Assignment 2 Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 2 Example\n",
    "\n",
    "*Last updated: 9/17/2019, Andrew Therriault*\n",
    "\n",
    "This notebook gives an example of what a completed Week 2 assignment might look like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the environment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting the working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('c:/working/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing numpy and pandas for analysis and setting display options for pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_colwidth = 1000\n",
    "pd.options.display.max_columns = 1000\n",
    "pd.options.display.max_rows = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing seaborn and matplotlib for plotting \n",
    "Also invoking matplotlib inline cell magic so that all plots will display inline in the notebook (otherwise it only displays one at a time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_color_codes(\"pastel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Ohio voter file from TargetSmart\n",
    "This is a 10% sample of the Ohio voter file, with several additional appends from TargetSmart (including their VoterBase demographics and model scores)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vf = pd.read_csv('ohio_voterfile_10pct.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the dimensions (rows, columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(760097, 159)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at some sample records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>193937</th>\n",
       "      <th>728929</th>\n",
       "      <th>232322</th>\n",
       "      <th>735524</th>\n",
       "      <th>544988</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>voterbase_id</th>\n",
       "      <td>OH-000001878359</td>\n",
       "      <td>OH-000002704362</td>\n",
       "      <td>OH-000002123672</td>\n",
       "      <td>OH-10557322</td>\n",
       "      <td>OH-000004984672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_city</th>\n",
       "      <td>HOLLAND</td>\n",
       "      <td>CINCINNATI</td>\n",
       "      <td>PAINESVILLE</td>\n",
       "      <td>SHADE</td>\n",
       "      <td>CAMDEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_state</th>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_zip</th>\n",
       "      <td>43528</td>\n",
       "      <td>45248</td>\n",
       "      <td>44077</td>\n",
       "      <td>45776</td>\n",
       "      <td>45311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_reg_cass_city</th>\n",
       "      <td>HOLLAND</td>\n",
       "      <td>CINCINNATI</td>\n",
       "      <td>PAINESVILLE</td>\n",
       "      <td>SHADE</td>\n",
       "      <td>CAMDEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_reg_cass_state</th>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_reg_cass_zip</th>\n",
       "      <td>43528</td>\n",
       "      <td>45248</td>\n",
       "      <td>44077</td>\n",
       "      <td>45776</td>\n",
       "      <td>45311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_registration_date</th>\n",
       "      <td>19870316.0</td>\n",
       "      <td>19890717.0</td>\n",
       "      <td>19640917.0</td>\n",
       "      <td>20121106.0</td>\n",
       "      <td>20020515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_earliest_registration_date</th>\n",
       "      <td>19870316.0</td>\n",
       "      <td>19890717.0</td>\n",
       "      <td>19640917.0</td>\n",
       "      <td>20121106.0</td>\n",
       "      <td>20020515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_party</th>\n",
       "      <td>Unaffiliated</td>\n",
       "      <td>Unaffiliated</td>\n",
       "      <td>Unaffiliated</td>\n",
       "      <td>Unaffiliated</td>\n",
       "      <td>Unaffiliated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_county_code</th>\n",
       "      <td>95</td>\n",
       "      <td>61</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_county_name</th>\n",
       "      <td>LUCAS</td>\n",
       "      <td>HAMILTON</td>\n",
       "      <td>LAKE</td>\n",
       "      <td>ATHENS</td>\n",
       "      <td>PREBLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_cd</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_sd</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_hd</th>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>61</td>\n",
       "      <td>94</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_township</th>\n",
       "      <td>SPRINGFIELD UNINCORP</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>PAINESVILLE</td>\n",
       "      <td>LODI</td>\n",
       "      <td>GRATIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_ward</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_precinct_id</th>\n",
       "      <td>48ASR</td>\n",
       "      <td>31BGP</td>\n",
       "      <td>43AGR</td>\n",
       "      <td>05ABY</td>\n",
       "      <td>68AAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_precinct_name</th>\n",
       "      <td>SPRINGFIELD 12 (48ASR)</td>\n",
       "      <td>GREEN CC (31-BGP)</td>\n",
       "      <td>PAINESVILLE TWP I (43AGR)</td>\n",
       "      <td>LODI TOWNSHIP (05ABY)</td>\n",
       "      <td>GRATIS NORTH (68AAN)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_national_precinct_code</th>\n",
       "      <td>OH_LUCAS_48ASR</td>\n",
       "      <td>OH_HAMILTON_31BGP</td>\n",
       "      <td>OH_LAKE_43AGR</td>\n",
       "      <td>OH_ATHENS_05ABY</td>\n",
       "      <td>OH_PREBLE_68AAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_county_council</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_city_council</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_municipal_district</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_school_district</th>\n",
       "      <td>SPRINGFIELD LOCAL SD (LUCAS)</td>\n",
       "      <td>OAK HILLS LOCAL SD (HAMILTON)</td>\n",
       "      <td>RIVERSIDE LOCAL SD (LAKE)</td>\n",
       "      <td>ALEXANDER LOCAL SD (ATHENS)</td>\n",
       "      <td>PREBLE SHAWNEE LOCAL SD (PREBLE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_judicial_district</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_census_id</th>\n",
       "      <td>390950000000000.0</td>\n",
       "      <td>390610000000000.0</td>\n",
       "      <td>390852000000000.0</td>\n",
       "      <td>390100000000000.0</td>\n",
       "      <td>391355000000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_dma</th>\n",
       "      <td>547.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_dma_name</th>\n",
       "      <td>Toledo OH</td>\n",
       "      <td>Cincinnati OH</td>\n",
       "      <td>Cleveland-Akron (Canton) OH</td>\n",
       "      <td>Columbus OH</td>\n",
       "      <td>Dayton OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_place</th>\n",
       "      <td>nan</td>\n",
       "      <td>3921742.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_place_name</th>\n",
       "      <td>nan</td>\n",
       "      <td>Dent</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_county_code</th>\n",
       "      <td>95.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_county_name</th>\n",
       "      <td>LUCAS</td>\n",
       "      <td>HAMILTON</td>\n",
       "      <td>LAKE</td>\n",
       "      <td>ATHENS</td>\n",
       "      <td>PREBLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_cd</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_sd</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_hd</th>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_township</th>\n",
       "      <td>SPRINGFIELD UNINCORP</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>PAINESVILLE</td>\n",
       "      <td>LODI</td>\n",
       "      <td>GRATIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_ward</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_precinct_id</th>\n",
       "      <td>48ASR</td>\n",
       "      <td>31BGP</td>\n",
       "      <td>43AGR</td>\n",
       "      <td>05ABY</td>\n",
       "      <td>68AAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_precinct_name</th>\n",
       "      <td>SPRINGFIELD 12 (48ASR)</td>\n",
       "      <td>GREEN CC (31-BGP)</td>\n",
       "      <td>PAINESVILLE TWP I (43AGR)</td>\n",
       "      <td>LODI TOWNSHIP (05ABY)</td>\n",
       "      <td>GRATIS NORTH (68AAN)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_county_council</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_city_council</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_municipal_district</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_school_district</th>\n",
       "      <td>SPRINGFIELD LOCAL SD (LUCAS)</td>\n",
       "      <td>OAK HILLS LOCAL SD (HAMILTON)</td>\n",
       "      <td>RIVERSIDE LOCAL SD (LAKE)</td>\n",
       "      <td>ALEXANDER LOCAL SD (ATHENS)</td>\n",
       "      <td>PREBLE SHAWNEE LOCAL SD (PREBLE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_judicial_district</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_census_id</th>\n",
       "      <td>390950000000000.0</td>\n",
       "      <td>390610000000000.0</td>\n",
       "      <td>390852000000000.0</td>\n",
       "      <td>390100000000000.0</td>\n",
       "      <td>391355000000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_dma</th>\n",
       "      <td>547.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_dma_name</th>\n",
       "      <td>Toledo OH</td>\n",
       "      <td>Cincinnati OH</td>\n",
       "      <td>Cleveland-Akron (Canton) OH</td>\n",
       "      <td>Columbus OH</td>\n",
       "      <td>Dayton OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_place</th>\n",
       "      <td>nan</td>\n",
       "      <td>3921742.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_place_name</th>\n",
       "      <td>nan</td>\n",
       "      <td>Dent</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2018</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2017</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2016</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2015</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2014</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2013</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2012</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2011</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2010</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2009</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2008</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2007</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2006</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2005</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2004</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2003</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2002</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2001</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_g2000</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2019</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2019_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2018</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2018_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2017</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2017_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2016</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2016_party</th>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2015</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2015_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2014</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2014_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2013</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2013_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2012</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2012_party</th>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2011</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2011_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2010</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2010_party</th>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2009</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2009_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2008</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2008_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2007</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2007_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2006</th>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2006_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2005</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2005_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2004</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2004_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2003</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2003_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2002</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2002_