FinalProject_group083.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our primary goal in our research is to look through the available data and determine whether there is a disparity in overall health and mortality for Americans residing in various communities, ranging from infancy to adulthood. We also take into account some predictive factors, such as smoking, binge drinking, physical activity, and insurance coverage to measure their effects on these values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Sean Reynolds\n",
    "- Kari Garcia\n",
    "- Sam Kapusta\n",
    "- Tam Tran\n",
    "- Randy Kim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does living in a larger metropolitan area affect the health of individuals residing in those areas (i.e. Do the populations in these cities have better or worse overall health and mortality than smaller populations)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the CDC, several of the leading health conditions affecting mortality rates in the United States are preventable.(1)  In that same article, the CDC notes that rural populations are older and sicker than urban populations.  Some explanations behind that include poor habits, less physical activity, and restricted access to healthcare. Our goal is to take these different factors into consideration when performing our analysis.\n",
    "\n",
    "While there are many factors in determining individual risks, we seek to utilize our analysis to further educate ourselves on whether living in different populations bare any relation. Additionally, we hope our research provides some insight on where Americans need to focus their effort on when it comes to improving public health wherever they reside.\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.cdc.gov/media/releases/2017/p0112-rural-death-risk.html\n",
    "- 2) https://www.census.gov/library/stories/2017/11/income-poverty-rural-america.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that individuals living in larger cities would be rated in poorer overall health as opposed to those residing in smaller, less populated cities. Some potential reasons would include low air quality (smog), higher rates of poverty, and insufficient health coverage.\n",
    "\n",
    "We will be focusing our data analysis on the following variables provided in our dataset: Poor or fair health, life expectancy, infant mortality, diabetes prevalence, drug overdose deaths, HIV prevalence, flu vaccinations, adult obesity, adult smoking, insufficient sleep, excessive drinking, physical inactivity, and uninsured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: County Health Rankings: 2019 CHR CSV Analytic Data\n",
    "- Link to the dataset: https://www.countyhealthrankings.org/sites/default/files/media/document/analytic_data2019.csv\n",
    "- Dataset Website: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/2020-measures\n",
    "- Number of observations: 3195\n",
    "- Description: This dataset has different health indicators such as 'Premature death raw value', 'Premature death numerator', 'Premature death denominator', and more. Additionally, it has observations by diffferent counties.\n",
    "\n",
    "\n",
    "- Dataset Name: CDC: NCHS Urban-Rural Classification Scheme for Counties\n",
    "- Link to the dataset: https://www.cdc.gov/nchs/data/data_acces_files/NCHSurbruralcodes2013.sas7bdat\n",
    "- Dataset website: https://www.cdc.gov/nchs/data_access/urban_rural.htm\n",
    "- Number of observations: 3149\n",
    "- Description: This dataset also has observations by diffferent counties, and it has different codes corresponding  to certain population types.\n",
    "\n",
    "We are combining these datasets to group information about health conditions in different counties from the CHR dataset and categorize them by their urban and rural rankings from the CDC dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We imported the numpy, pandas, matplotlib, and seaborn libraries to aid us in our analysis. The data we worked with is stored in a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest, kstest\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "#pd.options.display.max_columns = None\n",
    "\n",
    "# read_csv automatically uncompresses zip files. Alternate strategy is to use zipfile library\n",
    "health_df = pd.read_csv('analytic_data2019.csv', low_memory=False)\n",
    "county_df = pd.read_csv('NCHSURCodes2013.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>United States</td>\n",
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       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9917.2328984</td>\n",
       "      <td>80440</td>\n",
       "      <td>13636816</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alabama</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
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       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8824.0571232</td>\n",
       "      <td>815</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Autauga County</td>\n",
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       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>01003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7224.6321603</td>\n",
       "      <td>2827</td>\n",
       "      <td>576496</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baldwin County</td>\n",
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       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9586.165037</td>\n",
       "      <td>451</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barbour County</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01</td>\n",
       "      <td>007</td>\n",
       "      <td>01007</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11783.543675</td>\n",
       "      <td>445</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>009</td>\n",
       "      <td>01009</td>\n",
       "      <td>AL</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10908.101822</td>\n",
       "      <td>1050</td>\n",
       "      <td>161107</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blount County</td>\n",
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       "      <td>011</td>\n",
       "      <td>01011</td>\n",
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       "      <td>Bullock County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12066.910263</td>\n",
       "      <td>205</td>\n",
       "      <td>29266</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bullock County</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>01013</td>\n",
       "      <td>AL</td>\n",
       "      <td>Butler County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>14018.606262</td>\n",
       "      <td>393</td>\n",
       "      <td>54804</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Butler County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01</td>\n",
       "      <td>015</td>\n",
       "      <td>01015</td>\n",
       "      <td>AL</td>\n",
       "      <td>Calhoun County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12217.759984</td>\n",
       "      <td>2333</td>\n",
       "      <td>321406</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Calhoun County</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01</td>\n",
       "      <td>017</td>\n",
       "      <td>01017</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chambers County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11273.17098</td>\n",
       "      <td>691</td>\n",
       "      <td>93770</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chambers County</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01</td>\n",
       "      <td>019</td>\n",
       "      <td>01019</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cherokee County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11294.41054</td>\n",
       "      <td>575</td>\n",
       "      <td>71014</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherokee County</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01</td>\n",
       "      <td>021</td>\n",
       "      <td>01021</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chilton County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10831.917358</td>\n",
       "      <td>850</td>\n",
       "      <td>123617</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chilton County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01</td>\n",
       "      <td>023</td>\n",
       "      <td>01023</td>\n",
       "      <td>AL</td>\n",
       "      <td>Choctaw County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12047.187077</td>\n",
       "      <td>273</td>\n",
       "      <td>35489</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Choctaw County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01</td>\n",
       "      <td>025</td>\n",
       "      <td>01025</td>\n",
       "      <td>AL</td>\n",
       "      <td>Clarke County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9688.3738435</td>\n",
       "      <td>412</td>\n",
       "      <td>67037</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clarke County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>01</td>\n",
       "      <td>027</td>\n",
       "      <td>01027</td>\n",
       "      <td>AL</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9666.0412363</td>\n",
       "      <td>271</td>\n",
       "      <td>36916</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clay County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01</td>\n",
       "      <td>029</td>\n",
       "      <td>01029</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cleburne County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11985.555305</td>\n",
       "      <td>334</td>\n",
       "      <td>41389</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cleburne County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>01</td>\n",
       "      <td>031</td>\n",
       "      <td>01031</td>\n",
       "      <td>AL</td>\n",
       "      <td>Coffee County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7933.1296664</td>\n",
       "      <td>743</td>\n",
       "      <td>143899</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Coffee County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>01</td>\n",
       "      <td>033</td>\n",
       "      <td>01033</td>\n",
       "      <td>AL</td>\n",
       "      <td>Colbert County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10332.484038</td>\n",
       "      <td>1016</td>\n",
       "      <td>149595</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colbert County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01</td>\n",
       "      <td>035</td>\n",
       "      <td>01035</td>\n",
       "      <td>AL</td>\n",
       "      <td>Conecuh County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10635.365164</td>\n",
       "      <td>272</td>\n",
       "      <td>34170</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conecuh County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>01</td>\n",
       "      <td>037</td>\n",
       "      <td>01037</td>\n",
       "      <td>AL</td>\n",
       "      <td>Coosa County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12713.326366</td>\n",
       "      <td>228</td>\n",
       "      <td>29528</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Coosa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01</td>\n",
       "      <td>039</td>\n",
       "      <td>01039</td>\n",
       "      <td>AL</td>\n",
       "      <td>Covington County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11346.013021</td>\n",
       "      <td>754</td>\n",
       "      <td>102221</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Covington County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>01</td>\n",
       "      <td>041</td>\n",
       "      <td>01041</td>\n",
       "      <td>AL</td>\n",
       "      <td>Crenshaw County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9259.1296172</td>\n",
       "      <td>271</td>\n",
       "      <td>38691</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Crenshaw County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>01</td>\n",
       "      <td>043</td>\n",
       "      <td>01043</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cullman County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10007.614724</td>\n",
       "      <td>1493</td>\n",
       "      <td>229016</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cullman County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01</td>\n",
       "      <td>045</td>\n",
       "      <td>01045</td>\n",
       "      <td>AL</td>\n",
       "      <td>Dale County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9637.4564807</td>\n",
       "      <td>836</td>\n",
       "      <td>138090</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dale County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>01</td>\n",
       "      <td>047</td>\n",
       "      <td>01047</td>\n",
       "      <td>AL</td>\n",
       "      <td>Dallas County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>13923.693981</td>\n",
       "      <td>893</td>\n",
       "      <td>112186</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dallas County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>01</td>\n",
       "      <td>049</td>\n",
       "      <td>01049</td>\n",
       "      <td>AL</td>\n",
       "      <td>DeKalb County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>10995.276873</td>\n",
       "      <td>1313</td>\n",
       "      <td>199916</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DeKalb County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>01</td>\n",
       "      <td>051</td>\n",
       "      <td>01051</td>\n",
       "      <td>AL</td>\n",
       "      <td>Elmore County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8400.1348816</td>\n",
       "      <td>1170</td>\n",
       "      <td>231228</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elmore County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>01</td>\n",
       "      <td>053</td>\n",
       "      <td>01053</td>\n",
       "      <td>AL</td>\n",
       "      <td>Escambia County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>11920.569925</td>\n",
       "      <td>786</td>\n",
       "      <td>104898</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Escambia County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>55</td>\n",
       "      <td>131</td>\n",
       "      <td>55131</td>\n",
       "      <td>WI</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4914.2432933</td>\n",
       "      <td>1195</td>\n",
       "      <td>374028</td>\n",
       "      <td>...</td>\n",
       "      <td>19955</td>\n",
       "      <td>10285</td>\n",
       "      <td>66474</td>\n",
       "      <td>14737</td>\n",
       "      <td>20130</td>\n",
       "      <td>20271</td>\n",
       "      <td>12664</td>\n",
       "      <td>67802</td>\n",
       "      <td>0.0181139915</td>\n",
       "      <td>Washington County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>55</td>\n",
       "      <td>133</td>\n",
       "      <td>55133</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waukesha County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4632.5612366</td>\n",
       "      <td>3387</td>\n",
       "      <td>1104413</td>\n",
       "      <td>...</td>\n",
       "      <td>58476</td>\n",
       "      <td>31968</td>\n",
       "      <td>194680</td>\n",
       "      <td>41487</td>\n",
       "      <td>59795</td>\n",
       "      <td>61147</td>\n",
       "      <td>40063</td>\n",
       "      <td>202492</td>\n",
       "      <td>0.01867445</td>\n",
       "      <td>Waukesha County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3167</th>\n",
       "      <td>55</td>\n",
       "      <td>135</td>\n",
       "      <td>55135</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waupaca County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6999.3376391</td>\n",
       "      <td>748</td>\n",
       "      <td>140214</td>\n",
       "      <td>...</td>\n",
       "      <td>7643</td>\n",
       "      <td>6113</td>\n",
       "      <td>26486</td>\n",
       "      <td>4945</td>\n",
       "      <td>7077</td>\n",
       "      <td>7549</td>\n",
       "      <td>5980</td>\n",
       "      <td>25551</td>\n",
       "      <td>-0.007116962</td>\n",
       "      <td>Waupaca County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>55</td>\n",
       "      <td>137</td>\n",
       "      <td>55137</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waushara County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6262.7244141</td>\n",
       "      <td>357</td>\n",
       "      <td>65436</td>\n",
       "      <td>...</td>\n",
       "      <td>4092</td>\n",
       "      <td>2793</td>\n",
       "      <td>12923</td>\n",
       "      <td>2180</td>\n",
       "      <td>2932</td>\n",
       "      <td>3599</td>\n",
       "      <td>2842</td>\n",
       "      <td>11553</td>\n",
       "      <td>-0.00081646</td>\n",
       "      <td>Waushara County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>55</td>\n",
       "      <td>139</td>\n",
       "      <td>55139</td>\n",
       "      <td>WI</td>\n",
       "      <td>Winnebago County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5780.1509689</td>\n",
       "      <td>1728</td>\n",
       "      <td>474352</td>\n",
       "      <td>...</td>\n",
       "      <td>23005</td>\n",
       "      <td>12140</td>\n",
       "      <td>85436</td>\n",
       "      <td>17969</td>\n",
       "      <td>28909</td>\n",
       "      <td>22243</td>\n",
       "      <td>14927</td>\n",
       "      <td>84048</td>\n",
       "      <td>0.0149107154</td>\n",
       "      <td>Winnebago County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>55</td>\n",
       "      <td>141</td>\n",
       "      <td>55141</td>\n",
       "      <td>WI</td>\n",
       "      <td>Wood County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6800.0438242</td>\n",
       "      <td>913</td>\n",
       "      <td>199789</td>\n",
       "      <td>...</td>\n",
       "      <td>10613</td>\n",
       "      <td>6855</td>\n",
       "      <td>36667</td>\n",
       "      <td>7601</td>\n",
       "      <td>10912</td>\n",
       "      <td>10989</td>\n",
       "      <td>8345</td>\n",
       "      <td>37847</td>\n",
       "      <td>-0.003143855</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3171</th>\n",
       "      <td>56</td>\n",
       "      <td>000</td>\n",
       "      <td>56000</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7307.8246671</td>\n",
       "      <td>6960</td>\n",
       "      <td>1646791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3172</th>\n",
       "      <td>56</td>\n",
       "      <td>001</td>\n",
       "      <td>56001</td>\n",
       "      <td>WY</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5209.8477947</td>\n",
       "      <td>248</td>\n",
       "      <td>109971</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albany County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3173</th>\n",
       "      <td>56</td>\n",
       "      <td>003</td>\n",
       "      <td>56003</td>\n",
       "      <td>WY</td>\n",
       "      <td>Big Horn County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8077.3170112</td>\n",
       "      <td>185</td>\n",
       "      <td>32933</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Big Horn County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174</th>\n",
       "      <td>56</td>\n",
       "      <td>005</td>\n",
       "      <td>56005</td>\n",
       "      <td>WY</td>\n",
       "      <td>Campbell County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6213.9351247</td>\n",
       "      <td>431</td>\n",
       "      <td>140495</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Campbell County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3175</th>\n",
       "      <td>56</td>\n",
       "      <td>007</td>\n",
       "      <td>56007</td>\n",
       "      <td>WY</td>\n",
       "      <td>Carbon County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>9836.0629598</td>\n",
       "      <td>240</td>\n",
       "      <td>43868</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carbon County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3176</th>\n",
       "      <td>56</td>\n",
       "      <td>009</td>\n",
       "      <td>56009</td>\n",
       "      <td>WY</td>\n",
       "      <td>Converse County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8589.6866887</td>\n",
       "      <td>199</td>\n",
       "      <td>39731</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Converse County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>56</td>\n",
       "      <td>011</td>\n",
       "      <td>56011</td>\n",
       "      <td>WY</td>\n",
       "      <td>Crook County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5494.8237726</td>\n",
       "      <td>89</td>\n",
       "      <td>20689</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Crook County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>56</td>\n",
       "      <td>013</td>\n",
       "      <td>56013</td>\n",
       "      <td>WY</td>\n",
       "      <td>Fremont County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12248.191891</td>\n",
       "      <td>664</td>\n",
       "      <td>111994</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fremont County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>56</td>\n",
       "      <td>015</td>\n",
       "      <td>56015</td>\n",
       "      <td>WY</td>\n",
       "      <td>Goshen County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6820.8706886</td>\n",
       "      <td>184</td>\n",
       "      <td>36361</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Goshen County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3180</th>\n",
       "      <td>56</td>\n",
       "      <td>017</td>\n",
       "      <td>56017</td>\n",
       "      <td>WY</td>\n",
       "      <td>Hot Springs County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>12173.116982</td>\n",
       "      <td>108</td>\n",
       "      <td>12511</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hot Springs County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3181</th>\n",
       "      <td>56</td>\n",
       "      <td>019</td>\n",
       "      <td>56019</td>\n",
       "      <td>WY</td>\n",
       "      <td>Johnson County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7224.571317</td>\n",
       "      <td>121</td>\n",
       "      <td>23183</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3182</th>\n",
       "      <td>56</td>\n",
       "      <td>021</td>\n",
       "      <td>56021</td>\n",
       "      <td>WY</td>\n",
       "      <td>Laramie County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7533.3209436</td>\n",
       "      <td>1171</td>\n",
       "      <td>275458</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Laramie County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3183</th>\n",
       "      <td>56</td>\n",
       "      <td>023</td>\n",
       "      <td>56023</td>\n",
       "      <td>WY</td>\n",
       "      <td>Lincoln County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5639.0561283</td>\n",
       "      <td>183</td>\n",
       "      <td>53883</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3184</th>\n",
       "      <td>56</td>\n",
       "      <td>025</td>\n",
       "      <td>56025</td>\n",
       "      <td>WY</td>\n",
       "      <td>Natrona County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7905.5006997</td>\n",
       "      <td>1035</td>\n",
       "      <td>228730</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natrona County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3185</th>\n",
       "      <td>56</td>\n",
       "      <td>027</td>\n",
       "      <td>56027</td>\n",
       "      <td>WY</td>\n",
       "      <td>Niobrara County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4406.6551734</td>\n",
       "      <td>30</td>\n",
       "      <td>6694</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Niobrara County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3186</th>\n",
       "      <td>56</td>\n",
       "      <td>029</td>\n",
       "      <td>56029</td>\n",
       "      <td>WY</td>\n",
       "      <td>Park County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6161.1609218</td>\n",
       "      <td>350</td>\n",
       "      <td>80511</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Park County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3187</th>\n",
       "      <td>56</td>\n",
       "      <td>031</td>\n",
       "      <td>56031</td>\n",
       "      <td>WY</td>\n",
       "      <td>Platte County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6147.1231932</td>\n",
       "      <td>112</td>\n",
       "      <td>23387</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Platte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>56</td>\n",
       "      <td>033</td>\n",
       "      <td>56033</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sheridan County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8365.102601</td>\n",
       "      <td>455</td>\n",
       "      <td>83326</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sheridan County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>56</td>\n",
       "      <td>035</td>\n",
       "      <td>56035</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sublette County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5657.7721954</td>\n",
       "      <td>84</td>\n",
       "      <td>27888</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sublette County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3190</th>\n",
       "      <td>56</td>\n",
       "      <td>037</td>\n",
       "      <td>56037</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sweetwater County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7497.4399519</td>\n",
       "      <td>495</td>\n",
       "      <td>127427</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sweetwater County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3191</th>\n",
       "      <td>56</td>\n",
       "      <td>039</td>\n",
       "      <td>56039</td>\n",
       "      <td>WY</td>\n",
       "      <td>Teton County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3786.1282258</td>\n",
       "      <td>124</td>\n",
       "      <td>66351</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teton County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3192</th>\n",
       "      <td>56</td>\n",
       "      <td>041</td>\n",
       "      <td>56041</td>\n",
       "      <td>WY</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7790.3020428</td>\n",
       "      <td>262</td>\n",
       "      <td>59466</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uinta County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3193</th>\n",
       "      <td>56</td>\n",
       "      <td>043</td>\n",
       "      <td>56043</td>\n",
       "      <td>WY</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5504.6509699</td>\n",
       "      <td>108</td>\n",
       "      <td>22335</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Washakie County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3194</th>\n",
       "      <td>56</td>\n",
       "      <td>045</td>\n",
       "      <td>56045</td>\n",
       "      <td>WY</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4991.1682734</td>\n",
       "      <td>82</td>\n",
       "      <td>19599</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weston County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3195 rows × 535 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State FIPS Code County FIPS Code 5-digit FIPS Code State Abbreviation  \\\n",
       "0          statecode       countycode          fipscode              state   \n",
       "1                 00              000             00000                 US   \n",
       "2                 01              000             01000                 AL   \n",
       "3                 01              001             01001                 AL   \n",
       "4                 01              003             01003                 AL   \n",
       "5                 01              005             01005                 AL   \n",
       "6                 01              007             01007                 AL   \n",
       "7                 01              009             01009                 AL   \n",
       "8                 01              011             01011                 AL   \n",
       "9                 01              013             01013                 AL   \n",
       "10                01              015             01015                 AL   \n",
       "11                01              017             01017                 AL   \n",
       "12                01              019             01019                 AL   \n",
       "13                01              021             01021                 AL   \n",
       "14                01              023             01023                 AL   \n",
       "15                01              025             01025                 AL   \n",
       "16                01              027             01027                 AL   \n",
       "17                01              029             01029                 AL   \n",
       "18                01              031             01031                 AL   \n",
       "19                01              033             01033                 AL   \n",
       "20                01              035             01035                 AL   \n",
       "21                01              037             01037                 AL   \n",
       "22                01              039             01039                 AL   \n",
       "23                01              041             01041                 AL   \n",
       "24                01              043             01043                 AL   \n",
       "25                01              045             01045                 AL   \n",
       "26                01              047             01047                 AL   \n",
       "27                01              049             01049                 AL   \n",
       "28                01              051             01051                 AL   \n",
       "29                01              053             01053                 AL   \n",
       "...              ...              ...               ...                ...   \n",
       "3165              55              131             55131                 WI   \n",
       "3166              55              133             55133                 WI   \n",
       "3167              55              135             55135                 WI   \n",
       "3168              55              137             55137                 WI   \n",
       "3169              55              139             55139                 WI   \n",
       "3170              55              141             55141                 WI   \n",
       "3171              56              000             56000                 WY   \n",
       "3172              56              001             56001                 WY   \n",
       "3173              56              003             56003                 WY   \n",
       "3174              56              005             56005                 WY   \n",
       "3175              56              007             56007                 WY   \n",
       "3176              56              009             56009                 WY   \n",
       "3177              56              011             56011                 WY   \n",
       "3178              56              013             56013                 WY   \n",
       "3179              56              015             56015                 WY   \n",
       "3180              56              017             56017                 WY   \n",
       "3181              56              019             56019                 WY   \n",
       "3182              56              021             56021                 WY   \n",
       "3183              56              023             56023                 WY   \n",
       "3184              56              025             56025                 WY   \n",
       "3185              56              027             56027                 WY   \n",
       "3186              56              029             56029                 WY   \n",
       "3187              56              031             56031                 WY   \n",
       "3188              56              033             56033                 WY   \n",
       "3189              56              035             56035                 WY   \n",
       "3190              56              037             56037                 WY   \n",
       "3191              56              039             56039                 WY   \n",
       "3192              56              041             56041                 WY   \n",
       "3193              56              043             56043                 WY   \n",
       "3194              56              045             56045                 WY   \n",
       "\n",
       "                    Name Release Year County Ranked (Yes=1/No=0)  \\\n",
       "0                 county         year              county_ranked   \n",
       "1          United States         2019                        NaN   \n",
       "2                Alabama         2019                        NaN   \n",
       "3         Autauga County         2019                          1   \n",
       "4         Baldwin County         2019                          1   \n",
       "5         Barbour County         2019                          1   \n",
       "6            Bibb County         2019                          1   \n",
       "7          Blount County         2019                          1   \n",
       "8         Bullock County         2019                          1   \n",
       "9          Butler County         2019                          1   \n",
       "10        Calhoun County         2019                          1   \n",
       "11       Chambers County         2019                          1   \n",
       "12       Cherokee County         2019                          1   \n",
       "13        Chilton County         2019                          1   \n",
       "14        Choctaw County         2019                          1   \n",
       "15         Clarke County         2019                          1   \n",
       "16           Clay County         2019                          1   \n",
       "17       Cleburne County         2019                          1   \n",
       "18         Coffee County         2019                          1   \n",
       "19        Colbert County         2019                          1   \n",
       "20        Conecuh County         2019                          1   \n",
       "21          Coosa County         2019                          1   \n",
       "22      Covington County         2019                          1   \n",
       "23       Crenshaw County         2019                          1   \n",
       "24        Cullman County         2019                          1   \n",
       "25           Dale County         2019                          1   \n",
       "26         Dallas County         2019                          1   \n",
       "27         DeKalb County         2019                          1   \n",
       "28         Elmore County         2019                          1   \n",
       "29       Escambia County         2019                          1   \n",
       "...                  ...          ...                        ...   \n",
       "3165   Washington County         2019                          1   \n",
       "3166     Waukesha County         2019                          1   \n",
       "3167      Waupaca County         2019                          1   \n",
