HCA 203 PUF and Counties Aggregate Description 2 (OLD).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Exploration PUF and Counties Aggregate Data\n",
    "\n",
    "DeSYNPuf, RWJF and BRFSS Aggregate Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.rcsetup as mrc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file PUF_BRFSS_MERGED.csv contains aggregated Medicare data from 2008-2010 (DeSYNPuf Synthetic Public Use Files), RWJF County Health Factors and Outcomes Ranking Data from 2010, and BRFSS aggregated 2010 data for 302 out of the 3012 counties.\n",
    "\n",
    "2960 out of 3012 counties (98%) had a Medicare beneficiary with at least one inpatient or outpatient claim filed between 2008 and 2010. \n",
    "\n",
    "The file PUF_BRFSS_MERGED_50.csv contains this same data, but counties with fewer than 50 Medicare beneficiaries were removed, leaving 491 counties for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 491 entries, 0 to 490\n",
      "Columns: 104 entries, FIPS_STATE_CODE to ObeseWMean\n",
      "dtypes: float64(93), int64(8), object(3)\n",
      "memory usage: 399.1+ KB\n"
     ]
    }
   ],
   "source": [
    "medicare_counties = pd.read_csv(\"/home/kylier/python/DS/data/PUF/PUF_BRFSS_MERGED_50.csv\")\n",
    "medicare_counties.drop(columns=medicare_counties.columns[0], axis=1, inplace=True, errors=\"ignore\")\n",
    "medicare_counties.columns=medicare_counties.columns.str.strip()\n",
    "medicare_counties.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously, I had showed counties with no claims.\n",
    "\n",
    "This time there aren't any. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>COUNTY</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [STATE, COUNTY, CBSA_NAME, HF_PROP_RANK, HO_PROP_RANK]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_claims = medicare_counties[medicare_counties[\"MEAN_AGE\"].isnull()]\n",
    "nc_summary = no_claims[[\"STATE\", \"COUNTY\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "nc_summary.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Age, Sex and Race Codes\n",
    "\n",
    "Mean Age of beneficiaries in a county is self-explanatory.\n",
    "\n",
    "But Mean Sex and Race Codes? \n",
    "\n",
    "Here's my rationale.  Recoding Sex as 0=Female, and 1=Male the mean will show the mean proportion of males to females for the beneficiaries from that county. \n",
    "\n",
    "Race codes are organized with white as 1, and minority groups as 2-5, including \"other\".  The more diverse a county's beneficiary population is, the more it's Mean Race will trend away from 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "medicare_counties[\"MEAN_AGE\"] = medicare_counties[\"MEAN_AGE\"].round(decimals=1)\n",
    "\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(30, 10))\n",
    "medicare_counties.hist(\"MEAN_AGE\", ax=axes[0])\n",
    "medicare_counties.hist(\"MEAN_SEX\", ax=axes[1])\n",
    "medicare_counties.hist(\"MEAN_RACE_CD\", ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean age has now broadened out a bit, as has mean sex and race codes.  \n",
    "\n",
    "We can see the diversity of the US better in this picture!  \n",
    "\n",
    "We are now seeing a more urban population, compared to the one we saw before, where almost everyone was white and there were many more outliers in terms of small towns and beneficiary populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>COUNTY</th>\n",
       "      <th>MEAN_RACE_CD</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>2.354167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Maui</td>\n",
       "      <td>2.377358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>California</td>\n",
       "      <td>Monterey</td>\n",
       "      <td>1.728814</td>\n",
       "      <td>Salinas, CA</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>2.281553</td>\n",
       "      <td>Honolulu, HI</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>California</td>\n",
       "      <td>Napa</td>\n",
       "      <td>1.886792</td>\n",
       "      <td>Napa, CA</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>California</td>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>1.712121</td>\n",
       "      <td>Santa Cruz-Watsonville, CA</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>California</td>\n",
       "      <td>San Mateo</td>\n",
       "      <td>1.753191</td>\n",
       "      <td>San Francisco-San Mateo-Redwood City,CA</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          STATE      COUNTY  MEAN_RACE_CD  \\\n",
       "127      Hawaii      Hawaii      2.354167   \n",
       "129      Hawaii        Maui      2.377358   \n",
       "39   California    Monterey      1.728814   \n",
       "128      Hawaii    Honolulu      2.281553   \n",
       "40   California        Napa      1.886792   \n",
       "53   California  Santa Cruz      1.712121   \n",
       "50   California   San Mateo      1.753191   \n",
       "\n",
       "                                   CBSA_NAME  HF_PROP_RANK  HO_PROP_RANK  \n",
       "127                                      NaN         0.000         0.000  \n",
       "129                                      NaN         0.250         0.250  \n",
       "39                               Salinas, CA         0.482         0.714  \n",
       "128                             Honolulu, HI         0.750         0.750  \n",
       "40                                  Napa, CA         0.821         0.768  \n",
       "53                Santa Cruz-Watsonville, CA         0.857         0.857  \n",
       "50   San Francisco-San Mateo-Redwood City,CA         0.929         0.911  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_white = medicare_counties[medicare_counties[\"MEAN_RACE_CD\"]>1.7]\n",
    "nw = not_white[[\"STATE\", \"COUNTY\", \"MEAN_RACE_CD\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "nw.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>COUNTY</th>\n",
       "      <th>MEAN_RACE_CD</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>1.594156</td>\n",
       "      <td>Washington-Arlington-Alexandria DC-VA</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>California</td>\n",
       "      <td>Tulare</td>\n",
       "      <td>1.561404</td>\n",
       "      <td>Visalia-Porterville, CA</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>California</td>\n",
       "      <td>Kern</td>\n",
       "      <td>1.627451</td>\n",
       "      <td>Bakersfield-Delano, CA</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Mohave</td>\n",
       "      <td>1.588235</td>\n",
       "      <td>Lake Havasu City-Kingman, AZ</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Potter</td>\n",
       "      <td>1.602740</td>\n",
       "      <td>Amarillo, TX</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>California</td>\n",
       "      <td>San Joaquin</td>\n",
       "      <td>1.511765</td>\n",
       "      <td>Stockton, CA</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>California</td>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>1.695349</td>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>1.596477</td>\n",
       "      <td>Los Angeles-Long Beach-Santa Ana, CA</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>1.596477</td>\n",
       "      <td>Los Angeles-Long Beach-Santa Ana, CA</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>San Juan</td>\n",
       "      <td>1.557143</td>\n",
       "      <td>Farmington, NM</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>California</td>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>1.688742</td>\n",
       "      <td>Modesto, CA</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Texas</td>\n",
       "      <td>McLennan</td>\n",
       "      <td>1.620253</td>\n",
       "      <td>Waco, TX</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>California</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>Riverside-San Bernardino-Ontario, CA</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>California</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>1.611111</td>\n",
       "      <td>Redding, CA</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>California</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>1.612346</td>\n",
       "      <td>Sacramento--Arden-Arcade--Roseville, CA</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Dona Ana</td>\n",
       "      <td>1.531915</td>\n",
       "      <td>Las Cruces, NM</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>California</td>\n",
       "      <td>Butte</td>\n",
       "      <td>1.643836</td>\n",
       "      <td>Chico, CA</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Highlands</td>\n",
       "      <td>1.507937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Cochise</td>\n",
       "      <td>1.568966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>New York</td>\n",
       "      <td>Onondaga</td>\n",
       "      <td>1.514793</td>\n",
       "      <td>Syracuse, NY</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>California</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>1.610390</td>\n",
       "      <td>San Diego-Carlsbad-San Marcos, CA</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>1.672131</td>\n",
       "      <td>Santa Barbara-Santa Maria-Goleta, CA</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>California</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>1.594937</td>\n",
       "      <td>Oxnard-Thousand Oaks-Ventura, CA</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1.607317</td>\n",
       "      <td>Oakland-Fremont-Hayward, CA</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>California</td>\n",
       "      <td>Contra Costa</td>\n",