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>R</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2001</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2001_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2000</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_p2000_party</th>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2019</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2018</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2017</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2016</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2015</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2014</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2013</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2012</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2011</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2010</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2009</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2008</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2007</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2006</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2005</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2004</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2003</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2002</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2001</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_m2000</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2020</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2020_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2016</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2016_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2012</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2012_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2008</th>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>nan</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2008_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2004</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2004_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2000</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pp2000_party</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_partisan_score</th>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>99.2</td>\n",
       "      <td>99.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_presidential_general_turnout_score</th>\n",
       "      <td>98.6</td>\n",
       "      <td>98.4</td>\n",
       "      <td>97.5</td>\n",
       "      <td>85.7</td>\n",
       "      <td>95.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_midterm_general_turnout_score</th>\n",
       "      <td>90.7</td>\n",
       "      <td>96.6</td>\n",
       "      <td>95.7</td>\n",
       "      <td>31.3</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_midterm_general_enthusiasm_score</th>\n",
       "      <td>85.4</td>\n",
       "      <td>86.6</td>\n",
       "      <td>90.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_offyear_general_turnout_score</th>\n",
       "      <td>84.0</td>\n",
       "      <td>89.8</td>\n",
       "      <td>86.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_presidential_primary_turnout_score</th>\n",
       "      <td>78.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>38.7</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tsmart_non_presidential_primary_turnout_score</th>\n",
       "      <td>37.0</td>\n",
       "      <td>76.3</td>\n",
       "      <td>73.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_age</th>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_race</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_marital_status</th>\n",
       "      <td>Married</td>\n",
       "      <td>Married</td>\n",
       "      <td>Married</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_voter_status</th>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_deceased_flag</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deceased_flag_date_of_death</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voterbase_mailable_flag</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_missing_occupancy_flag</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_absentee_status</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_early_voter_status</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vf_pav</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     193937  \\\n",
       "voterbase_id                                                OH-000001878359   \n",
       "tsmart_city                                                         HOLLAND   \n",
       "tsmart_state                                                             OH   \n",
       "tsmart_zip                                                            43528   \n",
       "vf_reg_cass_city                                                    HOLLAND   \n",
       "vf_reg_cass_state                                                        OH   \n",
       "vf_reg_cass_zip                                                       43528   \n",
       "vf_registration_date                                             19870316.0   \n",
       "vf_earliest_registration_date                                    19870316.0   \n",
       "vf_party                                                       Unaffiliated   \n",
       "vf_county_code                                                           95   \n",
       "vf_county_name                                                        LUCAS   \n",
       "vf_cd                                                                     5   \n",
       "vf_sd                                                                    11   \n",
       "vf_hd                                                                    46   \n",
       "vf_township                                            SPRINGFIELD UNINCORP   \n",
       "vf_ward                                                                 nan   \n",
       "vf_precinct_id                                                        48ASR   \n",
       "vf_precinct_name                                     SPRINGFIELD 12 (48ASR)   \n",
       "vf_national_precinct_code                                    OH_LUCAS_48ASR   \n",
       "vf_county_council                                                       nan   \n",
       "vf_city_council                                                         nan   \n",
       "vf_municipal_district                                                   nan   \n",
       "vf_school_district                             SPRINGFIELD LOCAL SD (LUCAS)   \n",
       "vf_judicial_district                                                    nan   \n",
       "reg_census_id                                             390950000000000.0   \n",
       "reg_dma                                                               547.0   \n",
       "reg_dma_name                                                      Toledo OH   \n",
       "reg_place                                                               nan   \n",
       "reg_place_name                                                          nan   \n",
       "tsmart_county_code                                                     95.0   \n",
       "tsmart_county_name                                                    LUCAS   \n",
       "tsmart_cd                                                               5.0   \n",
       "tsmart_sd                                                              11.0   \n",
       "tsmart_hd                                                              46.0   \n",
       "tsmart_township                                        SPRINGFIELD UNINCORP   \n",
       "tsmart_ward                                                             nan   \n",
       "tsmart_precinct_id                                                    48ASR   \n",
       "tsmart_precinct_name                                 SPRINGFIELD 12 (48ASR)   \n",
       "tsmart_county_council                                                   nan   \n",
       "tsmart_city_council                                                     nan   \n",
       "tsmart_municipal_district                                               nan   \n",
       "tsmart_school_district                         SPRINGFIELD LOCAL SD (LUCAS)   \n",
       "tsmart_judicial_district                                                nan   \n",
       "tsmart_census_id                                          390950000000000.0   \n",
       "tsmart_dma                                                            547.0   \n",
       "tsmart_dma_name                                                   Toledo OH   \n",
       "tsmart_place                                                            nan   \n",
       "tsmart_place_name                                                       nan   \n",
       "vf_g2018                                                                  Y   \n",
       "vf_g2017                                                                  Y   \n",
       "vf_g2016                                                                  Y   \n",
       "vf_g2015                                                                  Y   \n",
       "vf_g2014                                                                  Y   \n",
       "vf_g2013                                                                nan   \n",
       "vf_g2012                                                                  Y   \n",
       "vf_g2011                                                                  Y   \n",
       "vf_g2010                                                                  Y   \n",
       "vf_g2009                                                                  Y   \n",
       "vf_g2008                                                                  Y   \n",
       "vf_g2007                                                                  Y   \n",
       "vf_g2006                                                                  Y   \n",
       "vf_g2005                                                                  Y   \n",
       "vf_g2004                                                                  Y   \n",
       "vf_g2003                                                                  Y   \n",
       "vf_g2002                                                                  Y   \n",
       "vf_g2001                                                                  Y   \n",
       "vf_g2000                                                                  Y   \n",
       "vf_p2019                                                                nan   \n",
       "vf_p2019_party                                                          nan   \n",
       "vf_p2018                                                                nan   \n",
       "vf_p2018_party                                                          nan   \n",
       "vf_p2017                                                                nan   \n",
       "vf_p2017_party                                                          nan   \n",
       "vf_p2016                                                                  Y   \n",
       "vf_p2016_party                                                            R   \n",
       "vf_p2015                                                                nan   \n",
       "vf_p2015_party                                                          nan   \n",
       "vf_p2014                                                                nan   \n",
       "vf_p2014_party                                                          nan   \n",
       "vf_p2013                                                                nan   \n",
       "vf_p2013_party                                                          nan   \n",
       "vf_p2012                                                                  Y   \n",
       "vf_p2012_party                                                            R   \n",
       "vf_p2011                                                                nan   \n",
       "vf_p2011_party                                                          nan   \n",
       "vf_p2010                                                                  Y   \n",
       "vf_p2010_party                                                            R   \n",
       "vf_p2009                                                                nan   \n",
       "vf_p2009_party                                                          nan   \n",
       "vf_p2008                                                                nan   \n",
       "vf_p2008_party                                                          nan   \n",
       "vf_p2007                                                                nan   \n",
       "vf_p2007_party                                                          nan   \n",
       "vf_p2006                                                                  Y   \n",
       "vf_p2006_party                                                          nan   \n",
       "vf_p2005                                                                nan   \n",
