draft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1700,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from scipy import stats\n",
    "from scipy.stats import ttest_1samp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1701,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2776905057.py:1: DtypeWarning: Columns (359,360,363,364,613,614,617,618) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"../replication-materials-yuanninghuang-1/CECC/CES22_Common.csv\")\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"../replication-materials-yuanninghuang-1/CECC/CES22_Common.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1728,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseid</th>\n",
       "      <th>tookpost</th>\n",
       "      <th>commonweight</th>\n",
       "      <th>commonpostweight</th>\n",
       "      <th>CCEStake</th>\n",
       "      <th>add_confirm</th>\n",
       "      <th>inputzip</th>\n",
       "      <th>birthyr</th>\n",
       "      <th>gender4</th>\n",
       "      <th>gender4_t</th>\n",
       "      <th>educ</th>\n",
       "      <th>race</th>\n",
       "      <th>race_other</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>multrace_1</th>\n",
       "      <th>multrace_2</th>\n",
       "      <th>multrace_3</th>\n",
       "      <th>multrace_4</th>\n",
       "      <th>multrace_5</th>\n",
       "      <th>multrace_8</th>\n",
       "      <th>multrace_97</th>\n",
       "      <th>multrace_98</th>\n",
       "      <th>comptype</th>\n",
       "      <th>votereg</th>\n",
       "      <th>votereg_f</th>\n",
       "      <th>regzip</th>\n",
       "      <th>pid3</th>\n",
       "      <th>pid3_t</th>\n",
       "      <th>pid7</th>\n",
       "      <th>inputstate</th>\n",
       "      <th>region</th>\n",
       "      <th>ccesmodule</th>\n",
       "      <th>CC22_300_1</th>\n",
       "      <th>CC22_300_2</th>\n",
       "      <th>CC22_300_3</th>\n",
       "      <th>CC22_300_4</th>\n",
       "      <th>CC22_300_5</th>\n",
       "      <th>CC22_300a</th>\n",
       "      <th>CC22_300c</th>\n",
       "      <th>CC22_300b_1</th>\n",
       "      <th>CC22_300b_2</th>\n",
       "      <th>CC22_300b_3</th>\n",
       "      <th>CC22_300b_4</th>\n",
       "      <th>CC22_300b_5</th>\n",
       "      <th>CC22_300b_6</th>\n",
       "      <th>CC22_300b_7</th>\n",
       "      <th>CC22_300b_8</th>\n",
       "      <th>CC22_300d_1</th>\n",
       "      <th>CC22_300d_2</th>\n",
       "      <th>CC22_300d_3</th>\n",
       "      <th>CC22_300d_4</th>\n",
       "      <th>CC22_300d_5</th>\n",
       "      <th>CC22_300d_6</th>\n",
       "      <th>CC22_302</th>\n",
       "      <th>CC22_303</th>\n",
       "      <th>CC22_304</th>\n",
       "      <th>CC22_305_1</th>\n",
       "      <th>CC22_305_2</th>\n",
       "      <th>CC22_305_3</th>\n",
       "      <th>CC22_305_4</th>\n",
       "      <th>CC22_305_5</th>\n",
       "      <th>CC22_305_6</th>\n",
       "      <th>CC22_305_7</th>\n",
       "      <th>CC22_305_9</th>\n",
       "      <th>CC22_305_10</th>\n",
       "      <th>CC22_305_11</th>\n",
       "      <th>CC22_305_12</th>\n",
       "      <th>CC22_305_13</th>\n",
       "      <th>CC22_307</th>\n",
       "      <th>CC22_309a_1</th>\n",
       "      <th>CC22_309a_2</th>\n",
       "      <th>CC22_309a_3</th>\n",
       "      <th>CC22_309a_4</th>\n",
       "      <th>CC22_309a_5</th>\n",
       "      <th>CC22_306</th>\n",
       "      <th>CC22_309b_1</th>\n",
       "      <th>CC22_309b_2</th>\n",
       "      <th>CC22_309b_3</th>\n",
       "      <th>CC22_309b_4</th>\n",
       "      <th>CC22_309c_1</th>\n",
       "      <th>CC22_309c_2</th>\n",
       "      <th>CC22_309c_3</th>\n",
       "      <th>CC22_309c_4</th>\n",
       "      <th>CC22_309c_5</th>\n",
       "      <th>CC22_309c_6</th>\n",
       "      <th>CC22_309c_7</th>\n",
       "      <th>CC22_309c_8</th>\n",
       "      <th>CC22_309c_9</th>\n",
       "      <th>CC22_309c_10</th>\n",
       "      <th>CC22_309dx_1</th>\n",
       "      <th>CC22_309dx_2</th>\n",
       "      <th>CC22_309dx_3</th>\n",
       "      <th>CC22_309dx_4</th>\n",
       "      <th>CC22_309dx_5</th>\n",
       "      <th>CC22_309dx_6</th>\n",
       "      <th>CC22_309dx_7</th>\n",
       "      <th>CC22_309dx_8</th>\n",
       "      <th>CC22_309dx_9</th>\n",
       "      <th>CC22_309d_t</th>\n",
       "      <th>CC22_309e</th>\n",
       "      <th>CC22_309f</th>\n",
       "      <th>CC22_310a</th>\n",
       "      <th>CC22_310b</th>\n",
       "      <th>CC22_310c</th>\n",
       "      <th>CC22_310d</th>\n",
       "      <th>CC22_311a</th>\n",
       "      <th>CC22_311b</th>\n",
       "      <th>CC22_311c</th>\n",
       "      <th>CC22_311d</th>\n",
       "      <th>CC22_320a</th>\n",
       "      <th>CC22_320b</th>\n",
       "      <th>CC22_320c</th>\n",
       "      <th>CC22_320d</th>\n",
       "      <th>CC22_320e</th>\n",
       "      <th>CC22_320f</th>\n",
       "      <th>CC22_320g</th>\n",
       "      <th>CC22_320h</th>\n",
       "      <th>cit1</th>\n",
       "      <th>immstat</th>\n",
       "      <th>CC22_321_1</th>\n",
       "      <th>CC22_321_2</th>\n",
       "      <th>CC22_321_3</th>\n",
       "      <th>CC22_321_4</th>\n",
       "      <th>CC22_321_5</th>\n",
       "      <th>CC22_321_6</th>\n",
       "      <th>CC22_321_7</th>\n",
       "      <th>CC22_321_8</th>\n",
       "      <th>CC22_327a</th>\n",
       "      <th>CC22_327b</th>\n",
       "      <th>CC22_327c</th>\n",
       "      <th>CC22_327d</th>\n",
       "      <th>CC22_330a</th>\n",
       "      <th>CC22_330b</th>\n",
       "      <th>CC22_330c</th>\n",
       "      <th>CC22_330d</th>\n",
       "      <th>CC22_330e</th>\n",
       "      <th>CC22_330f</th>\n",
       "      <th>CC22_331a</th>\n",
       "      <th>CC22_331b</th>\n",
       "      <th>CC22_331c</th>\n",
       "      <th>CC22_331d</th>\n",
       "      <th>CC22_332a</th>\n",
       "      <th>CC22_332b</th>\n",
       "      <th>CC22_332c</th>\n",
       "      <th>CC22_332d</th>\n",
       "      <th>CC22_332e</th>\n",
       "      <th>CC22_332f</th>\n",
       "      <th>CC22_333</th>\n",
       "      <th>CC22_333a</th>\n",
       "      <th>CC22_333b</th>\n",
       "      <th>CC22_333c</th>\n",
       "      <th>CC22_333d</th>\n",
       "      <th>CC22_333e</th>\n",
       "      <th>CC22_334a</th>\n",
       "      <th>CC22_334b</th>\n",
       "      <th>CC22_334c</th>\n",
       "      <th>CC22_334d</th>\n",
       "      <th>CC22_334e</th>\n",
       "      <th>CC22_334f</th>\n",
       "      <th>CC22_334g</th>\n",
       "      <th>CC22_334h</th>\n",
       "      <th>CC22_340a</th>\n",
       "      <th>CC22_340b</th>\n",
       "      <th>CC22_340c</th>\n",
       "      <th>CC22_340d</th>\n",
       "      <th>CC22_340e</th>\n",
       "      <th>CC22_340f</th>\n",
       "      <th>CC22_340g</th>\n",
       "      <th>CC22_340h</th>\n",
       "      <th>CC22_340i</th>\n",
       "      <th>CC22_340j</th>\n",
       "      <th>CC22_340k</th>\n",
       "      <th>CC22_340l</th>\n",
       "      <th>CC22_340m</th>\n",
       "      <th>CC22_340n</th>\n",
       "      <th>CC22_350a</th>\n",
       "      <th>CC22_350b</th>\n",
       "      <th>CC22_350c</th>\n",
       "      <th>CC22_350d</th>\n",
       "      <th>CC22_350e</th>\n",
       "      <th>CC22_350f</th>\n",
       "      <th>CC22_350g</th>\n",
       "      <th>CC22_350h</th>\n",
       "      <th>CC22_355a</th>\n",
       "      <th>CC22_355b</th>\n",
       "      <th>CC22_355c</th>\n",
       "      <th>CC22_355d</th>\n",
       "      <th>CC22_355e</th>\n",
       "      <th>CC22_360</th>\n",
       "      <th>CC22_361</th>\n",
       "      <th>urbancity</th>\n",
       "      <th>urbancity_t</th>\n",
       "      <th>presvote20post</th>\n",
       "      <th>presvote20post_t</th>\n",
       "      <th>CC22_363</th>\n",
       "      <th>CC22_365_voted</th>\n",
       "      <th>CC22_365_voted_t</th>\n",
       "      <th>CC22_365b_voted</th>\n",
       "      <th>CC22_365b_voted_t</th>\n",
       "      <th>CC22_366_voted</th>\n",
       "      <th>CC22_366_voted_t</th>\n",
       "      <th>CC22_367_voted</th>\n",
       "      <th>CC22_367_voted_t</th>\n",
       "      <th>CC22_365</th>\n",
       "      <th>CC22_365_t</th>\n",
       "      <th>CC22_365a</th>\n",
       "      <th>CC22_365a_t</th>\n",
       "      <th>CC22_365b</th>\n",
       "      <th>CC22_365b_t</th>\n",
       "      <th>CC22_365c</th>\n",
       "      <th>CC22_365c_t</th>\n",
       "      <th>CC22_366</th>\n",
       "      <th>CC22_366_t</th>\n",
       "      <th>CC22_367</th>\n",
       "      <th>CC22_367_t</th>\n",
       "      <th>CC22_367a</th>\n",
       "      <th>CC22_367a_t</th>\n",
       "      <th>ideo5</th>\n",
       "      <th>employ</th>\n",
       "      <th>employ_t</th>\n",
       "      <th>hadjob</th>\n",
       "      <th>investor</th>\n",
       "      <th>pew_bornagain</th>\n",
       "      <th>pew_religimp</th>\n",
       "      <th>pew_churatd</th>\n",
       "      <th>pew_prayer</th>\n",
       "      <th>religpew</th>\n",
       "      <th>religpew_t</th>\n",
       "      <th>religpew_protestant</th>\n",
       "      <th>religpew_protestant_t</th>\n",
       "      <th>religpew_baptist</th>\n",
       "      <th>religpew_baptist_t</th>\n",
       "      <th>religpew_methodist</th>\n",
       "      <th>religpew_methodist_t</th>\n",
       "      <th>religpew_nondenom</th>\n",
       "      <th>religpew_nondenom_t</th>\n",
       "      <th>religpew_lutheran</th>\n",
       "      <th>religpew_lutheran_t</th>\n",
       "      <th>religpew_presby</th>\n",
       "      <th>religpew_presby_t</th>\n",
       "      <th>religpew_pentecost</th>\n",
       "      <th>religpew_pentecost_t</th>\n",
       "      <th>religpew_episcop</th>\n",
       "      <th>religpew_episcop_t</th>\n",
       "      <th>religpew_christian</th>\n",
       "      <th>religpew_christian_t</th>\n",
       "      <th>religpew_congreg</th>\n",
       "      <th>religpew_congreg_t</th>\n",
       "      <th>religpew_holiness</th>\n",
       "      <th>religpew_holiness_t</th>\n",
       "      <th>religpew_reformed</th>\n",
       "      <th>religpew_reformed_t</th>\n",
       "      <th>religpew_advent</th>\n",
       "      <th>religpew_advent_t</th>\n",
       "      <th>religpew_catholic</th>\n",
       "      <th>religpew_catholic_t</th>\n",
       "      <th>religpew_mormon</th>\n",
       "      <th>religpew_mormon_t</th>\n",
       "      <th>religpew_orthodox</th>\n",
       "      <th>religpew_orthodox_t</th>\n",
       "      <th>religpew_jewish</th>\n",
       "      <th>religpew_jewish_t</th>\n",
       "      <th>religpew_muslim</th>\n",
       "      <th>religpew_muslim_t</th>\n",
       "      <th>religpew_buddhist</th>\n",
       "      <th>religpew_buddhist_t</th>\n",
       "      <th>religpew_hindu</th>\n",
       "      <th>religpew_hindu_t</th>\n",
       "      <th>marstat</th>\n",
       "      <th>union</th>\n",
       "      <th>union_coverage</th>\n",
       "      <th>unionhh</th>\n",
       "      <th>dualcit</th>\n",
       "      <th>dualctry</th>\n",
       "      <th>ownhome</th>\n",
       "      <th>ownhome_t</th>\n",
       "      <th>newsint</th>\n",
       "      <th>faminc_new</th>\n",
       "      <th>milstat_1</th>\n",
       "      <th>milstat_2</th>\n",
       "      <th>milstat_3</th>\n",
       "      <th>milstat_4</th>\n",
       "      <th>milstat_5</th>\n",
       "      <th>child18</th>\n",
       "      <th>child18num</th>\n",
       "      <th>healthins_1</th>\n",
       "      <th>healthins_2</th>\n",
       "      <th>healthins_3</th>\n",
       "      <th>healthins_4</th>\n",
       "      <th>healthins_5</th>\n",
       "      <th>healthins_6</th>\n",
       "      <th>healthins2</th>\n",
       "      <th>phone</th>\n",
       "      <th>internethome</th>\n",
       "      <th>internetwork</th>\n",
       "      <th>CC22_hisp_1</th>\n",
       "      <th>CC22_hisp_2</th>\n",
       "      <th>CC22_hisp_3</th>\n",
       "      <th>CC22_hisp_4</th>\n",
       "      <th>CC22_hisp_5</th>\n",
       "      <th>CC22_hisp_6</th>\n",
       "      <th>CC22_hisp_7</th>\n",
       "      <th>CC22_hisp_8</th>\n",
       "      <th>CC22_hisp_9</th>\n",
       "      <th>CC22_hisp_10</th>\n",
       "      <th>CC22_hisp_11</th>\n",
       "      <th>CC22_hisp_12</th>\n",
       "      <th>CC22_hisp_t</th>\n",
       "      <th>CC22_asian_1</th>\n",
       "      <th>CC22_asian_2</th>\n",
       "      <th>CC22_asian_3</th>\n",
       "      <th>CC22_asian_4</th>\n",
       "      <th>CC22_asian_5</th>\n",
       "      <th>CC22_asian_6</th>\n",
       "      <th>CC22_asian_7</th>\n",
       "      <th>CC22_asian_8</th>\n",
       "      <th>CC22_asian_9</th>\n",
       "      <th>CC22_asian_10</th>\n",
       "      <th>CC22_asian_11</th>\n",
       "      <th>CC22_asian_12</th>\n",
       "      <th>CC22_asian_13</th>\n",
       "      <th>CC22_asian_14</th>\n",
       "      <th>CC22_asian_15</th>\n",
       "      <th>CC22_asian_t</th>\n",
       "      <th>presvote16post</th>\n",
       "      <th>presvote16post_t</th>\n",
       "      <th>industry</th>\n",
       "      <th>sexuality</th>\n",
       "      <th>transgender</th>\n",
       "      <th>cdid117</th>\n",
       "      <th>cdid118</th>\n",
       "      <th>lookupzip</th>\n",
       "      <th>LegName</th>\n",
       "      <th>LowerChamberName</th>\n",
       "      <th>UpperChamberName</th>\n",
       "      <th>CurrentGovName</th>\n",
       "      <th>CurrentGovParty</th>\n",
       "      <th>CurrentHouseName</th>\n",
       "      <th>CurrentHouseParty</th>\n",
       "      <th>CurrentSen1Name</th>\n",
       "      <th>CurrentSen1Party</th>\n",
       "      <th>CurrentSen2Name</th>\n",
       "      <th>CurrentSen2Party</th>\n",
       "      <th>GovCand1Name</th>\n",
       "      <th>GovCand1Party</th>\n",
       "      <th>GovCand2Name</th>\n",
       "      <th>GovCand2Party</th>\n",
       "      <th>GovCand3Name</th>\n",
       "      <th>GovCand3Party</th>\n",
       "      <th>HouseCand1Name</th>\n",
       "      <th>HouseCand1Party</th>\n",
       "      <th>HouseCand2Name</th>\n",
       "      <th>HouseCand2Party</th>\n",
       "      <th>HouseCand3Name</th>\n",
       "      <th>HouseCand3Party</th>\n",
       "      <th>HouseCand4Name</th>\n",
       "      <th>HouseCand4Party</th>\n",
       "      <th>HouseCand5Name</th>\n",
       "      <th>HouseCand5Party</th>\n",
       "      <th>HouseCand6Name</th>\n",
       "      <th>HouseCand6Party</th>\n",
       "      <th>HouseCand7Name</th>\n",
       "      <th>HouseCand7Party</th>\n",
       "      <th>HouseCand8Name</th>\n",
       "      <th>HouseCand8Party</th>\n",
       "      <th>SenCand1Name</th>\n",
       "      <th>SenCand1Party</th>\n",
       "      <th>SenCand2Name</th>\n",
       "      <th>SenCand2Party</th>\n",
       "      <th>SenCand3Name</th>\n",
       "      <th>SenCand3Party</th>\n",
       "      <th>SenCand4Name</th>\n",
       "      <th>SenCand4Party</th>\n",
       "      <th>SenCand1Name2</th>\n",
       "      <th>SenCand1Party2</th>\n",
       "      <th>SenCand2Name2</th>\n",
       "      <th>SenCand2Party2</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>countyname</th>\n",
       "      <th>page_CC22_300_timing</th>\n",
       "      <th>page_CC22_300b_timing</th>\n",
       "      <th>page_CC22_300d_timing</th>\n",
       "      <th>page_CC19_302_timing</th>\n",
       "      <th>page_CC22_303_timing</th>\n",
       "      <th>page_CC22_304_timing</th>\n",
       "      <th>page_CC22_305_timing</th>\n",
       "      <th>page_CC22_307_timing</th>\n",
       "      <th>page_CC22_309a_timing</th>\n",
       "      <th>page_CC22_306_timing</th>\n",
       "      <th>page_CC22_309b_timing</th>\n",
       "      <th>page_CC22_309c_timing</th>\n",
       "      <th>page_CC22_309dx_timing</th>\n",
       "      <th>page_pg_health_timing</th>\n",
       "      <th>page_CC22_310grid_timing</th>\n",
       "      <th>page_CC22_311grid_timing</th>\n",
       "      <th>page_CC22_320grid_timing</th>\n",
       "      <th>page_cit1_timing</th>\n",
       "      <th>page_immstat_timing</th>\n",
       "      <th>page_CC22_321_timing</th>\n",
       "      <th>page_CC22_327grid_timing</th>\n",
       "      <th>page_CC22_330grid_timing</th>\n",
       "      <th>page_CC22_331grid_timing</th>\n",
       "      <th>page_CC22_332grid_timing</th>\n",
       "      <th>page_CC22_333_timing</th>\n",
       "      <th>page_CC22_333grid_timing</th>\n",
       "      <th>page_CC22_334grid_timing</th>\n",
       "      <th>page_CC22_340grid_timing</th>\n",
       "      <th>page_CC22_350grid_timing</th>\n",
       "      <th>page_CC22_355grid_timing</th>\n",
       "      <th>page_CC22_360_timing</th>\n",
       "      <th>page_urbanarea_timing</th>\n",
       "      <th>page_CC22_363_timing</th>\n",
       "      <th>page_CC22_365_voted_timing</th>\n",
       "      <th>page_CC22_365b_voted_timing</th>\n",
       "      <th>page_CC22_366_voted_timing</th>\n",
       "      <th>page_CC22_367_voted_timing</th>\n",
       "      <th>page_CC22_365_timing</th>\n",
       "      <th>page_CC22_365a_timing</th>\n",
       "      <th>page_CC22_365b_timing</th>\n",
       "      <th>page_CC22_365c_timing</th>\n",
       "      <th>page_CC22_366_timing</th>\n",
       "      <th>page_CC22_367_timing</th>\n",
       "      <th>page_CC22_367a_timing</th>\n",
       "      <th>page_ideo5_timing</th>\n",
       "      <th>page_employ_timing</th>\n",
       "      <th>page_hadjob_timing</th>\n",
       "      <th>page_investor_timing</th>\n",
       "      <th>page_pew_bornagain_timing</th>\n",
       "      <th>page_pew_religimp_timing</th>\n",
       "      <th>page_pew_churatd_timing</th>\n",
       "      <th>page_pew_prayer_timing</th>\n",
       "      <th>page_religpew_timing</th>\n",
       "      <th>page_religpew_protestant_timing</th>\n",
       "      <th>page_religion_denom_timing</th>\n",
       "      <th>page_marstat_timing</th>\n",
       "      <th>page_union_timing</th>\n",
       "      <th>page_union_coverage_timing</th>\n",
       "      <th>page_unionhh_timing</th>\n",
       "      <th>page_dualcit_timing</th>\n",
       "      <th>page_dualctry_timing</th>\n",
       "      <th>page_ownhome_timing</th>\n",
       "      <th>page_demos2c_timing</th>\n",
       "      <th>page_milstat_timing</th>\n",
       "      <th>page_child18_timing</th>\n",
       "      <th>page_healthins_timing</th>\n",
       "      <th>page_healthins2_timing</th>\n",
       "      <th>page_demos8_timing</th>\n",
       "      <th>page_CC22_hisp_timing</th>\n",
       "      <th>page_CC22_asian_timing</th>\n",
       "      <th>page_presvote20post_timing</th>\n",
       "      <th>page_presvote16post_timing</th>\n",
       "      <th>page_industry_timing</th>\n",
       "      <th>page_sex_timing</th>\n",
       "      <th>regzip_confirm_post</th>\n",
       "      <th>reszip_confirm_post</th>\n",
       "      <th>inputzip_post</th>\n",
       "      <th>votereg_post</th>\n",
       "      <th>votereg_f_post</th>\n",
       "      <th>regzip_post</th>\n",
       "      <th>inputstate_post</th>\n",
       "      <th>birthyr_post</th>\n",
       "      <th>gender4_post</th>\n",
       "      <th>CC22_401</th>\n",
       "      <th>CC22_402a</th>\n",
       "      <th>CC22_402a_t</th>\n",
       "      <th>CC22_402b</th>\n",
       "      <th>CC22_402b_t</th>\n",
       "      <th>CC22_403</th>\n",
       "      <th>CC22_403b</th>\n",
       "      <th>CC22_403c</th>\n",
       "      <th>CC22_403c_t</th>\n",
       "      <th>CC22_403d</th>\n",
       "      <th>CC22_403d_t</th>\n",
       "      <th>CC22_404</th>\n",
       "      <th>CC22_404_t</th>\n",
       "      <th>CC22_405</th>\n",
       "      <th>CC22_406a</th>\n",
       "      <th>CC22_406b_1</th>\n",
       "      <th>CC22_406b_2</th>\n",
       "      <th>CC22_406b_3</th>\n",
       "      <th>CC22_406b_4</th>\n",
       "      <th>CC22_406b_t</th>\n",
       "      <th>CC22_406c</th>\n",
       "      <th>CC22_411</th>\n",
       "      <th>CC22_411_t</th>\n",
       "      <th>CC22_411b</th>\n",
       "      <th>CC22_411b_t</th>\n",
       "      <th>CC22_412</th>\n",
       "      <th>CC22_412_t</th>\n",
       "      <th>CC22_413</th>\n",
       "      <th>CC22_413_t</th>\n",
       "      <th>CC22_411_nv</th>\n",
       "      <th>CC22_411_nv_t</th>\n",
       "      <th>CC22_411b_nv</th>\n",
       "      <th>CC22_411b_nv_t</th>\n",
       "      <th>CC22_412_nv</th>\n",
       "      <th>CC22_412_nv_t</th>\n",
       "      <th>CC22_413_nv</th>\n",
       "      <th>CC22_413_nv_t</th>\n",
       "      <th>CC22_414a</th>\n",
       "      <th>CC22_414b</th>\n",
       "      <th>CC22_415c</th>\n",
       "      <th>CC22_415d</th>\n",
       "      <th>CC22_416a</th>\n",
       "      <th>CC22_416b</th>\n",
       "      <th>CC22_416c</th>\n",
       "      <th>CC22_416a_other</th>\n",
       "      <th>CC22_416b_other</th>\n",
       "      <th>CC22_416c_other</th>\n",
       "      <th>CC22_420_1</th>\n",
       "      <th>CC22_420_2</th>\n",
       "      <th>CC22_420_3</th>\n",
       "      <th>CC22_420_4</th>\n",
       "      <th>CC22_420_5</th>\n",
       "      <th>CC22_420_6</th>\n",
       "      <th>CC22_420_7</th>\n",
       "      <th>CC22_421r</th>\n",
       "      <th>CC22_421r_dk_flag</th>\n",
       "      <th>CC22_422r</th>\n",
       "      <th>CC22_422r_dk_flag</th>\n",
       "      <th>CC22_423</th>\n",
       "      <th>CC22_424</th>\n",
       "      <th>CC22_430a_1</th>\n",
       "      <th>CC22_430a_2</th>\n",
       "      <th>CC22_430a_3</th>\n",
       "      <th>CC22_430a_4</th>\n",
       "      <th>CC22_430a_5</th>\n",
       "      <th>CC22_430a_6</th>\n",
       "      <th>CC22_430a_7</th>\n",
       "      <th>CC22_430a_8</th>\n",
       "      <th>CC22_430b_1</th>\n",
       "      <th>CC22_430b_2</th>\n",
       "      <th>CC22_430b_3</th>\n",
       "      <th>CC22_430b_4</th>\n",
       "      <th>CC22_430b_5</th>\n",
       "      <th>CC22_430b_6</th>\n",
       "      <th>CC22_430b_7</th>\n",
       "      <th>CC22_430b_8</th>\n",
       "      <th>CC22_430b_9</th>\n",
       "      <th>CC22_430b_10</th>\n",
       "      <th>CC22_430b_t</th>\n",
       "      <th>CC22_430c</th>\n",
       "      <th>CC22_431a</th>\n",
       "      <th>CC22_431b_1</th>\n",
       "      <th>CC22_431b_2</th>\n",
       "      <th>CC22_431b_3</th>\n",
       "      <th>CC22_431b_4</th>\n",
       "      <th>CC22_432a</th>\n",
       "      <th>CC22_432b_1</th>\n",
       "      <th>CC22_432b_2</th>\n",
       "      <th>CC22_432b_3</th>\n",
       "      <th>CC22_432b_4</th>\n",
       "      <th>CC22_432b_5</th>\n",
       "      <th>CC22_432b_6</th>\n",
       "      <th>CC22_432b_7</th>\n",
       "      <th>CC22_432b_8</th>\n",
       "      <th>CC22_432b_9</th>\n",
       "      <th>CC22_432b_10</th>\n",
       "      <th>CC22_432b_11</th>\n",
       "      <th>CC22_432b_t</th>\n",
       "      <th>CC22_433a</th>\n",
       "      <th>CC22_433_t</th>\n",
       "      <th>CC22_433_dem</th>\n",
       "      <th>CC22_433_rep</th>\n",
       "      <th>CC22_433b</th>\n",
       "      <th>CC22_pid7</th>\n",
       "      <th>CC22_440a</th>\n",
       "      <th>CC22_440b</th>\n",
       "      <th>CC22_440c</th>\n",
       "      <th>CC22_440d</th>\n",
       "      <th>CC22_441a</th>\n",
       "      <th>CC22_441b</th>\n",
       "      <th>CC22_441e</th>\n",
       "      <th>CC22_441f</th>\n",
       "      <th>CC22_441g</th>\n",
       "      <th>CC22_443_1</th>\n",
       "      <th>CC22_443_2</th>\n",
       "      <th>CC22_443_3</th>\n",
       "      <th>CC22_443_4</th>\n",
       "      <th>CC22_443_5</th>\n",
       "      <th>gunown</th>\n",
       "      <th>numchildren</th>\n",
       "      <th>edloan</th>\n",
       "      <th>student</th>\n",
       "      <th>cdid117_post</th>\n",
       "      <th>cdid118_post</th>\n",
       "      <th>lookupzip_post</th>\n",
       "      <th>LegName_post</th>\n",
       "      <th>LowerChamberName_post</th>\n",
       "      <th>CurrentGovName_post</th>\n",
       "      <th>CurrentGovParty_post</th>\n",
       "      <th>CurrentHouseName_post</th>\n",
       "      <th>CurrentHouseParty_post</th>\n",
       "      <th>CurrentSen1Name_post</th>\n",
       "      <th>CurrentSen1Party_post</th>\n",
       "      <th>CurrentSen2Name_post</th>\n",
       "      <th>CurrentSen2Party_post</th>\n",
       "      <th>GovCand1Name_post</th>\n",
       "      <th>GovCand1Party_post</th>\n",
       "      <th>GovCand2Name_post</th>\n",
       "      <th>GovCand2Party_post</th>\n",
       "      <th>GovCand3Name_post</th>\n",
       "      <th>GovCand3Party_post</th>\n",
       "      <th>HouseCand1Name_post</th>\n",
       "      <th>HouseCand1Party_post</th>\n",
       "      <th>HouseCand2Name_post</th>\n",
       "      <th>HouseCand2Party_post</th>\n",
       "      <th>HouseCand3Name_post</th>\n",
       "      <th>HouseCand3Party_post</th>\n",
       "      <th>HouseCand4Name_post</th>\n",
       "      <th>HouseCand4Party_post</th>\n",
       "      <th>HouseCand5Name_post</th>\n",
       "      <th>HouseCand5Party_post</th>\n",
       "      <th>HouseCand6Name_post</th>\n",
       "      <th>HouseCand6Party_post</th>\n",
       "      <th>HouseCand7Name_post</th>\n",
       "      <th>HouseCand7Party_post</th>\n",
       "      <th>HouseCand8Name_post</th>\n",
       "      <th>HouseCand8Party_post</th>\n",
       "      <th>SenCand1Name_post</th>\n",
       "      <th>SenCand1Party_post</th>\n",
       "      <th>SenCand2Name_post</th>\n",
       "      <th>SenCand2Party_post</th>\n",
       "      <th>SenCand3Name_post</th>\n",
       "      <th>SenCand3Party_post</th>\n",
       "      <th>SenCand4Name_post</th>\n",
       "      <th>SenCand4Party_post</th>\n",
       "      <th>SenCand1Name2_post</th>\n",
       "      <th>SenCand1Party2_post</th>\n",
       "      <th>SenCand2Name2_post</th>\n",
       "      <th>SenCand2Party2_post</th>\n",
       "      <th>AttCand1Name</th>\n",
       "      <th>AttCand2Name</th>\n",
       "      <th>AttCand3Name</th>\n",
       "      <th>AttCand1Party</th>\n",
       "      <th>AttCand2Party</th>\n",
       "      <th>AttCand3Party</th>\n",
       "      <th>SecCand1Name</th>\n",
       "      <th>SecCand2Name</th>\n",
       "      <th>SecCand3Name</th>\n",
       "      <th>SecCand1Party</th>\n",
       "      <th>SecCand2Party</th>\n",
       "      <th>SecCand3Party</th>\n",
       "      <th>page_CC22_401_timing</th>\n",
       "      <th>page_CC22_402a_timing</th>\n",
       "      <th>page_CC22_402b_timing</th>\n",
       "      <th>page_CC22_403_timing</th>\n",
       "      <th>page_CC22_403c_timing</th>\n",
       "      <th>page_CC22_403d_timing</th>\n",
       "      <th>page_CC22_404_timing</th>\n",
       "      <th>page_CC22_405_timing</th>\n",
       "      <th>page_CC22_406a_timing</th>\n",
       "      <th>page_CC22_406b_timing</th>\n",
       "      <th>page_CC22_406c_timing</th>\n",
       "      <th>page_CC22_411_timing</th>\n",
       "      <th>page_CC22_411b_timing</th>\n",
       "      <th>page_CC22_412_timing</th>\n",
       "      <th>page_CC22_413_timing</th>\n",
       "      <th>page_CC22_411_nv_timing</th>\n",
       "      <th>page_CC22_411b_nv_timing</th>\n",
       "      <th>page_CC22_412_nv_timing</th>\n",
       "      <th>page_CC22_413_nv_timing</th>\n",
       "      <th>page_CC22_414a_timing</th>\n",
       "      <th>page_CC22_414b_timing</th>\n",
       "      <th>page_CC22_415_grid_timing</th>\n",
       "      <th>page_CC22_416_grid_timing</th>\n",
       "      <th>page_CC22_416a_other_timing</th>\n",
       "      <th>page_CC22_416b_other_timing</th>\n",
       "      <th>page_CC22_416c_other_timing</th>\n",
       "      <th>page_CC22_420_timing</th>\n",
       "      <th>page_CC22_421r_timing</th>\n",
       "      <th>page_CC22_422r_timing</th>\n",
       "      <th>page_CC22_423_timing</th>\n",
       "      <th>page_CC22_424_timing</th>\n",
       "      <th>page_CC22_430a_timing</th>\n",
       "      <th>page_CC22_430b_timing</th>\n",
       "      <th>page_CC22_430c_timing</th>\n",
       "      <th>page_CC22_431a_timing</th>\n",
       "      <th>page_CC22_431b_timing</th>\n",
       "      <th>page_CC22_432a_timing</th>\n",
       "      <th>page_CC22_432b_timing</th>\n",
       "      <th>page_CC22_433a_timing</th>\n",
       "      <th>page_CC22_433_dem_timing</th>\n",
       "      <th>page_CC22_433_rep_timing</th>\n",
       "      <th>page_CC22_433b_timing</th>\n",
       "      <th>page_CC22_pid7_timing</th>\n",
       "      <th>page_CC22_440_grid_timing</th>\n",
       "      <th>page_CC22_441_grid_timing</th>\n",
       "      <th>page_CC22_443_timing</th>\n",
       "      <th>page_gunown_timing</th>\n",
       "      <th>page_numchildren_timing</th>\n",
       "      <th>page_edloan_timing</th>\n",
       "      <th>page_student_timing</th>\n",
       "      <th>starttime</th>\n",
       "      <th>endtime</th>\n",
       "      <th>starttime_post</th>\n",
       "      <th>endtime_post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983126005</td>\n",
       "      <td>2</td>\n",
       "      <td>3.649671</td>\n",
       "      <td>3.525008</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48152.0</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
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       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>48152</td>\n",
       "      <td>Michigan State Legislature</td>\n",
       "      <td>Michigan State House of Representatives</td>\n",
       "      <td>Michigan State Senate</td>\n",
       "      <td>Gretchen Whitmer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Haley Stevens</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Debbie Stabenow</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Gary Peters</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Gretchen Whitmer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Tudor Dixon</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rashida Tlaib</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Steven Elliott</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Gary Walkowicz</td>\n",
       "      <td>Working Class</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>26163.0</td>\n",
       "      <td>Wayne MI</td>\n",
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       "      <td>11.061</td>\n",
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       "      <td>4.848</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.144</td>\n",
       "      <td>17.960</td>\n",
       "      <td>5.600</td>\n",
       "      <td>14.913</td>\n",
       "      <td>8.679</td>\n",
       "      <td>19.551</td>\n",
       "      <td>2.296</td>\n",
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       "      <td>11.144</td>\n",
       "      <td>21.717</td>\n",
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       "      <td>18.317</td>\n",
       "      <td>34.790</td>\n",
       "      <td>29.379</td>\n",
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       "      <td>7.679</td>\n",
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       "      <td>__NA__</td>\n",
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       "      <td>__NA__</td>\n",
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       "      <td>Democratic</td>\n",
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       "      <td>Democratic</td>\n",
       "      <td>Gary Peters</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Gretchen Whitmer</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
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       "      <td>8.439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.653</td>\n",
       "      <td>14.715</td>\n",
       "      <td>7.229</td>\n",
       "      <td>4.724</td>\n",
       "      <td>5.115</td>\n",
       "      <td>7.679</td>\n",
       "      <td>16.768</td>\n",
       "      <td>9.035</td>\n",
       "      <td>4.738</td>\n",
       "      <td>5.883</td>\n",
       "      <td>3.505</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.628</td>\n",
       "      <td>2.071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.141</td>\n",
       "      <td>12.128</td>\n",
       "      <td>12.506</td>\n",
       "      <td>6.337</td>\n",
       "      <td>6.265</td>\n",
       "      <td>2.863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.985230e+12</td>\n",
       "      <td>1.985230e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983126559</td>\n",
       "      <td>2</td>\n",
       "      <td>0.780431</td>\n",
       "      <td>0.818539</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>98.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>7.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1331</td>\n",
       "      <td>Massachusetts State Legislature</td>\n",
       "      <td>Massachusetts State House of Representatives</td>\n",
       "      <td>Massachusetts State Senate</td>\n",
       "      <td>Charlie Baker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Jim McGovern</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Elizabeth Warren</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ed Markey</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Maura Healey</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Geoff Diehl</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jim McGovern</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Jeffrey Sossa-Paquette</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>25027.0</td>\n",
       "      <td>Worcester MA</td>\n",
       "      <td>22.725</td>\n",
       "      <td>4.317</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.914</td>\n",
       "      <td>4.775</td>\n",
       "      <td>3.506</td>\n",
       "      <td>11.900</td>\n",
       "      <td>2.619</td>\n",
       "      <td>5.318</td>\n",
       "      <td>1.975</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16.312</td>\n",
       "      <td>8.235</td>\n",
       "      <td>4.285</td>\n",
       "      <td>10.790</td>\n",
       "      <td>14.268</td>\n",
       "      <td>26.893</td>\n",
       "      <td>2.224</td>\n",
       "      <td>7.372</td>\n",
       "      <td>9.706</td>\n",
       "      <td>23.907</td>\n",
       "      <td>21.601</td>\n",
       "      <td>21.525</td>\n",
       "      <td>25.929</td>\n",
       "      <td>15.617</td>\n",
       "      <td>17.945</td>\n",
       "      <td>27.952</td>\n",
       "      <td>34.156</td>\n",
       "      <td>39.665</td>\n",
       "      <td>17.425</td>\n",
       "      <td>5.596</td>\n",
       "      <td>5.889</td>\n",
       "      <td>2.276</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.007</td>\n",
       "      <td>2.607</td>\n",
       "      <td>2.878</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.625</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.224</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.108</td>\n",
       "      <td>2.346</td>\n",
       "      <td>3.427</td>\n",
       "      <td>4.511</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.841</td>\n",
       "      <td>2.424</td>\n",
       "      <td>3.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.179</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1331</td>\n",
       "      <td>Massachusetts State Legislature</td>\n",
       "      <td>Massachusetts State House of Representatives</td>\n",
       "      <td>Charlie Baker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Jim McGovern</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Elizabeth Warren</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ed Markey</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Maura Healey</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Geoff Diehl</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jim McGovern</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Jeffrey Sossa-Paquette</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>Andrea Campbell</td>\n",
       "      <td>Jay McMahon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>William F. Galvin</td>\n",
       "      <td>Rayla Campbell</td>\n",
       "      <td>Juan Sanchez</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Green-Rainbow</td>\n",
       "      <td>4.619</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.433</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.510</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.592</td>\n",
       "      <td>9.807</td>\n",
       "      <td>6.211</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.731</td>\n",
       "      <td>2.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.632</td>\n",
       "      <td>2.168</td>\n",
       "      <td>9.432</td>\n",
       "      <td>11.622</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.823</td>\n",
       "      <td>13.408</td>\n",
       "      <td>7.497</td>\n",
       "      <td>6.218</td>\n",
       "      <td>4.698</td>\n",
       "      <td>9.950</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.842</td>\n",
       "      <td>2.275</td>\n",
       "      <td>4.104</td>\n",
       "      <td>13.099</td>\n",
       "      <td>2.421</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.042</td>\n",
       "      <td>18.707</td>\n",
       "      <td>21.805</td>\n",
       "      <td>3.126</td>\n",
       "      <td>9.023</td>\n",
       "      <td>3.722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.986030e+12</td>\n",
       "      <td>1.986030e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983126197</td>\n",
       "      <td>2</td>\n",
       "      <td>0.891555</td>\n",
       "      <td>0.774314</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>99.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>17603</td>\n",
       "      <td>Pennsylvania State General Assembly</td>\n",
       "      <td>Pennsylvania State House of Representatives</td>\n",
       "      <td>Pennsylvania State Senate</td>\n",
       "      <td>Tom Wolf</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lloyd Smucker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Bob Casey Jr.</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Pat Toomey</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Josh Shapiro</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Doug Mastriano</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bob Hollister</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lloyd Smucker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Fetterman</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Mehmet Oz</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>42071.0</td>\n",
       "      <td>Lancaster PA</td>\n",
       "      <td>4.251</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.477</td>\n",
       "      <td>24.067</td>\n",
       "      <td>2.611</td>\n",
       "      <td>4.376</td>\n",
       "      <td>7.118</td>\n",
       "      <td>10.069</td>\n",
       "      <td>5.980</td>\n",
       "      <td>4.300</td>\n",
       "      <td>9.401</td>\n",
       "      <td>2.745</td>\n",
       "      <td>12.960</td>\n",
       "      <td>5.985</td>\n",
       "      <td>5.716</td>\n",
       "      <td>4.946</td>\n",
       "      <td>16.029</td>\n",
       "      <td>11.749</td>\n",
       "      <td>2.613</td>\n",
       "      <td>5.731</td>\n",
       "      <td>28.611</td>\n",
       "      <td>67.336</td>\n",
       "      <td>29.973</td>\n",
       "      <td>34.603</td>\n",
       "      <td>84.301</td>\n",
       "      <td>22.516</td>\n",
       "      <td>28.195</td>\n",
       "      <td>27.405</td>\n",
       "      <td>35.782</td>\n",
       "      <td>14.193</td>\n",
       "      <td>3.381</td>\n",
       "      <td>4.847</td>\n",
       "      <td>10.892</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.595</td>\n",
       "      <td>3.079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.348</td>\n",
       "      <td>3.139</td>\n",
       "      <td>2.319</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.840</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.281</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.902</td>\n",
       "      <td>1.736</td>\n",
       "      <td>3.921</td>\n",
       "      <td>6.664</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.452</td>\n",
       "      <td>2.303</td>\n",
       "      <td>2.330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.404</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17603</td>\n",
       "      <td>Pennsylvania State General Assembly</td>\n",
       "      <td>Pennsylvania State House of Representatives</td>\n",
       "      <td>Tom Wolf</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lloyd Smucker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Bob Casey Jr.</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Pat Toomey</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Josh Shapiro</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Doug Mastriano</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bob Hollister</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lloyd Smucker</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Fetterman</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Mehmet Oz</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.711</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.703</td>\n",
       "      <td>8.581</td>\n",