       "3168     Waushara County         2019                          1   \n",
       "3169    Winnebago County         2019                          1   \n",
       "3170         Wood County         2019                          1   \n",
       "3171             Wyoming         2019                        NaN   \n",
       "3172       Albany County         2019                          1   \n",
       "3173     Big Horn County         2019                          1   \n",
       "3174     Campbell County         2019                          1   \n",
       "3175       Carbon County         2019                          1   \n",
       "3176     Converse County         2019                          1   \n",
       "3177        Crook County         2019                          1   \n",
       "3178      Fremont County         2019                          1   \n",
       "3179       Goshen County         2019                          1   \n",
       "3180  Hot Springs County         2019                          1   \n",
       "3181      Johnson County         2019                          1   \n",
       "3182      Laramie County         2019                          1   \n",
       "3183      Lincoln County         2019                          1   \n",
       "3184      Natrona County         2019                          1   \n",
       "3185     Niobrara County         2019                          1   \n",
       "3186         Park County         2019                          1   \n",
       "3187       Platte County         2019                          1   \n",
       "3188     Sheridan County         2019                          1   \n",
       "3189     Sublette County         2019                          1   \n",
       "3190   Sweetwater County         2019                          1   \n",
       "3191        Teton County         2019                          1   \n",
       "3192        Uinta County         2019                          1   \n",
       "3193     Washakie County         2019                          1   \n",
       "3194       Weston County         2019                          1   \n",
       "\n",
       "     Premature death raw value Premature death numerator  \\\n",
       "0                v001_rawvalue            v001_numerator   \n",
       "1                  6900.630354                   3745538   \n",
       "2                 9917.2328984                     80440   \n",
       "3                 8824.0571232                       815   \n",
       "4                 7224.6321603                      2827   \n",
       "5                  9586.165037                       451   \n",
       "6                 11783.543675                       445   \n",
       "7                 10908.101822                      1050   \n",
       "8                 12066.910263                       205   \n",
       "9                 14018.606262                       393   \n",
       "10                12217.759984                      2333   \n",
       "11                 11273.17098                       691   \n",
       "12                 11294.41054                       575   \n",
       "13                10831.917358                       850   \n",
       "14                12047.187077                       273   \n",
       "15                9688.3738435                       412   \n",
       "16                9666.0412363                       271   \n",
       "17                11985.555305                       334   \n",
       "18                7933.1296664                       743   \n",
       "19                10332.484038                      1016   \n",
       "20                10635.365164                       272   \n",
       "21                12713.326366                       228   \n",
       "22                11346.013021                       754   \n",
       "23                9259.1296172                       271   \n",
       "24                10007.614724                      1493   \n",
       "25                9637.4564807                       836   \n",
       "26                13923.693981                       893   \n",
       "27                10995.276873                      1313   \n",
       "28                8400.1348816                      1170   \n",
       "29                11920.569925                       786   \n",
       "...                        ...                       ...   \n",
       "3165              4914.2432933                      1195   \n",
       "3166              4632.5612366                      3387   \n",
       "3167              6999.3376391                       748   \n",
       "3168              6262.7244141                       357   \n",
       "3169              5780.1509689                      1728   \n",
       "3170              6800.0438242                       913   \n",
       "3171              7307.8246671                      6960   \n",
       "3172              5209.8477947                       248   \n",
       "3173              8077.3170112                       185   \n",
       "3174              6213.9351247                       431   \n",
       "3175              9836.0629598                       240   \n",
       "3176              8589.6866887                       199   \n",
       "3177              5494.8237726                        89   \n",
       "3178              12248.191891                       664   \n",
       "3179              6820.8706886                       184   \n",
       "3180              12173.116982                       108   \n",
       "3181               7224.571317                       121   \n",
       "3182              7533.3209436                      1171   \n",
       "3183              5639.0561283                       183   \n",
       "3184              7905.5006997                      1035   \n",
       "3185              4406.6551734                        30   \n",
       "3186              6161.1609218                       350   \n",
       "3187              6147.1231932                       112   \n",
       "3188               8365.102601                       455   \n",
       "3189              5657.7721954                        84   \n",
       "3190              7497.4399519                       495   \n",
       "3191              3786.1282258                       124   \n",
       "3192              7790.3020428                       262   \n",
       "3193              5504.6509699                       108   \n",
       "3194              4991.1682734                        82   \n",
       "\n",
       "     Premature death denominator  ... Male population 45-64 raw value  \\\n",
       "0               v001_denominator  ...                   v016_rawvalue   \n",
       "1                      908266078  ...                             NaN   \n",
       "2                       13636816  ...                             NaN   \n",
       "3                         156132  ...                             NaN   \n",
       "4                         576496  ...                             NaN   \n",
       "5                          72222  ...                             NaN   \n",
       "6                          63653  ...                             NaN   \n",
       "7                         161107  ...                             NaN   \n",
       "8                          29266  ...                             NaN   \n",
       "9                          54804  ...                             NaN   \n",
       "10                        321406  ...                             NaN   \n",
       "11                         93770  ...                             NaN   \n",
       "12                         71014  ...                             NaN   \n",
       "13                        123617  ...                             NaN   \n",
       "14                         35489  ...                             NaN   \n",
       "15                         67037  ...                             NaN   \n",
       "16                         36916  ...                             NaN   \n",
       "17                         41389  ...                             NaN   \n",
       "18                        143899  ...                             NaN   \n",
       "19                        149595  ...                             NaN   \n",
       "20                         34170  ...                             NaN   \n",
       "21                         29528  ...                             NaN   \n",
       "22                        102221  ...                             NaN   \n",
       "23                         38691  ...                             NaN   \n",
       "24                        229016  ...                             NaN   \n",
       "25                        138090  ...                             NaN   \n",
       "26                        112186  ...                             NaN   \n",
       "27                        199916  ...                             NaN   \n",
       "28                        231228  ...                             NaN   \n",
       "29                        104898  ...                             NaN   \n",
       "...                          ...  ...                             ...   \n",
       "3165                      374028  ...                           19955   \n",
       "3166                     1104413  ...                           58476   \n",
       "3167                      140214  ...                            7643   \n",
       "3168                       65436  ...                            4092   \n",
       "3169                      474352  ...                           23005   \n",
       "3170                      199789  ...                           10613   \n",
       "3171                     1646791  ...                             NaN   \n",
       "3172                      109971  ...                             NaN   \n",
       "3173                       32933  ...                             NaN   \n",
       "3174                      140495  ...                             NaN   \n",
       "3175                       43868  ...                             NaN   \n",
       "3176                       39731  ...                             NaN   \n",
       "3177                       20689  ...                             NaN   \n",
       "3178                      111994  ...                             NaN   \n",
       "3179                       36361  ...                             NaN   \n",
       "3180                       12511  ...                             NaN   \n",
       "3181                       23183  ...                             NaN   \n",
       "3182                      275458  ...                             NaN   \n",
       "3183                       53883  ...                             NaN   \n",
       "3184                      228730  ...                             NaN   \n",
       "3185                        6694  ...                             NaN   \n",
       "3186                       80511  ...                             NaN   \n",
       "3187                       23387  ...                             NaN   \n",
       "3188                       83326  ...                             NaN   \n",
       "3189                       27888  ...                             NaN   \n",
       "3190                      127427  ...                             NaN   \n",
       "3191                       66351  ...                             NaN   \n",
       "3192                       59466  ...                             NaN   \n",
       "3193                       22335  ...                             NaN   \n",
       "3194                       19599  ...                             NaN   \n",
       "\n",
       "     Male population 65+ raw value Total male population raw value  \\\n",
       "0                    v017_rawvalue                   v025_rawvalue   \n",
       "1                              NaN                             NaN   \n",
       "2                              NaN                             NaN   \n",
       "3                              NaN                             NaN   \n",
       "4                              NaN                             NaN   \n",
       "5                              NaN                             NaN   \n",
       "6                              NaN                             NaN   \n",
       "7                              NaN                             NaN   \n",
       "8                              NaN                             NaN   \n",
       "9                              NaN                             NaN   \n",
       "10                             NaN                             NaN   \n",
       "11                             NaN                             NaN   \n",
       "12                             NaN                             NaN   \n",
       "13                             NaN                             NaN   \n",
       "14                             NaN                             NaN   \n",
       "15                             NaN                             NaN   \n",
       "16                             NaN                             NaN   \n",
       "17                             NaN                             NaN   \n",
       "18                             NaN                             NaN   \n",
       "19                             NaN                             NaN   \n",
       "20                             NaN                             NaN   \n",
       "21                             NaN                             NaN   \n",
       "22                             NaN                             NaN   \n",
       "23                             NaN                             NaN   \n",
       "24                             NaN                             NaN   \n",
       "25                             NaN                             NaN   \n",
       "26                             NaN                             NaN   \n",
       "27                             NaN                             NaN   \n",
       "28                             NaN                             NaN   \n",
       "29                             NaN                             NaN   \n",
       "...                            ...                             ...   \n",
       "3165                         10285                           66474   \n",
       "3166                         31968                          194680   \n",
       "3167                          6113                           26486   \n",
       "3168                          2793                           12923   \n",
       "3169                         12140                           85436   \n",
       "3170                          6855                           36667   \n",
       "3171                           NaN                             NaN   \n",
       "3172                           NaN                             NaN   \n",
       "3173                           NaN                             NaN   \n",
       "3174                           NaN                             NaN   \n",
       "3175                           NaN                             NaN   \n",
       "3176                           NaN                             NaN   \n",
       "3177                           NaN                             NaN   \n",
       "3178                           NaN                             NaN   \n",
       "3179                           NaN                             NaN   \n",
       "3180                           NaN                             NaN   \n",
       "3181                           NaN                             NaN   \n",
       "3182                           NaN                             NaN   \n",
       "3183                           NaN                             NaN   \n",
       "3184                           NaN                             NaN   \n",
       "3185                           NaN                             NaN   \n",
       "3186                           NaN                             NaN   \n",
       "3187                           NaN                             NaN   \n",
       "3188                           NaN                             NaN   \n",
       "3189                           NaN                             NaN   \n",
       "3190                           NaN                             NaN   \n",
       "3191                           NaN                             NaN   \n",
       "3192                           NaN                             NaN   \n",
       "3193                           NaN                             NaN   \n",
       "3194                           NaN                             NaN   \n",
       "\n",
       "     Female population 0-17 raw value Female population 18-44 raw value  \\\n",
       "0                       v026_rawvalue                     v027_rawvalue   \n",
       "1                                 NaN                               NaN   \n",
       "2                                 NaN                               NaN   \n",
       "3                                 NaN                               NaN   \n",
       "4                                 NaN                               NaN   \n",
       "5                                 NaN                               NaN   \n",
       "6                                 NaN                               NaN   \n",
       "7                                 NaN                               NaN   \n",
       "8                                 NaN                               NaN   \n",
       "9                                 NaN                               NaN   \n",
       "10                                NaN                               NaN   \n",
       "11                                NaN                               NaN   \n",
       "12                                NaN                               NaN   \n",
       "13                                NaN                               NaN   \n",
       "14                                NaN                               NaN   \n",
       "15                                NaN                               NaN   \n",
       "16                                NaN                               NaN   \n",
       "17                                NaN                               NaN   \n",
       "18                                NaN                               NaN   \n",
       "19                                NaN                               NaN   \n",
       "20                                NaN                               NaN   \n",
       "21                                NaN                               NaN   \n",
       "22                                NaN                               NaN   \n",
       "23                                NaN                               NaN   \n",
       "24                                NaN                               NaN   \n",
       "25                                NaN                               NaN   \n",
       "26                                NaN                               NaN   \n",
       "27                                NaN                               NaN   \n",
       "28                                NaN                               NaN   \n",
       "29                                NaN                               NaN   \n",
       "...                               ...                               ...   \n",
       "3165                            14737                             20130   \n",
       "3166                            41487                             59795   \n",
       "3167                             4945                              7077   \n",
       "3168                             2180                              2932   \n",
       "3169                            17969                             28909   \n",
       "3170                             7601                             10912   \n",
       "3171                              NaN                               NaN   \n",
       "3172                              NaN                               NaN   \n",
       "3173                              NaN                               NaN   \n",
       "3174                              NaN                               NaN   \n",
       "3175                              NaN                               NaN   \n",
       "3176                              NaN                               NaN   \n",
       "3177                              NaN                               NaN   \n",
       "3178                              NaN                               NaN   \n",
       "3179                              NaN                               NaN   \n",
       "3180                              NaN                               NaN   \n",
       "3181                              NaN                               NaN   \n",
       "3182                              NaN                               NaN   \n",
       "3183                              NaN                               NaN   \n",
       "3184                              NaN                               NaN   \n",
       "3185                              NaN                               NaN   \n",
       "3186                              NaN                               NaN   \n",
       "3187                              NaN                               NaN   \n",
       "3188                              NaN                               NaN   \n",
       "3189                              NaN                               NaN   \n",
       "3190                              NaN                               NaN   \n",
       "3191                              NaN                               NaN   \n",
       "3192                              NaN                               NaN   \n",
       "3193                              NaN                               NaN   \n",
       "3194                              NaN                               NaN   \n",
       "\n",
       "     Female population 45-64 raw value Female population 65+ raw value  \\\n",
       "0                        v031_rawvalue                   v032_rawvalue   \n",
       "1                                  NaN                             NaN   \n",
       "2                                  NaN                             NaN   \n",
       "3                                  NaN                             NaN   \n",
       "4                                  NaN                             NaN   \n",
       "5                                  NaN                             NaN   \n",
       "6                                  NaN                             NaN   \n",
       "7                                  NaN                             NaN   \n",
       "8                                  NaN                             NaN   \n",
       "9                                  NaN                             NaN   \n",
       "10                                 NaN                             NaN   \n",
       "11                                 NaN                             NaN   \n",
       "12                                 NaN                             NaN   \n",
       "13                                 NaN                             NaN   \n",
       "14                                 NaN                             NaN   \n",
       "15                                 NaN                             NaN   \n",
       "16                                 NaN                             NaN   \n",
       "17                                 NaN                             NaN   \n",
       "18                                 NaN                             NaN   \n",
       "19                                 NaN                             NaN   \n",
       "20                                 NaN                             NaN   \n",
       "21                                 NaN                             NaN   \n",
       "22                                 NaN                             NaN   \n",
       "23                                 NaN                             NaN   \n",
       "24                                 NaN                             NaN   \n",
       "25                                 NaN                             NaN   \n",
       "26                                 NaN                             NaN   \n",
       "27                                 NaN                             NaN   \n",
       "28                                 NaN                             NaN   \n",
       "29                                 NaN                             NaN   \n",
       "...                                ...                             ...   \n",
       "3165                             20271                           12664   \n",
       "3166                             61147                           40063   \n",
       "3167                              7549                            5980   \n",
       "3168                              3599                            2842   \n",
       "3169                             22243                           14927   \n",
       "3170                             10989                            8345   \n",
       "3171                               NaN                             NaN   \n",
       "3172                               NaN                             NaN   \n",
       "3173                               NaN                             NaN   \n",
       "3174                               NaN                             NaN   \n",
       "3175                               NaN                             NaN   \n",
       "3176                               NaN                             NaN   \n",
       "3177                               NaN                             NaN   \n",
       "3178                               NaN                             NaN   \n",
       "3179                               NaN                             NaN   \n",
       "3180                               NaN                             NaN   \n",
       "3181                               NaN                             NaN   \n",
       "3182                               NaN                             NaN   \n",
       "3183                               NaN                             NaN   \n",
       "3184                               NaN                             NaN   \n",
       "3185                               NaN                             NaN   \n",
       "3186                               NaN                             NaN   \n",
       "3187                               NaN                             NaN   \n",
       "3188                               NaN                             NaN   \n",
       "3189                               NaN                             NaN   \n",
       "3190                               NaN                             NaN   \n",
       "3191                               NaN                             NaN   \n",
       "3192                               NaN                             NaN   \n",
       "3193                               NaN                             NaN   \n",
       "3194                               NaN                             NaN   \n",
       "\n",
       "     Total female population raw value Population growth raw value  \\\n",
       "0                        v035_rawvalue               v097_rawvalue   \n",
       "1                                  NaN                         NaN   \n",
       "2                                  NaN                         NaN   \n",
       "3                                  NaN                         NaN   \n",
       "4                                  NaN                         NaN   \n",
       "5                                  NaN                         NaN   \n",
       "6                                  NaN                         NaN   \n",
       "7                                  NaN                         NaN   \n",
       "8                                  NaN                         NaN   \n",
       "9                                  NaN                         NaN   \n",
       "10                                 NaN                         NaN   \n",
       "11                                 NaN                         NaN   \n",
       "12                                 NaN                         NaN   \n",
       "13                                 NaN                         NaN   \n",
       "14                                 NaN                         NaN   \n",
       "15                                 NaN                         NaN   \n",
       "16                                 NaN                         NaN   \n",
       "17                                 NaN                         NaN   \n",
       "18                                 NaN                         NaN   \n",
       "19                                 NaN                         NaN   \n",
       "20                                 NaN                         NaN   \n",
       "21                                 NaN                         NaN   \n",
       "22                                 NaN                         NaN   \n",
       "23                                 NaN                         NaN   \n",
       "24                                 NaN                         NaN   \n",
       "25                                 NaN                         NaN   \n",
       "26                                 NaN                         NaN   \n",
       "27                                 NaN                         NaN   \n",
       "28                                 NaN                         NaN   \n",
       "29                                 NaN                         NaN   \n",
       "...                                ...                         ...   \n",
       "3165                             67802                0.0181139915   \n",
       "3166                            202492                  0.01867445   \n",
       "3167                             25551                -0.007116962   \n",
       "3168                             11553                 -0.00081646   \n",
       "3169                             84048                0.0149107154   \n",
       "3170                             37847                -0.003143855   \n",
       "3171                               NaN                         NaN   \n",
       "3172                               NaN                         NaN   \n",
       "3173                               NaN                         NaN   \n",
       "3174                               NaN                         NaN   \n",
       "3175                               NaN                         NaN   \n",
       "3176                               NaN                         NaN   \n",
       "3177                               NaN                         NaN   \n",
       "3178                               NaN                         NaN   \n",
       "3179                               NaN                         NaN   \n",
       "3180                               NaN                         NaN   \n",
       "3181                               NaN                         NaN   \n",
       "3182                               NaN                         NaN   \n",
       "3183                               NaN                         NaN   \n",
       "3184                               NaN                         NaN   \n",
       "3185                               NaN                         NaN   \n",
       "3186                               NaN                         NaN   \n",
       "3187                               NaN                         NaN   \n",
       "3188                               NaN                         NaN   \n",
       "3189                               NaN                         NaN   \n",
       "3190                               NaN                         NaN   \n",
       "3191                               NaN                         NaN   \n",
       "3192                               NaN                         NaN   \n",
       "3193                               NaN                         NaN   \n",
       "3194                               NaN                         NaN   \n",
       "\n",
       "             County name  \n",
       "0                 county  \n",
       "1          United States  \n",
       "2                Alabama  \n",
       "3         Autauga County  \n",
       "4         Baldwin County  \n",
       "5         Barbour County  \n",
       "6            Bibb County  \n",
       "7          Blount County  \n",
       "8         Bullock County  \n",
       "9          Butler County  \n",
       "10        Calhoun County  \n",
       "11       Chambers County  \n",
       "12       Cherokee County  \n",
       "13        Chilton County  \n",
       "14        Choctaw County  \n",
       "15         Clarke County  \n",
       "16           Clay County  \n",
       "17       Cleburne County  \n",
       "18         Coffee County  \n",
       "19        Colbert County  \n",
       "20        Conecuh County  \n",
       "21          Coosa County  \n",
       "22      Covington County  \n",
       "23       Crenshaw County  \n",
       "24        Cullman County  \n",
       "25           Dale County  \n",
       "26         Dallas County  \n",
       "27         DeKalb County  \n",
       "28         Elmore County  \n",
       "29       Escambia County  \n",
       "...                  ...  \n",
       "3165   Washington County  \n",
       "3166     Waukesha County  \n",
       "3167      Waupaca County  \n",
       "3168     Waushara County  \n",
       "3169    Winnebago County  \n",
       "3170         Wood County  \n",
       "3171             Wyoming  \n",
       "3172       Albany County  \n",
       "3173     Big Horn County  \n",
       "3174     Campbell County  \n",
       "3175       Carbon County  \n",
       "3176     Converse County  \n",
       "3177        Crook County  \n",
       "3178      Fremont County  \n",
       "3179       Goshen County  \n",
       "3180  Hot Springs County  \n",
       "3181      Johnson County  \n",
       "3182      Laramie County  \n",
       "3183      Lincoln County  \n",
       "3184      Natrona County  \n",
       "3185     Niobrara County  \n",
       "3186         Park County  \n",
       "3187       Platte County  \n",
       "3188     Sheridan County  \n",
       "3189     Sublette County  \n",
       "3190   Sweetwater County  \n",
       "3191        Teton County  \n",
       "3192        Uinta County  \n",
       "3193     Washakie County  \n",
       "3194       Weston County  \n",
       "\n",
       "[3195 rows x 535 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_df['County name'] = health_df['Name']\n",
    "\n",
    "health_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>FIPS code</th>\n",
       "      <th>State Abr.</th>\n",
       "      <th>County name</th>\n",
       "      <th>CBSA title</th>\n",
       "      <th>CBSA 2012 pop</th>\n",
       "      <th>County 2012 pop</th>\n",
       "      <th>2013 code</th>\n",
       "      <th>2006 code</th>\n",
       "      <th>1990-based code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>377149</td>\n",
       "      <td>55514</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Daphne-Fairhope-Foley, AL</td>\n",
       "      <td>190790</td>\n",
       "      <td>190790</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>27201</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>Birmingham-Hoover, AL</td>\n",
       "      <td>1136650</td>\n",
       "      <td>22597</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>AL</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>Birmingham-Hoover, AL</td>\n",
       "      <td>1136650</td>\n",
       "      <td>57826</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1011</td>\n",
       "      <td>AL</td>\n",
       "      <td>Bullock County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>10474</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1013</td>\n",
       "      <td>AL</td>\n",
