SF Elections.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SF Voter Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll be taking a look at some voter data from the 2020 election and comparing it to historical data. The focus will be on creating a reusable pipeline to analyze election results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tl;dr:\n",
    "\n",
    "Analysis of the 2020 SF Election\n",
    "\n",
    "Summary:\n",
    "\n",
    "Candidate 2/B won a total of 31 precincts, receiving almost 15 thousand votes in the 11th Preliminary Report of RCV tabulation. From this analysis, the following insights were gleaned:\n",
    "    \n",
    "* In 2016 and 2020, Candidate 2/B won precincts 24, 12,  9, 19, 53, 41, 22, 34, 31, 28, 36, 11, 13,  8, 35, 33, 25,26, 18, 16,  3, 27, 32,  2 both times.\n",
    "* In 2016 and 2020, Candidate 2/B lost precincts 43, 4, 45, 47, 48, 46, 49 both times.\n",
    "* Candidate 2/B won 2 additional precincts in 2020 (total went from 29 to 31)\n",
    "* In Precincts 25 and 11, Candidate 2/B increased their vote count from 2016 to 2020 by 208 and 201 respectively, the highest of all the precinct totals.\n",
    "* Candidate 2/B won precincts 6,  7, 15, 23, 42, 44, 52 in 2020. Precincts that the candidate lost in 2016.\n",
    "\n",
    "Tools/Packages Used:\n",
    "\n",
    "Nothing special, but we developed some new functions specific to analyzing voter/election data. All are reusable and a part of the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set()\n",
    "sns.set_context(\"talk\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by importing our data. Please keep in mind that due to the nature of the 2020 election, results take a little longer than normal. We'll be using Preliminary Report 11 from the SF Department of Elections website (https://sfelections.sfgov.org/november-3-2020-election-results-detailed-reports). It is possible that these numbers will change as more votes are processed. This data was uploaded to the SF DOE website on 11/10/2020 at 3:46 PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precinct</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1055</td>\n",
       "      <td>836</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>409</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1017</td>\n",
       "      <td>837</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>439</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Precinct  Registered Voters  Total Votes  Undervotes  Overvotes  \\\n",
       "0         1                605          477          38          0   \n",
       "1         2               1055          836          68          1   \n",
       "2         3               1017          837          55          4   \n",
       "3         4               1098          814          88          3   \n",
       "4         6                833          629          76          0   \n",
       "\n",
       "   Candidate 1  Candidate 2  Candidate 3  Candidate 4  Write-in  \n",
       "0          233          214           30            0         0  \n",
       "1          348          409           79            0         0  \n",
       "2          328          439           70            0         0  \n",
       "3          403          336           75            0         0  \n",
       "4          219          343           67            0         0  "
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string2020=r\"C:\\Users\\am513\\Desktop\\LinkedIn\\DataSciPortfolio\\2020.csv\"\n",
    "result2020=pd.read_csv(string2020)\n",
    "result2020.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that this file looks a little different than the Statement of the Vote Excel file that you get from the DOE website. Other than that, the data is 100% the same. There are a couple of reasons for this: \n",
    "* The original file isn't friendly for data science\n",
    "* I want to protect exactly what candidates and contest this data is from"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precinct seems to be the unique key for this data set. Let's double check and make it the index if this is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(result2020[\"Precinct\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1055</td>\n",
       "      <td>836</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>409</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017</td>\n",
       "      <td>837</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>439</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "1                       605          477          38          0          233   \n",
       "2                      1055          836          68          1          348   \n",
       "3                      1017          837          55          4          328   \n",
       "4                      1098          814          88          3          403   \n",
       "6                       833          629          76          0          219   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in  \n",
       "Precinct                                                   \n",
       "1                 214           30            0         0  \n",
       "2                 409           79            0         0  \n",
       "3                 439           70            0         0  \n",
       "4                 336           75            0         0  \n",
       "6                 343           67            0         0  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020=result2020.set_index(\"Precinct\")\n",
    "result2020.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see who won this contest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Candidate 2    14998\n",
       "Candidate 1    13285\n",
       "Candidate 3     2712\n",
       "Write-in          15\n",
       "Candidate 4       15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[[\"Candidate 1\",\"Candidate 2\",\"Candidate 3\",\"Candidate 4\",\"Write-in\"]].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like Candidate 2 won. Congrats! It also seems that Write-In and Candidate 4 have the same tally of votes. It's probable that Candidate 4 is actually a write in candidate. Let's rework this into a function that works regardless of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "def who_won(dataframe,cand=[]):\n",
    "    num_of_cands=len(cand)\n",
    "    assert (num_of_cands>0),\"Please enter the number of candidates. (Must be more than 0)\"\n",
    "    if num_of_cands==1:\n",
    "        return cand[0]\n",
    "    if num_of_cands==2:\n",
    "        return dataframe[[cand[0],cand[1]]].sum().sort_values(ascending=False).index[0]\n",
    "    if num_of_cands==3:\n",
    "        return dataframe[[cand[0],cand[1],cand[2]]].sum().sort_values(ascending=False).index[0]\n",
    "    if num_of_cands==4:\n",
    "        return dataframe[[cand[0],cand[1],cand[2],cand[3]]].sum().sort_values(ascending=False).index[0]\n",
    "    if num_of_cands==5:\n",
    "        return dataframe[[cand[0],cand[1],cand[2],cand[3],cand[4]]].sum().sort_values(ascending=False).index[0]\n",
    "    if num_of_cands==6:\n",
    "        return dataframe[[cand[0],cand[1],cand[2],cand[3],cand[4],cand[5]]].sum().sort_values(ascending=False).index[0]\n",
    "    if num_of_cands==7:\n",
    "        return dataframe[[cand[0],cand[1],cand[2],cand[3],cand[4],cand[5],cand[6]]].sum().sort_values(ascending=False).index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Candidate 2'"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_won(dataframe=result2020,cand=[\"Candidate 1\",\"Candidate 2\",\"Candidate 3\",\"Candidate 4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at turn out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Registered Voters    41337\n",
       "Total Votes          31010\n",
       "dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[[\"Registered Voters\",\"Total Votes\"]].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.02"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnout=(result2020[[\"Registered Voters\",\"Total Votes\"]].sum().sort_values(ascending=False)[1]/result2020[[\"Registered Voters\",\"Total Votes\"]].sum().sort_values(ascending=False)[0])*100\n",
    "np.round(turnout,decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "75% turn out isn't horrible, but it is below the SF average of 86% for the 2020 election"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precinct Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how our candidates did in each precinct using ```idxmax```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1055</td>\n",