       "      <td>1.535714</td>\n",
       "      <td>Oakland-Fremont-Hayward, CA</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>San Francisco-San Mateo-Redwood City,CA</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>1.676259</td>\n",
       "      <td>Santa Rosa-Petaluma, CA</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Collier</td>\n",
       "      <td>1.503311</td>\n",
       "      <td>Naples-Marco Island, FL</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>1.561893</td>\n",
       "      <td>Santa Ana-Anaheim-Irvine, CA</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>California</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>1.510638</td>\n",
       "      <td>San Luis Obispo-Paso Robles, CA</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>California</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1.577528</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>California</td>\n",
       "      <td>Placer</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>Sacramento--Arden-Arcade--Roseville, CA</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Florida</td>\n",
       "      <td>St. Johns</td>\n",
       "      <td>1.577465</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Collin</td>\n",
       "      <td>1.507042</td>\n",
       "      <td>Dallas-Plano-Irving, TX</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    STATE                COUNTY  MEAN_RACE_CD  \\\n",
       "79   District of Columbia  District of Columbia      1.594156   \n",
       "58             California                Tulare      1.561404   \n",
       "35             California                  Kern      1.627451   \n",
       "16                Arizona                Mohave      1.588235   \n",
       "435                 Texas                Potter      1.602740   \n",
       "48             California           San Joaquin      1.511765   \n",
       "45             California        San Bernardino      1.695349   \n",
       "36             California           Los Angeles      1.596477   \n",
       "37             California           Los Angeles      1.596477   \n",
       "270            New Mexico              San Juan      1.557143   \n",
       "57             California            Stanislaus      1.688742   \n",
       "432                 Texas              McLennan      1.620253   \n",
       "43             California             Riverside      1.571429   \n",
       "54             California                Shasta      1.611111   \n",
       "44             California            Sacramento      1.612346   \n",
       "268            New Mexico              Dona Ana      1.531915   \n",
       "30             California                 Butte      1.643836   \n",
       "92                Florida             Highlands      1.507937   \n",
       "13                Arizona               Cochise      1.568966   \n",
       "285              New York              Onondaga      1.514793   \n",
       "46             California             San Diego      1.610390   \n",
       "51             California         Santa Barbara      1.672131   \n",
       "59             California               Ventura      1.594937   \n",
       "29             California               Alameda      1.607317   \n",
       "31             California          Contra Costa      1.535714   \n",
       "47             California         San Francisco      1.666667   \n",
       "56             California                Sonoma      1.676259   \n",
       "87                Florida               Collier      1.503311   \n",
       "41             California                Orange      1.561893   \n",
       "49             California       San Luis Obispo      1.510638   \n",
       "52             California           Santa Clara      1.577528   \n",
       "42             California                Placer      1.660000   \n",
       "109               Florida             St. Johns      1.577465   \n",
       "418                 Texas                Collin      1.507042   \n",
       "\n",
       "                                   CBSA_NAME  HF_PROP_RANK  HO_PROP_RANK  \n",
       "79     Washington-Arlington-Alexandria DC-VA         0.000         0.000  \n",
       "58                   Visalia-Porterville, CA         0.000         0.125  \n",
       "35                    Bakersfield-Delano, CA         0.018         0.089  \n",
       "16              Lake Havasu City-Kingman, AZ         0.067         0.267  \n",
       "435                             Amarillo, TX         0.086         0.081  \n",
       "48                              Stockton, CA         0.089         0.321  \n",
       "45      Riverside-San Bernardino-Ontario, CA         0.107         0.196  \n",
       "36      Los Angeles-Long Beach-Santa Ana, CA         0.214         0.536  \n",
       "37      Los Angeles-Long Beach-Santa Ana, CA         0.214         0.536  \n",
       "270                           Farmington, NM         0.219         0.656  \n",
       "57                               Modesto, CA         0.232         0.232  \n",
       "432                                 Waco, TX         0.235         0.520  \n",
       "43      Riverside-San Bernardino-Ontario, CA         0.286         0.518  \n",
       "54                               Redding, CA         0.339         0.339  \n",
       "44   Sacramento--Arden-Arcade--Roseville, CA         0.393         0.429  \n",
       "268                           Las Cruces, NM         0.406         0.844  \n",
       "30                                 Chico, CA         0.411         0.375  \n",
       "92                                       NaN         0.478         0.388  \n",
       "13                                       NaN         0.600         0.400  \n",
       "285                             Syracuse, NY         0.661         0.387  \n",
       "46         San Diego-Carlsbad-San Marcos, CA         0.661         0.732  \n",
       "51      Santa Barbara-Santa Maria-Goleta, CA         0.696         0.643  \n",
       "59          Oxnard-Thousand Oaks-Ventura, CA         0.714         0.696  \n",
       "29               Oakland-Fremont-Hayward, CA         0.732         0.589  \n",
       "31               Oakland-Fremont-Hayward, CA         0.768         0.661  \n",
       "47   San Francisco-San Mateo-Redwood City,CA         0.786         0.571  \n",
       "56                   Santa Rosa-Petaluma, CA         0.804         0.839  \n",
       "87                   Naples-Marco Island, FL         0.836         0.985  \n",
       "41              Santa Ana-Anaheim-Irvine, CA         0.839         0.875  \n",
       "49           San Luis Obispo-Paso Robles, CA         0.875         0.804  \n",
       "52        San Jose-Sunnyvale-Santa Clara, CA         0.946         0.929  \n",
       "42   Sacramento--Arden-Arcade--Roseville, CA         0.964         0.893  \n",
       "109                         Jacksonville, FL         0.985         0.970  \n",
       "418                  Dallas-Plano-Irving, TX         0.995         0.991  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_white2 = medicare_counties[(medicare_counties[\"MEAN_RACE_CD\"]>1.5) & (medicare_counties[\"MEAN_RACE_CD\"]<1.7)]\n",
    "nw2 = not_white2[[\"STATE\", \"COUNTY\", \"MEAN_RACE_CD\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "nw2.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>COUNTY</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Randolph</td>\n",
       "      <td>67.6</td>\n",
       "      <td>Greensboro-High Point, NC</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>67.6</td>\n",
       "      <td>Pascagoula, MS</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Washington</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Fayetteville-Springdale-Rogers, AR-MO</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              STATE      COUNTY  MEAN_AGE  \\\n",
       "318  North Carolina    Randolph      67.6   \n",
       "229     Mississippi     Jackson      67.6   \n",
       "28         Arkansas  Washington      67.1   \n",
       "\n",
       "                                 CBSA_NAME  HF_PROP_RANK  HO_PROP_RANK  \n",
       "318              Greensboro-High Point, NC         0.590         0.870  \n",
       "229                         Pascagoula, MS         0.827         0.827  \n",
       "28   Fayetteville-Springdale-Rogers, AR-MO         0.973         0.973  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "young = medicare_counties[medicare_counties[\"MEAN_AGE\"]<68]\n",
    "y = young[[\"STATE\", \"COUNTY\", \"MEAN_AGE\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "y.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"]).head(10)"
   ]
  },
  {
   "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>COUNTY</th>\n",
       "      <th>MEAN_SEX</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Macon</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>Decatur, IL</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Osceola</td>\n",
       "      <td>0.258427</td>\n",
       "      <td>Orlando-Kissimmee-Sanford, FL</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Porter</td>\n",
       "      <td>0.283019</td>\n",
       "      <td>Gary, IN</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        STATE   COUNTY  MEAN_SEX                      CBSA_NAME  HF_PROP_RANK  \\\n",
       "140  Illinois    Macon  0.254545                    Decatur, IL         0.079   \n",
       "104   Florida  Osceola  0.258427  Orlando-Kissimmee-Sanford, FL         0.582   \n",
       "157   Indiana   Porter  0.283019                       Gary, IN         0.859   \n",
       "\n",
       "     HO_PROP_RANK  \n",
       "140         0.139  \n",