       "      <td>Butler County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>20307</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1015</td>\n",
       "      <td>AL</td>\n",
       "      <td>Calhoun County</td>\n",
       "      <td>Anniston-Oxford-Jacksonville, AL</td>\n",
       "      <td>117296</td>\n",
       "      <td>117296</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1017</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chambers County</td>\n",
       "      <td>Valley, AL</td>\n",
       "      <td>34064</td>\n",
       "      <td>34064</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1019</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cherokee County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>26021</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1021</td>\n",
       "      <td>AL</td>\n",
       "      <td>Chilton County</td>\n",
       "      <td>Birmingham-Hoover, AL</td>\n",
       "      <td>1136650</td>\n",
       "      <td>43819</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1023</td>\n",
       "      <td>AL</td>\n",
       "      <td>Choctaw County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>13633</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1025</td>\n",
       "      <td>AL</td>\n",
       "      <td>Clarke County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>25161</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1027</td>\n",
       "      <td>AL</td>\n",
       "      <td>Clay County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>13435</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1029</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cleburne County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>14832</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1031</td>\n",
       "      <td>AL</td>\n",
       "      <td>Coffee County</td>\n",
       "      <td>Enterprise, AL</td>\n",
       "      <td>51252</td>\n",
       "      <td>51252</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1033</td>\n",
       "      <td>AL</td>\n",
       "      <td>Colbert County</td>\n",
       "      <td>Florence-Muscle Shoals, AL</td>\n",
       "      <td>146988</td>\n",
       "      <td>54446</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1035</td>\n",
       "      <td>AL</td>\n",
       "      <td>Conecuh County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>12981</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1037</td>\n",
       "      <td>AL</td>\n",
       "      <td>Coosa County</td>\n",
       "      <td>Talladega-Sylacauga, AL</td>\n",
       "      <td>92728</td>\n",
       "      <td>10966</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1039</td>\n",
       "      <td>AL</td>\n",
       "      <td>Covington County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>37955</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1041</td>\n",
       "      <td>AL</td>\n",
       "      <td>Crenshaw County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>14083</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1043</td>\n",
       "      <td>AL</td>\n",
       "      <td>Cullman County</td>\n",
       "      <td>Cullman, AL</td>\n",
       "      <td>80440</td>\n",
       "      <td>80440</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1045</td>\n",
       "      <td>AL</td>\n",
       "      <td>Dale County</td>\n",
       "      <td>Ozark, AL</td>\n",
       "      <td>50444</td>\n",
       "      <td>50444</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1047</td>\n",
       "      <td>AL</td>\n",
       "      <td>Dallas County</td>\n",
       "      <td>Selma, AL</td>\n",
       "      <td>42864</td>\n",
       "      <td>42864</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1049</td>\n",
       "      <td>AL</td>\n",
       "      <td>DeKalb County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>71080</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1051</td>\n",
       "      <td>AL</td>\n",
       "      <td>Elmore County</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>377149</td>\n",
       "      <td>80629</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1053</td>\n",
       "      <td>AL</td>\n",
       "      <td>Escambia County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>37994</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1055</td>\n",
       "      <td>AL</td>\n",
       "      <td>Etowah County</td>\n",
       "      <td>Gadsden, AL</td>\n",
       "      <td>104392</td>\n",
       "      <td>104392</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1057</td>\n",
       "      <td>AL</td>\n",
       "      <td>Fayette County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>16983</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1059</td>\n",
       "      <td>AL</td>\n",
       "      <td>Franklin County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>31761</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3119</th>\n",
       "      <td>55129</td>\n",
       "      <td>WI</td>\n",
       "      <td>Washburn County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>15826</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3120</th>\n",
       "      <td>55131</td>\n",
       "      <td>WI</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>Milwaukee-Waukesha-West Allis, WI</td>\n",
       "      <td>1566981</td>\n",
       "      <td>132661</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121</th>\n",
       "      <td>55133</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waukesha County</td>\n",
       "      <td>Milwaukee-Waukesha-West Allis, WI</td>\n",
       "      <td>1566981</td>\n",
       "      <td>392292</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3122</th>\n",
       "      <td>55135</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waupaca County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>52131</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123</th>\n",
       "      <td>55137</td>\n",
       "      <td>WI</td>\n",
       "      <td>Waushara County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>24461</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3124</th>\n",
       "      <td>55139</td>\n",
       "      <td>WI</td>\n",
       "      <td>Winnebago County</td>\n",
       "      <td>Oshkosh-Neenah, WI</td>\n",
       "      <td>168794</td>\n",
       "      <td>168794</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>55141</td>\n",
       "      <td>WI</td>\n",
       "      <td>Wood County</td>\n",
       "      <td>Wisconsin Rapids-Marshfield, WI</td>\n",
       "      <td>74424</td>\n",
       "      <td>74424</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126</th>\n",
       "      <td>56001</td>\n",
       "      <td>WY</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Laramie, WY</td>\n",
       "      <td>37276</td>\n",
       "      <td>37276</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3127</th>\n",
       "      <td>56003</td>\n",
       "      <td>WY</td>\n",
       "      <td>Big Horn County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>11794</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3128</th>\n",
       "      <td>56005</td>\n",
       "      <td>WY</td>\n",
       "      <td>Campbell County</td>\n",
       "      <td>Gillette, WY</td>\n",
       "      <td>47874</td>\n",
       "      <td>47874</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>56007</td>\n",
       "      <td>WY</td>\n",
       "      <td>Carbon County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>15666</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3130</th>\n",
       "      <td>56009</td>\n",
       "      <td>WY</td>\n",
       "      <td>Converse County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>14008</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3131</th>\n",
       "      <td>56011</td>\n",
       "      <td>WY</td>\n",
       "      <td>Crook County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>7155</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>56013</td>\n",
       "      <td>WY</td>\n",
       "      <td>Fremont County</td>\n",
       "      <td>Riverton, WY</td>\n",
       "      <td>41110</td>\n",
       "      <td>41110</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>56015</td>\n",
       "      <td>WY</td>\n",
       "      <td>Goshen County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>13636</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>56017</td>\n",
       "      <td>WY</td>\n",
       "      <td>Hot Springs County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>4822</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>56019</td>\n",
       "      <td>WY</td>\n",
       "      <td>Johnson County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>8615</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>56021</td>\n",
       "      <td>WY</td>\n",
       "      <td>Laramie County</td>\n",
       "      <td>Cheyenne, WY</td>\n",
       "      <td>94483</td>\n",
       "      <td>94483</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3137</th>\n",
       "      <td>56023</td>\n",
       "      <td>WY</td>\n",
       "      <td>Lincoln County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>17961</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>56025</td>\n",
       "      <td>WY</td>\n",
       "      <td>Natrona County</td>\n",
       "      <td>Casper, WY</td>\n",
       "      <td>78621</td>\n",
       "      <td>78621</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>56027</td>\n",
       "      <td>WY</td>\n",
       "      <td>Niobrara County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>2456</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3140</th>\n",
       "      <td>56029</td>\n",
       "      <td>WY</td>\n",
       "      <td>Park County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>28702</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3141</th>\n",
       "      <td>56031</td>\n",
       "      <td>WY</td>\n",
       "      <td>Platte County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>8756</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3142</th>\n",
       "      <td>56033</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sheridan County</td>\n",
       "      <td>Sheridan, WY</td>\n",
       "      <td>29596</td>\n",
       "      <td>29596</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3143</th>\n",
       "      <td>56035</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sublette County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>10368</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>56037</td>\n",
       "      <td>WY</td>\n",
       "      <td>Sweetwater County</td>\n",
       "      <td>Rock Springs, WY</td>\n",
       "      <td>45267</td>\n",
       "      <td>45267</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3145</th>\n",
       "      <td>56039</td>\n",
       "      <td>WY</td>\n",
       "      <td>Teton County</td>\n",
       "      <td>Jackson, WY-ID</td>\n",
       "      <td>31727</td>\n",
       "      <td>21675</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>56041</td>\n",
       "      <td>WY</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>Evanston, WY</td>\n",
       "      <td>21025</td>\n",
       "      <td>21025</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3147</th>\n",
       "      <td>56043</td>\n",
       "      <td>WY</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>8464</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3148</th>\n",
       "      <td>56045</td>\n",
       "      <td>WY</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>7082</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3149 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPS code State Abr.         County name  \\\n",
       "0          1001         AL      Autauga County   \n",
       "1          1003         AL      Baldwin County   \n",
       "2          1005         AL      Barbour County   \n",
       "3          1007         AL         Bibb County   \n",
       "4          1009         AL       Blount County   \n",
       "5          1011         AL      Bullock County   \n",
       "6          1013         AL       Butler County   \n",
       "7          1015         AL      Calhoun County   \n",
       "8          1017         AL     Chambers County   \n",
       "9          1019         AL     Cherokee County   \n",
       "10         1021         AL      Chilton County   \n",
       "11         1023         AL      Choctaw County   \n",
       "12         1025         AL       Clarke County   \n",
       "13         1027         AL         Clay County   \n",
       "14         1029         AL     Cleburne County   \n",
       "15         1031         AL       Coffee County   \n",
       "16         1033         AL      Colbert County   \n",
       "17         1035         AL      Conecuh County   \n",
       "18         1037         AL        Coosa County   \n",
       "19         1039         AL    Covington County   \n",
       "20         1041         AL     Crenshaw County   \n",
       "21         1043         AL      Cullman County   \n",
       "22         1045         AL         Dale County   \n",
       "23         1047         AL       Dallas County   \n",
       "24         1049         AL       DeKalb County   \n",
       "25         1051         AL       Elmore County   \n",
       "26         1053         AL     Escambia County   \n",
       "27         1055         AL       Etowah County   \n",
       "28         1057         AL      Fayette County   \n",
       "29         1059         AL     Franklin County   \n",
       "...         ...        ...                 ...   \n",
       "3119      55129         WI     Washburn County   \n",
       "3120      55131         WI   Washington County   \n",
       "3121      55133         WI     Waukesha County   \n",
       "3122      55135         WI      Waupaca County   \n",
       "3123      55137         WI     Waushara County   \n",
       "3124      55139         WI    Winnebago County   \n",
       "3125      55141         WI         Wood County   \n",
       "3126      56001         WY       Albany County   \n",
       "3127      56003         WY     Big Horn County   \n",
       "3128      56005         WY     Campbell County   \n",
       "3129      56007         WY       Carbon County   \n",
       "3130      56009         WY     Converse County   \n",
       "3131      56011         WY        Crook County   \n",
       "3132      56013         WY      Fremont County   \n",
       "3133      56015         WY       Goshen County   \n",
       "3134      56017         WY  Hot Springs County   \n",
       "3135      56019         WY      Johnson County   \n",
       "3136      56021         WY      Laramie County   \n",
       "3137      56023         WY      Lincoln County   \n",
       "3138      56025         WY      Natrona County   \n",
       "3139      56027         WY     Niobrara County   \n",
       "3140      56029         WY         Park County   \n",
       "3141      56031         WY       Platte County   \n",
       "3142      56033         WY     Sheridan County   \n",
       "3143      56035         WY     Sublette County   \n",
       "3144      56037         WY   Sweetwater County   \n",
       "3145      56039         WY        Teton County   \n",
       "3146      56041         WY        Uinta County   \n",
       "3147      56043         WY     Washakie County   \n",
       "3148      56045         WY       Weston County   \n",
       "\n",
       "                             CBSA title CBSA 2012 pop County 2012 pop  \\\n",
       "0                        Montgomery, AL        377149           55514   \n",
       "1             Daphne-Fairhope-Foley, AL        190790          190790   \n",
       "2                                   NaN             .           27201   \n",
       "3                 Birmingham-Hoover, AL       1136650           22597   \n",
       "4                 Birmingham-Hoover, AL       1136650           57826   \n",
       "5                                   NaN             .           10474   \n",
       "6                                   NaN             .           20307   \n",
       "7      Anniston-Oxford-Jacksonville, AL        117296          117296   \n",
       "8                            Valley, AL         34064           34064   \n",
       "9                                   NaN             .           26021   \n",
       "10                Birmingham-Hoover, AL       1136650           43819   \n",
       "11                                  NaN             .           13633   \n",
       "12                                  NaN             .           25161   \n",
       "13                                  NaN             .           13435   \n",
       "14                                  NaN             .           14832   \n",
       "15                       Enterprise, AL         51252           51252   \n",
       "16           Florence-Muscle Shoals, AL        146988           54446   \n",
       "17                                  NaN             .           12981   \n",
       "18              Talladega-Sylacauga, AL         92728           10966   \n",
       "19                                  NaN             .           37955   \n",
       "20                                  NaN             .           14083   \n",
       "21                          Cullman, AL         80440           80440   \n",
       "22                            Ozark, AL         50444           50444   \n",
       "23                            Selma, AL         42864           42864   \n",
       "24                                  NaN             .           71080   \n",
       "25                       Montgomery, AL        377149           80629   \n",
       "26                                  NaN             .           37994   \n",
       "27                          Gadsden, AL        104392          104392   \n",
       "28                                  NaN             .           16983   \n",
       "29                                  NaN             .           31761   \n",
       "...                                 ...           ...             ...   \n",
       "3119                                NaN             .           15826   \n",
       "3120  Milwaukee-Waukesha-West Allis, WI       1566981          132661   \n",
       "3121  Milwaukee-Waukesha-West Allis, WI       1566981          392292   \n",
       "3122                                NaN             .           52131   \n",
       "3123                                NaN             .           24461   \n",
       "3124                 Oshkosh-Neenah, WI        168794          168794   \n",
       "3125    Wisconsin Rapids-Marshfield, WI         74424           74424   \n",
       "3126                        Laramie, WY         37276           37276   \n",
       "3127                                NaN             .           11794   \n",
       "3128                       Gillette, WY         47874           47874   \n",
       "3129                                NaN             .           15666   \n",
       "3130                                NaN             .           14008   \n",
       "3131                                NaN             .            7155   \n",
       "3132                       Riverton, WY         41110           41110   \n",
       "3133                                NaN             .           13636   \n",
       "3134                                NaN             .            4822   \n",
       "3135                                NaN             .            8615   \n",
       "3136                       Cheyenne, WY         94483           94483   \n",
       "3137                                NaN             .           17961   \n",
       "3138                         Casper, WY         78621           78621   \n",
       "3139                                NaN             .            2456   \n",
       "3140                                NaN             .           28702   \n",
       "3141                                NaN             .            8756   \n",
       "3142                       Sheridan, WY         29596           29596   \n",
       "3143                                NaN             .           10368   \n",
       "3144                   Rock Springs, WY         45267           45267   \n",
       "3145                     Jackson, WY-ID         31727           21675   \n",
       "3146                       Evanston, WY         21025           21025   \n",
       "3147                                NaN             .            8464   \n",
       "3148                                NaN             .            7082   \n",
       "\n",
       "      2013 code  2006 code 1990-based code  \n",
       "0             3          3               3  \n",
       "1             4          5               3  \n",
       "2             6          5               5  \n",
       "3             2          2               6  \n",
       "4             2          2               3  \n",
       "5             6          6               6  \n",
       "6             6          6               6  \n",
       "7             4          4               4  \n",
       "8             5          5               6  \n",
       "9             6          6               6  \n",
       "10            2          2               6  \n",
       "11            6          6               6  \n",
       "12            6          6               6  \n",
       "13            6          6               6  \n",
       "14            6          6               6  \n",
       "15            5          5               5  \n",
       "16            4          4               4  \n",
       "17            6          6               6  \n",
       "18            5          5               6  \n",
       "19            6          6               6  \n",
       "20            6          6               6  \n",
       "21            5          5               5  \n",
       "22            5          5               4  \n",
       "23            5          5               5  \n",
       "24            6          5               5  \n",
       "25            3          3               3  \n",
       "26            6          6               6  \n",
       "27            4          4               4  \n",
       "28            6          6               6  \n",
       "29            6          6               6  \n",
       "...         ...        ...             ...  \n",
       "3119          6          6               6  \n",
       "3120          2          2               2  \n",
       "3121          2          2               2  \n",
       "3122          6          6               6  \n",
       "3123          6          6               6  \n",
       "3124          4          4               3  \n",
       "3125          5          5               5  \n",
       "3126          5          5               5  \n",
       "3127          6          6               6  \n",
       "3128          5          5               5  \n",
       "3129          6          6               6  \n",
       "3130          6          6               6  \n",
       "3131          6          6               6  \n",
       "3132          5          5               6  \n",
       "3133          6          6               6  \n",
       "3134          6          6               6  \n",
       "3135          6          6               6  \n",
       "3136          4          4               4  \n",
       "3137          6          6               6  \n",
       "3138          4          4               4  \n",
       "3139          6          6               6  \n",
       "3140          6          6               6  \n",
       "3141          6          6               6  \n",
       "3142          5          5               5  \n",
       "3143          6          6               6  \n",
       "3144          5          5               5  \n",
       "3145          5          5               6  \n",
       "3146          5          5               5  \n",
       "3147          6          6               6  \n",
       "3148          6          6               6  \n",
       "\n",
       "[3149 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(health_df, county_df, on='County name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State FIPS Code', 'County FIPS Code', '5-digit FIPS Code',\n",
       "       'State Abbreviation', 'Name', 'Release Year',\n",
       "       'County Ranked (Yes=1/No=0)', 'Premature death raw value',\n",
       "       'Premature death numerator', 'Premature death denominator',\n",
       "       ...\n",
       "       'Male population 45-64 raw value', 'Male population 65+ raw value',\n",
       "       'Total male population raw value', 'Female population 0-17 raw value',\n",
       "       'Female population 18-44 raw value',\n",
       "       'Female population 45-64 raw value', 'Female population 65+ raw value',\n",
       "       'Total female population raw value', 'Population growth raw value',\n",
       "       'County name'],\n",
       "      dtype='object', length=535)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPS code', 'State Abr.', 'County name', 'CBSA title', 'CBSA 2012 pop',\n",
       "       'County 2012 pop', '2013 code', '2006 code', '1990-based code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        9.6230761366\n",
       "1        16.322611618\n",
       "2        16.322611618\n",
       "3                 NaN\n",
       "4                 NaN\n",
       "5                 NaN\n",
       "6                 NaN\n",
       "7                 NaN\n",
       "8                 NaN\n",
       "9        22.093263028\n",
       "10       22.093263028\n",
       "11       11.320516477\n",
       "12       11.320516477\n",
       "13       24.799584751\n",
       "14       24.799584751\n",
       "15       31.359173984\n",
       "16       31.359173984\n",
       "17                NaN\n",
       "18                NaN\n",
       "19                NaN\n",
       "20                NaN\n",
       "21                NaN\n",
       "22                NaN\n",
       "23                NaN\n",
       "24                NaN\n",
       "25                NaN\n",
       "26                NaN\n",
       "27                NaN\n",
       "28                NaN\n",
       "29                NaN\n",
       "             ...     \n",
       "14492    12.326909644\n",
       "14493    32.852567463\n",
       "14494             NaN\n",
       "14495    11.194770959\n",
       "14496    10.204351589\n",
       "14497             NaN\n",
       "14498             NaN\n",
       "14499    20.297087965\n",
       "14500    7.9471702397\n",
       "14501    18.791890255\n",
       "14502             NaN\n",
       "14503    11.354328027\n",
       "14504    19.056418548\n",
       "14505             NaN\n",
       "14506    18.603209054\n",
       "14507             NaN\n",
       "14508    15.306980234\n",
       "14509    8.4031983866\n",
       "14510             NaN\n",
       "14511             NaN\n",
       "14512             NaN\n",
       "14513             NaN\n",
       "14514    13.965338711\n",
       "14515    17.300752995\n",
       "14516             NaN\n",
       "14517             NaN\n",
       "14518    18.892877385\n",
       "14519    24.158479626\n",
       "14520             NaN\n",
       "14521             NaN\n",
       "Name: Drug overdose deaths raw value, Length: 14522, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drug overdose deaths raw value'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above loads in two different data sets, the data on health statistics  for each county in the U.S. and the other for showing what type of population each county is classified as. Once the data sets are loaded in, we merged the two datasets on the 'County name' column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cutting down the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our dataset has too many variables(columns) that we don't need, we're going to pick out the variables that are the most relevant to our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['County name', '2013 code', 'Poor or fair health raw value', 'Life expectancy raw value', 'Infant mortality raw value', 'Diabetes prevalence raw value', 'Drug overdose deaths raw value', 'HIV prevalence raw value', 'Flu vaccinations raw value', 'Adult obesity raw value', 'Adult smoking raw value', 'Insufficient sleep raw value', 'Excessive drinking raw value','Physical inactivity raw value', 'Uninsured raw value', 'Homicides raw value', 'Firearm fatalities raw value', 'Residential segregation - non-white/white raw value', 'Frequent physical distress raw value', 'Frequent mental distress raw value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>County name</th>\n",
       "      <th>2013 code</th>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Infant mortality raw value</th>\n",
       "      <th>Diabetes prevalence raw value</th>\n",
       "      <th>Drug overdose deaths raw value</th>\n",
       "      <th>HIV prevalence raw value</th>\n",
       "      <th>Flu vaccinations raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Homicides raw value</th>\n",
       "      <th>Firearm fatalities raw value</th>\n",
       "      <th>Residential segregation - non-white/white raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1841112436</td>\n",
       "      <td>76.330589011</td>\n",
       "      <td>7.6992753623</td>\n",
       "      <td>0.142</td>\n",
       "      <td>9.6230761366</td>\n",
       "      <td>225.5</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.191246585</td>\n",
       "      <td>0.3590540606</td>\n",
       "      <td>0.1687801512</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.085009665</td>\n",
       "      <td>5.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>27.14756927</td>\n",
       "      <td>0.1282443447</td>\n",
       "      <td>0.1263835707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1806045782</td>\n",
       "      <td>78.599497656</td>\n",
       "      <td>5.8513374486</td>\n",
       "      <td>0.113</td>\n",
       "      <td>16.322611618</td>\n",
       "      <td>163.9</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.1679548515</td>\n",
       "      <td>0.3330586814</td>\n",
       "      <td>0.1671496869</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.1069928804</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>33.240059197</td>\n",
       "      <td>0.1262200157</td>\n",
       "      <td>0.1282255819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1806045782</td>\n",
       "      <td>78.599497656</td>\n",
       "      <td>5.8513374486</td>\n",
       "      <td>0.113</td>\n",
       "      <td>16.322611618</td>\n",
       "      <td>163.9</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.1679548515</td>\n",
       "      <td>0.3330586814</td>\n",
       "      <td>0.1671496869</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.1069928804</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>33.240059197</td>\n",
       "      <td>0.1262200157</td>\n",
       "      <td>0.1282255819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2350329359</td>\n",
       "      <td>75.244220557</td>\n",
       "      <td>9.9875156055</td>\n",
       "      <td>0.124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.2410759283</td>\n",
       "      <td>0.3861825744</td>\n",
       "      <td>0.1397612912</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.150347537</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>16.801580617</td>\n",
       "      <td>0.149511436</td>\n",
       "      <td>0.1431755912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2350329359</td>\n",
       "      <td>75.244220557</td>\n",
       "      <td>9.9875156055</td>\n",
       "      <td>0.124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.2410759283</td>\n",
       "      <td>0.3861825744</td>\n",
       "      <td>0.1397612912</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.150347537</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>16.801580617</td>\n",
       "      <td>0.149511436</td>\n",
       "      <td>0.1431755912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2577341563</td>\n",
       "      <td>75.77945737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.2154087757</td>\n",
       "      <td>0.3856316713</td>\n",
       "      <td>0.1269871542</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.1251319664</td>\n",
       "      <td>7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.31376525</td>\n",
       "      <td>0.1621615961</td>\n",
       "      <td>0.1463231059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2577341563</td>\n",
       "      <td>75.77945737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.2154087757</td>\n",
       "      <td>0.3856316713</td>\n",
       "      <td>0.1269871542</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.1251319664</td>\n",
       "      <td>7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.31376525</td>\n",
       "      <td>0.1621615961</td>\n",
       "      <td>0.1463231059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2446104682</td>\n",
       "      <td>76.437139837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.2325190522</td>\n",
       "      <td>0.3963743803</td>\n",
       "      <td>0.120231635</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.0720151611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "      <td>34.342650909</td>\n",
       "      <td>0.1642065675</td>\n",
       "      <td>0.1630094747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2446104682</td>\n",
       "      <td>76.437139837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.2325190522</td>\n",
       "      <td>0.3963743803</td>\n",
       "      <td>0.120231635</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.0720151611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "      <td>34.342650909</td>\n",
       "      <td>0.1642065675</td>\n",
       "      <td>0.1630094747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bibb County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1999691186</td>\n",
       "      <td>73.928271076</td>\n",
       "      <td>14.792899408</td>\n",
       "      <td>0.149</td>\n",
       "      <td>22.093263028</td>\n",
       "      <td>191.9</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.1991640424</td>\n",
       "      <td>0.381488654</td>\n",
       "      <td>0.1592515079</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.0968007528</td>\n",
       "      <td>8.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>38.49942387</td>\n",
       "      <td>0.1316296828</td>\n",
       "      <td>0.1285403606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bibb County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1999691186</td>\n",
       "      <td>73.928271076</td>\n",
       "      <td>14.792899408</td>\n",
       "      <td>0.149</td>\n",
       "      <td>22.093263028</td>\n",
       "      <td>191.9</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.1991640424</td>\n",
       "      <td>0.381488654</td>\n",
       "      <td>0.1592515079</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.0968007528</td>\n",
       "      <td>8.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>38.49942387</td>\n",
       "      <td>0.1316296828</td>\n",
       "      <td>0.1285403606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bibb County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2214088597</td>\n",
       "      <td>74.065883188</td>\n",
       "      <td>12.497621011</td>\n",
       "      <td>0.14</td>\n",
       "      <td>11.320516477</td>\n",
       "      <td>761.3</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.2130915904</td>\n",
       "      <td>0.4020257281</td>\n",
       "      <td>0.1417133997</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.1402776092</td>\n",
       "      <td>12.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>54.559740227</td>\n",
       "      <td>0.1393942462</td>\n",