       "      <td>4.787</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.681</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.003</td>\n",
       "      <td>2.452</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.166</td>\n",
       "      <td>5.314</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.824</td>\n",
       "      <td>5.637</td>\n",
       "      <td>3.063</td>\n",
       "      <td>3.913</td>\n",
       "      <td>3.400</td>\n",
       "      <td>9.479</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.632</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.763</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.203</td>\n",
       "      <td>2.511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.285</td>\n",
       "      <td>6.448</td>\n",
       "      <td>4.443</td>\n",
       "      <td>2.105</td>\n",
       "      <td>3.108</td>\n",
       "      <td>3.622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.984610e+12</td>\n",
       "      <td>1.984610e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979974411</td>\n",
       "      <td>2</td>\n",
       "      <td>1.103598</td>\n",
       "      <td>1.207156</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6716.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>3</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Socialist</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>Connecticut State House of Representatives</td>\n",
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       "      <td>Democratic</td>\n",
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       "      <td>Democratic</td>\n",
       "      <td>Chris Murphy</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ned Lamont</td>\n",
       "      <td>Democratic</td>\n",
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       "      <td>George Logan</td>\n",
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       "      <td>Richard Blumenthal</td>\n",
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       "      <td>10.843</td>\n",
       "      <td>5.421</td>\n",
       "      <td>4.947</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.980030e+12</td>\n",
       "      <td>1.984480e+12</td>\n",
       "      <td>1.984490e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983130427</td>\n",
       "      <td>2</td>\n",
       "      <td>0.542923</td>\n",
       "      <td>0.327550</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21401.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>6.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>99.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>90.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>7.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>21401</td>\n",
       "      <td>Maryland State General Assembly</td>\n",
       "      <td>Maryland State House of Delegates</td>\n",
       "      <td>Maryland State Senate</td>\n",
       "      <td>Larry Hogan</td>\n",
       "      <td>Republican</td>\n",
       "      <td>John Sarbanes</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ben Cardin</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Chris Van Hollen</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Wes Moore</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Dan Cox</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Sarbanes</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Yuripzy Morgan</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chris Van Hollen</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Chris Chaffee</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>24003.0</td>\n",
       "      <td>Anne Arundel MD</td>\n",
       "      <td>11.848</td>\n",
       "      <td>2.627</td>\n",
       "      <td>5.367</td>\n",
       "      <td>4.522</td>\n",
       "      <td>4.522</td>\n",
       "      <td>2.463</td>\n",
       "      <td>12.644</td>\n",
       "      <td>1.603</td>\n",
       "      <td>5.443</td>\n",
       "      <td>2.879</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.855</td>\n",
       "      <td>6.847</td>\n",
       "      <td>5.276</td>\n",
       "      <td>119.441</td>\n",
       "      <td>79.162</td>\n",
       "      <td>13.047</td>\n",
       "      <td>1.966</td>\n",
       "      <td>7.138</td>\n",
       "      <td>11.035</td>\n",
       "      <td>20.405</td>\n",
       "      <td>37.792</td>\n",
       "      <td>33.588</td>\n",
       "      <td>33.479</td>\n",
       "      <td>4.863</td>\n",
       "      <td>35.380</td>\n",
       "      <td>69.826</td>\n",
       "      <td>30.797</td>\n",
       "      <td>54.624</td>\n",
       "      <td>16.253</td>\n",
       "      <td>2.935</td>\n",
       "      <td>10.245</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.261</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.790</td>\n",
       "      <td>2.114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.078</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.750</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.398</td>\n",
       "      <td>17.583</td>\n",
       "      <td>7.197</td>\n",
       "      <td>3.133</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.899</td>\n",
       "      <td>2.481</td>\n",
       "      <td>5.702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.772</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>The government system is bullshit and a busine...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>It is a sham</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>7.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>11.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Maryland State General Assembly</td>\n",
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       "      <td>1.985620e+12</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>33774</td>\n",
       "      <td>Florida State Legislature</td>\n",
       "      <td>Florida State House of Representatives</td>\n",
       "      <td>Florida State Senate</td>\n",
       "      <td>Ron DeSantis</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Rick Scott</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Charlie Crist</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ron DeSantis</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eric Lynn</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Anna Paulina Luna</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Frank Craft</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Val Demings</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>12103.0</td>\n",
       "      <td>Pinellas FL</td>\n",
       "      <td>13.735</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.283</td>\n",
       "      <td>4.702</td>\n",
       "      <td>9.008</td>\n",
       "      <td>17.469</td>\n",
       "      <td>10.046</td>\n",
       "      <td>9.962</td>\n",
       "      <td>4.501</td>\n",
       "      <td>6.861</td>\n",
       "      <td>25.693</td>\n",
       "      <td>12.716</td>\n",
       "      <td>9.749</td>\n",
       "      <td>12.649</td>\n",
       "      <td>16.394</td>\n",
       "      <td>27.169</td>\n",
       "      <td>5.068</td>\n",
       "      <td>8191.718</td>\n",
       "      <td>21.015</td>\n",
       "      <td>26.345</td>\n",
       "      <td>32.242</td>\n",
       "      <td>32.328</td>\n",
       "      <td>42.122</td>\n",
       "      <td>9.035</td>\n",
       "      <td>14.108</td>\n",
       "      <td>57.483</td>\n",
       "      <td>35.325</td>\n",
       "      <td>43.132</td>\n",
       "      <td>18.825</td>\n",
       "      <td>7.185</td>\n",
       "      <td>8.995</td>\n",
       "      <td>5.446</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.814</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.584</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.790</td>\n",
       "      <td>3.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.420</td>\n",
       "      <td>4.955</td>\n",
       "      <td>4.654</td>\n",
       "      <td>6.470</td>\n",
       "      <td>10.588</td>\n",
       "      <td>13.601</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.970</td>\n",
       "      <td>4.692</td>\n",
       "      <td>5.565</td>\n",
       "      <td>4.576</td>\n",
       "      <td>4.051</td>\n",
       "      <td>13.775</td>\n",
       "      <td>4.457</td>\n",
       "      <td>5.156</td>\n",
       "      <td>6.267</td>\n",
       "      <td>6.877</td>\n",
       "      <td>11.220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.700</td>\n",
       "      <td>14.687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.982</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.629</td>\n",
       "      <td>4.599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.983500e+12</td>\n",
       "      <td>1.983540e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>2045981019</td>\n",
       "      <td>2</td>\n",
       "      <td>1.478491</td>\n",
       "      <td>0.930118</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33578.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>__NA__</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>84.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Florida State House of Representatives</td>\n",
       "      <td>Florida State Senate</td>\n",
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       "      <td>Republican</td>\n",
       "      <td>Scott Franklin</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Rick Scott</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Charlie Crist</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ron DeSantis</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jan Schneider</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Vern Buchanan</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Val Demings</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>12057.0</td>\n",
       "      <td>Hillsborough FL</td>\n",
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       "      <td>9.525</td>\n",
       "      <td>2.902</td>\n",
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       "      <td>3.939</td>\n",
       "      <td>3.595</td>\n",
       "      <td>3.252</td>\n",
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       "      <td>10.406</td>\n",
       "      <td>43.355</td>\n",
       "      <td>4.864</td>\n",
       "      <td>4.943</td>\n",
       "      <td>17.404</td>\n",
       "      <td>12.077</td>\n",
       "      <td>23.412</td>\n",
       "      <td>12.069</td>\n",
       "      <td>19.193</td>\n",
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       "      <td>25.546</td>\n",
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       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Val Demings</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
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       "      <td>Ashley Moody</td>\n",
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       "      <td>Democratic</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.150</td>\n",
       "      <td>9.945</td>\n",
       "      <td>21.826</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.850</td>\n",
       "      <td>15.273</td>\n",
       "      <td>10.222</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.338</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.566</td>\n",
       "      <td>6.847</td>\n",
       "      <td>3.244</td>\n",
       "      <td>3.819</td>\n",
       "      <td>1.393</td>\n",
       "      <td>5.963</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.548</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.337</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.735</td>\n",
       "      <td>1.959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.574</td>\n",
       "      <td>26.387</td>\n",
       "      <td>10.620</td>\n",
       "      <td>31.372</td>\n",
       "      <td>10.299</td>\n",
       "      <td>10.694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.983500e+12</td>\n",
       "      <td>1.983500e+12</td>\n",
       "      <td>1.984340e+12</td>\n",
       "      <td>1.984340e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>2045852523</td>\n",
       "      <td>1</td>\n",
       "      <td>0.195928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17901.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>98.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17901</td>\n",
       "      <td>Pennsylvania State General Assembly</td>\n",
       "      <td>Pennsylvania State House of Representatives</td>\n",
       "      <td>Pennsylvania State Senate</td>\n",
       "      <td>Tom Wolf</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Dan Meuser</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Bob Casey Jr.</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Pat Toomey</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Josh Shapiro</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Doug Mastriano</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amanda Waldman</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Dan Meuser</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Fetterman</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Mehmet Oz</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>42107.0</td>\n",
       "      <td>Schuylkill PA</td>\n",
       "      <td>6.560</td>\n",
       "      <td>0.000</td>\n",
       "      <td>20.158</td>\n",
       "      <td>9.690</td>\n",
       "      <td>4.248</td>\n",
       "      <td>3.435</td>\n",
       "      <td>27.000</td>\n",
       "      <td>7.678</td>\n",
       "      <td>8.884</td>\n",
       "      <td>11.916</td>\n",
       "      <td>3.734</td>\n",
       "      <td>10.627</td>\n",
       "      <td>7.647</td>\n",
       "      <td>5.455</td>\n",
       "      <td>16.494</td>\n",
       "      <td>15.790</td>\n",
       "      <td>21.367</td>\n",
       "      <td>2.356</td>\n",
       "      <td>4.919</td>\n",
       "      <td>11.223</td>\n",
       "      <td>17.721</td>\n",
       "      <td>18.518</td>\n",
       "      <td>15.878</td>\n",
       "      <td>30.220</td>\n",
       "      <td>7.410</td>\n",
       "      <td>25.340</td>\n",
       "      <td>42.688</td>\n",
       "      <td>53.903</td>\n",
       "      <td>43.197</td>\n",
       "      <td>18.489</td>\n",
       "      <td>5.563</td>\n",
       "      <td>10.283</td>\n",
       "      <td>3.584</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.262</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.431</td>\n",
       "      <td>13.733</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.244</td>\n",
       "      <td>3.998</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.407</td>\n",
       "      <td>3.606</td>\n",
       "      <td>3.297</td>\n",
       "      <td>3.920</td>\n",
       "      <td>7.150</td>\n",
       "      <td>5.504</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.965</td>\n",
       "      <td>6.424</td>\n",
       "      <td>3.854</td>\n",
       "      <td>4.984</td>\n",
       "      <td>4.229</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.707</td>\n",
       "      <td>7.332</td>\n",
       "      <td>6.445</td>\n",
       "      <td>6.854</td>\n",
       "      <td>4.774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.081</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.629</td>\n",
       "      <td>3.333</td>\n",
       "      <td>6.408</td>\n",
       "      <td>10.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.983510e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10016</td>\n",
       "      <td>New York State Legislature</td>\n",
       "      <td>New York State Assembly</td>\n",
       "      <td>New York State Senate</td>\n",
       "      <td>Kathy Hochul</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Carolyn Maloney</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Chuck Schumer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Kirsten Gillibrand</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Kathy Hochul</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lee Zeldin</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jerry Nadler</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Michael Zumbluskas</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Mikhail Itkis</td>\n",
       "      <td>Independent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chuck Schumer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Joe Pinion</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>36061.0</td>\n",
       "      <td>New York NY</td>\n",
       "      <td>11.607</td>\n",
       "      <td>18.210</td>\n",
       "      <td>5.876</td>\n",
       "      <td>15.882</td>\n",
       "      <td>2.113</td>\n",
       "      <td>1.799</td>\n",
       "      <td>16.922</td>\n",
       "      <td>2.204</td>\n",
       "      <td>4.284</td>\n",
       "      <td>972.906</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.785</td>\n",
       "      <td>11.461</td>\n",
       "      <td>5.469</td>\n",
       "      <td>13.729</td>\n",
       "      <td>8.853</td>\n",
       "      <td>29.977</td>\n",
       "      <td>16.859</td>\n",
       "      <td>10.034</td>\n",
       "      <td>22.456</td>\n",
       "      <td>17.512</td>\n",
       "      <td>37.406</td>\n",
       "      <td>16.294</td>\n",
       "      <td>43.221</td>\n",
       "      <td>8.822</td>\n",
       "      <td>17.542</td>\n",
       "      <td>58.643</td>\n",
       "      <td>70.707</td>\n",
       "      <td>13.661</td>\n",
       "      <td>19.061</td>\n",
       "      <td>12.025</td>\n",
       "      <td>24.948</td>\n",
       "      <td>6.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.354</td>\n",
       "      <td>5.724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.021</td>\n",
       "      <td>8.324</td>\n",
       "      <td>36.204</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.925</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.364</td>\n",
       "      <td>3.866</td>\n",
       "      <td>2.108</td>\n",
       "      <td>3.546</td>\n",
       "      <td>5.127</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.969</td>\n",
       "      <td>16.573</td>\n",
       "      <td>0.000</td>\n",
       "      <td>30.220</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.448</td>\n",
       "      <td>2.009</td>\n",
       "      <td>26.853</td>\n",
       "      <td>5.441</td>\n",
       "      <td>2.664</td>\n",
       "      <td>9.214</td>\n",
       "      <td>8.380</td>\n",
       "      <td>6.523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>3.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10016</td>\n",
       "      <td>New York State Legislature</td>\n",
       "      <td>New York State Assembly</td>\n",
       "      <td>Kathy Hochul</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Carolyn Maloney</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Chuck Schumer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Kirsten Gillibrand</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Kathy Hochul</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Lee Zeldin</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jerry Nadler</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Michael Zumbluskas</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Mikhail Itkis</td>\n",
       "      <td>Independent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chuck Schumer</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Joe Pinion</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>Letitia James</td>\n",
       "      <td>Michael Henry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jeffrey Powell</td>\n",
       "      <td>Michael Howe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.331</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.397</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.229</td>\n",
       "      <td>3.424</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.602</td>\n",
       "      <td>3.790</td>\n",
       "      <td>8.433</td>\n",
       "      <td>14.458</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.223</td>\n",
       "      <td>6.840</td>\n",
       "      <td>4.426</td>\n",
       "      <td>3.252</td>\n",
       "      <td>2.558</td>\n",
       "      <td>30.590</td>\n",
       "      <td>26.445</td>\n",
       "      <td>12.501</td>\n",
       "      <td>8.096</td>\n",
       "      <td>5.266</td>\n",
       "      <td>5.288</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.444</td>\n",
       "      <td>1.983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.297</td>\n",
       "      <td>179.778</td>\n",
       "      <td>16.411</td>\n",
       "      <td>4.572</td>\n",
       "      <td>7.346</td>\n",
       "      <td>3.487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.983510e+12</td>\n",
       "      <td>1.983510e+12</td>\n",
       "      <td>1.984750e+12</td>\n",
       "      <td>1.984750e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>2039693715</td>\n",
       "      <td>2</td>\n",
       "      <td>1.293247</td>\n",
       "      <td>1.017285</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>__NA__</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>__NA__</td>\n",
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       "      <td>33810</td>\n",
       "      <td>Florida State Legislature</td>\n",
       "      <td>Florida State House of Representatives</td>\n",
       "      <td>Florida State Senate</td>\n",
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       "      <td>Republican</td>\n",
       "      <td>Scott Franklin</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Rick Scott</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Charlie Crist</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Ron DeSantis</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alan Cohn</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Laurel Lee</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Val Demings</td>\n",
       "      <td>Democratic</td>\n",
       "      <td>Marco Rubio</td>\n",
       "      <td>Republican</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>__NA__</td>\n",
       "      <td>12105.0</td>\n",
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       "      <td>16.340</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.873</td>\n",
       "      <td>6.329</td>\n",
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       "      <td>9.899</td>\n",
       "      <td>13.166</td>\n",
       "      <td>6.799</td>\n",
       "      <td>14.233</td>\n",
       "      <td>8.017</td>\n",
       "      <td>10.358</td>\n",
       "      <td>19.204</td>\n",
       "      <td>19.199</td>\n",
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       "      <td>16.125</td>\n",
       "      <td>14.825</td>\n",
       "      <td>25.593</td>\n",
       "      <td>3.815</td>\n",
       "      <td>9.223</td>\n",
       "      <td>5.703</td>\n",
       "      <td>22.270</td>\n",
       "      <td>35.218</td>\n",
       "      <td>35.044</td>\n",
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       "      <td>13.115</td>\n",
       "      <td>31.398</td>\n",
       "      <td>38.008</td>\n",
       "      <td>32.734</td>\n",
       "      <td>50.790</td>\n",
       "      <td>20.142</td>\n",
       "      <td>4.779</td>\n",
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       "      <td>3.843</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.828</td>\n",
       "      <td>2.882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.293</td>\n",
       "      <td>32.854</td>\n",
       "      <td>26.144</td>\n",
       "      <td>5.721</td>\n",
       "      <td>6.560</td>\n",
       "      <td>7.903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.983510e+12</td>\n",
       "      <td>1.983520e+12</td>\n",
       "      <td>1.985970e+12</td>\n",
       "      <td>1.985980e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 697 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           caseid  tookpost  commonweight  commonpostweight  CCEStake  \\\n",
       "0      1983126005         2      3.649671          3.525008         1   \n",
       "1      1983126559         2      0.780431          0.818539         1   \n",
       "2      1983126197         2      0.891555          0.774314         1   \n",
       "3      1979974411         2      1.103598          1.207156         1   \n",
       "4      1983130427         2      0.542923          0.327550         1   \n",
       "...           ...       ...           ...               ...       ...   \n",
       "59995  2045974583         1      0.255190               NaN         1   \n",
       "59996  2045981019         2      1.478491          0.930118         1   \n",
       "59997  2045852523         1      0.195928               NaN         1   \n",
       "59998  2045338427         2      1.167724          1.256827         1   \n",
       "59999  2039693715         2      1.293247          1.017285         1   \n",
       "\n",
       "       add_confirm  inputzip  birthyr  gender4 gender4_t  educ  race  \\\n",
       "0              1.0       NaN     1992        1    __NA__     6     1   \n",
       "1              NaN    1331.0     1957        1    __NA__     3     1   \n",
       "2              1.0       NaN     1978        2    __NA__     5     1   \n",
       "3              NaN    6716.0     1991        3    __NA__     6     1   \n",
       "4              2.0   21401.0     1991        1    __NA__     6     1   \n",
       "...            ...       ...      ...      ...       ...   ...   ...   \n",
       "59995          NaN   33774.0     1985        2    __NA__     2     3   \n",
       "59996          NaN   33578.0     1996        2    __NA__     4     6   \n",
       "59997          NaN   17901.0     1989        1    __NA__     2     1   \n",
       "59998          NaN   10016.0     1990        1    __NA__     6     2   \n",
       "59999          1.0       NaN     1977        2    __NA__     6     2   \n",
       "\n",
       "      race_other  hispanic  multrace_1  multrace_2  multrace_3  multrace_4  \\\n",
       "0         __NA__       2.0           2           2           2           2   \n",
       "1         __NA__       2.0           2           2           2           2   \n",
       "2         __NA__       2.0           2           2           2           2   \n",
       "3         __NA__       2.0           1           2           2           2   \n",
       "4         __NA__       2.0           1           2           2           2   \n",
       "...          ...       ...         ...         ...         ...         ...   \n",
       "59995     __NA__       NaN           2           2           2           2   \n",
       "59996     __NA__       2.0           1           1           2           2   \n",
       "59997     __NA__       2.0           2           2           2           2   \n",
       "59998     __NA__       2.0           2           2           2           2   \n",
       "59999     __NA__       2.0           2           1           2           2   \n",
       "\n",
       "       multrace_5  multrace_8  multrace_97  multrace_98  comptype  votereg  \\\n",
       "0               2           2          2.0            2         1        1   \n",
       "1               2           2          2.0            2         1        1   \n",
       "2               2           2          2.0            2         3        1   \n",
       "3               2           2          2.0            2         1        1   \n",
       "4               2           2          2.0            2         1        1   \n",
       "...           ...         ...          ...          ...       ...      ...   \n",
       "59995           2           2          2.0            2         1        1   \n",
       "59996           2           2          NaN            2         1        2   \n",
       "59997           2           2          2.0            2         1        1   \n",
       "59998           2           2          2.0            2         1        1   \n",
       "59999           2           2          2.0            2         1        1   \n",
       "\n",
       "       votereg_f   regzip  pid3     pid3_t  pid7  inputstate  region  \\\n",
       "0            2.0  48152.0     1     __NA__     1          26       2   \n",
       "1            1.0      NaN     3     __NA__     5          25       1   \n",
       "2            1.0      NaN     1     __NA__     1          42       1   \n",
       "3            1.0      NaN     4  Socialist     4           9       1   \n",
       "4            1.0      NaN     3     __NA__     4          24       3   \n",
       "...          ...      ...   ...        ...   ...         ...     ...   \n",
       "59995        1.0      NaN     5     __NA__     4          12       3   \n",
       "59996        NaN      NaN     1     __NA__     2          12       3   \n",
       "59997        1.0      NaN     3     __NA__     4          42       1   \n",
       "59998        1.0      NaN     1     __NA__     1          36       1   \n",
       "59999        1.0      NaN     1     __NA__     2          12       3   \n",
       "\n",
       "       ccesmodule  CC22_300_1  CC22_300_2  CC22_300_3  CC22_300_4  CC22_300_5  \\\n",
       "0              11           1           1           1           2           2   \n",
       "1              38           2           1           1           1           2   \n",
       "2               1           1           2           2           2           2   \n",
       "3              32           1           2           2           2           2   \n",
       "4              38           1           1           2           1           2   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "59995           5           2           1           2           2           2   \n",
       "59996          12           1           2           2           2           2   \n",
       "59997          50           1           2           2           2           2   \n",
       "59998          10           1           1           1           1           2   \n",
       "59999           8           1           2           1           2           2   \n",
       "\n",
       "       CC22_300a  CC22_300c  CC22_300b_1  CC22_300b_2  CC22_300b_3  \\\n",
       "0            3.0        2.0          1.0          2.0          2.0   \n",
       "1            3.0        1.0          1.0          2.0          2.0   \n",
       "2            NaN        NaN          NaN          NaN          NaN   \n",
       "3            NaN        NaN          NaN          NaN          NaN   \n",
       "4            2.0        NaN          2.0          2.0          2.0   \n",
       "...          ...        ...          ...          ...          ...   \n",
       "59995        1.0        NaN          NaN          NaN          NaN   \n",
       "59996        NaN        NaN          NaN          NaN          NaN   \n",
       "59997        NaN        NaN          NaN          NaN          NaN   \n",
       "59998        3.0        1.0          1.0          2.0          1.0   \n",
       "59999        NaN        2.0          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_300b_4  CC22_300b_5  CC22_300b_6  CC22_300b_7  CC22_300b_8  \\\n",
       "0              1.0          2.0          2.0          2.0          2.0   \n",
       "1              2.0          1.0          2.0          2.0          2.0   \n",
       "2              NaN          NaN          NaN          NaN          NaN   \n",
       "3              NaN          NaN          NaN          NaN          NaN   \n",
       "4              2.0          2.0          2.0          2.0          1.0   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          NaN          NaN          NaN          NaN          NaN   \n",
       "59996          NaN          NaN          NaN          NaN          NaN   \n",
       "59997          NaN          NaN          NaN          NaN          NaN   \n",
       "59998          1.0          1.0          1.0          2.0          2.0   \n",
       "59999          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_300d_1  CC22_300d_2  CC22_300d_3  CC22_300d_4  CC22_300d_5  \\\n",
       "0              1.0          1.0          1.0          2.0          1.0   \n",
       "1              NaN          NaN          NaN          NaN          NaN   \n",
       "2              2.0          2.0          2.0          2.0          2.0   \n",
       "3              2.0          2.0          2.0          2.0          2.0   \n",
       "4              2.0          2.0          2.0          2.0          2.0   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          NaN          NaN          NaN          NaN          NaN   \n",
       "59996          1.0          2.0          2.0          2.0          2.0   \n",
       "59997          2.0          2.0          1.0          2.0          2.0   \n",
       "59998          1.0          1.0          1.0          1.0          1.0   \n",
       "59999          1.0          2.0          1.0          2.0          2.0   \n",
       "\n",
       "       CC22_300d_6  CC22_302  CC22_303  CC22_304  CC22_305_1  CC22_305_2  \\\n",
       "0              2.0       2.0       3.0       2.0           2           2   \n",
       "1              NaN       5.0       3.0       1.0           2           2   \n",
       "2              1.0       4.0       3.0       2.0           2           2   \n",
       "3              1.0       6.0       2.0       1.0           2           2   \n",
       "4              1.0       4.0       1.0       1.0           2           2   \n",
       "...            ...       ...       ...       ...         ...         ...   \n",
       "59995          NaN       5.0       4.0       1.0           2           2   \n",
       "59996          2.0       4.0       4.0       2.0           1           2   \n",
       "59997          2.0       4.0       4.0       2.0           2           2   \n",
       "59998          2.0       1.0       1.0       1.0           2           2   \n",
       "59999          2.0       6.0       5.0       1.0           2           2   \n",
       "\n",
       "       CC22_305_3  CC22_305_4  CC22_305_5  CC22_305_6  CC22_305_7  CC22_305_9  \\\n",
       "0               2           2           2           2           2           2   \n",
       "1               2           2           2           2           2           2   \n",
       "2               2           2           2           2           1           2   \n",
       "3               2           2           2           1           2           2   \n",
       "4               2           2           2           2           1           2   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "59995           2           2           2           2           1           1   \n",
       "59996           2           2           2           2           2           2   \n",
       "59997           2           2           2           2           1           2   \n",
       "59998           2           2           2           2           2           2   \n",
       "59999           2           2           2           2           1           2   \n",
       "\n",
       "       CC22_305_10  CC22_305_11  CC22_305_12  CC22_305_13  CC22_307  \\\n",
       "0                2            1            1            2       2.0   \n",
       "1                2            1            2            2       2.0   \n",
       "2                2            2            2            2       2.0   \n",
       "3                2            1            1            2       3.0   \n",
       "4                2            1            2            2       1.0   \n",
       "...            ...          ...          ...          ...       ...   \n",
       "59995            1            1            2            1       2.0   \n",
       "59996            2            2            2            2       3.0   \n",
       "59997            1            2            2            2       3.0   \n",
       "59998            2            1            1            2       1.0   \n",
       "59999            2            1            2            2       2.0   \n",
       "\n",
       "       CC22_309a_1  CC22_309a_2  CC22_309a_3  CC22_309a_4  CC22_309a_5  \\\n",
       "0                1            2            2            2            2   \n",
       "1                1            2            2            2            2   \n",
       "2                2            2            1            2            2   \n",
       "3                2            1            1            1            2   \n",
       "4                1            2            2            2            2   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995            1            1            1            1            2   \n",
       "59996            2            1            2            2            2   \n",
       "59997            1            1            1            1            2   \n",
       "59998            1            2            2            2            2   \n",
       "59999            2            1            1            1            2   \n",
       "\n",
       "       CC22_306  CC22_309b_1  CC22_309b_2  CC22_309b_3  CC22_309b_4  \\\n",
       "0           1.0          NaN          NaN          NaN          NaN   \n",
       "1           1.0          NaN          NaN          NaN          NaN   \n",
       "2           2.0          NaN          2.0          NaN          1.0   \n",
       "3           1.0          2.0          2.0          2.0          1.0   \n",
       "4           1.0          NaN          NaN          NaN          NaN   \n",
       "...         ...          ...          ...          ...          ...   \n",
       "59995       4.0          2.0          2.0          2.0          1.0   \n",
       "59996       2.0          1.0          NaN          NaN          2.0   \n",
       "59997       4.0          2.0          2.0          1.0          2.0   \n",
       "59998       1.0          NaN          NaN          NaN          NaN   \n",
       "59999       1.0          2.0          1.0          2.0          2.0   \n",
       "\n",
       "       CC22_309c_1  CC22_309c_2  CC22_309c_3  CC22_309c_4  CC22_309c_5  \\\n",
       "0                2            2            2            2            2   \n",
       "1                2            2            2            2            2   \n",
       "2                2            2            2            2            2   \n",
       "3                2            1            2            2            2   \n",
       "4                2            2            2            2            2   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995            1            2            2            2            2   \n",
       "59996            2            2            2            2            2   \n",
       "59997            2            2            2            2            2   \n",
       "59998            2            1            2            2            2   \n",
       "59999            1            2            2            2            2   \n",
       "\n",
       "       CC22_309c_6  CC22_309c_7  CC22_309c_8  CC22_309c_9  CC22_309c_10  \\\n",
       "0                2            2            2            2             1   \n",
       "1                2            2            2            2             1   \n",
       "2                2            2            2            2             1   \n",
       "3                2            2            2            2             2   \n",
       "4                2            2            2            2             1   \n",
       "...            ...          ...          ...          ...           ...   \n",
       "59995            2            2            2            2             2   \n",
       "59996            2            2            2            2             1   \n",
       "59997            2            1            2            2             2   \n",
       "59998            2            2            2            2             2   \n",
       "59999            2            2            2            2             2   \n",
       "\n",
       "       CC22_309dx_1  CC22_309dx_2  CC22_309dx_3  CC22_309dx_4  CC22_309dx_5  \\\n",
       "0                 1             2             1             2             2   \n",
       "1                 1             2             2             2             2   \n",
       "2                 1             2             2             2             2   \n",
       "3                 1             2             2             2             2   \n",
       "4                 1             2             2             2             2   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "59995             2             2             2             2             2   \n",
       "59996             2             2             2             2             2   \n",
       "59997             2             2             2             2             1   \n",
       "59998             1             2             2             2             2   \n",
       "59999             2             1             2             2             2   \n",
       "\n",
       "       CC22_309dx_6  CC22_309dx_7  CC22_309dx_8  CC22_309dx_9 CC22_309d_t  \\\n",
       "0                 2             2             2             2      __NA__   \n",