       "104         0.612  \n",
       "157         0.826  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female = medicare_counties[medicare_counties[\"MEAN_SEX\"]<0.3]\n",
    "f = female[[\"STATE\", \"COUNTY\", \"MEAN_SEX\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "f.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"]).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "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>COUNTY</th>\n",
       "      <th>MEAN_SEX</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Dona Ana</td>\n",
       "      <td>0.606383</td>\n",
       "      <td>Las Cruces, NM</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Craighead</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>Jonesboro, AR</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             STATE     COUNTY  MEAN_SEX       CBSA_NAME  HF_PROP_RANK  \\\n",
       "268     New Mexico   Dona Ana  0.606383  Las Cruces, NM         0.406   \n",
       "474  West Virginia    Raleigh  0.634615             NaN         0.673   \n",
       "23        Arkansas  Craighead  0.627451   Jonesboro, AR         0.920   \n",
       "\n",
       "     HO_PROP_RANK  \n",
       "268         0.844  \n",
       "474         0.255  \n",
       "23          0.707  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = medicare_counties[medicare_counties[\"MEAN_SEX\"]>0.6]\n",
    "m = male[[\"STATE\", \"COUNTY\", \"MEAN_SEX\", \"CBSA_NAME\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "m.sort_values(by=[\"HF_PROP_RANK\", \"HO_PROP_RANK\"]).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Counts\n",
    "\n",
    "I carefully ensured that for each beneficiary there was only one possible positive case recorded for each chronic condition, so I could sum these the total number of cases of a particular condition among beneficiaries in a particular county.  \n",
    "\n",
    "Let's see how these case counts correlate with the County Health Rankings, Total Costs and Stay Information, but first, let's face the fact - this is zero-heavy data.  We're glad about this, but if we don't do anything about it, outliers will skew correlation results.\n",
    "\n",
    "Since we are interested in super-utilizers, let's try to identify those counties with the highest MEAN_INP_STAY and highest total costs (ALLCOSTS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0     0.057026\n",
       "13.0     0.048880\n",
       "10.0     0.046843\n",
       "9.0      0.044807\n",
       "14.0     0.042770\n",
       "           ...   \n",
       "117.0    0.002037\n",
       "158.0    0.002037\n",
       "77.0     0.002037\n",
       "63.0     0.002037\n",
       "70.0     0.002037\n",
       "Name: ESRD_CASES, Length: 99, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medicare_counties[\"ESRD_CASES\"].value_counts(normalize=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n",
    "medicare_counties.hist(\"ESRD_CASES\", ax=axes[0])\n",
    "medicare_counties.hist(\"DEPRESSN_CASES\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n",
    "medicare_counties.hist(\"CNCR_CASES\", ax=axes[0])\n",
    "medicare_counties.hist(\"ISCHMCHT_CASES\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\")\n",
    "sb.barplot(x=\"FIPS_COUNTY_CODE\", y=\"USE_PROPORTION\", data=medicare_counties[medicare_counties[\"USE_PROPORTION\"]<0.6], palette=\"mako\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>COUNTY</th>\n",
       "      <th>MEAN_SEX</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>MEAN_RACE_CD</th>\n",
       "      <th>CBSA_NAME</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "      <th>ALLCOSTS</th>\n",
       "      <th>TOTAL_INP_DAYS</th>\n",
       "      <th>DrinksPerDayWMean</th>\n",
       "      <th>SmokerWMean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Calhoun</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>69.1</td>\n",
       "      <td>1.373333</td>\n",
       "      <td>Anniston-Oxford, AL</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.313</td>\n",
       "      <td>4871596.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Etowah</td>\n",
       "      <td>0.410256</td>\n",
       "      <td>71.7</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>Gadsden, AL</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.493</td>\n",
       "      <td>2537332.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>73.8</td>\n",
       "      <td>1.347826</td>\n",
       "      <td>Decatur, AL</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.925</td>\n",
       "      <td>3868268.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Coconino</td>\n",
       "      <td>0.410714</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.267857</td>\n",
       "      <td>Flagstaff, AZ</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.667</td>\n",
       "      <td>3534384.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>72.4</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>Yuma, AZ</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.933</td>\n",
       "      <td>2408292.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Baxter</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>71.1</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.893</td>\n",
       "      <td>2598990.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Craighead</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>70.3</td>\n",
       "      <td>1.235294</td>\n",
       "      <td>Jonesboro, AR</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.707</td>\n",
       "      <td>1868298.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Faulkner</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1.196429</td>\n",
       "      <td>Little Rock-N.Little Rock-Conway,AR</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.947</td>\n",
       "      <td>3257434.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Adams</td>\n",
       "      <td>0.524138</td>\n",
       "      <td>74.5</td>\n",
       "      <td>1.227586</td>\n",
       "      <td>Denver-Aurora-Broomfield, CO</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.357</td>\n",
       "      <td>7241992.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>0.006740</td>\n",
       "      <td>0.006381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>0.495495</td>\n",
       "      <td>70.8</td>\n",
       "      <td>1.153153</td>\n",
       "      <td>Boulder, CO</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.946</td>\n",
       "      <td>3242974.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Denver</td>\n",
       "      <td>0.436170</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.191489</td>\n",
       "      <td>Denver-Aurora-Broomfield, CO</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.161</td>\n",
       "      <td>7967044.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>0.007308</td>\n",
       "      <td>0.006263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>0.458515</td>\n",
       "      <td>71.8</td>\n",
       "      <td>1.227074</td>\n",
       "      <td>Colorado Springs, CO</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.482</td>\n",
       "      <td>11615912.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>0.004642</td>\n",
       "      <td>0.004550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>0.489177</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.173160</td>\n",
       "      <td>Denver-Aurora-Broomfield, CO</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.768</td>\n",
       "      <td>7832308.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>0.003476</td>\n",
       "      <td>0.003424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Larimer</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>74.6</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>Fort Collins-Loveland, CO</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>3732902.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0.008071</td>\n",
       "      <td>0.009170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>0.434211</td>\n",
       "      <td>72.8</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>Grand Junction, CO</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.464</td>\n",
       "      <td>3129468.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Pueblo</td>\n",
       "      <td>0.475728</td>\n",
       "      <td>75.5</td>\n",
       "      <td>1.126214</td>\n",
       "      <td>Pueblo, CO</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.107</td>\n",
       "      <td>4643098.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Weld</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>75.2</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>Greeley, CO</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.589</td>\n",
       "      <td>1604550.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent</td>\n",
       "      <td>0.471910</td>\n",
       "      <td>71.9</td>\n",
       "      <td>1.123596</td>\n",
       "      <td>Dover, DE</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2568654.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>0.004471</td>\n",
       "      <td>0.003988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>0.435065</td>\n",
       "      <td>74.4</td>\n",
       "      <td>1.594156</td>\n",
       "      <td>Washington-Arlington-Alexandria DC-VA</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16557510.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>0.002334</td>\n",
       "      <td>0.002564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>0.427184</td>\n",
       "      <td>74.2</td>\n",
       "      <td>2.281553</td>\n",
       "      <td>Honolulu, HI</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.750</td>\n",
