ms2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('20201103__ms__general__precinct.csv')\n",
    "df = df.replace(np.nan, '', regex = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding change to fix near-duplicates due to scraping errors\n",
    "df.loc[(df['precinct'].str.upper()=='MT ISREAL BAPTIST CHURCH')&(df.drop(columns = 'votes').duplicated()),'precinct']='MULTIPURPOSE CENTER'\n",
    "df.loc[(df['precinct'].str.upper()=='PALMER')&(df.drop(columns = 'votes').duplicated()),'precinct']='PEOPLES'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['office'] = df['office'].replace({'President': 'US PRESIDENT',\n",
    "                                          'U.S. Senate': 'US SENATE',\n",
    "                                           'U.S. House': 'US HOUSE',\n",
    "                                           'US Senate': 'US SENATE',\n",
    "                                          })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['party_detailed'] = df['party'].replace({'D': 'DEMOCRAT',\n",
    "                                            'R': 'REPUBLICAN',\n",
    "                                            'AC': 'CONSTITUTION',\n",
    "                                            'AS': 'AMERICAN SOLIDARITY',\n",
    "                                            'IND': 'INDEPENDENT',\n",
    "                                            'G': 'GREEN',\n",
    "                                            'L': 'LIBERTARIAN',\n",
    "                                            'I': 'INDEPENDENT',\n",
    "                                            'DEM': 'DEMOCRAT',\n",
    "                                            'REP': 'REPUBLICAN',\n",
    "                                            'GRN': 'GREEN',\n",
    "                                            'LIB': 'LIBERTARIAN'})\n",
    "\n",
    "df['party_simplified'] = df['party_detailed'].replace({'GREEN': 'OTHER',\n",
    "                                            'INDEPENDENT': 'OTHER',\n",
    "                                            'AMERICAN SOLIDARITY': 'OTHER',\n",
    "                                            'CONSTITUTION': 'OTHER'})\n",
    "\n",
    "df = df.drop(['party'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"candidate\"] = df[\"candidate\"].str.replace('.', '', regex=True)\n",
    "df[\"candidate\"] = df[\"candidate\"].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['votes'] = df['votes'].replace({'x': '0'}) # ask what this is\n",
    "df['votes'] = df['votes'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['district'] = df['district'].astype(str)\n",
    "df['district'] = df['district'].str.replace('\\.0', '',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDataverse(x):\n",
    "    if 'US PRESIDENT' in x:\n",
    "        return \"PRESIDENT\"\n",
    "    elif \"SENATE\" in x:\n",
    "        return \"SENATE\"\n",
    "    elif \"US HOUSE\" in x:\n",
    "        return \"HOUSE\"\n",
    "    else: \n",
    "        return ''\n",
    "    \n",
    "df['dataverse'] = df['office'].apply(getDataverse)\n",
    "df['dataverse'] = df['dataverse'].str.replace('\\s+', ' ',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"state\"] = 'MISSISSIPPI'\n",
    "df[\"state_po\"] = 'MS'\n",
    "df[\"state_fips\"] = '28'\n",
    "df[\"state_cen\"] = '64'\n",
    "df[\"state_ic\"] = '46'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stage\"] = \"GEN\"\n",
    "df[\"date\"] = \"2020-11-03\"\n",
    "df[\"year\"] = \"2020\"\n",
    "\n",
    "df[\"mode\"] = \"TOTAL\"\n",
    "\n",
    "df[\"special\"] = \"FALSE\"\n",
    "df[\"writein\"] = \"FALSE\"\n",
    "df['magnitude'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After county name fix, append on fips codes\n",
    "fips = pd.read_csv('../../../help-files/county-fips-codes.csv')\n",
    "\n",
    "fips['state'] = fips['state'].str.upper()\n",
    "fips = fips.applymap(str)\n",
    "df['county_name'] = df['county'].str.upper()\n",
    "df = df.drop(['county'], axis=1)\n",
    "df=df.applymap(lambda x: x.strip() if type(x)==str else x)\n",
    "df = pd.merge(df, fips, on = ['state','county_name'], \n",
    "             how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['jurisdiction_name'] = df['county_name']\n",
    "df['jurisdiction_fips'] = df['county_fips']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['office'].str.contains('US SENATE') | df['office'].str.contains('US PRESIDENT'), 'district'] = 'STATEWIDE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['district'].str.contains('1'), 'district'] = '001'\n",
    "df.loc[df['district'].str.contains('2'), 'district'] = '002'\n",
    "df.loc[df['district'].str.contains('3'), 'district'] = '003'\n",
    "df.loc[df['district'].str.contains('4'), 'district'] = '004'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['candidate'] = df['candidate'].replace({'ANTONIA ELLASON': 'ANTONIA ELIASON'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~(df.precinct.str.contains(\"TOTAL\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#manual fixes from scraping errors\n",
    "\n",
    "## US PRESIDENT\n",