       "1                 2             2             2             2      __NA__   \n",
       "2                 2             2             2             2      __NA__   \n",
       "3                 2             2             2             2      __NA__   \n",
       "4                 2             2             2             2      __NA__   \n",
       "...             ...           ...           ...           ...         ...   \n",
       "59995             2             2             1             2      __NA__   \n",
       "59996             2             2             1             2      __NA__   \n",
       "59997             2             2             2             2      __NA__   \n",
       "59998             2             2             2             2      __NA__   \n",
       "59999             2             1             2             2      __NA__   \n",
       "\n",
       "       CC22_309e  CC22_309f  CC22_310a  CC22_310b  CC22_310c  CC22_310d  \\\n",
       "0            3.0        4.0        2.0        2.0        1.0        1.0   \n",
       "1            1.0        1.0        2.0        2.0        2.0        2.0   \n",
       "2            2.0        3.0        4.0        4.0        4.0        4.0   \n",
       "3            3.0        4.0        2.0        2.0        2.0        2.0   \n",
       "4            1.0        1.0        2.0        1.0        2.0        2.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        4.0        1.0        3.0        3.0        4.0        3.0   \n",
       "59996        2.0        2.0        2.0        1.0        1.0        1.0   \n",
       "59997        2.0        2.0        1.0        1.0        2.0        1.0   \n",
       "59998        1.0        1.0        2.0        2.0        2.0        2.0   \n",
       "59999        2.0        4.0        2.0        2.0        1.0        1.0   \n",
       "\n",
       "       CC22_311a  CC22_311b  CC22_311c  CC22_311d  CC22_320a  CC22_320b  \\\n",
       "0            3.0        3.0        3.0        3.0        2.0        3.0   \n",
       "1            2.0        3.0        3.0        3.0        4.0        4.0   \n",
       "2            5.0        5.0        5.0        5.0        2.0        5.0   \n",
       "3            2.0        3.0        3.0        3.0        2.0        4.0   \n",
       "4            2.0        3.0        3.0        3.0        4.0        4.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        5.0        5.0        5.0        NaN        4.0        4.0   \n",
       "59996        1.0        1.0        1.0        1.0        2.0        5.0   \n",
       "59997        3.0        2.0        3.0        2.0        4.0        3.0   \n",
       "59998        3.0        3.0        3.0        3.0        1.0        2.0   \n",
       "59999        2.0        2.0        2.0        2.0        2.0        2.0   \n",
       "\n",
       "       CC22_320c  CC22_320d  CC22_320e  CC22_320f  CC22_320g  CC22_320h  cit1  \\\n",
       "0            4.0        1.0        4.0        3.0        2.0        1.0   1.0   \n",
       "1            2.0        2.0        3.0        4.0        4.0       -2.0   1.0   \n",
       "2            3.0        3.0        5.0        5.0        5.0        0.0   1.0   \n",
       "3            4.0        1.0        5.0        1.0        2.0        2.0   1.0   \n",
       "4            4.0        4.0        4.0        4.0        4.0       -2.0   1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   ...   \n",
       "59995        4.0        2.0        4.0        NaN        5.0        0.0   1.0   \n",
       "59996        5.0        4.0        5.0        5.0        5.0        0.0   1.0   \n",
       "59997        3.0        3.0        4.0        2.0        2.0        1.0   1.0   \n",
       "59998        2.0        2.0        1.0        2.0        1.0        2.0   1.0   \n",
       "59999        2.0        3.0        5.0        5.0        4.0       -2.0   1.0   \n",
       "\n",
       "       immstat  CC22_321_1  CC22_321_2  CC22_321_3  CC22_321_4  CC22_321_5  \\\n",
       "0          4.0           2           2           1           1           2   \n",
       "1          4.0           2           2           1           1           2   \n",
       "2          5.0           1           2           2           2           2   \n",
       "3          5.0           2           2           1           1           2   \n",
       "4          4.0           2           1           2           2           2   \n",
       "...        ...         ...         ...         ...         ...         ...   \n",
       "59995      5.0           2           1           2           2           2   \n",
       "59996      3.0           2           2           2           2           2   \n",
       "59997      1.0           2           2           2           2           2   \n",
       "59998      3.0           2           2           1           1           1   \n",
       "59999      5.0           2           1           2           2           2   \n",
       "\n",
       "       CC22_321_6  CC22_321_7  CC22_321_8  CC22_327a  CC22_327b  CC22_327c  \\\n",
       "0               2           2           2        1.0        1.0        2.0   \n",
       "1               2           2           2        1.0        1.0        2.0   \n",
       "2               2           2           2        1.0        1.0        1.0   \n",
       "3               2           2           2        1.0        1.0        2.0   \n",
       "4               2           2           2        1.0        1.0        2.0   \n",
       "...           ...         ...         ...        ...        ...        ...   \n",
       "59995           2           2           2        1.0        1.0        1.0   \n",
       "59996           2           2           1        1.0        2.0        2.0   \n",
       "59997           1           2           2        2.0        1.0        1.0   \n",
       "59998           2           1           2        1.0        1.0        1.0   \n",
       "59999           2           2           2        1.0        1.0        2.0   \n",
       "\n",
       "       CC22_327d  CC22_330a  CC22_330b  CC22_330c  CC22_330d  CC22_330e  \\\n",
       "0            1.0        2.0        1.0        2.0        1.0        1.0   \n",
       "1            1.0        1.0        2.0        1.0        2.0        1.0   \n",
       "2            2.0        2.0        1.0        2.0        1.0        1.0   \n",
       "3            2.0        2.0        1.0        2.0        1.0        1.0   \n",
       "4            1.0        2.0        2.0        1.0        2.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59996        1.0        1.0        2.0        2.0        2.0        2.0   \n",
       "59997        2.0        1.0        2.0        1.0        2.0        2.0   \n",
       "59998        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59999        2.0        2.0        1.0        2.0        1.0        1.0   \n",
       "\n",
       "       CC22_330f  CC22_331a  CC22_331b  CC22_331c  CC22_331d  CC22_332a  \\\n",
       "0            2.0        1.0        2.0        2.0        2.0        1.0   \n",
       "1            2.0        1.0        1.0        2.0        1.0        1.0   \n",
       "2            2.0        1.0        1.0        2.0        2.0        1.0   \n",
       "3            2.0        1.0        2.0        2.0        2.0        1.0   \n",
       "4            1.0        1.0        2.0        1.0        2.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        1.0        1.0        2.0        2.0        2.0        1.0   \n",
       "59996        1.0        2.0        2.0        2.0        2.0        1.0   \n",
       "59997        1.0        2.0        1.0        1.0        1.0        2.0   \n",
       "59998        1.0        1.0        1.0        1.0        1.0        2.0   \n",
       "59999        2.0        1.0        1.0        1.0        2.0        1.0   \n",
       "\n",
       "       CC22_332b  CC22_332c  CC22_332d  CC22_332e  CC22_332f  CC22_333  \\\n",
       "0            2.0        2.0        2.0        2.0        2.0       1.0   \n",
       "1            1.0        2.0        2.0        1.0        2.0       4.0   \n",
       "2            2.0        2.0        2.0        2.0        2.0       2.0   \n",
       "3            2.0        2.0        2.0        2.0        2.0       1.0   \n",
       "4            2.0        1.0        2.0        2.0        2.0       1.0   \n",
       "...          ...        ...        ...        ...        ...       ...   \n",
       "59995        1.0        2.0        1.0        2.0        2.0       1.0   \n",
       "59996        1.0        2.0        2.0        2.0        2.0       4.0   \n",
       "59997        2.0        2.0        2.0        2.0        2.0       1.0   \n",
       "59998        1.0        1.0        1.0        1.0        2.0       1.0   \n",
       "59999        2.0        1.0        2.0        2.0        2.0       2.0   \n",
       "\n",
       "       CC22_333a  CC22_333b  CC22_333c  CC22_333d  CC22_333e  CC22_334a  \\\n",
       "0            1.0        1.0        1.0        1.0        2.0        1.0   \n",
       "1            2.0        2.0        1.0        1.0        1.0        2.0   \n",
       "2            1.0        2.0        2.0        1.0        2.0        1.0   \n",
       "3            1.0        1.0        1.0        1.0        2.0        1.0   \n",
       "4            1.0        1.0        1.0        1.0        2.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59996        2.0        1.0        2.0        1.0        1.0        1.0   \n",
       "59997        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59998        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59999        1.0        1.0        2.0        1.0        1.0        1.0   \n",
       "\n",
       "       CC22_334b  CC22_334c  CC22_334d  CC22_334e  CC22_334f  CC22_334g  \\\n",
       "0            1.0        2.0        1.0        1.0        1.0        1.0   \n",
       "1            1.0        1.0        2.0        1.0        2.0        2.0   \n",
       "2            1.0        2.0        1.0        1.0        1.0        1.0   \n",
       "3            1.0        2.0        1.0        1.0        1.0        1.0   \n",
       "4            1.0        2.0        1.0        1.0        1.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        1.0        1.0        2.0        2.0        1.0        1.0   \n",
       "59996        1.0        2.0        2.0        1.0        1.0        1.0   \n",
       "59997        1.0        2.0        1.0        1.0        1.0        2.0   \n",
       "59998        1.0        1.0        2.0        2.0        1.0        2.0   \n",
       "59999        1.0        2.0        1.0        1.0        1.0        2.0   \n",
       "\n",
       "       CC22_334h  CC22_340a  CC22_340b  CC22_340c  CC22_340d  CC22_340e  \\\n",
       "0            1.0        1.0        3.0        4.0        7.0        3.0   \n",
       "1            1.0        6.0        5.0        1.0        6.0        1.0   \n",
       "2            1.0        2.0        8.0        2.0        7.0        2.0   \n",
       "3            1.0        1.0        3.0        5.0        7.0        5.0   \n",
       "4            1.0        4.0        4.0        4.0        4.0        4.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        1.0        1.0        3.0        7.0        7.0        7.0   \n",
       "59996        1.0        2.0        8.0        1.0        7.0        7.0   \n",
       "59997        1.0        1.0        4.0        8.0        1.0        8.0   \n",
       "59998        1.0        1.0        2.0        2.0        7.0        2.0   \n",
       "59999        1.0        4.0        8.0        8.0        8.0        8.0   \n",
       "\n",
       "       CC22_340f  CC22_340g  CC22_340h  CC22_340i  CC22_340j  CC22_340k  \\\n",
       "0            7.0        7.0        3.0        3.0        NaN        NaN   \n",
       "1            5.0        5.0        1.0        1.0        NaN        NaN   \n",
       "2            7.0        6.0        8.0        8.0        2.0        7.0   \n",
       "3            7.0        7.0        NaN        2.0        4.0        7.0   \n",
       "4            4.0        4.0        4.0        NaN        4.0        4.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        7.0        8.0        NaN        7.0        8.0        6.0   \n",
       "59996        7.0        8.0        NaN        8.0        8.0        8.0   \n",
       "59997        1.0        5.0        3.0        4.0        4.0        4.0   \n",
       "59998        6.0        6.0        NaN        3.0        2.0        6.0   \n",
       "59999        8.0        8.0        NaN        8.0        8.0        8.0   \n",
       "\n",
       "       CC22_340l  CC22_340m  CC22_340n  CC22_350a  CC22_350b  CC22_350c  \\\n",
       "0            1.0        7.0        NaN        1.0        1.0        1.0   \n",
       "1            1.0        8.0        NaN        2.0        1.0        1.0   \n",
       "2            8.0        8.0        NaN        2.0        1.0        1.0   \n",
       "3            2.0        8.0        NaN        1.0        1.0        1.0   \n",
       "4            4.0        4.0        NaN        2.0        1.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        8.0        8.0        NaN        2.0        1.0        1.0   \n",
       "59996        8.0        8.0        NaN        2.0        1.0        2.0   \n",
       "59997        4.0        4.0        NaN        1.0        2.0        1.0   \n",
       "59998        2.0        3.0        NaN        1.0        1.0        1.0   \n",
       "59999        8.0        8.0        8.0        1.0        1.0        1.0   \n",
       "\n",
       "       CC22_350d  CC22_350e  CC22_350f  CC22_350g  CC22_350h  CC22_355a  \\\n",
       "0            1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "1            1.0        1.0        1.0        2.0        1.0        2.0   \n",
       "2            1.0        2.0        2.0        1.0        1.0        1.0   \n",
       "3            1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "4            1.0        2.0        2.0        2.0        1.0        1.0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        2.0        2.0        1.0        2.0        2.0        1.0   \n",
       "59996        1.0        1.0        1.0        2.0        2.0        2.0   \n",
       "59997        2.0        1.0        1.0        2.0        2.0        1.0   \n",
       "59998        1.0        1.0        1.0        1.0        1.0        1.0   \n",
       "59999        1.0        2.0        2.0        1.0        1.0        1.0   \n",
       "\n",
       "       CC22_355b  CC22_355c  CC22_355d  CC22_355e  CC22_360  CC22_361  \\\n",
       "0            1.0        1.0        1.0        1.0       NaN       4.0   \n",
       "1            2.0        2.0        1.0        2.0       1.0       5.0   \n",
       "2            1.0        2.0        1.0        2.0       2.0       6.0   \n",
       "3            1.0        1.0        1.0        1.0       1.0       6.0   \n",
       "4            1.0        1.0        1.0        2.0       1.0       1.0   \n",
       "...          ...        ...        ...        ...       ...       ...   \n",
       "59995        1.0        1.0        1.0        2.0       4.0       5.0   \n",
       "59996        2.0        1.0        1.0        1.0       NaN       1.0   \n",
       "59997        1.0        1.0        1.0        2.0       1.0       3.0   \n",
       "59998        1.0        1.0        1.0        1.0       2.0       4.0   \n",
       "59999        1.0        1.0        1.0        2.0       2.0       3.0   \n",
       "\n",
       "       urbancity urbancity_t  presvote20post presvote20post_t  CC22_363  \\\n",
       "0              2      __NA__             1.0           __NA__       1.0   \n",
       "1              3      __NA__             1.0           __NA__       1.0   \n",
       "2              1      __NA__             1.0           __NA__       5.0   \n",
       "3              3      __NA__             1.0           __NA__       1.0   \n",
       "4              1      __NA__             6.0           __NA__       5.0   \n",
       "...          ...         ...             ...              ...       ...   \n",
       "59995          2      __NA__             1.0           __NA__       1.0   \n",
       "59996          3      __NA__             6.0           __NA__       2.0   \n",
       "59997          1      __NA__             2.0           __NA__       1.0   \n",
       "59998          1      __NA__             1.0           __NA__       1.0   \n",
       "59999          4      __NA__             1.0           __NA__       3.0   \n",
       "\n",
       "       CC22_365_voted CC22_365_voted_t  CC22_365b_voted CC22_365b_voted_t  \\\n",
       "0                 NaN           __NA__              NaN            __NA__   \n",
       "1                 NaN           __NA__              NaN            __NA__   \n",
       "2                 NaN           __NA__              NaN            __NA__   \n",
       "3                 NaN           __NA__              NaN            __NA__   \n",
       "4                 NaN           __NA__              NaN            __NA__   \n",
       "...               ...              ...              ...               ...   \n",
       "59995             NaN           __NA__              NaN            __NA__   \n",
       "59996             NaN           __NA__              NaN            __NA__   \n",
       "59997             NaN           __NA__              NaN            __NA__   \n",
       "59998             NaN           __NA__              NaN            __NA__   \n",
       "59999             1.0           __NA__              NaN            __NA__   \n",
       "\n",
       "       CC22_366_voted CC22_366_voted_t  CC22_367_voted CC22_367_voted_t  \\\n",
       "0                 NaN           __NA__             NaN           __NA__   \n",
       "1                 NaN           __NA__             NaN           __NA__   \n",
       "2                 NaN           __NA__             NaN           __NA__   \n",
       "3                 NaN           __NA__             NaN           __NA__   \n",
       "4                 NaN           __NA__             NaN           __NA__   \n",
       "...               ...              ...             ...              ...   \n",
       "59995             NaN           __NA__             NaN           __NA__   \n",
       "59996             NaN           __NA__             NaN           __NA__   \n",
       "59997             NaN           __NA__             NaN           __NA__   \n",
       "59998             NaN           __NA__             NaN           __NA__   \n",
       "59999             1.0           __NA__             1.0           __NA__   \n",
       "\n",
       "       CC22_365 CC22_365_t  CC22_365a CC22_365a_t  CC22_365b CC22_365b_t  \\\n",
       "0           NaN     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "1           NaN     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "2           1.0     __NA__        9.0      __NA__        NaN      __NA__   \n",
       "3           1.0     __NA__        9.0      __NA__        NaN      __NA__   \n",
       "4           9.0     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "...         ...        ...        ...         ...        ...         ...   \n",
       "59995       8.0     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "59996       9.0     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "59997       9.0     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "59998       1.0     __NA__        2.0      __NA__        NaN      __NA__   \n",
       "59999       NaN     __NA__        NaN      __NA__        NaN      __NA__   \n",
       "\n",
       "       CC22_365c CC22_365c_t  CC22_366 CC22_366_t  CC22_367 CC22_367_t  \\\n",
       "0            NaN      __NA__       1.0     __NA__       1.0     __NA__   \n",
       "1            NaN      __NA__       2.0     __NA__       2.0     __NA__   \n",
       "2            NaN      __NA__       1.0     __NA__       1.0     __NA__   \n",
       "3            NaN      __NA__       1.0     __NA__       1.0     __NA__   \n",
       "4            NaN      __NA__       9.0     __NA__      99.0     __NA__   \n",
       "...          ...         ...       ...        ...       ...        ...   \n",
       "59995        NaN      __NA__       2.0     __NA__      98.0     __NA__   \n",
       "59996        NaN      __NA__       8.0     __NA__      98.0     __NA__   \n",
       "59997        NaN      __NA__       2.0     __NA__      98.0     __NA__   \n",
       "59998        NaN      __NA__       1.0     __NA__       1.0     __NA__   \n",
       "59999        NaN      __NA__       NaN     __NA__       NaN     __NA__   \n",
       "\n",
       "       CC22_367a CC22_367a_t  ideo5  employ employ_t  hadjob  investor  \\\n",
       "0           99.0      __NA__    1.0     1.0   __NA__     NaN       1.0   \n",
       "1           98.0      __NA__    4.0     1.0   __NA__     NaN       1.0   \n",
       "2           99.0      __NA__    2.0     1.0   __NA__     NaN       2.0   \n",
       "3           99.0      __NA__    1.0     1.0   __NA__     NaN       1.0   \n",
       "4            NaN      __NA__    2.0     1.0   __NA__     NaN       2.0   \n",
       "...          ...         ...    ...     ...      ...     ...       ...   \n",
       "59995        NaN      __NA__    1.0     2.0   __NA__     NaN       2.0   \n",
       "59996        NaN      __NA__    1.0     4.0   __NA__     2.0       1.0   \n",
       "59997        NaN      __NA__    3.0     1.0   __NA__     NaN       2.0   \n",
       "59998        2.0      __NA__    1.0     1.0   __NA__     NaN       1.0   \n",
       "59999        NaN      __NA__    6.0     4.0   __NA__     1.0       1.0   \n",
       "\n",
       "       pew_bornagain  pew_religimp  pew_churatd  pew_prayer  religpew  \\\n",
       "0                2.0           4.0          6.0         7.0      11.0   \n",
       "1                1.0           2.0          4.0         1.0       1.0   \n",
       "2                2.0           4.0          6.0         7.0      11.0   \n",
       "3                2.0           4.0          6.0         7.0      10.0   \n",
       "4                2.0           3.0          5.0         6.0      11.0   \n",
       "...              ...           ...          ...         ...       ...   \n",
       "59995            1.0           2.0          5.0         6.0      12.0   \n",
       "59996            1.0           1.0          2.0         1.0       1.0   \n",
       "59997            2.0           4.0          6.0         8.0       9.0   \n",
       "59998            1.0           1.0          1.0         1.0       1.0   \n",
       "59999            2.0           2.0          5.0         1.0      11.0   \n",
       "\n",
       "      religpew_t  religpew_protestant religpew_protestant_t  religpew_baptist  \\\n",
       "0         __NA__                  NaN                __NA__               NaN   \n",
       "1         __NA__                  7.0                __NA__               NaN   \n",
       "2         __NA__                  NaN                __NA__               NaN   \n",
       "3         __NA__                  NaN                __NA__               NaN   \n",
       "4         __NA__                 90.0                __NA__               NaN   \n",
       "...          ...                  ...                   ...               ...   \n",
       "59995  Christian                  NaN                __NA__               NaN   \n",
       "59996  Christian                  1.0                __NA__               7.0   \n",
       "59997     __NA__                  NaN                __NA__               NaN   \n",
       "59998     __NA__                  6.0                __NA__               NaN   \n",
       "59999     __NA__                  NaN                __NA__               NaN   \n",
       "\n",
       "      religpew_baptist_t  religpew_methodist religpew_methodist_t  \\\n",
       "0                 __NA__                 NaN               __NA__   \n",
       "1                 __NA__                 NaN               __NA__   \n",
       "2                 __NA__                 NaN               __NA__   \n",
       "3                 __NA__                 NaN               __NA__   \n",
       "4                 __NA__                 NaN               __NA__   \n",
       "...                  ...                 ...                  ...   \n",
       "59995             __NA__                 NaN               __NA__   \n",
       "59996             __NA__                 NaN               __NA__   \n",
       "59997             __NA__                 NaN               __NA__   \n",
       "59998             __NA__                 NaN               __NA__   \n",
       "59999             __NA__                 NaN               __NA__   \n",
       "\n",
       "       religpew_nondenom religpew_nondenom_t  religpew_lutheran  \\\n",
       "0                    NaN              __NA__                NaN   \n",
       "1                    NaN              __NA__                NaN   \n",
       "2                    NaN              __NA__                NaN   \n",
       "3                    NaN              __NA__                NaN   \n",
       "4                    NaN              __NA__                NaN   \n",
       "...                  ...                 ...                ...   \n",
       "59995                NaN              __NA__                NaN   \n",
       "59996                NaN              __NA__                NaN   \n",
       "59997                NaN              __NA__                NaN   \n",
       "59998                NaN              __NA__                NaN   \n",
       "59999                NaN              __NA__                NaN   \n",
       "\n",
       "      religpew_lutheran_t  religpew_presby religpew_presby_t  \\\n",
       "0                  __NA__              NaN            __NA__   \n",
       "1                  __NA__              NaN            __NA__   \n",
       "2                  __NA__              NaN            __NA__   \n",
       "3                  __NA__              NaN            __NA__   \n",
       "4                  __NA__              NaN            __NA__   \n",
       "...                   ...              ...               ...   \n",
       "59995              __NA__              NaN            __NA__   \n",
       "59996              __NA__              NaN            __NA__   \n",
       "59997              __NA__              NaN            __NA__   \n",
       "59998              __NA__              NaN            __NA__   \n",
       "59999              __NA__              NaN            __NA__   \n",
       "\n",
       "       religpew_pentecost religpew_pentecost_t  religpew_episcop  \\\n",
       "0                     NaN               __NA__               NaN   \n",
       "1                     NaN               __NA__               3.0   \n",
       "2                     NaN               __NA__               NaN   \n",
       "3                     NaN               __NA__               NaN   \n",
       "4                     NaN               __NA__               NaN   \n",
       "...                   ...                  ...               ...   \n",
       "59995                 NaN               __NA__               NaN   \n",
       "59996                 NaN               __NA__               NaN   \n",
       "59997                 NaN               __NA__               NaN   \n",
       "59998                 1.0               __NA__               NaN   \n",
       "59999                 NaN               __NA__               NaN   \n",
       "\n",
       "      religpew_episcop_t  religpew_christian religpew_christian_t  \\\n",
       "0                 __NA__                 NaN               __NA__   \n",
       "1                 __NA__                 NaN               __NA__   \n",
       "2                 __NA__                 NaN               __NA__   \n",
       "3                 __NA__                 NaN               __NA__   \n",
       "4                 __NA__                 NaN               __NA__   \n",
       "...                  ...                 ...                  ...   \n",
       "59995             __NA__                 NaN               __NA__   \n",
       "59996             __NA__                 NaN               __NA__   \n",
       "59997             __NA__                 NaN               __NA__   \n",
       "59998             __NA__                 NaN               __NA__   \n",
       "59999             __NA__                 NaN               __NA__   \n",
       "\n",
       "       religpew_congreg religpew_congreg_t  religpew_holiness  \\\n",
       "0                   NaN             __NA__                NaN   \n",
       "1                   NaN             __NA__                NaN   \n",
       "2                   NaN             __NA__                NaN   \n",
       "3                   NaN             __NA__                NaN   \n",
       "4                   NaN             __NA__                NaN   \n",
       "...                 ...                ...                ...   \n",
       "59995               NaN             __NA__                NaN   \n",
       "59996               NaN             __NA__                NaN   \n",
       "59997               NaN             __NA__                NaN   \n",
       "59998               NaN             __NA__                NaN   \n",
       "59999               NaN             __NA__                NaN   \n",
       "\n",
       "      religpew_holiness_t  religpew_reformed religpew_reformed_t  \\\n",
       "0                  __NA__                NaN              __NA__   \n",
       "1                  __NA__                NaN              __NA__   \n",
       "2                  __NA__                NaN              __NA__   \n",
       "3                  __NA__                NaN              __NA__   \n",
       "4                  __NA__                NaN              __NA__   \n",
       "...                   ...                ...                 ...   \n",
       "59995              __NA__                NaN              __NA__   \n",
       "59996              __NA__                NaN              __NA__   \n",
       "59997              __NA__                NaN              __NA__   \n",
       "59998              __NA__                NaN              __NA__   \n",
       "59999              __NA__                NaN              __NA__   \n",
       "\n",
       "       religpew_advent religpew_advent_t  religpew_catholic  \\\n",
       "0                  NaN            __NA__                NaN   \n",
       "1                  NaN            __NA__                NaN   \n",
       "2                  NaN            __NA__                NaN   \n",
       "3                  NaN            __NA__                NaN   \n",
       "4                  NaN            __NA__                1.0   \n",
       "...                ...               ...                ...   \n",
       "59995              NaN            __NA__                NaN   \n",
       "59996              NaN            __NA__                NaN   \n",
       "59997              NaN            __NA__                NaN   \n",
       "59998              NaN            __NA__                NaN   \n",
       "59999              NaN            __NA__                NaN   \n",
       "\n",
       "      religpew_catholic_t  religpew_mormon religpew_mormon_t  \\\n",
       "0                  __NA__              NaN            __NA__   \n",
       "1                  __NA__              NaN            __NA__   \n",
       "2                  __NA__              NaN            __NA__   \n",
       "3                  __NA__              NaN            __NA__   \n",
       "4                  __NA__              NaN            __NA__   \n",
       "...                   ...              ...               ...   \n",
       "59995              __NA__              NaN            __NA__   \n",
       "59996              __NA__              NaN            __NA__   \n",
       "59997              __NA__              NaN            __NA__   \n",
       "59998              __NA__              NaN            __NA__   \n",
       "59999              __NA__              NaN            __NA__   \n",
       "\n",
       "       religpew_orthodox religpew_orthodox_t  religpew_jewish  \\\n",
       "0                    NaN              __NA__              NaN   \n",
       "1                    NaN              __NA__              NaN   \n",
       "2                    NaN              __NA__              NaN   \n",
       "3                    NaN              __NA__              NaN   \n",
       "4                    NaN              __NA__              NaN   \n",
       "...                  ...                 ...              ...   \n",
       "59995                NaN              __NA__              NaN   \n",
       "59996                NaN              __NA__              NaN   \n",
       "59997                NaN              __NA__              NaN   \n",
       "59998                NaN              __NA__              NaN   \n",
       "59999                NaN              __NA__              NaN   \n",
       "\n",
       "      religpew_jewish_t  religpew_muslim religpew_muslim_t  religpew_buddhist  \\\n",
       "0                __NA__              NaN            __NA__                NaN   \n",
       "1                __NA__              NaN            __NA__                NaN   \n",
       "2                __NA__              NaN            __NA__                NaN   \n",
       "3                __NA__              NaN            __NA__                NaN   \n",
       "4                __NA__              NaN            __NA__                NaN   \n",
       "...                 ...              ...               ...                ...   \n",
       "59995            __NA__              NaN            __NA__                NaN   \n",
       "59996            __NA__              NaN            __NA__                NaN   \n",
       "59997            __NA__              NaN            __NA__                NaN   \n",
       "59998            __NA__              NaN            __NA__                NaN   \n",
       "59999            __NA__              NaN            __NA__                NaN   \n",
       "\n",
       "      religpew_buddhist_t  religpew_hindu religpew_hindu_t  marstat  union  \\\n",
       "0                  __NA__             NaN           __NA__      5.0    2.0   \n",
       "1                  __NA__             NaN           __NA__      3.0    3.0   \n",
       "2                  __NA__             NaN           __NA__      5.0    3.0   \n",
       "3                  __NA__             NaN           __NA__      1.0    2.0   \n",
       "4                  __NA__             NaN           __NA__      3.0    3.0   \n",
       "...                   ...             ...              ...      ...    ...   \n",
       "59995              __NA__             NaN           __NA__      1.0    3.0   \n",
       "59996              __NA__             NaN           __NA__      1.0    3.0   \n",
       "59997              __NA__             NaN           __NA__      3.0    3.0   \n",
       "59998              __NA__             NaN           __NA__      1.0    1.0   \n",
       "59999              __NA__             NaN           __NA__      5.0    3.0   \n",
       "\n",
       "       union_coverage  unionhh  dualcit  dualctry  ownhome ownhome_t  newsint  \\\n",
       "0                 2.0      3.0      2.0       NaN      1.0    __NA__      1.0   \n",
       "1                 2.0      3.0      2.0       NaN      2.0    __NA__      1.0   \n",
       "2                 2.0      1.0      2.0       NaN      1.0    __NA__      3.0   \n",
       "3                 2.0      2.0      2.0       NaN      1.0    __NA__      2.0   \n",
       "4                 2.0      3.0      2.0       NaN      2.0    __NA__      2.0   \n",
       "...               ...      ...      ...       ...      ...       ...      ...   \n",
       "59995             2.0      3.0      1.0     630.0      1.0    __NA__      2.0   \n",
       "59996             2.0      3.0      1.0      84.0      2.0    __NA__      3.0   \n",
       "59997             2.0      3.0      2.0       NaN      2.0    __NA__      3.0   \n",
       "59998             NaN      1.0      1.0     404.0      1.0    __NA__      1.0   \n",
       "59999             2.0      3.0      2.0       NaN      1.0    __NA__      2.0   \n",
       "\n",
       "       faminc_new  milstat_1  milstat_2  milstat_3  milstat_4  milstat_5  \\\n",
       "0            11.0          2          2          2          2          1   \n",
       "1             8.0          2          2          2          2          1   \n",
       "2             6.0          2          2          2          2          1   \n",
       "3            11.0          2          1          2          1          2   \n",
       "4             9.0          2          2          2          2          1   \n",
       "...           ...        ...        ...        ...        ...        ...   \n",
       "59995         4.0          2          2          2          2          1   \n",
       "59996         5.0          2          2          2          1          2   \n",
       "59997         3.0          2          2          2          2          1   \n",
       "59998        16.0          2          2          2          1          2   \n",
       "59999         4.0          2          2          2          1          2   \n",
       "\n",
       "       child18  child18num  healthins_1  healthins_2  healthins_3  \\\n",
       "0          2.0         NaN            1            2            2   \n",
       "1          2.0         NaN            1            2            2   \n",
       "2          2.0         NaN            1            2            2   \n",
       "3          1.0         1.0            1            2            2   \n",
       "4          2.0         NaN            2            1            2   \n",
       "...        ...         ...          ...          ...          ...   \n",
       "59995      1.0         3.0            2            1            2   \n",
       "59996      1.0         2.0            1            2            2   \n",
       "59997      1.0         5.0            1            2            2   \n",
       "59998      1.0         2.0            1            2            2   \n",
       "59999      2.0         NaN            2            1            2   \n",
       "\n",
       "       healthins_4  healthins_5  healthins_6  healthins2  phone  internethome  \\\n",
       "0                2            2            2         NaN    2.0           1.0   \n",
       "1                2            2            2         NaN    2.0           1.0   \n",
       "2                2            2            2         NaN    1.0           1.0   \n",
       "3                2            2            2         NaN    2.0           1.0   \n",
       "4                2            2            2         NaN    2.0           1.0   \n",
       "...            ...          ...          ...         ...    ...           ...   \n",
       "59995            2            2            2         NaN    2.0           1.0   \n",
       "59996            2            2            2         NaN    2.0           3.0   \n",
       "59997            2            2            2         NaN    2.0           3.0   \n",
       "59998            2            2            2         NaN    2.0           2.0   \n",
       "59999            2            2            2         NaN    2.0           1.0   \n",
       "\n",
       "       internetwork  CC22_hisp_1  CC22_hisp_2  CC22_hisp_3  CC22_hisp_4  \\\n",
       "0               1.0          NaN          NaN          NaN          NaN   \n",
       "1               1.0          NaN          NaN          NaN          NaN   \n",
       "2               1.0          NaN          NaN          NaN          NaN   \n",
       "3               1.0          NaN          NaN          NaN          NaN   \n",
       "4               1.0          NaN          NaN          NaN          NaN   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "59995           1.0          2.0          2.0          2.0          1.0   \n",
       "59996           3.0          NaN          NaN          NaN          NaN   \n",
       "59997           3.0          NaN          NaN          NaN          NaN   \n",
       "59998           1.0          NaN          NaN          NaN          NaN   \n",
       "59999           1.0          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_hisp_5  CC22_hisp_6  CC22_hisp_7  CC22_hisp_8  CC22_hisp_9  \\\n",
       "0              NaN          NaN          NaN          NaN          NaN   \n",
       "1              NaN          NaN          NaN          NaN          NaN   \n",
       "2              NaN          NaN          NaN          NaN          NaN   \n",
       "3              NaN          NaN          NaN          NaN          NaN   \n",
       "4              NaN          NaN          NaN          NaN          NaN   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          2.0          2.0          2.0          2.0          2.0   \n",