       "      <td>0.135679163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bibb County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2214088597</td>\n",
       "      <td>74.065883188</td>\n",
       "      <td>12.497621011</td>\n",
       "      <td>0.14</td>\n",
       "      <td>11.320516477</td>\n",
       "      <td>761.3</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.2130915904</td>\n",
       "      <td>0.4020257281</td>\n",
       "      <td>0.1417133997</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.1402776092</td>\n",
       "      <td>12.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>54.559740227</td>\n",
       "      <td>0.1393942462</td>\n",
       "      <td>0.135679163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Blount County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2109532316</td>\n",
       "      <td>74.597766875</td>\n",
       "      <td>6.1846875666</td>\n",
       "      <td>0.143</td>\n",
       "      <td>24.799584751</td>\n",
       "      <td>95.4</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.1965215818</td>\n",
       "      <td>0.3594501042</td>\n",
       "      <td>0.1537872189</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.1211400416</td>\n",
       "      <td>7.4</td>\n",
       "      <td>19.7</td>\n",
       "      <td>21.205761317</td>\n",
       "      <td>0.1372005681</td>\n",
       "      <td>0.138279427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Blount County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2109532316</td>\n",
       "      <td>74.597766875</td>\n",
       "      <td>6.1846875666</td>\n",
       "      <td>0.143</td>\n",
       "      <td>24.799584751</td>\n",
       "      <td>95.4</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.1965215818</td>\n",
       "      <td>0.3594501042</td>\n",
       "      <td>0.1537872189</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.1211400416</td>\n",
       "      <td>7.4</td>\n",
       "      <td>19.7</td>\n",
       "      <td>21.205761317</td>\n",
       "      <td>0.1372005681</td>\n",
       "      <td>0.138279427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Blount County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1673236491</td>\n",
       "      <td>77.568325769</td>\n",
       "      <td>5.4871220605</td>\n",
       "      <td>0.146</td>\n",
       "      <td>31.359173984</td>\n",
       "      <td>108.6</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.1905873098</td>\n",
       "      <td>0.3348718277</td>\n",
       "      <td>0.1414591533</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.1013770018</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.8</td>\n",
       "      <td>36.352174622</td>\n",
       "      <td>0.1326045813</td>\n",
       "      <td>0.1304879414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Blount County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1673236491</td>\n",
       "      <td>77.568325769</td>\n",
       "      <td>5.4871220605</td>\n",
       "      <td>0.146</td>\n",
       "      <td>31.359173984</td>\n",
       "      <td>108.6</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.1905873098</td>\n",
       "      <td>0.3348718277</td>\n",
       "      <td>0.1414591533</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.1013770018</td>\n",
       "      <td>1.9</td>\n",
       "      <td>13.8</td>\n",
       "      <td>36.352174622</td>\n",
       "      <td>0.1326045813</td>\n",
       "      <td>0.1304879414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bullock County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2940771388</td>\n",
       "      <td>73.117530117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>691.3</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.2347829118</td>\n",
       "      <td>0.4502063587</td>\n",
       "      <td>0.1289631731</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.1320648344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19.543192348</td>\n",
       "      <td>0.1663247935</td>\n",
       "      <td>0.1484005471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.265026132</td>\n",
       "      <td>73.505385022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.2219765552</td>\n",
       "      <td>0.419110839</td>\n",
       "      <td>0.1304281127</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.1066708425</td>\n",
       "      <td>17</td>\n",
       "      <td>23.9</td>\n",
       "      <td>34.341641067</td>\n",
       "      <td>0.1619792823</td>\n",
       "      <td>0.1547420506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1081696135</td>\n",
       "      <td>79.17315688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1449631315</td>\n",
       "      <td>0.2539921925</td>\n",
       "      <td>0.2028526936</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0452041619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.980623654</td>\n",
       "      <td>0.0840204491</td>\n",
       "      <td>0.0957601455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1081696135</td>\n",
       "      <td>79.17315688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1449631315</td>\n",
       "      <td>0.2539921925</td>\n",
       "      <td>0.2028526936</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0452041619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.980623654</td>\n",
       "      <td>0.0840204491</td>\n",
       "      <td>0.0957601455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1081696135</td>\n",
       "      <td>79.17315688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1449631315</td>\n",
       "      <td>0.2539921925</td>\n",
       "      <td>0.2028526936</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0452041619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.980623654</td>\n",
       "      <td>0.0840204491</td>\n",
       "      <td>0.0957601455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Butler County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1081696135</td>\n",
       "      <td>79.17315688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1449631315</td>\n",
       "      <td>0.2539921925</td>\n",
       "      <td>0.2028526936</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0452041619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.980623654</td>\n",
       "      <td>0.0840204491</td>\n",
       "      <td>0.0957601455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14492</th>\n",
       "      <td>Marinette County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1432988233</td>\n",
       "      <td>78.56855937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12.326909644</td>\n",
       "      <td>48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.1783823882</td>\n",
       "      <td>0.3420792094</td>\n",
       "      <td>0.2264064275</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.0678512155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.7</td>\n",
       "      <td>28.618307702</td>\n",
       "      <td>0.1057703359</td>\n",
       "      <td>0.1131916786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14493</th>\n",
       "      <td>Milwaukee County</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1921289489</td>\n",
       "      <td>77.139929809</td>\n",
       "      <td>8.4439205804</td>\n",
       "      <td>0.105</td>\n",
       "      <td>32.852567463</td>\n",
       "      <td>357.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.1817158651</td>\n",
       "      <td>0.3596534246</td>\n",
       "      <td>0.2508574705</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.0776077723</td>\n",
       "      <td>12.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>63.50010861</td>\n",
       "      <td>0.1216212899</td>\n",
       "      <td>0.1293983398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14494</th>\n",
       "      <td>Oconto County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1233547889</td>\n",
       "      <td>79.30574613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.1551766621</td>\n",
       "      <td>0.3267447012</td>\n",
       "      <td>0.2391994239</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0616864743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.8</td>\n",
       "      <td>20.549489251</td>\n",
       "      <td>0.0976229733</td>\n",
       "      <td>0.1063386442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14495</th>\n",
       "      <td>Outagamie County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1121109844</td>\n",
       "      <td>79.973371483</td>\n",
       "      <td>4.9526954092</td>\n",
       "      <td>0.089</td>\n",
       "      <td>11.194770959</td>\n",
       "      <td>61.2</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.1491430447</td>\n",
       "      <td>0.2677922728</td>\n",
       "      <td>0.2539834149</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.0552278171</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>39.303193283</td>\n",
       "      <td>0.0917799481</td>\n",
       "      <td>0.1032322716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14496</th>\n",
       "      <td>Ozaukee County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1055496175</td>\n",
       "      <td>81.978810754</td>\n",
       "      <td>4.4786814762</td>\n",
       "      <td>0.086</td>\n",
       "      <td>10.204351589</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.1242034483</td>\n",
       "      <td>0.3206615916</td>\n",
       "      <td>0.2650625239</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.0376412956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>23.460020561</td>\n",
       "      <td>0.0839281182</td>\n",
       "      <td>0.0956251031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14497</th>\n",
       "      <td>Pepin County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1324051635</td>\n",
       "      <td>82.473027223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.1564442553</td>\n",
       "      <td>0.3086177551</td>\n",
       "      <td>0.2303746698</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.0732993496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1037059752</td>\n",
       "      <td>0.1102144009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14498</th>\n",
       "      <td>Price County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1355354385</td>\n",
       "      <td>78.731989129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.1611068254</td>\n",
       "      <td>0.3295866746</td>\n",
       "      <td>0.2154160029</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.0667937017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>11.474749103</td>\n",
       "      <td>0.1070312523</td>\n",
       "      <td>0.1126096528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14499</th>\n",
       "      <td>Racine County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1418249994</td>\n",
       "      <td>78.188217563</td>\n",
       "      <td>7.9761904762</td>\n",
       "      <td>0.089</td>\n",
       "      <td>20.297087965</td>\n",
       "      <td>125.1</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.1632715483</td>\n",
       "      <td>0.3632486408</td>\n",
       "      <td>0.2358073652</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.0638174687</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>47.702592755</td>\n",
       "      <td>0.1092645332</td>\n",
       "      <td>0.1099465872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14500</th>\n",
       "      <td>St. Croix County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1152573243</td>\n",
       "      <td>80.72477986</td>\n",
       "      <td>4.7775047775</td>\n",
       "      <td>0.079</td>\n",
       "      <td>7.9471702397</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.1537321974</td>\n",
       "      <td>0.3138139573</td>\n",
       "      <td>0.2575833478</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.0468182412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>25.80921523</td>\n",
       "      <td>0.0882154779</td>\n",
       "      <td>0.0974551768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14501</th>\n",
       "      <td>Sauk County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1294918297</td>\n",
       "      <td>79.059040704</td>\n",
       "      <td>6.1890472618</td>\n",
       "      <td>0.092</td>\n",
       "      <td>18.791890255</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.1531593737</td>\n",
       "      <td>0.3212126956</td>\n",
       "      <td>0.2458978787</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.0763393972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>23.337994839</td>\n",
       "      <td>0.1020628925</td>\n",
       "      <td>0.1078010924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14502</th>\n",
       "      <td>Sawyer County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.17023575</td>\n",
       "      <td>76.212063091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.1945276474</td>\n",
       "      <td>0.3214879701</td>\n",
       "      <td>0.2254326953</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.1027974948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.1</td>\n",
       "      <td>64.79266977</td>\n",
       "      <td>0.1240583882</td>\n",
       "      <td>0.1335562246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14503</th>\n",
       "      <td>Shawano County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14153587</td>\n",
       "      <td>79.111303755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114</td>\n",
       "      <td>11.354328027</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.1623920109</td>\n",
       "      <td>0.3307135788</td>\n",
       "      <td>0.2367845554</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.0835777126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>49.493718326</td>\n",
       "      <td>0.1088814849</td>\n",
       "      <td>0.1169249252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14504</th>\n",
       "      <td>Sheboygan County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1395246618</td>\n",
       "      <td>79.547583283</td>\n",
       "      <td>4.0797824116</td>\n",
       "      <td>0.087</td>\n",
       "      <td>19.056418548</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.1464081728</td>\n",
       "      <td>0.3346723876</td>\n",
       "      <td>0.2524936362</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0554449298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>43.438792738</td>\n",
       "      <td>0.0960511592</td>\n",
       "      <td>0.1014490926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14505</th>\n",
       "      <td>Trempealeau County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1329887068</td>\n",
       "      <td>79.106999658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.1507338967</td>\n",
       "      <td>0.3088920323</td>\n",
       "      <td>0.2430955496</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.0802502382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.2</td>\n",
       "      <td>54.375384299</td>\n",
       "      <td>0.1022304871</td>\n",
       "      <td>0.106744626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14506</th>\n",
       "      <td>Vilas County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1486787638</td>\n",
       "      <td>78.313946531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124</td>\n",
       "      <td>18.603209054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.1641977149</td>\n",
       "      <td>0.282522472</td>\n",
       "      <td>0.2205388068</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.110003391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.8</td>\n",
       "      <td>66.579574451</td>\n",
       "      <td>0.1132667761</td>\n",
       "      <td>0.1200848997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14507</th>\n",
       "      <td>Washburn County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1352200267</td>\n",
       "      <td>78.294222908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.1</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.156314827</td>\n",
       "      <td>0.2985289914</td>\n",
       "      <td>0.2210787246</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.0730882608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.3</td>\n",
       "      <td>21.392146089</td>\n",
       "      <td>0.1059300592</td>\n",
       "      <td>0.1143784937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14508</th>\n",
       "      <td>Waukesha County</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1079117996</td>\n",
       "      <td>81.753391382</td>\n",
       "      <td>4.2269611577</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.306980234</td>\n",
       "      <td>48.4</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.1314751328</td>\n",
       "      <td>0.3054972124</td>\n",
       "      <td>0.2482202414</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.036014953</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>35.08117458</td>\n",
       "      <td>0.0858703172</td>\n",
       "      <td>0.0948753889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14509</th>\n",
       "      <td>Waupaca County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1302271561</td>\n",
       "      <td>77.250502939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107</td>\n",
       "      <td>8.4031983866</td>\n",
       "      <td>38.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.1645465736</td>\n",
       "      <td>0.3408883292</td>\n",
       "      <td>0.2410256934</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.067295613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.7</td>\n",
       "      <td>26.117325969</td>\n",
       "      <td>0.1022803268</td>\n",
       "      <td>0.1075829677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14510</th>\n",
       "      <td>Waushara County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.145003344</td>\n",
       "      <td>79.734246065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.1628115474</td>\n",
       "      <td>0.3082043802</td>\n",
       "      <td>0.2256986826</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.0918349633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.3</td>\n",
       "      <td>30.4425635</td>\n",
       "      <td>0.1083877546</td>\n",
       "      <td>0.1087131383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14511</th>\n",
       "      <td>Converse County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1327863578</td>\n",
       "      <td>77.141258179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.1653301801</td>\n",
       "      <td>0.2832697691</td>\n",
       "      <td>0.2021688978</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.1159996686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.4</td>\n",
       "      <td>37.248062875</td>\n",
       "      <td>0.0990308323</td>\n",
       "      <td>0.1029174585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14512</th>\n",
       "      <td>Goshen County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1548256363</td>\n",
       "      <td>80.323740626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.1631855978</td>\n",
       "      <td>0.279536428</td>\n",
       "      <td>0.1590390357</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.1539032669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.3</td>\n",
       "      <td>16.241080843</td>\n",
       "      <td>0.1128507969</td>\n",
       "      <td>0.1169905256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14513</th>\n",
       "      <td>Hot Springs County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1348788675</td>\n",
       "      <td>74.005612971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.1511418934</td>\n",
       "      <td>0.3137856747</td>\n",
       "      <td>0.1605903108</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.1360230548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5837156126</td>\n",
       "      <td>0.1018654084</td>\n",
       "      <td>0.1085565077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14514</th>\n",
       "      <td>Laramie County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1459795381</td>\n",
       "      <td>78.930951442</td>\n",
       "      <td>5.3155394707</td>\n",
       "      <td>0.092</td>\n",
       "      <td>13.965338711</td>\n",
       "      <td>109.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.1854070154</td>\n",
       "      <td>0.3227794959</td>\n",
       "      <td>0.1743697428</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.1065858734</td>\n",
       "      <td>3.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>26.510193527</td>\n",
       "      <td>0.1025904316</td>\n",
       "      <td>0.1085687316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14515</th>\n",
       "      <td>Natrona County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1493701957</td>\n",
       "      <td>77.677236931</td>\n",
       "      <td>6.545174538</td>\n",
       "      <td>0.082</td>\n",
       "      <td>17.300752995</td>\n",
       "      <td>52</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.1691582423</td>\n",
       "      <td>0.302283542</td>\n",
       "      <td>0.1983601586</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.1379169642</td>\n",
       "      <td>3.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>29.593933228</td>\n",
       "      <td>0.1083090127</td>\n",
       "      <td>0.1145470364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14516</th>\n",
       "      <td>Niobrara County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1465300727</td>\n",
       "      <td>79.761175391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.1729088333</td>\n",
       "      <td>0.3059812685</td>\n",
       "      <td>0.1598001965</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.1599532983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1160021564</td>\n",
       "      <td>0.1237660612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14517</th>\n",
       "      <td>Sublette County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1182759398</td>\n",
       "      <td>84.264011258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.144150147</td>\n",
       "      <td>0.2900073756</td>\n",
       "      <td>0.189502586</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.1364503817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0917464438</td>\n",
       "      <td>0.0984426529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14518</th>\n",
       "      <td>Sweetwater County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1537716794</td>\n",
       "      <td>77.840130855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084</td>\n",
       "      <td>18.892877385</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.1756694329</td>\n",
       "      <td>0.3117090434</td>\n",
       "      <td>0.203251108</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.1275085219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.1</td>\n",
       "      <td>36.378234657</td>\n",
       "      <td>0.1056730897</td>\n",
       "      <td>0.1069329758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14519</th>\n",
       "      <td>Uinta County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1588575499</td>\n",
       "      <td>78.397789976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093</td>\n",
       "      <td>24.158479626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.1818058611</td>\n",
       "      <td>0.3109048541</td>\n",
       "      <td>0.1605855909</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.1256897609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.2</td>\n",
       "      <td>13.865106863</td>\n",
       "      <td>0.1136863571</td>\n",
       "      <td>0.1195647153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14520</th>\n",
       "      <td>Washakie County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1612605867</td>\n",
       "      <td>80.465988523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.1560757263</td>\n",
       "      <td>0.2847248449</td>\n",
       "      <td>0.1634299492</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.1670822943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.590562315</td>\n",
       "      <td>0.1125748293</td>\n",
       "      <td>0.114908562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14521</th>\n",
       "      <td>Weston County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1323753459</td>\n",
       "      <td>81.596294386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.1713812941</td>\n",
       "      <td>0.3022593971</td>\n",
       "      <td>0.1785701205</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.1435218325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.506029071</td>\n",
       "      <td>0.1081269651</td>\n",
       "      <td>0.1116178411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14522 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              County name  2013 code Poor or fair health raw value  \\\n",
       "0          Autauga County          3                  0.1841112436   \n",
       "1          Baldwin County          4                  0.1806045782   \n",
       "2          Baldwin County          5                  0.1806045782   \n",
       "3          Baldwin County          4                  0.2350329359   \n",
       "4          Baldwin County          5                  0.2350329359   \n",
       "5          Barbour County          6                  0.2577341563   \n",
       "6          Barbour County          6                  0.2577341563   \n",
       "7          Barbour County          6                  0.2446104682   \n",
       "8          Barbour County          6                  0.2446104682   \n",
       "9             Bibb County          2                  0.1999691186   \n",
       "10            Bibb County          4                  0.1999691186   \n",
       "11            Bibb County          2                  0.2214088597   \n",
       "12            Bibb County          4                  0.2214088597   \n",
       "13          Blount County          2                  0.2109532316   \n",
       "14          Blount County          3                  0.2109532316   \n",
       "15          Blount County          2                  0.1673236491   \n",
       "16          Blount County          3                  0.1673236491   \n",
       "17         Bullock County          6                  0.2940771388   \n",
       "18          Butler County          6                   0.265026132   \n",
       "19          Butler County          6                   0.265026132   \n",
       "20          Butler County          3                   0.265026132   \n",
       "21          Butler County          4                   0.265026132   \n",
       "22          Butler County          5                   0.265026132   \n",
       "23          Butler County          6                   0.265026132   \n",
       "24          Butler County          2                   0.265026132   \n",
       "25          Butler County          2                   0.265026132   \n",
       "26          Butler County          6                  0.1081696135   \n",
       "27          Butler County          6                  0.1081696135   \n",
       "28          Butler County          3                  0.1081696135   \n",
       "29          Butler County          4                  0.1081696135   \n",
       "...                   ...        ...                           ...   \n",
       "14492    Marinette County          5                  0.1432988233   \n",
       "14493    Milwaukee County          1                  0.1921289489   \n",
       "14494       Oconto County          3                  0.1233547889   \n",
       "14495    Outagamie County          4                  0.1121109844   \n",
       "14496      Ozaukee County          2                  0.1055496175   \n",
       "14497        Pepin County          6                  0.1324051635   \n",
       "14498        Price County          6                  0.1355354385   \n",
       "14499       Racine County          4                  0.1418249994   \n",
       "14500    St. Croix County          2                  0.1152573243   \n",
       "14501         Sauk County          5                  0.1294918297   \n",
       "14502       Sawyer County          6                    0.17023575   \n",
       "14503      Shawano County          5                    0.14153587   \n",
       "14504    Sheboygan County          4                  0.1395246618   \n",
       "14505  Trempealeau County          6                  0.1329887068   \n",
       "14506        Vilas County          6                  0.1486787638   \n",
       "14507     Washburn County          6                  0.1352200267   \n",
       "14508     Waukesha County          2                  0.1079117996   \n",
       "14509      Waupaca County          6                  0.1302271561   \n",
       "14510     Waushara County          6                   0.145003344   \n",
       "14511     Converse County          6                  0.1327863578   \n",
       "14512       Goshen County          6                  0.1548256363   \n",
       "14513  Hot Springs County          6                  0.1348788675   \n",
       "14514      Laramie County          4                  0.1459795381   \n",
       "14515      Natrona County          4                  0.1493701957   \n",
       "14516     Niobrara County          6                  0.1465300727   \n",
       "14517     Sublette County          6                  0.1182759398   \n",
       "14518   Sweetwater County          5                  0.1537716794   \n",
       "14519        Uinta County          5                  0.1588575499   \n",
       "14520     Washakie County          6                  0.1612605867   \n",
       "14521       Weston County          6                  0.1323753459   \n",
       "\n",
       "      Life expectancy raw value Infant mortality raw value  \\\n",
       "0                  76.330589011               7.6992753623   \n",
       "1                  78.599497656               5.8513374486   \n",
       "2                  78.599497656               5.8513374486   \n",
       "3                  75.244220557               9.9875156055   \n",
       "4                  75.244220557               9.9875156055   \n",
       "5                   75.77945737                        NaN   \n",
       "6                   75.77945737                        NaN   \n",
       "7                  76.437139837                        NaN   \n",
       "8                  76.437139837                        NaN   \n",
       "9                  73.928271076               14.792899408   \n",
       "10                 73.928271076               14.792899408   \n",
       "11                 74.065883188               12.497621011   \n",
       "12                 74.065883188               12.497621011   \n",
       "13                 74.597766875               6.1846875666   \n",
       "14                 74.597766875               6.1846875666   \n",
       "15                 77.568325769               5.4871220605   \n",
       "16                 77.568325769               5.4871220605   \n",
       "17                 73.117530117                        NaN   \n",
       "18                 73.505385022                        NaN   \n",
       "19                 73.505385022                        NaN   \n",
       "20                 73.505385022                        NaN   \n",
       "21                 73.505385022                        NaN   \n",
       "22                 73.505385022                        NaN   \n",
       "23                 73.505385022                        NaN   \n",
       "24                 73.505385022                        NaN   \n",
       "25                 73.505385022                        NaN   \n",
       "26                  79.17315688                        NaN   \n",
       "27                  79.17315688                        NaN   \n",
       "28                  79.17315688                        NaN   \n",
       "29                  79.17315688                        NaN   \n",
       "...                         ...                        ...   \n",
       "14492               78.56855937                        NaN   \n",
       "14493              77.139929809               8.4439205804   \n",
       "14494               79.30574613                        NaN   \n",
       "14495              79.973371483               4.9526954092   \n",
       "14496              81.978810754               4.4786814762   \n",
       "14497              82.473027223                        NaN   \n",
       "14498              78.731989129                        NaN   \n",
       "14499              78.188217563               7.9761904762   \n",
       "14500               80.72477986               4.7775047775   \n",
       "14501              79.059040704               6.1890472618   \n",
       "14502              76.212063091                        NaN   \n",
       "14503              79.111303755                        NaN   \n",
       "14504              79.547583283               4.0797824116   \n",
       "14505              79.106999658                        NaN   \n",
       "14506              78.313946531                        NaN   \n",
       "14507              78.294222908                        NaN   \n",
       "14508              81.753391382               4.2269611577   \n",
       "14509              77.250502939                        NaN   \n",
       "14510              79.734246065                        NaN   \n",
       "14511              77.141258179                        NaN   \n",
       "14512              80.323740626                        NaN   \n",
       "14513              74.005612971                        NaN   \n",
       "14514              78.930951442               5.3155394707   \n",
       "14515              77.677236931                6.545174538   \n",
       "14516              79.761175391                        NaN   \n",
       "14517              84.264011258                        NaN   \n",
       "14518              77.840130855                        NaN   \n",
       "14519              78.397789976                        NaN   \n",
       "14520              80.465988523                        NaN   \n",
       "14521              81.596294386                        NaN   \n",
       "\n",
       "      Diabetes prevalence raw value Drug overdose deaths raw value  \\\n",
       "0                             0.142                   9.6230761366   \n",
       "1                             0.113                   16.322611618   \n",
       "2                             0.113                   16.322611618   \n",
       "3                             0.124                            NaN   \n",
       "4                             0.124                            NaN   \n",
       "5                              0.18                            NaN   \n",
       "6                              0.18                            NaN   \n",
       "7                             0.168                            NaN   \n",
       "8                             0.168                            NaN   \n",
       "9                             0.149                   22.093263028   \n",
       "10                            0.149                   22.093263028   \n",
       "11                             0.14                   11.320516477   \n",
       "12                             0.14                   11.320516477   \n",
       "13                            0.143                   24.799584751   \n",
       "14                            0.143                   24.799584751   \n",
       "15                            0.146                   31.359173984   \n",
       "16                            0.146                   31.359173984   \n",
