2024 Election Prediction - Bayesian Method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b28576c0",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "686fa90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5affb954",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25f1c333",
   "metadata": {},
   "outputs": [],
   "source": [
    "US_STATES = [\n",
    "    'alabama', 'alaska', 'arizona', 'arkansas', 'california', 'colorado',\n",
    "    'connecticut', 'delaware', 'florida', 'georgia', 'hawaii', 'idaho',\n",
    "    'illinois', 'indiana', 'iowa', 'kansas', 'kentucky', 'louisiana', 'maine',\n",
    "    'maryland', 'massachusetts', 'michigan', 'minnesota', 'mississippi', \n",
    "    'missouri', 'montana', 'nebraska', 'nevada', 'new-hampshire', 'new-jersey',\n",
    "    'new-mexico', 'new-york', 'north-carolina', 'north-dakota', 'ohio',\n",
    "    'oklahoma', 'oregon', 'pennsylvania', 'rhode-island', 'south-carolina',\n",
    "    'south-dakota', 'tennessee', 'texas', 'utah', 'vermont', 'virginia',\n",
    "    'washington', 'west-virginia', 'wisconsin', 'wyoming', 'district-of-columbia'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48effcd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATES_TO_FULL_NAMES = state_mapping = {\n",
    "    'alabama': 'Alabama',\n",
    "    'alaska': 'Alaska',\n",
    "    'arizona': 'Arizona',\n",
    "    'arkansas': 'Arkansas',\n",
    "    'california': 'California',\n",
    "    'colorado': 'Colorado',\n",
    "    'connecticut': 'Connecticut',\n",
    "    'delaware': 'Delaware',\n",
    "    'florida': 'Florida',\n",
    "    'georgia': 'Georgia',\n",
    "    'hawaii': 'Hawaii',\n",
    "    'idaho': 'Idaho',\n",
    "    'illinois': 'Illinois',\n",
    "    'indiana': 'Indiana',\n",
    "    'iowa': 'Iowa',\n",
    "    'kansas': 'Kansas',\n",
    "    'kentucky': 'Kentucky',\n",
    "    'louisiana': 'Louisiana',\n",
    "    'maine': 'Maine',\n",
    "    'maryland': 'Maryland',\n",
    "    'massachusetts': 'Massachusetts',\n",
    "    'michigan': 'Michigan',\n",
    "    'minnesota': 'Minnesota',\n",
    "    'mississippi': 'Mississippi',\n",
    "    'missouri': 'Missouri',\n",
    "    'montana': 'Montana',\n",
    "    'nebraska': 'Nebraska',\n",
    "    'nevada': 'Nevada',\n",
    "    'new-hampshire': 'New Hampshire',\n",
    "    'new-jersey': 'New Jersey',\n",
    "    'new-mexico': 'New Mexico',\n",
    "    'new-york': 'New York',\n",
    "    'north-carolina': 'North Carolina',\n",
    "    'north-dakota': 'North Dakota',\n",
    "    'ohio': 'Ohio',\n",
    "    'oklahoma': 'Oklahoma',\n",
    "    'oregon': 'Oregon',\n",
    "    'pennsylvania': 'Pennsylvania',\n",
    "    'rhode-island': 'Rhode Island',\n",
    "    'south-carolina': 'South Carolina',\n",
    "    'south-dakota': 'South Dakota',\n",
    "    'tennessee': 'Tennessee',\n",
    "    'texas': 'Texas',\n",
    "    'utah': 'Utah',\n",
    "    'vermont': 'Vermont',\n",
    "    'virginia': 'Virginia',\n",
    "    'washington': 'Washington',\n",
    "    'west-virginia': 'West Virginia',\n",
    "    'wisconsin': 'Wisconsin',\n",
    "    'wyoming': 'Wyoming',\n",
    "    'district-of-columbia': 'District Of Columbia'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1231e594",
   "metadata": {},
   "source": [
    "# Read 2020 Election Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb6ec20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the file content\n",
    "with open('2020_election_results.txt', 'r') as file:\n",
    "    text = file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d48547b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regular expression pattern to match the required fields\n",
    "pattern = (\n",
    "    r\"President:\\s*(?P<state>[A-Za-z\\s]+)\\n\"\n",
    "    r\"(?P<electoral_votes>\\d+)\\s*Electoral Votes\\n\"\n",
    "    r\"(?P<winner>Trump|Biden)\\nPROJECTED WINNER\\n\\+ FOLLOW\\n\"\n",
    "    r\"Candidate\\t%\\t\\tVotes\\n(?:Trump|Biden)\\n(?:\\d+\\.\\d+%)?\\s*\\n?(?P<trump_votes>[\\d,]+)\\n(?:Trump|Biden)\\n(?:\\d+\\.\\d+%)?\\s*\\n?(?P<biden_votes>[\\d,]+)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51133d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the regex pattern\n",
    "regex = re.compile(pattern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5561c0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find all matches in the text\n",
    "matches = regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3505697",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process the matches\n",
    "results = []\n",
    "for match in matches:\n",
    "    state = match[0]\n",
    "    electoral_votes = int(match[1])\n",
    "    winner = match[2]\n",
    "    trump_votes = int(match[3].replace(',', ''))\n",
    "    biden_votes = int(match[4].replace(',', ''))\n",
    "    \n",
    "    # Append the extracted information to the results list\n",
    "    results.append({\n",
    "        \"State\": state,\n",