       "59996          NaN          NaN          NaN          NaN          NaN   \n",
       "59997          NaN          NaN          NaN          NaN          NaN   \n",
       "59998          NaN          NaN          NaN          NaN          NaN   \n",
       "59999          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_hisp_10  CC22_hisp_11  CC22_hisp_12 CC22_hisp_t  CC22_asian_1  \\\n",
       "0               NaN           NaN           NaN      __NA__           NaN   \n",
       "1               NaN           NaN           NaN      __NA__           NaN   \n",
       "2               NaN           NaN           NaN      __NA__           NaN   \n",
       "3               NaN           NaN           NaN      __NA__           NaN   \n",
       "4               NaN           NaN           NaN      __NA__           NaN   \n",
       "...             ...           ...           ...         ...           ...   \n",
       "59995           2.0           1.0           2.0      Canada           NaN   \n",
       "59996           NaN           NaN           NaN      __NA__           NaN   \n",
       "59997           NaN           NaN           NaN      __NA__           NaN   \n",
       "59998           NaN           NaN           NaN      __NA__           NaN   \n",
       "59999           NaN           NaN           NaN      __NA__           NaN   \n",
       "\n",
       "       CC22_asian_2  CC22_asian_3  CC22_asian_4  CC22_asian_5  CC22_asian_6  \\\n",
       "0               NaN           NaN           NaN           NaN           NaN   \n",
       "1               NaN           NaN           NaN           NaN           NaN   \n",
       "2               NaN           NaN           NaN           NaN           NaN   \n",
       "3               NaN           NaN           NaN           NaN           NaN   \n",
       "4               NaN           NaN           NaN           NaN           NaN   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "59995           NaN           NaN           NaN           NaN           NaN   \n",
       "59996           NaN           NaN           NaN           NaN           NaN   \n",
       "59997           NaN           NaN           NaN           NaN           NaN   \n",
       "59998           NaN           NaN           NaN           NaN           NaN   \n",
       "59999           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "       CC22_asian_7  CC22_asian_8  CC22_asian_9  CC22_asian_10  CC22_asian_11  \\\n",
       "0               NaN           NaN           NaN            NaN            NaN   \n",
       "1               NaN           NaN           NaN            NaN            NaN   \n",
       "2               NaN           NaN           NaN            NaN            NaN   \n",
       "3               NaN           NaN           NaN            NaN            NaN   \n",
       "4               NaN           NaN           NaN            NaN            NaN   \n",
       "...             ...           ...           ...            ...            ...   \n",
       "59995           NaN           NaN           NaN            NaN            NaN   \n",
       "59996           NaN           NaN           NaN            NaN            NaN   \n",
       "59997           NaN           NaN           NaN            NaN            NaN   \n",
       "59998           NaN           NaN           NaN            NaN            NaN   \n",
       "59999           NaN           NaN           NaN            NaN            NaN   \n",
       "\n",
       "       CC22_asian_12  CC22_asian_13  CC22_asian_14  CC22_asian_15  \\\n",
       "0                NaN            NaN            NaN            NaN   \n",
       "1                NaN            NaN            NaN            NaN   \n",
       "2                NaN            NaN            NaN            NaN   \n",
       "3                NaN            NaN            NaN            NaN   \n",
       "4                NaN            NaN            NaN            NaN   \n",
       "...              ...            ...            ...            ...   \n",
       "59995            NaN            NaN            NaN            NaN   \n",
       "59996            NaN            NaN            NaN            NaN   \n",
       "59997            NaN            NaN            NaN            NaN   \n",
       "59998            NaN            NaN            NaN            NaN   \n",
       "59999            NaN            NaN            NaN            NaN   \n",
       "\n",
       "      CC22_asian_t  presvote16post presvote16post_t  industry  sexuality  \\\n",
       "0           __NA__             1.0           __NA__      17.0        1.0   \n",
       "1           __NA__             2.0           __NA__       5.0        1.0   \n",
       "2           __NA__             1.0           __NA__       8.0        1.0   \n",
       "3           __NA__             1.0           __NA__       7.0        4.0   \n",
       "4           __NA__             7.0           __NA__       7.0        1.0   \n",
       "...            ...             ...              ...       ...        ...   \n",
       "59995       __NA__             1.0           __NA__      12.0        1.0   \n",
       "59996       __NA__             7.0           __NA__       NaN        1.0   \n",
       "59997       __NA__             2.0           __NA__      16.0        4.0   \n",
       "59998       __NA__             1.0           __NA__      14.0        1.0   \n",
       "59999       __NA__             1.0           __NA__       8.0        1.0   \n",
       "\n",
       "       transgender  cdid117  cdid118  lookupzip  \\\n",
       "0              2.0       11       12      48152   \n",
       "1              2.0        2        2       1331   \n",
       "2              2.0       11       11      17603   \n",
       "3              1.0        5        5       6716   \n",
       "4              2.0        3        3      21401   \n",
       "...            ...      ...      ...        ...   \n",
       "59995          2.0       13       13      33774   \n",
       "59996          2.0       15       16      33578   \n",
       "59997          2.0        9        9      17901   \n",
       "59998          2.0       12       12      10016   \n",
       "59999          2.0       15       15      33810   \n",
       "\n",
       "                                   LegName  \\\n",
       "0               Michigan State Legislature   \n",
       "1          Massachusetts State Legislature   \n",
       "2      Pennsylvania State General Assembly   \n",
       "3       Connecticut State General Assembly   \n",
       "4          Maryland State General Assembly   \n",
       "...                                    ...   \n",
       "59995            Florida State Legislature   \n",
       "59996            Florida State Legislature   \n",
       "59997  Pennsylvania State General Assembly   \n",
       "59998           New York State Legislature   \n",
       "59999            Florida State Legislature   \n",
       "\n",
       "                                   LowerChamberName  \\\n",
       "0           Michigan State House of Representatives   \n",
       "1      Massachusetts State House of Representatives   \n",
       "2       Pennsylvania State House of Representatives   \n",
       "3        Connecticut State House of Representatives   \n",
       "4                 Maryland State House of Delegates   \n",
       "...                                             ...   \n",
       "59995        Florida State House of Representatives   \n",
       "59996        Florida State House of Representatives   \n",
       "59997   Pennsylvania State House of Representatives   \n",
       "59998                       New York State Assembly   \n",
       "59999        Florida State House of Representatives   \n",
       "\n",
       "                 UpperChamberName    CurrentGovName CurrentGovParty  \\\n",
       "0           Michigan State Senate  Gretchen Whitmer      Democratic   \n",
       "1      Massachusetts State Senate     Charlie Baker      Republican   \n",
       "2       Pennsylvania State Senate          Tom Wolf      Democratic   \n",
       "3        Connecticut State Senate        Ned Lamont      Democratic   \n",
       "4           Maryland State Senate       Larry Hogan      Republican   \n",
       "...                           ...               ...             ...   \n",
       "59995        Florida State Senate      Ron DeSantis      Republican   \n",
       "59996        Florida State Senate      Ron DeSantis      Republican   \n",
       "59997   Pennsylvania State Senate          Tom Wolf      Democratic   \n",
       "59998       New York State Senate      Kathy Hochul      Democratic   \n",
       "59999        Florida State Senate      Ron DeSantis      Republican   \n",
       "\n",
       "      CurrentHouseName CurrentHouseParty     CurrentSen1Name CurrentSen1Party  \\\n",
       "0        Haley Stevens        Democratic     Debbie Stabenow       Democratic   \n",
       "1         Jim McGovern        Democratic    Elizabeth Warren       Democratic   \n",
       "2        Lloyd Smucker        Republican       Bob Casey Jr.       Democratic   \n",
       "3         Jahana Hayes        Democratic  Richard Blumenthal       Democratic   \n",
       "4        John Sarbanes        Democratic          Ben Cardin       Democratic   \n",
       "...                ...               ...                 ...              ...   \n",
       "59995              NaN               NaN         Marco Rubio       Republican   \n",
       "59996   Scott Franklin        Republican         Marco Rubio       Republican   \n",
       "59997       Dan Meuser        Republican       Bob Casey Jr.       Democratic   \n",
       "59998  Carolyn Maloney        Democratic       Chuck Schumer       Democratic   \n",
       "59999   Scott Franklin        Republican         Marco Rubio       Republican   \n",
       "\n",
       "          CurrentSen2Name CurrentSen2Party      GovCand1Name GovCand1Party  \\\n",
       "0             Gary Peters       Democratic  Gretchen Whitmer    Democratic   \n",
       "1               Ed Markey       Democratic      Maura Healey    Democratic   \n",
       "2              Pat Toomey       Republican      Josh Shapiro    Democratic   \n",
       "3            Chris Murphy       Democratic        Ned Lamont    Democratic   \n",
       "4        Chris Van Hollen       Democratic         Wes Moore    Democratic   \n",
       "...                   ...              ...               ...           ...   \n",
       "59995          Rick Scott       Republican     Charlie Crist    Democratic   \n",
       "59996          Rick Scott       Republican     Charlie Crist    Democratic   \n",
       "59997          Pat Toomey       Republican      Josh Shapiro    Democratic   \n",
       "59998  Kirsten Gillibrand       Democratic      Kathy Hochul    Democratic   \n",
       "59999          Rick Scott       Republican     Charlie Crist    Democratic   \n",
       "\n",
       "          GovCand2Name GovCand2Party GovCand3Name GovCand3Party  \\\n",
       "0          Tudor Dixon    Republican          NaN           NaN   \n",
       "1          Geoff Diehl    Republican          NaN           NaN   \n",
       "2       Doug Mastriano    Republican          NaN           NaN   \n",
       "3      Bob Stefanowski    Republican          NaN           NaN   \n",
       "4              Dan Cox    Republican          NaN           NaN   \n",
       "...                ...           ...          ...           ...   \n",
       "59995     Ron DeSantis    Republican          NaN           NaN   \n",
       "59996     Ron DeSantis    Republican          NaN           NaN   \n",
       "59997   Doug Mastriano    Republican          NaN           NaN   \n",
       "59998       Lee Zeldin    Republican          NaN           NaN   \n",
       "59999     Ron DeSantis    Republican          NaN           NaN   \n",
       "\n",
       "       HouseCand1Name HouseCand1Party          HouseCand2Name HouseCand2Party  \\\n",
       "0       Rashida Tlaib      Democratic          Steven Elliott      Republican   \n",
       "1        Jim McGovern      Democratic  Jeffrey Sossa-Paquette      Republican   \n",
       "2       Bob Hollister      Democratic           Lloyd Smucker      Republican   \n",
       "3        Jahana Hayes      Democratic            George Logan      Republican   \n",
       "4       John Sarbanes      Democratic          Yuripzy Morgan      Republican   \n",
       "...               ...             ...                     ...             ...   \n",
       "59995       Eric Lynn      Democratic       Anna Paulina Luna      Republican   \n",
       "59996   Jan Schneider      Democratic           Vern Buchanan      Republican   \n",
       "59997  Amanda Waldman      Democratic              Dan Meuser      Republican   \n",
       "59998    Jerry Nadler      Democratic      Michael Zumbluskas      Republican   \n",
       "59999       Alan Cohn      Democratic              Laurel Lee      Republican   \n",
       "\n",
       "       HouseCand3Name HouseCand3Party HouseCand4Name HouseCand4Party  \\\n",
       "0      Gary Walkowicz   Working Class            NaN             NaN   \n",
       "1                 NaN             NaN            NaN             NaN   \n",
       "2                 NaN             NaN            NaN             NaN   \n",
       "3                 NaN             NaN            NaN             NaN   \n",
       "4                 NaN             NaN            NaN             NaN   \n",
       "...               ...             ...            ...             ...   \n",
       "59995     Frank Craft     Libertarian            NaN             NaN   \n",
       "59996             NaN             NaN            NaN             NaN   \n",
       "59997             NaN             NaN            NaN             NaN   \n",
       "59998   Mikhail Itkis     Independent            NaN             NaN   \n",
       "59999             NaN             NaN            NaN             NaN   \n",
       "\n",
       "      HouseCand5Name HouseCand5Party HouseCand6Name HouseCand6Party  \\\n",
       "0                NaN             NaN            NaN             NaN   \n",
       "1                NaN             NaN            NaN             NaN   \n",
       "2                NaN             NaN            NaN             NaN   \n",
       "3                NaN             NaN            NaN             NaN   \n",
       "4                NaN             NaN            NaN             NaN   \n",
       "...              ...             ...            ...             ...   \n",
       "59995            NaN             NaN            NaN             NaN   \n",
       "59996            NaN             NaN            NaN             NaN   \n",
       "59997            NaN             NaN            NaN             NaN   \n",
       "59998            NaN             NaN            NaN             NaN   \n",
       "59999            NaN             NaN            NaN             NaN   \n",
       "\n",
       "      HouseCand7Name HouseCand7Party HouseCand8Name HouseCand8Party  \\\n",
       "0                NaN             NaN            NaN             NaN   \n",
       "1                NaN             NaN            NaN             NaN   \n",
       "2                NaN             NaN            NaN             NaN   \n",
       "3                NaN             NaN            NaN             NaN   \n",
       "4                NaN             NaN            NaN             NaN   \n",
       "...              ...             ...            ...             ...   \n",
       "59995            NaN             NaN            NaN             NaN   \n",
       "59996            NaN             NaN            NaN             NaN   \n",
       "59997            NaN             NaN            NaN             NaN   \n",
       "59998            NaN             NaN            NaN             NaN   \n",
       "59999            NaN             NaN            NaN             NaN   \n",
       "\n",
       "             SenCand1Name SenCand1Party   SenCand2Name SenCand2Party  \\\n",
       "0                     NaN           NaN            NaN           NaN   \n",
       "1                     NaN           NaN            NaN           NaN   \n",
       "2          John Fetterman    Democratic      Mehmet Oz    Republican   \n",
       "3      Richard Blumenthal    Democratic     Leora Levy    Republican   \n",
       "4        Chris Van Hollen    Democratic  Chris Chaffee    Republican   \n",
       "...                   ...           ...            ...           ...   \n",
       "59995         Val Demings    Democratic    Marco Rubio    Republican   \n",
       "59996         Val Demings    Democratic    Marco Rubio    Republican   \n",
       "59997      John Fetterman    Democratic      Mehmet Oz    Republican   \n",
       "59998       Chuck Schumer    Democratic     Joe Pinion    Republican   \n",
       "59999         Val Demings    Democratic    Marco Rubio    Republican   \n",
       "\n",
       "      SenCand3Name SenCand3Party SenCand4Name SenCand4Party SenCand1Name2  \\\n",
       "0              NaN           NaN          NaN           NaN        __NA__   \n",
       "1              NaN           NaN          NaN           NaN        __NA__   \n",
       "2              NaN           NaN          NaN           NaN        __NA__   \n",
       "3              NaN           NaN          NaN           NaN        __NA__   \n",
       "4              NaN           NaN          NaN           NaN        __NA__   \n",
       "...            ...           ...          ...           ...           ...   \n",
       "59995          NaN           NaN          NaN           NaN        __NA__   \n",
       "59996          NaN           NaN          NaN           NaN        __NA__   \n",
       "59997          NaN           NaN          NaN           NaN        __NA__   \n",
       "59998          NaN           NaN          NaN           NaN        __NA__   \n",
       "59999          NaN           NaN          NaN           NaN        __NA__   \n",
       "\n",
       "      SenCand1Party2 SenCand2Name2 SenCand2Party2  countyfips  \\\n",
       "0             __NA__        __NA__         __NA__     26163.0   \n",
       "1             __NA__        __NA__         __NA__     25027.0   \n",
       "2             __NA__        __NA__         __NA__     42071.0   \n",
       "3             __NA__        __NA__         __NA__      9009.0   \n",
       "4             __NA__        __NA__         __NA__     24003.0   \n",
       "...              ...           ...            ...         ...   \n",
       "59995         __NA__        __NA__         __NA__     12103.0   \n",
       "59996         __NA__        __NA__         __NA__     12057.0   \n",
       "59997         __NA__        __NA__         __NA__     42107.0   \n",
       "59998         __NA__        __NA__         __NA__     36061.0   \n",
       "59999         __NA__        __NA__         __NA__     12105.0   \n",
       "\n",
       "            countyname  page_CC22_300_timing  page_CC22_300b_timing  \\\n",
       "0             Wayne MI                11.026                  5.523   \n",
       "1         Worcester MA                22.725                  4.317   \n",
       "2         Lancaster PA                 4.251                  0.000   \n",
       "3         New Haven CT                 9.135                  0.000   \n",
       "4      Anne Arundel MD                11.848                  2.627   \n",
       "...                ...                   ...                    ...   \n",
       "59995      Pinellas FL                13.735                  0.000   \n",
       "59996  Hillsborough FL                 3.548                  0.000   \n",
       "59997    Schuylkill PA                 6.560                  0.000   \n",
       "59998      New York NY                11.607                 18.210   \n",
       "59999          Polk FL                16.340                  0.000   \n",
       "\n",
       "       page_CC22_300d_timing  page_CC19_302_timing  page_CC22_303_timing  \\\n",
       "0                     11.061                 4.373                 3.488   \n",
       "1                      0.000                 6.914                 4.775   \n",
       "2                      3.477                24.067                 2.611   \n",
       "3                      7.150                23.711                 4.466   \n",
       "4                      5.367                 4.522                 4.522   \n",
       "...                      ...                   ...                   ...   \n",
       "59995                  0.000                 8.283                 4.702   \n",
       "59996                  7.676                 9.525                 2.902   \n",
       "59997                 20.158                 9.690                 4.248   \n",
       "59998                  5.876                15.882                 2.113   \n",
       "59999                 14.873                 6.329                 8.379   \n",
       "\n",
       "       page_CC22_304_timing  page_CC22_305_timing  page_CC22_307_timing  \\\n",
       "0                     3.099                12.033                 4.114   \n",
       "1                     3.506                11.900                 2.619   \n",
       "2                     4.376                 7.118                10.069   \n",
       "3                     2.752                16.211                 3.486   \n",
       "4                     2.463                12.644                 1.603   \n",
       "...                     ...                   ...                   ...   \n",
       "59995                 9.008                17.469                10.046   \n",
       "59996                 3.770                 3.939                 3.595   \n",
       "59997                 3.435                27.000                 7.678   \n",
       "59998                 1.799                16.922                 2.204   \n",
       "59999                 9.899                13.166                 6.799   \n",
       "\n",
       "       page_CC22_309a_timing  page_CC22_306_timing  page_CC22_309b_timing  \\\n",
       "0                      3.546                 4.848                  0.000   \n",
       "1                      5.318                 1.975                  0.000   \n",
       "2                      5.980                 4.300                  9.401   \n",
       "3                      9.797                 3.630                  4.295   \n",
       "4                      5.443                 2.879                  0.000   \n",
       "...                      ...                   ...                    ...   \n",
       "59995                  9.962                 4.501                  6.861   \n",
       "59996                  3.252                 5.130                 10.406   \n",
       "59997                  8.884                11.916                  3.734   \n",
       "59998                  4.284               972.906                  0.000   \n",
       "59999                 14.233                 8.017                 10.358   \n",
       "\n",
       "       page_CC22_309c_timing  page_CC22_309dx_timing  page_pg_health_timing  \\\n",
       "0                      6.144                  17.960                  5.600   \n",
       "1                     16.312                   8.235                  4.285   \n",
       "2                      2.745                  12.960                  5.985   \n",
       "3                     21.901                  18.282                  4.959   \n",
       "4                      8.855                   6.847                  5.276   \n",
       "...                      ...                     ...                    ...   \n",
       "59995                 25.693                  12.716                  9.749   \n",
       "59996                 43.355                   4.864                  4.943   \n",
       "59997                 10.627                   7.647                  5.455   \n",
       "59998                 12.785                  11.461                  5.469   \n",
       "59999                 19.204                  19.199                  9.801   \n",
       "\n",
       "       page_CC22_310grid_timing  page_CC22_311grid_timing  \\\n",
       "0                        14.913                     8.679   \n",
       "1                        10.790                    14.268   \n",
       "2                         5.716                     4.946   \n",
       "3                        28.597                    12.221   \n",
       "4                       119.441                    79.162   \n",
       "...                         ...                       ...   \n",
       "59995                    12.649                    16.394   \n",
       "59996                    17.404                    12.077   \n",
       "59997                    16.494                    15.790   \n",
       "59998                    13.729                     8.853   \n",
       "59999                    16.125                    14.825   \n",
       "\n",
       "       page_CC22_320grid_timing  page_cit1_timing  page_immstat_timing  \\\n",
       "0                        19.551             2.296                6.599   \n",
       "1                        26.893             2.224                7.372   \n",
       "2                        16.029            11.749                2.613   \n",
       "3                        28.029             1.844                7.447   \n",
       "4                        13.047             1.966                7.138   \n",
       "...                         ...               ...                  ...   \n",
       "59995                    27.169             5.068             8191.718   \n",
       "59996                    23.412            12.069               19.193   \n",
       "59997                    21.367             2.356                4.919   \n",
       "59998                    29.977            16.859               10.034   \n",
       "59999                    25.593             3.815                9.223   \n",
       "\n",
       "       page_CC22_321_timing  page_CC22_327grid_timing  \\\n",
       "0                     8.530                    11.144   \n",
       "1                     9.706                    23.907   \n",
       "2                     5.731                    28.611   \n",
       "3                    52.610                    31.865   \n",
       "4                    11.035                    20.405   \n",
       "...                     ...                       ...   \n",
       "59995                21.015                    26.345   \n",
       "59996                19.446                    25.546   \n",
       "59997                11.223                    17.721   \n",
       "59998                22.456                    17.512   \n",
       "59999                 5.703                    22.270   \n",
       "\n",
       "       page_CC22_330grid_timing  page_CC22_331grid_timing  \\\n",
       "0                        21.717                    19.971   \n",
       "1                        21.601                    21.525   \n",
       "2                        67.336                    29.973   \n",
       "3                        68.529                    22.650   \n",
       "4                        37.792                    33.588   \n",
       "...                         ...                       ...   \n",
       "59995                    32.242                    32.328   \n",
       "59996                    20.389                    26.778   \n",
       "59997                    18.518                    15.878   \n",
       "59998                    37.406                    16.294   \n",
       "59999                    35.218                    35.044   \n",
       "\n",
       "       page_CC22_332grid_timing  page_CC22_333_timing  \\\n",
       "0                        27.536                 5.559   \n",
       "1                        25.929                15.617   \n",
       "2                        34.603                84.301   \n",
       "3                        27.854                 7.879   \n",
       "4                        33.479                 4.863   \n",
       "...                         ...                   ...   \n",
       "59995                    42.122                 9.035   \n",
       "59996                    33.375                24.093   \n",
       "59997                    30.220                 7.410   \n",
       "59998                    43.221                 8.822   \n",
       "59999                    26.478                13.115   \n",
       "\n",
       "       page_CC22_333grid_timing  page_CC22_334grid_timing  \\\n",
       "0                        14.511                    18.317   \n",
       "1                        17.945                    27.952   \n",
       "2                        22.516                    28.195   \n",
       "3                        25.123                    31.758   \n",
       "4                        35.380                    69.826   \n",
       "...                         ...                       ...   \n",
       "59995                    14.108                    57.483   \n",
       "59996                    18.888                    29.267   \n",
       "59997                    25.340                    42.688   \n",
       "59998                    17.542                    58.643   \n",
       "59999                    31.398                    38.008   \n",
       "\n",
       "       page_CC22_340grid_timing  page_CC22_350grid_timing  \\\n",
       "0                        34.790                    29.379   \n",
       "1                        34.156                    39.665   \n",
       "2                        27.405                    35.782   \n",
       "3                        34.423                    68.851   \n",
       "4                        30.797                    54.624   \n",
       "...                         ...                       ...   \n",
       "59995                    35.325                    43.132   \n",
       "59996                    42.445                    22.625   \n",
       "59997                    53.903                    43.197   \n",
       "59998                    70.707                    13.661   \n",
       "59999                    32.734                    50.790   \n",
       "\n",
       "       page_CC22_355grid_timing  page_CC22_360_timing  page_urbanarea_timing  \\\n",
       "0                        11.067                 0.000                  7.679   \n",
       "1                        17.425                 5.596                  5.889   \n",
       "2                        14.193                 3.381                  4.847   \n",
       "3                        21.463                 5.131                 22.886   \n",
       "4                        16.253                 2.935                 10.245   \n",
       "...                         ...                   ...                    ...   \n",
       "59995                    18.825                 7.185                  8.995   \n",
       "59996                    10.393                 0.000                 12.335   \n",
       "59997                    18.489                 5.563                 10.283   \n",
       "59998                    19.061                12.025                 24.948   \n",
       "59999                    20.142                 4.779                  8.104   \n",
       "\n",
       "       page_CC22_363_timing  page_CC22_365_voted_timing  \\\n",
       "0                     3.758                       0.000   \n",
       "1                     2.276                       0.000   \n",
       "2                    10.892                       0.000   \n",
       "3                     2.656                       0.000   \n",
       "4                     3.500                       0.000   \n",
       "...                     ...                         ...   \n",
       "59995                 5.446                       0.000   \n",
       "59996                 3.166                       0.000   \n",
       "59997                 3.584                       0.000   \n",
       "59998                 6.361                       0.000   \n",
       "59999                 5.244                       6.872   \n",
       "\n",
       "       page_CC22_365b_voted_timing  page_CC22_366_voted_timing  \\\n",
       "0                              0.0                       0.000   \n",
       "1                              0.0                       0.000   \n",
       "2                              0.0                       0.000   \n",
       "3                              0.0                       0.000   \n",
       "4                              0.0                       0.000   \n",
       "...                            ...                         ...   \n",
       "59995                          0.0                       0.000   \n",
       "59996                          0.0                       0.000   \n",
       "59997                          0.0                       0.000   \n",
       "59998                          0.0                       0.000   \n",
       "59999                          0.0                       3.465   \n",
       "\n",
       "       page_CC22_367_voted_timing  page_CC22_365_timing  \\\n",
       "0                           0.000                 0.000   \n",
       "1                           0.000                 0.000   \n",
       "2                           0.000                 3.595   \n",
       "3                           0.000                 5.049   \n",
       "4                           0.000                 3.261   \n",
       "...                           ...                   ...   \n",
       "59995                       0.000                 8.814   \n",
       "59996                       0.000                 5.982   \n",
       "59997                       0.000                 6.262   \n",
       "59998                       0.000                 4.354   \n",
       "59999                       4.199                 0.000   \n",
       "\n",
       "       page_CC22_365a_timing  page_CC22_365b_timing  page_CC22_365c_timing  \\\n",
       "0                      0.000                    0.0                    0.0   \n",
       "1                      0.000                    0.0                    0.0   \n",
       "2                      3.079                    0.0                    0.0   \n",
       "3                      7.490                    0.0                    0.0   \n",
       "4                      0.000                    0.0                    0.0   \n",
       "...                      ...                    ...                    ...   \n",
       "59995                  0.000                    0.0                    0.0   \n",
       "59996                  0.000                    0.0                    0.0   \n",
       "59997                  0.000                    0.0                    0.0   \n",
       "59998                  5.724                    0.0                    0.0   \n",
       "59999                  0.000                    0.0                    0.0   \n",
       "\n",
       "       page_CC22_366_timing  page_CC22_367_timing  page_CC22_367a_timing  \\\n",
       "0                     2.287                 2.806                  5.600   \n",
       "1                     2.007                 2.607                  2.878   \n",
       "2                     2.348                 3.139                  2.319   \n",
       "3                     3.156                 9.476                  2.691   \n",
       "4                     3.790                 2.114                  0.000   \n",
       "...                     ...                   ...                    ...   \n",
       "59995                 3.584                 3.714                  0.000   \n",
       "59996                12.742                 1.703                  0.000   \n",
       "59997                 3.431                13.733                  0.000   \n",
       "59998                 5.021                 8.324                 36.204   \n",
       "59999                 0.000                 0.000                  0.000   \n",
       "\n",
       "       page_ideo5_timing  page_employ_timing  page_hadjob_timing  \\\n",
       "0                  0.000               6.192               0.000   \n",
       "1                  0.000               2.625               0.000   \n",
       "2                  0.000               1.840               0.000   \n",
       "3                  0.000               4.264               0.000   \n",
       "4                  0.000               6.078               0.000   \n",
       "...                  ...                 ...                 ...   \n",
       "59995              8.790               3.079               0.000   \n",
       "59996              6.998               5.360              13.086   \n",
       "59997              4.244               3.998               0.000   \n",
       "59998              0.000               3.925               0.000   \n",
       "59999              0.000               5.585               6.080   \n",
       "\n",
       "       page_investor_timing  page_pew_bornagain_timing  \\\n",
       "0                     6.228                      0.000   \n",
       "1                     4.224                      0.000   \n",
       "2                     2.281                      0.000   \n",
       "3                     3.646                      0.000   \n",
       "4                     3.750                      0.000   \n",
       "...                     ...                        ...   \n",
       "59995                 4.420                      4.955   \n",
       "59996                 3.370                      2.590   \n",
       "59997                 4.407                      3.606   \n",
       "59998                23.364                      3.866   \n",
       "59999                 4.915                      6.317   \n",
       "\n",
       "       page_pew_religimp_timing  page_pew_churatd_timing  \\\n",
       "0                         0.000                    0.000   \n",
       "1                         0.000                    0.000   \n",
       "2                         0.000                    0.000   \n",
       "3                         0.000                    0.000   \n",
       "4                         0.000                    0.000   \n",
       "...                         ...                      ...   \n",
       "59995                     4.654                    6.470   \n",
       "59996                     3.910                   13.862   \n",
       "59997                     3.297                    3.920   \n",
       "59998                     2.108                    3.546   \n",
       "59999                     4.550                    5.762   \n",
       "\n",
       "       page_pew_prayer_timing  page_religpew_timing  \\\n",
       "0                       0.000                 0.000   \n",
       "1                       0.000                 0.000   \n",
       "2                       0.000                 0.000   \n",
       "3                       0.000                 0.000   \n",
       "4                       0.000                 0.000   \n",
       "...                       ...                   ...   \n",
       "59995                  10.588                13.601   \n",
       "59996                   6.300                21.288   \n",
       "59997                   7.150                 5.504   \n",
       "59998                   5.127                 0.000   \n",
       "59999                  12.319                 7.372   \n",
       "\n",
       "       page_religpew_protestant_timing  page_religion_denom_timing  \\\n",
       "0                                0.000                       0.000   \n",
       "1                                0.000                       0.000   \n",
       "2                                0.000                       0.000   \n",
       "3                                0.000                       0.000   \n",
       "4                                0.000                       0.000   \n",
       "...                                ...                         ...   \n",
       "59995                            0.000                       0.000   \n",
       "59996                            0.000                       8.418   \n",
       "59997                            0.000                       0.000   \n",
       "59998                           17.969                      16.573   \n",
       "59999                            0.000                       0.000   \n",
       "\n",
       "       page_marstat_timing  page_union_timing  page_union_coverage_timing  \\\n",
       "0                    0.000              0.000                       2.991   \n",
       "1                    0.000              3.108                       2.346   \n",
       "2                    0.000              2.902                       1.736   \n",
       "3                    0.000              0.000                       1.259   \n",
       "4                    0.000              3.398                      17.583   \n",
       "...                    ...                ...                         ...   \n",
       "59995                1.970              4.692                       5.565   \n",