       "17                              0.2                            NaN   \n",
       "18                            0.179                            NaN   \n",
       "19                            0.179                            NaN   \n",
       "20                            0.179                            NaN   \n",
       "21                            0.179                            NaN   \n",
       "22                            0.179                            NaN   \n",
       "23                            0.179                            NaN   \n",
       "24                            0.179                            NaN   \n",
       "25                            0.179                            NaN   \n",
       "26                            0.107                            NaN   \n",
       "27                            0.107                            NaN   \n",
       "28                            0.107                            NaN   \n",
       "29                            0.107                            NaN   \n",
       "...                             ...                            ...   \n",
       "14492                           0.1                   12.326909644   \n",
       "14493                         0.105                   32.852567463   \n",
       "14494                          0.11                            NaN   \n",
       "14495                         0.089                   11.194770959   \n",
       "14496                         0.086                   10.204351589   \n",
       "14497                         0.118                            NaN   \n",
       "14498                         0.099                            NaN   \n",
       "14499                         0.089                   20.297087965   \n",
       "14500                         0.079                   7.9471702397   \n",
       "14501                         0.092                   18.791890255   \n",
       "14502                         0.114                            NaN   \n",
       "14503                         0.114                   11.354328027   \n",
       "14504                         0.087                   19.056418548   \n",
       "14505                         0.096                            NaN   \n",
       "14506                         0.124                   18.603209054   \n",
       "14507                         0.109                            NaN   \n",
       "14508                         0.092                   15.306980234   \n",
       "14509                         0.107                   8.4031983866   \n",
       "14510                         0.113                            NaN   \n",
       "14511                         0.092                            NaN   \n",
       "14512                           0.1                            NaN   \n",
       "14513                          0.12                            NaN   \n",
       "14514                         0.092                   13.965338711   \n",
       "14515                         0.082                   17.300752995   \n",
       "14516                          0.08                            NaN   \n",
       "14517                         0.079                            NaN   \n",
       "14518                         0.084                   18.892877385   \n",
       "14519                         0.093                   24.158479626   \n",
       "14520                         0.109                            NaN   \n",
       "14521                         0.094                            NaN   \n",
       "\n",
       "      HIV prevalence raw value Flu vaccinations raw value  \\\n",
       "0                        225.5                       0.41   \n",
       "1                        163.9                       0.45   \n",
       "2                        163.9                       0.45   \n",
       "3                        319.9                        0.4   \n",
       "4                        319.9                        0.4   \n",
       "5                          436                       0.37   \n",
       "6                          436                       0.37   \n",
       "7                        110.1                       0.28   \n",
       "8                        110.1                       0.28   \n",
       "9                        191.9                       0.39   \n",
       "10                       191.9                       0.39   \n",
       "11                       761.3                       0.41   \n",
       "12                       761.3                       0.41   \n",
       "13                        95.4                       0.38   \n",
       "14                        95.4                       0.38   \n",
       "15                       108.6                       0.54   \n",
       "16                       108.6                       0.54   \n",
       "17                       691.3                       0.26   \n",
       "18                       268.4                       0.35   \n",
       "19                       268.4                       0.35   \n",
       "20                       268.4                       0.35   \n",
       "21                       268.4                       0.35   \n",
       "22                       268.4                       0.35   \n",
       "23                       268.4                       0.35   \n",
       "24                       268.4                       0.35   \n",
       "25                       268.4                       0.35   \n",
       "26                         NaN                       0.45   \n",
       "27                         NaN                       0.45   \n",
       "28                         NaN                       0.45   \n",
       "29                         NaN                       0.45   \n",
       "...                        ...                        ...   \n",
       "14492                       48                       0.39   \n",
       "14493                    357.8                        0.5   \n",
       "14494                     18.7                       0.35   \n",
       "14495                     61.2                       0.53   \n",
       "14496                     34.7                       0.57   \n",
       "14497                      NaN                        0.5   \n",
       "14498                      NaN                       0.47   \n",
       "14499                    125.1                       0.52   \n",
       "14500                     27.9                        0.5   \n",
       "14501                     54.4                       0.49   \n",
       "14502                      NaN                       0.45   \n",
       "14503                     40.1                       0.49   \n",
       "14504                     59.6                       0.55   \n",
       "14505                     20.6                       0.44   \n",
       "14506                      NaN                       0.51   \n",
       "14507                     67.1                       0.51   \n",
       "14508                     48.4                       0.57   \n",
       "14509                     38.2                        0.5   \n",
       "14510                     52.6                        0.5   \n",
       "14511                      NaN                       0.38   \n",
       "14512                     43.1                       0.43   \n",
       "14513                      NaN                       0.33   \n",
       "14514                    109.5                       0.42   \n",
       "14515                       52                       0.42   \n",
       "14516                      NaN                       0.13   \n",
       "14517                      NaN                       0.41   \n",
       "14518                     86.5                       0.37   \n",
       "14519                      NaN                       0.33   \n",
       "14520                      NaN                       0.43   \n",
       "14521                      NaN                       0.17   \n",
       "\n",
       "      Adult obesity raw value Adult smoking raw value  \\\n",
       "0                       0.375             0.191246585   \n",
       "1                        0.31            0.1679548515   \n",
       "2                        0.31            0.1679548515   \n",
       "3                       0.341            0.2410759283   \n",
       "4                       0.341            0.2410759283   \n",
       "5                       0.443            0.2154087757   \n",
       "6                       0.443            0.2154087757   \n",
       "7                       0.393            0.2325190522   \n",
       "8                       0.393            0.2325190522   \n",
       "9                       0.378            0.1991640424   \n",
       "10                      0.378            0.1991640424   \n",
       "11                      0.356            0.2130915904   \n",
       "12                      0.356            0.2130915904   \n",
       "13                      0.344            0.1965215818   \n",
       "14                      0.344            0.1965215818   \n",
       "15                      0.346            0.1905873098   \n",
       "16                      0.346            0.1905873098   \n",
       "17                      0.394            0.2347829118   \n",
       "18                      0.402            0.2219765552   \n",
       "19                      0.402            0.2219765552   \n",
       "20                      0.402            0.2219765552   \n",
       "21                      0.402            0.2219765552   \n",
       "22                      0.402            0.2219765552   \n",
       "23                      0.402            0.2219765552   \n",
       "24                      0.402            0.2219765552   \n",
       "25                      0.402            0.2219765552   \n",
       "26                      0.327            0.1449631315   \n",
       "27                      0.327            0.1449631315   \n",
       "28                      0.327            0.1449631315   \n",
       "29                      0.327            0.1449631315   \n",
       "...                       ...                     ...   \n",
       "14492                   0.336            0.1783823882   \n",
       "14493                   0.337            0.1817158651   \n",
       "14494                   0.346            0.1551766621   \n",
       "14495                   0.288            0.1491430447   \n",
       "14496                   0.239            0.1242034483   \n",
       "14497                   0.346            0.1564442553   \n",
       "14498                   0.318            0.1611068254   \n",
       "14499                   0.331            0.1632715483   \n",
       "14500                   0.271            0.1537321974   \n",
       "14501                   0.315            0.1531593737   \n",
       "14502                   0.322            0.1945276474   \n",
       "14503                   0.354            0.1623920109   \n",
       "14504                   0.297            0.1464081728   \n",
       "14505                   0.312            0.1507338967   \n",
       "14506                   0.315            0.1641977149   \n",
       "14507                   0.322             0.156314827   \n",
       "14508                   0.264            0.1314751328   \n",
       "14509                   0.332            0.1645465736   \n",
       "14510                   0.319            0.1628115474   \n",
       "14511                   0.296            0.1653301801   \n",
       "14512                    0.32            0.1631855978   \n",
       "14513                   0.296            0.1511418934   \n",
       "14514                   0.305            0.1854070154   \n",
       "14515                   0.315            0.1691582423   \n",
       "14516                   0.245            0.1729088333   \n",
       "14517                   0.258             0.144150147   \n",
       "14518                   0.302            0.1756694329   \n",
       "14519                   0.318            0.1818058611   \n",
       "14520                   0.297            0.1560757263   \n",
       "14521                   0.309            0.1713812941   \n",
       "\n",
       "      Insufficient sleep raw value Excessive drinking raw value  \\\n",
       "0                     0.3590540606                 0.1687801512   \n",
       "1                     0.3330586814                 0.1671496869   \n",
       "2                     0.3330586814                 0.1671496869   \n",
       "3                     0.3861825744                 0.1397612912   \n",
       "4                     0.3861825744                 0.1397612912   \n",
       "5                     0.3856316713                 0.1269871542   \n",
       "6                     0.3856316713                 0.1269871542   \n",
       "7                     0.3963743803                  0.120231635   \n",
       "8                     0.3963743803                  0.120231635   \n",
       "9                      0.381488654                 0.1592515079   \n",
       "10                     0.381488654                 0.1592515079   \n",
       "11                    0.4020257281                 0.1417133997   \n",
       "12                    0.4020257281                 0.1417133997   \n",
       "13                    0.3594501042                 0.1537872189   \n",
       "14                    0.3594501042                 0.1537872189   \n",
       "15                    0.3348718277                 0.1414591533   \n",
       "16                    0.3348718277                 0.1414591533   \n",
       "17                    0.4502063587                 0.1289631731   \n",
       "18                     0.419110839                 0.1304281127   \n",
       "19                     0.419110839                 0.1304281127   \n",
       "20                     0.419110839                 0.1304281127   \n",
       "21                     0.419110839                 0.1304281127   \n",
       "22                     0.419110839                 0.1304281127   \n",
       "23                     0.419110839                 0.1304281127   \n",
       "24                     0.419110839                 0.1304281127   \n",
       "25                     0.419110839                 0.1304281127   \n",
       "26                    0.2539921925                 0.2028526936   \n",
       "27                    0.2539921925                 0.2028526936   \n",
       "28                    0.2539921925                 0.2028526936   \n",
       "29                    0.2539921925                 0.2028526936   \n",
       "...                            ...                          ...   \n",
       "14492                 0.3420792094                 0.2264064275   \n",
       "14493                 0.3596534246                 0.2508574705   \n",
       "14494                 0.3267447012                 0.2391994239   \n",
       "14495                 0.2677922728                 0.2539834149   \n",
       "14496                 0.3206615916                 0.2650625239   \n",
       "14497                 0.3086177551                 0.2303746698   \n",
       "14498                 0.3295866746                 0.2154160029   \n",
       "14499                 0.3632486408                 0.2358073652   \n",
       "14500                 0.3138139573                 0.2575833478   \n",
       "14501                 0.3212126956                 0.2458978787   \n",
       "14502                 0.3214879701                 0.2254326953   \n",
       "14503                 0.3307135788                 0.2367845554   \n",
       "14504                 0.3346723876                 0.2524936362   \n",
       "14505                 0.3088920323                 0.2430955496   \n",
       "14506                  0.282522472                 0.2205388068   \n",
       "14507                 0.2985289914                 0.2210787246   \n",
       "14508                 0.3054972124                 0.2482202414   \n",
       "14509                 0.3408883292                 0.2410256934   \n",
       "14510                 0.3082043802                 0.2256986826   \n",
       "14511                 0.2832697691                 0.2021688978   \n",
       "14512                  0.279536428                 0.1590390357   \n",
       "14513                 0.3137856747                 0.1605903108   \n",
       "14514                 0.3227794959                 0.1743697428   \n",
       "14515                  0.302283542                 0.1983601586   \n",
       "14516                 0.3059812685                 0.1598001965   \n",
       "14517                 0.2900073756                  0.189502586   \n",
       "14518                 0.3117090434                  0.203251108   \n",
       "14519                 0.3109048541                 0.1605855909   \n",
       "14520                 0.2847248449                 0.1634299492   \n",
       "14521                 0.3022593971                 0.1785701205   \n",
       "\n",
       "      Physical inactivity raw value Uninsured raw value Homicides raw value  \\\n",
       "0                             0.311         0.085009665                 5.2   \n",
       "1                             0.238        0.1069928804                 2.9   \n",
       "2                             0.238        0.1069928804                 2.9   \n",
       "3                             0.239         0.150347537                 9.1   \n",
       "4                             0.239         0.150347537                 9.1   \n",
       "5                             0.282        0.1251319664                   7   \n",
       "6                             0.282        0.1251319664                   7   \n",
       "7                             0.312        0.0720151611                 NaN   \n",
       "8                             0.312        0.0720151611                 NaN   \n",
       "9                             0.349        0.0968007528                 8.2   \n",
       "10                            0.349        0.0968007528                 8.2   \n",
       "11                            0.298        0.1402776092                12.6   \n",
       "12                            0.298        0.1402776092                12.6   \n",
       "13                            0.285        0.1211400416                 7.4   \n",
       "14                            0.285        0.1211400416                 7.4   \n",
       "15                            0.259        0.1013770018                 1.9   \n",
       "16                            0.259        0.1013770018                 1.9   \n",
       "17                            0.292        0.1320648344                 NaN   \n",
       "18                            0.347        0.1066708425                  17   \n",
       "19                            0.347        0.1066708425                  17   \n",
       "20                            0.347        0.1066708425                  17   \n",
       "21                            0.347        0.1066708425                  17   \n",
       "22                            0.347        0.1066708425                  17   \n",
       "23                            0.347        0.1066708425                  17   \n",
       "24                            0.347        0.1066708425                  17   \n",
       "25                            0.347        0.1066708425                  17   \n",
       "26                            0.259        0.0452041619                 NaN   \n",
       "27                            0.259        0.0452041619                 NaN   \n",
       "28                            0.259        0.0452041619                 NaN   \n",
       "29                            0.259        0.0452041619                 NaN   \n",
       "...                             ...                 ...                 ...   \n",
       "14492                         0.265        0.0678512155                 NaN   \n",
       "14493                         0.215        0.0776077723                12.8   \n",
       "14494                          0.25        0.0616864743                 NaN   \n",
       "14495                         0.163        0.0552278171                 1.5   \n",
       "14496                         0.158        0.0376412956                 NaN   \n",
       "14497                         0.215        0.0732993496                 NaN   \n",
       "14498                         0.215        0.0667937017                 NaN   \n",
       "14499                         0.212        0.0638174687                 2.6   \n",
       "14500                         0.178        0.0468182412                 NaN   \n",
       "14501                         0.173        0.0763393972                 NaN   \n",
       "14502                         0.231        0.1027974948                 NaN   \n",
       "14503                         0.221        0.0835777126                 NaN   \n",
       "14504                          0.21        0.0554449298                 NaN   \n",
       "14505                         0.197        0.0802502382                 NaN   \n",
       "14506                         0.277         0.110003391                 NaN   \n",
       "14507                         0.236        0.0730882608                 NaN   \n",
       "14508                         0.168         0.036014953                 1.1   \n",
       "14509                         0.225         0.067295613                 NaN   \n",
       "14510                         0.209        0.0918349633                 NaN   \n",
       "14511                         0.255        0.1159996686                 NaN   \n",
       "14512                         0.287        0.1539032669                 NaN   \n",
       "14513                         0.271        0.1360230548                 NaN   \n",
       "14514                         0.229        0.1065858734                 3.4   \n",
       "14515                         0.245        0.1379169642                 3.9   \n",
       "14516                         0.283        0.1599532983                 NaN   \n",
       "14517                         0.193        0.1364503817                 NaN   \n",
       "14518                         0.256        0.1275085219                 NaN   \n",
       "14519                         0.235        0.1256897609                 NaN   \n",
       "14520                          0.26        0.1670822943                 NaN   \n",
       "14521                         0.225        0.1435218325                 NaN   \n",
       "\n",
       "      Firearm fatalities raw value  \\\n",
       "0                             17.7   \n",
       "1                             14.4   \n",
       "2                             14.4   \n",
       "3                             23.3   \n",
       "4                             23.3   \n",
       "5                             15.2   \n",
       "6                             15.2   \n",
       "7                             13.2   \n",
       "8                             13.2   \n",
       "9                             21.3   \n",
       "10                            21.3   \n",
       "11                            19.5   \n",
       "12                            19.5   \n",
       "13                            19.7   \n",
       "14                            19.7   \n",
       "15                            13.8   \n",
       "16                            13.8   \n",
       "17                              19   \n",
       "18                            23.9   \n",
       "19                            23.9   \n",
       "20                            23.9   \n",
       "21                            23.9   \n",
       "22                            23.9   \n",
       "23                            23.9   \n",
       "24                            23.9   \n",
       "25                            23.9   \n",
       "26                             NaN   \n",
       "27                             NaN   \n",
       "28                             NaN   \n",
       "29                             NaN   \n",
       "...                            ...   \n",
       "14492                         12.7   \n",
       "14493                         16.8   \n",
       "14494                         11.8   \n",
       "14495                          8.3   \n",
       "14496                          6.6   \n",
       "14497                          NaN   \n",
       "14498                         20.6   \n",
       "14499                          8.2   \n",
       "14500                          6.6   \n",
       "14501                          8.2   \n",
       "14502                         23.1   \n",
       "14503                          8.2   \n",
       "14504                          6.2   \n",
       "14505                         10.2   \n",
       "14506                         15.8   \n",
       "14507                         15.3   \n",
       "14508                          8.3   \n",
       "14509                         12.7   \n",
       "14510                          8.3   \n",
       "14511                         18.4   \n",
       "14512                         19.3   \n",
       "14513                          NaN   \n",
       "14514                         15.4   \n",
       "14515                         18.5   \n",
       "14516                          NaN   \n",
       "14517                         26.2   \n",
       "14518                         17.1   \n",
       "14519                         20.2   \n",
       "14520                          NaN   \n",
       "14521                          NaN   \n",
       "\n",
       "      Residential segregation - non-white/white raw value  \\\n",
       "0                                            27.14756927    \n",
       "1                                           33.240059197    \n",
       "2                                           33.240059197    \n",
       "3                                           16.801580617    \n",
       "4                                           16.801580617    \n",
       "5                                            24.31376525    \n",
       "6                                            24.31376525    \n",
       "7                                           34.342650909    \n",
       "8                                           34.342650909    \n",
       "9                                            38.49942387    \n",
       "10                                           38.49942387    \n",
       "11                                          54.559740227    \n",
       "12                                          54.559740227    \n",
       "13                                          21.205761317    \n",
       "14                                          21.205761317    \n",
       "15                                          36.352174622    \n",
       "16                                          36.352174622    \n",
       "17                                          19.543192348    \n",
       "18                                          34.341641067    \n",
       "19                                          34.341641067    \n",
       "20                                          34.341641067    \n",
       "21                                          34.341641067    \n",
       "22                                          34.341641067    \n",
       "23                                          34.341641067    \n",
       "24                                          34.341641067    \n",
       "25                                          34.341641067    \n",
       "26                                          40.980623654    \n",
       "27                                          40.980623654    \n",
       "28                                          40.980623654    \n",
       "29                                          40.980623654    \n",
       "...                                                  ...    \n",
       "14492                                       28.618307702    \n",
       "14493                                        63.50010861    \n",
       "14494                                       20.549489251    \n",
       "14495                                       39.303193283    \n",
       "14496                                       23.460020561    \n",
       "14497                                                NaN    \n",
       "14498                                       11.474749103    \n",
       "14499                                       47.702592755    \n",
       "14500                                        25.80921523    \n",
       "14501                                       23.337994839    \n",
       "14502                                        64.79266977    \n",
       "14503                                       49.493718326    \n",
       "14504                                       43.438792738    \n",
       "14505                                       54.375384299    \n",
       "14506                                       66.579574451    \n",
       "14507                                       21.392146089    \n",
       "14508                                        35.08117458    \n",
       "14509                                       26.117325969    \n",
       "14510                                         30.4425635    \n",
       "14511                                       37.248062875    \n",
       "14512                                       16.241080843    \n",
       "14513                                       0.5837156126    \n",
       "14514                                       26.510193527    \n",
       "14515                                       29.593933228    \n",
       "14516                                                NaN    \n",
       "14517                                                NaN    \n",
       "14518                                       36.378234657    \n",
       "14519                                       13.865106863    \n",
       "14520                                       13.590562315    \n",
       "14521                                       14.506029071    \n",
       "\n",
       "      Frequent physical distress raw value Frequent mental distress raw value  \n",
       "0                             0.1282443447                       0.1263835707  \n",
       "1                             0.1262200157                       0.1282255819  \n",
       "2                             0.1262200157                       0.1282255819  \n",
       "3                              0.149511436                       0.1431755912  \n",
       "4                              0.149511436                       0.1431755912  \n",
       "5                             0.1621615961                       0.1463231059  \n",
       "6                             0.1621615961                       0.1463231059  \n",
       "7                             0.1642065675                       0.1630094747  \n",
       "8                             0.1642065675                       0.1630094747  \n",
       "9                             0.1316296828                       0.1285403606  \n",
       "10                            0.1316296828                       0.1285403606  \n",
       "11                            0.1393942462                        0.135679163  \n",
       "12                            0.1393942462                        0.135679163  \n",
       "13                            0.1372005681                        0.138279427  \n",
       "14                            0.1372005681                        0.138279427  \n",
       "15                            0.1326045813                       0.1304879414  \n",
       "16                            0.1326045813                       0.1304879414  \n",
       "17                            0.1663247935                       0.1484005471  \n",
       "18                            0.1619792823                       0.1547420506  \n",
       "19                            0.1619792823                       0.1547420506  \n",
       "20                            0.1619792823                       0.1547420506  \n",
       "21                            0.1619792823                       0.1547420506  \n",
       "22                            0.1619792823                       0.1547420506  \n",
       "23                            0.1619792823                       0.1547420506  \n",
       "24                            0.1619792823                       0.1547420506  \n",
       "25                            0.1619792823                       0.1547420506  \n",
       "26                            0.0840204491                       0.0957601455  \n",
       "27                            0.0840204491                       0.0957601455  \n",
       "28                            0.0840204491                       0.0957601455  \n",
       "29                            0.0840204491                       0.0957601455  \n",
       "...                                    ...                                ...  \n",
       "14492                         0.1057703359                       0.1131916786  \n",
       "14493                         0.1216212899                       0.1293983398  \n",
       "14494                         0.0976229733                       0.1063386442  \n",
       "14495                         0.0917799481                       0.1032322716  \n",
       "14496                         0.0839281182                       0.0956251031  \n",
       "14497                         0.1037059752                       0.1102144009  \n",
       "14498                         0.1070312523                       0.1126096528  \n",
       "14499                         0.1092645332                       0.1099465872  \n",
       "14500                         0.0882154779                       0.0974551768  \n",
       "14501                         0.1020628925                       0.1078010924  \n",
       "14502                         0.1240583882                       0.1335562246  \n",
       "14503                         0.1088814849                       0.1169249252  \n",
       "14504                         0.0960511592                       0.1014490926  \n",
       "14505                         0.1022304871                        0.106744626  \n",
       "14506                         0.1132667761                       0.1200848997  \n",
       "14507                         0.1059300592                       0.1143784937  \n",
       "14508                         0.0858703172                       0.0948753889  \n",
       "14509                         0.1022803268                       0.1075829677  \n",
       "14510                         0.1083877546                       0.1087131383  \n",
       "14511                         0.0990308323                       0.1029174585  \n",
       "14512                         0.1128507969                       0.1169905256  \n",
       "14513                         0.1018654084                       0.1085565077  \n",
       "14514                         0.1025904316                       0.1085687316  \n",
       "14515                         0.1083090127                       0.1145470364  \n",
       "14516                         0.1160021564                       0.1237660612  \n",
       "14517                         0.0917464438                       0.0984426529  \n",
       "14518                         0.1056730897                       0.1069329758  \n",
       "14519                         0.1136863571                       0.1195647153  \n",
       "14520                         0.1125748293                        0.114908562  \n",
       "14521                         0.1081269651                       0.1116178411  \n",
       "\n",
       "[14522 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "County name                                            object\n",
       "2013 code                                               int64\n",
       "Poor or fair health raw value                          object\n",
       "Life expectancy raw value                              object\n",
       "Infant mortality raw value                             object\n",