    "        \"Electoral Votes\": electoral_votes,\n",
    "        \"Winner\": winner,\n",
    "        \"Winner Votes\": trump_votes,\n",
    "        \"Loser Votes\": biden_votes,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d37af36",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23816202",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_df['Trump Votes'] = voting_df.apply(lambda row: row['Winner Votes'] if row.Winner == 'Trump' else row['Loser Votes'], 1)\n",
    "voting_df['Biden Votes'] = voting_df.apply(lambda row: row['Winner Votes'] if row.Winner == 'Biden' else row['Loser Votes'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f88b4d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_df['Trump Electoral Votes'] = voting_df.apply(lambda row: row['Electoral Votes'] if row.Winner == 'Trump' else 0, 1)\n",
    "voting_df['Biden Electoral Votes'] = voting_df.apply(lambda row: row['Electoral Votes'] if row.Winner == 'Biden' else 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d3b4d64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_df = voting_df[[c for c in voting_df.columns if c not in ['Winner Votes', 'Loser Votes']]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5917806",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_df.State = voting_df.State.apply(lambda x: x.split('\\n')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d7742ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#there was a few states missing\n",
    "missing_df = pd.DataFrame(\n",
    "    columns=voting_df.columns,\n",
    "    data=[['Maine', 4, 'Biden', 360737, 435072, 1, 3], ['Nebraska', 5, 'Trump', 556846, 374583, 4, 1]]\n",
    ")\n",
    "\n",
    "voting_df = pd.concat([voting_df, missing_df])\n",
    "\n",
    "voting_df = voting_df.sort_values('State').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a7272ce",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Electoral Votes</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Trump Votes</th>\n",
       "      <th>Biden Votes</th>\n",
       "      <th>Trump Electoral Votes</th>\n",
       "      <th>Biden Electoral Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>9</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1441170</td>\n",
       "      <td>849624</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>Trump</td>\n",
       "      <td>189951</td>\n",
       "      <td>153778</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>11</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1661686</td>\n",
       "      <td>1672143</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Trump</td>\n",
       "      <td>760647</td>\n",
       "      <td>423932</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>55</td>\n",
       "      <td>Biden</td>\n",
       "      <td>6006429</td>\n",
       "      <td>11110250</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>9</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1364607</td>\n",
       "      <td>1804352</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>7</td>\n",
       "      <td>Biden</td>\n",
       "      <td>714717</td>\n",
       "      <td>1080831</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>3</td>\n",
       "      <td>Biden</td>\n",
       "      <td>200603</td>\n",
       "      <td>296268</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District Of Columbia</td>\n",
       "      <td>3</td>\n",
       "      <td>Biden</td>\n",
       "      <td>18586</td>\n",
       "      <td>317323</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>29</td>\n",
       "      <td>Trump</td>\n",
       "      <td>5668731</td>\n",
       "      <td>5297045</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>16</td>\n",
       "      <td>Biden</td>\n",
       "      <td>2461854</td>\n",
       "      <td>2473633</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>4</td>\n",
       "      <td>Biden</td>\n",
       "      <td>196864</td>\n",
       "      <td>366130</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>4</td>\n",
       "      <td>Trump</td>\n",
       "      <td>554119</td>\n",
       "      <td>287021</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>20</td>\n",
       "      <td>Biden</td>\n",
       "      <td>2446891</td>\n",
       "      <td>3471915</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>11</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1729857</td>\n",
       "      <td>1242498</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>6</td>\n",
       "      <td>Trump</td>\n",
       "      <td>897672</td>\n",
       "      <td>759061</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6</td>\n",
       "      <td>Trump</td>\n",
       "      <td>771406</td>\n",
       "      <td>570323</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>8</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1326646</td>\n",
       "      <td>772474</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>8</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1255776</td>\n",