       "59996                3.077              6.528                      12.778   \n",
       "59997                3.965              6.424                       3.854   \n",
       "59998                0.000             30.220                       0.000   \n",
       "59999                0.000              5.674                       4.346   \n",
       "\n",
       "       page_unionhh_timing  page_dualcit_timing  page_dualctry_timing  \\\n",
       "0                    0.000                2.918                 0.000   \n",
       "1                    3.427                4.511                 0.000   \n",
       "2                    3.921                6.664                 0.000   \n",
       "3                    0.000                2.570                 0.000   \n",
       "4                    7.197                3.133                 0.000   \n",
       "...                    ...                  ...                   ...   \n",
       "59995                4.576                4.051                13.775   \n",
       "59996                6.836                6.551                10.557   \n",
       "59997                4.984                4.229                 0.000   \n",
       "59998                8.448                2.009                26.853   \n",
       "59999                5.128                4.727                 0.000   \n",
       "\n",
       "       page_ownhome_timing  page_demos2c_timing  page_milstat_timing  \\\n",
       "0                    0.000                0.000               14.464   \n",
       "1                    0.000                0.000                3.841   \n",
       "2                    0.000                0.000                2.452   \n",
       "3                    0.000                0.000               13.131   \n",
       "4                    0.000                0.000                3.899   \n",
       "...                    ...                  ...                  ...   \n",
       "59995                4.457                5.156                6.267   \n",
       "59996                1.660                9.037               15.333   \n",
       "59997                2.707                7.332                6.445   \n",
       "59998                5.441                2.664                9.214   \n",
       "59999                8.272               21.734               14.385   \n",
       "\n",
       "       page_child18_timing  page_healthins_timing  page_healthins2_timing  \\\n",
       "0                    3.261                  6.646                     0.0   \n",
       "1                    2.424                  3.030                     0.0   \n",
       "2                    2.303                  2.330                     0.0   \n",
       "3                    5.349                  5.116                     0.0   \n",
       "4                    2.481                  5.702                     0.0   \n",
       "...                    ...                    ...                     ...   \n",
       "59995                6.877                 11.220                     0.0   \n",
       "59996                7.911                 10.605                     0.0   \n",
       "59997                6.854                  4.774                     0.0   \n",
       "59998                8.380                  6.523                     0.0   \n",
       "59999                5.387                  6.440                     0.0   \n",
       "\n",
       "       page_demos8_timing  page_CC22_hisp_timing  page_CC22_asian_timing  \\\n",
       "0                   8.918                  0.000                     0.0   \n",
       "1                   0.000                  0.000                     0.0   \n",
       "2                   0.000                  0.000                     0.0   \n",
       "3                   9.390                  0.000                     0.0   \n",
       "4                   0.000                  0.000                     0.0   \n",
       "...                   ...                    ...                     ...   \n",
       "59995               5.700                 14.687                     0.0   \n",
       "59996              23.908                  0.000                     0.0   \n",
       "59997               9.081                  0.000                     0.0   \n",
       "59998              20.236                  0.000                     0.0   \n",
       "59999              14.084                  0.000                     0.0   \n",
       "\n",
       "       page_presvote20post_timing  page_presvote16post_timing  \\\n",
       "0                           0.000                       0.000   \n",
       "1                           0.000                       0.000   \n",
       "2                           0.000                       0.000   \n",
       "3                           0.000                       0.000   \n",
       "4                           0.000                       0.000   \n",
       "...                           ...                         ...   \n",
       "59995                       4.982                       0.000   \n",
       "59996                       8.393                       7.850   \n",
       "59997                       4.629                       3.333   \n",
       "59998                       0.000                       0.000   \n",
       "59999                       0.000                       0.000   \n",
       "\n",
       "       page_industry_timing  page_sex_timing  regzip_confirm_post  \\\n",
       "0                     6.521            0.000                  1.0   \n",
       "1                     3.179            0.000                  1.0   \n",
       "2                     6.404            0.000                  1.0   \n",
       "3                     4.282            0.000                  1.0   \n",
       "4                     5.772            0.000                  1.0   \n",
       "...                     ...              ...                  ...   \n",
       "59995                 5.629            4.599                  NaN   \n",
       "59996                 0.000            7.238                  NaN   \n",
       "59997                 6.408           10.683                  NaN   \n",
       "59998                10.704            0.000                  1.0   \n",
       "59999                 6.560            5.675                  1.0   \n",
       "\n",
       "       reszip_confirm_post  inputzip_post  votereg_post  votereg_f_post  \\\n",
       "0                      NaN            NaN           1.0             NaN   \n",
       "1                      NaN            NaN           1.0             NaN   \n",
       "2                      NaN            NaN           1.0             NaN   \n",
       "3                      NaN            NaN           1.0             NaN   \n",
       "4                      NaN            NaN           1.0             NaN   \n",
       "...                    ...            ...           ...             ...   \n",
       "59995                  NaN            NaN           NaN             NaN   \n",
       "59996                  1.0            NaN           1.0             1.0   \n",
       "59997                  NaN            NaN           NaN             NaN   \n",
       "59998                  NaN            NaN           1.0             NaN   \n",
       "59999                  NaN            NaN           1.0             NaN   \n",
       "\n",
       "       regzip_post  inputstate_post  birthyr_post  gender4_post  CC22_401  \\\n",
       "0              NaN             26.0        1992.0           1.0       5.0   \n",
       "1              NaN             25.0        1957.0           1.0       5.0   \n",
       "2              NaN             42.0        1978.0           2.0       5.0   \n",
       "3              NaN              9.0        1991.0           3.0       5.0   \n",
       "4              NaN             24.0        1991.0           1.0       1.0   \n",
       "...            ...              ...           ...           ...       ...   \n",
       "59995          NaN              NaN           NaN           NaN       NaN   \n",
       "59996          NaN             12.0        1996.0           2.0       1.0   \n",
       "59997          NaN              NaN           NaN           NaN       NaN   \n",
       "59998          NaN             36.0        1990.0           1.0       5.0   \n",
       "59999          NaN             12.0        1977.0           2.0       5.0   \n",
       "\n",
       "       CC22_402a                                        CC22_402a_t  \\\n",
       "0            NaN                                             __NA__   \n",
       "1            NaN                                             __NA__   \n",
       "2            NaN                                             __NA__   \n",
       "3            NaN                                             __NA__   \n",
       "4           17.0  The government system is bullshit and a busine...   \n",
       "...          ...                                                ...   \n",
       "59995        NaN                                                 -1   \n",
       "59996       14.0                                             __NA__   \n",
       "59997        NaN                                                 -1   \n",
       "59998        NaN                                             __NA__   \n",
       "59999        NaN                                             __NA__   \n",
       "\n",
       "       CC22_402b   CC22_402b_t  CC22_403  CC22_403b  CC22_403c CC22_403c_t  \\\n",
       "0            NaN        __NA__       3.0        NaN        4.0      __NA__   \n",
       "1            NaN        __NA__       1.0        1.0        NaN      __NA__   \n",
       "2            NaN        __NA__       3.0        NaN        1.0      __NA__   \n",
       "3            NaN        __NA__       3.0        NaN        4.0      __NA__   \n",
       "4           17.0  It is a sham       NaN        NaN        NaN      __NA__   \n",
       "...          ...           ...       ...        ...        ...         ...   \n",
       "59995        NaN            -1       NaN        NaN        NaN          -1   \n",
       "59996       13.0        __NA__       NaN        NaN        NaN      __NA__   \n",
       "59997        NaN            -1       NaN        NaN        NaN          -1   \n",
       "59998        NaN        __NA__       1.0        2.0        NaN      __NA__   \n",
       "59999        NaN        __NA__       3.0        NaN        1.0      __NA__   \n",
       "\n",
       "       CC22_403d CC22_403d_t  CC22_404 CC22_404_t  CC22_405  CC22_406a  \\\n",
       "0            1.0      __NA__       NaN     __NA__       2.0        1.0   \n",
       "1            NaN      __NA__       2.0     __NA__       NaN        2.0   \n",
       "2            1.0      __NA__       NaN     __NA__       NaN        1.0   \n",
       "3            1.0      __NA__       NaN     __NA__       2.0        1.0   \n",
       "4            NaN      __NA__       NaN     __NA__       NaN        NaN   \n",
       "...          ...         ...       ...        ...       ...        ...   \n",
       "59995        NaN          -1       NaN         -1       NaN        NaN   \n",
       "59996        NaN      __NA__       NaN     __NA__       NaN        NaN   \n",
       "59997        NaN          -1       NaN         -1       NaN        NaN   \n",
       "59998        NaN      __NA__       3.0     __NA__       NaN        1.0   \n",
       "59999        1.0      __NA__       NaN     __NA__       NaN        1.0   \n",
       "\n",
       "       CC22_406b_1  CC22_406b_2  CC22_406b_3  CC22_406b_4 CC22_406b_t  \\\n",
       "0              NaN          NaN          NaN          NaN      __NA__   \n",
       "1              2.0          2.0          1.0          2.0      __NA__   \n",
       "2              NaN          NaN          NaN          NaN      __NA__   \n",
       "3              NaN          NaN          NaN          NaN      __NA__   \n",
       "4              NaN          NaN          NaN          NaN      __NA__   \n",
       "...            ...          ...          ...          ...         ...   \n",
       "59995          NaN          NaN          NaN          NaN          -1   \n",
       "59996          NaN          NaN          NaN          NaN      __NA__   \n",
       "59997          NaN          NaN          NaN          NaN          -1   \n",
       "59998          NaN          NaN          NaN          NaN      __NA__   \n",
       "59999          NaN          NaN          NaN          NaN      __NA__   \n",
       "\n",
       "       CC22_406c  CC22_411 CC22_411_t  CC22_411b CC22_411b_t  CC22_412  \\\n",
       "0            NaN       NaN     __NA__        NaN      __NA__       1.0   \n",
       "1            3.0       NaN     __NA__        NaN      __NA__       2.0   \n",
       "2            NaN       1.0     __NA__        NaN      __NA__       1.0   \n",
       "3            NaN       1.0     __NA__        NaN      __NA__       1.0   \n",
       "4            NaN       NaN     __NA__        NaN      __NA__       NaN   \n",
       "...          ...       ...        ...        ...         ...       ...   \n",
       "59995        NaN       NaN         -1        NaN          -1       NaN   \n",
       "59996        NaN       NaN     __NA__        NaN      __NA__       NaN   \n",
       "59997        NaN       NaN         -1        NaN          -1       NaN   \n",
       "59998        NaN       1.0     __NA__        NaN      __NA__       1.0   \n",
       "59999        NaN       1.0     __NA__        NaN      __NA__       1.0   \n",
       "\n",
       "      CC22_412_t  CC22_413 CC22_413_t  CC22_411_nv CC22_411_nv_t  \\\n",
       "0         __NA__       1.0     __NA__          NaN        __NA__   \n",
       "1         __NA__       2.0     __NA__          NaN        __NA__   \n",
       "2         __NA__       1.0     __NA__          NaN        __NA__   \n",
       "3         __NA__       1.0     __NA__          NaN        __NA__   \n",
       "4         __NA__       NaN     __NA__          7.0        __NA__   \n",
       "...          ...       ...        ...          ...           ...   \n",
       "59995         -1       NaN         -1          NaN            -1   \n",
       "59996     __NA__       NaN     __NA__          7.0        __NA__   \n",
       "59997         -1       NaN         -1          NaN            -1   \n",
       "59998     __NA__       1.0     __NA__          NaN        __NA__   \n",
       "59999     __NA__       1.0     __NA__          NaN        __NA__   \n",
       "\n",
       "       CC22_411b_nv CC22_411b_nv_t  CC22_412_nv CC22_412_nv_t  CC22_413_nv  \\\n",
       "0               NaN         __NA__          NaN        __NA__          NaN   \n",
       "1               NaN         __NA__          NaN        __NA__          NaN   \n",
       "2               NaN         __NA__          NaN        __NA__          NaN   \n",
       "3               NaN         __NA__          NaN        __NA__          NaN   \n",
       "4               NaN         __NA__         11.0        __NA__          9.0   \n",
       "...             ...            ...          ...           ...          ...   \n",
       "59995           NaN             -1          NaN            -1          NaN   \n",
       "59996           NaN         __NA__         11.0        __NA__          9.0   \n",
       "59997           NaN             -1          NaN            -1          NaN   \n",
       "59998           NaN         __NA__          NaN        __NA__          NaN   \n",
       "59999           NaN         __NA__          NaN        __NA__          NaN   \n",
       "\n",
       "      CC22_413_nv_t  CC22_414a  CC22_414b  CC22_415c  CC22_415d  CC22_416a  \\\n",
       "0            __NA__        1.0        1.0        1.0        1.0        5.0   \n",
       "1            __NA__        2.0        2.0        2.0        2.0        1.0   \n",
       "2            __NA__        NaN        NaN        1.0        1.0        6.0   \n",
       "3            __NA__        1.0        1.0        1.0        1.0        2.0   \n",
       "4            __NA__        5.0        NaN        NaN        NaN        6.0   \n",
       "...             ...        ...        ...        ...        ...        ...   \n",
       "59995            -1        NaN        NaN        NaN        NaN        NaN   \n",
       "59996        __NA__        5.0        NaN        NaN        NaN        6.0   \n",
       "59997            -1        NaN        NaN        NaN        NaN        NaN   \n",
       "59998        __NA__        1.0        1.0        1.0        1.0        2.0   \n",
       "59999        __NA__        1.0        NaN        1.0        1.0        NaN   \n",
       "\n",
       "       CC22_416b  CC22_416c CC22_416a_other CC22_416b_other CC22_416c_other  \\\n",
       "0            1.0        NaN     Palestinian          __NA__          __NA__   \n",
       "1            1.0        NaN          __NA__          __NA__          __NA__   \n",
       "2            1.0        NaN          __NA__          __NA__          __NA__   \n",
       "3            2.0        NaN          __NA__          __NA__          __NA__   \n",
       "4            6.0        NaN          __NA__          __NA__          __NA__   \n",
       "...          ...        ...             ...             ...             ...   \n",
       "59995        NaN        NaN              -1              -1              -1   \n",
       "59996        6.0        NaN          __NA__          __NA__          __NA__   \n",
       "59997        NaN        NaN              -1              -1              -1   \n",
       "59998        1.0        NaN          __NA__          __NA__          __NA__   \n",
       "59999        1.0        1.0          __NA__          __NA__          __NA__   \n",
       "\n",
       "       CC22_420_1  CC22_420_2  CC22_420_3  CC22_420_4  CC22_420_5  CC22_420_6  \\\n",
       "0             2.0         2.0         2.0         2.0         2.0         2.0   \n",
       "1             1.0         1.0         1.0         2.0         1.0         1.0   \n",
       "2             2.0         1.0         2.0         2.0         1.0         2.0   \n",
       "3             2.0         2.0         1.0         2.0         1.0         1.0   \n",
       "4             2.0         2.0         2.0         2.0         2.0         2.0   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "59995         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "59996         2.0         2.0         2.0         2.0         2.0         1.0   \n",
       "59997         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "59998         1.0         1.0         1.0         1.0         1.0         1.0   \n",
       "59999         2.0         1.0         1.0         2.0         1.0         2.0   \n",
       "\n",
       "       CC22_420_7  CC22_421r  CC22_421r_dk_flag  CC22_422r  CC22_422r_dk_flag  \\\n",
       "0             1.0        0.0                0.0       90.0                0.0   \n",
       "1             2.0       45.0                0.0       35.0                0.0   \n",
       "2             2.0        NaN                1.0        NaN                1.0   \n",
       "3             2.0       50.0                0.0       80.0                0.0   \n",
       "4             1.0      100.0                0.0        0.0                0.0   \n",
       "...           ...        ...                ...        ...                ...   \n",
       "59995         NaN        NaN                NaN        NaN                NaN   \n",
       "59996         2.0       50.0                0.0       51.0                0.0   \n",
       "59997         NaN        NaN                NaN        NaN                NaN   \n",
       "59998         2.0       57.0                0.0       53.0                0.0   \n",
       "59999         2.0       51.0                0.0       42.0                0.0   \n",
       "\n",
       "       CC22_423  CC22_424  CC22_430a_1  CC22_430a_2  CC22_430a_3  CC22_430a_4  \\\n",
       "0           2.0       2.0          1.0          1.0          1.0          1.0   \n",
       "1           8.0       3.0          1.0          1.0          2.0          2.0   \n",
       "2           3.0       3.0          2.0          2.0          2.0          2.0   \n",
       "3           8.0       8.0          2.0          1.0          2.0          2.0   \n",
       "4           8.0       8.0          2.0          2.0          2.0          2.0   \n",
       "...         ...       ...          ...          ...          ...          ...   \n",
       "59995       NaN       NaN          NaN          NaN          NaN          NaN   \n",
       "59996       3.0       3.0          2.0          2.0          2.0          2.0   \n",
       "59997       NaN       NaN          NaN          NaN          NaN          NaN   \n",
       "59998       1.0       1.0          1.0          1.0          2.0          1.0   \n",
       "59999       3.0       3.0          2.0          2.0          2.0          2.0   \n",
       "\n",
       "       CC22_430a_5  CC22_430a_6  CC22_430a_7  CC22_430a_8  CC22_430b_1  \\\n",
       "0              1.0          1.0          2.0          2.0          2.0   \n",
       "1              1.0          2.0          2.0          2.0          NaN   \n",
       "2              2.0          2.0          1.0          2.0          NaN   \n",
       "3              1.0          1.0          2.0          2.0          2.0   \n",
       "4              2.0          2.0          2.0          1.0          NaN   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          NaN          NaN          NaN          NaN          NaN   \n",
       "59996          2.0          2.0          2.0          1.0          NaN   \n",
       "59997          NaN          NaN          NaN          NaN          NaN   \n",
       "59998          2.0          1.0          1.0          2.0          2.0   \n",
       "59999          2.0          2.0          2.0          1.0          NaN   \n",
       "\n",
       "       CC22_430b_2  CC22_430b_3  CC22_430b_4  CC22_430b_5  CC22_430b_6  \\\n",
       "0              2.0          2.0          2.0          2.0          1.0   \n",
       "1              NaN          NaN          NaN          NaN          NaN   \n",
       "2              NaN          NaN          NaN          NaN          NaN   \n",
       "3              2.0          1.0          2.0          2.0          2.0   \n",
       "4              NaN          NaN          NaN          NaN          NaN   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          NaN          NaN          NaN          NaN          NaN   \n",
       "59996          NaN          NaN          NaN          NaN          NaN   \n",
       "59997          NaN          NaN          NaN          NaN          NaN   \n",
       "59998          1.0          2.0          1.0          2.0          2.0   \n",
       "59999          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_430b_7  CC22_430b_8  CC22_430b_9  CC22_430b_10 CC22_430b_t  \\\n",
       "0              2.0          2.0          2.0           2.0      __NA__   \n",
       "1              NaN          NaN          NaN           NaN      __NA__   \n",
       "2              NaN          NaN          NaN           NaN      __NA__   \n",
       "3              2.0          2.0          2.0           2.0      __NA__   \n",
       "4              NaN          NaN          NaN           NaN      __NA__   \n",
       "...            ...          ...          ...           ...         ...   \n",
       "59995          NaN          NaN          NaN           NaN          -1   \n",
       "59996          NaN          NaN          NaN           NaN      __NA__   \n",
       "59997          NaN          NaN          NaN           NaN          -1   \n",
       "59998          2.0          2.0          2.0           2.0      __NA__   \n",
       "59999          NaN          NaN          NaN           NaN      __NA__   \n",
       "\n",
       "       CC22_430c  CC22_431a  CC22_431b_1  CC22_431b_2  CC22_431b_3  \\\n",
       "0         3500.0        1.0          1.0          1.0          1.0   \n",
       "1            NaN        1.0          2.0          1.0          2.0   \n",
       "2            NaN        2.0          NaN          NaN          NaN   \n",
       "3            NaN        1.0          2.0          1.0          1.0   \n",
       "4            NaN        2.0          NaN          NaN          NaN   \n",
       "...          ...        ...          ...          ...          ...   \n",
       "59995        NaN        NaN          NaN          NaN          NaN   \n",
       "59996        NaN        2.0          NaN          NaN          NaN   \n",
       "59997        NaN        NaN          NaN          NaN          NaN   \n",
       "59998     1900.0        1.0          2.0          2.0          1.0   \n",
       "59999        NaN        1.0          2.0          2.0          1.0   \n",
       "\n",
       "       CC22_431b_4  CC22_432a  CC22_432b_1  CC22_432b_2  CC22_432b_3  \\\n",
       "0              1.0        2.0          NaN          NaN          NaN   \n",
       "1              2.0        1.0          2.0          2.0          1.0   \n",
       "2              NaN        2.0          NaN          NaN          NaN   \n",
       "3              1.0        1.0          2.0          1.0          2.0   \n",
       "4              NaN        2.0          NaN          NaN          NaN   \n",
       "...            ...        ...          ...          ...          ...   \n",
       "59995          NaN        NaN          NaN          NaN          NaN   \n",
       "59996          NaN        2.0          NaN          NaN          NaN   \n",
       "59997          NaN        NaN          NaN          NaN          NaN   \n",
       "59998          2.0        2.0          NaN          NaN          NaN   \n",
       "59999          1.0        2.0          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_432b_4  CC22_432b_5  CC22_432b_6  CC22_432b_7  CC22_432b_8  \\\n",
       "0              NaN          NaN          NaN          NaN          NaN   \n",
       "1              2.0          2.0          2.0          2.0          2.0   \n",
       "2              NaN          NaN          NaN          NaN          NaN   \n",
       "3              2.0          2.0          2.0          2.0          2.0   \n",
       "4              NaN          NaN          NaN          NaN          NaN   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "59995          NaN          NaN          NaN          NaN          NaN   \n",
       "59996          NaN          NaN          NaN          NaN          NaN   \n",
       "59997          NaN          NaN          NaN          NaN          NaN   \n",
       "59998          NaN          NaN          NaN          NaN          NaN   \n",
       "59999          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       CC22_432b_9  CC22_432b_10  CC22_432b_11 CC22_432b_t  CC22_433a  \\\n",
       "0              NaN           NaN           NaN      __NA__        1.0   \n",
       "1              2.0           2.0           2.0      __NA__        3.0   \n",
       "2              NaN           NaN           NaN      __NA__        1.0   \n",
       "3              2.0           2.0           2.0      __NA__        4.0   \n",
       "4              NaN           NaN           NaN      __NA__        3.0   \n",
       "...            ...           ...           ...         ...        ...   \n",
       "59995          NaN           NaN           NaN          -1        NaN   \n",
       "59996          NaN           NaN           NaN      __NA__        1.0   \n",
       "59997          NaN           NaN           NaN          -1        NaN   \n",
       "59998          NaN           NaN           NaN      __NA__        1.0   \n",
       "59999          NaN           NaN           NaN      __NA__        1.0   \n",
       "\n",
       "      CC22_433_t  CC22_433_dem  CC22_433_rep  CC22_433b  CC22_pid7  CC22_440a  \\\n",
       "0         __NA__           1.0           NaN        NaN        1.0        1.0   \n",
       "1         __NA__           NaN           NaN        2.0        5.0        4.0   \n",
       "2         __NA__           1.0           NaN        NaN        1.0        2.0   \n",
       "3        Leftist           NaN           NaN        1.0        3.0        1.0   \n",
       "4         __NA__           NaN           NaN        3.0        4.0        5.0   \n",
       "...          ...           ...           ...        ...        ...        ...   \n",
       "59995         -1           NaN           NaN        NaN        NaN        NaN   \n",
       "59996     __NA__           2.0           NaN        NaN        2.0        2.0   \n",
       "59997         -1           NaN           NaN        NaN        NaN        NaN   \n",
       "59998     __NA__           1.0           NaN        NaN        1.0        1.0   \n",
       "59999     __NA__           2.0           NaN        NaN        2.0        1.0   \n",
       "\n",
       "       CC22_440b  CC22_440c  CC22_440d  CC22_441a  CC22_441b  CC22_441e  \\\n",
       "0            5.0        5.0        5.0        5.0        1.0        NaN   \n",
       "1            1.0        3.0        2.0        1.0        5.0        NaN   \n",
       "2            4.0        4.0        4.0        3.0        2.0        NaN   \n",
       "3            5.0        5.0        5.0        5.0        1.0        NaN   \n",
       "4            5.0        5.0        5.0        4.0        2.0        NaN   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "59995        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "59996        NaN        2.0        3.0        NaN        2.0        3.0   \n",
       "59997        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "59998        4.0        1.0        2.0        2.0        2.0        1.0   \n",
       "59999        5.0        4.0        4.0        5.0        2.0        1.0   \n",
       "\n",
       "       CC22_441f  CC22_441g  CC22_443_1  CC22_443_2  CC22_443_3  CC22_443_4  \\\n",
       "0            NaN        NaN         1.0         1.0         1.0         2.0   \n",
       "1            NaN        NaN         3.0         3.0         3.0         2.0   \n",
       "2            NaN        NaN         3.0         3.0         3.0         3.0   \n",
       "3            NaN        NaN         1.0         1.0         1.0         4.0   \n",
       "4            NaN        NaN         1.0         1.0         1.0         5.0   \n",
       "...          ...        ...         ...         ...         ...         ...   \n",
       "59995        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "59996        3.0        4.0         1.0         1.0         1.0         1.0   \n",
       "59997        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "59998        2.0        2.0         1.0         1.0         1.0         1.0   \n",
       "59999        1.0        2.0         3.0         1.0         1.0         3.0   \n",
       "\n",
       "       CC22_443_5  gunown  numchildren  edloan  student  cdid117_post  \\\n",
       "0             1.0     3.0          0.0     2.0      NaN          11.0   \n",
       "1             1.0     3.0          2.0     2.0      NaN           2.0   \n",
       "2             3.0     3.0          1.0     2.0      NaN          11.0   \n",
       "3             1.0     3.0          1.0     1.0      NaN           5.0   \n",
       "4             1.0     3.0          0.0     2.0      NaN           3.0   \n",
       "...           ...     ...          ...     ...      ...           ...   \n",
       "59995         NaN     NaN          NaN     NaN      NaN           NaN   \n",
       "59996         1.0     3.0          0.0     2.0      NaN          15.0   \n",
       "59997         NaN     NaN          NaN     NaN      NaN           NaN   \n",
       "59998         1.0     1.0          2.0     1.0      NaN          12.0   \n",
       "59999         2.0     1.0          1.0     1.0      NaN          15.0   \n",
       "\n",
       "       cdid118_post  lookupzip_post                         LegName_post  \\\n",
       "0              12.0           48152           Michigan State Legislature   \n",
       "1               2.0            1331      Massachusetts State Legislature   \n",
       "2              11.0           17603  Pennsylvania State General Assembly   \n",
       "3               5.0            6716   Connecticut State General Assembly   \n",
       "4               3.0           21401      Maryland State General Assembly   \n",
       "...             ...             ...                                  ...   \n",
       "59995           NaN              -1                                   -1   \n",
       "59996          16.0           33578            Florida State Legislature   \n",
       "59997           NaN              -1                                   -1   \n",
       "59998          12.0           10016           New York State Legislature   \n",
       "59999          15.0           33810            Florida State Legislature   \n",
       "\n",
       "                              LowerChamberName_post CurrentGovName_post  \\\n",
       "0           Michigan State House of Representatives    Gretchen Whitmer   \n",
       "1      Massachusetts State House of Representatives       Charlie Baker   \n",
       "2       Pennsylvania State House of Representatives            Tom Wolf   \n",
       "3        Connecticut State House of Representatives          Ned Lamont   \n",
       "4                 Maryland State House of Delegates         Larry Hogan   \n",
       "...                                             ...                 ...   \n",
       "59995                                            -1                  -1   \n",
       "59996        Florida State House of Representatives        Ron DeSantis   \n",
       "59997                                            -1                  -1   \n",
       "59998                       New York State Assembly        Kathy Hochul   \n",
       "59999        Florida State House of Representatives        Ron DeSantis   \n",
       "\n",
       "      CurrentGovParty_post CurrentHouseName_post CurrentHouseParty_post  \\\n",
       "0               Democratic         Haley Stevens             Democratic   \n",
       "1               Republican          Jim McGovern             Democratic   \n",
       "2               Democratic         Lloyd Smucker             Republican   \n",
       "3               Democratic          Jahana Hayes             Democratic   \n",
       "4               Republican         John Sarbanes             Democratic   \n",
       "...                    ...                   ...                    ...   \n",
       "59995                   -1                    -1                     -1   \n",
       "59996           Republican        Scott Franklin             Republican   \n",
       "59997                   -1                    -1                     -1   \n",
       "59998           Democratic       Carolyn Maloney             Democratic   \n",
       "59999           Republican        Scott Franklin             Republican   \n",
       "\n",
       "      CurrentSen1Name_post CurrentSen1Party_post CurrentSen2Name_post  \\\n",
       "0          Debbie Stabenow            Democratic          Gary Peters   \n",
       "1         Elizabeth Warren            Democratic            Ed Markey   \n",
       "2            Bob Casey Jr.            Democratic           Pat Toomey   \n",
       "3       Richard Blumenthal            Democratic         Chris Murphy   \n",
       "4               Ben Cardin            Democratic     Chris Van Hollen   \n",
       "...                    ...                   ...                  ...   \n",
       "59995                   -1                    -1                   -1   \n",
       "59996          Marco Rubio            Republican           Rick Scott   \n",
       "59997                   -1                    -1                   -1   \n",
       "59998        Chuck Schumer            Democratic   Kirsten Gillibrand   \n",
       "59999          Marco Rubio            Republican           Rick Scott   \n",
       "\n",
       "      CurrentSen2Party_post GovCand1Name_post GovCand1Party_post  \\\n",
       "0                Democratic  Gretchen Whitmer         Democratic   \n",
       "1                Democratic      Maura Healey         Democratic   \n",
       "2                Republican      Josh Shapiro         Democratic   \n",
       "3                Democratic        Ned Lamont         Democratic   \n",
       "4                Democratic         Wes Moore         Democratic   \n",
       "...                     ...               ...                ...   \n",
       "59995                    -1                -1                 -1   \n",
       "59996            Republican     Charlie Crist         Democratic   \n",
       "59997                    -1                -1                 -1   \n",
       "59998            Democratic      Kathy Hochul         Democratic   \n",
       "59999            Republican     Charlie Crist         Democratic   \n",
       "\n",
       "      GovCand2Name_post GovCand2Party_post GovCand3Name_post  \\\n",
       "0           Tudor Dixon         Republican               NaN   \n",
       "1           Geoff Diehl         Republican               NaN   \n",
       "2        Doug Mastriano         Republican               NaN   \n",
       "3       Bob Stefanowski         Republican               NaN   \n",
       "4               Dan Cox         Republican               NaN   \n",
       "...                 ...                ...               ...   \n",
       "59995                -1                 -1                -1   \n",
       "59996      Ron DeSantis         Republican               NaN   \n",
       "59997                -1                 -1                -1   \n",
       "59998        Lee Zeldin         Republican               NaN   \n",
       "59999      Ron DeSantis         Republican               NaN   \n",
       "\n",
       "      GovCand3Party_post HouseCand1Name_post HouseCand1Party_post  \\\n",
       "0                    NaN       Rashida Tlaib           Democratic   \n",
       "1                    NaN        Jim McGovern           Democratic   \n",
       "2                    NaN       Bob Hollister           Democratic   \n",
       "3                    NaN        Jahana Hayes           Democratic   \n",
       "4                    NaN       John Sarbanes           Democratic   \n",
       "...                  ...                 ...                  ...   \n",
       "59995                 -1                  -1                   -1   \n",
       "59996                NaN       Jan Schneider           Democratic   \n",
       "59997                 -1                  -1                   -1   \n",
       "59998                NaN        Jerry Nadler           Democratic   \n",
       "59999                NaN           Alan Cohn           Democratic   \n",
       "\n",
       "          HouseCand2Name_post HouseCand2Party_post HouseCand3Name_post  \\\n",
       "0              Steven Elliott           Republican      Gary Walkowicz   \n",
       "1      Jeffrey Sossa-Paquette           Republican                 NaN   \n",
       "2               Lloyd Smucker           Republican                 NaN   \n",
       "3                George Logan           Republican                 NaN   \n",
       "4              Yuripzy Morgan           Republican                 NaN   \n",
       "...                       ...                  ...                 ...   \n",
       "59995                      -1                   -1                  -1   \n",
       "59996           Vern Buchanan           Republican                 NaN   \n",
       "59997                      -1                   -1                  -1   \n",
       "59998      Michael Zumbluskas           Republican       Mikhail Itkis   \n",
       "59999              Laurel Lee           Republican                 NaN   \n",
       "\n",
       "      HouseCand3Party_post HouseCand4Name_post HouseCand4Party_post  \\\n",
       "0            Working Class                 NaN                  NaN   \n",
       "1                      NaN                 NaN                  NaN   \n",
       "2                      NaN                 NaN                  NaN   \n",
       "3                      NaN                 NaN                  NaN   \n",
       "4                      NaN                 NaN                  NaN   \n",
       "...                    ...                 ...                  ...   \n",
       "59995                   -1                  -1                   -1   \n",
       "59996                  NaN                 NaN                  NaN   \n",
       "59997                   -1                  -1                   -1   \n",
       "59998          Independent                 NaN                  NaN   \n",
       "59999                  NaN                 NaN                  NaN   \n",
       "\n",
       "      HouseCand5Name_post HouseCand5Party_post HouseCand6Name_post  \\\n",
       "0                     NaN                  NaN                 NaN   \n",
       "1                     NaN                  NaN                 NaN   \n",