       "Diabetes prevalence raw value                          object\n",
       "Drug overdose deaths raw value                         object\n",
       "HIV prevalence raw value                               object\n",
       "Flu vaccinations raw value                             object\n",
       "Adult obesity raw value                                object\n",
       "Adult smoking raw value                                object\n",
       "Insufficient sleep raw value                           object\n",
       "Excessive drinking raw value                           object\n",
       "Physical inactivity raw value                          object\n",
       "Uninsured raw value                                    object\n",
       "Homicides raw value                                    object\n",
       "Firearm fatalities raw value                           object\n",
       "Residential segregation - non-white/white raw value    object\n",
       "Frequent physical distress raw value                   object\n",
       "Frequent mental distress raw value                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, our variables are being stored in the dataframe as incorrect data types, but we will fix that in the <b>Data Cleaning</b> section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "County name                                               0\n",
       "2013 code                                                 0\n",
       "Poor or fair health raw value                             0\n",
       "Life expectancy raw value                               184\n",
       "Infant mortality raw value                             8729\n",
       "Diabetes prevalence raw value                             0\n",
       "Drug overdose deaths raw value                         6306\n",
       "HIV prevalence raw value                               2795\n",
       "Flu vaccinations raw value                               15\n",
       "                                                       ... \n",
       "Insufficient sleep raw value                              0\n",
       "Excessive drinking raw value                              0\n",
       "Physical inactivity raw value                             0\n",
       "Uninsured raw value                                       1\n",
       "Homicides raw value                                    8829\n",
       "Firearm fatalities raw value                           3915\n",
       "Residential segregation - non-white/white raw value    1177\n",
       "Frequent physical distress raw value                      0\n",
       "Frequent mental distress raw value                        0\n",
       "Length: 20, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 19)\n",
    "n_nan = df.loc[:, :].isnull().sum()\n",
    "n_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of NaN entries in our dataframe, but we still want to keep any observations with NaN entries since their other variables will help in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping county codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The '2013 code' column in our dataframe carries numerical codes that correspond to a certain population type, which is important in our analysis for classifying the different population sizes that each observation falls under."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_2013_code(code):\n",
    "    if code == 1:\n",
    "        code = 'LCM' # Large Central Metro\n",
    "    elif code == 2:\n",
    "        code = 'LFM' # Large Fringe Metro\n",
    "    elif code == 3:\n",
    "        code = 'MM' # Medium Metro\n",
    "    elif code == 4:\n",
    "        code = 'SM' # Small Metro\n",
    "    elif code == 5:\n",
    "        code = 'MI' # Micropolitan\n",
    "    elif code == 6:\n",
    "        code = 'NC' # Non-Core\n",
    "    else:\n",
    "        code = code\n",
    "    \n",
    "    return code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>County name</th>\n",
       "      <th>2013 code</th>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Infant mortality raw value</th>\n",
       "      <th>Diabetes prevalence raw value</th>\n",
       "      <th>Drug overdose deaths raw value</th>\n",
       "      <th>HIV prevalence raw value</th>\n",
       "      <th>Flu vaccinations raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>...</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Homicides raw value</th>\n",
       "      <th>Firearm fatalities raw value</th>\n",
       "      <th>Residential segregation - non-white/white raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "      <th>County Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga County</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1841112436</td>\n",
       "      <td>76.330589011</td>\n",
       "      <td>7.6992753623</td>\n",
       "      <td>0.142</td>\n",
       "      <td>9.6230761366</td>\n",
       "      <td>225.5</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3590540606</td>\n",
       "      <td>0.1687801512</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.085009665</td>\n",
       "      <td>5.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>27.14756927</td>\n",
       "      <td>0.1282443447</td>\n",
       "      <td>0.1263835707</td>\n",
       "      <td>MM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1806045782</td>\n",
       "      <td>78.599497656</td>\n",
       "      <td>5.8513374486</td>\n",
       "      <td>0.113</td>\n",
       "      <td>16.322611618</td>\n",
       "      <td>163.9</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3330586814</td>\n",
       "      <td>0.1671496869</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.1069928804</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>33.240059197</td>\n",
       "      <td>0.1262200157</td>\n",
       "      <td>0.1282255819</td>\n",
       "      <td>SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1806045782</td>\n",
       "      <td>78.599497656</td>\n",
       "      <td>5.8513374486</td>\n",
       "      <td>0.113</td>\n",
       "      <td>16.322611618</td>\n",
       "      <td>163.9</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3330586814</td>\n",
       "      <td>0.1671496869</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.1069928804</td>\n",
       "      <td>2.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>33.240059197</td>\n",
       "      <td>0.1262200157</td>\n",
       "      <td>0.1282255819</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2350329359</td>\n",
       "      <td>75.244220557</td>\n",
       "      <td>9.9875156055</td>\n",
       "      <td>0.124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.341</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3861825744</td>\n",
       "      <td>0.1397612912</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.150347537</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>16.801580617</td>\n",
       "      <td>0.149511436</td>\n",
       "      <td>0.1431755912</td>\n",
       "      <td>SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2350329359</td>\n",
       "      <td>75.244220557</td>\n",
       "      <td>9.9875156055</td>\n",
       "      <td>0.124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.341</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3861825744</td>\n",
       "      <td>0.1397612912</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.150347537</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>16.801580617</td>\n",
       "      <td>0.149511436</td>\n",
       "      <td>0.1431755912</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2577341563</td>\n",
       "      <td>75.77945737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.443</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3856316713</td>\n",
       "      <td>0.1269871542</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.1251319664</td>\n",
       "      <td>7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.31376525</td>\n",
       "      <td>0.1621615961</td>\n",
       "      <td>0.1463231059</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2577341563</td>\n",
       "      <td>75.77945737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.443</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3856316713</td>\n",
       "      <td>0.1269871542</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.1251319664</td>\n",
       "      <td>7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.31376525</td>\n",
       "      <td>0.1621615961</td>\n",
       "      <td>0.1463231059</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2446104682</td>\n",
       "      <td>76.437139837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3963743803</td>\n",
       "      <td>0.120231635</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.0720151611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "      <td>34.342650909</td>\n",
       "      <td>0.1642065675</td>\n",
       "      <td>0.1630094747</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2446104682</td>\n",
       "      <td>76.437139837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3963743803</td>\n",
       "      <td>0.120231635</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.0720151611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.2</td>\n",
       "      <td>34.342650909</td>\n",
       "      <td>0.1642065675</td>\n",
       "      <td>0.1630094747</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14513</th>\n",
       "      <td>Hot Springs County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1348788675</td>\n",
       "      <td>74.005612971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3137856747</td>\n",
       "      <td>0.1605903108</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.1360230548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5837156126</td>\n",
       "      <td>0.1018654084</td>\n",
       "      <td>0.1085565077</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14514</th>\n",
       "      <td>Laramie County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1459795381</td>\n",
       "      <td>78.930951442</td>\n",
       "      <td>5.3155394707</td>\n",
       "      <td>0.092</td>\n",
       "      <td>13.965338711</td>\n",
       "      <td>109.5</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3227794959</td>\n",
       "      <td>0.1743697428</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.1065858734</td>\n",
       "      <td>3.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>26.510193527</td>\n",
       "      <td>0.1025904316</td>\n",
       "      <td>0.1085687316</td>\n",
       "      <td>SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14515</th>\n",
       "      <td>Natrona County</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1493701957</td>\n",
       "      <td>77.677236931</td>\n",
       "      <td>6.545174538</td>\n",
       "      <td>0.082</td>\n",
       "      <td>17.300752995</td>\n",
       "      <td>52</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.315</td>\n",
       "      <td>...</td>\n",
       "      <td>0.302283542</td>\n",
       "      <td>0.1983601586</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.1379169642</td>\n",
       "      <td>3.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>29.593933228</td>\n",
       "      <td>0.1083090127</td>\n",
       "      <td>0.1145470364</td>\n",
       "      <td>SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14516</th>\n",
       "      <td>Niobrara County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1465300727</td>\n",
       "      <td>79.761175391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3059812685</td>\n",
       "      <td>0.1598001965</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.1599532983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1160021564</td>\n",
       "      <td>0.1237660612</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14517</th>\n",
       "      <td>Sublette County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1182759398</td>\n",
       "      <td>84.264011258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2900073756</td>\n",
       "      <td>0.189502586</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.1364503817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0917464438</td>\n",
       "      <td>0.0984426529</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14518</th>\n",
       "      <td>Sweetwater County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1537716794</td>\n",
       "      <td>77.840130855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084</td>\n",
       "      <td>18.892877385</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3117090434</td>\n",
       "      <td>0.203251108</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.1275085219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.1</td>\n",
       "      <td>36.378234657</td>\n",
       "      <td>0.1056730897</td>\n",
       "      <td>0.1069329758</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14519</th>\n",
       "      <td>Uinta County</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1588575499</td>\n",
       "      <td>78.397789976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093</td>\n",
       "      <td>24.158479626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3109048541</td>\n",
       "      <td>0.1605855909</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.1256897609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.2</td>\n",
       "      <td>13.865106863</td>\n",
       "      <td>0.1136863571</td>\n",
       "      <td>0.1195647153</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14520</th>\n",
       "      <td>Washakie County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1612605867</td>\n",
       "      <td>80.465988523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2847248449</td>\n",
       "      <td>0.1634299492</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.1670822943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.590562315</td>\n",
       "      <td>0.1125748293</td>\n",
       "      <td>0.114908562</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14521</th>\n",
       "      <td>Weston County</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1323753459</td>\n",
       "      <td>81.596294386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3022593971</td>\n",
       "      <td>0.1785701205</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.1435218325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.506029071</td>\n",
       "      <td>0.1081269651</td>\n",
       "      <td>0.1116178411</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14522 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              County name  2013 code Poor or fair health raw value  \\\n",
       "0          Autauga County          3                  0.1841112436   \n",
       "1          Baldwin County          4                  0.1806045782   \n",
       "2          Baldwin County          5                  0.1806045782   \n",
       "3          Baldwin County          4                  0.2350329359   \n",
       "4          Baldwin County          5                  0.2350329359   \n",
       "5          Barbour County          6                  0.2577341563   \n",
       "6          Barbour County          6                  0.2577341563   \n",
       "7          Barbour County          6                  0.2446104682   \n",
       "8          Barbour County          6                  0.2446104682   \n",
       "...                   ...        ...                           ...   \n",
       "14513  Hot Springs County          6                  0.1348788675   \n",
       "14514      Laramie County          4                  0.1459795381   \n",
       "14515      Natrona County          4                  0.1493701957   \n",
       "14516     Niobrara County          6                  0.1465300727   \n",
       "14517     Sublette County          6                  0.1182759398   \n",
       "14518   Sweetwater County          5                  0.1537716794   \n",
       "14519        Uinta County          5                  0.1588575499   \n",
       "14520     Washakie County          6                  0.1612605867   \n",
       "14521       Weston County          6                  0.1323753459   \n",
       "\n",
       "      Life expectancy raw value Infant mortality raw value  \\\n",
       "0                  76.330589011               7.6992753623   \n",
       "1                  78.599497656               5.8513374486   \n",
       "2                  78.599497656               5.8513374486   \n",
       "3                  75.244220557               9.9875156055   \n",
       "4                  75.244220557               9.9875156055   \n",
       "5                   75.77945737                        NaN   \n",
       "6                   75.77945737                        NaN   \n",
       "7                  76.437139837                        NaN   \n",
       "8                  76.437139837                        NaN   \n",
       "...                         ...                        ...   \n",
       "14513              74.005612971                        NaN   \n",
       "14514              78.930951442               5.3155394707   \n",
       "14515              77.677236931                6.545174538   \n",
       "14516              79.761175391                        NaN   \n",
       "14517              84.264011258                        NaN   \n",
       "14518              77.840130855                        NaN   \n",
       "14519              78.397789976                        NaN   \n",
       "14520              80.465988523                        NaN   \n",
       "14521              81.596294386                        NaN   \n",
       "\n",
       "      Diabetes prevalence raw value Drug overdose deaths raw value  \\\n",
       "0                             0.142                   9.6230761366   \n",
       "1                             0.113                   16.322611618   \n",
       "2                             0.113                   16.322611618   \n",
       "3                             0.124                            NaN   \n",
       "4                             0.124                            NaN   \n",
       "5                              0.18                            NaN   \n",
       "6                              0.18                            NaN   \n",
       "7                             0.168                            NaN   \n",
       "8                             0.168                            NaN   \n",
       "...                             ...                            ...   \n",
       "14513                          0.12                            NaN   \n",
       "14514                         0.092                   13.965338711   \n",
       "14515                         0.082                   17.300752995   \n",
       "14516                          0.08                            NaN   \n",
       "14517                         0.079                            NaN   \n",
       "14518                         0.084                   18.892877385   \n",
       "14519                         0.093                   24.158479626   \n",
       "14520                         0.109                            NaN   \n",
       "14521                         0.094                            NaN   \n",
       "\n",
       "      HIV prevalence raw value Flu vaccinations raw value  \\\n",
       "0                        225.5                       0.41   \n",
       "1                        163.9                       0.45   \n",
       "2                        163.9                       0.45   \n",
       "3                        319.9                        0.4   \n",
       "4                        319.9                        0.4   \n",
       "5                          436                       0.37   \n",
       "6                          436                       0.37   \n",
       "7                        110.1                       0.28   \n",
       "8                        110.1                       0.28   \n",
       "...                        ...                        ...   \n",
       "14513                      NaN                       0.33   \n",
       "14514                    109.5                       0.42   \n",
       "14515                       52                       0.42   \n",
       "14516                      NaN                       0.13   \n",
       "14517                      NaN                       0.41   \n",
       "14518                     86.5                       0.37   \n",
       "14519                      NaN                       0.33   \n",
       "14520                      NaN                       0.43   \n",
       "14521                      NaN                       0.17   \n",
       "\n",
       "      Adult obesity raw value  ... Insufficient sleep raw value  \\\n",
       "0                       0.375  ...                 0.3590540606   \n",
       "1                        0.31  ...                 0.3330586814   \n",
       "2                        0.31  ...                 0.3330586814   \n",
       "3                       0.341  ...                 0.3861825744   \n",
       "4                       0.341  ...                 0.3861825744   \n",
       "5                       0.443  ...                 0.3856316713   \n",
       "6                       0.443  ...                 0.3856316713   \n",
       "7                       0.393  ...                 0.3963743803   \n",
       "8                       0.393  ...                 0.3963743803   \n",
       "...                       ...  ...                          ...   \n",
       "14513                   0.296  ...                 0.3137856747   \n",
       "14514                   0.305  ...                 0.3227794959   \n",
       "14515                   0.315  ...                  0.302283542   \n",
       "14516                   0.245  ...                 0.3059812685   \n",
       "14517                   0.258  ...                 0.2900073756   \n",
       "14518                   0.302  ...                 0.3117090434   \n",
       "14519                   0.318  ...                 0.3109048541   \n",
       "14520                   0.297  ...                 0.2847248449   \n",
       "14521                   0.309  ...                 0.3022593971   \n",
       "\n",
       "      Excessive drinking raw value Physical inactivity raw value  \\\n",
       "0                     0.1687801512                         0.311   \n",
       "1                     0.1671496869                         0.238   \n",
       "2                     0.1671496869                         0.238   \n",
       "3                     0.1397612912                         0.239   \n",
       "4                     0.1397612912                         0.239   \n",
       "5                     0.1269871542                         0.282   \n",
       "6                     0.1269871542                         0.282   \n",
       "7                      0.120231635                         0.312   \n",
       "8                      0.120231635                         0.312   \n",
       "...                            ...                           ...   \n",
       "14513                 0.1605903108                         0.271   \n",
       "14514                 0.1743697428                         0.229   \n",
       "14515                 0.1983601586                         0.245   \n",
       "14516                 0.1598001965                         0.283   \n",
       "14517                  0.189502586                         0.193   \n",
       "14518                  0.203251108                         0.256   \n",
       "14519                 0.1605855909                         0.235   \n",
       "14520                 0.1634299492                          0.26   \n",
       "14521                 0.1785701205                         0.225   \n",
       "\n",
       "      Uninsured raw value Homicides raw value Firearm fatalities raw value  \\\n",
       "0             0.085009665                 5.2                         17.7   \n",
       "1            0.1069928804                 2.9                         14.4   \n",
       "2            0.1069928804                 2.9                         14.4   \n",
       "3             0.150347537                 9.1                         23.3   \n",
       "4             0.150347537                 9.1                         23.3   \n",
       "5            0.1251319664                   7                         15.2   \n",
       "6            0.1251319664                   7                         15.2   \n",
       "7            0.0720151611                 NaN                         13.2   \n",
       "8            0.0720151611                 NaN                         13.2   \n",
       "...                   ...                 ...                          ...   \n",
       "14513        0.1360230548                 NaN                          NaN   \n",
       "14514        0.1065858734                 3.4                         15.4   \n",
       "14515        0.1379169642                 3.9                         18.5   \n",
       "14516        0.1599532983                 NaN                          NaN   \n",
       "14517        0.1364503817                 NaN                         26.2   \n",
       "14518        0.1275085219                 NaN                         17.1   \n",
       "14519        0.1256897609                 NaN                         20.2   \n",
       "14520        0.1670822943                 NaN                          NaN   \n",
       "14521        0.1435218325                 NaN                          NaN   \n",
       "\n",
       "      Residential segregation - non-white/white raw value  \\\n",
       "0                                            27.14756927    \n",
       "1                                           33.240059197    \n",
       "2                                           33.240059197    \n",
       "3                                           16.801580617    \n",
       "4                                           16.801580617    \n",
       "5                                            24.31376525    \n",
       "6                                            24.31376525    \n",
       "7                                           34.342650909    \n",
       "8                                           34.342650909    \n",
       "...                                                  ...    \n",
       "14513                                       0.5837156126    \n",
       "14514                                       26.510193527    \n",
       "14515                                       29.593933228    \n",
       "14516                                                NaN    \n",
       "14517                                                NaN    \n",
       "14518                                       36.378234657    \n",
       "14519                                       13.865106863    \n",
       "14520                                       13.590562315    \n",
       "14521                                       14.506029071    \n",
       "\n",
       "      Frequent physical distress raw value Frequent mental distress raw value  \\\n",
       "0                             0.1282443447                       0.1263835707   \n",
       "1                             0.1262200157                       0.1282255819   \n",
       "2                             0.1262200157                       0.1282255819   \n",
       "3                              0.149511436                       0.1431755912   \n",
       "4                              0.149511436                       0.1431755912   \n",
       "5                             0.1621615961                       0.1463231059   \n",
       "6                             0.1621615961                       0.1463231059   \n",
       "7                             0.1642065675                       0.1630094747   \n",
       "8                             0.1642065675                       0.1630094747   \n",
       "...                                    ...                                ...   \n",
       "14513                         0.1018654084                       0.1085565077   \n",
       "14514                         0.1025904316                       0.1085687316   \n",
       "14515                         0.1083090127                       0.1145470364   \n",
       "14516                         0.1160021564                       0.1237660612   \n",
       "14517                         0.0917464438                       0.0984426529   \n",
       "14518                         0.1056730897                       0.1069329758   \n",
       "14519                         0.1136863571                       0.1195647153   \n",
       "14520                         0.1125748293                        0.114908562   \n",
       "14521                         0.1081269651                       0.1116178411   \n",
       "\n",
       "      County Classification  \n",
       "0                        MM  \n",
       "1                        SM  \n",
       "2                        MI  \n",
       "3                        SM  \n",
       "4                        MI  \n",
       "5                        NC  \n",
       "6                        NC  \n",
       "7                        NC  \n",
       "8                        NC  \n",
       "...                     ...  \n",
       "14513                    NC  \n",
       "14514                    SM  \n",
       "14515                    SM  \n",
       "14516                    NC  \n",
       "14517                    NC  \n",
       "14518                    MI  \n",
       "14519                    MI  \n",
       "14520                    NC  \n",
       "14521                    NC  \n",
       "\n",
       "[14522 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['County Classification'] = df.loc[:,'2013 code'].apply(convert_2013_code)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code cells above are mapping the numerical values under the '2013 code' column to what kind of population types they represent. The following is a list of what population type each acronym represents.\n",
    "    - LCM/1 = Large Central Metro\n",
    "    - LFM/2 = Large Fringe Metro\n",
    "    - MM/3 = Medium Metro\n",
    "    - SM/4 = Small Metro\n",
    "    - MI/5 = Micropolitan\n",
    "    - NC/6 = Non-Core\n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our variables of analysis are measured numerically(floats) so they must be converted from strings to floats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_float(obj):\n",
    "    if obj == str:\n",
    "        obj = obj.strip()\n",
    "    \n",
    "    return float(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Poor or fair health raw value'] = df['Poor or fair health raw value'].apply(convert_to_float)\n",
    "df['Life expectancy raw value'] = df['Life expectancy raw value'].apply(convert_to_float)\n",
    "df['Infant mortality raw value'] = df['Infant mortality raw value'].apply(convert_to_float)\n",
    "df['Diabetes prevalence raw value'] = df['Diabetes prevalence raw value'].apply(convert_to_float)\n",
    "df['Drug overdose deaths raw value'] = df['Drug overdose deaths raw value'].apply(convert_to_float)\n",
    "df['HIV prevalence raw value'] = df['HIV prevalence raw value'].apply(convert_to_float)\n",
    "df['Flu vaccinations raw value'] = df['Flu vaccinations raw value'].apply(convert_to_float)\n",
    "df['Adult obesity raw value'] = df['Adult obesity raw value'].apply(convert_to_float)\n",
    "df['Adult smoking raw value'] = df['Adult smoking raw value'].apply(convert_to_float)\n",
    "df['Insufficient sleep raw value'] = df['Insufficient sleep raw value'].apply(convert_to_float)\n",
    "df['Excessive drinking raw value'] = df['Excessive drinking raw value'].apply(convert_to_float)\n",
    "df['Physical inactivity raw value'] = df['Physical inactivity raw value'].apply(convert_to_float)\n",
    "df['Uninsured raw value'] = df['Uninsured raw value'].apply(convert_to_float)\n",
    "df['Homicides raw value'] = df['Homicides raw value'].apply(convert_to_float)\n",
    "df['Firearm fatalities raw value'] = df['Firearm fatalities raw value'].apply(convert_to_float)\n",
    "df['Residential segregation - non-white/white raw value'] = df['Residential segregation - non-white/white raw value'].apply(convert_to_float)\n",
    "df['Frequent physical distress raw value'] = df['Frequent physical distress raw value'].apply(convert_to_float)\n",
    "df['Frequent mental distress raw value'] = df['Frequent mental distress raw value'].apply(convert_to_float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "County name                                             object\n",
       "2013 code                                                int64\n",
       "Poor or fair health raw value                          float64\n",
       "Life expectancy raw value                              float64\n",
       "Infant mortality raw value                             float64\n",
       "Diabetes prevalence raw value                          float64\n",
       "Drug overdose deaths raw value                         float64\n",
       "HIV prevalence raw value                               float64\n",
       "Flu vaccinations raw value                             float64\n",
       "                                                        ...   \n",
       "Excessive drinking raw value                           float64\n",
       "Physical inactivity raw value                          float64\n",
       "Uninsured raw value                                    float64\n",
       "Homicides raw value                                    float64\n",
       "Firearm fatalities raw value                           float64\n",
       "Residential segregation - non-white/white raw value    float64\n",
       "Frequent physical distress raw value                   float64\n",
       "Frequent mental distress raw value                     float64\n",
       "County Classification                                   object\n",
       "Length: 21, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's take a look at the distribution of the counts of each county by population type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1866"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = df.groupby('County name', group_keys=False).apply(lambda df: df.sample(1))\n",