       "      <td>856034</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>4</td>\n",
       "      <td>Biden</td>\n",
       "      <td>360737</td>\n",
       "      <td>435072</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>976414</td>\n",
       "      <td>1985023</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>11</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1167202</td>\n",
       "      <td>2382202</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>16</td>\n",
       "      <td>Biden</td>\n",
       "      <td>2649852</td>\n",
       "      <td>2804040</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1484065</td>\n",
       "      <td>1717077</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>6</td>\n",
       "      <td>Trump</td>\n",
       "      <td>756764</td>\n",
       "      <td>539398</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>10</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1718736</td>\n",
       "      <td>1253014</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>3</td>\n",
       "      <td>Trump</td>\n",
       "      <td>343602</td>\n",
       "      <td>244786</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>5</td>\n",
       "      <td>Trump</td>\n",
       "      <td>556846</td>\n",
       "      <td>374583</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>6</td>\n",
       "      <td>Biden</td>\n",
       "      <td>669890</td>\n",
       "      <td>703486</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>4</td>\n",
       "      <td>Biden</td>\n",
       "      <td>365660</td>\n",
       "      <td>424937</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>14</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1883313</td>\n",
       "      <td>2608400</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>5</td>\n",
       "      <td>Biden</td>\n",
       "      <td>401894</td>\n",
       "      <td>501614</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>29</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3251997</td>\n",
       "      <td>5244886</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>15</td>\n",
       "      <td>Trump</td>\n",
       "      <td>2758775</td>\n",
       "      <td>2684292</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Trump</td>\n",
       "      <td>235595</td>\n",
       "      <td>114902</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>18</td>\n",
       "      <td>Trump</td>\n",
       "      <td>3154834</td>\n",
       "      <td>2679165</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>7</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1020280</td>\n",
       "      <td>503890</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>7</td>\n",
       "      <td>Biden</td>\n",
       "      <td>958448</td>\n",
       "      <td>1340383</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>20</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3378263</td>\n",
       "      <td>3459923</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>4</td>\n",
       "      <td>Biden</td>\n",
       "      <td>199922</td>\n",
       "      <td>307486</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>9</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1385103</td>\n",
       "      <td>1091541</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3</td>\n",
       "      <td>Trump</td>\n",
       "      <td>261043</td>\n",
       "      <td>150471</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>11</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1852475</td>\n",
       "      <td>1143711</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>38</td>\n",
       "      <td>Trump</td>\n",
       "      <td>5890347</td>\n",
       "      <td>5259126</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Utah</td>\n",
       "      <td>6</td>\n",
       "      <td>Trump</td>\n",
       "      <td>865140</td>\n",
       "      <td>560282</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>3</td>\n",
       "      <td>Biden</td>\n",
       "      <td>112704</td>\n",
       "      <td>242820</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>13</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1962430</td>\n",
       "      <td>2413568</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Washington</td>\n",
       "      <td>12</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1584651</td>\n",
       "      <td>2369612</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5</td>\n",
       "      <td>Trump</td>\n",
       "      <td>545382</td>\n",
       "      <td>235984</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1610184</td>\n",
       "      <td>1630866</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>3</td>\n",
       "      <td>Trump</td>\n",
       "      <td>193559</td>\n",
       "      <td>73491</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State  Electoral Votes Winner  Trump Votes  Biden Votes  \\\n",