       "2                     NaN                  NaN                 NaN   \n",
       "3                     NaN                  NaN                 NaN   \n",
       "4                     NaN                  NaN                 NaN   \n",
       "...                   ...                  ...                 ...   \n",
       "59995                  -1                   -1                  -1   \n",
       "59996                 NaN                  NaN                 NaN   \n",
       "59997                  -1                   -1                  -1   \n",
       "59998                 NaN                  NaN                 NaN   \n",
       "59999                 NaN                  NaN                 NaN   \n",
       "\n",
       "      HouseCand6Party_post HouseCand7Name_post HouseCand7Party_post  \\\n",
       "0                      NaN                 NaN                  NaN   \n",
       "1                      NaN                 NaN                  NaN   \n",
       "2                      NaN                 NaN                  NaN   \n",
       "3                      NaN                 NaN                  NaN   \n",
       "4                      NaN                 NaN                  NaN   \n",
       "...                    ...                 ...                  ...   \n",
       "59995                   -1                  -1                   -1   \n",
       "59996                  NaN                 NaN                  NaN   \n",
       "59997                   -1                  -1                   -1   \n",
       "59998                  NaN                 NaN                  NaN   \n",
       "59999                  NaN                 NaN                  NaN   \n",
       "\n",
       "      HouseCand8Name_post HouseCand8Party_post   SenCand1Name_post  \\\n",
       "0                     NaN                  NaN                 NaN   \n",
       "1                     NaN                  NaN                 NaN   \n",
       "2                     NaN                  NaN      John Fetterman   \n",
       "3                     NaN                  NaN  Richard Blumenthal   \n",
       "4                     NaN                  NaN    Chris Van Hollen   \n",
       "...                   ...                  ...                 ...   \n",
       "59995                -1.0                 -1.0                  -1   \n",
       "59996                 NaN                  NaN         Val Demings   \n",
       "59997                -1.0                 -1.0                  -1   \n",
       "59998                 NaN                  NaN       Chuck Schumer   \n",
       "59999                 NaN                  NaN         Val Demings   \n",
       "\n",
       "      SenCand1Party_post SenCand2Name_post SenCand2Party_post  \\\n",
       "0                    NaN               NaN                NaN   \n",
       "1                    NaN               NaN                NaN   \n",
       "2             Democratic         Mehmet Oz         Republican   \n",
       "3             Democratic        Leora Levy         Republican   \n",
       "4             Democratic     Chris Chaffee         Republican   \n",
       "...                  ...               ...                ...   \n",
       "59995                 -1                -1                 -1   \n",
       "59996         Democratic       Marco Rubio         Republican   \n",
       "59997                 -1                -1                 -1   \n",
       "59998         Democratic        Joe Pinion         Republican   \n",
       "59999         Democratic       Marco Rubio         Republican   \n",
       "\n",
       "      SenCand3Name_post SenCand3Party_post SenCand4Name_post  \\\n",
       "0                   NaN                NaN               NaN   \n",
       "1                   NaN                NaN               NaN   \n",
       "2                   NaN                NaN               NaN   \n",
       "3                   NaN                NaN               NaN   \n",
       "4                   NaN                NaN               NaN   \n",
       "...                 ...                ...               ...   \n",
       "59995                -1                 -1                -1   \n",
       "59996               NaN                NaN               NaN   \n",
       "59997                -1                 -1                -1   \n",
       "59998               NaN                NaN               NaN   \n",
       "59999               NaN                NaN               NaN   \n",
       "\n",
       "      SenCand4Party_post SenCand1Name2_post SenCand1Party2_post  \\\n",
       "0                    NaN             __NA__              __NA__   \n",
       "1                    NaN             __NA__              __NA__   \n",
       "2                    NaN             __NA__              __NA__   \n",
       "3                    NaN             __NA__              __NA__   \n",
       "4                    NaN             __NA__              __NA__   \n",
       "...                  ...                ...                 ...   \n",
       "59995                 -1                 -1                  -1   \n",
       "59996                NaN             __NA__              __NA__   \n",
       "59997                 -1                 -1                  -1   \n",
       "59998                NaN             __NA__              __NA__   \n",
       "59999                NaN             __NA__              __NA__   \n",
       "\n",
       "      SenCand2Name2_post SenCand2Party2_post      AttCand1Name  \\\n",
       "0                 __NA__              __NA__       Dana Nessel   \n",
       "1                 __NA__              __NA__   Andrea Campbell   \n",
       "2                 __NA__              __NA__               NaN   \n",
       "3                 __NA__              __NA__      William Tong   \n",
       "4                 __NA__              __NA__  Anthony G. Brown   \n",
       "...                  ...                 ...               ...   \n",
       "59995                 -1                  -1                -1   \n",
       "59996             __NA__              __NA__      Aramis Ayala   \n",
       "59997                 -1                  -1                -1   \n",
       "59998             __NA__              __NA__     Letitia James   \n",
       "59999             __NA__              __NA__      Aramis Ayala   \n",
       "\n",
       "           AttCand2Name   AttCand3Name AttCand1Party AttCand2Party  \\\n",
       "0       Matthew DePerno  Joseph McHugh    Democratic    Republican   \n",
       "1           Jay McMahon            NaN    Democratic    Republican   \n",
       "2                   NaN            NaN           NaN           NaN   \n",
       "3        Jessica Kordas            NaN    Democratic    Republican   \n",
       "4      Michael Peroutka            NaN    Democratic    Republican   \n",
       "...                 ...            ...           ...           ...   \n",
       "59995                -1             -1            -1            -1   \n",
       "59996      Ashley Moody            NaN    Democratic    Republican   \n",
       "59997                -1             -1            -1            -1   \n",
       "59998     Michael Henry            NaN    Democratic    Republican   \n",
       "59999      Ashley Moody            NaN    Democratic    Republican   \n",
       "\n",
       "      AttCand3Party       SecCand1Name     SecCand2Name      SecCand3Name  \\\n",
       "0       Libertarian     Jocelyn Benson  Kristina Karamo  Gregory Stempfle   \n",
       "1               NaN  William F. Galvin   Rayla Campbell      Juan Sanchez   \n",
       "2               NaN                NaN              NaN               NaN   \n",
       "3               NaN   Stephanie Thomas   Dominic Rapini     Harold Harris   \n",
       "4               NaN                NaN              NaN               NaN   \n",
       "...             ...                ...              ...               ...   \n",
       "59995            -1                 -1               -1                -1   \n",
       "59996           NaN                NaN              NaN               NaN   \n",
       "59997            -1                 -1               -1                -1   \n",
       "59998           NaN     Jeffrey Powell     Michael Howe               NaN   \n",
       "59999           NaN                NaN              NaN               NaN   \n",
       "\n",
       "      SecCand1Party SecCand2Party  SecCand3Party  page_CC22_401_timing  \\\n",
       "0        Democratic    Republican    Libertarian                 3.838   \n",
       "1        Democratic    Republican  Green-Rainbow                 4.619   \n",
       "2               NaN           NaN            NaN                 4.711   \n",
       "3        Democratic    Republican     Republican                 4.665   \n",
       "4               NaN           NaN            NaN                 2.811   \n",
       "...             ...           ...            ...                   ...   \n",
       "59995            -1            -1             -1                   NaN   \n",
       "59996           NaN           NaN            NaN                12.150   \n",
       "59997            -1            -1             -1                   NaN   \n",
       "59998    Democratic    Republican            NaN                11.331   \n",
       "59999           NaN           NaN            NaN                 6.120   \n",
       "\n",
       "       page_CC22_402a_timing  page_CC22_402b_timing  page_CC22_403_timing  \\\n",
       "0                      0.000                  0.000                 3.195   \n",
       "1                      0.000                  0.000                 5.433   \n",
       "2                      0.000                  0.000                 2.703   \n",
       "3                      0.000                  0.000                 3.297   \n",
       "4                     36.077                 13.455                 0.000   \n",
       "...                      ...                    ...                   ...   \n",
       "59995                    NaN                    NaN                   NaN   \n",
       "59996                  9.945                 21.826                 0.000   \n",
       "59997                    NaN                    NaN                   NaN   \n",
       "59998                  0.000                  0.000                13.005   \n",
       "59999                  0.000                  0.000                 4.138   \n",
       "\n",
       "       page_CC22_403c_timing  page_CC22_403d_timing  page_CC22_404_timing  \\\n",
       "0                     11.716                  7.395                 0.000   \n",
       "1                      0.000                  0.000                 5.510   \n",
       "2                      8.581                  4.787                 0.000   \n",
       "3                     37.923                  5.639                 0.000   \n",
       "4                      0.000                  0.000                 0.000   \n",
       "...                      ...                    ...                   ...   \n",
       "59995                    NaN                    NaN                   NaN   \n",
       "59996                  0.000                  0.000                 0.000   \n",
       "59997                    NaN                    NaN                   NaN   \n",
       "59998                  0.000                  0.000                 6.397   \n",
       "59999                 13.535                 13.610                 0.000   \n",
       "\n",
       "       page_CC22_405_timing  page_CC22_406a_timing  page_CC22_406b_timing  \\\n",
       "0                     3.801                  5.123                  0.000   \n",
       "1                     0.000                  3.592                  9.807   \n",
       "2                     0.000                  2.681                  0.000   \n",
       "3                     5.341                  3.293                  0.000   \n",
       "4                     0.000                  0.000                  0.000   \n",
       "...                     ...                    ...                    ...   \n",
       "59995                   NaN                    NaN                    NaN   \n",
       "59996                 0.000                  0.000                  0.000   \n",
       "59997                   NaN                    NaN                    NaN   \n",
       "59998                 0.000                  4.897                  0.000   \n",
       "59999                 0.000                  2.936                  0.000   \n",
       "\n",
       "       page_CC22_406c_timing  page_CC22_411_timing  page_CC22_411b_timing  \\\n",
       "0                      0.000                 0.000                    0.0   \n",
       "1                      6.211                 0.000                    0.0   \n",
       "2                      0.000                 2.727                    0.0   \n",
       "3                      0.000                 2.712                    0.0   \n",
       "4                      0.000                 0.000                    0.0   \n",
       "...                      ...                   ...                    ...   \n",
       "59995                    NaN                   NaN                    NaN   \n",
       "59996                  0.000                 0.000                    0.0   \n",
       "59997                    NaN                   NaN                    NaN   \n",
       "59998                  0.000                 6.989                    0.0   \n",
       "59999                  0.000                 4.066                    0.0   \n",
       "\n",
       "       page_CC22_412_timing  page_CC22_413_timing  page_CC22_411_nv_timing  \\\n",
       "0                     3.070                 7.917                    0.000   \n",
       "1                     3.731                 2.001                    0.000   \n",
       "2                     2.003                 2.452                    0.000   \n",
       "3                     1.754                 1.911                    0.000   \n",
       "4                     0.000                 0.000                    1.764   \n",
       "...                     ...                   ...                      ...   \n",
       "59995                   NaN                   NaN                      NaN   \n",
       "59996                 0.000                 0.000                    3.351   \n",
       "59997                   NaN                   NaN                      NaN   \n",
       "59998                10.229                 3.424                    0.000   \n",
       "59999                 4.993                 6.955                    0.000   \n",
       "\n",
       "       page_CC22_411b_nv_timing  page_CC22_412_nv_timing  \\\n",
       "0                           0.0                    0.000   \n",
       "1                           0.0                    0.000   \n",
       "2                           0.0                    0.000   \n",
       "3                           0.0                    0.000   \n",
       "4                           0.0                    1.331   \n",
       "...                         ...                      ...   \n",
       "59995                       NaN                      NaN   \n",
       "59996                       0.0                    3.850   \n",
       "59997                       NaN                      NaN   \n",
       "59998                       0.0                    0.000   \n",
       "59999                       0.0                    0.000   \n",
       "\n",
       "       page_CC22_413_nv_timing  page_CC22_414a_timing  page_CC22_414b_timing  \\\n",
       "0                        0.000                  2.699                  5.474   \n",
       "1                        0.000                  2.632                  2.168   \n",
       "2                        0.000                  0.000                  0.000   \n",
       "3                        0.000                  2.602                  5.487   \n",
       "4                        0.873                  1.031                  0.000   \n",
       "...                        ...                    ...                    ...   \n",
       "59995                      NaN                    NaN                    NaN   \n",
       "59996                   15.273                 10.222                  0.000   \n",
       "59997                      NaN                    NaN                    NaN   \n",
       "59998                    0.000                  4.602                  3.790   \n",
       "59999                    0.000                  5.359                  0.000   \n",
       "\n",
       "       page_CC22_415_grid_timing  page_CC22_416_grid_timing  \\\n",
       "0                          5.270                      8.890   \n",
       "1                          9.432                     11.622   \n",
       "2                          3.166                      5.314   \n",
       "3                         21.356                      6.250   \n",
       "4                          0.000                      4.303   \n",
       "...                          ...                        ...   \n",
       "59995                        NaN                        NaN   \n",
       "59996                      0.000                     10.338   \n",
       "59997                        NaN                        NaN   \n",
       "59998                      8.433                     14.458   \n",
       "59999                      4.718                     14.886   \n",
       "\n",
       "       page_CC22_416a_other_timing  page_CC22_416b_other_timing  \\\n",
       "0                            8.439                          0.0   \n",
       "1                            0.000                          0.0   \n",
       "2                            0.000                          0.0   \n",
       "3                            0.000                          0.0   \n",
       "4                            0.000                          0.0   \n",
       "...                            ...                          ...   \n",
       "59995                          NaN                          NaN   \n",
       "59996                        0.000                          0.0   \n",
       "59997                          NaN                          NaN   \n",
       "59998                        0.000                          0.0   \n",
       "59999                        0.000                          0.0   \n",
       "\n",
       "       page_CC22_416c_other_timing  page_CC22_420_timing  \\\n",
       "0                              0.0                14.653   \n",
       "1                              0.0                27.823   \n",
       "2                              0.0                13.824   \n",
       "3                              0.0                16.149   \n",
       "4                              0.0                 9.665   \n",
       "...                            ...                   ...   \n",
       "59995                          NaN                   NaN   \n",
       "59996                          0.0                 3.566   \n",
       "59997                          NaN                   NaN   \n",
       "59998                          0.0                19.223   \n",
       "59999                          0.0                16.641   \n",
       "\n",
       "       page_CC22_421r_timing  page_CC22_422r_timing  page_CC22_423_timing  \\\n",
       "0                     14.715                  7.229                 4.724   \n",
       "1                     13.408                  7.497                 6.218   \n",
       "2                      5.637                  3.063                 3.913   \n",
       "3                     15.548                 10.454                 3.303   \n",
       "4                     20.104                  6.577                 4.109   \n",
       "...                      ...                    ...                   ...   \n",
       "59995                    NaN                    NaN                   NaN   \n",
       "59996                  6.847                  3.244                 3.819   \n",
       "59997                    NaN                    NaN                   NaN   \n",
       "59998                  6.840                  4.426                 3.252   \n",
       "59999                 35.861                 16.629                16.053   \n",
       "\n",
       "       page_CC22_424_timing  page_CC22_430a_timing  page_CC22_430b_timing  \\\n",
       "0                     5.115                  7.679                 16.768   \n",
       "1                     4.698                  9.950                  0.000   \n",
       "2                     3.400                  9.479                  0.000   \n",
       "3                     5.372                  9.185                  6.350   \n",
       "4                     1.443                  3.500                  0.000   \n",
       "...                     ...                    ...                    ...   \n",
       "59995                   NaN                    NaN                    NaN   \n",
       "59996                 1.393                  5.963                  0.000   \n",
       "59997                   NaN                    NaN                    NaN   \n",
       "59998                 2.558                 30.590                 26.445   \n",
       "59999                 6.000                 10.279                  0.000   \n",
       "\n",
       "       page_CC22_430c_timing  page_CC22_431a_timing  page_CC22_431b_timing  \\\n",
       "0                      9.035                  4.738                  5.883   \n",
       "1                      0.000                  5.842                  2.275   \n",
       "2                      0.000                  2.632                  0.000   \n",
       "3                      1.361                  4.563                  1.080   \n",
       "4                      0.000                  3.714                  0.000   \n",
       "...                      ...                    ...                    ...   \n",
       "59995                    NaN                    NaN                    NaN   \n",
       "59996                  0.000                  8.548                  0.000   \n",
       "59997                    NaN                    NaN                    NaN   \n",
       "59998                 12.501                  8.096                  5.266   \n",
       "59999                  0.000                 11.621                  8.181   \n",
       "\n",
       "       page_CC22_432a_timing  page_CC22_432b_timing  page_CC22_433a_timing  \\\n",
       "0                      3.505                  0.000                  2.628   \n",
       "1                      4.104                 13.099                  2.421   \n",
       "2                      1.763                  0.000                  2.203   \n",
       "3                      2.013                  3.814                  7.642   \n",
       "4                      0.848                  0.000                  1.066   \n",
       "...                      ...                    ...                    ...   \n",
       "59995                    NaN                    NaN                    NaN   \n",
       "59996                  7.337                  0.000                  2.735   \n",
       "59997                    NaN                    NaN                    NaN   \n",
       "59998                  5.288                  0.000                  3.444   \n",
       "59999                  3.843                  0.000                  3.828   \n",
       "\n",
       "       page_CC22_433_dem_timing  page_CC22_433_rep_timing  \\\n",
       "0                         2.071                       0.0   \n",
       "1                         0.000                       0.0   \n",
       "2                         2.511                       0.0   \n",
       "3                         0.000                       0.0   \n",
       "4                         0.000                       0.0   \n",
       "...                         ...                       ...   \n",
       "59995                       NaN                       NaN   \n",
       "59996                     1.959                       0.0   \n",
       "59997                       NaN                       NaN   \n",
       "59998                     1.983                       0.0   \n",
       "59999                     2.882                       0.0   \n",
       "\n",
       "       page_CC22_433b_timing  page_CC22_pid7_timing  \\\n",
       "0                      0.000                    0.0   \n",
       "1                      2.542                    0.0   \n",
       "2                      0.000                    0.0   \n",
       "3                      5.006                    0.0   \n",
       "4                      1.175                    0.0   \n",
       "...                      ...                    ...   \n",
       "59995                    NaN                    NaN   \n",
       "59996                  0.000                    0.0   \n",
       "59997                    NaN                    NaN   \n",
       "59998                  0.000                    0.0   \n",
       "59999                  0.000                    0.0   \n",
       "\n",
       "       page_CC22_440_grid_timing  page_CC22_441_grid_timing  \\\n",
       "0                         13.141                     12.128   \n",
       "1                         21.042                     18.707   \n",
       "2                         11.285                      6.448   \n",
       "3                         11.151                     11.287   \n",
       "4                         15.117                     12.222   \n",
       "...                          ...                        ...   \n",
       "59995                        NaN                        NaN   \n",
       "59996                     10.574                     26.387   \n",
       "59997                        NaN                        NaN   \n",
       "59998                     34.297                    179.778   \n",
       "59999                     19.293                     32.854   \n",
       "\n",
       "       page_CC22_443_timing  page_gunown_timing  page_numchildren_timing  \\\n",
       "0                    12.506               6.337                    6.265   \n",
       "1                    21.805               3.126                    9.023   \n",
       "2                     4.443               2.105                    3.108   \n",
       "3                    10.843               5.421                    4.947   \n",
       "4                    19.317               3.458                    3.843   \n",
       "...                     ...                 ...                      ...   \n",
       "59995                   NaN                 NaN                      NaN   \n",
       "59996                10.620              31.372                   10.299   \n",
       "59997                   NaN                 NaN                      NaN   \n",
       "59998                16.411               4.572                    7.346   \n",
       "59999                26.144               5.721                    6.560   \n",
       "\n",
       "       page_edloan_timing  page_student_timing     starttime       endtime  \\\n",
       "0                   2.863                  0.0  1.980030e+12  1.980030e+12   \n",
       "1                   3.722                  0.0  1.980030e+12  1.980030e+12   \n",
       "2                   3.622                  0.0  1.980030e+12  1.980030e+12   \n",
       "3                   3.000                  0.0  1.980030e+12  1.980030e+12   \n",
       "4                   4.173                  0.0  1.980030e+12  1.980030e+12   \n",
       "...                   ...                  ...           ...           ...   \n",
       "59995                 NaN                  NaN  1.983500e+12  1.983540e+12   \n",
       "59996              10.694                  0.0  1.983500e+12  1.983500e+12   \n",
       "59997                 NaN                  NaN  1.983500e+12  1.983510e+12   \n",
       "59998               3.487                  0.0  1.983510e+12  1.983510e+12   \n",
       "59999               7.903                  0.0  1.983510e+12  1.983520e+12   \n",
       "\n",
       "       starttime_post  endtime_post  \n",
       "0        1.985230e+12  1.985230e+12  \n",
       "1        1.986030e+12  1.986030e+12  \n",
       "2        1.984610e+12  1.984610e+12  \n",
       "3        1.984480e+12  1.984490e+12  \n",
       "4        1.985620e+12  1.985620e+12  \n",
       "...               ...           ...  \n",
       "59995             NaN           NaN  \n",
       "59996    1.984340e+12  1.984340e+12  \n",
       "59997             NaN           NaN  \n",
       "59998    1.984750e+12  1.984750e+12  \n",
       "59999    1.985970e+12  1.985980e+12  \n",
       "\n",
       "[60000 rows x 697 columns]"
      ]
     },
     "execution_count": 1728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1703,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseid</th>\n",
       "      <th>if_voted_2022</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>senate_vote_choice</th>\n",
       "      <th>senator_name</th>\n",
       "      <th>state</th>\n",
       "      <th>vote_2022</th>\n",
       "      <th>turn_out_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983126005</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983126559</td>\n",
       "      <td>1</td>\n",
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       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983126197</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>John Fetterman</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979974411</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Richard Blumenthal</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       caseid  if_voted_2022  vote_2020  senate_vote_choice  \\\n",
       "0  1983126005              1        1.0                 NaN   \n",
       "1  1983126559              1        1.0                 NaN   \n",
       "2  1983126197              1        1.0                 1.0   \n",
       "3  1979974411              1        1.0                 1.0   \n",
       "4  1983130427              0        0.0                 NaN   \n",
       "\n",
       "         senator_name  state  vote_2022  turn_out_diff  \n",
       "0                 NaN   26.0          0           -1.0  \n",
       "1                 NaN   25.0          0           -1.0  \n",
       "2      John Fetterman   42.0          1            0.0  \n",
       "3  Richard Blumenthal    9.0          1            0.0  \n",
       "4                 NaN   24.0          0            0.0  "
      ]
     },
     "execution_count": 1703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe with 'caseid', 'voted', and 'senate_vote_choice' columns\n",
    "new_df = df[['caseid']].copy()\n",
    "\n",
    "# Create the 'voted' variable\n",
    "new_df['if_voted_2022'] = (df['CC22_401'] == 5).astype(int)\n",
    "\n",
    "def get_senate_vote_choice(row):\n",
    "    vote_20 = row['presvote20post']\n",
    "    pre_vote = row['CC22_365_voted']\n",
    "    post_vote = row['CC22_411']\n",
    "    pre_state = row['inputstate']\n",
    "    post_state = row['inputstate_post']\n",
    "\n",
    "    def get_candidate_info(vote, suffix=''):\n",
    "        if 1 <= vote <= 4:\n",
    "            return row[f'SenCand{int(vote)}Name{suffix}'], row[f'SenCand{int(vote)}Party{suffix}']\n",
    "        else:\n",
    "            return None, None\n",
    "\n",
    "    #if individual voted for trump in 2020, code -1, if voted for Biden code 1, others and missing and did not vote code 0\n",
    "    if vote_20 == 2:\n",
    "        vote_20 = -1\n",
    "    elif vote_20 == 1:\n",
    "        vote_20 = 1\n",
    "    else:\n",
    "        vote_20 = 0\n",
    "\n",
    "\n",
    "    if pd.notna(pre_vote) and pd.notna(post_vote):\n",
    "        if pre_vote == post_vote:\n",
    "            name, party = get_candidate_info(post_vote, '_post')\n",
    "            return vote_20, post_vote, name, party, post_state\n",
    "        else:\n",
    "            return vote_20, float('nan'), None, None, None  # set as missing if contradictory responses\n",
    "    elif pd.notna(pre_vote):\n",
    "        name, party = get_candidate_info(pre_vote)\n",
    "        return vote_20, pre_vote, name, party, pre_state\n",
    "    elif pd.notna(post_vote):\n",
    "        name, party = get_candidate_info(post_vote, '_post')\n",
    "        return vote_20, post_vote, name, party, post_state\n",
    "    else:\n",
    "        if pd.isna(pre_state):\n",
    "            return vote_20, float('nan'), None, None, post_state\n",
    "        elif pd.isna(pre_state):\n",
    "            return vote_20, float('nan'), None, None, pre_state\n",
    "        elif pre_state == post_state:\n",
    "            return vote_20, float('nan'), None, None, pre_state  \n",
    "        else:\n",
    "            return vote_20, float('nan'), None, None, None \n",
    "\n",
    "\n",
    "# Apply the updated function to the dataframe\n",
    "new_df[['vote_2020', 'senate_vote_choice', 'senator_name', 'party', 'state']] = df.apply(get_senate_vote_choice, axis=1, result_type='expand')\n",
    "\n",
    "#if voted for republican senator, party_voted is -1, if voted for democrats, party_voted is 1. if other or missing, the value is 0\n",
    "new_df['vote_2022'] = new_df['party'].apply(lambda party: 1 if party == 'Democratic' else (-1 if party == 'Republican' else 0))\n",
    "new_df = new_df.drop(columns=['party'])\n",
    "\n",
    "#turn_out_diff measures how people's voting behavior changes.\n",
    "# If the variable is negative, people voted more for republican in the midterm compared to 2020\n",
    "new_df['turn_out_diff'] = new_df['vote_2022'] - new_df['vote_2020']\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1704,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the specified columns from 'df' to 'new_df'\n",
    "control_vr = ['birthyr', 'gender4', 'educ', 'race', 'faminc_new', 'marstat', 'urbancity', 'region', 'presvote16post', \\\n",
    "    'pew_bornagain', 'pew_religimp', 'pew_churatd', 'pew_prayer', 'religpew', 'ideo5', 'CC22_320a', 'CC22_320g', 'CC22_320h', 'CurrentSen1Party', 'CurrentSen2Party',\\\n",
    "    'CC22_430a_8', 'CC22_310b', 'CC22_303', 'CC22_304']\n",
    "important_vr = ['pid3', 'pid7', 'CC22_332a', 'CC22_332b', 'CC22_332c', 'CC22_332d', 'CC22_332e', 'CC22_332f', 'CC22_350d']\n",
    "new_df = pd.concat([new_df, df[control_vr]], axis=1)\n",
    "new_df = pd.concat([new_df, df[important_vr]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1705,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning columns\n",
    "new_df['age'] = 2022 - df['birthyr']\n",
    "new_df = new_df.drop('birthyr', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1706,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if there is political involvement, give vaue 1, else 0\n",
    "new_df['pol_involve'] = new_df['CC22_430a_8'].apply(lambda x: 0 if x > 6 else 1)\n",
    "new_df = new_df.drop(columns=['CC22_430a_8'])\n",
    "\n",
    "#Calculate approval for senators in different political parties\n",
    "recode_map = {1: 2, 2: 1, 5: 0, 3: -1, 4: -2}\n",
    "new_df['CC22_320g'] = new_df['CC22_320g'].map(recode_map)\n",
    "df['CC22_320h'] = df['CC22_320h'].map(recode_map)\n",
    "\n",
    "# Define a function to calculate the average rating for a given party\n",
    "def get_party_attitude(row, party):\n",
    "    ratings = []\n",
    "    if row['CurrentSen1Party'] == party:\n",
    "        ratings.append(row['CC22_320g'])\n",
    "    if row['CurrentSen2Party'] == party:\n",
    "        ratings.append(row['CC22_320h'])\n",
    "    \n",
    "    return sum(ratings) / len(ratings) if ratings else None\n",
    "\n",
    "# Create new columns 'attitude_towards_party' as the average rating for each party\n",
    "new_df['attitude_towards_dem'] = new_df.apply(lambda row: get_party_attitude(row, 'Democratic'), axis=1)\n",
    "new_df['attitude_towards_rep'] = new_df.apply(lambda row: get_party_attitude(row, 'Republican'), axis=1)\n",
    "\n",
    "# Drop the original columns\n",
    "new_df = new_df.drop(columns=['CC22_320g', 'CC22_320h', 'CurrentSen1Party', 'CurrentSen2Party'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1707,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = {'senator_name': 'senator_voted', 'gender4':'gender', 'faminc_new': 'fam_income',\\\n",
    "    'ideo5': 'pol_ideo', 'CC22_320a': 'approval_biden', 'CC22_310b': 'know_senate', 'CC22_303': 'income_change', 'CC22_304': 'price_change',\\\n",
    "    'pid3': 'party_identified', 'pid7': 'party_strength', 'CC22_332a':'abortion_all_cases', 'CC22_332b':'abortion_some_cases', \\\n",
    "    'CC22_332c': 'abortion_before_20weeks', 'CC22_332d': 'abortion_coverage', 'CC22_332e': 'abortion_funds', 'CC22_332f': 'abortion_illegal', 'CC22_350d': 'congress_abortion'}\n",
    "\n",
    "new_df = new_df.rename(columns=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1708,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create column for states with abortion on ballot\n",
    "#source: https://ballotpedia.org/2022_abortion-related_ballot_measures\n",
    "abortion_states = [6, 21, 26, 30, 50]\n",
    "new_df['abortion_ballot'] = new_df['state'].apply(lambda row: 1 if row in abortion_states else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1709,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change coding for gender. Man is 0, Woman is 1. Change non-binary and others as NaN\n",
    "new_df['gender'] = new_df['gender'].apply(lambda value: 1 if value == 2 else (0 if value == 1 else None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1710,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code education. Did not graduate from high school as 0, high school grad as 1, some colege (no degree yet) as 2, 2 year college as 3\n",
    "# 4 year college as 4, postgrad as 5\n",
    "new_df['educ'] = new_df['educ'].apply(lambda value: value - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1711,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dummy(df, old_col, column_mapping):\n",
    "    dummies = pd.get_dummies(df[old_col], prefix=old_col)\n",
    "    dummies = dummies.rename(columns=column_mapping)\n",
    "    df = pd.concat([df, dummies], axis=1)\n",
    "    df = df.drop(columns=old_col)\n",
    "    return df\n",
    "\n",
    "#Race\n",
    "# Create dummy variables for the new 'race_mapped' column, excluding the 'White' category\n",
    "# Code Native American, Middle Eastern, two + races, other as missing\n",
    "new_df['race'] = new_df['race'].apply(lambda value: None if value in [5,6,7, 8] else value)\n",
    "column_mapping = {'race_1.0': 'race_white', 'race_2.0': 'race_black', 'race_3.0': 'race_hispanic', 'race_4.0': 'race_asian'}\n",
    "new_df = get_dummy(new_df, 'race', column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1712,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code family income to start with 0, change prefer not to say to None\n",
    "new_df['fam_income'] = new_df['fam_income'].apply(lambda value: None if value in [97, -1] else value - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1713,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code marstat to start with 0, create dummy variable\n",
    "new_df['marstat'] = new_df['marstat'].apply(lambda value: None if value == -1 else value - 1)\n",
    "column_mapping = {'marstat_0.0': 'marstat_married', 'marstat_1.0': 'marstat_separated', 'marstat_2.0': 'marstat_divorced', 'marstat_3.0': 'marstat_widowed',\\\n",
    "    'marstat_4.0': 'marstat_never_married', 'marstat_5.0': 'marstat_domestic'}\n",
    "new_df = get_dummy(new_df, 'marstat', column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1714,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code urbancity, if respondent live in city, code 3, suburb = 2, town =1, rural area = 0. other and missing as NaN\n",
    "new_df['urbancity'] = new_df['urbancity'].apply(lambda value: 0 if value == 4 else (1 if value == 3 else (2 if value == 2 else (3 if value == 1 else None))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1715,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dummy variable for region\n",
    "column_mapping = {'region_1': 'region_northeast', 'region_2': 'region_midwest', 'region_3': 'region_south', 'region_4': 'region_west'}\n",
    "new_df = get_dummy(new_df, 'region', column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1716,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For voting outcome in 2016, Code voted for Clinton as 1, Trump as -1, other candidates as 2, did not vote as 0\n",
    "new_df['presvote16post'] = new_df['presvote16post'].apply(lambda value: -1 if value == 2 else (2 if value in [3,4,5,6] else (1 if value == 1 else (0 if value == 7 else None))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1717,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code born-again/evangelical Christian as 1, No as 0\n",
    "new_df['pew_bornagain'] = new_df['pew_bornagain'].apply(lambda value: 0 if value == 2 else (1 if value == 1 else None))\n",
    "\n",