    "df.loc[(df['precinct']=='Springridge')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='HINDS'),\n",
    "       'votes'] = 1553\n",
    "df.loc[(df['precinct']=='Beat 5/Steele')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='SCOTT'),\n",
    "       'votes'] = 324\n",
    "df.loc[(df['precinct']=='Pace')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='BOLIVAR'),\n",
    "       'votes'] = 228\n",
    "df.loc[(df['precinct']=='Sanford')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COVINGTON'),\n",
    "       'votes'] = 75\n",
    "df.loc[(df['precinct']=='Riceville')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='AMITE'),\n",
    "       'votes'] = 194\n",
    "df.loc[(df['precinct']=='South Liberty')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='AMITE'),\n",
    "       'votes'] = 102\n",
    "df.loc[(df['precinct']=='Kellis Store')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='KEMPER'),\n",
    "       'votes'] = 28\n",
    "\n",
    "df.loc[(df['precinct']=='Lula')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 125\n",
    "df.loc[(df['precinct']=='Cagle Crossing')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 21\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 374\n",
    "df.loc[(df['precinct']=='Clarksdale # 4')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1009\n",
    "df.loc[(df['precinct']=='Clarksdale # 4 North')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 98\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1021\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 71\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='JOSEPH R BIDEN')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 428\n",
    "\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 87\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 70\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 185\n",
    "df.loc[(df['precinct']=='Clarksdale # 4 North')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 59\n",
    "df.loc[(df['precinct']=='Rena Lara')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 181\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='DONALD J TRUMP')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 21\n",
    "\n",
    "df.loc[(df['precinct']=='Red House 55')&(df['candidate']=='BRIAN CARROLL')&(df['county_name']=='JEFFERSON DAVIS'),\n",
    "       'votes'] = 0\n",
    "df.loc[(df['precinct']=='Red House 55')&(df['candidate']=='DON BLANKENSHIP')&(df['county_name']=='JEFFERSON DAVIS'),\n",
    "       'votes'] = 0\n",
    "df.loc[(df['precinct']=='Precinct 54')&(df['candidate']=='KANYE WEST')&(df['county_name']=='HINDS'),\n",
    "       'votes'] = 1\n",
    "df.loc[(df['precinct']=='Crosby')&(df['candidate']=='KANYE WEST')&(df['county_name']=='AMITE'),\n",
    "       'votes'] = 1\n",
    "\n",
    "## US SENATE\n",
    "df.loc[(df['precinct']=='Learned')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='HINDS'),\n",
    "       'votes'] = 301\n",
    "df.loc[(df['precinct']=='Beat 3 Coxburg (A-Z)')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='HOLMES'),\n",
    "       'votes'] = 51\n",
    "df.loc[(df['precinct']=='Belzoni South - 5bs 5010')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='HUMPHREYS'),\n",
    "       'votes'] = 294\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 77\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 58\n",
    "df.loc[(df['precinct']=='Clarksdale # 4')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 46\n",
    "df.loc[(df['precinct']=='Clarksdale # 4 North')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 55\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 150\n",
    "df.loc[(df['precinct']=='Rena Lara')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 167\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='CINDY HYDE-SMITH')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 8\n",
    "\n",
    "\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 83\n",
    "df.loc[(df['precinct']=='Cagle Crossing')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 22\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 383\n",
    "df.loc[(df['precinct']=='Clarksdale # 4')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1056\n",
    "df.loc[(df['precinct']=='Clarksdale # 4 North')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 106\n",
    "df.loc[(df['precinct']=='Lyon')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 371\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1065\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='MIKE ESPY')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 449\n",
    "\n",
    "# US HOUSE\n",
    "df.loc[(df['precinct']=='Cagle Crossing')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 18\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 370\n",
    "df.loc[(df['precinct']=='Clarksdale # 4')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1028\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 1028\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 76\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='BENNIE G THOMPSON')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 440\n",
