSRJkmTQPCDpu0Q2yNeK79GgOtEls8Zb2xQri1LbA8cDa9t+orLeQDyc2mBQ3dhs717rs5PW+LmkN9v+RS2BQR2fPeWZE4ANCE+nJtm64c9LBsOCxPG4ePn5X+CWWmKZqZQkBUk/J0oZbu7ZviHwOdvb9X9nkiRJMuxIWgh4E5GldJekZYnAwQUD3rVkLqGULj4NPEsLpYuS9u+zeaqHk+1FmtRrE0mvBbYCPgQc1/XU48DPbN9VSfflwDHAa4j/w0uJUsL7a+glzSPpceI8+Gf5afz8G+DxeQ9xXIq4ztwDfMH2pTX0krGHpOMJz7THgauAK4Era2fMZlApSQqSbrW91ijPjfBZSpIkScYXklYB7rf9tKStiDLpU20/Otg9S5J2PZzaYhDd2CT9CvgB8N9l067ALra3aWsfkrFDG93YkmR2kHQ+sBRwK+GndAVwqysHfTKolCQFSXfbXnV2n0uSJEmGH0k3Ev4EKwK/BM4BXmn7zYPcr2R8M+weTpIuariz1cz0pvPjadOjJ3nhSBJxPqxk+1BJrwCWtX11Ba1J9OksWeuYlTQv8J/AlmXTZOC7tp+poZeMTco5sCZRorkZ0VH2L4RZ9+dqaKanUpJM4xpJH7T9ve6NkvYgOkgkSZIk45fnbT9bPPeOsn2MpBsGvVPJzCkt1K+0/dSg96VJ2vZwGhA3SDqH9rqxPSxpV6Y1a9kZGPNd9MYZ3waeB14PHAo8AXwL2KiC1gFdv0/txlZBp8N3gHmJ7wjwnrLtAxU1kzFGyUq6VdKjwN/Kz1uBVwNVgkqZqZQkBUnLAD8h6q87QaQNgfmAd9r+86D2LUmSJBkskq4CjgIOBrazfc+MyqaTuQdJpwKbEMGBS8rPpWM9o6dtD6dB0HY3NknLA8cSnZJMlI/skyVOY4dO97XuLmaSbrK9bkv6jXZj6/ns6b5Hm98tmfuRtDeRnfQa4BngMqIE7jLCE/L5GrqZqZQkBdsPAptJeh2RJghwru2LBrhbSZIkydzB7oQp65dKQGkl4H8GvE/JLGB7NwBJywHvIrIWlmOMj4NtD333wba7sdn+I/C2NjWTxnmm+HEZQNLSROZS47TUja2b5yStYvt3RX9l4LmKesnYY0XgLODjtv/UlmhmKiVJkiRJkiRDSyln2gJYG3iY6Oh1ie0rBrpjyUxpqxubpP+awdO2fWiTekk9JO0C7AisT/iMvQv4jO0fVdBqtRubpK2Bk4HfF80VgN1tT6qhlySzSgaVkiRJkiRJZoKk1YCvAGsQ3hkA2F55YDuVzBKSHgZ+R7T+nmT7D4Pdo2RWaasbm6T9+2xemOimt6TtRZrUS+oi6V+ArYnAy69t3z7gXWoMSfMDryS+2x22nx7wLiVJBpWSJEmSJElmhqRLCYPLbwDbEeVwqtVJJWkWSWsSHZM2B1YD7rT9nsHuVTIzBtGNTdKiwD5EQOlM4EjbD9XSS5pB0gJEifKqwC3AibZrmma31o1N0kbAfR1/V0m7Eabg9wKH2P5Lk3pJMrsMfS12kiRJkiRJAyxo+9dEIOle24cQ3YWSuRxJiwHLE6UiKwKLU8ljJWmchyXtKmli+dmVSt3YJC0h6YvAzYTf1vq2P5kBpTHDKUSDnVuAbYEjWtD8DuGj9O3ys0HZ1jTfJRoJIWlL4KvAqURXr+Mr6CXJbDGmDQqTJEmSJEla4h+SJgB3Sfoo8ADwkgHvUzJrXNr1c2zTfjxJVd5PdGP7BtO6sTXe+U3S4cD2xAR9bdtPNK2RVGcN22sDSDoRuLoFzY16Oq9dJOmmCjoTu7KRdgSOt302cLakGyvoJclskUGlJEmSJEmSmbMvsBCwN3AokaX03oHuUTJL2F4HQNLCtp8c9P4ks06L3dj2B54GPgMcLKmzXbEbXqyFfUheGFNLzmw/2/V/WJO2urFNlDRPKefbGtiz67mczycDJw/CJEmSJEmSmWD7mvLrE4SfUjJGkLQpcCKwCLC8pHWBvWx/eLB7loxG293YbKclyNhnXUmPld8FLFge1wwMHghMkjSiG1sFndOBKaXpwN+BSwAkrUqUwCXJQEmj7iRJkiRJklGQdM6MnrfdRhZF8gKQdBXRVvwc2/9att1qe63B7lkyGtmNLRkrtNWNTdImwLLABZ2MS0mrA4vYvr6GZpLMKpmplCRJkiRJMjqbAvcRK8VXEROHZIxh+76ecpgaJSpJQ9g+svN7Vze23YEzgCNHe1+StEF3NzbbT0taj9KNTVKVbmy2r+yz7bdN6yTJnJCpnkmSJEmSJKPzUuAgYC3gaGAb4GHbU2xPGeieJbPKfZI2AyxpPkkHALcPeqeSGZPd2JK5mOzGliRdZFApSZIkSZJkFGw/Z/t82+8FNgHuBiZL+tiAdy2ZdT4EfAR4GXA/sF55nMyllG5s1wCPE93YDrH91wHvVpJ06NuNzfZngVUHuF9JMhDSUylJkiRJkmQGFM+MtwA7AysC5wAn2X5gkPuVJMOKpOeJbmzPAt2TlezGlgwcSbcC65Uuc3cAe9q+uPNc+rUl4430VEqSJEmSJBkFSacQpW/nAZ+3feuAdymZRdruIJY0R3ZjS+ZyshtbknSRmUpJkiRJkiSjUDImniwPM2NiDJEdxJIkqUV2Y0uSaWRQKUmSJEmSJBlqujqI7QGcCRyZhs9JkiRJ8sLJ8rckSZIkSZJkKJG0BLAfsAtwCtFBLA2fkyRJkqQhMqiUJEmSJEmSDB2lg9j2RIvvtW0/MeBdSpIkSZKhI8vfkiRJkiRJkqEjO4glSZIkSX0yqJQkSZIkSZIkSZIkSZLMNtmuM0mSJEmSJEmSJEmSJJltMqiUJEmSJEmSJEmSJEmSzDYZVEqSJEmSJEmSJEmSJElmmwwqJUmSJEmSzCGSDpZ0m6SbJd0oaWNJ+0paaBbeO0uvS5IkSZIkmVtJo+4kSZIkSZI5QNKmwNeBrWw/LWkpYD7gcmBD2w/P5P1/mJXXJUmSJEmSzK1kplKSJEmSJMmcsSzwsO2nAUpw6F3AcsAkSZMAJH1H0rUlo+nzZdvefV73BklXSLpe0o8kLTKIL5UkSZIkSTKrZKZSkiRJkiTJHFCCPpcCCwEXAj+0PaU3A0nSErb/Imki8Gtgb9s3d7+uZDn9GNjW9pOSPgnMb/sLA/hqSZIkSZIks8Q8g96BJEmSJEmSsYjtJyRtAGwBvA74oaRP9XnpDpL2JMZdywJrADf3vGaTsv0ySRBldFfU2vckSZIkSZImyKBSkiRJkiTJHGL7OWAyMFnSLcB7u5+XtBJwALCR7b9K+j6wQJ+PEvAr2zvX3eMkSZIkSZLmSE+lJEmSJEmSOUDSKyWt1rVpPeBe4HFg0bJtMeBJ4G+SlgG27Xp99+uuBF4jadXy2QtJWr3m/idJkiRJkrxQMlMpSZIkSZJkzlgEOEbSi4BngbuBPYGdgfMk/cn26yTdANwG/B64rOv9x/e87n3A6ZLmL89/BvhtS98lSZIkSZJktkmj7iRJkiRJkiRJkiRJkmS2yfK3JEmSJEmSJEmSJEmSZLbJoFKSJEmSJEmSJEmSJEky22RQKUmSJEmSJEmSJEmSJJltMqiUJEmSJEmSJEmSJEmSzDYZVEqSJEmSJEmSJEmSJElmmwwqJUmSJEmSJEmSJEmSJLNNBpWSJEmSJEmSJEmSJEmS2SaDSkmSJEmSJEmSJEmSJMls8/8Bx5fI40gIKCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvt_merged.plot(x='State', y='Asthma Mortality', kind=\"bar\", figsize=(20,3))\n",
