DrDataVisualizations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/41768629/normal-distribution-plot-by-name-from-pandas-dataframe\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.density.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMedicaidInState = pd.read_csv('MedicaidCoverageByState2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfMedicaidInState.sort_values('Medicaid', axis=0, ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Location</th>\n",
       "      <th>Medicaid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>UT</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ND</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WY</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NE</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Medicaid\n",
       "43       UT      0.11\n",
       "33       ND      0.11\n",
       "45       VA      0.12\n",
       "49       WY      0.13\n",
       "26       NE      0.13"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMedicaidInState.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfMedicaidInState['Location']\n",
    "Y = dfMedicaidInState['Medicaid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20364cffe10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "sns.barplot(x = X, y = Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDrsMedicaidInState = pd.read_csv('DoctorsMedicaidCoverageByState2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Location</th>\n",
       "      <th>Medicaid</th>\n",
       "      <th>PhysiciansPer100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>0.21</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>0.22</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0.22</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>0.27</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>0.26</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location  Medicaid  PhysiciansPer100k\n",
       "0       AL      0.21                328\n",
       "1       AK      0.22                546\n",
       "2       AZ      0.22                366\n",
       "3       AR      0.27                334\n",
       "4       CA      0.26                380"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDrsMedicaidInState.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dfDrsMedicaidInState.iloc[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDrsMedicaidInState2 = dfDrsMedicaidInState.set_index('Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Medicaid</th>\n",
       "      <th>PhysiciansPer100k</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.21</td>\n",
       "      <td>328</td>\n",
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       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>0.22</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.22</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>0.27</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.26</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Medicaid  PhysiciansPer100k\n",
       "Location                             \n",
       "AL            0.21                328\n",
       "AK            0.22                546\n",
       "AZ            0.22                366\n",
       "AR            0.27                334\n",
       "CA            0.26                380"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDrsMedicaidInState2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDrsMedicaidInState3 = dfDrsMedicaidInState.pivot(\"Location\",\"Medicaid\",\"PhysiciansPer100k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Medicaid</th>\n",
       "      <th>0.11</th>\n",
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       "      <th>0.14</th>\n",
       "      <th>0.15</th>\n",
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       "      <th>0.18</th>\n",
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       "      <th>0.28</th>\n",
       "      <th>0.34</th>\n",
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       "      <th>Location</th>\n",
       "      <th></th>\n",
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       "      <th>AK</th>\n",
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       "      <th>AL</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>AZ</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>CA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ND</th>\n",
       "      <td>514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>352.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Medicaid   0.11   0.12   0.13   0.14   0.15   0.17   0.18   0.19   0.20  \\\n",
       "Location                                                                  \n",
       "AK          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "AL          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "AR          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "AZ          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "CO          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  395.0   \n",
       "CT          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  487.0   \n",
       "DE          0.0    0.0    0.0    0.0    0.0    0.0  553.0    0.0    0.0   \n",
       "FL          0.0    0.0    0.0    0.0    0.0    0.0    0.0  359.0    0.0   \n",
       "GA          0.0    0.0    0.0    0.0    0.0  349.0    0.0    0.0    0.0   \n",
       "HI          0.0    0.0    0.0    0.0    0.0    0.0  662.0    0.0    0.0   \n",
       "IA          0.0    0.0    0.0    0.0    0.0    0.0  381.0    0.0    0.0   \n",
       "ID          0.0    0.0    0.0    0.0    0.0    0.0  355.0    0.0    0.0   \n",
       "IL          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  387.0   \n",
       "IN          0.0    0.0    0.0    0.0    0.0    0.0  426.0    0.0    0.0   \n",
       "KS          0.0    0.0    0.0  329.0    0.0    0.0    0.0    0.0    0.0   \n",
       "KY          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "LA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "MA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "MD          0.0    0.0    0.0    0.0    0.0    0.0  502.0    0.0    0.0   \n",
       "ME          0.0    0.0    0.0    0.0    0.0    0.0  509.0    0.0    0.0   \n",
       "MI          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "MN          0.0    0.0    0.0    0.0    0.0  426.0    0.0    0.0    0.0   \n",
       "MO          0.0    0.0    0.0    0.0  423.0    0.0    0.0    0.0    0.0   \n",
       "MS          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "MT          0.0    0.0    0.0    0.0    0.0    0.0    0.0  503.0    0.0   \n",
       "NC          0.0    0.0    0.0    0.0    0.0    0.0  382.0    0.0    0.0   \n",
       "ND        514.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "NE          0.0    0.0  488.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "NH          0.0    0.0    0.0  544.0    0.0    0.0    0.0    0.0    0.0   \n",
       "NJ          0.0    0.0    0.0    0.0    0.0  416.0    0.0    0.0    0.0   \n",
       "NM          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "NV          0.0    0.0    0.0    0.0    0.0    0.0    0.0  301.0    0.0   \n",
