Fulton_County_GA_Benfords_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "# Data downloaded as XLS from https://results.enr.clarityelections.com/GA/Fulton/105430/web.264614/#/detail/1\n",
    "# worksheet 1 saved to csv to ga_fulton_county.csv\n",
    "\n",
    "data = pd.read_csv('ga_fulton_county.csv', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Donald J. Trump (I) (Rep)</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Joseph R. Biden (Dem)</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Jo Jorgensen (Lib)</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>County</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Election Day Votes</td>\n",
       "      <td>Advanced Voting Votes</td>\n",
       "      <td>Absentee by Mail Votes</td>\n",
       "      <td>Provisional Votes</td>\n",
       "      <td>Total Votes</td>\n",
       "      <td>Election Day Votes</td>\n",
       "      <td>Advanced Voting Votes</td>\n",
       "      <td>Absentee by Mail Votes</td>\n",
       "      <td>Provisional Votes</td>\n",
       "      <td>Total Votes</td>\n",
       "      <td>Election Day Votes</td>\n",
       "      <td>Advanced Voting Votes</td>\n",
       "      <td>Absentee by Mail Votes</td>\n",
       "      <td>Provisional Votes</td>\n",
       "      <td>Total Votes</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01A</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "      <td>160</td>\n",
       "      <td>1638</td>\n",
       "      <td>703</td>\n",
       "      <td>0</td>\n",
       "      <td>2501</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01B</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>158</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>294</td>\n",
       "      <td>242</td>\n",
       "      <td>1824</td>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>2944</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0         Unnamed: 1 Donald J. Trump (I) (Rep)  \\\n",
       "0     County  Registered Voters        Election Day Votes   \n",
       "1        01A                  0                        41   \n",
       "2        01B                  0                        79   \n",
       "\n",
       "              Unnamed: 3              Unnamed: 4         Unnamed: 5  \\\n",
       "0  Advanced Voting Votes  Absentee by Mail Votes  Provisional Votes   \n",
       "1                    143                      50                  0   \n",
       "2                    158                      57                  0   \n",
       "\n",
       "    Unnamed: 6 Joseph R. Biden (Dem)             Unnamed: 8  \\\n",
       "0  Total Votes    Election Day Votes  Advanced Voting Votes   \n",
       "1          234                   160                   1638   \n",
       "2          294                   242                   1824   \n",
       "\n",
       "               Unnamed: 9        Unnamed: 10  Unnamed: 11  Jo Jorgensen (Lib)  \\\n",
       "0  Absentee by Mail Votes  Provisional Votes  Total Votes  Election Day Votes   \n",
       "1                     703                  0         2501                   3   \n",
       "2                     878                  0         2944                  11   \n",
       "\n",
       "             Unnamed: 13             Unnamed: 14        Unnamed: 15  \\\n",
       "0  Advanced Voting Votes  Absentee by Mail Votes  Provisional Votes   \n",
       "1                     10                       9                  0   \n",
       "2                     16                      15                  0   \n",
       "\n",
       "   Unnamed: 16 Unnamed: 17  \n",
       "0  Total Votes       Total  \n",
       "1           22        2757  \n",
       "2           42        3280  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_csv('data/ga_fulton_county.csv', skiprows=2)\n",
    "data = data[data.columns[[0, 6, 11, 16]]]\n",
    "\n",
    "# first column is mislabled (not County)\n",
    "data.columns = ['Precinct', 'Donald Trump', 'Joe Biden', 'Jo Jorgensen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Donald Trump</th>\n",
       "      <th>Joe Biden</th>\n",
       "      <th>Jo Jorgensen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precinct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01A</th>\n",
       "      <td>234</td>\n",
       "      <td>2501</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01B</th>\n",
       "      <td>294</td>\n",
       "      <td>2944</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01C</th>\n",
       "      <td>49</td>\n",
       "      <td>710</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Donald Trump  Joe Biden  Jo Jorgensen\n",
       "Precinct                                       \n",
       "01A                234       2501            22\n",
       "01B                294       2944            42\n",
       "01C                 49        710            13"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index('Precinct')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>count</th>\n",
       "      <th>Precinct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>234</td>\n",
       "      <td>01A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>294</td>\n",
       "      <td>01B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>49</td>\n",
       "      <td>01C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      candidate  count Precinct\n",
       "0  Donald Trump    234      01A\n",
       "1  Donald Trump    294      01B\n",
       "2  Donald Trump     49      01C"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstacked_data = data.unstack().reset_index().rename(\n",
    "    columns={0: 'count', 'level_0': 'candidate'})\n",
    "unstacked_data = unstacked_data[['candidate', 'count', 'Precinct']]\n",
    "unstacked_data['count'] = unstacked_data['count'].astype(int)\n",
    "unstacked_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Fulton')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 317,
       "width": 923
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "fig.tight_layout(rect=[0.1, 0.0, 0.9, 0.9])\n",
    "plt.subplots_adjust(hspace=0.4, wspace=0.25)\n",
    "\n",
    "axs = np.array(axs).flatten()\n",
    "candidates = unstacked_data.candidate.unique()\n",
    "digit = np.arange(1, 10)\n",
    "\n",
    "for i in range(len(candidates)):\n",
    "\n",
    "    ax = axs[i]\n",
    "    candidate = candidates[i]\n",
    "    \n",
    "    N = len(data[data[candidate] > 0])\n",
    "    \n",
    "    # expected distribution, from benford's law, normalize by non-zero counts\n",
    "    expected = np.log10(1 + 1/digit) * N\n",
    "\n",
    "    cur_data = unstacked_data\n",
    "    cur_data = cur_data.groupby(['candidate', 'Precinct']).sum()\n",
    "    digits = cur_data.loc[candidate]['count'].astype(str).str[0].astype(int)\n",
    "    digits = digits[digits > 0]\n",
    "    \n",
    "    v = digits.value_counts()\n",
    "    ax.bar(v.index, v.values)\n",
    "    ax.set_xticks(range(1, 10))\n",
    "\n",
    "    ax.set_title(\"%s, N=%d\" % (candidate, N))\n",
    "    ax.plot(digit, expected, marker='D', color='red', label='Expected (Benford\\'s law)')\n",
    "    \n",
    "    ax.set_xlabel(\"Leading Digit\")\n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "    \n",
    "plt.legend(loc=3, bbox_to_anchor=(0.0, -0.25))\n",
    "plt.suptitle(\"Fulton\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}