    "plt.title(\"Asthma mortality per State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel(\"Asthma Mortality\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pvt_merged['Asthma Mortality'], pvt_merged['value_O3 Mean'], marker='o')\n",
    "plt.title(\"Mean Pollutant vs Year\")\n",
    "plt.xlabel(\"Asthma Mortality\")\n",
    "plt.ylabel(\"value_O3 Mean\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col1 {\n",
       "            background-color:  #a4bcda;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col2 {\n",
       "            background-color:  #63a2cb;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col3 {\n",
       "            background-color:  #e8e4f0;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col4 {\n",
       "            background-color:  #023858;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col5 {\n",
       "            background-color:  #a5bddb;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col0 {\n",
       "            background-color:  #cdd0e5;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col1 {\n",
       "            background-color:  #023858;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col2 {\n",
       "            background-color:  #4094c3;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col3 {\n",
       "            background-color:  #f0eaf4;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col4 {\n",
       "            background-color:  #afc1dd;\n",
       "        }    #T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col5 {\n",
       "            background-color:  #023858;\n",
       "        }</style>  \n",
       "<table id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Asthma Mortality</th> \n",
       "        <th class=\"col_heading level0 col1\" >value_CO Mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >value_NO2 Mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >value_O3 Mean</th> \n",
       "        <th class=\"col_heading level0 col4\" >value_SO2 Mean</th> \n",
       "        <th class=\"col_heading level0 col5\" >value_State Average</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row0\" class=\"row_heading level0 row0\" >Asthma Mortality</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col1\" class=\"data row0 col1\" >0.2156</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col2\" class=\"data row0 col2\" >0.180544</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col3\" class=\"data row0 col3\" >-0.0505675</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col4\" class=\"data row0 col4\" >0.125801</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row0_col5\" class=\"data row0 col5\" >0.222388</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row1\" class=\"row_heading level0 row1\" >value_CO Mean</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col0\" class=\"data row1 