       "      <td>11574870.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.002004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Bernalillo</td>\n",
       "      <td>0.487603</td>\n",
       "      <td>72.8</td>\n",
       "      <td>1.358127</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.781</td>\n",
       "      <td>9547464.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.003985</td>\n",
       "      <td>0.004436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Dona Ana</td>\n",
       "      <td>0.606383</td>\n",
       "      <td>72.8</td>\n",
       "      <td>1.531915</td>\n",
       "      <td>Las Cruces, NM</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.844</td>\n",
       "      <td>5276138.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>0.011025</td>\n",
       "      <td>0.011543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Sandoval</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>75.1</td>\n",
       "      <td>1.476190</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.906</td>\n",
       "      <td>2193180.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.009325</td>\n",
       "      <td>0.008645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>71.4</td>\n",
       "      <td>1.356164</td>\n",
       "      <td>Santa Fe, NM</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>1381736.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>0.008032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>Cass</td>\n",
       "      <td>0.554054</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Fargo, ND-MN</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.932</td>\n",
       "      <td>2130002.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.009589</td>\n",
       "      <td>0.009788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Coos</td>\n",
       "      <td>0.508475</td>\n",
       "      <td>73.6</td>\n",
       "      <td>1.118644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.273</td>\n",
       "      <td>1859222.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Deschutes</td>\n",
       "      <td>0.492537</td>\n",
       "      <td>73.9</td>\n",
       "      <td>1.029851</td>\n",
       "      <td>Bend, OR</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.818</td>\n",
       "      <td>2331828.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Josephine</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.068966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.182</td>\n",
       "      <td>884940.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Marion</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>74.4</td>\n",
       "      <td>1.118519</td>\n",
       "      <td>Salem, OR</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.697</td>\n",
       "      <td>3507158.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Multnomah</td>\n",
       "      <td>0.502008</td>\n",
       "      <td>69.9</td>\n",
       "      <td>1.088353</td>\n",
       "      <td>Portland-Vancouver-Hillsboro, OR-WA</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.364</td>\n",
       "      <td>8779034.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>0.009247</td>\n",
       "      <td>0.008653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>72.8</td>\n",
       "      <td>1.075000</td>\n",
       "      <td>Portland-Vancouver-Hillsboro, OR-WA</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.939</td>\n",
       "      <td>6964640.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>0.010901</td>\n",
       "      <td>0.012161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Kent</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>73.5</td>\n",
       "      <td>1.122449</td>\n",
       "      <td>Providence-New Bedford-Fall River, RI-M</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1827324.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0.004828</td>\n",
       "      <td>0.004353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Newport</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>74.1</td>\n",
       "      <td>1.087719</td>\n",
       "      <td>Providence-New Bedford-Fall River, RI-M</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2021844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.008770</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Providence</td>\n",
       "      <td>0.533156</td>\n",
       "      <td>72.8</td>\n",
       "      <td>1.119363</td>\n",
       "      <td>Providence-New Bedford-Fall River, RI-M</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12035338.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.001530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Washington</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>Providence-New Bedford-Fall River, RI-M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.800</td>\n",
       "      <td>3004630.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.005710</td>\n",
       "      <td>0.006435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Salt Lake</td>\n",
       "      <td>0.511050</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.102210</td>\n",
       "      <td>Salt Lake City, UT</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.640</td>\n",
       "      <td>15377532.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>0.001297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Utah</td>\n",
       "      <td>0.483607</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.122951</td>\n",
       "      <td>Provo-Orem, UT</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.840</td>\n",
       "      <td>3535582.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.005596</td>\n",
       "      <td>0.004529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Cabell</td>\n",
       "      <td>0.486842</td>\n",
       "      <td>69.4</td>\n",
       "      <td>1.026316</td>\n",
       "      <td>Huntington-Ashland, WV-KY-OH</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.236</td>\n",
       "      <td>3936406.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0.448276</td>\n",
       "      <td>74.2</td>\n",
       "      <td>1.137931</td>\n",
       "      <td>Green Bay, WI</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.569</td>\n",
       "      <td>2945928.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Laramie</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>71.9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Cheyenne, WY</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.261</td>\n",
       "      <td>1927352.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.007385</td>\n",
       "      <td>0.007301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Natrona</td>\n",
       "      <td>0.560606</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Casper, WY</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1830310.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.008542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    STATE                COUNTY  MEAN_SEX  MEAN_AGE  \\\n",
       "1                 Alabama               Calhoun  0.466667      69.1   \n",
       "2                 Alabama                Etowah  0.410256      71.7   \n",
       "10                Alabama                Morgan  0.347826      73.8   \n",
       "14                Arizona              Coconino  0.410714      74.0   \n",
       "20                Arizona                  Yuma  0.516667      72.4   \n",
       "21               Arkansas                Baxter  0.500000      71.1   \n",
       "23               Arkansas             Craighead  0.627451      70.3   \n",
       "24               Arkansas              Faulkner  0.571429      70.1   \n",
       "60               Colorado                 Adams  0.524138      74.5   \n",
       "62               Colorado               Boulder  0.495495      70.8   \n",
       "63               Colorado                Denver  0.436170      75.0   \n",
       "64               Colorado               El Paso  0.458515      71.8   \n",
       "65               Colorado             Jefferson  0.489177      74.0   \n",
       "66               Colorado               Larimer  0.453846      74.6   \n",
       "67               Colorado                  Mesa  0.434211      72.8   \n",
       "68               Colorado                Pueblo  0.475728      75.5   \n",
       "69               Colorado                  Weld  0.515152      75.2   \n",
       "76               Delaware                  Kent  0.471910      71.9   \n",
       "79   District of Columbia  District of Columbia  0.435065      74.4   \n",
       "128                Hawaii              Honolulu  0.427184      74.2   \n",
       "267            New Mexico            Bernalillo  0.487603      72.8   \n",
       "268            New Mexico              Dona Ana  0.606383      72.8   \n",
       "269            New Mexico              Sandoval  0.444444      75.1   \n",
       "271            New Mexico              Santa Fe  0.520548      71.4   \n",
       "324          North Dakota                  Cass  0.554054      73.2   \n",
       "346                Oregon                  Coos  0.508475      73.6   \n",
       "347                Oregon             Deschutes  0.492537      73.9   \n",
       "350                Oregon             Josephine  0.551724      74.0   \n",
       "353                Oregon                Marion  0.444444      74.4   \n",
       "354                Oregon             Multnomah  0.502008      69.9   \n",
       "355                Oregon            Washington  0.450000      72.8   \n",
       "385          Rhode Island                  Kent  0.571429      73.5   \n",