       "      <td>836</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>409</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017</td>\n",
       "      <td>837</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>439</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "1                       605          477          38          0          233   \n",
       "2                      1055          836          68          1          348   \n",
       "3                      1017          837          55          4          328   \n",
       "4                      1098          814          88          3          403   \n",
       "6                       833          629          76          0          219   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  \n",
       "Precinct                                                                \n",
       "1                 214           30            0         0  Candidate 1  \n",
       "2                 409           79            0         0  Candidate 2  \n",
       "3                 439           70            0         0  Candidate 2  \n",
       "4                 336           75            0         0  Candidate 1  \n",
       "6                 343           67            0         0  Candidate 2  "
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[\"Who won?\"]=result2020[[\"Candidate 1\",\"Candidate 2\", \"Candidate 3\",\"Candidate 4\"]].idxmax(axis=1)\n",
    "result2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Candidate 2    31\n",
       "Candidate 1    12\n",
       "Name: Who won?, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[\"Who won?\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our winner Candidate 2 won 31 precincts and Candidate 1 won 12 precincts. It seems that Candidate 3 and 4 did not get enough votes to win a precinct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1055</td>\n",
       "      <td>836</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>409</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017</td>\n",
       "      <td>837</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>439</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "1                       605          477          38          0          233   \n",
       "2                      1055          836          68          1          348   \n",
       "3                      1017          837          55          4          328   \n",
       "4                      1098          814          88          3          403   \n",
       "6                       833          629          76          0          219   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  Diff  \n",
       "Precinct                                                                      \n",
       "1                 214           30            0         0  Candidate 1   -19  \n",
       "2                 409           79            0         0  Candidate 2    61  \n",
       "3                 439           70            0         0  Candidate 2   111  \n",
       "4                 336           75            0         0  Candidate 1   -67  \n",
       "6                 343           67            0         0  Candidate 2   124  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[\"Diff\"]=result2020[\"Candidate 2\"]-result2020[\"Candidate 1\"]\n",
    "result2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>949</td>\n",
       "      <td>732</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>363</td>\n",
       "      <td>308</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1061</td>\n",
       "      <td>818</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>397</td>\n",
       "      <td>353</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1160</td>\n",
       "      <td>863</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>416</td>\n",
       "      <td>376</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>940</td>\n",
       "      <td>759</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>370</td>\n",
       "      <td>334</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1000</td>\n",
       "      <td>781</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "      <td>381</td>\n",
       "      <td>345</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1093</td>\n",
       "      <td>837</td>\n",
       "      <td>78</td>\n",
       "      <td>8</td>\n",
       "      <td>409</td>\n",
       "      <td>377</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1148</td>\n",
       "      <td>848</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>394</td>\n",
       "      <td>368</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1095</td>\n",
       "      <td>827</td>\n",
       "      <td>66</td>\n",
       "      <td>6</td>\n",
       "      <td>385</td>\n",
       "      <td>359</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>911</td>\n",
       "      <td>650</td>\n",
       "      <td>73</td>\n",
       "      <td>3</td>\n",
       "      <td>302</td>\n",
       "      <td>296</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>765</td>\n",
       "      <td>608</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>273</td>\n",
       "      <td>272</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "4                      1098          814          88          3          403   \n",
       "47                      949          732          72          2          363   \n",
       "14                     1061          818          67          1          397   \n",
       "46                     1160          863          65          5          416   \n",
       "45                      940          759          56          2          370   \n",
       "49                     1000          781          63          5          381   \n",
       "48                     1093          837          78          8          409   \n",
       "29                     1148          848          82          4          394   \n",
       "43                     1095          827          66          6          385   \n",
       "1                       605          477          38          0          233   \n",
       "17                      911          650          73          3          302   \n",
       "21                      765          608          63          2          273   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  Diff  \n",
       "Precinct                                                                      \n",
       "4                 336           75            0         0  Candidate 1   -67  \n",
       "47                308           61            0         0  Candidate 1   -55  \n",
       "14                353           68            0         0  Candidate 1   -44  \n",
       "46                376           70            1         1  Candidate 1   -40  \n",
       "45                334           55            0         0  Candidate 1   -36  \n",
       "49                345           55            0         0  Candidate 1   -36  \n",
       "48                377           51            0         0  Candidate 1   -32  \n",
       "29                368           86            0         0  Candidate 1   -26  \n",
       "43                359           82            1         1  Candidate 1   -26  \n",
       "1                 214           30            0         0  Candidate 1   -19  \n",
       "17                296           52            0         0  Candidate 1    -6  \n",
       "21                272           62            1         1  Candidate 1    -1  "
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cand1won=result2020[result2020[\"Diff\"]<0].sort_values(\"Diff\")\n",
    "cand1won"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result2020[result2020[\"Diff\"]<0].sort_values(\"Diff\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like Candidate 2 didn't do so well in the region that contains the precincts labeled 40. Let's do some visualizations further explore this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stack_data(dataframe,cands=[],index=\"\"):\n",
    "    stackked=dataframe[cands].stack().reset_index().set_index(index)\n",
    "    return stackked.rename(columns={\"level_1\":\"Candidate\",0:\"Number of Votes\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Number of Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Candidate  Number of Votes\n",
       "Precinct                              \n",
       "1         Candidate 1              233\n",
       "1         Candidate 2              214\n",