    "df_sample.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell above shows that there are 1866 different counties in our data. Now let's take a look at how they are distributed by population type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JHUNBktQxFCRJHUNBktQxFCRJHUNBktQxFCRJHUNBktTpLRSSfDDJTUmuGGrbI8n5Sa5uz7u39iQ5Ocm6JJclObCvuiRJm9fnkcKHgWdv1HYicEFVLQMuaPMAhwPL2mMVcGqPdUmSNqO3UKiqLwE/3qh5ObCmTa8BjhxqP70GLgQWJtm3r9okSTOb62sK+1TVDQDtee/Wvhi4fqjf+ta2iSSrkkwmmZyamuq1WEna2cyXC82Zoa1m6lhVq6tqoqomFi1a1HNZkrRzmetQuHH6tFB7vqm1rwf2G+q3BNgwx7VJ0k5vrkPhXGBlm14JnDPUfmy7C+lg4Nbp00ySpLmzoK8NJzkTOBTYK8l64G+Ak4CzkhwPXAcc3bqfBxwBrANuB47rqy5J0ub1FgpV9YLNLDpshr4FnNBXLZKk0cyXC82SpHnAUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdXr78pqknc+7/+LT4y5hm7z0bX847hLmHY8UJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0GS1DEUJEmdeRUKSZ6d5LtJ1iU5cdz1SNLOZt6EQpJdgPcAhwP7Ay9Isv94q5Kkncu8CQXgCcC6qrqmqn4JrAWWj7kmSdqppKrGXQMASY4Cnl1VL2nzLwaeWFUv3ajfKmBVm30U8N05LHMv4OY5fL255v5tv3bkfQP3b7b9RlUtmmnBfBolNTO0bZJYVbUaWN1/OZtKMllVE+N47bng/m2/duR9A/dvLs2n00frgf2G5pcAG8ZUiyTtlOZTKFwELEvyiCT3BVYA5465Jknaqcyb00dVdWeSlwL/DOwCfLCqvj3msjY2ltNWc8j9237tyPsG7t+cmTcXmiVJ4zefTh9JksbMUJAkdQyFzUhSSc4Yml+QZCrJZ9r8nyZ59/gq3HYj7lMlOWyozx+1tqPGUfO2arW+bWj+1UlePzR/bJIrknw7yZVJXj2WQu+FJK9r9V+W5NIkT0zyhSTXJclQv08l+dk4ax3F5mrc3HuV5MNJbk/ykKG+72rv/V5zVfeoZtq/JK9P8oP2/l2a5KTWPvb30VDYvH8HfifJA9r8M4EfjLGe2TDKPl0OvGBofgXwrTmobbbcATx/pj8OSQ4HXgn8QVX9NnAgcOsc13evJHkS8FzgwKp6DPAM4Pq2+BbgkNZvIbDvWIqcBSO8V+toIx4kuQ/wNLa//z/fUVUHtMfwWG9jfR8NhS37R+A5bfoFwJljrGW2bG2fvgw8IcmuSR4MPBK4dA7ru7fuZHAnx6tmWPZa4NVVtQGgqn5RVe+fy+Jmwb7AzVV1B0BV3Ty9PwyGhlnRpp8PfGIM9c2Wrb1XZwLHtOlDga8yeO93BGN9Hw2FLVsLrEhyf+AxwNfHXM9s2No+FfAvwLMYfBLbHr8r8h7ghUl226j9d4CLx1DPbPossF+Sf03y3iRPHVp2AfCUNrjkCuBjY6lwdmztvboaWJRkdwYfbtbOSVWz61VDp4+eNdQ+1vfRUNiCqroMWMrgP7rzxlvN7Bhxn6Y/qaxgOzw6qqrbgNOBl4+7ltlWVT8DDmIw/tcU8LEkf9oW/xr4CoNP0A+oqmvHUeMc+gSD/0afyOAId3szfPron4fax/o+Ggpbdy7wVrbDP45bsMV9qqpvMPiktldV/etcFjaL3gkcDzxoqO3bDP6gbteq6tdV9YWq+hvgpcB/HVq8FjgFOGssxc2eUd6rtcCbgPOr6j/6L2lOje19NBS27oPAG6vq8nEXMotG2afXAn81R/XMuqr6MYP/oY4fav7fwP9J8jCAJPdLsl0dTSR5VJJlQ00HAN8fmv8yg/3c3j/EbPW9qqrrgNcB7x1DfX0b2/s4b4a5mK+qaj3wrnHXMZtG2aeq+sc5KqdPb2PwSRqAqjovyT7Av7Rb/opBQG5PHgyc0u5KuZPBXTirgLMBajBEwVvHV9498sAk64fm315Vbx/lvaqq/ztXRd4Lm+zf1lYY5/voMBeSpI6njyRJHUNBktQxFCRJHUNBktQxFCRJHUNB80aShyVZm+R7bVTM85L8l1l+jUOTPPkerLelETtnZQTZJA9PcvbQ/JltJNRXJXljkmfcg20uTfInQ/MTSU6ejXq1Y/J7CpoX2r3onwTWVNWK1nYAsA8wm9+qPhT4GfC1bahteMTODW3cqBfPYk0AtMHfjmqv+TDgyVX1G/dys0uBPwE+2l5jEpi8l9vUDswjBc0XTwN+VVXvm26oqkur6ssZ+Lv2Sf3yJMdA96n/M9P9k7x7ehygJNcmeUOSS9o6j06yFPgf3DUQ2e8n+bcku7Z1HtrW23Wj2kYaXTXJXye5qNW5enpM/CQvb0cXlyVZ29qeOjQY2jeTPKR9qr+ibe6zwN5DdXZHJEken+RrSb6V5BtD63657e8lQ0dDJwG/37bzquF/syR7ZDBW/2VJLkzymNb++iQfzGBs/2u2t299697xSEHzxZZGxXw+g+EcHgvsBVyU5EsjbPPmqjowyf9k8Ef9JUneB/ysqt4Kgx81YTCU+KcYDK728ar61TbUNuzdVfXGtt0zGPzuwaeBE4FHVNUd7ZvIAK8GTqiqr2YwRPkvNtrW84DPVNUBbXvHt+f7Mhg185iquijJQ4GfAzcBz6yqX7RhMM4EJtprv7qqntvWP3ToNd4AfLOqjkzydAaDCB7Qlj2aQVA/BPhuklNn+HfRDsgjBW0Pfg84sw0EdyPwReDxI6w3PQ79xQxOo8zkA8Bxbfo44EP3os6nJfl6ksuBpwO/3dovAz6S5EXcNeb/V4G3t0/hC6tq1N8CeBRwQ1VdBIMRYdu6uwLvb6/9D8D+I2zr94Az2nY+B+yZu4Yb/39VdUdV3cwgcPYZsT5t5wwFzRdbGhUzm2m/k7v/N3z/jZbf0Z5/zWaOiqvqq8DSDH6XYJequmKGblsdsbNdZ3gvcFRV/S7w/qF6nsPgNx4OAi5OsqCqTgJeAjwAuDDJo7e0/eGXYjAO0MZeBdzI4GhqArjviNva2PS27xhq2+y/n3Y8hoLmi88B90vy36cb2rnzpwJfAo5JskuSRcBTgG8wGB10/wxG0NwNOGymDW/kpwxOiQw7ncHpls0dJYwyuup0ANzcTgdNn/+/D7BfVX0eeA2wEHhwkt+sqsur6i0MLvyOGgrfAR6e5PFt+w9JsgDYjcERxH8wuAi+yxb2d9qXgBe27RzK4HTbbSPWoR2U6a95oaoqyR8B70xyIoNz7NcyuOvnS8CTGPxWdAGvqaofAiQ5i8HpmauBb47wUp8Gzk6yHHhZVX0Z+AjwZjb/+xJbHV21qm5J8n4Gv3F9LXBRW7QL8PcttMLgh1VuSfKmJE9j8Cn8SgY/k7rV3+Ktql+2C+2nZPBb2z9n8DvN7wU+nuRo4PMMfo8bBv82dyb5FvBh7v5v9HrgQ0kuA24HVm7t9bXjc5RU7fTaXT3Lq2rWbzOVtjceKWinluQU4HDgiHHXIs0HHilIkjpeaJYkdQwFSVLHUJAkdQwFSVLHUJAkdf4/8XYgs8yOXx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='County Classification', data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram above shows that a large portion of counties in our data are considered Non-Core and Micropolitan which means the results for those population types will be more precise.\n",
    "\n",
    "In our analysis, we will be separating observations into 3 different categories:\n",
    "    1. Large population: consists of Large Central/Fringe Metro counties\n",
    "    2. Medium population: consists of Medium/Small Metro counties\n",
    "    3. Small population: consists of Micropolitan/Non-Core counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "large_pop_df = df[df['County Classification'].isin(['LCM', 'LFM'])]\n",
    "medium_pop_df = df[df['County Classification'].isin(['MM', 'SM'])]\n",
    "small_pop_df = df[df['County Classification'].isin(['MI', 'NC'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>2013 code</th>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Infant mortality raw value</th>\n",
       "      <th>Diabetes prevalence raw value</th>\n",
       "      <th>Drug overdose deaths raw value</th>\n",
       "      <th>HIV prevalence raw value</th>\n",
       "      <th>Flu vaccinations raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Homicides raw value</th>\n",
       "      <th>Firearm fatalities raw value</th>\n",
       "      <th>Residential segregation - non-white/white raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>964.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1281.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>957.000000</td>\n",
       "      <td>1591.000000</td>\n",
       "      <td>1914.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.850347</td>\n",
       "      <td>0.168002</td>\n",
       "      <td>77.632732</td>\n",
       "      <td>6.458775</td>\n",
       "      <td>0.116625</td>\n",
       "      <td>22.538501</td>\n",
       "      <td>204.699768</td>\n",
       "      <td>0.426572</td>\n",
       "      <td>0.320151</td>\n",
       "      <td>0.177348</td>\n",
       "      <td>0.334176</td>\n",
       "      <td>0.175432</td>\n",
       "      <td>0.254444</td>\n",
       "      <td>0.099587</td>\n",
       "      <td>6.034692</td>\n",
       "      <td>13.972156</td>\n",
       "      <td>32.052265</td>\n",
       "      <td>0.117308</td>\n",
       "      <td>0.120782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.356820</td>\n",
       "      <td>0.042843</td>\n",
       "      <td>2.796021</td>\n",
       "      <td>2.079580</td>\n",
       "      <td>0.025635</td>\n",
       "      <td>12.379896</td>\n",
       "      <td>255.184959</td>\n",
       "      <td>0.086820</td>\n",
       "      <td>0.045544</td>\n",
       "      <td>0.034592</td>\n",
       "      <td>0.040374</td>\n",
       "      <td>0.032266</td>\n",
       "      <td>0.052520</td>\n",
       "      <td>0.042795</td>\n",
       "      <td>5.243044</td>\n",
       "      <td>6.631750</td>\n",
       "      <td>12.656156</td>\n",
       "      <td>0.021904</td>\n",
       "      <td>0.017717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082892</td>\n",
       "      <td>67.958718</td>\n",
       "      <td>2.321843</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>4.470030</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.071693</td>\n",
       "      <td>0.236942</td>\n",
       "      <td>0.092878</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.020682</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.169576</td>\n",
       "      <td>0.069370</td>\n",
       "      <td>0.080346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.135695</td>\n",
       "      <td>75.689665</td>\n",
       "      <td>4.822284</td>\n",
       "      <td>0.099000</td>\n",
       "      <td>13.748720</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.151146</td>\n",
       "      <td>0.306822</td>\n",
       "      <td>0.152355</td>\n",
       "      <td>0.219250</td>\n",
       "      <td>0.064403</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>23.790263</td>\n",
       "      <td>0.100318</td>\n",
       "      <td>0.107667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.161917</td>\n",
       "      <td>77.778541</td>\n",
       "      <td>6.171026</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>19.265461</td>\n",
       "      <td>127.100000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.324000</td>\n",
       "      <td>0.174996</td>\n",
       "      <td>0.334373</td>\n",
       "      <td>0.174898</td>\n",
       "      <td>0.254000</td>\n",
       "      <td>0.093969</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.769146</td>\n",
       "      <td>0.114845</td>\n",
       "      <td>0.118888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.194551</td>\n",
       "      <td>79.624968</td>\n",
       "      <td>7.753608</td>\n",
       "      <td>0.134000</td>\n",
       "      <td>28.529314</td>\n",
       "      <td>234.800000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.349000</td>\n",
       "      <td>0.202119</td>\n",
       "      <td>0.363187</td>\n",
       "      <td>0.197109</td>\n",
       "      <td>0.292000</td>\n",
       "      <td>0.128083</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>40.051431</td>\n",
       "      <td>0.132243</td>\n",
       "      <td>0.133419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.345652</td>\n",
       "      <td>91.007047</td>\n",
       "      <td>17.142857</td>\n",
       "      <td>0.201000</td>\n",
       "      <td>85.487650</td>\n",
       "      <td>2590.200000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>0.292537</td>\n",
       "      <td>0.458932</td>\n",
       "      <td>0.280822</td>\n",
       "      <td>0.402000</td>\n",
       "      <td>0.274373</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>72.910662</td>\n",
       "      <td>0.191473</td>\n",
       "      <td>0.175169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2013 code  Poor or fair health raw value  Life expectancy raw value  \\\n",
       "count  2018.000000                    2018.000000                2008.000000   \n",
       "mean      1.850347                       0.168002                  77.632732   \n",
       "std       0.356820                       0.042843                   2.796021   \n",
       "min       1.000000                       0.082892                  67.958718   \n",
       "25%       2.000000                       0.135695                  75.689665   \n",
       "50%       2.000000                       0.161917                  77.778541   \n",
       "75%       2.000000                       0.194551                  79.624968   \n",
       "max       2.000000                       0.345652                  91.007047   \n",
       "\n",
       "       Infant mortality raw value  Diabetes prevalence raw value  \\\n",
       "count                  964.000000                    2018.000000   \n",
       "mean                     6.458775                       0.116625   \n",
       "std                      2.079580                       0.025635   \n",
       "min                      2.321843                       0.047000   \n",
       "25%                      4.822284                       0.099000   \n",
       "50%                      6.171026                       0.116000   \n",
       "75%                      7.753608                       0.134000   \n",
       "max                     17.142857                       0.201000   \n",
       "\n",
       "       Drug overdose deaths raw value  HIV prevalence raw value  \\\n",
       "count                     1281.000000               1725.000000   \n",
       "mean                        22.538501                204.699768   \n",
       "std                         12.379896                255.184959   \n",
       "min                          4.470030                 18.000000   \n",
       "25%                         13.748720                 78.500000   \n",
       "50%                         19.265461                127.100000   \n",
       "75%                         28.529314                234.800000   \n",
       "max                         85.487650               2590.200000   \n",
       "\n",
       "       Flu vaccinations raw value  Adult obesity raw value  \\\n",
       "count                 2016.000000              2018.000000   \n",
       "mean                     0.426572                 0.320151   \n",
       "std                      0.086820                 0.045544   \n",
       "min                      0.080000                 0.158000   \n",
       "25%                      0.380000                 0.294000   \n",
       "50%                      0.440000                 0.324000   \n",
       "75%                      0.490000                 0.349000   \n",
       "max                      0.620000                 0.495000   \n",
       "\n",
       "       Adult smoking raw value  Insufficient sleep raw value  \\\n",
       "count              2018.000000                   2018.000000   \n",
       "mean                  0.177348                      0.334176   \n",
       "std                   0.034592                      0.040374   \n",
       "min                   0.071693                      0.236942   \n",
       "25%                   0.151146                      0.306822   \n",
       "50%                   0.174996                      0.334373   \n",
       "75%                   0.202119                      0.363187   \n",
       "max                   0.292537                      0.458932   \n",
       "\n",
       "       Excessive drinking raw value  Physical inactivity raw value  \\\n",
       "count                   2018.000000                    2018.000000   \n",
       "mean                       0.175432                       0.254444   \n",
       "std                        0.032266                       0.052520   \n",
       "min                        0.092878                       0.096000   \n",
       "25%                        0.152355                       0.219250   \n",
       "50%                        0.174898                       0.254000   \n",
       "75%                        0.197109                       0.292000   \n",
       "max                        0.280822                       0.402000   \n",
       "\n",
       "       Uninsured raw value  Homicides raw value  Firearm fatalities raw value  \\\n",
       "count          2018.000000           957.000000                   1591.000000   \n",
       "mean              0.099587             6.034692                     13.972156   \n",
       "std               0.042795             5.243044                      6.631750   \n",
       "min               0.020682             0.700000                      1.700000   \n",
       "25%               0.064403             2.700000                      9.400000   \n",
       "50%               0.093969             4.400000                     13.000000   \n",
       "75%               0.128083             7.600000                     17.500000   \n",
       "max               0.274373            39.500000                     51.100000   \n",
       "\n",
       "       Residential segregation - non-white/white raw value  \\\n",
       "count                                        1914.000000     \n",
       "mean                                           32.052265     \n",
       "std                                            12.656156     \n",
       "min                                             0.169576     \n",
       "25%                                            23.790263     \n",
       "50%                                            31.769146     \n",
       "75%                                            40.051431     \n",
       "max                                            72.910662     \n",
       "\n",
       "       Frequent physical distress raw value  \\\n",
       "count                           2018.000000   \n",
       "mean                               0.117308   \n",
       "std                                0.021904   \n",
       "min                                0.069370   \n",
       "25%                                0.100318   \n",
       "50%                                0.114845   \n",
       "75%                                0.132243   \n",
       "max                                0.191473   \n",
       "\n",
       "       Frequent mental distress raw value  \n",
       "count                         2018.000000  \n",
       "mean                             0.120782  \n",
       "std                              0.017717  \n",
       "min                              0.080346  \n",
       "25%                              0.107667  \n",
       "50%                              0.118888  \n",
       "75%                              0.133419  \n",
       "max                              0.175169  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_pop_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013 code</th>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Infant mortality raw value</th>\n",
       "      <th>Diabetes prevalence raw value</th>\n",
       "      <th>Drug overdose deaths raw value</th>\n",
       "      <th>HIV prevalence raw value</th>\n",
       "      <th>Flu vaccinations raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Homicides raw value</th>\n",
       "      <th>Firearm fatalities raw value</th>\n",
       "      <th>Residential segregation - non-white/white raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3273.000000</td>\n",
       "      <td>1532.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>2046.000000</td>\n",
       "      <td>2826.000000</td>\n",
       "      <td>3297.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3297.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>2557.000000</td>\n",
       "      <td>3109.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.503032</td>\n",
       "      <td>0.173221</td>\n",
       "      <td>77.304337</td>\n",
       "      <td>6.778518</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>21.833899</td>\n",
       "      <td>182.222930</td>\n",
       "      <td>0.421608</td>\n",
       "      <td>0.324918</td>\n",
       "      <td>0.181275</td>\n",
       "      <td>0.333839</td>\n",
       "      <td>0.173321</td>\n",
       "      <td>0.259116</td>\n",
       "      <td>0.102568</td>\n",
       "      <td>5.984865</td>\n",
       "      <td>14.577669</td>\n",
       "      <td>32.102492</td>\n",
       "      <td>0.120188</td>\n",
       "      <td>0.123193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500067</td>\n",
       "      <td>0.044299</td>\n",
       "      <td>2.701575</td>\n",
       "      <td>2.038436</td>\n",
       "      <td>0.026191</td>\n",
       "      <td>11.758381</td>\n",
       "      <td>192.290339</td>\n",
       "      <td>0.089279</td>\n",
       "      <td>0.042701</td>\n",
       "      <td>0.034169</td>\n",
       "      <td>0.040821</td>\n",
       "      <td>0.033141</td>\n",
       "      <td>0.052302</td>\n",
       "      <td>0.043452</td>\n",
       "      <td>4.521403</td>\n",
       "      <td>6.190808</td>\n",
       "      <td>12.416754</td>\n",
       "      <td>0.022203</td>\n",
       "      <td>0.017870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.082892</td>\n",
       "      <td>67.745553</td>\n",
       "      <td>3.061615</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>3.212149</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.143000</td>\n",
       "      <td>0.067354</td>\n",
       "      <td>0.230283</td>\n",
       "      <td>0.092878</td>\n",
       "      <td>0.084000</td>\n",
       "      <td>0.025498</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.169576</td>\n",
       "      <td>0.069370</td>\n",
       "      <td>0.080346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.139919</td>\n",
       "      <td>75.492452</td>\n",
       "      <td>5.272828</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>13.560504</td>\n",
       "      <td>77.200000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.299000</td>\n",
       "      <td>0.155076</td>\n",
       "      <td>0.305421</td>\n",
       "      <td>0.149780</td>\n",
       "      <td>0.226000</td>\n",
       "      <td>0.066489</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>24.370147</td>\n",
       "      <td>0.103296</td>\n",
       "      <td>0.110313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.167604</td>\n",
       "      <td>77.379989</td>\n",
       "      <td>6.559767</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>19.052337</td>\n",
       "      <td>115.600000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.327000</td>\n",
       "      <td>0.178940</td>\n",
       "      <td>0.334483</td>\n",
       "      <td>0.173020</td>\n",
       "      <td>0.260500</td>\n",
       "      <td>0.096920</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>31.288155</td>\n",
       "      <td>0.117635</td>\n",
       "      <td>0.122792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.200758</td>\n",
       "      <td>79.198328</td>\n",
       "      <td>7.902299</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>27.129325</td>\n",
       "      <td>214.750000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.352000</td>\n",
       "      <td>0.204622</td>\n",
       "      <td>0.363014</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.296000</td>\n",
       "      <td>0.130359</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>39.949143</td>\n",
       "      <td>0.136179</td>\n",
       "      <td>0.136055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.378066</td>\n",
       "      <td>94.659083</td>\n",
       "      <td>31.117397</td>\n",
       "      <td>0.201000</td>\n",
       "      <td>111.540052</td>\n",
       "      <td>2445.200000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>0.383231</td>\n",
       "      <td>0.467078</td>\n",
       "      <td>0.294401</td>\n",
       "      <td>0.402000</td>\n",
       "      <td>0.309625</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>84.936407</td>\n",
       "      <td>0.206885</td>\n",
       "      <td>0.180517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2013 code  Poor or fair health raw value  Life expectancy raw value  \\\n",
       "count  3298.000000                    3298.000000                3273.000000   \n",
       "mean      3.503032                       0.173221                  77.304337   \n",
       "std       0.500067                       0.044299                   2.701575   \n",
       "min       3.000000                       0.082892                  67.745553   \n",
       "25%       3.000000                       0.139919                  75.492452   \n",
       "50%       4.000000                       0.167604                  77.379989   \n",
       "75%       4.000000                       0.200758                  79.198328   \n",
       "max       4.000000                       0.378066                  94.659083   \n",
       "\n",
       "       Infant mortality raw value  Diabetes prevalence raw value  \\\n",
       "count                 1532.000000                    3298.000000   \n",
       "mean                     6.778518                       0.118644   \n",
       "std                      2.038436                       0.026191   \n",
       "min                      3.061615                       0.042000   \n",
       "25%                      5.272828                       0.100000   \n",
       "50%                      6.559767                       0.118000   \n",
       "75%                      7.902299                       0.137000   \n",
       "max                     31.117397                       0.201000   \n",
       "\n",
       "       Drug overdose deaths raw value  HIV prevalence raw value  \\\n",
       "count                     2046.000000               2826.000000   \n",
       "mean                        21.833899                182.222930   \n",
       "std                         11.758381                192.290339   \n",
       "min                          3.212149                 18.000000   \n",
       "25%                         13.560504                 77.200000   \n",
       "50%                         19.052337                115.600000   \n",
       "75%                         27.129325                214.750000   \n",
       "max                        111.540052               2445.200000   \n",
       "\n",
       "       Flu vaccinations raw value  Adult obesity raw value  \\\n",
       "count                 3297.000000              3298.000000   \n",
       "mean                     0.421608                 0.324918   \n",
       "std                      0.089279                 0.042701   \n",
       "min                      0.060000                 0.143000   \n",
       "25%                      0.370000                 0.299000   \n",
       "50%                      0.430000                 0.327000   \n",
       "75%                      0.490000                 0.352000   \n",
       "max                      0.650000                 0.495000   \n",
       "\n",
       "       Adult smoking raw value  Insufficient sleep raw value  \\\n",
       "count              3298.000000                   3298.000000   \n",
       "mean                  0.181275                      0.333839   \n",
       "std                   0.034169                      0.040821   \n",
       "min                   0.067354                      0.230283   \n",
       "25%                   0.155076                      0.305421   \n",
       "50%                   0.178940                      0.334483   \n",
       "75%                   0.204622                      0.363014   \n",
       "max                   0.383231                      0.467078   \n",
       "\n",
       "       Excessive drinking raw value  Physical inactivity raw value  \\\n",
       "count                   3298.000000                    3298.000000   \n",
       "mean                       0.173321                       0.259116   \n",
       "std                        0.033141                       0.052302   \n",
       "min                        0.092878                       0.084000   \n",
       "25%                        0.149780                       0.226000   \n",
       "50%                        0.173020                       0.260500   \n",
       "75%                        0.197246                       0.296000   \n",
       "max                        0.294401                       0.402000   \n",
       "\n",
       "       Uninsured raw value  Homicides raw value  Firearm fatalities raw value  \\\n",
       "count          3297.000000          1480.000000                   2557.000000   \n",
       "mean              0.102568             5.984865                     14.577669   \n",
       "std               0.043452             4.521403                      6.190808   \n",
       "min               0.025498             0.800000                      2.200000   \n",
       "25%               0.066489             3.000000                     10.200000   \n",
       "50%               0.096920             4.800000                     13.800000   \n",
       "75%               0.130359             7.600000                     17.800000   \n",
       "max               0.309625            30.300000                     56.500000   \n",
       "\n",
       "       Residential segregation - non-white/white raw value  \\\n",
       "count                                        3109.000000     \n",
       "mean                                           32.102492     \n",
       "std                                            12.416754     \n",
       "min                                             0.169576     \n",
       "25%                                            24.370147     \n",
       "50%                                            31.288155     \n",
       "75%                                            39.949143     \n",
       "max                                            84.936407     \n",
       "\n",
       "       Frequent physical distress raw value  \\\n",
       "count                           3298.000000   \n",
       "mean                               0.120188   \n",
       "std                                0.022203   \n",
       "min                                0.069370   \n",
       "25%                                0.103296   \n",
       "50%                                0.117635   \n",
       "75%                                0.136179   \n",
       "max                                0.206885   \n",
       "\n",
       "       Frequent mental distress raw value  \n",
       "count                         3298.000000  \n",
       "mean                             0.123193  \n",
       "std                              0.017870  \n",
       "min                              0.080346  \n",
       "25%                              0.110313  \n",
       "50%                              0.122792  \n",
       "75%                              0.136055  \n",
       "max                              0.180517  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medium_pop_df.describe()"
   ]
  },
  {
   "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>2013 code</th>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Infant mortality raw value</th>\n",
       "      <th>Diabetes prevalence raw value</th>\n",
       "      <th>Drug overdose deaths raw value</th>\n",
       "      <th>HIV prevalence raw value</th>\n",
       "      <th>Flu vaccinations raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Homicides raw value</th>\n",
       "      <th>Firearm fatalities raw value</th>\n",
       "      <th>Residential segregation - non-white/white raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9057.000000</td>\n",
       "      <td>3297.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>4889.000000</td>\n",
       "      <td>7176.000000</td>\n",
       "      <td>9194.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>3256.000000</td>\n",
       "      <td>6459.000000</td>\n",
       "      <td>8322.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "      <td>9206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.669237</td>\n",
       "      <td>0.175609</td>\n",
       "      <td>77.132676</td>\n",
       "      <td>7.042744</td>\n",
       "      <td>0.119790</td>\n",
       "      <td>22.835352</td>\n",
       "      <td>170.034532</td>\n",
       "      <td>0.404817</td>\n",
       "      <td>0.327046</td>\n",
       "      <td>0.183668</td>\n",
       "      <td>0.331022</td>\n",
       "      <td>0.171099</td>\n",
       "      <td>0.264520</td>\n",
       "      <td>0.106438</td>\n",
       "      <td>6.601443</td>\n",
       "      <td>15.438659</td>\n",
       "      <td>31.490229</td>\n",
       "      <td>0.121459</td>\n",
       "      <td>0.123903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.470513</td>\n",
       "      <td>0.045753</td>\n",
       "      <td>2.815815</td>\n",
       "      <td>2.082546</td>\n",
       "      <td>0.026038</td>\n",
       "      <td>12.248008</td>\n",
       "      <td>192.715298</td>\n",
       "      <td>0.094689</td>\n",
       "      <td>0.042931</td>\n",
       "      <td>0.035753</td>\n",
       "      <td>0.042161</td>\n",
       "      <td>0.032657</td>\n",
       "      <td>0.051565</td>\n",
       "      <td>0.045067</td>\n",
       "      <td>4.946415</td>\n",
       "      <td>6.867051</td>\n",
       "      <td>13.016166</td>\n",
       "      <td>0.023318</td>\n",
       "      <td>0.018970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.082892</td>\n",
       "      <td>62.441612</td>\n",
       "      <td>3.039791</td>\n",
       "      <td>0.033000</td>\n",
       "      <td>3.212149</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.068218</td>\n",
       "      <td>0.232268</td>\n",
       "      <td>0.092652</td>\n",
       "      <td>0.084000</td>\n",
       "      <td>0.026564</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.045076</td>\n",
       "      <td>0.069370</td>\n",
       "      <td>0.080346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.141695</td>\n",
       "      <td>75.237452</td>\n",
       "      <td>5.581915</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>14.630776</td>\n",
       "      <td>69.900000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.301000</td>\n",
       "      <td>0.156364</td>\n",
       "      <td>0.301076</td>\n",
       "      <td>0.147127</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.069988</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>22.852883</td>\n",
       "      <td>0.104063</td>\n",
       "      <td>0.110026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.168538</td>\n",
       "      <td>77.134802</td>\n",
       "      <td>6.815366</td>\n",
       "      <td>0.119000</td>\n",
       "      <td>19.621422</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>0.180698</td>\n",
       "      <td>0.331213</td>\n",
       "      <td>0.170723</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.098911</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>31.118866</td>\n",
       "      <td>0.119067</td>\n",
       "      <td>0.123190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.205903</td>\n",
       "      <td>79.077963</td>\n",
       "      <td>8.225617</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>28.344097</td>\n",
       "      <td>191.400000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>0.207901</td>\n",
       "      <td>0.362144</td>\n",
       "      <td>0.195046</td>\n",
       "      <td>0.302000</td>\n",
       "      <td>0.134641</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>39.209799</td>\n",
       "      <td>0.138030</td>\n",
       "      <td>0.137049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.407324</td>\n",
       "      <td>97.965235</td>\n",
       "      <td>17.473118</td>\n",
       "      <td>0.209000</td>\n",
       "      <td>87.059479</td>\n",
       "      <td>2445.200000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>0.427541</td>\n",
       "      <td>0.458932</td>\n",
       "      <td>0.281579</td>\n",
       "      <td>0.451000</td>\n",
       "      <td>0.334537</td>\n",
       "      <td>34.600000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>91.099085</td>\n",
       "      <td>0.246185</td>\n",
       "      <td>0.222063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2013 code  Poor or fair health raw value  Life expectancy raw value  \\\n",
       "count  9206.000000                    9206.000000                9057.000000   \n",
       "mean      5.669237                       0.175609                  77.132676   \n",
       "std       0.470513                       0.045753                   2.815815   \n",
       "min       5.000000                       0.082892                  62.441612   \n",
       "25%       5.000000                       0.141695                  75.237452   \n",
       "50%       6.000000                       0.168538                  77.134802   \n",
       "75%       6.000000                       0.205903                  79.077963   \n",
       "max       6.000000                       0.407324                  97.965235   \n",
       "\n",
       "       Infant mortality raw value  Diabetes prevalence raw value  \\\n",
       "count                 3297.000000                    9206.000000   \n",
       "mean                     7.042744                       0.119790   \n",
       "std                      2.082546                       0.026038   \n",
       "min                      3.039791                       0.033000   \n",
       "25%                      5.581915                       0.101000   \n",
       "50%                      6.815366                       0.119000   \n",
       "75%                      8.225617                       0.137000   \n",
       "max                     17.473118                       0.209000   \n",
       "\n",
       "       Drug overdose deaths raw value  HIV prevalence raw value  \\\n",
       "count                     4889.000000               7176.000000   \n",
       "mean                        22.835352                170.034532   \n",
       "std                         12.248008                192.715298   \n",
       "min                          3.212149                 10.400000   \n",
       "25%                         14.630776                 69.900000   \n",
       "50%                         19.621422                103.500000   \n",
       "75%                         28.344097                191.400000   \n",
       "max                         87.059479               2445.200000   \n",
       "\n",
       "       Flu vaccinations raw value  Adult obesity raw value  \\\n",
       "count                 9194.000000              9206.000000   \n",
       "mean                     0.404817                 0.327046   \n",
       "std                      0.094689                 0.042931   \n",
       "min                      0.030000                 0.136000   \n",
       "25%                      0.350000                 0.301000   \n",
       "50%                      0.420000                 0.329000   \n",
       "75%                      0.470000                 0.354000   \n",
       "max                      0.620000                 0.495000   \n",
       "\n",
       "       Adult smoking raw value  Insufficient sleep raw value  \\\n",
       "count              9206.000000                   9206.000000   \n",
       "mean                  0.183668                      0.331022   \n",
       "std                   0.035753                      0.042161   \n",
       "min                   0.068218                      0.232268   \n",
       "25%                   0.156364                      0.301076   \n",
       "50%                   0.180698                      0.331213   \n",
       "75%                   0.207901                      0.362144   \n",
       "max                   0.427541                      0.458932   \n",