    "#Code religion importance, not important as 0, very important as 3\n",
    "new_df['pew_religimp'] = new_df['pew_religimp'].apply(lambda value: 0 if value == 4 else (1 if value == 3 else (2 if value ==2 else (4 if value == 1 else None))))\n",
    "\n",
    "#Code church attendance, never and don't know as 0, more than once a week as 5\n",
    "new_df['pew_churatd'] = new_df['pew_churatd'].apply(lambda value: 0 if value in [6,7] else (1 if value == 5 else (2 if value == 4 else (3 if value == 3 else (4 if value == 2 else (5 if value == 1 else None))))))\n",
    "\n",
    "#Code prayer, never and don't know as 0, several times a day as 6\n",
    "new_df['pew_prayer'] = new_df['pew_prayer'].apply(lambda value: 0 if value in [7,8] else (1 if value == 6 else (2 if value == 5 else (3 if value == 4 else (4 if value == 3 else (5 if value == 2 else (6 if value == 1 else None)))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1718,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code present religion, combine atheist, agnostic, nothing in particular together and code them as 0. Code something else and missing as NaN\n",
    "new_df['religpew'] = new_df['religpew'].apply(lambda value: 0 if value in [9, 10, 11] else (None if value in [12, -1] else value))\n",
    "column_mapping = {'religpew_0.0': 'religpew_not_religious', 'religpew_1.0': 'religpew_protestant', 'religpew_2.0': 'religpew_catholic', 'religpew_3.0': 'religpew_mormon',\\\n",
    "     'religpew_4.0': 'religpew_orthodox', 'religpew_5.0': 'religpew_jewish','religpew_6.0': 'religpew_muslim','religpew_7.0': 'religpew_buddhist','religpew_8.0': 'religpew_hindu'}\n",
    "new_df = get_dummy(new_df, 'religpew', column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1719,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code political viewpoint, very liberal as 2, moderate as 0, very conservative as -2. Not sure and missing as NaN\n",
    "new_df['pol_ideo'] = new_df['pol_ideo'].apply(lambda value: 0 if value == 3 else (1 if value == 2 else (2 if value == 1 else (-1 if value == 4 else (-2 if value == 5 else None)))))\n",
    "\n",
    "#Code approval for biden, strongly approve as 2, strongly disapprove as -2, not sure as 0. \n",
    "new_df['approval_biden'] = new_df['approval_biden'].apply(lambda value: 0 if value == 5 else (1 if value == 2 else (2 if value == 1 else (-1 if value == 3 else (-2 if value == 4 else None)))))\n",
    "\n",
    "#Code knwoledge of us senate, if the person correctly answered the question Which party has a majority of seats in the US Senate (democrats), they receive 1\n",
    "# If they respond incorrectly (republican, neither), they receive -1\n",
    "# If they are not sure, receive 0. Missing is coded as None\n",
    "new_df['know_senate'] = new_df['know_senate'].apply(lambda value: 0 if value == 4 else (1 if value == 2 else (-1 if value in [1, 3] else None)))\n",
    "\n",
    "#Code change in annual income. Code increased a lot as 2, decreased a lot as -2, same as 0\n",
    "new_df['income_change'] = new_df['income_change'].apply(lambda value: 0 if value == 3 else (1 if value == 2 else (2 if value == 1 else (-1 if value == 4 else (-2 if value == 5 else None)))))\n",
    "\n",
    "#Code change in prices of everyday goods. Code increased a lot as 2, decreased a lot as -2, same as 0\n",
    "new_df['price_change'] = new_df['price_change'].apply(lambda value: 0 if value == 3 else (1 if value == 2 else (2 if value == 1 else (-1 if value == 4 else (-2 if value == 5 else None)))))\n",
    "\n",
    "#Code party registered. Registered democrats as 1, repblican as -1, no party, independent as 0, others as NaN\n",
    "# new_df['party_registered'] = new_df['party_registered'].apply(lambda value: 0 if value == 1 else (1 if value == 2 else (-1 if value ==3 else None)))\n",
    "\n",
    "#Code party identified, democrats as 1, repblican as -1, no party, independent and not sure as 0, others as NaN\n",
    "new_df['party_identified'] = new_df['party_identified'].apply(lambda value: 0 if value in [3,5] else (1 if value == 1 else (-1 if value == 2 else None)))\n",
    "\n",
    "#Code party strength, strong democrats as 3, not very strong democrats as 2, democratic party as 1. Neither and not sure as 0\n",
    "new_df['party_strength'] = new_df['party_strength'].apply(lambda value: 0 if value in [4, 8, 9] else (1 if value == 3 else (2 if value == 2 else (3 if value == 1 else (-1 if value == 5 else (-2 if value == 6 else (-3 if value == 7 else None)))))))\n",
    "\n",
    "#Code support of various statements of abortion\n",
    "for var in ['abortion_all_cases', 'abortion_some_cases', 'abortion_before_20weeks', 'abortion_coverage', 'abortion_funds', 'abortion_illegal', 'congress_abortion']:\n",
    "    new_df[var] = new_df[var].apply(lambda value: 0 if value == 2 else (1 if value == 1 else None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1720,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Only select states that participated in the senate election in 2022\n",
    "new_df = new_df[new_df['state'].isin([1, 2, 4, 5, 6, 8, 9, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 29, 32, 33, 36, 37, 38, 39, 40, 41, 42, 45, 46, 49, 50, 53, 55])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1721,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop individuals who did not vote or who vote for both other parties in 2020 and 2022 \n",
    "new_df = new_df[(new_df[\"vote_2020\"] != 0) & (new_df[\"vote_2022\"] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1722,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.drop(columns=['caseid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1723,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender                       145\n",
      "fam_income                  2173\n",
      "urbancity                    142\n",
      "presvote16post               240\n",
      "pew_bornagain                 10\n",
      "pew_religimp                   6\n",
      "pew_churatd                   11\n",
      "pew_prayer                     6\n",
      "pol_ideo                     503\n",
      "approval_biden                11\n",
      "know_senate                   10\n",
      "income_change                 29\n",
      "price_change                  56\n",
      "party_identified             946\n",
      "abortion_all_cases            15\n",
      "abortion_some_cases           19\n",
      "abortion_before_20weeks       13\n",
      "abortion_coverage             10\n",
      "abortion_funds                14\n",
      "abortion_illegal              16\n",
      "congress_abortion              1\n",
      "attitude_towards_dem        9400\n",
      "attitude_towards_rep       13371\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#check for missing value\n",
    "missing_values_count = new_df.isnull().sum()\n",
    "missing_values = missing_values_count[missing_values_count > 0]\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1724,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handle missing value \n",
    "new_df = new_df[new_df['presvote16post'].notna()]\n",
    "new_df = new_df.drop(columns=['attitude_towards_dem', 'attitude_towards_rep', 'senate_vote_choice', 'senator_voted'])\n",
    "new_df = new_df[new_df['pol_ideo'].notnull() & new_df['party_identified'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1725,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For missing values in all columns, fill them with mean\n",
    "new_df[new_df.columns.to_list()] = new_df[new_df.columns.to_list()].fillna(new_df[new_df.columns.to_list()].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1726,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "#check for missing value\n",
    "missing_values_count = new_df.isnull().sum()\n",
    "missing_values = missing_values_count[missing_values_count > 0]\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1727,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OnDmjAwcO5Prc2rZtq8OHD+vkyZNKTU3Vzz//rBdeeEHp6enWC+a+++47tWvXzma7f/9OLBaLqlevrnPnzlmfY8mSJXPMK89+PmZ3mqhUqZICAgJspmX88MMPOn36tHWUOD+vrX/btm2bDMPIcT4LCAjQ5cuXtXPnTh06dEhnz57N8YlDp06drlkzkF9c9AfYSdeuXfXWW29pw4YN6t+/vzZu3KisrCw99thjkq5cGCVJd9xxR45t77jjDu3bty/XfrO3e+211/Taa6/lWJ89p7ggTp06JWdnZ1WoUCHHuqZNm+rdd9/V0qVLFRkZqYULF6py5coaMGCAevXqZdqvm5vbdfd99XEoUaKEKlSoYP0PvzBkT6fIvtNENgcHB1WsWNFmru7Vd3bIflNgNt9Tyv336ebmZn0erq6uevjhh7Vu3ToNHjxYn376qWrVqpWnALpgwQJr7Y6OjqpYsaKqVq1q0yY1NVVvvPGG1q5dq4yMDNWoUUNNmzaVg4NDjuk6udV6tc6dO2vq1Klat26d+vfvr88///y6v++rJScn57hY8d/7//fvOC81SVd+hwsWLLA+dnJykru7u8qXL29ddu7cOWVlZWnx4sVavHhxjj5KlSplre9ar0PDMHThwgXrsipVqti0yb5zRG5/p//8848Mw1CzZs1yfQ6nT59W/fr1cyy/9957VapUKf34449yd3dXyZIl1a5dO9WpU0exsbFyc3NTYmJijsB89d9siRIlrL/z5ORkVaxYUQ4OtrEg++/p6nnqVwsODtaAAQO0Z88eNW7cWGvXrpWXl5caNmxo7f/f/WXL7bX1b9nns2tdoHnq1CnrMb76Lh1X7wu4EQRmwE7KlSun9u3ba/369erfv7/WrFmjgIAAa3jMDqV//fVXjm3PnDmjihUrXrNfSRo9erRatmyZY/2/A0N+ZGZmKjY2Vs2aNVPJkiVzbdOmTRu1adNGKSkp2rZtm95//31NnTpVTZo0kY+PT4H2m+3qwJGZmamkpCTr8bJYLDnuE53fixyzj82ZM2dsAlx6erqSkpKueczzI7fgdObMGTVt2tT6OCgoSJ9++qn27NmjTZs25fnezZ6enrkGz3+bMmWKNm3apFmzZum+++6zzjO+99578/Es/k+ZMmX08MMP6/PPP1f9+vV17tw5Pf744/nqo3z58tf8O5dUoOPu5ORkvcDvWsqUKSOLxaLevXvnGsiyA2Ze6st+I5od8LKdPXtWUu5vCsuWLavSpUvr/fffz7W+WrVq5brcxcVFLVu21I8//qg777xTzZo1k6Ojo1q1aqXY2FiVKVNG1atXz9MnN9nKly+vpKQkZWRk2ITm7Od1vd/B/fffL3d3d61fv1533323vvzyS40aNcqmfyn/r63s89myZctUpkyZHOvvvPNO62sq+1hnu/p3AdwIpmQAdhQcHKz4+HjFxsZq165d1ukYkuTh4aHKlSvnuPr86NGj+vnnn62jUleH17vvvltubm46duyYGjVqZP3n7u6umTNnXnNk+nqioqJ0+vTpa36pwFtvvaXg4GAZhiEXFxe1a9fO+iUl2Xf1KFGi4KecH3/80eZiw02bNikjI0OtWrWSdCX8JCUl2dzN4qeffrLp41pBP1v2G4yrj/lnn32mzMxMNW/evMD1Z9u1a5fNaNqePXt0/Phx+fn5WZf5+vqqdu3amj59upKSkvIdQM3s3LlTrVq10kMPPWQNy7/88ov+/vvv646OX+v3FxwcrN9//11LliyRn5+f7rzzznzV5Ovrq127dlnvFpJt3bp1qly58jWD441ydXWVt7e3Dh06ZPNaueeeezRv3jzrNAFfX98cd7XJzMzUZ599pkaNGsnJycm6/KuvvrLZx6ZNm+Ti4pLrG8aWLVvq0qVLMgzDZv8HDhzQ/Pnzc724Nlvbtm21bds27dixw/oa8PPz0969e/XFF1/kGF2+npYtWyozMzPHHTqyL7q83t9+iRIlFBgYqM2bN+urr76SYRg20y/y+tq6+m/M19dX0pWLV/99jP755x/NmjVL//zzj2rXrq1q1arluPj066+/zuvTB66LEWbAjvz8/FSjRg29+uqrcnd31/33329dV6JECY0YMULjxo3T8OHD9fjjjyspKUnz5s1T+fLl1adPH0myzqvcunWrypcvLy8vLw0fPlwTJ060flR77tw5hYeH69SpUzZ3p8jNhQsXrPMts7KylJSUpO+//14rVqzQo48+qg4dOuS63b333qv33ntPY8eO1aOPPqr09HRFRESoQoUK1jBYrlw57dq1SzExMfm+h/Nff/2lIUOGqEePHjp8+LDeeecdtW7d2joy2q5dOy1fvlyvvPKKnnjiCR04cEBLliyxCcnZxyomJkZ16tTJEWLq1q2rwMBAzZs3T6mpqWrVqpX279+vefPmqVWrVmrTpk2+as5NVlaWnnvuOb3wwgtKSkrSzJkz5enpmWPua1BQkGbOnKnWrVurWrVqN7zfbI0bN9bnn3+ujz/+WHXq1FF8fLwWLFggi8Vy3fnX2aN9mzdv1gMPPKA6depIuhKm7r77bsXGxmrGjBn5rqlPnz5at26d+vTpo8GDB6tixYpas2aNtm3bpqlTp97QG63rGTFihJ577jm9/PLLevTRR5WZmaklS5Zo9+7d1ruCDB48WN9++6169uyp5557Tk5OTvrggw909OhRRURE2PT3v//9T3fccYf8/f0VGxurDz/8UMOHD8/1jiH+/v7y9fXVwIEDNXDgQNWpU0d79uzR3Llzdf/99+f6RSD/3vaNN97QX3/9Zb21Y8uWLZWRkaG9e/fqpZdeytdxeOCBB9SqVStNmjRJp0+flre3t2JjY7V48WIFBgaqbt261+0jKChICxcu1Pz58633m8+W19dW9t/Yhg0b5OPjY31tvPrqqzp+/LgaNmyohIQEhYWFqUaNGqpdu7YsFotGjhypl19+WRMmTNDDDz+sn3/+WR9//HG+jgFghsAM2JHFYlG3bt00Z84cDRo0KEcw6Natm8qUKaNFixZp0KBBcnV1VZs2bTRixAjr/Lx77rlHjzzyiD788EN999132rBhg5544gmVKVNGERERWrFihUqXLq1mzZppxowZOS6sutq+ffv05JNPSroS2t3c3OTh4aE333wzxwU7//bAAw9oxowZWrJkifVCv+bNm+v999+3Ti959tln9csvv2jAgAGaNm1ajvmeZrp3767U1FQNGjTIei/rUaNGWecOt27dWmPGjNHy5cv1xRdfqEGDBpo3b56eeuopax+urq7q06ePVqxYoa1bt+Z6AeSUKVNUq1YtRUdHKzIyUlWqVFGPHj1y/f0URLt27VSzZk2NGjVKGRkZateuncaPH2+dL5utbdu2mjlzZqF9g2O2sWPHKj09XbNmzVJaWppq1KihF198UQcPHtRXX32VY1rLv7Vq1Ur33XefZs6cqZiYGL377rs29Z45c0bt27fPd02VK1fWxx9/rJkzZ2rKlClKT0+Xl5eXwsPDC3TruPy4//77FRkZqXnz5mno0KFydHRUgwYN9N5771lvcXjPPffoo48+0jvvvKNXXnlFFotFjRs31vvvv59jbvmwYcMUGxurFStWqFq1apo4ceI1P5UpUaKE3n33Xc2ePVuLFi3S2bNnVbVqVfXu3VuDBg0yrTv7wsSTJ09a5wlXrFhRnp6eOnr0aK7TscxYLBYtWrRIc+bM0fvvv6+///5bNWrU0PDhw61vzq/nrrvuUqtWrbRt27Zcr5/Iy2urQ4cOWrt2rcaOHavg4GCFhoZq2rRpWrRokaKiopSYmCg3Nzd17txZL730kvUN8SOPPKISJUooPDxca9eulaenp15//XWNGDEiX8cBuBaLcfVVHgAAu1u8eLEiIiL03Xff2XzkXxRlf/zeqlWrYnvv2+wvxpk2bVqhv8kBYH+MMANAEfLpp5/q999/10cffWT9+L+ounDhgpYuXaq9e/fq8OHDBfrmPQD4LyAwA0AREh8fr6ioKD300EMaMGCAvcsx5ezsrKioKGVlZWnKlCk296UGgNsJUzIAAAAAE9xWDgAAADBBYAYAAABMEJgBAAAAE1z0dxPs2rVLhmHI0dHR3qUAAAAgF+np6bJYLGratOl12xKYbwLDMMS1lAAAAEVXfrIagfkmyB5ZbtSokZ0rAQAAQG727t2b57bMYQYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAFHlGVpa9SwBwk/wXXt8O9i4AAIDrsZQooYQNi5Vy9qS9SwFQiFzcqsnjkQH2LuO6CMwAgP+ElLMnlXLqT3uXAaAYYkoGAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYKJIBebw8HD16NHjmusnTJiggIAAm2VZWVmaM2eO2rRpIx8fH/Xt21dHjhyxabN//36FhISoSZMmatu2rSIjI/PdBwAAAIqnIhOYly5dqjlz5lxz/ZdffqlVq1blWB4eHq6oqChNnjxZK1askMVi0YABA5SWliZJSkpKUp8+fVS7dm1FR0dryJAhmj17tqKjo/PcBwAAAIovuwfmU6dOqX///po9e7Y8PDxybXP69Gm9+uqratmypc3ytLQ0LVmyREOGDJG/v7+8vLwUFhamU6dOafPmzZKklStXysnJSaGhoapTp46CgoLUu3dvLV68OM99AAAAoPiye2D+9ddfVb58ea1bt04+Pj451huGobFjx+qxxx7LEZjj4+N18eJF+fn5WZeVK1dO3t7e2rFjhyQpLi5Ovr6+cnBwsLbx8/NTQkKCzp49m6c+AAAAUHw5XL/JzRUQEJBjXvK/LV26VGfOnNHChQu1aNEim3WJiYmSpGrVqtksr1Klik6ePGlt4+npmWO9JJ04cSJPfRSEYRi6dOlSgbcHAFxhsVjk4uJi7zIA3EQpKSkyDOOW7tMwDFksljy1tXtgNhMfH6958+bpww8/lJOTU471KSkpkpRjXalSpZScnCxJSk1NzXW9JF2+fDlPfRREenq69u/fX+DtAQBXuLi4yNvb295lALiJEhISrJnsVsotX+amyAbmy5cva+TIkXrxxRfl5eWVaxtnZ2dJV+YhZ/+cvW32aISzs3OOi/cuX74sSSpdunSe+igIR0dH1a1bt8DbAwCuyOsIEID/Lg8Pj1s+wnzw4ME8ty2ygXn37t06cOCA5s2bp/nz50u6MmqbkZGhpk2b6rXXXlPt2rUlXbkosGbNmtZtT58+bQ3Z7u7uOn36tE3f2Y+rVq2qjIyM6/ZREBaLRaVLly7w9gAAAMWFPaZd5efNeJENzI0bN9YXX3xhs2z58uX64osvtHz5crm5ucnJyUmurq7avn27NeyeO3dO+/btU0hIiCTJ19dXUVFRyszMVMmSJSVJMTEx8vDwkJubm8qWLXvdPgAAAFB8FdnA7OzsrFq1atksK1++vBwcHGyWh4SEaMaMGapUqZKqV6+u6dOny93dXe3bt5ckBQUFKSIiQuPHj1f//v21Z88eLVu2TK+99pqkK3NXrtcHAAAAiq8iG5jzaujQocrIyNCECROUmpoqX19fRUZGWidxu7m5KSIiQlOmTFFgYKAqV66s0aNHKzAwMM99AAAAoPiyGLd6hnUxsHfvXklSo0aN7FwJANw+9i17XSmn/rR3GQAKkUvVmvLuNdEu+85PXrP7F5cAAAAARRmBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMEJgBAAAAEwRmAAAAwASBGQAAADBBYAYAAABMFKnAHB4erh49etgs++qrrxQUFKSmTZsqICBAb731llJTU63rs7KyNGfOHLVp00Y+Pj7q27evjhw5YtPH/v37FRISoiZNmqht27aKjIy0WZ+XPgAAAFA8FZnAvHTpUs2ZM8dmWVxcnAYPHqyOHTtqzZo1Cg0N1eeff67XXnvN2iY8PFxRUVGaPHmyVqxYIYvFogEDBigtLU2SlJSUpD59+qh27dqKjo7WkCFDNHv2bEVHR+e5DwAAABRfdg/Mp06dUv/+/TV79mx5eHjYrIuKipKfn5+ee+451apVSw888ICGDx+udevWKS0tTWlpaVqyZImGDBkif39/eXl5KSwsTKdOndLmzZslSStXrpSTk5NCQ0NVp04dBQUFqXfv3lq8eLEk5akPAAAAFF92D8y//vqrypcvr3Xr1snHx8dmXd++fTV69Ogc22RkZOjChQuKj4/XxYsX5efnZ11Xrlw5eXt7a8eOHZKujFL7+vrKwcHB2sbPz08JCQk6e/ZsnvoAAABA8eVw/SY3V0BAgAICAnJd5+3tbfM4LS1N7733nho0aKBKlSopLi5OklStWjWbdlWqVNHJkyclSYmJifL09MyxXpJOnDihxMTE6/ZREIZh6NKlSwXeHgBwhcVikYuLi73LAHATpaSkyDCMW7pPwzBksVjy1NbugTmvMjIyNHr0aB08eFAffvihpCsHV5KcnJxs2pYqVUrJycmSpNTU1FzXS9Lly5fz1EdBpKena//+/QXeHgBwhYuLS44BFAC3l4SEBGsmu5Wuzn/X8p8IzBcuXNBLL72k7du3a86cOdapG87OzpKujDxn/yxdCcLZoxHOzs45Lt67fPmyJKl06dJ56qMgHB0dVbdu3QJvDwC4Iq8jQAD+uzw8PG75CPPBgwfz3LbIB+bTp09rwIABOnbsmBYvXmwz1zh7GsXp06dVs2ZNm228vLwkSe7u7jp9+nSOPiWpatWqysjIuG4fBWGxWFS6dOkCbw8AAFBc2GPaVX7ejNv9oj8zycnJ6tWrl/7++2999NFHNmFZkry8vOTq6qrt27dbl507d0779u1TixYtJEm+vr7auXOnMjMzrW1iYmLk4eEhNze3PPUBAACA4qtIjzBPmzZNR48eVUREhCpVqqQzZ85Y11WqVElOTk4KCQnRjBkzVKlSJVWvXl3Tp0+Xu7u72rdvL0kKCgpSRESExo8fr/79+2vPnj1atmyZ9V7OeekDAAAAxVeRDcxZWVnauHGj0tPT1atXrxzrt2zZoho1amjo0KHKyMjQhAkTlJqaKl9fX0VGRloncbu5uSkiIkJTpkxRYGCgKleurNGjRyswMNDa1/X6AAAAQPFlMW71DOtiYO/evZKkRo0a2bkSALh97Fv2ulJO/WnvMgAUIpeqNeXda6Jd9p2fvFak5zADAAAA9kZgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgvg1lZRn2LgHATcLrGwBuPQd7F4DCV6KERfM//kHHTyfbuxQAhah6lfIa9HRre5cBAMUOgfk2dfx0sg4fT7J3GQAAAP95TMkAAAAATBCYAQAAABNFKjCHh4erR48eNsv279+vkJAQNWnSRG3btlVkZKTN+qysLM2ZM0dt2rSRj4+P+vbtqyNHjhR6HwAAACieikxgXrp0qebMmWOzLCkpSX369FHt2rUVHR2tIUOGaPbs2YqOjra2CQ8PV1RUlCZPnqwVK1bIYrFowIABSktLK7Q+AAAAUHzZPTCfOnVK/fv31+zZs+Xh4WGzbuXKlXJyclJoaKjq1KmjoKAg9e7dW4sXL5YkpaWlacmSJRoyZIj8/f3l5eWlsLAwnTp1Sps3by60PgAAAFB82T0w//rrrypfvrzWrVsnHx8fm3VxcXHy9fWVg8P/3czDz89PCQkJOnv2rOLj43Xx4kX5+flZ15crV07e3t7asWNHofUBAACA4svut5ULCAhQQEBArusSExPl6elps6xKlSqSpBMnTigxMVGSVK1atRxtTp48WWh9AAAAoPiye2A2k5qaKicnJ5tlpUqVkiRdvnxZKSkpkpRrm+Tk5ELroyAMw9ClS5cKvH1BWSwWubi43PL9Arh1UlJSZBjF5xv/OK8Btz97nNcMw5DFYslT2yIdmJ2dnXNceHf58mVJUunSpeXs7Czpyjzk7J+z22SfXAujj4JIT0/X/v37C7x9Qbm4uMjb2/uW7xfArZOQkGB9s18ccF4Dbn/2Oq9dPWB6LUU6MLu7u+v06dM2y7IfV61aVRkZGdZlNWvWtGnj5eVVaH0UhKOjo+rWrVvg7Qsqr++UAPx3eXh4FLsRZgC3N3uc1w4ePJjntgUKzBcuXNDFixdVtWpVpaWl6f3331diYqI6duwoX1/fgnSZK19fX0VFRSkzM1MlS5aUJMXExMjDw0Nubm4qW7asXF1dtX37dmvYPXfunPbt26eQkJBC66MgLBaLSpcufSNPHwByxfQEALcbe5zX8vNmPN93ydizZ48CAgK0fPlySdLkyZM1Y8YMrVu3Tr169dKWLVvy2+U1BQUF6cKFCxo/frwOHjyo1atXa9myZXr++eclXRlGDwkJ0YwZM7RlyxbFx8dr+PDhcnd3V/v27QutDwAAABRf+R5hDgsL0913360nn3xSqampWr9+vZ555hlNnDhREydO1MKFC/Xggw8WSnFubm6KiIjQlClTFBgYqMqVK2v06NEKDAy0thk6dKgyMjI0YcIEpaamytfXV5GRkdY5KYXRBwAAAIqvfAfm3bt3KywsTHfddZe+/vprpaam6rHHHpMkde7cWevWrStwMW+++WaOZY0bN9aKFSuuuU3JkiU1atQojRo16pptCqMPAAAAFE/5npJRokQJ68jrN998o3Llyqlx48aSrsxt/vedJgAAAID/unyPMDds2FCffPKJnJ2d9fnnn6tt27ayWCw6e/asFi9erIYNG96MOgEAAAC7yPcI8+jRoxUTE6Onn35aJUuW1IsvvihJeuSRR3T48GG99NJLhV0jAAAAYDf5HmH29vbWF198oT/++EP33HOP9dZpoaGhatasmSpXrlzoRQIAAAD2ku8RZklydXVVo0aN9Oeff+rbb7/VhQsX1KpVK8IyAAAAbjsF+uKStWvXaubMmTp9+rRKlCihVatWae7cuXJ0dNTMmTO5HRsAAABuG/keYd64caPGjBkjPz8/hYWFKSsrS5LUoUMHffvttwoPDy/0IgEAAAB7yfcI88KFC/XUU08pNDRUmZmZ1uXdunXT2bNntXLlSi78AwAAwG0j3yPMCQkJ1/zKaB8fH506deqGiwIAAACKinwHZjc3N/3xxx+5rvvjjz/k5uZ2w0UBAAAARUW+A3Pnzp01Z84c/e9//1NaWpokyWKx6JdfflF4eLgefvjhQi8SAAAAsJd8z2F+6aWX9Pvvv+ull15SiRJX8naPHj106dIltWjRQsOGDSv0IgEAAAB7yXdgdnJyUkREhH744Qdt27ZN//zzj8qWLauWLVvK399fFovlZtQJAAAA2EW+A/OaNWvk7++v1q1bq3Xr1jbrzpw5ozVr1mjAgAGFViAAAABgT/mewzxu3DgdPXo013X79+/XnDlzbrgoAAAAoKjI0wjz888/r4MHD0qSDMPQoEGDcv02v7Nnz6pmzZqFWyEAAABgR3kOzKtWrZIkffrpp/L29lalSpVs2pQoUULlypVTt27dCr9KAAAAwE7yFJibNWumZs2aWR8PHDhQd911100rCgAAACgq8n3R37Rp0yRd+ZKS2NhYnT9/XhUrVlTz5s119913F3qBAAAAgD3lOzBL0sSJE7Vq1SoZhmFdZrFYFBgYqClTpnBrOQAAANw28h2YFy9erOjoaA0dOlSPPvqoKleurNOnT2vt2rVasGCB7rnnHvXp0+dm1AoAAADccvkOzJ988on69++vF1980bqsRo0aGjRokNLT07Vq1SoCMwAAAG4b+b4P88mTJ+Xn55frulatWunYsWM3XBQAAABQVOQ7MFevXl3x8fG5rtu3b1+O280BAAAA/2X5DsyPPPKI5s6dq88++0xZWVmSpKysLG3YsEHz589X586dC71IAAAAwF7yPYd5wIABiouL08svv6wxY8aoQoUK+ueff5SZmamWLVtq2LBhN6NOAAAAwC7yFJijoqL0yCOPyNXVVU5OTnrvvff0zTffaMeOHUpOTlb58uXl6+srf3//m10vAAAAcEvlKTCHhobqrbfe0sMPP6zg4GA1b95c/v7+BGQAAADc9vI0h3nNmjXq3r27vvvuO4WEhOjhhx9WZGSkzp49e7PrAwAAAOwqT4HZy8tL48aN0zfffKPw8HDVq1dPs2fPlr+/vwYPHqxvvvnG5lv/AAAAgNtFvi76K1mypNq1a6d27drp/Pnz2rBhg9auXavnn39eVatWVWBgoIKDg1WjRo2bVS8AAABwS+X7tnLZypYtq6efflpRUVH64osv9Mwzz2jjxo3q2LFjYdYHAAAA2FWBA3O2s2fPauvWrfr222917NgxVa9evTDqAgAAAIqEfN+HWZIuXbqkzZs3a926ddq+fbscHBzUoUMHDRs2TC1btizsGgEAAAC7yXNgzszM1Pfff69169bpq6++UkpKiho2bKgJEyZY79EMAAAA3G7yFJjfeOMNff7550pKSlL58uX1xBNPKDg4WJ6enje7PgAAAMCu8hSYP/74Y913330KCgrSQw89JEdHx5tdFwAAAFAk5Ckwf/XVV3J3d7/ZtQAAAABFTp7ukkFYBgAAQHF1w7eVAwAAAG5nBGYAAADARJ4C89q1a5WUlHSzawEAAACKnDwF5tDQUCUkJEiSHnzwQcXHx9/UogAAAICiIk93yXByctLatWuVkZGh48eP6+eff9b58+ev2d7X17fQCgQAAADsKU+B+YknnlBERIRWrlwpi8Wi1157Ldd2hmHIYrFo//79hVokAAAAYC95CswjR47UY489pqSkJPXs2VMTJ05U3bp1b3ZtAAAAgN3lKTBL0j333CNJGjx4sB588EFVrVr1phUFAAAAFBX5vq3c4MGDVbFiRUVFRWn48OHq16+fRo4cqaioKF2+fPlm1Kj09HSFhYWpbdu2atq0qZ555hn99NNP1vX79+9XSEiImjRporZt2yoyMtJm+6ysLM2ZM0dt2rSRj4+P+vbtqyNHjti0uV4fAAAAKJ7yHZjPnTun7t27KzQ0VLt379aFCxf0008/KTQ0VMHBwaYXAxbUggULFB0drcmTJ2vNmjW6++67NWDAAJ06dUpJSUnq06ePateurejoaA0ZMkSzZ89WdHS0dfvw8HBFRUVp8uTJWrFihSwWiwYMGKC0tDRJylMfAAAAKJ7yPCUj28yZM5WYmKgPPvhALVq0sC6Pi4vT0KFDNXv2bE2YMKFQi9yyZYseeeQR3X///ZKksWPHatWqVfr55591+PBhOTk5KTQ0VA4ODqpTp46OHDmixYsXKygoSGlpaVqyZIlGjRolf39/SVJYWJjatGmjzZs3q0uXLlq5cqVpHwAAACi+8j3CvGXLFr300ks2YVmSWrRooaFDh+qLL74otOKyVahQQV9//bWOHTumzMxMrVixQk5OTqpfv77i4uLk6+srB4f/y/5+fn5KSEjQ2bNnFR8fr4sXL8rPz8+6vly5cvL29taOHTsk6bp9AAAAoPjK9wjzxYsXddddd+W67q677tI///xzozXlMH78eA0fPlwPPvigSpYsqRIlSmj27NmqWbOmEhMT5enpadO+SpUqkqQTJ04oMTFRklStWrUcbU6ePClJ1+3Dzc0t3zUbhqFLly7le7sbZbFY5OLicsv3C+DWSUlJkWEY9i7jluG8Btz+7HFey74dcl7kOzDffffd+vrrr9W6desc67Zs2aJatWrlt8vr+uOPP1SuXDnNnz9fVatW1apVqzRmzBh98MEHSk1NlZOTk037UqVKSZIuX76slJQUScq1TXJysiRdt4+CSE9Pt8v9qF1cXOTt7X3L9wvg1klISLCe24oDzmvA7c9e57Wr89+15Dsw9+vXTyNGjFBaWpq6du2qO+64Q3/99ZfWr1+vVatWKTQ0NL9dmjp+/LhGjRqlpUuXWqeBNGrUSAcPHtTcuXPl7OxsvXgvW3bILV26tJydnSVJaWlp1p+z22SPWFyvj4JwdHS0y72q8/pOCcB/l4eHR7EbYQZwe7PHee3gwYN5bpvvwNy5c2cdPnxYCxcu1KpVqyRdGdJ2cnLSoEGD9OSTT+a3S1N79uxRenq6GjVqZLPcx8dH3377re68806dPn3aZl3246pVqyojI8O6rGbNmjZtvLy8JEnu7u6mfRSExWIpcNgGADNMTwBwu7HHeS0/b8bzHZglaeDAgQoJCdHPP/+s5ORklS9fXj4+PipfvnxBujOVPff4t99+U+PGja3Lf//9d9WqVUtNmjRRVFSUMjMzVbJkSUlSTEyMPDw85ObmprJly8rV1VXbt2+3BuZz585p3759CgkJkST5+vqa9gEAAIDiK993ychWrlw5PfDAA+rataseeOCBmxKWJalx48Zq0aKFxowZo23btunw4cOaNWuWYmJi9NxzzykoKEgXLlzQ+PHjdfDgQa1evVrLli3T888/L+nK3JSQkBDNmDFDW7ZsUXx8vIYPHy53d3e1b99ekq7bBwAAAIqvAo0w30olSpRQeHi4Zs2apXHjxik5OVmenp5aunSpmjRpIkmKiIjQlClTFBgYqMqVK2v06NEKDAy09jF06FBlZGRowoQJSk1Nla+vryIjI60Tvd3c3K7bBwAAAIoni1Gcrhy5Rfbu3StJOeZd30qvzN6ow8eT7LZ/AIWvdvWKmjqss73LsJt9y15Xyqk/7V0GgELkUrWmvHtNtMu+85PXCjwlAwAAACgO8h2YFy5cqAMHDtyMWgAAAIAiJ9+BOSIiwvoNeQAAAMDtLt+BuXbt2owwAwAAoNjI910y2rZtq7CwMH399de65557ctyn2GKxaNCgQYVWIAAAAGBP+Q7M8+bNkyTFxcUpLi4ux3oCMwAAAG4n+Q7M8fHxN6MOAAAAoEi6odvKnT9/Xn/88YfS0tKUmZlZWDUBAAAARUaBAvP27dv1xBNPqGXLluratasOHDigl19+WW+++WZh1wcAAADYVb4Dc0xMjPr16ydnZ2eNHDlS2V8U6O3trffff1/vvfdeoRcJAAAA2Eu+A/OsWbP04IMPavny5erVq5c1MD/33HPq37+/Vq1aVehFAgAAAPaS78C8f/9+BQUFSbpyR4x/a926tY4fP144lQEAAABFQL4Dc9myZXXmzJlc1508eVJly5a94aIAAACAoiLfgfnBBx9UWFiY9u7da11msViUmJiohQsXqm3btoVZHwAAAGBX+b4P88svv6zdu3ere/fuuuOOOyRJI0aMUGJioqpVq6YRI0YUepEAAACAveQ7MJcvX16rVq3SmjVrtG3bNv3zzz8qW7asevTooW7dusnFxeVm1AkAAADYRb4DsyQ5OTmpe/fu6t69e2HXAwAAABQpBQrMf/zxhxYsWKCYmBglJyfLzc1Nfn5+evHFF1W7du1CLhEAAACwn3wH5piYGA0YMEAVK1ZU27Zt5ebmpjNnzuibb77Rl19+qQ8//FBeXl43o1YAAADglst3YA4LC5Ovr68WLVokJycn6/KLFy+qf//+mjp1qt5///1CLRIAAACwl3zfVi4+Pl69e/e2CcuSVKZMGT333HPavXt3oRUHAAAA2Fu+A3O1atV04sSJXNddvHjReqs5AAAA4HaQ78A8atQozZo1S59//rkyMzOty7dv36533nlHI0eOLNQCAQAAAHvK0xxmLy8vWSwW62PDMDRixAiVLFlSFSpU0Pnz55WWlqaSJUtqypQp6tSp000rGAAAALiV8hSYBw0aZBOYAQAAgOIiT4F5yJAhN7sOAAAAoEgq0BeXpKWl6dChQzp//nyu6319fW+oKAAAAKCoKNAXl7z88stKSkqSdGU+syRZLBYZhiGLxaL9+/cXbpUAAACAneQ7ME+dOlUVK1ZUaGioKlSocBNKAgAAAIqOfAfmP//8U2FhYQoICLgZ9QAAAABFSr7vw1yvXj3rdAwAAADgdpfvEeZXXnlFI0eOVIkSJdS4cWO5uLjkaHPnnXcWSnEAAACAvRX4LhmvvPLKNddz0R8AAABuF/kOzKGhoSpZsqSGDx+uypUr34yaAAAAgCIj34H50KFDmj17ttq1a3cz6gEAAACKlHxf9FerVi2lpKTcjFoAAACAIiffgXnYsGEKCwvTDz/8oIsXL96MmgAAAIAiI99TMmbOnKm//vpL/fv3z3W9xWLRvn37brgwAAAAoCjId2Du0qXLzagDAAAAKJLyHZgHDx58M+oAAAAAiqR8B+YTJ05ctw1fXAIAAIDbRb4Dc0BAgCwWi2kbvrgEAAAAt4t8B+apU6fmCMyXLl3Szp07tW3bNk2dOrXQigMAAADsLd+BuVu3brkuf/bZZ/XWW29p/fr1atu27Y3WBQAAABQJ+b4Ps5m2bdtq69athdklAAAAYFeFGph//vlnOTjke9AaAAAAKLLynW7HjRuXY1lWVpZOnjypuLg4BQcHF0phAAAAQFGQ78C8ffv2HMssFotcXV01YMAAvfDCC4VS2NXWrFmjd999V0ePHlXNmjU1ePBgderUSdKVu3JMmTJFv/zyiypUqKAePXqoX79+1m2zsrI0b948rVq1SufOnVPz5s01adIk1apVy9rmen0AAACgeMp3YP7qq69uRh2m1q5dq1deeUVjxoxR27ZttWHDBo0YMULu7u6qXbu2+vTpo4ceekivvfaafv75Z7322muqUKGCgoKCJEnh4eGKiorStGnTVLVqVU2fPl0DBgzQhg0b5OTkpKSkpOv2AQAAgOKpyE84NgxDs2fPVq9evdSrVy9J0qBBg/TTTz8pNjZWsbGxcnJyUmhoqBwcHFSnTh0dOXJEixcvVlBQkNLS0rRkyRKNGjVK/v7+kqSwsDC1adNGmzdvVpcuXbRy5UrTPgAAAFB85Skw5zZv+VosFkuh3ov50KFDOn78uLp27WqzPDIyUpI0YMAA+fr62lxs6Ofnp0WLFuns2bM6fvy4Ll68KD8/P+v6cuXKydvbWzt27FCXLl0UFxdn2oebm1uhPR8AAAD8t+QpMOc2b/lqSUlJSklJKfTAfPjwYUlXvhylX79+2rdvn2rUqKEXX3xRAQEBSkxMlKenp802VapUkXTla7wTExMlSdWqVcvR5uTJk5J03T4KEpgNw9ClS5fyvd2NslgscnFxueX7BXDrpKSkyDAMe5dxy3BeA25/9jivGYZx3W+vzpanwGw2bzk9PV0LFizQu+++qzvuuEOhoaF52nFeXbhwQZI0ZswYDR48WCNHjtSmTZs0cOBAvffee0pNTZWTk5PNNqVKlZIkXb58WSkpKZKUa5vk5GRJum4fBZGenm6Xrwh3cXGRt7f3Ld8vgFsnISHBem4rDjivAbc/e53Xrs5/13JDc5j379+vsWPH6vfff1eXLl306quvqnz58jfSZQ6Ojo6SpH79+ikwMFCSVL9+fe3bt0/vvfeenJ2dlZaWZrNNdsgtXbq0nJ2dJUlpaWnWn7PbZI9YXK+PgtZdt27dAm17I/L6TgnAf5eHh0exG2EGcHuzx3nt4MGDeW5boMCckZGh+fPna/HixapQoYLmzZunBx98sCBdXZe7u7sk5ZgyUbduXW3dulXVq1fX6dOnbdZlP65ataoyMjKsy2rWrGnTxsvLy7oPsz4KwmKxFDhsA4AZpicAuN3Y47yWnzfj+f6mv3379ikoKEgLFixQ586dtXHjxpsWliXJ29tbZcqU0e7du22W//7776pZs6Z8fX21c+dOZWZmWtfFxMTIw8NDbm5u8vLykqurq8087HPnzmnfvn1q0aKFJF23DwAAABRfeQ7MGRkZmjVrlrp3766kpCQtWLBAb7/9tsqVK3cz65Ozs7P69++v+fPna8OGDfrzzz+1YMEC/fDDD+rTp4+CgoJ04cIFjR8/XgcPHtTq1au1bNkyPf/885KuzE0JCQnRjBkztGXLFsXHx2v48OFyd3dX+/btJem6fQAAAKD4ytOUjF9//VVjx47VwYMH9fjjj2v8+PFydXW92bVZDRw4UC4uLgoLC9OpU6dUp04dzZ07V61atZIkRUREaMqUKQoMDFTlypU1evRo63xnSRo6dKgyMjI0YcIEpaamytfXV5GRkdaJ3m5ubtftAwAAAMWTxcjDDOsGDRooKytLZcuWtc77vWaHFouWLVtWaAX+F+3du1eS1KhRI7vV8MrsjTp8PMlu+wdQ+GpXr6ipwzrbuwy72bfsdaWc+tPeZQAoRC5Va8q710S77Ds/eS1PI8zNmjWz/ny9fF2crtwGAADA7S9PgXn58uU3uw4AAACgSMr3XTIAAACA4oTADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACAif9UYE5ISFDTpk21evVq67L9+/crJCRETZo0Udu2bRUZGWmzTVZWlubMmaM2bdrIx8dHffv21ZEjR2zaXK8PAAAAFF//mcCcnp6ukSNH6tKlS9ZlSUlJ6tOnj2rXrq3o6GgNGTJEs2fPVnR0tLVNeHi4oqKiNHnyZK1YsUIWi0UDBgxQWlpanvsAAABA8eVg7wLyau7cuSpTpozNspUrV8rJyUmhoaFycHBQnTp1dOTIES1evFhBQUFKS0vTkiVLNGrUKPn7+0uSwsLC1KZNG23evFldunS5bh8AAAAo3v4TI8w7duzQihUr9NZbb9ksj4uLk6+vrxwc/i/3+/n5KSEhQWfPnlV8fLwuXrwoPz8/6/py5crJ29tbO3bsyFMfAAAAKN6KfGA+d+6cRo8erQkTJqhatWo26xITE+Xu7m6zrEqVKpKkEydOKDExUZJybFelShWdPHkyT30AAACgeCvyUzJCQ0PVpEkTde3aNce61NRUOTk52SwrVaqUJOny5ctKSUmRpFzbJCcn56mPgjIMw2a+9a1isVjk4uJyy/cL4NZJSUmRYRj2LuOW4bwG3P7scV4zDEMWiyVPbYt0YF6zZo3i4uK0fv36XNc7OztbL97Llh1yS5cuLWdnZ0lSWlqa9efsNtkn3+v1UVDp6enav39/gbcvKBcXF3l7e9/y/QK4dRISEqwDAsUB5zXg9mev89rVg6bXUqQDc3R0tM6ePau2bdvaLJ80aZIiIyN155136vTp0zbrsh9XrVpVGRkZ1mU1a9a0aePl5SVJcnd3N+2joBwdHVW3bt0Cb19QeX2nBOC/y8PDo9iNMAO4vdnjvHbw4ME8ty3SgXnGjBlKTU21WdahQwcNHTpUnTt31meffaaoqChlZmaqZMmSkqSYmBh5eHjIzc1NZcuWlaurq7Zv324NzOfOndO+ffsUEhIiSfL19TXto6AsFssNjVADwLUwPQHA7cYe57X8vBkv0hf9Va1aVbVq1bL5J0lubm6qXr26goKCdOHCBY0fP14HDx7U6tWrtWzZMj3//POSrgyzh4SEaMaMGdqyZYvi4+M1fPhwubu7q3379pJ03T4AAABQvBXpEebrcXNzU0REhKZMmaLAwEBVrlxZo0ePVmBgoLXN0KFDlZGRoQkTJig1NVW+vr6KjIy0zlnJSx8AAAAovv5zgfm3336zedy4cWOtWLHimu1LliypUaNGadSoUddsc70+AAAAUHwV6SkZAAAAgL0RmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADARJEPzP/8848mTpyoBx54QM2aNdPTTz+tuLg46/r9+/crJCRETZo0Udu2bRUZGWmzfVZWlubMmaM2bdrIx8dHffv21ZEjR2zaXK8PAAAAFF9FPjCPGDFCu3fv1jvvvKNPPvlEDRo0UL9+/fTHH38oKSlJffr0Ue3atRUdHa0hQ4Zo9uzZio6Otm4fHh6uqKgoTZ48WStWrJDFYtGAAQOUlpYmSXnqAwAAAMWXg70LMHPkyBH98MMP+vjjj9WsWTNJ0vjx4/Xtt99qw4YNcnZ2lpOTk0JDQ+Xg4KA6deroyJEjWrx4sYKCgpSWlqYlS5Zo1KhR8vf3lySFhYWpTZs22rx5s7p06aKVK1ea9gEAAIDirUiPMFesWFHvvvuuGjZsaF1msVhkGIaSk5MVFxcnX19fOTj8X+738/NTQkKCzp49q/j4eF28eFF+fn7W9eXKlZO3t7d27NghSdftAwAAAMVbkR5hLleunHVkONvnn3+uP//8U/fff7/CwsLk6elps75KlSqSpBMnTigxMVGSVK1atRxtTp48KUlKTEw07cPNza1AtRuGoUuXLhVo2xthsVjk4uJyy/cL4NZJSUmRYRj2LuOW4bwG3P7scV4zDEMWiyVPbYt0YL7azp079corr+jBBx9UQECApk2bJicnJ5s2pUqVkiRdvnxZKSkpkpRrm+TkZElSamqqaR8FlZ6erv379xd4+4JycXGRt7f3Ld8vgFsnISHBen4rDjivAbc/e53Xrs6A1/KfCcxffvmlRo4cKR8fH73zzjuSJGdnZ+vFe9myQ27p0qXl7OwsSUpLS7P+nN0me7Tien0UlKOjo+rWrVvg7Qsqr++UAPx3eXh4FLsRZgC3N3uc1w4ePJjntv+JwPzBBx9oypQpat++vWbMmGF9N+Du7q7Tp0/btM1+XLVqVWVkZFiX1axZ06aNl5dXnvooKIvFckOBGwCuhekJAG439jiv5efNeJG+6E+SPvroI73xxht69tlnNWvWLJuhc19fX+3cuVOZmZnWZTExMfLw8JCbm5u8vLzk6uqq7du3W9efO3dO+/btU4sWLfLUBwAAAIq3Ih2YExISNHXqVLVv317PP/+8zp49qzNnzujMmTM6f/68goKCdOHCBY0fP14HDx7U6tWrtWzZMj3//POSrsxLCQkJ0YwZM7RlyxbFx8dr+PDhcnd3V/v27SXpun0AAACgeCvSUzI2bdqk9PR0bd68WZs3b7ZZFxgYqDfffFMRERGaMmWKAgMDVblyZY0ePVqBgYHWdkOHDlVGRoYmTJig1NRU+fr6KjIy0jpS7ebmdt0+AAAAUHxZjOJ05cgtsnfvXklSo0aN7FbDK7M36vDxJLvtH0Dhq129oqYO62zvMuxm37LXlXLqT3uXAaAQuVStKe9eE+2y7/zktSI9JQMAAACwNwIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMAMAAAAmCMwAAACACQIzAAAAYILADAAAAJggMP9/WVlZmjNnjtq0aSMfHx/17dtXR44csXdZAAAAsDMC8/8XHh6uqKgoTZ48WStWrJDFYtGAAQOUlpZm79IAAABgRwRmSWlpaVqyZImGDBkif39/eXl5KSwsTKdOndLmzZvtXR4AAADsiMAsKT4+XhcvXpSfn591Wbly5eTt7a0dO3bYsTIAAADYm4O9CygKEhMTJUnVqlWzWV6lShWdPHky3/2lp6fLMAzt2bOnUOrLL4vFoi4tKyszy80u+wdwc5QsUUJ79+6VYRj2LuWWs1gsyvB6SBbPTHuXAqAQXS5R0m7ntfT0dFksljy1JTBLSklJkSQ5OTnZLC9VqpSSk5Pz3V/2wc/rL+FmKOfqbLd9A7i57HlusSeH0mXtXQKAm8Qe5zWLxUJgzg9n5yvhMi0tzfqzJF2+fFkuLi757q9p06aFVhsAAADsiznM+r+pGKdPn7ZZfvr0abm7u9ujJAAAABQRBGZJXl5ecnV11fbt263Lzp07p3379qlFixZ2rAwAAAD2xpQMXZm7HBISohkzZqhSpUqqXr26pk+fLnd3d7Vv397e5QEAAMCOCMz/39ChQ5WRkaEJEyYoNTVVvr6+ioyMzHEhIAAAAIoXi1Ec708EAAAA5BFzmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBvKhR48eqlevns2/hg0bKiAgQFOmTFFqauotq2X79u2qV6+ejh07Zlrv2LFj89weQPEUEBCQ47zWtm1bvf7660pKSrJ3eTfFiRMn9Nlnn9m7DPxH8E1/QD516tRJ48ePtz6+dOmSvv/+e02bNk2ZmZmaOHGiHau7tqZNm+r7779XpUqV7F0KgCKob9++6tu3ryQpNTVVv//+u6ZPn64dO3bo448/lqurq50rLFxjxoxR9erV1aVLF3uXgv8AAjOQT87OzqpcubLNslq1aumXX37RZ599VmQDs5OTU466ASBb6dKlbc4Rd911l+rXr68uXbooMjJSw4YNs2N1gH0xJQMoJKVKlVKJEldeUmlpaZo+fbratGmjpk2bqnv37vr++++tbVevXq0HHnhA0dHR8vf3V9OmTTVo0CCdOnXK2iYgIEBz58612ce/p1hk+/rrr9WhQwc1btxYffr00dGjR3Ot7+opGRkZGZo7d64CAgLk4+Ojbt266dtvv7W2/+qrr/TUU0+padOmatSokYKDg/Xjjz/a1PLWW2/plVdeUYsWLdSsWTONGTNGFy9eLOARBFDU3HnnnWrfvr02bNggSTp//rxeffVV+fn5qXnz5urZs6f27t1rbT937lz17t1b77//vu6//341adJEI0aM0JkzZzR69Gg1bdpU/v7++vTTT63bZGZmaunSperYsaMaNWqkjh07auXKlTZ1HD16VIMGDVLz5s3VqlUrDR8+XH/99ZckaezYsRo8eLD69u2rZs2aadGiRTIMQxEREerUqZMaNmyo5s2b6/nnn7eeH3v06KHY2Fh9+umnCggIkCTt2bNHzzzzjJo2bSpfX18NGTJEJ06cuKnHF/8dBGbgBmVkZGjr1q1au3atHnvsMUnSuHHj9N1332n69On69NNP1alTJ73wwgvaunWrdbu///5bS5Ys0cyZM7Vs2TKdPHlS/fv3V0ZGRr72HxkZqVdffVWffPKJSpUqpaefflopKSnX3W7q1Kn68MMPNXLkSK1fv17+/v4aOHCgDh48qF9++UWDBg1Shw4dtG7dOq1atUpubm4aOXKk0tLSrH0sX75cd9xxh1atWqXJkydr48aNWrp0ab7qB1C0eXp66s8//9SFCxc0YMAAHT58WIsWLdLKlSvVpEkTPf3009q3b5+1fVxcnOLi4rRs2TLNmjVLmzZt0iOPPKL69esrOjpaDzzwgCZOnGidG/3mm28qPDxcgwcP1vr169WzZ0+9/vrrWr58uaQrIf2ZZ57RpUuXtHTpUi1dulTHjx/XkCFDrPvcvHmz7rvvPkVHR+vRRx/VsmXLtGjRIo0aNUqbNm1SeHi4EhIS9Oabb0q6EuybNm2qTp066ZNPPlFWVpaef/55+fr6at26dVq6dKlOnDihV1555RYeaRRlTMkA8mn9+vXatGmT9XFqaqruvPNO9evXTy+88IKOHDmiDRs26JNPPlGjRo0kSX369FF8fLwiIyPVtm1bSVJ6errefvttNWjQQJI0ffp0de7cWTExMWrTpk2e65kwYYK1/dtvvy1/f39t2LBBTzzxxDW3uXDhglauXKkJEyaoc+fOkqRhw4YpKytLFy9elJOTkyZMmKBnn33Wuk3Pnj3Vt29fnT17VtWqVZMk1alTRyNGjJAkeXh46LPPPtNPP/2U59oBFH3lypWTdOVTp127dikmJsZ6LcSIESP0008/6f3337eG0aysLE2ePFnlypVTnTp1VL9+fTk6OqpPnz6SpN69e2vlypU6cuSIHB0d9fHHH2vs2LHq2rWrJKl27do6evSoFi5cqJCQEG3cuFHnz59XWFiYKlSoIEmaMmWK1q5dq8uXL0uSypcvr/79+1trrlmzpt58803r6HH16tXVqVMn60V+FSpUkKOjo5ydnVWpUiUlJycrKSlJVapUUY0aNWSxWDRr1iydPXv2Jh9d/FcQmIF8CggI0MiRI5WVlaXdu3dr2rRpuu+++/TCCy/IwcHBOtLSs2dPm+3S09Ot//FIUpkyZaxhWboSPsuVK6fff/89X4G5RYsW1p/LlSun2rVr6/fffzfdJiEhQenp6WrSpInN8uHDh1t/Ll++vBYvXqyEhAQdPnxY+/fvl3Tl49N/1/xvZcuW1blz5/JcO4Ci7/z585Jknc7w4IMP2qxPS0uzBldJcnNzsznXubi4WN9kS1emr0nS5cuXdejQIaWnp6t58+Y2fbZo0ULvvfeezp49q99++021a9e2hmVJuueeezRy5Ejr41q1atlsHxAQoN27d2vOnDk6cuSI/vjjDx04cEBVq1bN9TlmB+433nhD8+bN03333acHHnhAHTt2vO7xQfFAYAbyqUyZMtaTs4eHh9zd3dWnTx+VLFlSoaGhMgxDkvThhx+qTJkyNttmz3GWJEdHxxx9G4ahkiVL2jz+t/T09Bzb/Lu9dCXQOjk5mT6H3Pb9bzt27FDfvn3l7++vFi1aqEuXLkpJSdGgQYNs2l1vPwD++3799VfVrl1bjo6OcnV11erVq3O0+fe5ILfzy7/Pff+WfY6zWCw2y7OysiRJDg4OcnBwyLH+as7OzjaPFy9erLlz56pbt25q2bKlevTooS1btpjeRm7kyJF65pln9M033ygmJkahoaFatGiR1qxZw7kOzGEGbpSfn5/69Omjjz/+WN9++63uueceSdLp06dVq1Yt67/Vq1crOjraut0///yjP//80/r4wIEDOn/+vLy9vSVd+U8ne2RHuvIfSG73UP7ll1+sP//99986fPiwtYZrqVWrlhwdHW0u1pGk4OBgRUREKDIyUq1atdK8efPUu3dvtW7dWidPnpSUM8QDuH0lJiZqy5Yt6tq1qzw9PXXhwgWlpaXZnNsWL16sLVu2FKj/u+++Ww4ODoqLi7NZHhcXp8qVK6t8+fKqW7euDh8+bHM+3Ldvn1q1aqXjx4/n2u+CBQs0ePBghYaG6sknn1STJk10+PDha56/Dh06pEmTJsnNzU1PP/205syZo4iICP3xxx+Kj48v0HPD7YXADBSCYcOGqXbt2po0aZLuvPNOtWvXTpMmTdKWLVt09OhRRUZGatGiRbrrrrtsths9erT27t2r3bt3W68g9/X1lSQ1a9ZMGzdu1I4dO5SQkKDQ0FCb/zCyTZw4UTExMdq/f7+GDx+uatWqWeclX4uLi4tCQkI0e/ZsbdmyRX/++afCwsJ08OBBtWvXTtWqVdNvv/2muLg4HTt2TNHR0Zo9e7Yk2Vz0B+D2cenSJZ05c0ZnzpzR0aNH9eWXX6p///6qUaOG+vTpozZt2qh+/fp66aWXFBMToyNHjuitt95SdHR0julZeVW2bFl1795dc+bM0fr163XkyBF9+OGH+uijj9S3b19ZLBZ17dpV5cuX16hRoxQfH69ffvlFoaGh8vT0VPXq1XPtt1q1avrhhx908OBBHTp0SGFhYfriiy9szl9lypTR8ePHlZiYqAoVKmjDhg2aOHGi/vjjDyUkJCg6Olrly5fX3XffXaDnhtsLUzKAQlCqVCm98cYb6tmzp8LCwqz/Jk2apOTkZN1111164403FBQUZLPdI488oueee07p6ekKCAjQ+PHjrR89Dh8+XMnJyRowYIBcXFz0xBNPqHPnzjlGSAYOHKhx48bp77//VqtWrRQREZGnjw9HjBghBwcHhYaG6ty5c6pXr57effdd1alTR0OHDtVff/2lF154QZJUt25dTZ06VaNGjdKePXsK/J8jgKJryZIlWrJkiaQr92R2d3dXhw4d1LdvX+v0siVLlmj69OkaPny4UlJSVKdOHc2dO1f33ntvgfc7fvx4VaxYUTNnztRff/2lWrVqaeLEierevbukK2/wIyMj9eabb+rpp5+Wk5OTAgICNHr06Gv2+fbbb+v1119XUFCQypQpIx8fH7322msKDQ3VsWPHVKNGDT311FMaM2aMHn30UcXExCgiIkIzZ85U9+7dlZmZqSZNmui999677b6wBQVjMfh8FbjlVq9erXHjxum3336zdykAAOA6mJIBAAAAmCAwAwAAACaYkgEAAACYYIQZAAAAMEFgBgAAAEwQmAEAAAATBGYAQKHgkhgAtyu+uAQAbnM9evRQbGyszTJHR0fdcccdateunV566SWVL1/+hvaxZcsWbdq0SW+//fYN9QMARRGBGQCKAW9vb02aNMn6OD09Xb/++qveeecd7d+/Xx9//LH1WyYLYunSpYVQJQAUTQRmACgGXF1d1aRJE5tlvr6+unjxoubMmaPdu3fnWA8AuII5zABQjDVs2FCSdOLECWVmZurdd9/VI488osaNG6tJkyZ66qmnFBMTY20/d+5ctW/fXvPmzVOrVq300EMPqWvXroqNjVVsbKzq1aunH3/8Uffff79efvnlHPvr1KmTxo0bd8ueHwAUBkaYAaAYS0hIkCTdddddmjFjhj766CONHDlS9erVU2JioubPn69hw4Zp69atKl26tKQr4Xrz5s165513lJSUJC8vL40aNUqSNGnSJNWtW1ePP/64li9frgsXLsjV1VWStHv3bh06dEivv/66fZ4sABQQgRkAigHDMJSRkWF9nJycrNjYWC1YsEBNmjRRw4YNtXTpUg0fPlw9evSwtnN2dtaQIUP022+/qWnTppKkjIwMjRkzRvfdd5+1XXYozp7WERQUpMWLF2vTpk0KCgqSJH366aeqWbOmWrRocbOfLgAUKgIzABQDO3bsUIMGDWyWlShRQvfee6/eeOMNWSwWzZw5U5L0999/68iRI0pISNBXX30l6cpFgv/m6elpuj8PDw81b95ca9euVVBQkNLS0rRx40b16tXrhi4uBAB7IDADQDHQoEEDvfbaa5Iki8WiUqVKqVq1ataRYUnau3evXnvtNe3du1fOzs6qW7euqlevLinnPZbvuOOO6+4zODhYr7zyik6cOKHdu3fr3LlzCgwMLMRnBQC3BoEZAIqBMmXKqFGjRtdcf+HCBfXv31/16tXThg0bVKdOHZUoUULffPONNm3aVKB9Pvzww5o8ebI2bdqkXbt26d5779Wdd95Z0KcAAHbDXTIAADp06JD++ecf9ezZU/fcc49KlLjy38O3334rScrKyjLdPrv9v5UuXVqdO3fWhg0b9N133zG6DOA/ixFmAIA8PDzk6uqqhQsXysHBQQ4ODtq0aZM++eQTSVJKSorp9uXKldOuXbsUExMjb29v6zcHBgcH68knn5Srq6s6dOhw058HANwMjDADAFS2bFmFh4fLMAwNGzZMo0eP1okTJ/TBBx+oTJkyiouLM93+2WeflaOjowYMGGAdlZau3DWjYsWK6tKli5ydnW/20wCAm8JiXH0lBwAAhWTPnj164oknFB0dbf2SFAD4r2FKBgCg0G3fvl3bt2/XmjVr5OfnR1gG8J/GlAwAQKFLSkrSe++9Jzc3N02bNs3e5QDADWFKBgAAAGCCEWYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADABIEZAAAAMEFgBgAAAEwQmAEAAAATBGYAAADAxP8DoWcG4BW/89UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "The number of FixedLocator locations (3), usually from a call to set_ticks, does not match the number of labels (5).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1727], line 24\u001b[0m\n\u001b[1;32m     22\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m     23\u001b[0m ax \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mcountplot(data\u001b[38;5;241m=\u001b[39mnew_df, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mturn_out_diff\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_xticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDem to Rep\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRep to No Vote / No Vote to Dem\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNo Change\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDem to No Vote / No Vote to Rep\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRep to Dem\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTurn Out Difference Distribution in the Dataset\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     26\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Respondents\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:74\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 74\u001b[0m     \u001b[39mreturn\u001b[39;00m get_method(\u001b[39mself\u001b[39;49m)(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/_api/deprecation.py:297\u001b[0m, in \u001b[0;36mrename_parameter.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    292\u001b[0m     warn_deprecated(\n\u001b[1;32m    293\u001b[0m         since, message\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe \u001b[39m\u001b[39m{\u001b[39;00mold\u001b[39m!r}\u001b[39;00m\u001b[39m parameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m() \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    294\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhas been renamed \u001b[39m\u001b[39m{\u001b[39;00mnew\u001b[39m!r}\u001b[39;00m\u001b[39m since Matplotlib \u001b[39m\u001b[39m{\u001b[39;00msince\u001b[39m}\u001b[39;00m\u001b[39m; support \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    295\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mfor the old name will be dropped %(removal)s.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    296\u001b[0m     kwargs[new] \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(old)\n\u001b[0;32m--> 297\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1969\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[0;34m(self, labels, minor, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1965\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(locator, mticker\u001b[39m.\u001b[39mFixedLocator):\n\u001b[1;32m   1966\u001b[0m     \u001b[39m# Passing [] as a list of labels is often used as a way to\u001b[39;00m\n\u001b[1;32m   1967\u001b[0m     \u001b[39m# remove all tick labels, so only error for > 0 labels\u001b[39;00m\n\u001b[1;32m   1968\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(locator\u001b[39m.\u001b[39mlocs) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(labels) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(labels) \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m-> 1969\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   1970\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mThe number of FixedLocator locations\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1971\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m (\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(locator\u001b[39m.\u001b[39mlocs)\u001b[39m}\u001b[39;00m\u001b[39m), usually from a call to\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1972\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m set_ticks, does not match\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1973\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m the number of labels (\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(labels)\u001b[39m}\u001b[39;00m\u001b[39m).\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   1974\u001b[0m     tickd \u001b[39m=\u001b[39m {loc: lab \u001b[39mfor\u001b[39;00m loc, lab \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(locator\u001b[39m.\u001b[39mlocs, labels)}\n\u001b[1;32m   1975\u001b[0m     func \u001b[39m=\u001b[39m functools\u001b[39m.\u001b[39mpartial(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_format_with_dict, tickd)\n",
      "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (3), usually from a call to set_ticks, does not match the number of labels (5)."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style for the plots\n",
    "sns.set(style='whitegrid')\n",
    "\n",
    "# Plot the distribution of how many people voted in the dataset\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.countplot(data=new_df, x='if_voted_2022')\n",
    "ax.set_xticklabels(['Did not vote', 'Voted'])\n",
    "ax.set_title('Voting Distribution in the Dataset')\n",
    "ax.set_ylabel('Number of Respondents')\n",
    "plt.show()\n",
    "\n",
    "# Plot the distribution of people who voted for each party\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.countplot(data=new_df[new_df['if_voted_2022'] == 1], x='vote_2022')\n",
    "ax.set_xticklabels(['Republican', 'Democrats'])\n",
    "ax.set_title('Vote Distribution by Party for People who Voted')\n",
    "ax.set_xlabel('Party')\n",
    "ax.set_ylabel('Number of Votes')\n",
    "plt.show()\n",
    "\n",
    "# Plot the distribution of turn_out_diff\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.countplot(data=new_df, x='turn_out_diff')\n",
    "ax.set_xticklabels(['Dem to Rep', 'Rep to No Vote / No Vote to Dem', 'No Change', 'Dem to No Vote / No Vote to Rep', 'Rep to Dem'])\n",
    "ax.set_title('Turn Out Difference Distribution in the Dataset')\n",
    "ax.set_ylabel('Number of Respondents')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create copy of dataframe for exact matching\n",
    "prop_match_df = new_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_var = ['abortion_ballot']\n",
    "Y_var = ['turn_out_diff']\n",
    "Z_var = [z for z in new_df.columns.to_list() if z not in ['if_voted_2022', 'vote_2020','vote_2022','state', 'turn_out_diff', 'abortion_ballot']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_matching(prop_match_df, Z_var, X_var, caliper=0.01):\n",
    "    # Prepare the feature set (Z) and the treatment indicator (W)\n",
    "    Z = prop_match_df[Z_var]\n",
    "    X = prop_match_df[X_var]\n",
    "\n",
    "    # Estimate propensity scores using logistic regression\n",
    "    lr = LogisticRegression(max_iter=1000)\n",
    "    lr.fit(Z, X)\n",
    "    prop_match_df['propensity_score'] = lr.predict_proba(Z)[:, 1]\n",
    "\n",
    "    # Split treated and control observations\n",
    "    treated = prop_match_df[prop_match_df['abortion_ballot'] == 1]\n",
    "    control = prop_match_df[prop_match_df['abortion_ballot'] == 0]\n",
    "\n",
    "    # Match treated and control observations based on propensity scores\n",
    "    nn = NearestNeighbors(n_neighbors=1)\n",
    "    nn.fit(control[['propensity_score']])\n",
    "    distances, indices = nn.kneighbors(treated[['propensity_score']])\n",
    "    indices = indices.reshape(-1)\n",
    "\n",
    "    # Create a matched dataset\n",
    "    matched_control_indices = []\n",
    "    matched_treated_indices = []\n",
    "    for i, (dist, idx) in enumerate(zip(distances, indices)):\n",
    "        if dist <= caliper:\n",
    "            matched_control_indices.append(idx)\n",
    "            matched_treated_indices.append(i)\n",
    "\n",
    "    matched_control = control.iloc[matched_control_indices]\n",
    "    matched_treated = treated.iloc[matched_treated_indices]\n",
    "\n",
    "    # Concatenate matched treated and control observations\n",
    "    matched_data = pd.concat([matched_treated, matched_control], axis=0).reset_index(drop=True)\n",
    "    rows, _ = matched_data.shape\n",
    "    print(\"Number of instances in total matches \", rows)\n",
    "    return matched_control, matched_treated, matched_data\n",
    "\n",
    "def validate_matching(Z_var, matched_control, matched_treated, matched_data):\n",
    "    #change data type of variables to prevent underflow\n",
    "    for var in Z_var:\n",
    "        matched_treated[var] = matched_treated[var].astype(int)\n",
    "        matched_control[var] = matched_control[var].astype(int)\n",
    "        matched_data[var] = matched_data[var].astype(int)\n",
    "\n",
    "    # Initialize an empty DataFrame to store the mean differences and t-test results\n",