       "vf_p2005_party                                                          nan   \n",
       "vf_p2004                                                                nan   \n",
       "vf_p2004_party                                                          nan   \n",
       "vf_p2003                                                                nan   \n",
       "vf_p2003_party                                                          nan   \n",
       "vf_p2002                                                                nan   \n",
       "vf_p2002_party                                                          nan   \n",
       "vf_p2001                                                                nan   \n",
       "vf_p2001_party                                                          nan   \n",
       "vf_p2000                                                                  Y   \n",
       "vf_p2000_party                                                            R   \n",
       "vf_m2019                                                                nan   \n",
       "vf_m2018                                                                nan   \n",
       "vf_m2017                                                                nan   \n",
       "vf_m2016                                                                nan   \n",
       "vf_m2015                                                                nan   \n",
       "vf_m2014                                                                nan   \n",
       "vf_m2013                                                                nan   \n",
       "vf_m2012                                                                nan   \n",
       "vf_m2011                                                                nan   \n",
       "vf_m2010                                                                nan   \n",
       "vf_m2009                                                                nan   \n",
       "vf_m2008                                                                nan   \n",
       "vf_m2007                                                                nan   \n",
       "vf_m2006                                                                nan   \n",
       "vf_m2005                                                                nan   \n",
       "vf_m2004                                                                nan   \n",
       "vf_m2003                                                                nan   \n",
       "vf_m2002                                                                nan   \n",
       "vf_m2001                                                                nan   \n",
       "vf_m2000                                                                nan   \n",
       "vf_pp2020                                                               nan   \n",
       "vf_pp2020_party                                                         nan   \n",
       "vf_pp2016                                                               nan   \n",
       "vf_pp2016_party                                                         nan   \n",
       "vf_pp2012                                                               nan   \n",
       "vf_pp2012_party                                                         nan   \n",
       "vf_pp2008                                                               nan   \n",
       "vf_pp2008_party                                                         nan   \n",
       "vf_pp2004                                                               nan   \n",
       "vf_pp2004_party                                                         nan   \n",
       "vf_pp2000                                                               nan   \n",
       "vf_pp2000_party                                                         nan   \n",
       "tsmart_partisan_score                                                   1.2   \n",
       "tsmart_presidential_general_turnout_score                              98.6   \n",
       "tsmart_midterm_general_turnout_score                                   90.7   \n",
       "tsmart_midterm_general_enthusiasm_score                                85.4   \n",
       "tsmart_offyear_general_turnout_score                                   84.0   \n",
       "tsmart_presidential_primary_turnout_score                              78.5   \n",
       "tsmart_non_presidential_primary_turnout_score                          37.0   \n",
       "voterbase_age                                                          66.0   \n",
       "voterbase_gender                                                       Male   \n",
       "voterbase_race                                                    Caucasian   \n",
       "voterbase_marital_status                                            Married   \n",
       "vf_voter_status                                                      Active   \n",
       "voterbase_deceased_flag                                                 nan   \n",
       "deceased_flag_date_of_death                                             nan   \n",
       "voterbase_mailable_flag                                                 Yes   \n",
       "vf_missing_occupancy_flag                                               nan   \n",
       "vf_absentee_status                                                      nan   \n",
       "vf_early_voter_status                                                   nan   \n",
       "vf_pav                                                                  nan   \n",
       "\n",
       "                                                                      728929  \\\n",
       "voterbase_id                                                 OH-000002704362   \n",
       "tsmart_city                                                       CINCINNATI   \n",
       "tsmart_state                                                              OH   \n",
       "tsmart_zip                                                             45248   \n",
       "vf_reg_cass_city                                                  CINCINNATI   \n",
       "vf_reg_cass_state                                                         OH   \n",
       "vf_reg_cass_zip                                                        45248   \n",
       "vf_registration_date                                              19890717.0   \n",
       "vf_earliest_registration_date                                     19890717.0   \n",
       "vf_party                                                        Unaffiliated   \n",
       "vf_county_code                                                            61   \n",
       "vf_county_name                                                      HAMILTON   \n",
       "vf_cd                                                                      1   \n",
       "vf_sd                                                                      8   \n",
       "vf_hd                                                                     30   \n",
       "vf_township                                                            GREEN   \n",
       "vf_ward                                                                  nan   \n",
       "vf_precinct_id                                                         31BGP   \n",
       "vf_precinct_name                                           GREEN CC (31-BGP)   \n",
       "vf_national_precinct_code                                  OH_HAMILTON_31BGP   \n",
       "vf_county_council                                                        nan   \n",
       "vf_city_council                                                          nan   \n",
       "vf_municipal_district                                                    nan   \n",
       "vf_school_district                             OAK HILLS LOCAL SD (HAMILTON)   \n",
       "vf_judicial_district                                                     nan   \n",
       "reg_census_id                                              390610000000000.0   \n",
       "reg_dma                                                                515.0   \n",
       "reg_dma_name                                                   Cincinnati OH   \n",
       "reg_place                                                          3921742.0   \n",
       "reg_place_name                                                          Dent   \n",
       "tsmart_county_code                                                      61.0   \n",
       "tsmart_county_name                                                  HAMILTON   \n",
       "tsmart_cd                                                                1.0   \n",
       "tsmart_sd                                                                8.0   \n",
       "tsmart_hd                                                               30.0   \n",
       "tsmart_township                                                        GREEN   \n",
       "tsmart_ward                                                              nan   \n",
       "tsmart_precinct_id                                                     31BGP   \n",
       "tsmart_precinct_name                                       GREEN CC (31-BGP)   \n",
       "tsmart_county_council                                                    nan   \n",
       "tsmart_city_council                                                      nan   \n",
       "tsmart_municipal_district                                                nan   \n",
       "tsmart_school_district                         OAK HILLS LOCAL SD (HAMILTON)   \n",
       "tsmart_judicial_district                                                 nan   \n",
       "tsmart_census_id                                           390610000000000.0   \n",
       "tsmart_dma                                                             515.0   \n",
       "tsmart_dma_name                                                Cincinnati OH   \n",
       "tsmart_place                                                       3921742.0   \n",
       "tsmart_place_name                                                       Dent   \n",
       "vf_g2018                                                                   Y   \n",
       "vf_g2017                                                                   Y   \n",
       "vf_g2016                                                                   Y   \n",
       "vf_g2015                                                                   Y   \n",
       "vf_g2014                                                                   Y   \n",
       "vf_g2013                                                                   Y   \n",
       "vf_g2012                                                                   Y   \n",
       "vf_g2011                                                                   Y   \n",
       "vf_g2010                                                                   Y   \n",
       "vf_g2009                                                                   Y   \n",
       "vf_g2008                                                                   Y   \n",
       "vf_g2007                                                                   Y   \n",
       "vf_g2006                                                                   Y   \n",
       "vf_g2005                                                                   Y   \n",
       "vf_g2004                                                                   Y   \n",
       "vf_g2003                                                                   Y   \n",
       "vf_g2002                                                                   Y   \n",
       "vf_g2001                                                                   Y   \n",
       "vf_g2000                                                                   Y   \n",
       "vf_p2019                                                                 nan   \n",
       "vf_p2019_party                                                           nan   \n",
       "vf_p2018                                                                   Y   \n",
       "vf_p2018_party                                                             R   \n",
       "vf_p2017                                                                   Y   \n",
       "vf_p2017_party                                                           nan   \n",
       "vf_p2016                                                                   Y   \n",
       "vf_p2016_party                                                             R   \n",
       "vf_p2015                                                                 nan   \n",
       "vf_p2015_party                                                           nan   \n",
       "vf_p2014                                                                   Y   \n",
       "vf_p2014_party                                                             R   \n",
       "vf_p2013                                                                 nan   \n",
       "vf_p2013_party                                                           nan   \n",