       "\n",
       "       Excessive drinking raw value  Physical inactivity raw value  \\\n",
       "count                   9206.000000                    9206.000000   \n",
       "mean                       0.171099                       0.264520   \n",
       "std                        0.032657                       0.051565   \n",
       "min                        0.092652                       0.084000   \n",
       "25%                        0.147127                       0.230000   \n",
       "50%                        0.170723                       0.265000   \n",
       "75%                        0.195046                       0.302000   \n",
       "max                        0.281579                       0.451000   \n",
       "\n",
       "       Uninsured raw value  Homicides raw value  Firearm fatalities raw value  \\\n",
       "count          9206.000000          3256.000000                   6459.000000   \n",
       "mean              0.106438             6.601443                     15.438659   \n",
       "std               0.045067             4.946415                      6.867051   \n",
       "min               0.026564             0.700000                      1.700000   \n",
       "25%               0.069988             3.200000                     10.500000   \n",
       "50%               0.098911             5.000000                     14.400000   \n",
       "75%               0.134641             8.300000                     18.800000   \n",
       "max               0.334537            34.600000                     76.800000   \n",
       "\n",
       "       Residential segregation - non-white/white raw value  \\\n",
       "count                                        8322.000000     \n",
       "mean                                           31.490229     \n",
       "std                                            13.016166     \n",
       "min                                             0.045076     \n",
       "25%                                            22.852883     \n",
       "50%                                            31.118866     \n",
       "75%                                            39.209799     \n",
       "max                                            91.099085     \n",
       "\n",
       "       Frequent physical distress raw value  \\\n",
       "count                           9206.000000   \n",
       "mean                               0.121459   \n",
       "std                                0.023318   \n",
       "min                                0.069370   \n",
       "25%                                0.104063   \n",
       "50%                                0.119067   \n",
       "75%                                0.138030   \n",
       "max                                0.246185   \n",
       "\n",
       "       Frequent mental distress raw value  \n",
       "count                         9206.000000  \n",
       "mean                             0.123903  \n",
       "std                              0.018970  \n",
       "min                              0.080346  \n",
       "25%                              0.110026  \n",
       "50%                              0.123190  \n",
       "75%                              0.137049  \n",
       "max                              0.222063  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_pop_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poor or fair health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Poor or fair health raw value' variable in our data is the percentage of adults reporting fair or poor health.\n",
    "\n",
    "Source/Description: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model/health-outcomes/quality-of-life/poor-or-fair-health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our analysis of the poor/fair health indicator, we did not notice a large differenc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    0.165684\n",
       "2    0.154684\n",
       "3    0.174214\n",
       "4    0.170465\n",
       "5    0.181334\n",
       "6    0.180490\n",
       "Name: Poor or fair health raw value, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['Poor or fair health raw value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"Poor or fair health raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of observations')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "large_hist = large_pop_df['Poor or fair health raw value'].hist(bins=25)\n",
    "large_hist.set_title('Large population data')\n",
    "large_hist.set_xlabel('Percentage of population reporting poor or fair health')\n",
    "large_hist.set_ylabel('# of observations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of observations')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "medium_hist = medium_pop_df['Poor or fair health raw value'].hist(bins=25)\n",
    "medium_hist.set_title('Medium population data')\n",
    "medium_hist.set_xlabel('Percentage of population reporting poor or fair health')\n",
    "medium_hist.set_ylabel('# of observations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of observations')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_hist = small_pop_df['Poor or fair health raw value'].hist(bins=25)\n",
    "small_hist.set_title('Medium population data')\n",
    "small_hist.set_xlabel('Percentage of population reporting poor or fair health')\n",
    "small_hist.set_ylabel('# of observations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visuals above show us that the 'Poor or fair health raw value' variable has a skewed right distribution that peaks at 15% of the population reporting a poor or fair health condition for all population sizes.\n",
    "\n",
    "There are outliers in each population category where counties show about 35% of the population in poor/fair health. The small population category has the largest outlier at 40%.\n",
    "\n",
    "This analysis shows us that, in general, there is a difference in health conditions across the three population sizes; more rural counties having higher rates of people in poor/fair health classifications.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pfh_large = large_pop_df['Poor or fair health raw value'].values\n",
    "pfh_medium = medium_pop_df['Poor or fair health raw value'].values\n",
    "pfh_small = small_pop_df['Poor or fair health raw value'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_large, p_large = stats.normaltest(pfh_large)\n",
    "st_medium, p_medium = stats.normaltest(pfh_medium)\n",
    "st_small, p_small = stats.normaltest(pfh_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Poor/Fair health variable in large population size counties follows a normal distribution:  False\n",
      "The Poor/Fair health variable in medium population size counties follows a normal distribution:  False\n",
      "The Poor/Fair health variable in small population size counties follows a normal distribution:  False\n"
     ]
    }
   ],
   "source": [
    "def check_p_val(p_val, alpha):\n",
    "    if p_val < alpha:\n",
    "        return False\n",
    "    else:\n",
    "        return True\n",
    "\n",
    "is_n_large = check_p_val(p_large, .01)\n",
    "is_n_medium = check_p_val(p_medium, .01)\n",
    "is_n_small = check_p_val(p_small, .01)\n",
    "\n",
    "print(\"The Poor/Fair health variable in large population size counties follows a normal distribution: \", is_n_large)\n",
    "print(\"The Poor/Fair health variable in medium population size counties follows a normal distribution: \", is_n_medium)\n",
    "print(\"The Poor/Fair health variable in small population size counties follows a normal distribution: \", is_n_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(pfh_large.min(), pfh_large.max(), 0.01)\n",
    "fit = stats.norm.pdf(xs, np.mean(pfh_large), np.std(pfh_large))\n",
    "# most easily done using matplotlib\n",
    "plt.plot(xs, fit, label = 'Normal Dist.', lw = 4)\n",
    "plt.hist(pfh_large, density = True, label = 'Actual Data');\n",
    "plt.title('Large population - Poor/Fair Health Data')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above confirms that the 'Poor or fair health raw value' variable in each population category does not follow normal distributions and are skewed right distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Life Expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Life Expectancy' variable in our data is the average number of years a person can expect to live..\n",
    "Source/Description: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model/health-outcomes/quality-of-life/poor-or-fair-health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing the mean's of our 3 groupings shows a consistent decline from urban cities life expectancy to rural counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.63273228477158"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_pop_df['Life expectancy raw value'].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.30433660905616"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medium_pop_df['Life expectancy raw value'].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.13267555040088"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_pop_df['Life expectancy raw value'].mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperating out the populations back into their six original groupings shows an even more drastic difference, with large central metrpolitan cities (1) having a 79.8 life expectancy average which consistently decreases in line with how urban/rural a location is down to 77.1 years in non-core (6) rural neighborhoods. This constitutes a difference of 2.7 years simply based on the grouping of where people live."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    79.789764\n",
       "2    78.584777\n",
       "3    77.840751\n",
       "4    77.813149\n",
       "5    77.363703\n",
       "6    77.117909\n",
       "Name: Life expectancy raw value, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['Life expectancy raw value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"Life expectancy raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    79.789764\n",
       "2    78.584777\n",
       "3    77.840751\n",
       "4    77.813149\n",
       "5    77.363703\n",
       "6    77.117909\n",
       "Name: Life expectancy raw value, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['Life expectancy raw value'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.boxplot(x='2013 code', y=\"Life expectancy raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supplemental Health Indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we explored health indicators besides general life expectancy and poor/fair health to analyze if trends continue to appear with different indicators. If a decrease is consistently among other health indicators like infant mortality, diabetes, and HIV, it serves as a good indicator that our main variables of life expectancy and self-reported health are good examples of the general health of these communities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infant Mortality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clear increase in infant mortality for more rural counties, with a spike in the most rural counties. This suggests decreased health quality for poorer counties. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    5.688175\n",
       "2    5.845090\n",
       "3    6.782994\n",
       "4    6.404465\n",
       "5    7.276575\n",
       "6    8.572936\n",
       "Name: Infant mortality raw value, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['Infant mortality raw value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"Infant mortality raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diabetes seems to have a much lower value for very large cities before holding consistent among all other county types. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    0.091714\n",
       "2    0.110060\n",
       "3    0.111791\n",
       "4    0.110722\n",
       "5    0.114730\n",
       "6    0.118110\n",
       "Name: Diabetes prevalence raw value, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['Diabetes prevalence raw value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"Diabetes prevalence raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HIV Prevelance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HIV Prevelance Indicator measures the average amount of people aged 13 and over per 100,000 people who tested positive for HIV: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model/health-outcomes/quality-of-life/hiv-prevalence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our analysis below shows the average amount of people with HIV in large cities is 700 compared to an average of roughly 200 across all other counties. This indicator serves as a marker for possible high risk behavior like intravenous drug use and unprotected sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013 code\n",
       "1    681.443750\n",
       "2    208.737681\n",
       "3    226.398058\n",
       "4    173.864500\n",
       "5    145.885329\n",
       "6    174.835198\n",
       "Name: HIV prevalence raw value, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.groupby('2013 code')['HIV prevalence raw value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"HIV prevalence raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Side-Note: Residential Segregation/White Flight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While not directly relevant to our original question, it is interesting to note the large spike in non-white populations in vary large cities, indicitive of the \"White Flight\" of the 1950's and 1960's towards suburban neighborhoods away from large cities as discussed in Cogs 9 and Cogs 108.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='2013 code', y=\"Residential segregation - non-white/white raw value\", data=df_sample)\n",
    "f1 = plt.gcf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indicators for Poor Health/Life Expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wanted to do some basic analysis of the various factors that could be contributing to the disparity between the healths of urban and rural neighborhoods. There are a numerous amount of different factors associated with this and many are outside of the range of our health dataset, often surronding the difference in healthcare infastructure, thus this analysis is purely corrolational and should not be used to demonstrate some type of casual analysis like poor sleep or smoking is causing this disparity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An initial look at overall corrolations is extremely convoluted due to the use of positive/negative values and sheer number of variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Poor or fair health raw value</th>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Poor or fair health raw value</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.589131</td>\n",
       "      <td>0.413209</td>\n",
       "      <td>0.695377</td>\n",
       "      <td>0.636567</td>\n",
       "      <td>-0.634408</td>\n",
       "      <td>0.538820</td>\n",
       "      <td>0.489119</td>\n",
       "      <td>0.925821</td>\n",
       "      <td>0.830065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life expectancy raw value</th>\n",
       "      <td>-0.589131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.544049</td>\n",
       "      <td>-0.677550</td>\n",
       "      <td>-0.495112</td>\n",
       "      <td>0.512152</td>\n",
       "      <td>-0.609108</td>\n",
       "      <td>-0.217249</td>\n",
       "      <td>-0.637787</td>\n",
       "      <td>-0.666141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adult obesity raw value</th>\n",
       "      <td>0.413209</td>\n",
       "      <td>-0.544049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568765</td>\n",
       "      <td>0.431967</td>\n",
       "      <td>-0.348423</td>\n",
       "      <td>0.707184</td>\n",
       "      <td>0.066437</td>\n",
       "      <td>0.400756</td>\n",
       "      <td>0.414061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adult smoking raw value</th>\n",
       "      <td>0.695377</td>\n",
       "      <td>-0.677550</td>\n",
       "      <td>0.568765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.616112</td>\n",
       "      <td>-0.396809</td>\n",
       "      <td>0.590453</td>\n",
       "      <td>0.190611</td>\n",
       "      <td>0.793960</td>\n",
       "      <td>0.807747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insufficient sleep raw value</th>\n",
       "      <td>0.636567</td>\n",
       "      <td>-0.495112</td>\n",
       "      <td>0.431967</td>\n",
       "      <td>0.616112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.429191</td>\n",
       "      <td>0.468634</td>\n",
       "      <td>0.075392</td>\n",
       "      <td>0.644815</td>\n",
       "      <td>0.663351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Excessive drinking raw value</th>\n",
       "      <td>-0.634408</td>\n",
       "      <td>0.512152</td>\n",
       "      <td>-0.348423</td>\n",
       "      <td>-0.396809</td>\n",
       "      <td>-0.429191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.541234</td>\n",
       "      <td>-0.345440</td>\n",
       "      <td>-0.634864</td>\n",
       "      <td>-0.633471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical inactivity raw value</th>\n",
       "      <td>0.538820</td>\n",
       "      <td>-0.609108</td>\n",
       "      <td>0.707184</td>\n",
       "      <td>0.590453</td>\n",
       "      <td>0.468634</td>\n",
       "      <td>-0.541234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257062</td>\n",
       "      <td>0.523179</td>\n",
       "      <td>0.522602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uninsured raw value</th>\n",
       "      <td>0.489119</td>\n",
       "      <td>-0.217249</td>\n",
       "      <td>0.066437</td>\n",
       "      <td>0.190611</td>\n",
       "      <td>0.075392</td>\n",
       "      <td>-0.345440</td>\n",
       "      <td>0.257062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.370173</td>\n",
       "      <td>0.278116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequent physical distress raw value</th>\n",
       "      <td>0.925821</td>\n",
       "      <td>-0.637787</td>\n",
       "      <td>0.400756</td>\n",
       "      <td>0.793960</td>\n",
       "      <td>0.644815</td>\n",
       "      <td>-0.634864</td>\n",
       "      <td>0.523179</td>\n",
       "      <td>0.370173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequent mental distress raw value</th>\n",
       "      <td>0.830065</td>\n",
       "      <td>-0.666141</td>\n",
       "      <td>0.414061</td>\n",
       "      <td>0.807747</td>\n",
       "      <td>0.663351</td>\n",
       "      <td>-0.633471</td>\n",
       "      <td>0.522602</td>\n",
       "      <td>0.278116</td>\n",
       "      <td>0.954229</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Poor or fair health raw value  \\\n",
       "Poor or fair health raw value                              1.000000   \n",
       "Life expectancy raw value                                 -0.589131   \n",
       "Adult obesity raw value                                    0.413209   \n",
       "Adult smoking raw value                                    0.695377   \n",
       "Insufficient sleep raw value                               0.636567   \n",
       "Excessive drinking raw value                              -0.634408   \n",
       "Physical inactivity raw value                              0.538820   \n",
       "Uninsured raw value                                        0.489119   \n",
       "Frequent physical distress raw value                       0.925821   \n",
       "Frequent mental distress raw value                         0.830065   \n",
       "\n",
       "                                      Life expectancy raw value  \\\n",
       "Poor or fair health raw value                         -0.589131   \n",
       "Life expectancy raw value                              1.000000   \n",
       "Adult obesity raw value                               -0.544049   \n",
       "Adult smoking raw value                               -0.677550   \n",
       "Insufficient sleep raw value                          -0.495112   \n",
       "Excessive drinking raw value                           0.512152   \n",
       "Physical inactivity raw value                         -0.609108   \n",
       "Uninsured raw value                                   -0.217249   \n",
       "Frequent physical distress raw value                  -0.637787   \n",
       "Frequent mental distress raw value                    -0.666141   \n",
       "\n",
       "                                      Adult obesity raw value  \\\n",
       "Poor or fair health raw value                        0.413209   \n",
       "Life expectancy raw value                           -0.544049   \n",
       "Adult obesity raw value                              1.000000   \n",
       "Adult smoking raw value                              0.568765   \n",
       "Insufficient sleep raw value                         0.431967   \n",
       "Excessive drinking raw value                        -0.348423   \n",
       "Physical inactivity raw value                        0.707184   \n",
       "Uninsured raw value                                  0.066437   \n",
       "Frequent physical distress raw value                 0.400756   \n",
       "Frequent mental distress raw value                   0.414061   \n",
       "\n",
       "                                      Adult smoking raw value  \\\n",
       "Poor or fair health raw value                        0.695377   \n",
       "Life expectancy raw value                           -0.677550   \n",
       "Adult obesity raw value                              0.568765   \n",
       "Adult smoking raw value                              1.000000   \n",
       "Insufficient sleep raw value                         0.616112   \n",
       "Excessive drinking raw value                        -0.396809   \n",
       "Physical inactivity raw value                        0.590453   \n",
       "Uninsured raw value                                  0.190611   \n",
       "Frequent physical distress raw value                 0.793960   \n",
       "Frequent mental distress raw value                   0.807747   \n",
       "\n",
       "                                      Insufficient sleep raw value  \\\n",
       "Poor or fair health raw value                             0.636567   \n",
       "Life expectancy raw value                                -0.495112   \n",
       "Adult obesity raw value                                   0.431967   \n",
       "Adult smoking raw value                                   0.616112   \n",
       "Insufficient sleep raw value                              1.000000   \n",
       "Excessive drinking raw value                             -0.429191   \n",
       "Physical inactivity raw value                             0.468634   \n",
       "Uninsured raw value                                       0.075392   \n",
       "Frequent physical distress raw value                      0.644815   \n",
       "Frequent mental distress raw value                        0.663351   \n",
       "\n",
       "                                      Excessive drinking raw value  \\\n",
       "Poor or fair health raw value                            -0.634408   \n",
       "Life expectancy raw value                                 0.512152   \n",
       "Adult obesity raw value                                  -0.348423   \n",
       "Adult smoking raw value                                  -0.396809   \n",
       "Insufficient sleep raw value                             -0.429191   \n",
       "Excessive drinking raw value                              1.000000   \n",
       "Physical inactivity raw value                            -0.541234   \n",
       "Uninsured raw value                                      -0.345440   \n",
       "Frequent physical distress raw value                     -0.634864   \n",
       "Frequent mental distress raw value                       -0.633471   \n",
       "\n",
       "                                      Physical inactivity raw value  \\\n",
       "Poor or fair health raw value                              0.538820   \n",
       "Life expectancy raw value                                 -0.609108   \n",
       "Adult obesity raw value                                    0.707184   \n",
       "Adult smoking raw value                                    0.590453   \n",
       "Insufficient sleep raw value                               0.468634   \n",
       "Excessive drinking raw value                              -0.541234   \n",
       "Physical inactivity raw value                              1.000000   \n",
       "Uninsured raw value                                        0.257062   \n",
       "Frequent physical distress raw value                       0.523179   \n",
       "Frequent mental distress raw value                         0.522602   \n",
       "\n",
       "                                      Uninsured raw value  \\\n",
       "Poor or fair health raw value                    0.489119   \n",
       "Life expectancy raw value                       -0.217249   \n",
       "Adult obesity raw value                          0.066437   \n",
       "Adult smoking raw value                          0.190611   \n",
       "Insufficient sleep raw value                     0.075392   \n",
       "Excessive drinking raw value                    -0.345440   \n",
       "Physical inactivity raw value                    0.257062   \n",
       "Uninsured raw value                              1.000000   \n",
       "Frequent physical distress raw value             0.370173   \n",
       "Frequent mental distress raw value               0.278116   \n",
       "\n",
       "                                      Frequent physical distress raw value  \\\n",
       "Poor or fair health raw value                                     0.925821   \n",
       "Life expectancy raw value                                        -0.637787   \n",
       "Adult obesity raw value                                           0.400756   \n",
       "Adult smoking raw value                                           0.793960   \n",
       "Insufficient sleep raw value                                      0.644815   \n",
       "Excessive drinking raw value                                     -0.634864   \n",
       "Physical inactivity raw value                                     0.523179   \n",
       "Uninsured raw value                                               0.370173   \n",
       "Frequent physical distress raw value                              1.000000   \n",
       "Frequent mental distress raw value                                0.954229   \n",
       "\n",
       "                                      Frequent mental distress raw value  \n",
       "Poor or fair health raw value                                   0.830065  \n",
       "Life expectancy raw value                                      -0.666141  \n",
       "Adult obesity raw value                                         0.414061  \n",
       "Adult smoking raw value                                         0.807747  \n",
       "Insufficient sleep raw value                                    0.663351  \n",
       "Excessive drinking raw value                                   -0.633471  \n",
       "Physical inactivity raw value                                   0.522602  \n",
       "Uninsured raw value                                             0.278116  \n",
       "Frequent physical distress raw value                            0.954229  \n",
       "Frequent mental distress raw value                              1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corrs = df_sample[['Poor or fair health raw value', 'Life expectancy raw value', 'Adult obesity raw value', 'Adult smoking raw value', 'Insufficient sleep raw value', 'Excessive drinking raw value', 'Physical inactivity raw value', 'Uninsured raw value', 'Frequent physical distress raw value', 'Frequent mental distress raw value']]\n",
    "corrs = df_corrs.corr()\n",
    "corrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To corrolate variables with poor/fair self-reported health, we sort values and set ascending to false as an increase in these values represents more people reporting poor health and a negative impact. We can see that the 3 most-highly corrolated values are frequent physical distress, frequent mental distress, and smoking. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Poor or fair health raw value           1.000000\n",
       "Frequent physical distress raw value    0.925821\n",
       "Frequent mental distress raw value      0.830065\n",
       "Adult smoking raw value                 0.695377\n",
       "Insufficient sleep raw value            0.636567\n",
       "Physical inactivity raw value           0.538820\n",
       "Uninsured raw value                     0.489119\n",
       "Adult obesity raw value                 0.413209\n",
       "Life expectancy raw value              -0.589131\n",
       "Excessive drinking raw value           -0.634408\n",
       "Name: Poor or fair health raw value, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs['Poor or fair health raw value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the same formula for life expectancy, but now setting ascending as False as the variables with the highest impact will be negatively corrolated since we are no seeing what decreases people's life expectancy. Doing the same formula for life expectancy, we see that the largest negative contributors to shortening one's life are smoking, frequent mental distress, and frequent physical distress. What's interesting is that these are the exact same 3 top values corrolated with 'Poor or fair health' suggesting how tied together these two values are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adult smoking raw value                -0.677550\n",
       "Frequent mental distress raw value     -0.666141\n",
       "Frequent physical distress raw value   -0.637787\n",
       "Physical inactivity raw value          -0.609108\n",
       "Poor or fair health raw value          -0.589131\n",
       "Adult obesity raw value                -0.544049\n",
       "Insufficient sleep raw value           -0.495112\n",
       "Uninsured raw value                    -0.217249\n",
       "Excessive drinking raw value            0.512152\n",
       "Life expectancy raw value               1.000000\n",
       "Name: Life expectancy raw value, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs['Life expectancy raw value'].sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main goal while deciding which data to use and what to do with it for this project was to answer our question as thoroughly and accurately as possible without accidentally harming or compromising the privacy of any individuals or groups. We realize that although our intent was clearly to avoid these things, we still had to take the following steps to ensure that loss of privacy or harm was not a biproduct of our work:\n",
    "\n",
    "1. Carefully check terms and conditions for the data\n",
    "2. Make sure that we are collecting isn't identifiable to any individual\n",
    "3. Don't target any specific group on age gender, race, religion, and/or culture, check if our data is biased\n",
    "4. Make sure any data included in anonymous, and consider using safe harbour method to filter out personal information\n",
    "5. Make sure that our data is protected and safe in order to prevent being found and/or hacked by someone is not part of this project\n",
    "6. Carefully discuss in group if there is anything violates ethics and privacy section of the project\n",
    "\n",
    "For our data frame that we retrieved, the data is already grouped into subsets of populations, and is therefore anonymous and we do not need to do anything else to ensure the privacy of the subjects is protected.\n",
    "\n",
    "In our analysis of the data, we made sure not to make generalizations about specific populations or to label specific populations as having certain traits, but instead focused on the type of cities they were and what effect that has on the conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizing two datasets provided by CHR and CDC, we sought to answer our question of how residing in various populations have an effect on various health conditions and mortality rates for individuals within those populations. Our initial hypothesis was that individuals living in larger populations would have poorer health and mortality than those living in rural populations.\n",
    "\n",
    "In our analysis, we separated the populations into three subsets; the first subset being the largest populations, second being medium populations, and last being small populations. The first variable we analyzed was the \"Poor or fair raw health value.\" Contrary to our hypothesis, rural counties reported higher quantities of population in poor or fair health over their large metropolitan counterparts. This is also the same for average life expectancy, however. Large metropolitan counties report similar, but slightly longer life expectancy than rural counties.\n",
    "\n",
    "Diving deeper into our analysis, we decided to take additional health indicators into account, such as infant mortality, diabetes, and HIV prevalence. Furthermore, we wanted to see if racial distribution within the various populations had any relation to the variables in our analysis. We found that rural counties are afflicted by higher rates of diabetes and infant mortality, while the largest metropolitan counties have substantially higher rates of HIV prevalence. While much of our analysis found poorer and poorer health conditions the more rural a county was, HIV prelance is a notable exception, likely due to high-risk behaviors like unprotected sex and intravanous drug-use, and is an issue most-notably seen in the largest of cities.\n",
    "\n",
    "Our analysis concludes that larger metropolitan populations are generally healthier than rural populations, with the exception of HIV prevalence. We used corrolation functions to find that the indicators of smoking, mental distress, obesity, insufficient sleep, and being uninsured serve as indicators for decreased self-reported health and life expectancy. Unfortunately, we cannot create a casual relationship between any of these variables.In order to explain the causes for these differences, we would need to perform a much more expansive study with access to a lot more variables in datasets pertaining to healthcare infastructure which are much less readily available then those concerning public health. While this can come from lobbing by large pharmaceutical instutions, it is also a result of the need for privacy for the sensitive nature of this data. It would be far more worrisome if the type of data we would need for extensive studies was made public in the same way census data is at it would raise serious ethical and privacy concerns pertaining users health. \n",
    "\n",
    "Our hypothesis was clearly wrong, however it was done before both conducting our own study/reading other studies and created under the belief that the artificial enviroment cities provide would  caused decreased health conditions in people compared to the romanticized view of living away from people and much closer to nature. While we cannot point to the specific elements that cause the health decline, what we can derive from our analysis is that there are clear differences between health indicators between urban and rural populations. We hope that our research brings attention to this so we can eliminate this disparity, because all Americans should have the opportunity for equal health regardless of where they reside."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sean Reynolds\n",
    "- Kari Garcia\n",
    "- Sam Kapusta\n",
    "- Tam Tran\n",
    "- Randy Kim"
   ]
  }
 ],
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