       "2         Candidate 1              348\n",
       "2         Candidate 2              409\n",
       "3         Candidate 1              328"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_data(result2020,[\"Candidate 1\",\"Candidate 2\"],\"Precinct\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              level_1    0\n",
       "Precinct                  \n",
       "1         Candidate 1  233\n",
       "1         Candidate 2  214\n",
       "2         Candidate 1  348\n",
       "2         Candidate 2  409\n",
       "3         Candidate 1  328"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack2020=result2020[[\"Candidate 1\",\"Candidate 2\"]].stack().reset_index().set_index(\"Precinct\")\n",
    "stack2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Number of Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Candidate  Number of Votes\n",
       "Precinct                              \n",
       "1         Candidate 1              233\n",
       "1         Candidate 2              214\n",
       "2         Candidate 1              348\n",
       "2         Candidate 2              409\n",
       "3         Candidate 1              328"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack2020=stack2020.rename(columns={\"level_1\":\"Candidate\",0:\"Number of Votes\"})\n",
    "stack2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>605</td>\n",
       "      <td>477</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>214</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1055</td>\n",
       "      <td>836</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>409</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017</td>\n",
       "      <td>837</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>328</td>\n",
       "      <td>439</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098</td>\n",
       "      <td>814</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>336</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "1                       605          477          38          0          233   \n",
       "2                      1055          836          68          1          348   \n",
       "3                      1017          837          55          4          328   \n",
       "4                      1098          814          88          3          403   \n",
       "6                       833          629          76          0          219   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  Diff  \n",
       "Precinct                                                                      \n",
       "1                 214           30            0         0  Candidate 1   -19  \n",
       "2                 409           79            0         0  Candidate 2    61  \n",
       "3                 439           70            0         0  Candidate 2   111  \n",
       "4                 336           75            0         0  Candidate 1   -67  \n",
       "6                 343           67            0         0  Candidate 2   124  "
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like using ```stack``` worked. Now let's visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prec=stack2020.index\n",
    "plt.figure(figsize=(40,10))\n",
    "sns.barplot(x=prec,y=\"Number of Votes\",hue=\"Candidate\",data=stack2020)\n",
    "plt.title(\"2020 Results\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preccand1=cand1won.index\n",
    "\n",
    "cand12020=stack2020.loc[preccand1]\n",
    "indexprec=cand12020.index\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=indexprec,y=\"Number of Votes\",hue=\"Candidate\",data=cand12020)\n",
    "plt.title(\"Precincts where Candidate 1 Won\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! Precinct 21 was only decided by 1 vote! That's razer close (and could change as more votes are processed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison to 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do a comparison to the 2016 election. This is gonna be a little funky with the privacy/data adjustments, but the most important thing to remember is **Candidate 2=Candidate B**. No other similarities (A=1, etc.) exist. Again, the data is from the DOE website (https://sfelections.sfgov.org/november-8-2016-election-results-detailed-reports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precinct</th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>ReportingType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>586</td>\n",
       "      <td>159</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>Election Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>586</td>\n",
       "      <td>287</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>112</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>VBM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>291</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>124</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>Election Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>519</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>228</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>VBM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>971</td>\n",
       "      <td>263</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>Election Day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Precinct  Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "0         1           586           159          20          0            4   \n",
       "1         1           586           287          18          0           19   \n",
       "2         2           996           291          26          1           26   \n",
       "3         2           996           519          25          2           43   \n",
       "4         3           971           263          22          0           22   \n",
       "\n",
       "   Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN ReportingType  \n",
       "0           54           25            5           48         0  Election Day  \n",
       "1          112           21           10           99         0           VBM  \n",
       "2          124           22           11           77         1  Election Day  \n",
       "3          228           41           24          143         3           VBM  \n",
       "4           83           34           15           78         2  Election Day  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string2016=r\"C:\\Users\\am513\\Desktop\\LinkedIn\\DataSciPortfolio\\2016.csv\"\n",
    "result2016=pd.read_csv(string2016)\n",
    "results2016=pd.read_csv(string2016)\n",
    "result2016.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start off doing a lot of the same analysis and cleaning we did with the 2020 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1172</td>\n",
       "      <td>446</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>166</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>810</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>352</td>\n",
       "      <td>63</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1942</td>\n",
       "      <td>781</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>305</td>\n",
       "      <td>75</td>\n",
       "      <td>36</td>\n",
       "      <td>236</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982</td>\n",
       "      <td>763</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>232</td>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>292</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1616</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "1                 1172           446          38          0           23   \n",
       "2                 1992           810          51          3           69   \n",
       "3                 1942           781          56          1           52   \n",
       "4                 1982           763          57          2           69   \n",
       "6                 1616           600          67          3           58   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \n",
       "Precinct                                                                \n",
       "1                 166           46           15          147         0  \n",
       "2                 352           63           35          220         4  \n",
       "3                 305           75           36          236         2  \n",
       "4                 232           55           39          292         6  \n",
       "6                 166           63           48          168         2  "