       "386          Rhode Island               Newport  0.438596      74.1   \n",
       "387          Rhode Island            Providence  0.533156      72.8   \n",
       "388          Rhode Island            Washington  0.480519      72.0   \n",
       "444                  Utah             Salt Lake  0.511050      73.0   \n",
       "445                  Utah                  Utah  0.483607      73.0   \n",
       "471         West Virginia                Cabell  0.486842      69.4   \n",
       "476             Wisconsin                 Brown  0.448276      74.2   \n",
       "489               Wyoming               Laramie  0.589744      71.9   \n",
       "490               Wyoming               Natrona  0.560606      75.0   \n",
       "\n",
       "     MEAN_RACE_CD                                CBSA_NAME  HF_PROP_RANK  \\\n",
       "1        1.373333                      Anniston-Oxford, AL         0.552   \n",
       "2        1.384615                              Gadsden, AL         0.582   \n",
       "10       1.347826                              Decatur, AL         0.687   \n",
       "14       1.267857                            Flagstaff, AZ         0.800   \n",
       "20       1.366667                                 Yuma, AZ         0.133   \n",
       "21       1.140000                                      NaN         0.840   \n",
       "23       1.235294                            Jonesboro, AR         0.920   \n",
       "24       1.196429      Little Rock-N.Little Rock-Conway,AR         0.960   \n",
       "60       1.227586             Denver-Aurora-Broomfield, CO         0.125   \n",
       "62       1.153153                              Boulder, CO         0.929   \n",
       "63       1.191489             Denver-Aurora-Broomfield, CO         0.107   \n",
       "64       1.227074                     Colorado Springs, CO         0.411   \n",
       "65       1.173160             Denver-Aurora-Broomfield, CO         0.732   \n",
       "66       1.200000                Fort Collins-Loveland, CO         0.857   \n",
       "67       1.263158                       Grand Junction, CO         0.554   \n",
       "68       1.126214                               Pueblo, CO         0.089   \n",
       "69       1.136364                              Greeley, CO         0.339   \n",
       "76       1.123596                                Dover, DE         0.333   \n",
       "79       1.594156    Washington-Arlington-Alexandria DC-VA         0.000   \n",
       "128      2.281553                             Honolulu, HI         0.750   \n",
       "267      1.358127                          Albuquerque, NM         0.875   \n",
       "268      1.531915                           Las Cruces, NM         0.406   \n",
       "269      1.476190                          Albuquerque, NM         0.906   \n",
       "271      1.356164                             Santa Fe, NM         0.938   \n",
       "324      1.000000                             Fargo, ND-MN         0.955   \n",
       "346      1.118644                                      NaN         0.152   \n",
       "347      1.029851                                 Bend, OR         0.939   \n",
       "350      1.068966                                      NaN         0.182   \n",
       "353      1.118519                                Salem, OR         0.394   \n",
       "354      1.088353      Portland-Vancouver-Hillsboro, OR-WA         0.727   \n",
       "355      1.075000      Portland-Vancouver-Hillsboro, OR-WA         0.909   \n",
       "385      1.122449  Providence-New Bedford-Fall River, RI-M         0.200   \n",
       "386      1.087719  Providence-New Bedford-Fall River, RI-M         0.400   \n",
       "387      1.119363  Providence-New Bedford-Fall River, RI-M         0.000   \n",
       "388      1.142857  Providence-New Bedford-Fall River, RI-M         0.600   \n",
       "444      1.102210                       Salt Lake City, UT         0.520   \n",
       "445      1.122951                           Provo-Orem, UT         0.880   \n",
       "471      1.026316             Huntington-Ashland, WV-KY-OH         0.764   \n",
       "476      1.137931                            Green Bay, WI         0.625   \n",
       "489      1.000000                             Cheyenne, WY         0.391   \n",
       "490      1.000000                               Casper, WY         0.304   \n",
       "\n",
       "     HO_PROP_RANK    ALLCOSTS  TOTAL_INP_DAYS  DrinksPerDayWMean  SmokerWMean  \n",
       "1           0.313   4871596.0           312.0                NaN          NaN  \n",
       "2           0.493   2537332.0           155.0                NaN          NaN  \n",
       "10          0.925   3868268.0           156.0                NaN          NaN  \n",
       "14          0.667   3534384.0           142.0                NaN          NaN  \n",
       "20          0.933   2408292.0            92.0                NaN          NaN  \n",
       "21          0.893   2598990.0            93.0                NaN          NaN  \n",
       "23          0.707   1868298.0            73.0                NaN          NaN  \n",
       "24          0.947   3257434.0           133.0                NaN          NaN  \n",
       "60          0.357   7241992.0           424.0           0.006740     0.006381  \n",
       "62          0.946   3242974.0           213.0                NaN          NaN  \n",
       "63          0.161   7967044.0           507.0           0.007308     0.006263  \n",
       "64          0.482  11615912.0           573.0           0.004642     0.004550  \n",
       "65          0.768   7832308.0           597.0           0.003476     0.003424  \n",
       "66          0.857   3732902.0           218.0           0.008071     0.009170  \n",
       "67          0.464   3129468.0           169.0                NaN          NaN  \n",
       "68          0.107   4643098.0           210.0                NaN          NaN  \n",
       "69          0.589   1604550.0            45.0                NaN          NaN  \n",
       "76          0.000   2568654.0           171.0           0.004471     0.003988  \n",
       "79          0.000  16557510.0           672.0           0.002334     0.002564  \n",
       "128         0.750  11574870.0           680.0           0.002133     0.002004  \n",
       "267         0.781   9547464.0           520.0           0.003985     0.004436  \n",
       "268         0.844   5276138.0           258.0           0.011025     0.011543  \n",
       "269         0.906   2193180.0           101.0           0.009325     0.008645  \n",
       "271         0.938   1381736.0            96.0           0.006662     0.008032  \n",
       "324         0.932   2130002.0            91.0           0.009589     0.009788  \n",
       "346         0.273   1859222.0           102.0                NaN          NaN  \n",
       "347         0.818   2331828.0           131.0                NaN          NaN  \n",
       "350         0.182    884940.0            57.0                NaN          NaN  \n",
       "353         0.697   3507158.0           169.0                NaN          NaN  \n",
       "354         0.364   8779034.0           496.0           0.009247     0.008653  \n",
       "355         0.939   6964640.0           411.0           0.010901     0.012161  \n",
       "385         0.200   1827324.0           116.0           0.004828     0.004353  \n",
       "386         0.600   2021844.0           101.0           0.008770     0.008013  \n",
       "387         0.000  12035338.0           601.0           0.001655     0.001530  \n",
       "388         0.800   3004630.0           173.0           0.005710     0.006435  \n",
       "444         0.640  15377532.0           819.0           0.001403     0.001297  \n",
       "445         0.840   3535582.0           205.0           0.005596     0.004529  \n",
       "471         0.236   3936406.0           253.0                NaN          NaN  \n",
       "476         0.569   2945928.0           236.0                NaN          NaN  \n",
       "489         0.261   1927352.0           132.0           0.007385     0.007301  \n",
       "490         0.348   1830310.0           133.0           0.008264     0.008542  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_users = medicare_counties[medicare_counties[\"USE_PROPORTION\"]<0.6]\n",
    "lower_users[[\"STATE\", \"COUNTY\", \"MEAN_SEX\", \"MEAN_AGE\", \"MEAN_RACE_CD\", \"CBSA_NAME\", \n",
    "                       \"HF_PROP_RANK\", \"HO_PROP_RANK\", \"ALLCOSTS\", \"TOTAL_INP_DAYS\", \n",
    "                       \"DrinksPerDayWMean\", \"SmokerWMean\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 104)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141207810.0    0.004073\n",
       "6807530.0      0.002037\n",
       "49651852.0     0.002037\n",
       "13309080.0     0.002037\n",
       "7735906.0      0.002037\n",
       "                 ...   \n",
       "18765338.0     0.002037\n",
       "2061668.0      0.002037\n",
       "1381736.0      0.002037\n",
       "4871596.0      0.002037\n",
       "10338314.0     0.002037\n",
       "Name: ALLCOSTS, Length: 490, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medicare_counties[\"ALLCOSTS\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "437.582777    0.004073\n",
       "728.925373    0.002037\n",
       "531.261988    0.002037\n",
       "423.556645    0.002037\n",
       "496.142494    0.002037\n",
       "                ...   \n",
       "286.700000    0.002037\n",
       "458.062567    0.002037\n",
       "446.156118    0.002037\n",
       "503.029663    0.002037\n",
       "515.303371    0.002037\n",