       "vf_p2012                                                                   Y   \n",
       "vf_p2012_party                                                             R   \n",
       "vf_p2011                                                                 nan   \n",
       "vf_p2011_party                                                           nan   \n",
       "vf_p2010                                                                   Y   \n",
       "vf_p2010_party                                                             R   \n",
       "vf_p2009                                                                 nan   \n",
       "vf_p2009_party                                                           nan   \n",
       "vf_p2008                                                                   Y   \n",
       "vf_p2008_party                                                             R   \n",
       "vf_p2007                                                                 nan   \n",
       "vf_p2007_party                                                           nan   \n",
       "vf_p2006                                                                 nan   \n",
       "vf_p2006_party                                                           nan   \n",
       "vf_p2005                                                                 nan   \n",
       "vf_p2005_party                                                           nan   \n",
       "vf_p2004                                                                   Y   \n",
       "vf_p2004_party                                                             R   \n",
       "vf_p2003                                                                 nan   \n",
       "vf_p2003_party                                                           nan   \n",
       "vf_p2002                                                                   Y   \n",
       "vf_p2002_party                                                             R   \n",
       "vf_p2001                                                                 nan   \n",
       "vf_p2001_party                                                           nan   \n",
       "vf_p2000                                                                   Y   \n",
       "vf_p2000_party                                                             R   \n",
       "vf_m2019                                                                 nan   \n",
       "vf_m2018                                                                 nan   \n",
       "vf_m2017                                                                 nan   \n",
       "vf_m2016                                                                 nan   \n",
       "vf_m2015                                                                 nan   \n",
       "vf_m2014                                                                 nan   \n",
       "vf_m2013                                                                 nan   \n",
       "vf_m2012                                                                 nan   \n",
       "vf_m2011                                                                 nan   \n",
       "vf_m2010                                                                 nan   \n",
       "vf_m2009                                                                 nan   \n",
       "vf_m2008                                                                 nan   \n",
       "vf_m2007                                                                 nan   \n",
       "vf_m2006                                                                 nan   \n",
       "vf_m2005                                                                 nan   \n",
       "vf_m2004                                                                 nan   \n",
       "vf_m2003                                                                 nan   \n",
       "vf_m2002                                                                 nan   \n",
       "vf_m2001                                                                 nan   \n",
       "vf_m2000                                                                 nan   \n",
       "vf_pp2020                                                                nan   \n",
       "vf_pp2020_party                                                          nan   \n",
       "vf_pp2016                                                                nan   \n",
       "vf_pp2016_party                                                          nan   \n",
       "vf_pp2012                                                                nan   \n",
       "vf_pp2012_party                                                          nan   \n",
       "vf_pp2008                                                                  Y   \n",
       "vf_pp2008_party                                                          nan   \n",
       "vf_pp2004                                                                nan   \n",
       "vf_pp2004_party                                                          nan   \n",
       "vf_pp2000                                                                nan   \n",
       "vf_pp2000_party                                                          nan   \n",
       "tsmart_partisan_score                                                    1.2   \n",
       "tsmart_presidential_general_turnout_score                               98.4   \n",
       "tsmart_midterm_general_turnout_score                                    96.6   \n",
       "tsmart_midterm_general_enthusiasm_score                                 86.6   \n",
       "tsmart_offyear_general_turnout_score                                    89.8   \n",
       "tsmart_presidential_primary_turnout_score                               92.0   \n",
       "tsmart_non_presidential_primary_turnout_score                           76.3   \n",
       "voterbase_age                                                           62.0   \n",
       "voterbase_gender                                                      Female   \n",
       "voterbase_race                                                     Caucasian   \n",
       "voterbase_marital_status                                             Married   \n",
       "vf_voter_status                                                       Active   \n",
       "voterbase_deceased_flag                                                  nan   \n",
       "deceased_flag_date_of_death                                              nan   \n",
       "voterbase_mailable_flag                                                  Yes   \n",
       "vf_missing_occupancy_flag                                                nan   \n",
       "vf_absentee_status                                                       nan   \n",
       "vf_early_voter_status                                                    nan   \n",
       "vf_pav                                                                   nan   \n",
       "\n",
       "                                                                    232322  \\\n",
       "voterbase_id                                               OH-000002123672   \n",
       "tsmart_city                                                    PAINESVILLE   \n",
       "tsmart_state                                                            OH   \n",
       "tsmart_zip                                                           44077   \n",
       "vf_reg_cass_city                                               PAINESVILLE   \n",
       "vf_reg_cass_state                                                       OH   \n",
       "vf_reg_cass_zip                                                      44077   \n",
       "vf_registration_date                                            19640917.0   \n",
       "vf_earliest_registration_date                                   19640917.0   \n",
       "vf_party                                                      Unaffiliated   \n",
       "vf_county_code                                                          85   \n",
       "vf_county_name                                                        LAKE   \n",
       "vf_cd                                                                   14   \n",
       "vf_sd                                                                   18   \n",
       "vf_hd                                                                   61   \n",
       "vf_township                                                    PAINESVILLE   \n",
       "vf_ward                                                                nan   \n",
       "vf_precinct_id                                                       43AGR   \n",
       "vf_precinct_name                                 PAINESVILLE TWP I (43AGR)   \n",
       "vf_national_precinct_code                                    OH_LAKE_43AGR   \n",
       "vf_county_council                                                      nan   \n",
       "vf_city_council                                                        nan   \n",
       "vf_municipal_district                                                  nan   \n",
       "vf_school_district                               RIVERSIDE LOCAL SD (LAKE)   \n",
       "vf_judicial_district                                                   nan   \n",
       "reg_census_id                                            390852000000000.0   \n",
       "reg_dma                                                              510.0   \n",
       "reg_dma_name                                   Cleveland-Akron (Canton) OH   \n",
       "reg_place                                                              nan   \n",
       "reg_place_name                                                         nan   \n",
       "tsmart_county_code                                                    85.0   \n",
       "tsmart_county_name                                                    LAKE   \n",
       "tsmart_cd                                                             14.0   \n",
       "tsmart_sd                                                             18.0   \n",
       "tsmart_hd                                                             61.0   \n",
       "tsmart_township                                                PAINESVILLE   \n",
       "tsmart_ward                                                            nan   \n",
       "tsmart_precinct_id                                                   43AGR   \n",
       "tsmart_precinct_name                             PAINESVILLE TWP I (43AGR)   \n",
       "tsmart_county_council                                                  nan   \n",
       "tsmart_city_council                                                    nan   \n",
       "tsmart_municipal_district                                              nan   \n",
       "tsmart_school_district                           RIVERSIDE LOCAL SD (LAKE)   \n",
       "tsmart_judicial_district                                               nan   \n",
       "tsmart_census_id                                         390852000000000.0   \n",
       "tsmart_dma                                                           510.0   \n",
       "tsmart_dma_name                                Cleveland-Akron (Canton) OH   \n",
       "tsmart_place                                                           nan   \n",
       "tsmart_place_name                                                      nan   \n",
       "vf_g2018                                                                 Y   \n",
       "vf_g2017                                                                 Y   \n",
       "vf_g2016                                                                 Y   \n",
       "vf_g2015                                                                 Y   \n",
       "vf_g2014                                                                 Y   \n",
       "vf_g2013                                                                 Y   \n",
       "vf_g2012                                                                 Y   \n",
       "vf_g2011                                                                 Y   \n",
       "vf_g2010                                                                 Y   \n",
       "vf_g2009                                                                 Y   \n",
       "vf_g2008                                                                 Y   \n",
       "vf_g2007                                                               nan   \n",
       "vf_g2006                                                                 Y   \n",
       "vf_g2005                                                                 Y   \n",
       "vf_g2004                                                                 Y   \n",
       "vf_g2003                                                                 Y   \n",
       "vf_g2002                                                                 Y   \n",
       "vf_g2001                                                                 Y   \n",
       "vf_g2000                                                                 Y   \n",
       "vf_p2019                                                               nan   \n",