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2016=result2016.groupby(\"Precinct\").sum()#.drop([\"Registration\",\"Ballots Cast\"],axis=1)\n",
    "results2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1172</td>\n",
       "      <td>446</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>166</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>586</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>810</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>352</td>\n",
       "      <td>63</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>996</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1942</td>\n",
       "      <td>781</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>305</td>\n",
       "      <td>75</td>\n",
       "      <td>36</td>\n",
       "      <td>236</td>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982</td>\n",
       "      <td>763</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>232</td>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>292</td>\n",
       "      <td>6</td>\n",
       "      <td>971</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1616</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>991</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "1                 1172           446          38          0           23   \n",
       "2                 1992           810          51          3           69   \n",
       "3                 1942           781          56          1           52   \n",
       "4                 1982           763          57          2           69   \n",
       "6                 1616           600          67          3           58   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \\\n",
       "Precinct                                                                 \n",
       "1                 166           46           15          147         0   \n",
       "2                 352           63           35          220         4   \n",
       "3                 305           75           36          236         2   \n",
       "4                 232           55           39          292         6   \n",
       "6                 166           63           48          168         2   \n",
       "\n",
       "          Registered Voters  Total Votes  \n",
       "Precinct                                  \n",
       "1                       586          287  \n",
       "2                       996          291  \n",
       "3                       996          519  \n",
       "4                       971          263  \n",
       "6                       991          329  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2016[\"Registered Voters\"]=result2016[\"Registration\"]\n",
    "results2016[\"Total Votes\"]=result2016[\"Ballots Cast\"]\n",
    "results2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Candidate B'"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_won(results2016,cand=[\"Candidate A\",\"Candidate B\",\"Candidate C\",\"Candidate C\",\"Candidate E\",\"WRITE-IN\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2016, Candidate B/Candidate 2 won as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Number of Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate B</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candidate E</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate B</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candidate E</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Candidate B</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Candidate  Number of Votes\n",
       "Precinct                              \n",
       "1         Candidate B              166\n",
       "1         Candidate E              147\n",
       "2         Candidate B              352\n",
       "2         Candidate E              220\n",
       "3         Candidate B              305"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack2016=stack_data(results2016,[\"Candidate B\",\"Candidate E\"],\"Precinct\")\n",
    "stack2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_2_cands(dataframe,year):\n",
    "    precinctlist=dataframe.index\n",
    "    plt.figure(figsize=(40,10))\n",
    "    sns.barplot(x=precinctlist,y=\"Number of Votes\",hue=\"Candidate\",data=dataframe)\n",
    "    plt.title(\"{} Results\".format(year))\n",
    "    return plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_2_cands(stack2016,2016);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff(dataframe,cand=[]):\n",
    "    #cand in position 0 will be subtracted from\n",
    "    dataframe[\"Diff\"]=dataframe[cand[0]]-dataframe[cand[1]]\n",
    "    \n",
    "def num_precinct_lost(dataframe):\n",
    "    return len(dataframe[dataframe[\"Diff\"]<0].sort_values(\"Diff\"))\n",
    "    \n",
    "def precinct_lost(dataframe):\n",
    "    return dataframe[dataframe[\"Diff\"]<0].sort_values(\"Diff\")\n",
    "    \n",
    "def who_won_pct(dataframe,cand=[]):\n",
    "    dataframe[\"Who won?\"]=dataframe[cand].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1172</td>\n",
       "      <td>446</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>166</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>586</td>\n",
       "      <td>287</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>810</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>352</td>\n",
       "      <td>63</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>996</td>\n",
       "      <td>291</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1942</td>\n",
       "      <td>781</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>305</td>\n",
       "      <td>75</td>\n",
       "      <td>36</td>\n",
       "      <td>236</td>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>519</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982</td>\n",
       "      <td>763</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>232</td>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>292</td>\n",
       "      <td>6</td>\n",
       "      <td>971</td>\n",
       "      <td>263</td>\n",
       "      <td>-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1616</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>991</td>\n",
       "      <td>329</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "1                 1172           446          38          0           23   \n",
       "2                 1992           810          51          3           69   \n",
       "3                 1942           781          56          1           52   \n",
       "4                 1982           763          57          2           69   \n",
       "6                 1616           600          67          3           58   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \\\n",
       "Precinct                                                                 \n",
       "1                 166           46           15          147         0   \n",
       "2                 352           63           35          220         4   \n",
       "3                 305           75           36          236         2   \n",
       "4                 232           55           39          292         6   \n",
       "6                 166           63           48          168         2   \n",
       "\n",
       "          Registered Voters  Total Votes  Diff  \n",
       "Precinct                                        \n",
       "1                       586          287    19  \n",
       "2                       996          291   132  \n",
       "3                       996          519    69  \n",
       "4                       971          263   -60  \n",
       "6                       991          329    -2  "
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(results2016,[\"Candidate B\",\"Candidate E\"])\n",
    "results2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_precinct_lost(results2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2112</td>\n",
       "      <td>801</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>207</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>326</td>\n",
       "      <td>2</td>\n",
       "      <td>1029</td>\n",
       "      <td>511</td>\n",
       "      <td>-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982</td>\n",
       "      <td>763</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>232</td>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>292</td>\n",
       "      <td>6</td>\n",
       "      <td>971</td>\n",
       "      <td>263</td>\n",
       "      <td>-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1862</td>\n",