    "\n",
    "df.loc[(df['precinct']=='Dublin')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 81\n",
    "df.loc[(df['precinct']=='Clarksdale # 3')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 59\n",
    "df.loc[(df['precinct']=='Clarksdale # 4')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 52\n",
    "df.loc[(df['precinct']=='Clarksdale # 4 North')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 52\n",
    "df.loc[(df['precinct']=='Clarksdale # 5')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 161\n",
    "df.loc[(df['precinct']=='Rena Lara')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 167\n",
    "df.loc[(df['precinct']=='Sasse Street Fire Station')&(df['candidate']=='BRIAN FLOWERS')&(df['county_name']=='COAHOMA'),\n",
    "       'votes'] = 10\n",
    "\n",
    "df.loc[(df['precinct']=='Old Red Star')&(df['candidate']=='DOROTHY DOT BENFORD')&(df['county_name']=='LINCOLN'),\n",
    "       'votes'] = 62\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Readme Check - No issues \n",
    "df[\"readme_check\"] = \"FALSE\"\n",
    "df['precinct'] = df['precinct'].str.upper().str.strip()\n",
    "\n",
    "# Final step: Remove all trailing white space and put columns in correct order. \n",
    "df=df.applymap(lambda x: x.strip() if type(x)==str else x)\n",
    "\n",
    "df=df[[\"precinct\", \"office\", \"party_detailed\", \"party_simplified\", \"mode\", \"votes\", \"county_name\", \"county_fips\", \"jurisdiction_name\",\n",
    "                      \"jurisdiction_fips\", \"candidate\", \"district\", \"dataverse\", \"year\", \"stage\", \"state\", \"special\", \"writein\", \"state_po\",\n",
    "                      \"state_fips\", \"state_cen\", \"state_ic\", \"date\", \"readme_check\", \"magnitude\"]]\n",
    "\n",
    "import csv\n",
    "df.to_csv('../2020-ms-precinct-general.csv',quoting=csv.QUOTE_NONNUMERIC,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for x in df['precinct'].unique():\n",
    "    if 'TOTAL' in x:\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precinct</th>\n",
       "      <th>office</th>\n",
       "      <th>party_detailed</th>\n",
       "      <th>party_simplified</th>\n",
       "      <th>mode</th>\n",
       "      <th>votes</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>jurisdiction_name</th>\n",
       "      <th>jurisdiction_fips</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>special</th>\n",
       "      <th>writein</th>\n",
       "      <th>state_po</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>state_cen</th>\n",
       "      <th>state_ic</th>\n",
       "      <th>date</th>\n",
       "      <th>readme_check</th>\n",
       "      <th>magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>627</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>53</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
       "      <td>CONSTITUTION</td>\n",
       "      <td>OTHER</td>\n",
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       "      <td>1</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>MARSHALL</td>\n",
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       "      <td>2020-11-03</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
       "      <td>AMERICAN SOLIDARITY</td>\n",
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       "      <td>28093</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
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       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
       "      <td>INDEPENDENT</td>\n",
       "      <td>OTHER</td>\n",
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       "      <td>2</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>28093</td>\n",
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       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25848</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>665</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25849</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>197</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25850</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>LIBERTARIAN</td>\n",
       "      <td>LIBERTARIAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>4</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25851</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US HOUSE</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>202</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25852</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US HOUSE</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>647</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24726 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  precinct        office       party_detailed  \\\n",
       "0                    1 North Holly Springs  US PRESIDENT             DEMOCRAT   \n",
       "1                    1 North Holly Springs  US PRESIDENT           REPUBLICAN   \n",
       "2                    1 North Holly Springs  US PRESIDENT         CONSTITUTION   \n",