       "vf_p2019_party                                                         nan   \n",
       "vf_p2018                                                                 Y   \n",
       "vf_p2018_party                                                           D   \n",
       "vf_p2017                                                               nan   \n",
       "vf_p2017_party                                                         nan   \n",
       "vf_p2016                                                                 Y   \n",
       "vf_p2016_party                                                           D   \n",
       "vf_p2015                                                               nan   \n",
       "vf_p2015_party                                                         nan   \n",
       "vf_p2014                                                                 Y   \n",
       "vf_p2014_party                                                           D   \n",
       "vf_p2013                                                               nan   \n",
       "vf_p2013_party                                                         nan   \n",
       "vf_p2012                                                                 Y   \n",
       "vf_p2012_party                                                           D   \n",
       "vf_p2011                                                                 Y   \n",
       "vf_p2011_party                                                         nan   \n",
       "vf_p2010                                                                 Y   \n",
       "vf_p2010_party                                                           D   \n",
       "vf_p2009                                                               nan   \n",
       "vf_p2009_party                                                         nan   \n",
       "vf_p2008                                                                 Y   \n",
       "vf_p2008_party                                                           D   \n",
       "vf_p2007                                                               nan   \n",
       "vf_p2007_party                                                         nan   \n",
       "vf_p2006                                                                 Y   \n",
       "vf_p2006_party                                                           D   \n",
       "vf_p2005                                                                 Y   \n",
       "vf_p2005_party                                                         nan   \n",
       "vf_p2004                                                                 Y   \n",
       "vf_p2004_party                                                           D   \n",
       "vf_p2003                                                                 Y   \n",
       "vf_p2003_party                                                         nan   \n",
       "vf_p2002                                                               nan   \n",
       "vf_p2002_party                                                         nan   \n",
       "vf_p2001                                                               nan   \n",
       "vf_p2001_party                                                         nan   \n",
       "vf_p2000                                                                 Y   \n",
       "vf_p2000_party                                                           D   \n",
       "vf_m2019                                                               nan   \n",
       "vf_m2018                                                               nan   \n",
       "vf_m2017                                                               nan   \n",
       "vf_m2016                                                               nan   \n",
       "vf_m2015                                                               nan   \n",
       "vf_m2014                                                               nan   \n",
       "vf_m2013                                                               nan   \n",
       "vf_m2012                                                               nan   \n",
       "vf_m2011                                                               nan   \n",
       "vf_m2010                                                               nan   \n",
       "vf_m2009                                                               nan   \n",
       "vf_m2008                                                               nan   \n",
       "vf_m2007                                                               nan   \n",
       "vf_m2006                                                               nan   \n",
       "vf_m2005                                                               nan   \n",
       "vf_m2004                                                               nan   \n",
       "vf_m2003                                                               nan   \n",
       "vf_m2002                                                               nan   \n",
       "vf_m2001                                                               nan   \n",
       "vf_m2000                                                               nan   \n",
       "vf_pp2020                                                              nan   \n",
       "vf_pp2020_party                                                        nan   \n",
       "vf_pp2016                                                              nan   \n",
       "vf_pp2016_party                                                        nan   \n",
       "vf_pp2012                                                              nan   \n",
       "vf_pp2012_party                                                        nan   \n",
       "vf_pp2008                                                                Y   \n",
       "vf_pp2008_party                                                        nan   \n",
       "vf_pp2004                                                              nan   \n",
       "vf_pp2004_party                                                        nan   \n",
       "vf_pp2000                                                              nan   \n",
       "vf_pp2000_party                                                        nan   \n",
       "tsmart_partisan_score                                                 99.2   \n",
       "tsmart_presidential_general_turnout_score                             97.5   \n",
       "tsmart_midterm_general_turnout_score                                  95.7   \n",
       "tsmart_midterm_general_enthusiasm_score                               90.6   \n",
       "tsmart_offyear_general_turnout_score                                  86.3   \n",
       "tsmart_presidential_primary_turnout_score                             90.4   \n",
       "tsmart_non_presidential_primary_turnout_score                         73.1   \n",
       "voterbase_age                                                         82.0   \n",
       "voterbase_gender                                                    Female   \n",
       "voterbase_race                                                   Caucasian   \n",
       "voterbase_marital_status                                           Married   \n",
       "vf_voter_status                                                     Active   \n",
       "voterbase_deceased_flag                                                nan   \n",
       "deceased_flag_date_of_death                                            nan   \n",
       "voterbase_mailable_flag                                                Yes   \n",
       "vf_missing_occupancy_flag                                              nan   \n",
       "vf_absentee_status                                                     nan   \n",
       "vf_early_voter_status                                                  nan   \n",
       "vf_pav                                                                 nan   \n",
       "\n",
       "                                                                    735524  \\\n",
       "voterbase_id                                                   OH-10557322   \n",
       "tsmart_city                                                          SHADE   \n",
       "tsmart_state                                                            OH   \n",
       "tsmart_zip                                                           45776   \n",
       "vf_reg_cass_city                                                     SHADE   \n",
       "vf_reg_cass_state                                                       OH   \n",
       "vf_reg_cass_zip                                                      45776   \n",
       "vf_registration_date                                            20121106.0   \n",
       "vf_earliest_registration_date                                   20121106.0   \n",
       "vf_party                                                      Unaffiliated   \n",
       "vf_county_code                                                           9   \n",
       "vf_county_name                                                      ATHENS   \n",
       "vf_cd                                                                   15   \n",
       "vf_sd                                                                   30   \n",
       "vf_hd                                                                   94   \n",
       "vf_township                                                           LODI   \n",
       "vf_ward                                                                nan   \n",
       "vf_precinct_id                                                       05ABY   \n",
       "vf_precinct_name                                     LODI TOWNSHIP (05ABY)   \n",
       "vf_national_precinct_code                                  OH_ATHENS_05ABY   \n",
       "vf_county_council                                                      nan   \n",
       "vf_city_council                                                        nan   \n",
       "vf_municipal_district                                                  nan   \n",
       "vf_school_district                             ALEXANDER LOCAL SD (ATHENS)   \n",
       "vf_judicial_district                                                   nan   \n",
       "reg_census_id                                            390100000000000.0   \n",
       "reg_dma                                                              535.0   \n",
       "reg_dma_name                                                   Columbus OH   \n",
       "reg_place                                                              nan   \n",
       "reg_place_name                                                         nan   \n",
       "tsmart_county_code                                                     9.0   \n",
       "tsmart_county_name                                                  ATHENS   \n",
       "tsmart_cd                                                             15.0   \n",
       "tsmart_sd                                                             30.0   \n",
       "tsmart_hd                                                             94.0   \n",
       "tsmart_township                                                       LODI   \n",
       "tsmart_ward                                                            nan   \n",
       "tsmart_precinct_id                                                   05ABY   \n",
       "tsmart_precinct_name                                 LODI TOWNSHIP (05ABY)   \n",
       "tsmart_county_council                                                  nan   \n",
       "tsmart_city_council                                                    nan   \n",
       "tsmart_municipal_district                                              nan   \n",
       "tsmart_school_district                         ALEXANDER LOCAL SD (ATHENS)   \n",
       "tsmart_judicial_district                                               nan   \n",
       "tsmart_census_id                                         390100000000000.0   \n",
       "tsmart_dma                                                           535.0   \n",
       "tsmart_dma_name                                                Columbus OH   \n",
       "tsmart_place                                                           nan   \n",
       "tsmart_place_name                                                      nan   \n",
       "vf_g2018                                                               nan   \n",
       "vf_g2017                                                               nan   \n",
       "vf_g2016                                                                 Y   \n",
       "vf_g2015                                                                 Y   \n",
       "vf_g2014                                                               nan   \n",
       "vf_g2013                                                               nan   \n",
       "vf_g2012                                                                 Y   \n",
       "vf_g2011                                                               nan   \n",