       "Name: MEANCOSTS, Length: 490, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medicare_counties[\"MEANCOSTS\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233.0     0.010183\n",
       "187.0     0.008147\n",
       "203.0     0.008147\n",
       "147.0     0.008147\n",
       "231.0     0.008147\n",
       "            ...   \n",
       "557.0     0.002037\n",
       "301.0     0.002037\n",
       "1295.0    0.002037\n",
       "1140.0    0.002037\n",
       "365.0     0.002037\n",
       "Name: TOTAL_INP_DAYS, Length: 388, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medicare_counties[\"TOTAL_INP_DAYS\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.000000    0.004073\n",
       "5.500000    0.004073\n",
       "4.000000    0.004073\n",
       "5.428936    0.004073\n",
       "4.973958    0.002037\n",
       "              ...   \n",
       "5.147727    0.002037\n",
       "5.073099    0.002037\n",
       "4.726852    0.002037\n",
       "5.404915    0.002037\n",
       "6.679348    0.002037\n",
       "Name: MEAN_INP_STAY, Length: 487, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medicare_counties[\"MEAN_INP_STAY\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(312, 104)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the outliers (we can explore them more later)\n",
    "cc_reasonable = medicare_counties[(medicare_counties[\"ALLCOSTS\"]<10000000) & (medicare_counties[\"TOTAL_INP_DAYS\"]<2000) & (medicare_counties[\"TOTAL_CC_CASES\"]<600) & (medicare_counties[\"TOTAL_CC_CASES\"]>100)]\n",
    "\n",
    "\n",
    "# Subset to higher utilzers\n",
    "cc_case_counties = cc_reasonable[(cc_reasonable[\"ALLCOSTS\"]>1000000) | (cc_reasonable[\"TOTAL_INP_DAYS\"]>500)]\n",
    "cc_case_counties.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIMe1v7tg34vo2NjUM4ycDsyrwAAHtqXz72eLVjs315XoD9TV21B4ChNrK2Lq23X1vtMQZs0ezzqz0CAFAQx0oA5XKmEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKNKAXmp41a1ZGjRqV0aNHJ0kuvPDCnHTSSVm7dm1aWlqyYcOGjBs3Lm1tbZk6depQzgsAAADAIBjwu49de+21Oeqoo15224IFC9Lc3Jy5c+dm2bJlaW1tzY033jjoQwIAAAAwuHb78rHe3t50dXWlqakpSdLU1JSurq6sX79+0IYDAAAAYGgM+EyhCy+8MJVKJY2NjbngggvS3d2dSZMmpba2NklSW1ubiRMnpru7O+PHjx+ygQEAAADYcwOKQjfddFPq6+vz4osv5sorr8yiRYvyV3/1V4MywJo1awZlO/u7zs7Oao8wbDU2NlZ7hF3m5+El1mHfYn/sO+wLAAAGw4CiUH19fZJk1KhRaW5uzl//9V/n85//fNatW5e+vr7U1tamr68vPT09/fcdqBkzZvS/gHWpOjs798twwdDx8+D3Yl9jf+w7hmJfbNmyxZM0AAAF2ulrCm3atCm///3vkySVSiW33XZbGhoaMmHChDQ0NKSjoyNJ0tHRkYaGBpeOAQAAAOwHdnqmUG9vb84777z09fVl+/btmTZtWhYsWJAkWbhwYVpaWrJkyZKMHTs2bW1tQz4wAAAAAHtup1FoypQpaW9vf9XPTZs2LUuXLh3smQAAAAAYYrv9lvQAAAAA7L9EIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUYhdtrVvW7VHAAAAAPZQXbUHYP8zsrYurbdfW+0xBmzR7POrPQIAAADsc5wpBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAD2G1v7tlV7hF2yv80LlKWu2gMAAAAM1MjaurTefm21xxiwRbPPr/YIADvkTCEAAACAAolCAAAAAAVy+RgAQGG2bNmS//bf/lvuueeejB49OjNnzszll1+etWvXpqWlJRs2bMi4cePS1taWqVOnVntcAGCIiEIAAIW5+uqrM3r06KxYsSI1NTV59tlnkyQLFixIc3Nz5s6dm2XLlqW1tTU33nhjlacFAIaKy8cAAAqycePGtLe3Z/78+ampqUmSHHrooent7U1XV1eampqSJE1NTenq6sr69eurOS4AMIScKQQAUJAnnngi48aNy3XXXZdVq1blwAMPzPz583PAAQdk0qRJqa2tTZLU1tZm4sSJ6e7uzvjx46s8NQAwFEQhAICCbNu2LU888USOOeaYXHTRRfnlL3+ZT3/607nmmmv2eNtr1qwZhAmHj87OzmqPMCCNjY3VHmHY2xd+FvaFGYYLazk4rOPg2NN1FIUAAApy+OGHp66urv8ysWOPPTaHHHJIDjjggKxbty59fX2pra1NX19fenp6Ul9fP+Btz5gxI6NHjx6q0fcrnZ2dYgv9qv2z4Odx8FjLwWEdB8eO1nHLli0DfqLGawoBABRk/PjxOeGEE3L33XcnSdauXZve3t5MnTo1DQ0N6ejoSJJ0dHSkoaHBpWMAMIw5UwgAoDCXXXZZLr744rS1taWuri5XXXVVxo4dm4ULF6alpSVLlizJ2LFj09bWVu1RAYAhJAoBABRmypQp+c53vvOK26dNm5alS5dWYSIAoBpcPgYAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRKF9wDEzjqn2CAAAAEBh6qo9AMnrRr8urbdfW+0xBmzR7POrPQIAAACwh5wpBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQ7GO29m2r9gi7ZH+bFwAAgJfUVXsA4OVG1tal9fZrqz3GgC2afX61RwAAAGA3OFMIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACjQLkWh6667LtOnT88jjzySJFm7dm3mzZuXU045JfPmzcujjz46FDMCAAAAMMgGHIV+9atf5Re/+EUOP/zw/tsWLFiQ5ubmrFixIs3NzWltbR2SIQEAAAAYXAOKQi+++GIWLVqUBQsWpKamJknS29ubrq6uNDU1JUmamprS1dWV9evXD920AAAAAAyKuoHc6Zprrslpp52WKVOm9N/W3d2dSZMmpba2NklSW1ubiRMnpru7O+PHjx/wAGvWrNnFkYefxsbGao8Ae6Szs3O/2i67x/7Yd9gXAAAMhp1GodWrV+fBBx/MhRdeOCQDzJgxI6NHjx6SbQN7x1CEzc7OTsF0H2J/7DuGYl9s2bLFkzQAAAXa6eVj9957b37729/m5JNPzqxZs/L000/nk5/8ZB5//PGsW7cufX19SZK+vr709PSkvr5+yIcGAAAAYM/sNAqdc845ueuuu7Jy5cqsXLkykydPzje/+c2ceuqpaWhoSEdHR5Kko6MjDQ0Nu3TpGAAAAADVMaDXFNqRhQsXpqWlJUuWLMnYsWPT1tY2WHMBAAAAMIR2OQqtXLmy/7+nTZuWpUuXDupAAAAAAAy9Ab0lPQAAAADDiygEAAAAUCBRCAAAAKBAohAAAABAgUQhAAAAgALt0VvSAwCw/5k1a1ZGjRqV0aNHJ0kuvPDCnHTSSVm7dm1aWlqyYcOGjBs3Lm1tbZk6dWp1hwUAhowoBABQoGuvvTZHHXXUy25bsGBBmpubM3fu3Cxbtiytra258cYbqzQhADDUXD4GAEB6e3vT1dWVpqamJElTU1O6urqyfv36Kk8GAAwVZwoBABTowgsvTKVSSWNjYy644IJ0d3dn0qRJqa2tTZLU1tZm4sSJ6e7uzvjx4we0zTVr1gzlyPudzs7Oao8wII2NjdUeYdjbF34W9oUZhgtrOTis4+DY03UUhQAACnPTTTelvr4+L774Yq688sosWrQof/VXf7XH250xY0b/6xSVrrOzU2yhX7V/Fvw8Dh5rOTis4+DY0Tpu2bJlwE/UuHwMAKAw9fX1SZJRo0alubk5999/f+rr67Nu3br09fUlSfr6+tLT09N/XwBg+BGFAAAKsmnTpvz+979PklQqldx2221paGjIhAkT0tDQkI6OjiRJR0dHGhoaBnzpGACw/3H5GABAQXp7e3Peeeelr68v27dvz7Rp07JgwYIkycKFC9PS0pIlS5Zk7NixaWtrq/K0AMBQEoUAAAoyZcqUtLe3v+rnpk2blqVLl+7dgQCAqnH5GAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAwiLb2bUtjY2O1xwCAnaqr9gAAADCcjKytS+vt11Z7jAFbNPv8ao8AQJU4UwgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiULAHtnat21IttvY2Dgk2x2qeQEAAPY3ddUeANi/jaytS+vt11Z7jAFbNPv8ao8AAACwT3CmEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAwBDZ2ret2iOksbFxwPfdF+YF9p66ag8AAAAwXI2srUvr7ddWe4wBWzT7/GqPAOxFzhQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAoEDXXXddpk+fnkceeSRJsnbt2sybNy+nnHJK5s2bl0cffbS6AwIAQ04UAgAozK9+9av84he/yOGHH95/24IFC9Lc3JwVK1akubk5ra2tVZwQANgbRCEAgIK8+OKLWbRoURYsWJCampokSW9vb7q6utLU1JQkaWpqSldXV9avX1/NUQGAISYKAQAU5Jprrslpp52WKVOm9N/W3d2dSZMmpba2NklSW1ubiRMnpru7u1pjAgB7QV21BwAAYO9YvXp1HnzwwVx44YVDsv01a9YMyXb3N42NjdUeAfZIZ2dntUfYp1mfwWEdB8eerqMoBABQiHvvvTe//e1vc/LJJydJnn766Xzyk5/M5z//+axbty59fX2pra1NX19fenp6Ul9fv0vbnzFjRkaPHj0UowN7kbC5Y52dndZnEFjHwbGjddyyZcuAn6hx+RgAQCHOOeec3HXXXVm5cmVWrlyZyZMn55vf/GZOPfXUNDQ0pKOjI0nS0dGRhoaGjB8/vsoTAwBDyZlCAABk4cKFaWlpyZIlSzJ27Ni0tbVVeyQAYIiJQgAAhVq5cmX/f0+bNi1Lly6t4jQAwN42oCh07rnn5sknn8yIESMyZsyYXHrppWloaMjatWvT0tKSDRs2ZNy4cWlra8vUqVOHeGQAAAAA9tSAolBbW1sOOuigJMkdd9yRiy++OLfeemsWLFiQ5ubmzJ07N8uWLUtra2tuvPHGIR0YAAAAgD03oBea/mMQSpI//OEPqampSW9vb7q6utLU1JQkaWpqSldXV9avXz80kwIAAAAwaAb8mkJf+MIXcvfdd6dSqeSGG25Id3d3Jk2alNra2iRJbW1tJk6cmO7ubu9UAQAAALCPG3AUuvLKK5Mk7e3tueqqqzJ//vxBGWDNmjWDsp39WWNjY7VHgGJs7duWkbX712vsb96yOV1ruqo9Rjo7O6s9Av8/+wIAgMGwy/8yev/735/W1tZMnjw569atS19fX2pra9PX15eenp7U19fv0vZmzJiR0aNH7+oYALtlZG1dWm+/ttpj7JJFs8+vejzu7Oys+gy8ZCj2xZYtWzxJAwBQoJ2+ptDGjRvT3d3d//HKlStz8MEHZ8KECWloaEhHR0eSpKOjIw0NDS4dAwAAANgP7PRMoc2bN2f+/PnZvHlzRowYkYMPPjjXX399ampqsnDhwrS0tGTJkiUZO3Zs2tra9sbMAAAAAOyhnUahQw89NLfccsurfm7atGlZunTpoA8FAAAAwNAa0FvSAwAAADC8iEIAAAAkeemdWvcn+9u8sK/Zv96XGQAAgCGzv71T66LZ51d7BNivOVMIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoEB11R4AAIC969xzz82TTz6ZESNGZMyYMbn00kvT0NCQtWvXpqWlJRs2bMi4cePS1taWqVOnVntcAGCIiEIAAIVpa2vLQQcdlCS54447cvHFF+fWW2/NggUL0tzcnLlz52bZsmVpbW3NjTfeWOVpAYCh4vIxAIDC/DEIJckf/vCH1NTUpLe3N11dXWlqakqSNDU1paurK+vXr6/WmADAEHOmEABAgb7whS/k7rvvTqVSyQ033JDu7u5MmjQptbW1SZLa2tpMnDgx3d3dGT9+fJWnBQCGgigEAFCgK6+8MknS3t6eq666KvPnz9/jba5Zs2aPtzEcNDY2VnsEKEpnZ+ew/nrDlXUcHHu6jqIQAEDB3v/+96e1tTWTJ0/OunXr0tfXl9ra2vT19aWnpyf19fUD3taMGTMyevToIZwW4JX2Zojt7OwUfgeBdRwcO1rHLVu2DPiJmmH5mkJb+7ZVewQAgH3Sxo0b093d3f/xypUrc/DBB2fChAlpaGhIR0dHkqSjoyMNDQ0uHQOAYWxYnik0srYurbdfW+0xBmzR7POrPQIAUIjNmzdn/vz52bx5c0aMGJGDDz44119/fWpqarJw4cK0tLRkyZIlGTt2bNra2qo9LgAwhIZlFAIA4NUdeuihueWWW171c9OmTcvSpUv38kQAQLUMy8vHAAAAAHhtohAAAABAgUQhAAAAgAKJQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAAAokCgEAAAAUCBRCAAAAKBAohAAAABAgUQhAAAAgAKJQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAAAokCgEAAAAUCBRCAAAAKBAohAAAABAgUQhAAAAgAKJQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAAAokCgEAAAAUKC6nd3hueeey9/+7d/m8ccfz6hRo3LkkUdm0aJFGT9+fNauXZuWlpZs2LAh48aNS1tbW6ZOnboXxgYAAABgT+z0TKGampp86lOfyooVK/L9738/U6ZMyZe//OUkyYIFC9Lc3JwVK1akubk5ra2tQz4wAAAAAHtup1Fo3LhxOeGEE/o/njlzZp566qn09vamq6srTU1NSZKmpqZ0dXVl/fr1QzctAAAAAINip5eP/Vvbt2/PzTffnFmzZqW7uzuTJk1KbW1tkqS2tjYTJ05Md3d3xo8fP+BtrlmzZtcmHoDGxsZB3yZANXV2dlZ7hH1iBl5iXwAAMBh2KQpdfvnlGTNmTD72sY+lq6trUAaYMWNGRo8ePSjbAhiuqh27Ozs7qz4DLxmKfbFly5YheZIGAIB924CjUFtbWx577LFcf/31GTFiROrr67Nu3br09fWltrY2fX196enpSX19/VDOCwAAAMAgGNBb0n/lK1/JmjVrsnjx4owaNSpJMmHChDQ0NKSjoyNJ0tHRkYaGhl26dAwAAACA6tjpmUK//vWvc/3112fq1Kn5yEc+kiQ54ogjsnjx4ixcuDAtLS1ZsmRJxo4dm7a2tiEfGAAAAIA9t9Mo9OY3vzkPP/zwq35u2rRpWbp06aAPBQAAAMDQGtDlYwAAAAAML6IQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIADKqtfduqPcIu2d/mBQCAwVJX7QEAGF5G1tal9fZrqz3GgC2afX61RwAAgKoQhQAACvLcc8/lb//2b/P4449n1KhROfLII7No0aKMHz8+a9euTUtLSzZs2JBx48alra0tU6dOrfbIAMAQcfkYAEBBampq8qlPfSorVqzI97///UyZMiVf/vKXkyQLFixIc3NzVqxYkebm5rS2tlZ5WgBgKIlCAAAFGTduXE444YT+j2fOnJmnnnoqvb296erqSlNTU5KkqakpXV1dWb9+fbVGBQCGmMvHAAAKtX379tx8882ZNWtWuru7M2nSpNTW1iZJamtrM3HixHR3d2f8+PED2t6aNWuGctz9RmNjY7VHgKJ0dnYO6683XFnHwbGn6ygKAQAU6vLLL8+YMWPysY99LF1dXXu8vRkzZmT06NGDMBnAwO3NENvZ2Sn8DgLrODh2tI5btmwZ8BM1ohAAQIHa2try2GOP5frrr8+IESNSX1+fdevWpa+vL7W1tenr60tPT0/q6+urPSoAMES8phAAQGG+8pWvZM2aNVm8eHFGjRqVJJkwYUIaGhrS0dGRJOno6EhDQ8OALx0DAPY/zhQCACjIr3/961x//fWZOnVqPvKRjyRJjjjiiCxevDgLFy5MS0tLlixZkrFjx6atra3K0wIAQ0kUAgAoyJvf/OY8/PDDr/q5adOmZenSpXt5IgCgWlw+BgAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAD7tK1926o9AgAMS3XVHgAAAF7LyNq6tN5+bbXHGLBFs8+v9ggAMCDOFAIAAAAokCgEAAAAUCBRCAAAAKBAohAAAABAgUQhAAAAgAKJQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAAAokCgEAAAAUCBRCAAAAKBAohAAAABAgUQhAAAAgAKJQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAAAokCgEsI/b2ret2iOksbGx2iMAAACDrK7aAwDw2kbW1qX19murPcaALZp9frVHAAAABsCZQgAAAAAFEoUAAAAACiQKAQAAABRIFAIAAGC/tLffkGNP33xjX3gDEfi3vNA0AAAA+yVvyAF7xplCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKARA0fa3dwE5ZsYx1R4BAIBhwruPAVA071oCAOwtW/u2ZWTt/vPP8P1tXnadvQsAAAB7gSej2Ne4fAwAAACgQKIQAAAAQIFEIQAAAIAC7TQKtbW1ZdasWZk+fXoeeeSR/tvXrl2befPm5ZRTTsm8efPy6KOPDuWcAAAAAAyinUahk08+OTfddFPe8IY3vOz2BQsWpLm5OStWrEhzc3NaW1uHbEgAAAAABtdOo9Dxxx+f+vr6l93W29ubrq6uNDU1JUmamprS1dWV9evXD82UAAAAAAyq3XpNoe7u7kyaNCm1tbVJktra2kycODHd3d2DOhwAAAAAQ6Ou2gOsWbNm0LfZ2Ng46NsEgH1FZ2dntUcAAGAY2K0oVF9fn3Xr1qWvry+1tbXp6+tLT0/PKy4zG4gZM2Zk9OjRuzMGABRpsJ/82LJly5A8SQMAwL5tty4fmzBhQhoaGtLR0ZEk6ejoSENDQ8aPHz+owwEAAAAwNHZ6ptAVV1yRH/7wh3n22Wdz1llnZdy4cVm+fHkWLlyYlpaWLFmyJGPHjk1bW9vemBcAAACAQbDTKHTJJZfkkksuecXt06ZNy9KlS4dkKAAAAACG1m5dPgYAAADA/k0UAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAFaWtry6xZszJ9+vQ88sgj/bevXbs28+bNyymnnJJ58+bl0Ucfrd6QAMBeIQoBABTk5JNPzk033ZQ3vOENL7t9wYIFaW5uzooVK9Lc3JzW1tYqTQgA7C2iEABAQY4//vjU19e/7Lbe3t50dXWlqakpSdLU1JSurq6sX7++GiMCAHuJKAQAULju7u5MmjQptbW1SZLa2tpMnDgx3d3dVZ4MABhKddUeAACA4WHNmjVDst3GxsYh2S4AO9fZ2blfbbc0e7qOohAAQOHq6+uzbt269PX1pba2Nn19fenp6XnFZWY7M2PGjIwePXqIpgSgGoYizHd2dgr+g2BH67hly5YBP1Hj8jEAgMJNmDAhDQ0N6ejoSJJ0dHSkoaEh48ePr/JkAMBQcqYQAEBBrrjiivzwhz/Ms88+m7POOivjxo3L8uXLs3DhwrS0tGTJkiUZO3Zs2traqj0qADDERCEAgIJccsklueSSS15x+7Rp07J06dIqTAQAVIvLxwAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAAF5ha9+2IdluY2PjkGw3GbqZh6u6ag8AAAAA7HtG1tal9fZrqz3GLlk0+/xqj7BfcaYQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAFAgUQgAAACgQKIQAAAAQIFEIQAAAIACiUIAAAAABRKFAAAAAAokCgEAAAAUSBQCAAAAKJAoBAAAAAwLW/u2VXuEXVLteeuq+tUBAAAABsnI2rq03n5ttccYsEWzz6/q13emEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoECiEAAAAECBRCEAAACAAu1xFFq7dm3mzZuXU045JfPmzcujjz46CGMBAFANju0AoBx7HIUWLFiQ5ubmrFixIs3NzWltbR2MuQAAqALHdgBQjro9eXBvb2+6urryrW99K0nS1NSUyy+/POvXr8/48eNf87GVSiVJ8uKLL+7JCDv0uhGjh2S7Q2HLli3mHULmHVrmHXr728zmHVpbtmwZ9G3+8f+L//j/zZRrd4/thvq4LnFsN5TMO7TMO7TMO7T2t3mT/W/mPT22e7XH78qxXU1lD44A16xZk4suuijLly/vv+3UU0/N1Vdfnbe85S2v+djf//73eeSRR3b3SwMAg+yoo47KQQcdVO0xqKLdPbZzXAcA+56BHNvt0ZlCe+LAAw/MUUcdlZEjR6ampqZaYwBA8SqVSrZu3ZoDDzyw2qOwn3JcBwD7jl05ttujKFRfX59169alr68vtbW16evrS09PT+rr63f62BEjRng2EgD2EQcccEC1R2AfsLvHdo7rAGDfMtBjuz16oekJEyakoaEhHR0dSZKOjo40NDTs9PWEAADY9zi2A4Cy7NFrCiXJb37zm7S0tOT555/P2LFj09bWlje96U2DNR8AAHuRYzsAKMceRyEAAAAA9j97dPkYAAAAAPsnUQgAAACgQKIQAAAAQIFEIQAAAIACiUJ7ydq1azNv3ryccsopmTdvXh599NFX3Gfx4sV53/vel9NOOy2nn356fvKTn+z9QQswkH3xR7/97W9z7LHHpq2tbe8NWJCB7ovbbrstc+bMSVNTU+bMmZNnn3127w5aiIHsj97e3pxzzjmZM2dO3vOe92ThwoXZtm3b3h92GGtra8usWbMyffr0PPLII696n76+vlx22WX5i7/4i8yePTtLly7dy1NC2Xb0e/paf0d35fijBM8991zOPvvsnHLKKZkzZ04+85nPZP369Ums4+4499xzc9ppp+X9739/mpub8y//8i9JrOXuuu666172+20dd82sWbPynve8J3Pnzs3cuXP7/11rHXfdli1bsmDBgvzH//gfM2fOnFx66aVJBnktK+wVZ555ZqW9vb1SqVQq7e3tlTPPPPMV97nzzjsrmzZtqlQqlcq//Mu/VBobGyubN2/eq3OWYCD7olKpVLZt21b52Mc+VrngggsqX/rSl/bmiMUYyL544IEHKu9973srPT09lUqlUnn++ecrL7zwwl6dsxQD2R9XXHFF/+/Diy++WPngBz9YWb58+V6dc7i79957K0899VTl3e9+d+Xhhx9+1fvceuutlU984hOVvr6+Sm9vb+Wkk06qPPHEE3t5UijXjn5PX+vv6ECPP0rx3HPPVX72s5/1f/ylL32p8vnPf75SqVjH3fH888/3//ftt99eef/731+pVKzl7lizZk3lk5/8ZOXP//zP+3+/reOu2dExjHXcdZdffnnlyiuvrGzfvr1SqVQqzzzzTKVSGdy1dKbQXtDb25uurq40NTUlSZqamtLV1dX/bMgfnXTSSXnd616XJJk+fXoqlUo2bNiwt8cd1ga6L5Lk61//ev78z/88U6dO3ctTlmGg++J//I//kU984hM57LDDkiQHHXRQRo8evdfnHe4Guj9qamqycePGbN++PS+++GK2bt2aSZMmVWPkYev4449PfX39a97ntttuy4c+9KGMGDEi48ePz1/8xV/kBz/4wV6aEHi139PX+ju6K8cfpRg3blxOOOGE/o9nzpyZp556yjrupoMOOqj/v//whz+kpqbGWu6GF198MYsWLcqCBQtSU1OTxO/2YLGOu27jxo1pb2/P/Pnz+38eDz300EFfy7qh/1bo7u7OpEmTUltbmySpra3NxIkT093dnfHjx7/qY9rb2/PGN74xkydP3pujDnsD3RcPPfRQ7rrrrtx4441ZsmRJtcYd1ga6L37zm9/kiCOOyBlnnJFNmzZl9uzZ+eu//uv+P4wMjoHuj3PPPTfnnXde3vGOd2Tz5s0544wz0tjYWK2xi9Xd3Z3DDz+8/+P6+vo8/fTTVZwIeK2/o5VKZZePBUuyffv23HzzzZk1a5Z13ANf+MIXcvfdd6dSqeSGG26wlrvhmmuuyWmnnZYpU6b032Ydd8+FF16YSqWSxsbGXHDBBdZxNzzxxBMZN25crrvuuqxatSoHHnhg5s+fnwMOOGBQ19KZQvugn//857nmmmvyd3/3d9UepUhbt27NpZdemssuu6z/l4nq6evry8MPP5xvfetb+c53vpM777wzy5Ytq/ZYxfrBD36Q6dOn56677sqdd96Z++67zxkqAOyRyy+/PGPGjMnHPvaxao+yX7vyyivz4x//OJ/97Gdz1VVXVXuc/c7q1avz4IMPprm5udqj7Pduuumm/OM//mO++93vplKpZNGiRdUeab+0bdu2PPHEEznmmGPyve99LxdeeGHOO++8bNq0aVC/jii0F9TX12fdunXp6+tL8tI/cnt6el718oDVq1fnc5/7XBYvXpw3velNe3vUYW8g++KZZ57J448/nnPOOSezZs3Kt7/97dxyyy39L+rF4Bjo78Xhhx+e97znPRk1alRe//rX5+STT84DDzxQjZGHtYHuj//5P/9nTjvttIwYMSIHHXRQZs2alVWrVlVj5KLV19fnqaee6v+4u7vbmaVQZa/1d3RXjgVL09bWlsceeyxf/epXM2LECOs4CN7//vdn1apVmTx5srXcBffee29++9vf5uSTT86sWbPy9NNP55Of/GQef/xx67iL/vj9jxo1Ks3Nzbn//vv9bu+Gww8/PHV1df2Xgh177LE55JBDcsABBwzqWopCe8GECRPS0NCQjo6OJElHR0caGhpecfrWAw88kM9+9rO59tpr85a3vKUaow57A9kXhx9+eFatWpWVK1dm5cqV+cu//Mt8+MMfzuWXX16tsYelgf5eNDU15a677kqlUsnWrVvzs5/9LEcffXQ1Rh7WBro/jjjiiNx5551JXrru/p577smb3/zmvT5v6d7znvdk6dKl2b59e9avX5877rgjp5xySrXHgqK91t/Rgf6NLc1XvvKVrFmzJosXL86oUaOSWMfdsXHjxnR3d/d/vHLlyhx88MHWchedc845ueuuu/r/DTB58uR885vfzKmnnmodd8GmTZvy+9//PklSqVRy2223paGhwc/jbhg/fnxOOOGE3H333Uleelex3t7eTJ06dVDXsqZSqVSG/tvhN7/5TVpaWvL8889n7NixaWtry5ve9KacffbZOf/88/Onf/qn+cAHPpDf/e53L3vR1quuuirTp0+v4uTDz0D2xb/1ta99LZs2bcpFF11UpYmHr4Hsi+3bt6etrS133nlnRowYkXe84x256KKLMmKEpj3YBrI/Hn/88SxYsCDPPvts+vr6csIJJ+QLX/hC6uq8RN1gueKKK/LDH/4wzz77bA455JCMGzcuy5cvf9l+6Ovry6JFi/oPEs4+++zMmzevypNDOXb0e7qjv6PJjv/GlurXv/51mpqaMnXq1BxwwAFJXnriYfHixdZxFz377LM599xzs3nz5owYMSIHH3xwLrroorzlLW+xlntg1qxZuf7663PUUUdZx13wxBNP5LzzzktfX1+2b9+eadOm5ZJLLsnEiROt42544okncvHFF2fDhg2pq6vL3/zN3+Rd73rXoK6lKAQAAABQIE+1AwAAABRIFAIAAAAokCgEAAAAUCBRCAAAAKBA3i4GAPZDbW1tWbFiRX73u9/l+9//fo466qjXvP/999+fyy67rP/j3t7eHHbYYbn11luHelQAAPZRohAA7IdOPvnkfPzjH88ZZ5wxoPu/9a1vzbJly/o/Pvfcc9PY2DhU4wEAsB9w+RgA7IeOP/741NfXv+L2X/7ylznzzDNz+umn5/TTT8+Pf/zjV9ynt7c3d999d+bOnbsXJgUAYF/lTCEAGCaef/75LFiwIF//+tczceLE9PT05IMf/GA6OjoyduzY/vu1t7fnxBNPzKGHHlrFaQEAqDZRCACGidWrV+fJJ5/M2Wef3X9bTU1NHnvssfzpn/5p/23f+973csEFF1RjRAAA9iGiEAAME5VKJdOnT89NN920w/v84he/yIYNG/Kud71rL04GAMC+yGsKAcAwcdxxx+Wxxx7Lz372s/7bHnjggVQqlf6Pv/vd72bu3Lmpq/O8EABA6Woq//ZIEQDYL1xxxRX54Q9/mGeffTaHHHJIxo0bl+XLl+eBBx7I1VdfnX/913/N1q1bM2XKlFx//fUZMWJEXnjhhbzjHe/I//7f/zvTpk2r9rcAAECViUIAAAAABXL5GAAAAECBRCEAAACAAolCAAAAAAUShQAAAAAKJAoBAAAAFEgUAgAAACiQKAQAAABQIFEIAAAAoED/H6mfgrc1BCuVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"crest\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n",
    "cc_case_counties.hist(\"ALLCOSTS\", ax=axes[0])\n",
    "cc_case_counties.hist(\"TOTAL_INP_DAYS\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"crest\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n",
    "cc_case_counties.hist(\"MEANCOSTS\", ax=axes[0])\n",
    "cc_case_counties.hist(\"USE_PROPORTION\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conditions = cc_case_counties[[\"ALZHDMTA_CASES\", \"CHF_CASES\", \"CNCR_CASES\", \"COPD_CASES\", \"DEPRESSN_CASES\", \n",
    "                                \"DIABETES_CASES\", \"ISCHMCHT_CASES\", \"OSTEOPRS_CASES\", \"CHRNKIDN_CASES\", \n",
    "                                \"RA_OA_CASES\", \"STRKETIA_CASES\", \"ESRD_CASES\", \"TOTAL_VISITS\", \"TOTAL_INP_DAYS\",\n",
    "                               \"MEAN_INP_STAY\", \"ALLCOSTS\", \"MEANCOSTS\", \"USE_PROPORTION\", \"HF_PROP_RANK\", \"HO_PROP_RANK\"]]\n",
    "conditions_corr = conditions.corr(method=\"pearson\")\n",
    "lower_corr = conditions_corr.where(np.tril(np.ones(conditions_corr.shape)).astype(np.bool))\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "conditions_hmap=sb.heatmap(lower_corr, annot=True, cmap=\"mako\", fmt=\".2f\")\n",
    "fig = conditions_hmap.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"counties_cases_heatmap.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    312.000000\n",
       "mean     314.128205\n",
       "std      104.024837\n",
       "min      127.000000\n",
       "25%      238.750000\n",
       "50%      295.500000\n",
       "75%      376.750000\n",
       "max      599.000000\n",
       "Name: TOTAL_CC_CASES, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_case_counties[\"TOTAL_CC_CASES\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(30, 10))\n",
    "cc_case_counties.hist(\"TOTAL_CC_CASES\", ax=axes[0])\n",
    "cc_case_counties.hist(\"TOTAL_VISITS\", ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, these are our 312 Super-Utilizers (but not the Super-Duper Outliers; we can tackle them later).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>ALLCOSTS</th>\n",
       "      <th>MEANCOSTS</th>\n",
       "      <th>TOTAL_INP_DAYS</th>\n",
       "      <th>MEAN_INP_STAY</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>MEAN_RACE_CD</th>\n",
       "      <th>HF_PROP_RANK</th>\n",
       "      <th>HO_PROP_RANK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <th>Caddo</th>\n",
       "      <td>9999944.0</td>\n",
       "      <td>490.938552</td>\n",
       "      <td>286.0</td>\n",
       "      <td>3.620760</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1.356061</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <th>Lake</th>\n",
       "      <td>9837300.0</td>\n",
       "      <td>692.093567</td>\n",
       "      <td>404.0</td>\n",
       "      <td>5.449550</td>\n",
       "      <td>74.2</td>\n",
       "      <td>1.094737</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <th>Washtenaw</th>\n",
       "      <td>9651634.0</td>\n",
       "      <td>622.537954</td>\n",
       "      <td>501.0</td>\n",
       "      <td>6.057353</td>\n",
       "      <td>72.2</td>\n",
       "      <td>1.188119</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <th>Yavapai</th>\n",
       "      <td>9468926.0</td>\n",
       "      <td>496.142494</td>\n",
       "      <td>394.0</td>\n",
       "      <td>5.156746</td>\n",
       "      <td>70.2</td>\n",
       "      <td>1.343511</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <th>Fayette</th>\n",
       "      <td>9420848.0</td>\n",
       "      <td>613.574671</td>\n",
       "      <td>288.0</td>\n",
       "      <td>4.014706</td>\n",
       "      <td>71.3</td>\n",
       "      <td>1.118280</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <th>Placer</th>\n",
       "      <td>9393820.0</td>\n",