       "      <td>753</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>228</td>\n",
       "      <td>79</td>\n",
       "      <td>31</td>\n",
       "      <td>286</td>\n",
       "      <td>3</td>\n",
       "      <td>954</td>\n",
       "      <td>447</td>\n",
       "      <td>-58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1854</td>\n",
       "      <td>734</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>217</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>911</td>\n",
       "      <td>412</td>\n",
       "      <td>-58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1984</td>\n",
       "      <td>727</td>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>58</td>\n",
       "      <td>201</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>907</td>\n",
       "      <td>237</td>\n",
       "      <td>-46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1856</td>\n",
       "      <td>713</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>239</td>\n",
       "      <td>69</td>\n",
       "      <td>24</td>\n",
       "      <td>278</td>\n",
       "      <td>3</td>\n",
       "      <td>954</td>\n",
       "      <td>254</td>\n",
       "      <td>-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1820</td>\n",
       "      <td>746</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>205</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>240</td>\n",
       "      <td>2</td>\n",
       "      <td>809</td>\n",
       "      <td>402</td>\n",
       "      <td>-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1908</td>\n",
       "      <td>646</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>177</td>\n",
       "      <td>71</td>\n",
       "      <td>38</td>\n",
       "      <td>204</td>\n",
       "      <td>3</td>\n",
       "      <td>971</td>\n",
       "      <td>444</td>\n",
       "      <td>-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2072</td>\n",
       "      <td>791</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>237</td>\n",
       "      <td>73</td>\n",
       "      <td>52</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>911</td>\n",
       "      <td>273</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1900</td>\n",
       "      <td>747</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>231</td>\n",
       "      <td>70</td>\n",
       "      <td>42</td>\n",
       "      <td>251</td>\n",
       "      <td>2</td>\n",
       "      <td>907</td>\n",
       "      <td>449</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1796</td>\n",
       "      <td>671</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>105</td>\n",
       "      <td>208</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "      <td>219</td>\n",
       "      <td>1</td>\n",
       "      <td>853</td>\n",
       "      <td>192</td>\n",
       "      <td>-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1562</td>\n",
       "      <td>506</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>153</td>\n",
       "      <td>50</td>\n",
       "      <td>27</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>991</td>\n",
       "      <td>434</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1716</td>\n",
       "      <td>667</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>208</td>\n",
       "      <td>67</td>\n",
       "      <td>40</td>\n",
       "      <td>216</td>\n",
       "      <td>4</td>\n",
       "      <td>1029</td>\n",
       "      <td>234</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1616</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>991</td>\n",
       "      <td>329</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "43                2112           801          53          4           63   \n",
       "4                 1982           763          57          2           69   \n",
       "45                1862           753          42          6           56   \n",
       "47                1854           734          40          8           75   \n",
       "48                1984           727          73         10           58   \n",
       "44                1856           713          39          1           50   \n",
       "15                1820           746          76          1           58   \n",
       "23                1908           646          59          1           70   \n",
       "46                2072           791          60          7           84   \n",
       "49                1900           747          59          7           73   \n",
       "52                1796           671          38          7          105   \n",
       "7                 1562           506          60          7           39   \n",
       "42                1716           667          45          5           59   \n",
       "6                 1616           600          67          3           58   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \\\n",
       "Precinct                                                                 \n",
       "43                207           75           50          326         2   \n",
       "4                 232           55           39          292         6   \n",
       "45                228           79           31          286         3   \n",
       "47                217           55           44          275         0   \n",
       "48                201           71           50          247         2   \n",
       "44                239           69           24          278         3   \n",
       "15                205           85           45          240         2   \n",
       "23                177           71           38          204         3   \n",
       "46                237           73           52          262         1   \n",
       "49                231           70           42          251         2   \n",
       "52                208           47           33          219         1   \n",
       "7                 153           50           27          161         1   \n",
       "42                208           67           40          216         4   \n",
       "6                 166           63           48          168         2   \n",
       "\n",
       "          Registered Voters  Total Votes  Diff  \n",
       "Precinct                                        \n",
       "43                     1029          511  -119  \n",
       "4                       971          263   -60  \n",
       "45                      954          447   -58  \n",
       "47                      911          412   -58  \n",
       "48                      907          237   -46  \n",
       "44                      954          254   -39  \n",
       "15                      809          402   -35  \n",
       "23                      971          444   -27  \n",
       "46                      911          273   -25  \n",
       "49                      907          449   -20  \n",
       "52                      853          192   -11  \n",
       "7                       991          434    -8  \n",
       "42                     1029          234    -8  \n",
       "6                       991          329    -2  "
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_lost(results2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Who won?</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1172</td>\n",
       "      <td>446</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>166</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>586</td>\n",
       "      <td>287</td>\n",
       "      <td>19</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>810</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>69</td>\n",
       "      <td>352</td>\n",
       "      <td>63</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>996</td>\n",
       "      <td>291</td>\n",
       "      <td>132</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1942</td>\n",
       "      <td>781</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>305</td>\n",
       "      <td>75</td>\n",
       "      <td>36</td>\n",
       "      <td>236</td>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>519</td>\n",
       "      <td>69</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982</td>\n",
       "      <td>763</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>232</td>\n",
       "      <td>55</td>\n",
       "      <td>39</td>\n",
       "      <td>292</td>\n",
       "      <td>6</td>\n",
       "      <td>971</td>\n",
       "      <td>263</td>\n",
       "      <td>-60</td>\n",