       "3                    1 North Holly Springs  US PRESIDENT  AMERICAN SOLIDARITY   \n",
       "4                    1 North Holly Springs  US PRESIDENT          INDEPENDENT   \n",
       "...                                    ...           ...                  ...   \n",
       "25848  Washington County Convention Center     US SENATE             DEMOCRAT   \n",
       "25849  Washington County Convention Center     US SENATE           REPUBLICAN   \n",
       "25850  Washington County Convention Center     US SENATE          LIBERTARIAN   \n",
       "25851  Washington County Convention Center      US HOUSE           REPUBLICAN   \n",
       "25852  Washington County Convention Center      US HOUSE             DEMOCRAT   \n",
       "\n",
       "      party_simplified   mode  votes county_name county_fips  \\\n",
       "0             DEMOCRAT  TOTAL    627    MARSHALL       28093   \n",
       "1           REPUBLICAN  TOTAL     53    MARSHALL       28093   \n",
       "2                OTHER  TOTAL      1    MARSHALL       28093   \n",
       "3                OTHER  TOTAL      2    MARSHALL       28093   \n",
       "4                OTHER  TOTAL      2    MARSHALL       28093   \n",
       "...                ...    ...    ...         ...         ...   \n",
       "25848         DEMOCRAT  TOTAL    665  WASHINGTON       28151   \n",
       "25849       REPUBLICAN  TOTAL    197  WASHINGTON       28151   \n",
       "25850      LIBERTARIAN  TOTAL      4  WASHINGTON       28151   \n",
       "25851       REPUBLICAN  TOTAL    202  WASHINGTON       28151   \n",
       "25852         DEMOCRAT  TOTAL    647  WASHINGTON       28151   \n",
       "\n",
       "      jurisdiction_name jurisdiction_fips  ...        state special writein  \\\n",
       "0              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "1              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "2              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "3              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "4              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "...                 ...               ...  ...          ...     ...     ...   \n",
       "25848        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25849        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25850        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25851        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25852        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "\n",
       "      state_po state_fips state_cen state_ic        date readme_check  \\\n",
       "0           MS         28        64       46  2020-11-03        FALSE   \n",
       "1           MS         28        64       46  2020-11-03        FALSE   \n",
       "2           MS         28        64       46  2020-11-03        FALSE   \n",
       "3           MS         28        64       46  2020-11-03        FALSE   \n",
       "4           MS         28        64       46  2020-11-03        FALSE   \n",
       "...        ...        ...       ...      ...         ...          ...   \n",
       "25848       MS         28        64       46  2020-11-03        FALSE   \n",
       "25849       MS         28        64       46  2020-11-03        FALSE   \n",
       "25850       MS         28        64       46  2020-11-03        FALSE   \n",
       "25851       MS         28        64       46  2020-11-03        FALSE   \n",
       "25852       MS         28        64       46  2020-11-03        FALSE   \n",
       "\n",
       "      magnitude  \n",
       "0             1  \n",
       "1             1  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "...         ...  \n",
       "25848         1  \n",
       "25849         1  \n",
       "25850         1  \n",
       "25851         1  \n",
       "25852         1  \n",
       "\n",
       "[24726 rows x 25 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precinct</th>\n",
       "      <th>office</th>\n",
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       "      <td>1 North Holly Springs</td>\n",
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       "      <td>REPUBLICAN</td>\n",
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       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 North Holly Springs</td>\n",
       "      <td>US PRESIDENT</td>\n",
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       "      <td>MARSHALL</td>\n",
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       "    <tr>\n",
       "      <th>25846</th>\n",
       "      <td>Washington County Convention Center</td>\n",
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       "      <th>25847</th>\n",
       "      <td>Washington County Convention Center</td>\n",
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       "    <tr>\n",
       "      <th>25848</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>DEMOCRAT</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>665</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25849</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>197</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25850</th>\n",