       "      <td>571.732222</td>\n",
       "      <td>295.0</td>\n",
       "      <td>5.505208</td>\n",
       "      <td>75.3</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <th>Butler</th>\n",
       "      <td>9334294.0</td>\n",
       "      <td>538.689316</td>\n",
       "      <td>581.0</td>\n",
       "      <td>5.217687</td>\n",
       "      <td>73.1</td>\n",
       "      <td>1.192308</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <th>Rock Island</th>\n",
       "      <td>9323692.0</td>\n",
       "      <td>655.102513</td>\n",
       "      <td>357.0</td>\n",
       "      <td>6.045977</td>\n",
       "      <td>73.5</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <th>Cumberland</th>\n",
       "      <td>9303972.0</td>\n",
       "      <td>520.006648</td>\n",
       "      <td>310.0</td>\n",
       "      <td>4.076608</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.188034</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <th>Washington</th>\n",
       "      <td>9233022.0</td>\n",
       "      <td>702.198889</td>\n",
       "      <td>472.0</td>\n",
       "      <td>4.463288</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <th>Atlantic</th>\n",
       "      <td>9168692.0</td>\n",
       "      <td>563.287462</td>\n",
       "      <td>328.0</td>\n",
       "      <td>5.181140</td>\n",
       "      <td>75.9</td>\n",
       "      <td>1.412844</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <th>Pulaski</th>\n",
       "      <td>8854808.0</td>\n",
       "      <td>416.812682</td>\n",
       "      <td>557.0</td>\n",
       "      <td>5.553283</td>\n",
       "      <td>70.3</td>\n",
       "      <td>1.083770</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <th>Cambria</th>\n",
       "      <td>8825920.0</td>\n",
       "      <td>645.058081</td>\n",
       "      <td>488.0</td>\n",
       "      <td>5.697980</td>\n",
       "      <td>71.5</td>\n",
       "      <td>1.170455</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <th>Rockland</th>\n",
       "      <td>8810778.0</td>\n",
       "      <td>781.971576</td>\n",
       "      <td>455.0</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>73.4</td>\n",
       "      <td>1.302326</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <th>Orleans</th>\n",
       "      <td>8724636.0</td>\n",
       "      <td>573.251129</td>\n",
       "      <td>491.0</td>\n",
       "      <td>5.258274</td>\n",
       "      <td>73.6</td>\n",
       "      <td>1.349593</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <th>Charlotte</th>\n",
       "      <td>8673742.0</td>\n",
       "      <td>522.304777</td>\n",
       "      <td>346.0</td>\n",
       "      <td>4.825581</td>\n",
       "      <td>74.8</td>\n",
       "      <td>1.252336</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <th>Bell</th>\n",
       "      <td>8601948.0</td>\n",
       "      <td>717.415895</td>\n",
       "      <td>395.0</td>\n",
       "      <td>6.641667</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <th>LaSalle</th>\n",
       "      <td>8594740.0</td>\n",
       "      <td>831.527778</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.604444</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <th>Alachua</th>\n",
       "      <td>8453762.0</td>\n",
       "      <td>677.600271</td>\n",
       "      <td>316.0</td>\n",
       "      <td>5.018056</td>\n",
       "      <td>73.4</td>\n",
       "      <td>1.426829</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <th>Norfolk</th>\n",
       "      <td>8389466.0</td>\n",
       "      <td>697.884367</td>\n",
       "      <td>450.0</td>\n",
       "      <td>5.467647</td>\n",
       "      <td>74.3</td>\n",
       "      <td>1.406977</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ALLCOSTS   MEANCOSTS  TOTAL_INP_DAYS  \\\n",
       "STATE          COUNTY                                               \n",
       "Louisiana      Caddo        9999944.0  490.938552           286.0   \n",
       "Ohio           Lake         9837300.0  692.093567           404.0   \n",
       "Michigan       Washtenaw    9651634.0  622.537954           501.0   \n",
       "Arizona        Yavapai      9468926.0  496.142494           394.0   \n",
       "Kentucky       Fayette      9420848.0  613.574671           288.0   \n",
       "California     Placer       9393820.0  571.732222           295.0   \n",
       "Ohio           Butler       9334294.0  538.689316           581.0   \n",
       "Illinois       Rock Island  9323692.0  655.102513           357.0   \n",
       "North Carolina Cumberland   9303972.0  520.006648           310.0   \n",
       "Pennsylvania   Washington   9233022.0  702.198889           472.0   \n",
       "New Jersey     Atlantic     9168692.0  563.287462           328.0   \n",
       "Arkansas       Pulaski      8854808.0  416.812682           557.0   \n",
       "Pennsylvania   Cambria      8825920.0  645.058081           488.0   \n",
       "New York       Rockland     8810778.0  781.971576           455.0   \n",
       "Louisiana      Orleans      8724636.0  573.251129           491.0   \n",
       "Florida        Charlotte    8673742.0  522.304777           346.0   \n",
       "Texas          Bell         8601948.0  717.415895           395.0   \n",
       "Illinois       LaSalle      8594740.0  831.527778           113.0   \n",
       "Florida        Alachua      8453762.0  677.600271           316.0   \n",
       "Virginia       Norfolk      8389466.0  697.884367           450.0   \n",
       "\n",
       "                            MEAN_INP_STAY  MEAN_AGE  MEAN_RACE_CD  \\\n",
       "STATE          COUNTY                                               \n",
       "Louisiana      Caddo             3.620760      71.0      1.356061   \n",
       "Ohio           Lake              5.449550      74.2      1.094737   \n",
       "Michigan       Washtenaw         6.057353      72.2      1.188119   \n",
       "Arizona        Yavapai           5.156746      70.2      1.343511   \n",
       "Kentucky       Fayette           4.014706      71.3      1.118280   \n",
       "California     Placer            5.505208      75.3      1.660000   \n",
       "Ohio           Butler            5.217687      73.1      1.192308   \n",
       "Illinois       Rock Island       6.045977      73.5      1.166667   \n",
       "North Carolina Cumberland        4.076608      73.0      1.188034   \n",
       "Pennsylvania   Washington        4.463288      77.0      1.100000   \n",
       "New Jersey     Atlantic          5.181140      75.9      1.412844   \n",
       "Arkansas       Pulaski           5.553283      70.3      1.083770   \n",
       "Pennsylvania   Cambria           5.697980      71.5      1.170455   \n",
       "New York       Rockland          6.266667      73.4      1.302326   \n",
       "Louisiana      Orleans           5.258274      73.6      1.349593   \n",
       "Florida        Charlotte         4.825581      74.8      1.252336   \n",
       "Texas          Bell              6.641667      75.0      1.222222   \n",
       "Illinois       LaSalle           2.604444      76.0      1.250000   \n",
       "Florida        Alachua           5.018056      73.4      1.426829   \n",
       "Virginia       Norfolk           5.467647      74.3      1.406977   \n",
       "\n",
       "                            HF_PROP_RANK  HO_PROP_RANK  \n",
       "STATE          COUNTY                                   \n",
       "Louisiana      Caddo               0.688         0.438  \n",
       "Ohio           Lake                0.807         0.852  \n",
       "Michigan       Washtenaw           0.988         0.915  \n",
       "Arizona        Yavapai             0.867         0.533  \n",
       "Kentucky       Fayette             0.983         0.917  \n",
       "California     Placer              0.964         0.893  \n",
       "Ohio           Butler              0.455         0.432  \n",
       "Illinois       Rock Island         0.396         0.614  \n",
       "North Carolina Cumberland          0.460         0.460  \n",
       "Pennsylvania   Washington          0.433         0.567  \n",
       "New Jersey     Atlantic            0.095         0.143  \n",
       "Arkansas       Pulaski             0.853         0.587  \n",
       "Pennsylvania   Cambria             0.239         0.045  \n",
       "New York       Rockland            0.935         0.871  \n",
       "Louisiana      Orleans             0.266         0.078  \n",
       "Florida        Charlotte           0.746         0.716  \n",
       "Texas          Bell                0.425         0.665  \n",
       "Illinois       LaSalle             0.099         0.208  \n",
       "Florida        Alachua             0.881         0.731  \n",
       "Virginia       Norfolk             0.083         0.205  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc = cc_case_counties[[\"STATE\", \"COUNTY\", \"MEAN_AGE\", \"MEAN_RACE_CD\", \n",
    "                       \"HF_PROP_RANK\", \"HO_PROP_RANK\", \"ALLCOSTS\", \"MEANCOSTS\", \"TOTAL_INP_DAYS\", \n",
    "                       \"MEAN_INP_STAY\"]]\n",
    "\n",
    "cc_super_states = cc.groupby([\"STATE\", \"COUNTY\"]).aggregate({\"ALLCOSTS\":\"sum\", \"MEANCOSTS\":\"mean\",\n",
    "                                                             \"TOTAL_INP_DAYS\":\"sum\", \"MEAN_INP_STAY\":\"mean\",\n",
    "                                                             \"MEAN_AGE\":\"mean\", \"MEAN_RACE_CD\":\"mean\",\n",
    "                                                            \"HF_PROP_RANK\":\"mean\", \"HO_PROP_RANK\":\"mean\"})\n",
    "cc_super_states.sort_values(by=\"ALLCOSTS\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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