       "      <td>Candidate E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1616</td>\n",
       "      <td>600</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>991</td>\n",
       "      <td>329</td>\n",
       "      <td>-2</td>\n",
       "      <td>Candidate E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "1                 1172           446          38          0           23   \n",
       "2                 1992           810          51          3           69   \n",
       "3                 1942           781          56          1           52   \n",
       "4                 1982           763          57          2           69   \n",
       "6                 1616           600          67          3           58   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \\\n",
       "Precinct                                                                 \n",
       "1                 166           46           15          147         0   \n",
       "2                 352           63           35          220         4   \n",
       "3                 305           75           36          236         2   \n",
       "4                 232           55           39          292         6   \n",
       "6                 166           63           48          168         2   \n",
       "\n",
       "          Registered Voters  Total Votes  Diff     Who won?  \n",
       "Precinct                                                     \n",
       "1                       586          287    19  Candidate B  \n",
       "2                       996          291   132  Candidate B  \n",
       "3                       996          519    69  Candidate B  \n",
       "4                       971          263   -60  Candidate E  \n",
       "6                       991          329    -2  Candidate E  "
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_won_pct(results2016,cand=[\"Candidate A\",\"Candidate B\",\"Candidate C\",\"Candidate C\",\"Candidate E\",\"WRITE-IN\"])\n",
    "results2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preccand1=cand1won.index\n",
    "\n",
    "cand12020=stack2020.loc[preccand1]\n",
    "indexprec=cand12020.index\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=indexprec,y=\"Number of Votes\",hue=\"Candidate\",data=cand12020)\n",
    "plt.title(\"Precincts where Candidate 1 Won\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_2_cands(dataframe,cand=[]):\n",
    "    lost_precincts=precinct_lost(dataframe).index\n",
    "    stackdf=stack_data(dataframe,cand,\"Precinct\")\n",
    "    pull_lost=stackdf.loc[lost_precincts]\n",
    "    index_prec=pull_lost.index\n",
    "    plt.figure(figsize=(20,10))\n",
    "    sns.barplot(x=index_prec,y=\"Number of Votes\",hue=\"Candidate\",data=pull_lost)\n",
    "    plt.title(\"Precincts where {} Won\".format(cand[1]))\n",
    "    return plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_2_cands(results2016,[\"Candidate B\",\"Candidate E\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to 2020, the precincts labeled 40 also did not vote for Candidate 2/B in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "def num_precinct_won(dataframe):\n",
    "    return len(dataframe[dataframe[\"Diff\"]>0].sort_values(\"Diff\"))\n",
    "    \n",
    "def precinct_won(dataframe):\n",
    "    return dataframe[dataframe[\"Diff\"]>0].sort_values(\"Diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both elections, Candidate 2/B lost these precincts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([43, 4, 45, 47, 48, 46, 49], dtype='int64', name='Precinct')"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_lost(results2016).index&precinct_lost(result2020).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both elections, Candidate 2/B won these precincts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([24, 12,  9, 19, 53, 41, 22, 34, 31, 28, 36, 11, 13,  8, 35, 33, 25,\n",
       "            26, 18, 16,  3, 27, 32,  2],\n",
       "           dtype='int64', name='Precinct')"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_won(results2016).index&precinct_won(result2020).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>843</td>\n",
       "      <td>647</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>291</td>\n",
       "      <td>300</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>838</td>\n",
       "      <td>603</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>272</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>981</td>\n",
       "      <td>745</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>334</td>\n",
       "      <td>348</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>985</td>\n",
       "      <td>723</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>331</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1014</td>\n",
       "      <td>790</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>342</td>\n",
       "      <td>364</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "34                      843          647          64          3          291   \n",
       "13                      838          603          67          1          260   \n",
       "42                      981          745          72          2          334   \n",
       "15                      985          723          79          2          310   \n",
       "44                     1014          790          60          2          342   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  Diff  \n",
       "Precinct                                                                      \n",
       "34                300           55            1         1  Candidate 2     9  \n",
       "13                272           71            0         0  Candidate 2    12  \n",
       "42                348           63            0         0  Candidate 2    14  \n",
       "15                331           82            0         0  Candidate 2    21  \n",
       "44                364           84            0         0  Candidate 2    22  "
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_won(result2020).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Registration</th>\n",
       "      <th>Ballots Cast</th>\n",
       "      <th>Under Vote</th>\n",
       "      <th>Over Vote</th>\n",
       "      <th>Candidate A</th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Candidate C</th>\n",
       "      <th>Candidate D</th>\n",
       "      <th>Candidate E</th>\n",
       "      <th>WRITE-IN</th>\n",
       "      <th>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Who won?</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1662</td>\n",
       "      <td>556</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>165</td>\n",
       "      <td>67</td>\n",
       "      <td>36</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>910</td>\n",
       "      <td>233</td>\n",
       "      <td>5</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1772</td>\n",
       "      <td>644</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>186</td>\n",
       "      <td>84</td>\n",
       "      <td>41</td>\n",
       "      <td>179</td>\n",
       "      <td>6</td>\n",
       "      <td>1031</td>\n",
       "      <td>471</td>\n",
       "      <td>7</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1530</td>\n",
       "      <td>557</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>180</td>\n",
       "      <td>46</td>\n",
       "      <td>28</td>\n",
       "      <td>166</td>\n",
       "      <td>3</td>\n",
       "      <td>951</td>\n",
       "      <td>250</td>\n",
       "      <td>14</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1618</td>\n",
       "      <td>617</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>195</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "      <td>808</td>\n",
       "      <td>383</td>\n",
       "      <td>15</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1566</td>\n",
       "      <td>587</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>191</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>174</td>\n",
       "      <td>4</td>\n",
       "      <td>765</td>\n",
       "      <td>384</td>\n",
       "      <td>17</td>\n",
       "      <td>Candidate B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registration  Ballots Cast  Under Vote  Over Vote  Candidate A  \\\n",
       "Precinct                                                                   \n",
       "24                1662           556          42          3           66   \n",
       "17                1772           644          56          3           60   \n",