       "vf_g2010                                                               nan   \n",
       "vf_g2009                                                               nan   \n",
       "vf_g2008                                                               nan   \n",
       "vf_g2007                                                               nan   \n",
       "vf_g2006                                                               nan   \n",
       "vf_g2005                                                               nan   \n",
       "vf_g2004                                                               nan   \n",
       "vf_g2003                                                               nan   \n",
       "vf_g2002                                                               nan   \n",
       "vf_g2001                                                               nan   \n",
       "vf_g2000                                                               nan   \n",
       "vf_p2019                                                               nan   \n",
       "vf_p2019_party                                                         nan   \n",
       "vf_p2018                                                               nan   \n",
       "vf_p2018_party                                                         nan   \n",
       "vf_p2017                                                               nan   \n",
       "vf_p2017_party                                                         nan   \n",
       "vf_p2016                                                                 Y   \n",
       "vf_p2016_party                                                           D   \n",
       "vf_p2015                                                               nan   \n",
       "vf_p2015_party                                                         nan   \n",
       "vf_p2014                                                               nan   \n",
       "vf_p2014_party                                                         nan   \n",
       "vf_p2013                                                               nan   \n",
       "vf_p2013_party                                                         nan   \n",
       "vf_p2012                                                               nan   \n",
       "vf_p2012_party                                                         nan   \n",
       "vf_p2011                                                               nan   \n",
       "vf_p2011_party                                                         nan   \n",
       "vf_p2010                                                               nan   \n",
       "vf_p2010_party                                                         nan   \n",
       "vf_p2009                                                               nan   \n",
       "vf_p2009_party                                                         nan   \n",
       "vf_p2008                                                               nan   \n",
       "vf_p2008_party                                                         nan   \n",
       "vf_p2007                                                               nan   \n",
       "vf_p2007_party                                                         nan   \n",
       "vf_p2006                                                               nan   \n",
       "vf_p2006_party                                                         nan   \n",
       "vf_p2005                                                               nan   \n",
       "vf_p2005_party                                                         nan   \n",
       "vf_p2004                                                               nan   \n",
       "vf_p2004_party                                                         nan   \n",
       "vf_p2003                                                               nan   \n",
       "vf_p2003_party                                                         nan   \n",
       "vf_p2002                                                               nan   \n",
       "vf_p2002_party                                                         nan   \n",
       "vf_p2001                                                               nan   \n",
       "vf_p2001_party                                                         nan   \n",
       "vf_p2000                                                               nan   \n",
       "vf_p2000_party                                                         nan   \n",
       "vf_m2019                                                               nan   \n",
       "vf_m2018                                                               nan   \n",
       "vf_m2017                                                               nan   \n",
       "vf_m2016                                                               nan   \n",
       "vf_m2015                                                               nan   \n",
       "vf_m2014                                                               nan   \n",
       "vf_m2013                                                               nan   \n",
       "vf_m2012                                                               nan   \n",
       "vf_m2011                                                               nan   \n",
       "vf_m2010                                                               nan   \n",
       "vf_m2009                                                               nan   \n",
       "vf_m2008                                                               nan   \n",
       "vf_m2007                                                               nan   \n",
       "vf_m2006                                                               nan   \n",
       "vf_m2005                                                               nan   \n",
       "vf_m2004                                                               nan   \n",
       "vf_m2003                                                               nan   \n",
       "vf_m2002                                                               nan   \n",
       "vf_m2001                                                               nan   \n",
       "vf_m2000                                                               nan   \n",
       "vf_pp2020                                                              nan   \n",
       "vf_pp2020_party                                                        nan   \n",
       "vf_pp2016                                                              nan   \n",
       "vf_pp2016_party                                                        nan   \n",
       "vf_pp2012                                                              nan   \n",
       "vf_pp2012_party                                                        nan   \n",
       "vf_pp2008                                                              nan   \n",
       "vf_pp2008_party                                                        nan   \n",
       "vf_pp2004                                                              nan   \n",
       "vf_pp2004_party                                                        nan   \n",
       "vf_pp2000                                                              nan   \n",
       "vf_pp2000_party                                                        nan   \n",
       "tsmart_partisan_score                                                 99.3   \n",
       "tsmart_presidential_general_turnout_score                             85.7   \n",
       "tsmart_midterm_general_turnout_score                                  31.3   \n",
       "tsmart_midterm_general_enthusiasm_score                               22.9   \n",
       "tsmart_offyear_general_turnout_score                                  11.1   \n",
       "tsmart_presidential_primary_turnout_score                             38.7   \n",
       "tsmart_non_presidential_primary_turnout_score                          6.3   \n",
       "voterbase_age                                                         30.0   \n",
       "voterbase_gender                                                      Male   \n",
       "voterbase_race                                                   Caucasian   \n",
       "voterbase_marital_status                                         Unmarried   \n",
       "vf_voter_status                                                     Active   \n",
       "voterbase_deceased_flag                                                nan   \n",
       "deceased_flag_date_of_death                                            nan   \n",
       "voterbase_mailable_flag                                                Yes   \n",
       "vf_missing_occupancy_flag                                              nan   \n",
       "vf_absentee_status                                                     nan   \n",
       "vf_early_voter_status                                                  nan   \n",
       "vf_pav                                                                 nan   \n",
       "\n",
       "                                                                         544988  \n",
       "voterbase_id                                                    OH-000004984672  \n",
       "tsmart_city                                                              CAMDEN  \n",
       "tsmart_state                                                                 OH  \n",
       "tsmart_zip                                                                45311  \n",
       "vf_reg_cass_city                                                         CAMDEN  \n",
       "vf_reg_cass_state                                                            OH  \n",
       "vf_reg_cass_zip                                                           45311  \n",
       "vf_registration_date                                                 20020515.0  \n",
       "vf_earliest_registration_date                                        20020515.0  \n",
       "vf_party                                                           Unaffiliated  \n",
       "vf_county_code                                                              135  \n",
       "vf_county_name                                                           PREBLE  \n",
       "vf_cd                                                                         8  \n",
       "vf_sd                                                                         5  \n",
       "vf_hd                                                                        43  \n",
       "vf_township                                                              GRATIS  \n",
       "vf_ward                                                                     nan  \n",
       "vf_precinct_id                                                            68AAN  \n",
       "vf_precinct_name                                           GRATIS NORTH (68AAN)  \n",
       "vf_national_precinct_code                                       OH_PREBLE_68AAN  \n",
       "vf_county_council                                                           nan  \n",
       "vf_city_council                                                             nan  \n",
       "vf_municipal_district                                                       nan  \n",
       "vf_school_district                             PREBLE SHAWNEE LOCAL SD (PREBLE)  \n",
       "vf_judicial_district                                                        nan  \n",
       "reg_census_id                                                 391355000000000.0  \n",
       "reg_dma                                                                   542.0  \n",
       "reg_dma_name                                                          Dayton OH  \n",
       "reg_place                                                                   nan  \n",
       "reg_place_name                                                              nan  \n",
       "tsmart_county_code                                                        135.0  \n",
       "tsmart_county_name                                                       PREBLE  \n",
       "tsmart_cd                                                                   8.0  \n",
       "tsmart_sd                                                                   5.0  \n",
       "tsmart_hd                                                                  43.0  \n",
       "tsmart_township                                                          GRATIS  \n",
       "tsmart_ward                                                                 nan  \n",
       "tsmart_precinct_id                                                        68AAN  \n",
       "tsmart_precinct_name                                       GRATIS NORTH (68AAN)  \n",
       "tsmart_county_council                                                       nan  \n",
       "tsmart_city_council                                                         nan  \n",
       "tsmart_municipal_district                                                   nan  \n",
       "tsmart_school_district                         PREBLE SHAWNEE LOCAL SD (PREBLE)  \n",
       "tsmart_judicial_district                                                    nan  \n",