    "    validation_results = pd.DataFrame(columns=['Variable', 'Mean Difference', 't-statistic', 'p-value'])\n",
    "\n",
    "    # Loop over all control variables\n",
    "    for var in Z_var:\n",
    "        # Compute the differences between matched pairs for this variable\n",
    "        differences = matched_treated[var].values - matched_control[var].values\n",
    "\n",
    "        # Compute the mean difference\n",
    "        mean_difference = np.mean(differences)\n",
    "        \n",
    "        # Perform a t-test to test if the mean difference is significantly different from 0\n",
    "        t_statistic, p_value = ttest_1samp(differences, 0)\n",
    "        \n",
    "        # Store the results in the validation_results DataFrame\n",
    "        validation_results = validation_results.append({\n",
    "            'Variable': var,\n",
    "            'Mean Difference': mean_difference,\n",
    "            't-statistic': t_statistic,\n",
    "            'p-value': p_value\n",
    "        }, ignore_index=True)\n",
    "\n",
    "    # Display the validation_results DataFrame\n",
    "    return validation_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances in total matches  6908\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>Variable</th>\n",
       "      <th>Mean Difference</th>\n",
       "      <th>t-statistic</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.004922</td>\n",
       "      <td>0.418081</td>\n",
       "      <td>0.675914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>educ</td>\n",
       "      <td>0.009844</td>\n",
       "      <td>0.292978</td>\n",
       "      <td>0.769557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fam_income</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>0.386874</td>\n",
       "      <td>0.698873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urbancity</td>\n",
       "      <td>0.080776</td>\n",
       "      <td>3.563989</td>\n",
       "      <td>0.00037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>presvote16post</td>\n",
       "      <td>0.007238</td>\n",
       "      <td>0.325017</td>\n",
       "      <td>0.745188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pew_bornagain</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.593203</td>\n",
       "      <td>0.553084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pew_religimp</td>\n",
       "      <td>0.042559</td>\n",
       "      <td>1.102124</td>\n",
       "      <td>0.270484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pew_churatd</td>\n",
       "      <td>0.099595</td>\n",
       "      <td>2.510803</td>\n",
       "      <td>0.012091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pew_prayer</td>\n",
       "      <td>0.051824</td>\n",
       "      <td>0.898407</td>\n",
       "      <td>0.369031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pol_ideo</td>\n",
       "      <td>-0.011581</td>\n",
       "      <td>-0.411731</td>\n",
       "      <td>0.680562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>approval_biden</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>0.038677</td>\n",
       "      <td>0.96915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>know_senate</td>\n",
       "      <td>0.005501</td>\n",
       "      <td>0.245276</td>\n",
       "      <td>0.806257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income_change</td>\n",
       "      <td>-0.010133</td>\n",
       "      <td>-0.454417</td>\n",
       "      <td>0.649558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>price_change</td>\n",
       "      <td>-0.00029</td>\n",
       "      <td>-0.020612</td>\n",
       "      <td>0.983556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>party_identified</td>\n",
       "      <td>-0.004053</td>\n",
       "      <td>-0.225482</td>\n",
       "      <td>0.821617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>party_strength</td>\n",
       "      <td>-0.002606</td>\n",
       "      <td>-0.051331</td>\n",
       "      <td>0.959065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abortion_all_cases</td>\n",
       "      <td>-0.005501</td>\n",
       "      <td>-0.511594</td>\n",
       "      <td>0.608968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abortion_some_cases</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>0.747155</td>\n",
       "      <td>0.455021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>abortion_before_20weeks</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>0.744389</td>\n",
       "      <td>0.456692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>abortion_coverage</td>\n",
       "      <td>0.015924</td>\n",
       "      <td>1.4562</td>\n",
       "      <td>0.145428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abortion_funds</td>\n",
       "      <td>0.015634</td>\n",
       "      <td>1.40386</td>\n",
       "      <td>0.16045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abortion_illegal</td>\n",
       "      <td>-0.00029</td>\n",
       "      <td>-0.036103</td>\n",
       "      <td>0.971203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>congress_abortion</td>\n",
       "      <td>-0.004922</td>\n",
       "      <td>-0.457076</td>\n",
       "      <td>0.647645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.074696</td>\n",
       "      <td>-0.205978</td>\n",
       "      <td>0.83682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pol_involve</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>race_white</td>\n",
       "      <td>0.007238</td>\n",
       "      <td>0.82374</td>\n",
       "      <td>0.410144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>race_black</td>\n",
       "      <td>0.011291</td>\n",
       "      <td>1.883632</td>\n",
       "      <td>0.059699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>race_hispanic</td>\n",
       "      <td>-0.003185</td>\n",
       "      <td>-0.471135</td>\n",
       "      <td>0.637574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>race_asian</td>\n",
       "      <td>-0.011581</td>\n",
       "      <td>-2.45434</td>\n",
       "      <td>0.014163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>marstat_married</td>\n",
       "      <td>0.008975</td>\n",
       "      <td>0.753811</td>\n",
       "      <td>0.451014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>marstat_separated</td>\n",
       "      <td>-0.001158</td>\n",
       "      <td>-0.441675</td>\n",
       "      <td>0.658752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>marstat_divorced</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>0.500316</td>\n",
       "      <td>0.616885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>marstat_widowed</td>\n",
       "      <td>-0.003474</td>\n",
       "      <td>-0.561896</td>\n",
       "      <td>0.574223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>marstat_never_married</td>\n",
       "      <td>-0.006948</td>\n",
       "      <td>-0.717087</td>\n",
       "      <td>0.473369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>marstat_domestic</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>-0.112138</td>\n",
       "      <td>0.91072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>region_northeast</td>\n",
       "      <td>-0.002027</td>\n",
       "      <td>-1.94223</td>\n",
       "      <td>0.052191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>region_midwest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>region_south</td>\n",
       "      <td>-0.002027</td>\n",
       "      <td>-1.299997</td>\n",
       "      <td>0.193689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>region_west</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>3.505716</td>\n",
       "      <td>0.000461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>religpew_not_religious</td>\n",
       "      <td>-0.00579</td>\n",
       "      <td>-0.497157</td>\n",
       "      <td>0.61911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>religpew_protestant</td>\n",
       "      <td>-0.010133</td>\n",
       "      <td>-0.971091</td>\n",
       "      <td>0.331571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>religpew_catholic</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>0.154798</td>\n",
       "      <td>0.876989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>religpew_mormon</td>\n",
       "      <td>0.004343</td>\n",
       "      <td>2.613367</td>\n",
       "      <td>0.009004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>religpew_orthodox</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>2.192097</td>\n",
       "      <td>0.028439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>religpew_jewish</td>\n",
       "      <td>0.007238</td>\n",
       "      <td>1.547774</td>\n",
       "      <td>0.121768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>religpew_muslim</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.755882</td>\n",
       "      <td>0.449771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>religpew_buddhist</td>\n",
       "      <td>-0.004922</td>\n",
       "      <td>-1.442147</td>\n",
       "      <td>0.149352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>religpew_hindu</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.942794</td>\n",
       "      <td>0.345852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Variable Mean Difference t-statistic   p-value\n",
       "0                    gender        0.004922    0.418081  0.675914\n",
       "1                      educ        0.009844    0.292978  0.769557\n",
       "2                fam_income        0.031558    0.386874  0.698873\n",
       "3                 urbancity        0.080776    3.563989   0.00037\n",
       "4            presvote16post        0.007238    0.325017  0.745188\n",
       "5             pew_bornagain         0.00608    0.593203  0.553084\n",
       "6              pew_religimp        0.042559    1.102124  0.270484\n",
       "7               pew_churatd        0.099595    2.510803  0.012091\n",
       "8                pew_prayer        0.051824    0.898407  0.369031\n",
       "9                  pol_ideo       -0.011581   -0.411731  0.680562\n",
       "10           approval_biden        0.001448    0.038677   0.96915\n",
       "11              know_senate        0.005501    0.245276  0.806257\n",
       "12            income_change       -0.010133   -0.454417  0.649558\n",
       "13             price_change        -0.00029   -0.020612  0.983556\n",
       "14         party_identified       -0.004053   -0.225482  0.821617\n",
       "15           party_strength       -0.002606   -0.051331  0.959065\n",
       "16       abortion_all_cases       -0.005501   -0.511594  0.608968\n",
       "17      abortion_some_cases        0.008686    0.747155  0.455021\n",
       "18  abortion_before_20weeks        0.008686    0.744389  0.456692\n",
       "19        abortion_coverage        0.015924      1.4562  0.145428\n",
       "20           abortion_funds        0.015634     1.40386   0.16045\n",
       "21         abortion_illegal        -0.00029   -0.036103  0.971203\n",
       "22        congress_abortion       -0.004922   -0.457076  0.647645\n",
       "23                      age       -0.074696   -0.205978   0.83682\n",
       "24              pol_involve             0.0         NaN       NaN\n",
       "25               race_white        0.007238     0.82374  0.410144\n",
       "26               race_black        0.011291    1.883632  0.059699\n",
       "27            race_hispanic       -0.003185   -0.471135  0.637574\n",
       "28               race_asian       -0.011581    -2.45434  0.014163\n",
       "29          marstat_married        0.008975    0.753811  0.451014\n",
       "30        marstat_separated       -0.001158   -0.441675  0.658752\n",
       "31         marstat_divorced        0.003764    0.500316  0.616885\n",
       "32          marstat_widowed       -0.003474   -0.561896  0.574223\n",
       "33    marstat_never_married       -0.006948   -0.717087  0.473369\n",
       "34         marstat_domestic       -0.000579   -0.112138   0.91072\n",
       "35         region_northeast       -0.002027    -1.94223  0.052191\n",
       "36           region_midwest             0.0         NaN       NaN\n",
       "37             region_south       -0.002027   -1.299997  0.193689\n",
       "38              region_west        0.004053    3.505716  0.000461\n",
       "39   religpew_not_religious        -0.00579   -0.497157   0.61911\n",
       "40      religpew_protestant       -0.010133   -0.971091  0.331571\n",
       "41        religpew_catholic        0.001448    0.154798  0.876989\n",
       "42          religpew_mormon        0.004343    2.613367  0.009004\n",
       "43        religpew_orthodox        0.003474    2.192097  0.028439\n",
       "44          religpew_jewish        0.007238    1.547774  0.121768\n",
       "45          religpew_muslim        0.001158    0.755882  0.449771\n",
       "46        religpew_buddhist       -0.004922   -1.442147  0.149352\n",
       "47           religpew_hindu        0.001158    0.942794  0.345852"
      ]
     },
     "execution_count": 1684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_control, matched_treated, matched_data = get_matching(prop_match_df, Z_var, X_var, caliper=0.005)\n",
    "validation_results = validate_matching(Z_var, matched_control, matched_treated, matched_data)\n",
    "validation_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_two_sample_t_test(matched_treated, matched_control):\n",
    "    # Perform two-sample t-test\n",
    "    t_stat, p_value = stats.ttest_ind(matched_treated['turn_out_diff'], matched_control['turn_out_diff'])\n",
    "    print('mean turn_out_diff in control is:', matched_treated['turn_out_diff'].mean())\n",
    "    print('mean turn_out_diff in treatment is:', matched_control['turn_out_diff'].mean())\n",
    "    print(\"t-statistic:\", t_stat)\n",
    "    print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='turn_out_diff', hue='abortion_ballot', data=matched_data)\n",
    "plt.title('Counts of Turn Out Difference for Treated and Control Groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean turn_out_diff in control is: -0.0023161551823972205\n",
      "mean turn_out_diff in treatment is: 0.015055008685581933\n",
      "t-statistic: -1.6618165081643976\n",
      "p-value: 0.09659495775620722\n"
     ]
    }
   ],
   "source": [
    "get_two_sample_t_test(matched_treated, matched_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we want to treat states that have abortion on ballot differently depending on the type of ballot question\n",
    "protect_abortion_states = [6, 26, 50]\n",
    "against_abortion_states = [21, 30]\n",
    "prop_match_df['abortion_ballot'] = prop_match_df['state'].apply(lambda row: 2 if row in protect_abortion_states else (1 if row in against_abortion_states else 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2884050334.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  prop_match_df_for['abortion_ballot'] = prop_match_df_for['abortion_ballot'].replace(2, 1)\n"
     ]
    }
   ],
   "source": [
    "# Compare only states that have ballot questions supporting abortion rights to states without abortion ballot questions\n",
    "prop_match_df_for = prop_match_df[prop_match_df['abortion_ballot'].isin([0,2])]\n",
    "prop_match_df_for['abortion_ballot'] = prop_match_df_for['abortion_ballot'].replace(2, 1)\n",
    "\n",
    "# Compare only states that have ballot questions prohibiting abortion rights to states without abortion ballot questions\n",
    "prop_match_df_against = prop_match_df[prop_match_df['abortion_ballot'].isin([0,1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  prop_match_df['propensity_score'] = lr.predict_proba(Z)[:, 1]\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances in total matches  5854\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>Variable</th>\n",
       "      <th>Mean Difference</th>\n",
       "      <th>t-statistic</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.02289</td>\n",
       "      <td>1.784322</td>\n",
       "      <td>0.074475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>educ</td>\n",
       "      <td>0.009224</td>\n",
       "      <td>0.25537</td>\n",
       "      <td>0.798455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fam_income</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.079305</td>\n",
       "      <td>0.936795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urbancity</td>\n",
       "      <td>0.012641</td>\n",
       "      <td>0.563067</td>\n",
       "      <td>0.573432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>presvote16post</td>\n",
       "      <td>0.031773</td>\n",
       "      <td>1.319748</td>\n",
       "      <td>0.187023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pew_bornagain</td>\n",
       "      <td>-0.009224</td>\n",
       "      <td>-0.851648</td>\n",
       "      <td>0.394479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pew_religimp</td>\n",
       "      <td>-0.054663</td>\n",
       "      <td>-1.306988</td>\n",
       "      <td>0.191319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pew_churatd</td>\n",
       "      <td>-0.050222</td>\n",
       "      <td>-1.16217</td>\n",
       "      <td>0.245261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pew_prayer</td>\n",
       "      <td>-0.017424</td>\n",
       "      <td>-0.278238</td>\n",
       "      <td>0.780849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pol_ideo</td>\n",
       "      <td>0.012641</td>\n",
       "      <td>0.430202</td>\n",
       "      <td>0.667081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>approval_biden</td>\n",
       "      <td>0.012299</td>\n",
       "      <td>0.305345</td>\n",
       "      <td>0.760125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>know_senate</td>\n",
       "      <td>0.059105</td>\n",
       "      <td>2.389775</td>\n",
       "      <td>0.016922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income_change</td>\n",
       "      <td>-0.004441</td>\n",
       "      <td>-0.179988</td>\n",
       "      <td>0.857174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>price_change</td>\n",
       "      <td>-0.002733</td>\n",
       "      <td>-0.178499</td>\n",
       "      <td>0.858343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>party_identified</td>\n",
       "      <td>0.009224</td>\n",
       "      <td>0.47926</td>\n",
       "      <td>0.631789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>party_strength</td>\n",
       "      <td>0.019816</td>\n",
       "      <td>0.370252</td>\n",
       "      <td>0.711221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abortion_all_cases</td>\n",
       "      <td>0.023574</td>\n",
       "      <td>2.075011</td>\n",
       "      <td>0.038073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abortion_some_cases</td>\n",
       "      <td>-0.011958</td>\n",
       "      <td>-0.960063</td>\n",
       "      <td>0.337103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>abortion_before_20weeks</td>\n",
       "      <td>-0.015032</td>\n",
       "      <td>-1.170187</td>\n",
       "      <td>0.242021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>abortion_coverage</td>\n",
       "      <td>-0.009908</td>\n",
       "      <td>-0.833993</td>\n",
       "      <td>0.404353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abortion_funds</td>\n",
       "      <td>-0.004441</td>\n",
       "      <td>-0.373823</td>\n",
       "      <td>0.708563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abortion_illegal</td>\n",
       "      <td>-0.003416</td>\n",
       "      <td>-0.411635</td>\n",
       "      <td>0.680637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>congress_abortion</td>\n",
       "      <td>0.010591</td>\n",
       "      <td>0.893003</td>\n",
       "      <td>0.371929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.148958</td>\n",
       "      <td>-0.36172</td>\n",
       "      <td>0.717587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pol_involve</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>race_white</td>\n",
       "      <td>0.009566</td>\n",
       "      <td>1.105114</td>\n",
       "      <td>0.269201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>race_black</td>\n",
       "      <td>-0.000342</td>\n",
       "      <td>-0.051769</td>\n",
       "      <td>0.958716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>race_hispanic</td>\n",
       "      <td>-0.000342</td>\n",
       "      <td>-0.041768</td>\n",
       "      <td>0.966686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>race_asian</td>\n",
       "      <td>0.004441</td>\n",
       "      <td>0.695813</td>\n",
       "      <td>0.486601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>marstat_married</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>0.453797</td>\n",
       "      <td>0.650009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>marstat_separated</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>2.178717</td>\n",
       "      <td>0.029432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>marstat_divorced</td>\n",
       "      <td>-0.00205</td>\n",
       "      <td>-0.250395</td>\n",
       "      <td>0.802299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>marstat_widowed</td>\n",
       "      <td>-0.009566</td>\n",
       "      <td>-1.410858</td>\n",
       "      <td>0.158393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>marstat_never_married</td>\n",
       "      <td>0.00205</td>\n",
       "      <td>0.191047</td>\n",
       "      <td>0.848502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>marstat_domestic</td>\n",
       "      <td>-0.000683</td>\n",
       "      <td>-0.122607</td>\n",
       "      <td>0.902427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>region_northeast</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>region_midwest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>region_south</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>region_west</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>religpew_not_religious</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.188062</td>\n",
       "      <td>0.850841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>religpew_protestant</td>\n",
       "      <td>-0.005125</td>\n",
       "      <td>-0.466641</td>\n",
       "      <td>0.640791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>religpew_catholic</td>\n",
       "      <td>-0.001708</td>\n",
       "      <td>-0.168618</td>\n",
       "      <td>0.866109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>religpew_mormon</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>1.257362</td>\n",
       "      <td>0.208723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>religpew_orthodox</td>\n",
       "      <td>-0.001367</td>\n",
       "      <td>-0.617148</td>\n",
       "      <td>0.537185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>religpew_jewish</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>1.048283</td>\n",
       "      <td>0.294595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>religpew_muslim</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.365094</td>\n",
       "      <td>0.715067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>religpew_buddhist</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.371336</td>\n",
       "      <td>0.710414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>religpew_hindu</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>0.534457</td>\n",
       "      <td>0.593066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Variable Mean Difference t-statistic   p-value\n",
       "0                    gender         0.02289    1.784322  0.074475\n",
       "1                      educ        0.009224     0.25537  0.798455\n",
       "2                fam_income        0.006833    0.079305  0.936795\n",
       "3                 urbancity        0.012641    0.563067  0.573432\n",
       "4            presvote16post        0.031773    1.319748  0.187023\n",
       "5             pew_bornagain       -0.009224   -0.851648  0.394479\n",
       "6              pew_religimp       -0.054663   -1.306988  0.191319\n",
       "7               pew_churatd       -0.050222    -1.16217  0.245261\n",
       "8                pew_prayer       -0.017424   -0.278238  0.780849\n",
       "9                  pol_ideo        0.012641    0.430202  0.667081\n",
       "10           approval_biden        0.012299    0.305345  0.760125\n",
       "11              know_senate        0.059105    2.389775  0.016922\n",
       "12            income_change       -0.004441   -0.179988  0.857174\n",
       "13             price_change       -0.002733   -0.178499  0.858343\n",
       "14         party_identified        0.009224     0.47926  0.631789\n",
       "15           party_strength        0.019816    0.370252  0.711221\n",
       "16       abortion_all_cases        0.023574    2.075011  0.038073\n",
       "17      abortion_some_cases       -0.011958   -0.960063  0.337103\n",
       "18  abortion_before_20weeks       -0.015032   -1.170187  0.242021\n",
       "19        abortion_coverage       -0.009908   -0.833993  0.404353\n",
       "20           abortion_funds       -0.004441   -0.373823  0.708563\n",
       "21         abortion_illegal       -0.003416   -0.411635  0.680637\n",
       "22        congress_abortion        0.010591    0.893003  0.371929\n",
       "23                      age       -0.148958    -0.36172  0.717587\n",
       "24              pol_involve             0.0         NaN       NaN\n",
       "25               race_white        0.009566    1.105114  0.269201\n",
       "26               race_black       -0.000342   -0.051769  0.958716\n",
       "27            race_hispanic       -0.000342   -0.041768  0.966686\n",
       "28               race_asian        0.004441    0.695813  0.486601\n",
       "29          marstat_married        0.005808    0.453797  0.650009\n",
       "30        marstat_separated        0.005466    2.178717  0.029432\n",
       "31         marstat_divorced        -0.00205   -0.250395  0.802299\n",
       "32          marstat_widowed       -0.009566   -1.410858  0.158393\n",
       "33    marstat_never_married         0.00205    0.191047  0.848502\n",
       "34         marstat_domestic       -0.000683   -0.122607  0.902427\n",
       "35         region_northeast             0.0         0.0       1.0\n",
       "36           region_midwest             0.0         0.0       1.0\n",
       "37             region_south             0.0         NaN       NaN\n",
       "38              region_west             0.0         NaN       NaN\n",
       "39   religpew_not_religious        0.002392    0.188062  0.850841\n",
       "40      religpew_protestant       -0.005125   -0.466641  0.640791\n",
       "41        religpew_catholic       -0.001708   -0.168618  0.866109\n",
       "42          religpew_mormon        0.002392    1.257362  0.208723\n",
       "43        religpew_orthodox       -0.001367   -0.617148  0.537185\n",
       "44          religpew_jewish        0.005808    1.048283  0.294595\n",
       "45          religpew_muslim        0.000683    0.365094  0.715067\n",
       "46        religpew_buddhist        0.001367    0.371336  0.710414\n",
       "47           religpew_hindu        0.000683    0.534457  0.593066"
      ]
     },
     "execution_count": 1692,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Abortion State vs No Abortion State\n",
    "matched_control, matched_treated, matched_data = get_matching(prop_match_df_for, Z_var, X_var, caliper=0.005)\n",
    "validation_results = validate_matching(Z_var, matched_control, matched_treated, matched_data)\n",
    "validation_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='turn_out_diff', hue='abortion_ballot', data=matched_data)\n",
    "plt.title('Counts of Turn Out Difference for Treated and Control Groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean turn_out_diff in control is: -0.004099760847283909\n",
      "mean turn_out_diff in treatment is: 0.020498804236419543\n",
      "t-statistic: -2.208252294868455\n",
      "p-value: 0.027265320654155357\n"
     ]
    }
   ],
   "source": [
    "get_two_sample_t_test(matched_treated, matched_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/violethuang/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  prop_match_df['propensity_score'] = lr.predict_proba(Z)[:, 1]\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  matched_treated[var] = matched_treated[var].astype(int)\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  matched_control[var] = matched_control[var].astype(int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of instances in total matches  1042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n",
      "/var/folders/zg/n9l4w3gd6w98b4wdky4vmq1m0000gn/T/ipykernel_66670/2642755188.py:60: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  validation_results = validation_results.append({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Variable</th>\n",
       "      <th>Mean Difference</th>\n",
       "      <th>t-statistic</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>-0.053743</td>\n",
       "      <td>-1.774524</td>\n",
       "      <td>0.076562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>educ</td>\n",
       "      <td>-0.042226</td>\n",
       "      <td>-0.460391</td>\n",
       "      <td>0.645428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fam_income</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>0.138066</td>\n",
       "      <td>0.890242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urbancity</td>\n",
       "      <td>0.107486</td>\n",
       "      <td>1.903351</td>\n",
       "      <td>0.057547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>presvote16post</td>\n",
       "      <td>-0.038388</td>\n",
       "      <td>-0.59778</td>\n",
       "      <td>0.550247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pew_bornagain</td>\n",
       "      <td>-0.019194</td>\n",
       "      <td>-0.664834</td>\n",
       "      <td>0.506452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pew_religimp</td>\n",
       "      <td>0.051823</td>\n",
       "      <td>0.557668</td>\n",
       "      <td>0.577311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pew_churatd</td>\n",
       "      <td>0.046065</td>\n",
       "      <td>0.429746</td>\n",
       "      <td>0.667558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pew_prayer</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>0.271722</td>\n",
       "      <td>0.785943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pol_ideo</td>\n",
       "      <td>-0.023033</td>\n",
       "      <td>-0.289766</td>\n",
       "      <td>0.772111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>approval_biden</td>\n",
       "      <td>-0.051823</td>\n",
       "      <td>-0.508348</td>\n",
       "      <td>0.611425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>know_senate</td>\n",
       "      <td>-0.015355</td>\n",
       "      <td>-0.271924</td>\n",
       "      <td>0.785789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income_change</td>\n",
       "      <td>-0.015355</td>\n",
       "      <td>-0.282593</td>\n",
       "      <td>0.777601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>price_change</td>\n",
       "      <td>0.026871</td>\n",
       "      <td>0.712244</td>\n",
       "      <td>0.476633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>party_identified</td>\n",
       "      <td>-0.007678</td>\n",
       "      <td>-0.144202</td>\n",
       "      <td>0.885397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>party_strength</td>\n",
       "      <td>-0.03263</td>\n",
       "      <td>-0.217875</td>\n",
       "      <td>0.827612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>abortion_all_cases</td>\n",
       "      <td>-0.021113</td>\n",
       "      <td>-0.711193</td>\n",
       "      <td>0.477283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>abortion_some_cases</td>\n",
       "      <td>0.03071</td>\n",
       "      <td>0.996109</td>\n",
       "      <td>0.31966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>abortion_before_20weeks</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.124397</td>\n",
       "      <td>0.901049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>abortion_coverage</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.063312</td>\n",
       "      <td>0.949543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abortion_funds</td>\n",
       "      <td>0.013436</td>\n",
       "      <td>0.432951</td>\n",
       "      <td>0.66523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abortion_illegal</td>\n",
       "      <td>-0.007678</td>\n",
       "      <td>-0.335387</td>\n",
       "      <td>0.737469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>congress_abortion</td>\n",
       "      <td>0.009597</td>\n",
       "      <td>0.342304</td>\n",
       "      <td>0.73226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>age</td>\n",
       "      <td>1.347409</td>\n",
       "      <td>1.426331</td>\n",
       "      <td>0.154373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>pol_involve</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>race_white</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>1.988091</td>\n",
       "      <td>0.047326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>race_black</td>\n",
       "      <td>-0.021113</td>\n",
       "      <td>-1.764982</td>\n",
       "      <td>0.078154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>race_hispanic</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.301248</td>\n",
       "      <td>0.763346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>race_asian</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.576981</td>\n",
       "      <td>0.564202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>marstat_married</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.064895</td>\n",
       "      <td>0.948283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>marstat_separated</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.816235</td>\n",
       "      <td>0.414739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>marstat_divorced</td>\n",
       "      <td>-0.013436</td>\n",
       "      <td>-0.710404</td>\n",
       "      <td>0.477772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>marstat_widowed</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.245961</td>\n",
       "      <td>0.80581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>marstat_never_married</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.083544</td>\n",
       "      <td>0.933451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>marstat_domestic</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.145728</td>\n",
       "      <td>0.884193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>region_northeast</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>region_midwest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>region_south</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>region_west</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>religpew_not_religious</td>\n",
       "      <td>-0.019194</td>\n",
       "      <td>-0.706767</td>\n",
       "      <td>0.480028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>religpew_protestant</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.066019</td>\n",
       "      <td>0.947388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>religpew_catholic</td>\n",
       "      <td>0.015355</td>\n",
       "      <td>0.675771</td>\n",
       "      <td>0.499487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>religpew_mormon</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.44687</td>\n",
       "      <td>0.655155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>religpew_orthodox</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.317776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>religpew_jewish</td>\n",
       "      <td>-0.001919</td>\n",
       "      <td>-0.333049</td>\n",
       "      <td>0.739232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>religpew_muslim</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>religpew_buddhist</td>\n",
       "      <td>-0.005758</td>\n",
       "      <td>-1.342674</td>\n",
       "      <td>0.179963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>religpew_hindu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Variable Mean Difference t-statistic   p-value\n",
       "0                    gender       -0.053743   -1.774524  0.076562\n",
       "1                      educ       -0.042226   -0.460391  0.645428\n",
       "2                fam_income        0.024952    0.138066  0.890242\n",
       "3                 urbancity        0.107486    1.903351  0.057547\n",
       "4            presvote16post       -0.038388    -0.59778  0.550247\n",
       "5             pew_bornagain       -0.019194   -0.664834  0.506452\n",
       "6              pew_religimp        0.051823    0.557668  0.577311\n",
       "7               pew_churatd        0.046065    0.429746  0.667558\n",
       "8                pew_prayer        0.038388    0.271722  0.785943\n",
       "9                  pol_ideo       -0.023033   -0.289766  0.772111\n",
       "10           approval_biden       -0.051823   -0.508348  0.611425\n",
       "11              know_senate       -0.015355   -0.271924  0.785789\n",
       "12            income_change       -0.015355   -0.282593  0.777601\n",
       "13             price_change        0.026871    0.712244  0.476633\n",
       "14         party_identified       -0.007678   -0.144202  0.885397\n",
       "15           party_strength        -0.03263   -0.217875  0.827612\n",
       "16       abortion_all_cases       -0.021113   -0.711193  0.477283\n",
       "17      abortion_some_cases         0.03071    0.996109   0.31966\n",
       "18  abortion_before_20weeks        0.003839    0.124397  0.901049\n",
       "19        abortion_coverage        0.001919    0.063312  0.949543\n",
       "20           abortion_funds        0.013436    0.432951   0.66523\n",
       "21         abortion_illegal       -0.007678   -0.335387  0.737469\n",
       "22        congress_abortion        0.009597    0.342304   0.73226\n",
       "23                      age        1.347409    1.426331  0.154373\n",
       "24              pol_involve             0.0         NaN       NaN\n",
       "25               race_white        0.024952    1.988091  0.047326\n",
       "26               race_black       -0.021113   -1.764982  0.078154\n",
       "27            race_hispanic       -0.001919   -0.301248  0.763346\n",
       "28               race_asian        0.001919    0.576981  0.564202\n",
       "29          marstat_married        0.001919    0.064895  0.948283\n",
       "30        marstat_separated        0.003839    0.816235  0.414739\n",
       "31         marstat_divorced       -0.013436   -0.710404  0.477772\n",
       "32          marstat_widowed        0.003839    0.245961   0.80581\n",
       "33    marstat_never_married        0.001919    0.083544  0.933451\n",
       "34         marstat_domestic        0.001919    0.145728  0.884193\n",
       "35         region_northeast             0.0         NaN       NaN\n",
       "36           region_midwest             0.0         NaN       NaN\n",
       "37             region_south             0.0         NaN       NaN\n",
       "38              region_west             0.0         NaN       NaN\n",
       "39   religpew_not_religious       -0.019194   -0.706767  0.480028\n",
       "40      religpew_protestant       -0.001919   -0.066019  0.947388\n",
       "41        religpew_catholic        0.015355    0.675771  0.499487\n",
       "42          religpew_mormon        0.001919     0.44687  0.655155\n",
       "43        religpew_orthodox        0.001919         1.0  0.317776\n",
       "44          religpew_jewish       -0.001919   -0.333049  0.739232\n",
       "45          religpew_muslim             0.0         NaN       NaN\n",
       "46        religpew_buddhist       -0.005758   -1.342674  0.179963\n",
       "47           religpew_hindu             0.0         NaN       NaN"
      ]
     },
     "execution_count": 1696,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Against Abortion State vs No Abortion State\n",
    "matched_control, matched_treated, matched_data = get_matching(prop_match_df_against, Z_var, X_var, caliper=0.005)\n",
    "validation_results = validate_matching(Z_var, matched_control, matched_treated, matched_data)\n",
    "validation_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='turn_out_diff', hue='abortion_ballot', data=matched_data)\n",
    "plt.title('Counts of Turn Out Difference for Treated and Control Groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean turn_out_diff in control is: 0.003838771593090211\n",
      "mean turn_out_diff in treatment is: 0.007677543186180422\n",
      "t-statistic: -0.17946063401938853\n",
      "p-value: 0.8576109820915202\n"
     ]
    }
   ],
   "source": [
    "get_two_sample_t_test(matched_treated, matched_control)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8da593020269d3b8f2f89629b67a40e1602e1b19a7674d304503744e42ece518"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}