col0\" >0.2156</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col1\" class=\"data row1 col1\" >1</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col2\" class=\"data row1 col2\" >0.397031</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col3\" class=\"data row1 col3\" >-0.315108</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col4\" class=\"data row1 col4\" >0.185583</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row1_col5\" class=\"data row1 col5\" >0.998124</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row2\" class=\"row_heading level0 row2\" >value_NO2 Mean</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col0\" class=\"data row2 col0\" >0.180544</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col1\" class=\"data row2 col1\" >0.397031</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col2\" class=\"data row2 col2\" >1</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col3\" class=\"data row2 col3\" >-0.435102</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col4\" class=\"data row2 col4\" >0.33076</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row2_col5\" class=\"data row2 col5\" >0.430464</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row3\" class=\"row_heading level0 row3\" >value_O3 Mean</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col0\" class=\"data row3 col0\" >-0.0505675</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col1\" class=\"data row3 col1\" >-0.315108</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col2\" class=\"data row3 col2\" >-0.435102</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col4\" class=\"data row3 col4\" >-0.230533</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row3_col5\" class=\"data row3 col5\" >-0.284538</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row4\" class=\"row_heading level0 row4\" >value_SO2 Mean</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col0\" class=\"data row4 col0\" >0.125801</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col1\" class=\"data row4 col1\" >0.185583</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col2\" class=\"data row4 col2\" >0.33076</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col3\" class=\"data row4 col3\" >-0.230533</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row4_col5\" class=\"data row4 col5\" >0.198729</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763level0_row5\" class=\"row_heading level0 row5\" >value_State Average</th> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col0\" class=\"data row5 col0\" >0.222388</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col1\" class=\"data row5 col1\" >0.998124</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col2\" class=\"data row5 col2\" >0.430464</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col3\" class=\"data row5 col3\" >-0.284538</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col4\" class=\"data row5 col4\" >0.198729</td> \n",
       "        <td id=\"T_98830fba_3f90_11e9_981a_98e0d97e0763row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x122d10940>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "corr = pvt_merged.corr()\n",
    "corr.style.background_gradient()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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 "nbformat": 4,
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