       "NY          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "OH          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "OK          0.0    0.0    0.0    0.0    0.0    0.0  335.0    0.0    0.0   \n",
       "OR          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "PA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  441.0   \n",
       "RI          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "SC          0.0    0.0    0.0    0.0    0.0    0.0    0.0  383.0    0.0   \n",
       "SD          0.0    0.0    0.0  440.0    0.0    0.0    0.0    0.0    0.0   \n",
       "TN          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "TX          0.0    0.0    0.0    0.0    0.0  280.0    0.0    0.0    0.0   \n",
       "UT        352.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "VA          0.0  450.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "VT          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "WA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "WI          0.0    0.0    0.0    0.0    0.0  463.0    0.0    0.0    0.0   \n",
       "WV          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "WY          0.0    0.0  645.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "Medicaid   0.21   0.22   0.23   0.24   0.26   0.27   0.28   0.34  \n",
       "Location                                                          \n",
       "AK          0.0  546.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "AL        328.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "AR          0.0    0.0    0.0    0.0    0.0  334.0    0.0    0.0  \n",
       "AZ          0.0  366.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "CA          0.0    0.0    0.0    0.0  380.0    0.0    0.0    0.0  \n",
       "CO          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "CT          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "DE          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "FL          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "GA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "HI          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "IA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "ID          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "IL          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "IN          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "KS          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "KY          0.0    0.0    0.0    0.0    0.0  418.0    0.0    0.0  \n",
       "LA          0.0    0.0    0.0    0.0    0.0    0.0  361.0    0.0  \n",
       "MA          0.0    0.0    0.0  512.0    0.0    0.0    0.0    0.0  \n",
       "MD          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "ME          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "MI          0.0  476.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "MN          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "MO          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "MS          0.0    0.0    0.0  343.0    0.0    0.0    0.0    0.0  \n",
       "MT          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NC          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "ND          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NE          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NH          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NJ          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NM          0.0    0.0    0.0    0.0    0.0    0.0    0.0  444.0  \n",
       "NV          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "NY          0.0    0.0    0.0    0.0  476.0    0.0    0.0    0.0  \n",
       "OH        401.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "OK          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "OR          0.0    0.0  370.0    0.0    0.0    0.0    0.0    0.0  \n",
       "PA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "RI          0.0    0.0    0.0  514.0    0.0    0.0    0.0    0.0  \n",
       "SC          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "SD          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "TN        346.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "TX          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "UT          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "VA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "VT          0.0    0.0    0.0    0.0    0.0    0.0  567.0    0.0  \n",
       "WA        394.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "WI          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "WV          0.0    0.0    0.0    0.0    0.0    0.0  434.0    0.0  \n",
       "WY          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDrsMedicaidInState3.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Medicaid</th>\n",
       "      <th>0.11</th>\n",
       "      <th>0.12</th>\n",
       "      <th>0.13</th>\n",
       "      <th>0.14</th>\n",
       "      <th>0.15</th>\n",
       "      <th>0.17</th>\n",
       "      <th>0.18</th>\n",
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       "      <th>0.21</th>\n",
       "      <th>0.22</th>\n",
       "      <th>0.23</th>\n",
       "      <th>0.24</th>\n",
       "      <th>0.26</th>\n",
       "      <th>0.27</th>\n",
       "      <th>0.28</th>\n",
       "      <th>0.34</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546.0</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>AL</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</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>AR</th>\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",
       "      <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",
       "      <td>334.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.0</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>CA</th>\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",
       "      <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>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "Medicaid  0.11  0.12  0.13  0.14  0.15  0.17  0.18  0.19  0.20   0.21   0.22  \\\n",
       "Location                                                                       \n",
       "AK         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN  546.0   \n",
       "AL         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  328.0    NaN   \n",
       "AR         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN    NaN   \n",
       "AZ         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN  366.0   \n",
       "CA         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN    NaN   \n",
       "\n",
       "Medicaid  0.23  0.24   0.26   0.27  0.28  0.34  \n",
       "Location                                        \n",
       "AK         NaN   NaN    NaN    NaN   NaN   NaN  \n",
       "AL         NaN   NaN    NaN    NaN   NaN   NaN  \n",
       "AR         NaN   NaN    NaN  334.0   NaN   NaN  \n",
       "AZ         NaN   NaN    NaN    NaN   NaN   NaN  \n",
       "CA         NaN   NaN  380.0    NaN   NaN   NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDrsMedicaidInState3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203659147b8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(dfDrsMedicaidInState3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}