       "      <td>Washington County Convention Center</td>\n",
       "      <td>US SENATE</td>\n",
       "      <td>LIBERTARIAN</td>\n",
       "      <td>LIBERTARIAN</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>4</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>28151</td>\n",
       "      <td>...</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>MS</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21192 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  precinct        office       party_detailed  \\\n",
       "0                    1 North Holly Springs  US PRESIDENT             DEMOCRAT   \n",
       "1                    1 North Holly Springs  US PRESIDENT           REPUBLICAN   \n",
       "2                    1 North Holly Springs  US PRESIDENT         CONSTITUTION   \n",
       "3                    1 North Holly Springs  US PRESIDENT  AMERICAN SOLIDARITY   \n",
       "4                    1 North Holly Springs  US PRESIDENT          INDEPENDENT   \n",
       "...                                    ...           ...                  ...   \n",
       "25846  Washington County Convention Center  US PRESIDENT          INDEPENDENT   \n",
       "25847  Washington County Convention Center  US PRESIDENT          INDEPENDENT   \n",
       "25848  Washington County Convention Center     US SENATE             DEMOCRAT   \n",
       "25849  Washington County Convention Center     US SENATE           REPUBLICAN   \n",
       "25850  Washington County Convention Center     US SENATE          LIBERTARIAN   \n",
       "\n",
       "      party_simplified   mode  votes county_name county_fips  \\\n",
       "0             DEMOCRAT  TOTAL    627    MARSHALL       28093   \n",
       "1           REPUBLICAN  TOTAL     53    MARSHALL       28093   \n",
       "2                OTHER  TOTAL      1    MARSHALL       28093   \n",
       "3                OTHER  TOTAL      2    MARSHALL       28093   \n",
       "4                OTHER  TOTAL      2    MARSHALL       28093   \n",
       "...                ...    ...    ...         ...         ...   \n",
       "25846            OTHER  TOTAL      0  WASHINGTON       28151   \n",
       "25847            OTHER  TOTAL      5  WASHINGTON       28151   \n",
       "25848         DEMOCRAT  TOTAL    665  WASHINGTON       28151   \n",
       "25849       REPUBLICAN  TOTAL    197  WASHINGTON       28151   \n",
       "25850      LIBERTARIAN  TOTAL      4  WASHINGTON       28151   \n",
       "\n",
       "      jurisdiction_name jurisdiction_fips  ...        state special writein  \\\n",
       "0              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "1              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "2              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "3              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "4              MARSHALL             28093  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "...                 ...               ...  ...          ...     ...     ...   \n",
       "25846        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25847        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25848        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25849        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "25850        WASHINGTON             28151  ...  MISSISSIPPI   FALSE   FALSE   \n",
       "\n",
       "      state_po state_fips state_cen state_ic        date readme_check  \\\n",
       "0           MS         28        64       46  2020-11-03        FALSE   \n",
       "1           MS         28        64       46  2020-11-03        FALSE   \n",
       "2           MS         28        64       46  2020-11-03        FALSE   \n",
       "3           MS         28        64       46  2020-11-03        FALSE   \n",
       "4           MS         28        64       46  2020-11-03        FALSE   \n",
       "...        ...        ...       ...      ...         ...          ...   \n",
       "25846       MS         28        64       46  2020-11-03        FALSE   \n",
       "25847       MS         28        64       46  2020-11-03        FALSE   \n",
       "25848       MS         28        64       46  2020-11-03        FALSE   \n",
       "25849       MS         28        64       46  2020-11-03        FALSE   \n",
       "25850       MS         28        64       46  2020-11-03        FALSE   \n",
       "\n",
       "      magnitude  \n",
       "0             1  \n",
       "1             1  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "...         ...  \n",
       "25846         1  \n",
       "25847         1  \n",
       "25848         1  \n",
       "25849         1  \n",
       "25850         1  \n",
       "\n",
       "[21192 rows x 25 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = df[df['office'].str.contains('US SENATE') | df['office'].str.contains('US PRESIDENT')]\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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