       "12                1530           557          57          3           53   \n",
       "9                 1618           617          66          4           47   \n",
       "19                1566           587          55          5           60   \n",
       "\n",
       "          Candidate B  Candidate C  Candidate D  Candidate E  WRITE-IN  \\\n",
       "Precinct                                                                 \n",
       "24                165           67           36          160         0   \n",
       "17                186           84           41          179         6   \n",
       "12                180           46           28          166         3   \n",
       "9                 195           59           38          180         5   \n",
       "19                191           45           40          174         4   \n",
       "\n",
       "          Registered Voters  Total Votes  Diff     Who won?  \n",
       "Precinct                                                     \n",
       "24                      910          233     5  Candidate B  \n",
       "17                     1031          471     7  Candidate B  \n",
       "12                      951          250    14  Candidate B  \n",
       "9                       808          383    15  Candidate B  \n",
       "19                      765          384    17  Candidate B  "
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_won(results2016).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14998"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(result2020[\"Candidate 2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9422"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(results2016[\"Candidate B\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a big difference! Let's see why.\n",
    "\n",
    "The large gap is because the 2016 data is from round 1 of the ranked choice vote and 2020 isn't. (If you didn't know, San Francisco has ranked choice voting. If you don't know what that is, check it out here: https://sfelections.sfgov.org/ranked-choice-voting) This is just an artifact of the data from DOE and unfortunately, there isn't anything that can be done about it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison to 2008"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll take a look at 2008. This time, we'll just look at the data of Candidate 1 and Candidate 2 (both ran against each other in 2008 and 2020). Data is from DOE (https://sfelections.sfgov.org/election-summary-november-4-2008)\n",
    "\n",
    "**IMPORTANT NOTE**: Due to redistricting in 2010, the Precincts in 2008 do not 100% match up to 2016 or 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precinct</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Precinct  Candidate 1  Candidate 2\n",
       "0       1.0        103.0         69.0\n",
       "1       1.0         75.0         92.0\n",
       "2       2.0         99.0        156.0\n",
       "3       2.0         94.0        154.0\n",
       "4       3.0         99.0         76.0"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string2008=r\"C:\\Users\\am513\\Desktop\\LinkedIn\\DataSciPortfolio\\2008.csv\"\n",
    "result2008=pd.read_csv(string2008)\n",
    "\n",
    "result2008.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "result2008=result2008.groupby(\"Precinct\").sum()\n",
    "result2008=result2008.astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Number of Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>Candidate 1</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Candidate  Number of Votes\n",
       "Precinct                              \n",
       "1.0       Candidate 1              178\n",
       "1.0       Candidate 2              161\n",
       "2.0       Candidate 1              193\n",
       "2.0       Candidate 2              310\n",
       "3.0       Candidate 1              165"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_data(result2008,[\"Candidate 1\",\"Candidate 2\"],\"Precinct\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff(dataframe,cand=[]):\n",
    "    #cand in position 0 will be subtracted from\n",
    "    dataframe[\"Diff\"]=dataframe[cand[0]]-dataframe[cand[1]]\n",
    "    \n",
    "def num_precinct_lost(dataframe):\n",
    "    return len(dataframe[dataframe[\"Diff\"]<0].sort_values(\"Diff\"))\n",
    "    \n",
    "def precinct_lost(dataframe):\n",
    "    return dataframe[dataframe[\"Diff\"]<0].sort_values(\"Diff\")\n",
    "    \n",
    "def who_won_pct(dataframe,cand=[]):\n",
    "    dataframe[\"Who won?\"]=dataframe[cand].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff(result2008,[\"Candidate 1\",\"Candidate 2\"])\n",
    "who_won_pct(result2008,[\"Candidate 1\",\"Candidate 2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Candidate 2 lost 16 precincts in 2008\n",
      "Candidate 2 won 26 precincts in 2008\n"
     ]
    }
   ],
   "source": [
    "print(\"Candidate 2 lost\",num_precinct_lost(result2008),\"precincts in 2008\")\n",
    "print(\"Candidate 2 won\",num_precinct_won(result2008),\"precincts in 2008\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Candidate 2 won these precincts in 2008, 2016, and 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([11, 35, 3, 26, 12, 18, 24, 31, 16, 25, 8, 13, 28, 27, 41, 22], dtype='object', name='Precinct')"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_won(result2008).index&precinct_won(results2016).index&precinct_won(result2020).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Candidate 2 lost these precincts in 2008, 2016, 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([4, 45, 46], dtype='object', name='Precinct')"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_lost(result2008).index&precinct_lost(results2016).index&precinct_lost(result2020).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Candidate 2 lost these precincts in 2008 and 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([4, 29, 21, 14, 45, 46], dtype='object', name='Precinct')"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_lost(result2008).index&precinct_lost(result2020).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our most complete data (2016 and 2020), let's take a look at the win/loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Won Int64Index([24, 12,  9, 19, 53, 41, 22, 34, 31, 28, 36, 11, 13,  8, 35, 33, 25,\n",
      "            26, 18, 16,  3, 27, 32,  2],\n",
      "           dtype='int64', name='Precinct')\n",
      "Lost Int64Index([43, 4, 45, 47, 48, 46, 49], dtype='int64', name='Precinct')\n"
     ]
    }
   ],
   "source": [
    "print(\"Won\",precinct_won(results2016).index&precinct_won(result2020).index)\n",
    "\n",
    "print(\"Lost\",precinct_lost(results2016).index&precinct_lost(result2020).index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_2_cands(result2008,[\"Candidate 2\",\"Candidate 1\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing 2016 and 2020 pt. 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how Candidate 2/B did compared to themselves in 2016 and 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "results2016[\"Year\"]=2016\n",
    "result2020[\"Year\"]=2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_of_precincts=results2016.index\n",
    "cand2_2020=result2020[\"Candidate 2\"]\n",
    "candb_2016=results2016[\"Candidate B\"]\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=list_of_precincts,y=cand2_2020,color=\"b\",label=\"2020\")\n",
    "sns.barplot(x=list_of_precincts,y=candb_2016,color=\"r\",label=\"2016\")\n",
    "plt.legend()\n",
    "plt.title(\"Candidate 2/B in 2016 vs 2020\")\n",
    "plt.ylabel(\"Total Votes\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cand2_2020-candb_2016).idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Change\n",
       "Precinct        \n",
       "25           208\n",
       "11           201\n",
       "6            177\n",
       "48           176\n",
       "33           170"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biggest_change=pd.DataFrame()\n",
    "biggest_change[\"Change\"]=(cand2_2020-candb_2016)\n",
    "biggest_change.sort_values(by=\"Change\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy2020=precinct_won(result2020).copy()\n",
    "copy2016=precinct_won(results2016).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy2020[\"precinct\"]=copy2020.index\n",
    "copy2016[\"precinct\"]=copy2016.index\n",
    "diff_change=copy2016[\"precinct\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precincts won in 2020 but not 2016\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>Registered Voters</th>\n",