       "tsmart_census_id                                              391355000000000.0  \n",
       "tsmart_dma                                                                542.0  \n",
       "tsmart_dma_name                                                       Dayton OH  \n",
       "tsmart_place                                                                nan  \n",
       "tsmart_place_name                                                           nan  \n",
       "vf_g2018                                                                      Y  \n",
       "vf_g2017                                                                      Y  \n",
       "vf_g2016                                                                      Y  \n",
       "vf_g2015                                                                      Y  \n",
       "vf_g2014                                                                    nan  \n",
       "vf_g2013                                                                    nan  \n",
       "vf_g2012                                                                      Y  \n",
       "vf_g2011                                                                      Y  \n",
       "vf_g2010                                                                      Y  \n",
       "vf_g2009                                                                      Y  \n",
       "vf_g2008                                                                      Y  \n",
       "vf_g2007                                                                    nan  \n",
       "vf_g2006                                                                    nan  \n",
       "vf_g2005                                                                    nan  \n",
       "vf_g2004                                                                      Y  \n",
       "vf_g2003                                                                    nan  \n",
       "vf_g2002                                                                    nan  \n",
       "vf_g2001                                                                    nan  \n",
       "vf_g2000                                                                    nan  \n",
       "vf_p2019                                                                    nan  \n",
       "vf_p2019_party                                                              nan  \n",
       "vf_p2018                                                                    nan  \n",
       "vf_p2018_party                                                              nan  \n",
       "vf_p2017                                                                      Y  \n",
       "vf_p2017_party                                                              nan  \n",
       "vf_p2016                                                                      Y  \n",
       "vf_p2016_party                                                                R  \n",
       "vf_p2015                                                                    nan  \n",
       "vf_p2015_party                                                              nan  \n",
       "vf_p2014                                                                    nan  \n",
       "vf_p2014_party                                                              nan  \n",
       "vf_p2013                                                                    nan  \n",
       "vf_p2013_party                                                              nan  \n",
       "vf_p2012                                                                    nan  \n",
       "vf_p2012_party                                                              nan  \n",
       "vf_p2011                                                                    nan  \n",
       "vf_p2011_party                                                              nan  \n",
       "vf_p2010                                                                    nan  \n",
       "vf_p2010_party                                                              nan  \n",
       "vf_p2009                                                                    nan  \n",
       "vf_p2009_party                                                              nan  \n",
       "vf_p2008                                                                      Y  \n",
       "vf_p2008_party                                                                D  \n",
       "vf_p2007                                                                    nan  \n",
       "vf_p2007_party                                                              nan  \n",
       "vf_p2006                                                                      Y  \n",
       "vf_p2006_party                                                              nan  \n",
       "vf_p2005                                                                    nan  \n",
       "vf_p2005_party                                                              nan  \n",
       "vf_p2004                                                                    nan  \n",
       "vf_p2004_party                                                              nan  \n",
       "vf_p2003                                                                    nan  \n",
       "vf_p2003_party                                                              nan  \n",
       "vf_p2002                                                                    nan  \n",
       "vf_p2002_party                                                              nan  \n",
       "vf_p2001                                                                    nan  \n",
       "vf_p2001_party                                                              nan  \n",
       "vf_p2000                                                                    nan  \n",
       "vf_p2000_party                                                              nan  \n",
       "vf_m2019                                                                    nan  \n",
       "vf_m2018                                                                    nan  \n",
       "vf_m2017                                                                    nan  \n",
       "vf_m2016                                                                    nan  \n",
       "vf_m2015                                                                    nan  \n",
       "vf_m2014                                                                    nan  \n",
       "vf_m2013                                                                    nan  \n",
       "vf_m2012                                                                    nan  \n",
       "vf_m2011                                                                    nan  \n",
       "vf_m2010                                                                    nan  \n",
       "vf_m2009                                                                    nan  \n",
       "vf_m2008                                                                    nan  \n",
       "vf_m2007                                                                    nan  \n",
       "vf_m2006                                                                    nan  \n",
       "vf_m2005                                                                    nan  \n",
       "vf_m2004                                                                    nan  \n",
       "vf_m2003                                                                    nan  \n",
       "vf_m2002                                                                    nan  \n",
       "vf_m2001                                                                    nan  \n",
       "vf_m2000                                                                    nan  \n",
       "vf_pp2020                                                                   nan  \n",
       "vf_pp2020_party                                                             nan  \n",
       "vf_pp2016                                                                   nan  \n",
       "vf_pp2016_party                                                             nan  \n",
       "vf_pp2012                                                                   nan  \n",
       "vf_pp2012_party                                                             nan  \n",
       "vf_pp2008                                                                     Y  \n",
       "vf_pp2008_party                                                             nan  \n",
       "vf_pp2004                                                                   nan  \n",
       "vf_pp2004_party                                                             nan  \n",
       "vf_pp2000                                                                   nan  \n",
       "vf_pp2000_party                                                             nan  \n",
       "tsmart_partisan_score                                                       9.7  \n",
       "tsmart_presidential_general_turnout_score                                  95.9  \n",
       "tsmart_midterm_general_turnout_score                                       81.1  \n",
       "tsmart_midterm_general_enthusiasm_score                                    49.1  \n",
       "tsmart_offyear_general_turnout_score                                       62.7  \n",
       "tsmart_presidential_primary_turnout_score                                  56.0  \n",
       "tsmart_non_presidential_primary_turnout_score                              27.4  \n",
       "voterbase_age                                                              56.0  \n",
       "voterbase_gender                                                           Male  \n",
       "voterbase_race                                                        Caucasian  \n",
       "voterbase_marital_status                                                Married  \n",
       "vf_voter_status                                                          Active  \n",
       "voterbase_deceased_flag                                                     nan  \n",
       "deceased_flag_date_of_death                                                 nan  \n",
       "voterbase_mailable_flag                                                     Yes  \n",
       "vf_missing_occupancy_flag                                                   nan  \n",
       "vf_absentee_status                                                          nan  \n",
       "vf_early_voter_status                                                       nan  \n",
       "vf_pav                                                                      nan  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vf.sample(5).T.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the distributions of some key demographic and political variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    760085.000000\n",
       "mean         49.902715\n",
       "std          18.508834\n",
       "min          17.000000\n",
       "25%          34.000000\n",
       "50%          50.000000\n",
       "75%          64.000000\n",
       "max         115.000000\n",
       "Name: voterbase_age, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vf.voterbase_age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for the seaborn distribution plot, we need to limit to non-null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb84e99ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(vf.voterbase_age[vf.voterbase_age.notnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling in null values with \"Missing\" for calculating proportions across the full population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Proportions by gender\n",
      "Female     0.486867\n",
      "Male       0.453746\n",
      "Unknown    0.059387\n",
      "Name: voterbase_gender, dtype: float64\n",
      "\n",
      "Proportions by race\n",
      "Caucasian           0.876057\n",
      "African-American    0.092105\n",
      "Uncoded             0.022166\n",
      "Hispanic            0.006036\n",
      "Asian               0.003513\n",
      "Native American     0.000124\n",
      "Name: voterbase_race, dtype: float64\n",
      "\n",
      "Proportions by marital_status\n",
      "Unmarried    0.503740\n",
      "Married      0.442940\n",
      "Unknown      0.053321\n",
      "Name: voterbase_marital_status, dtype: float64\n",
      "\n",
      "Proportions by deceased_flag\n",
      "Missing     0.999471\n",
      "Deceased    0.000529\n",
      "Name: voterbase_deceased_flag, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in ['gender','race','marital_status','deceased_flag']:\n",
    "    print('\\nProportions by {}'.format(i))\n",
    "    print(vf['voterbase_{}'.format(i)].fillna('Missing').value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Proportions by voter_status\n",
      "Active      0.899237\n",
      "Inactive    0.100763\n",
      "Name: vf_voter_status, dtype: float64\n",
      "\n",
      "Proportions by absentee_status\n",
      "Missing    0.864488\n",
      "Yes        0.135512\n",
      "Name: vf_absentee_status, dtype: float64\n",
      "\n",
      "Proportions by g2018\n",
      "Y          0.582862\n",
      "Missing    0.403435\n",
      "Z          0.008625\n",
      "R          0.002542\n",
      "B          0.001558\n",
      "F          0.000950\n",
      "S          0.000028\n",
      "Name: vf_g2018, dtype: float64\n",
      "\n",
      "Proportions by g2016\n",
      "Y          0.669586\n",
      "Missing    0.294211\n",
      "Z          0.026668\n",
      "R          0.004481\n",
      "B          0.002933\n",
      "F          0.002094\n",
      "S          0.000028\n",
      "Name: vf_g2016, dtype: float64\n",
      "\n",
      "Proportions by p2018_party\n",
      "Missing    0.794933\n",
      "R          0.111803\n",
      "D          0.092745\n",
      "G          0.000520\n",
      "Name: vf_p2018_party, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in ['voter_status','absentee_status','g2018', 'g2016', 'p2018_party']:\n",
    "    print('\\nProportions by {}'.format(i))\n",
    "    print(vf['vf_{}'.format(i)].fillna('Missing').value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    760097.000000\n",
      "mean         48.399587\n",
      "std          38.356573\n",
      "min           0.500000\n",
      "25%           9.900000\n",
      "50%          41.400000\n",
      "75%          93.300000\n",
      "max          99.900000\n",
      "Name: tsmart_partisan_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb80935128>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(vf.tsmart_partisan_score.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(vf.tsmart_partisan_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    760097.000000\n",
      "mean         74.766173\n",
      "std          26.097281\n",
      "min           1.100000\n",
      "25%          55.000000\n",
      "50%          87.600000\n",
      "75%          96.600000\n",
      "max          99.400000\n",
      "Name: tsmart_presidential_general_turnout_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb808c6d68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(vf.tsmart_presidential_general_turnout_score.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(vf.tsmart_presidential_general_turnout_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    760097.000000\n",
      "mean         53.346607\n",
      "std          34.278661\n",
      "min           0.400000\n",
      "25%          20.100000\n",
      "50%          58.400000\n",
      "75%          87.400000\n",
      "max          97.400000\n",
      "Name: tsmart_midterm_general_turnout_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb80ab0400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(vf.tsmart_midterm_general_turnout_score.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(vf.tsmart_midterm_general_turnout_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: How do the 2016 and 2018 electorates vary in terms of demographics from the overall registered voter populations?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flagging electorates in each year as well as those eligible (based on earliest registration date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "vf['voted16'] = (vf.vf_g2016.notnull())\n",
    "vf['eligible16'] = (vf.vf_earliest_registration_date <= 20161108)\n",
    "vf['voted18'] = (vf.vf_g2018.notnull())\n",
    "vf['eligible18'] = (vf.vf_earliest_registration_date <= 20181106)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating distributions for each demographic category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "By gender:\n",
      "          voted16  eligible16   voted18  eligible18\n",
      "Female   0.512006    0.495024  0.503062    0.486324\n",
      "Male     0.451911    0.457755  0.460482    0.455502\n",
      "Unknown  0.036082    0.047222  0.036456    0.058174\n",
      "\n",
      "By race:\n",
      "                   voted16  eligible16   voted18  eligible18\n",
      "African-American  0.077386    0.090700  0.073003    0.090368\n",
      "Asian             0.003016    0.003138  0.003231    0.003490\n",
      "Caucasian         0.897409    0.880237  0.903080    0.877808\n",
      "Hispanic          0.004153    0.005834  0.003189    0.006152\n",
      "Native American   0.000110    0.000124  0.000106    0.000124\n",
      "Uncoded           0.017927    0.019966  0.017391    0.022058\n",
      "\n",
      "By marital_status:\n",
      "            voted16  eligible16   voted18  eligible18\n",
      "Married    0.525248    0.460207  0.546172    0.445340\n",
      "Unknown    0.040012    0.045799  0.035949    0.044108\n",
      "Unmarried  0.434740    0.493994  0.417879    0.510551\n"
     ]
    }
   ],
   "source": [
    "for i in ['gender','race','marital_status']:\n",
    "    print('\\nBy {}:'.format(i))\n",
    "    x = pd.DataFrame()\n",
    "    for j in ['voted16','eligible16','voted18','eligible18']:\n",
    "        x[j] = vf.loc[vf[j],'voterbase_{}'.format(i)].value_counts(normalize=True).sort_index()\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating distributions by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average age (current) for voted16 = 53.9\n",
      "Average age (current) for eligible16 = 51.1\n",
      "Average age (current) for voted18 = 54.7\n",
      "Average age (current) for eligible18 = 49.6\n"
     ]
    }
   ],
   "source": [
    "for j in ['voted16','eligible16','voted18','eligible18']:\n",
    "    print('Average age (current) for {} = {}'.format(j, np.round(vf[vf[j]].voterbase_age.mean(),1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Compare partisanship scores across Dem primary voters in 2018, Rep primary voters, and those who didn't vote in a primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    70495.000000\n",
      "mean        79.656075\n",
      "std         31.400597\n",
      "min          0.700000\n",
      "25%         64.700000\n",
      "50%         98.800000\n",
      "75%         99.300000\n",
      "max         99.900000\n",
      "Name: tsmart_partisan_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb80f87d68>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dem_voter_scores = vf[vf.vf_p2018_party=='D'].tsmart_partisan_score\n",
    "print(dem_voter_scores.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(dem_voter_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    84981.000000\n",
      "mean        14.150463\n",
      "std         22.809783\n",
      "min          0.600000\n",
      "25%          1.300000\n",
      "50%          2.300000\n",
      "75%         16.600000\n",
      "max         99.700000\n",
      "Name: tsmart_partisan_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb80d4d710>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rep_voter_scores = vf[vf.vf_p2018_party=='R'].tsmart_partisan_score\n",
    "print(rep_voter_scores.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(rep_voter_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    604226.000000\n",
      "mean         49.563015\n",
      "std          37.163721\n",
      "min           0.500000\n",
      "25%          12.700000\n",
      "50%          44.400000\n",
      "75%          91.900000\n",
      "max          99.900000\n",
      "Name: tsmart_partisan_score, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb80f63b00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_voter_scores = vf[vf.vf_p2018_party.isnull()].tsmart_partisan_score\n",
    "print(non_voter_scores.describe())\n",
    "f, ax = plt.subplots(1, figsize=(12,6))\n",
    "sns.distplot(non_voter_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: How are partisanship scores correlated with demographics?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting ages into groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "vf['voterbase_age_group'] = pd.qcut(vf.voterbase_age,5).astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describing scores by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "By gender:\n",
      "              Female           Male       Unknown\n",
      "count  370066.000000  344891.000000  45140.000000\n",
      "mean       52.479572      41.511753     67.577448\n",
      "std        38.477440      37.359184     33.247574\n",
      "min         0.600000       0.500000      0.600000\n",
      "25%        12.500000       7.100000     38.800000\n",
      "50%        51.900000      27.300000     82.600000\n",
      "75%        95.700000      83.900000     97.100000\n",
      "max        99.900000      99.900000     99.900000\n",
      "\n",
      "By race:\n",
      "       African-American        Asian      Caucasian     Hispanic  \\\n",
      "count      70009.000000  2670.000000  665888.000000  4588.000000   \n",
      "mean          94.498537    60.929775      42.857654    81.939037   \n",
      "std           13.904150    32.205449      36.807006    24.306787   \n",
      "min            1.100000     0.900000       0.500000     0.800000   \n",
      "25%           95.900000    32.500000       7.800000    79.800000   \n",
      "50%           97.900000    68.500000      31.900000    92.100000   \n",
      "75%           99.200000    91.300000      82.100000    96.900000   \n",
      "max           99.900000    99.800000      99.900000    99.800000   \n",
      "\n",
      "       Native American       Uncoded  \n",
      "count        94.000000  16848.000000  \n",
      "mean         66.572340     64.658060  \n",
      "std          33.692932     32.457174  \n",
      "min           1.100000      0.700000  \n",
      "25%          44.400000     38.100000  \n",
      "50%          80.500000     75.400000  \n",
      "75%          95.875000     94.300000  \n",
      "max          99.600000     99.900000  \n",
      "\n",
      "By marital_status:\n",
      "             Married       Unknown      Unmarried\n",
      "count  336677.000000  40529.000000  382891.000000\n",
      "mean       35.453749     55.610923      59.019576\n",
      "std        36.875594     36.652535      36.291559\n",
      "min         0.500000      0.700000       0.600000\n",
      "25%         3.400000     18.900000      22.800000\n",
      "50%        18.500000     58.700000      66.900000\n",
      "75%        66.800000     94.800000      96.000000\n",
      "max        99.900000     99.900000      99.900000\n",
      "\n",
      "By age_group:\n",
      "       (16.999, 31.0]   (31.0, 43.0]   (43.0, 56.0]   (56.0, 67.0]  \\\n",
      "count   160721.000000  144541.000000  160339.000000  147479.000000   \n",
      "mean        57.525450      52.050759      44.032095      44.146287   \n",
      "std         34.984921      36.694011      38.118733      39.520413   \n",
      "min          0.600000       0.500000       0.600000       0.600000   \n",
      "25%         22.400000      15.500000       7.800000       5.200000   \n",
      "50%         63.600000      50.100000      31.200000      30.200000   \n",
      "75%         93.000000      92.900000      90.300000      94.000000   \n",
      "max         99.900000      99.900000      99.800000      99.800000   \n",
      "\n",
      "       (67.0, 115.0]        nan  \n",
      "count  147005.000000  12.000000  \n",
      "mean       43.862363  55.458333  \n",
      "std        40.367439  36.712135  \n",
      "min         0.600000   2.200000  \n",
      "25%         3.100000  27.475000  \n",
      "50%        29.000000  64.850000  \n",
      "75%        95.700000  89.350000  \n",
      "max        99.800000  98.900000  \n"
     ]
    }
   ],
   "source": [
    "for i in ['gender','race','marital_status','age_group']:\n",
    "    print('\\nBy {}:'.format(i))\n",
    "    x = pd.DataFrame()\n",
    "    for j in sorted(vf['voterbase_{}'.format(i)].unique()):\n",
    "        x[j] = vf.loc[vf['voterbase_{}'.format(i)]==j,'tsmart_partisan_score'].describe()\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4: Graph new / updated registrations by month over the past 8 years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting month and year from registration dates and limiting to last 8 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reg_dates = vf.vf_registration_date.astype(str).map(lambda x: x[:6])\n",
    "reg_dates = reg_dates[(reg_dates.astype(float) > 201107)]\n",
    "reg_by_month = reg_dates.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting registrations by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb93546f98>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 25))\n",
    "sns.countplot(y=reg_dates, color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5: How does the population of inactive registrants vary across counties?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating percentage of inactives by county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "inactive_pct_by_county = 100*(vf.vf_voter_status == 'Inactive').astype(int).groupby(\n",
    "                        vf.vf_county_name).mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting across counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb9353ca58>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 25))\n",
    "sns.barplot(x=inactive_pct_by_county, y=inactive_pct_by_county.index, color='b')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}