       "      <th>Total Votes</th>\n",
       "      <th>Undervotes</th>\n",
       "      <th>Overvotes</th>\n",
       "      <th>Candidate 1</th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Candidate 3</th>\n",
       "      <th>Candidate 4</th>\n",
       "      <th>Write-in</th>\n",
       "      <th>Who won?</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Year</th>\n",
       "      <th>precinct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>981</td>\n",
       "      <td>745</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>334</td>\n",
       "      <td>348</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>985</td>\n",
       "      <td>723</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>331</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>21</td>\n",
       "      <td>2020</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1014</td>\n",
       "      <td>790</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>342</td>\n",
       "      <td>364</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>22</td>\n",
       "      <td>2020</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>989</td>\n",
       "      <td>695</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>291</td>\n",
       "      <td>335</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>44</td>\n",
       "      <td>2020</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>970</td>\n",
       "      <td>713</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>361</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>76</td>\n",
       "      <td>2020</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>805</td>\n",
       "      <td>627</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>232</td>\n",
       "      <td>321</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>89</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>833</td>\n",
       "      <td>629</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>219</td>\n",
       "      <td>343</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Candidate 2</td>\n",
       "      <td>124</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Registered Voters  Total Votes  Undervotes  Overvotes  Candidate 1  \\\n",
       "Precinct                                                                       \n",
       "42                      981          745          72          2          334   \n",
       "15                      985          723          79          2          310   \n",
       "44                     1014          790          60          2          342   \n",
       "23                      989          695          79          2          291   \n",
       "52                      970          713          63          1          285   \n",
       "7                       805          627          54          2          232   \n",
       "6                       833          629          76          0          219   \n",
       "\n",
       "          Candidate 2  Candidate 3  Candidate 4  Write-in     Who won?  Diff  \\\n",
       "Precinct                                                                       \n",
       "42                348           63            0         0  Candidate 2    14   \n",
       "15                331           82            0         0  Candidate 2    21   \n",
       "44                364           84            0         0  Candidate 2    22   \n",
       "23                335           66            3         3  Candidate 2    44   \n",
       "52                361           67            0         0  Candidate 2    76   \n",
       "7                 321           74            0         0  Candidate 2    89   \n",
       "6                 343           67            0         0  Candidate 2   124   \n",
       "\n",
       "          Year  precinct  \n",
       "Precinct                  \n",
       "42        2020        42  \n",
       "15        2020        15  \n",
       "44        2020        44  \n",
       "23        2020        23  \n",
       "52        2020        52  \n",
       "7         2020         7  \n",
       "6         2020         6  "
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Precincts won in 2020 but not 2016\")\n",
    "copy2020[~copy2020[\"precinct\"].isin(diff_change)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_precinct_won(result2020)-num_precinct_won(results2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([24, 17, 12,  9, 19, 14,  1, 53, 41, 22, 34, 31, 28, 36, 11, 13,  8,\n",
       "            35, 33, 25, 26, 18, 16, 29,  3, 27, 32, 21,  2],\n",
       "           dtype='int64', name='Precinct')"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precinct_won(results2016).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "np2020=np.array([34, 13, 42, 15, 44, 19, 26, 28, 31, 41, 24, 23, 18, 25, 32,  2, 52,\n",
    "            35, 22, 27,  9,  7, 11,  3, 36, 12, 53, 33,  6, 16,  8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [],
   "source": [
    "np2016=np.array([24, 17, 12,  9, 19, 14,  1, 53, 41, 22, 34, 31, 28, 36, 11, 13,  8,\n",
    "            35, 33, 25, 26, 18, 16, 29,  3, 27, 32, 21,  2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Candidate 2/B won these precincts in 2020, but did not win them in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  7, 15, 23, 42, 44, 52])"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.setdiff1d(np2020,np2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate 2</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>409</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>439</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>336</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>343</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Candidate 2  Year\n",
       "Precinct                   \n",
       "1                 214  2020\n",
       "2                 409  2020\n",
       "3                 439  2020\n",
       "4                 336  2020\n",
       "6                 343  2020"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2020[[\"Candidate 2\",\"Year\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate B</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>352</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>305</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>232</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>166</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Candidate B  Year\n",
       "Precinct                   \n",
       "1                 166  2016\n",
       "2                 352  2016\n",
       "3                 305  2016\n",
       "4                 232  2016\n",
       "6                 166  2016"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2016[[\"Candidate B\",\"Year\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "copycopy2020=result2020.copy()\n",
    "copycopy2016=results2016.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "copycopy2020[\"precinct\"]=copycopy2020.index\n",
    "copycopy2016[\"precinct\"]=copycopy2016.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "appendedresult=copycopy2020[[\"Candidate 2\",\"Year\",\"precinct\"]].append(copycopy2016[[\"Candidate B\",\"Year\",\"precinct\"]])\n",
    "\n",
    "appendedresult.to_csv(\"comparison.zip\",index=False,compression=dict(method=\"zip\",archive_name=\"comparison.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate 2/B</th>\n",
       "      <th>Year</th>\n",
       "      <th>precinct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>214</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>409</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>439</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>336</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>343</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Candidate 2/B  Year  precinct\n",
       "0            214  2020         1\n",
       "1            409  2020         2\n",
       "2            439  2020         3\n",
       "3            336  2020         4\n",
       "4            343  2020         6"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stringcomp=r\"C:\\Users\\am513\\Desktop\\LinkedIn\\DataSciPortfolio\\comparison.csv\"\n",
    "resultcomp=pd.read_csv(stringcomp)\n",
    "\n",
    "resultcomp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(x=\"precinct\",y=\"Candidate 2/B\",data=resultcomp,hue=\"Year\")\n",
    "plt.title(\"2016 vs 2020 Vote Totals\")\n",
    "plt.xlabel(\"Precinct\")\n",
    "plt.ylabel(\"Total Votes\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}