       "0                Alabama                9  Trump      1441170       849624   \n",
       "1                 Alaska                3  Trump       189951       153778   \n",
       "2                Arizona               11  Biden      1661686      1672143   \n",
       "3               Arkansas                6  Trump       760647       423932   \n",
       "4             California               55  Biden      6006429     11110250   \n",
       "5               Colorado                9  Biden      1364607      1804352   \n",
       "6            Connecticut                7  Biden       714717      1080831   \n",
       "7               Delaware                3  Biden       200603       296268   \n",
       "8   District Of Columbia                3  Biden        18586       317323   \n",
       "9                Florida               29  Trump      5668731      5297045   \n",
       "10               Georgia               16  Biden      2461854      2473633   \n",
       "11                Hawaii                4  Biden       196864       366130   \n",
       "12                 Idaho                4  Trump       554119       287021   \n",
       "13              Illinois               20  Biden      2446891      3471915   \n",
       "14               Indiana               11  Trump      1729857      1242498   \n",
       "15                  Iowa                6  Trump       897672       759061   \n",
       "16                Kansas                6  Trump       771406       570323   \n",
       "17              Kentucky                8  Trump      1326646       772474   \n",
       "18             Louisiana                8  Trump      1255776       856034   \n",
       "19                 Maine                4  Biden       360737       435072   \n",
       "20              Maryland               10  Biden       976414      1985023   \n",
       "21         Massachusetts               11  Biden      1167202      2382202   \n",
       "22              Michigan               16  Biden      2649852      2804040   \n",
       "23             Minnesota               10  Biden      1484065      1717077   \n",
       "24           Mississippi                6  Trump       756764       539398   \n",
       "25              Missouri               10  Trump      1718736      1253014   \n",
       "26               Montana                3  Trump       343602       244786   \n",
       "27              Nebraska                5  Trump       556846       374583   \n",
       "28                Nevada                6  Biden       669890       703486   \n",
       "29         New Hampshire                4  Biden       365660       424937   \n",
       "30            New Jersey               14  Biden      1883313      2608400   \n",
       "31            New Mexico                5  Biden       401894       501614   \n",
       "32              New York               29  Biden      3251997      5244886   \n",
       "33        North Carolina               15  Trump      2758775      2684292   \n",
       "34          North Dakota                3  Trump       235595       114902   \n",
       "35                  Ohio               18  Trump      3154834      2679165   \n",
       "36              Oklahoma                7  Trump      1020280       503890   \n",
       "37                Oregon                7  Biden       958448      1340383   \n",
       "38          Pennsylvania               20  Biden      3378263      3459923   \n",
       "39          Rhode Island                4  Biden       199922       307486   \n",
       "40        South Carolina                9  Trump      1385103      1091541   \n",
       "41          South Dakota                3  Trump       261043       150471   \n",
       "42             Tennessee               11  Trump      1852475      1143711   \n",
       "43                 Texas               38  Trump      5890347      5259126   \n",
       "44                  Utah                6  Trump       865140       560282   \n",
       "45               Vermont                3  Biden       112704       242820   \n",
       "46              Virginia               13  Biden      1962430      2413568   \n",
       "47            Washington               12  Biden      1584651      2369612   \n",
       "48         West Virginia                5  Trump       545382       235984   \n",
       "49             Wisconsin               10  Biden      1610184      1630866   \n",
       "50               Wyoming                3  Trump       193559        73491   \n",
       "\n",
       "    Trump Electoral Votes  Biden Electoral Votes  \n",
       "0                       9                      0  \n",
       "1                       3                      0  \n",
       "2                       0                     11  \n",
       "3                       6                      0  \n",
       "4                       0                     55  \n",
       "5                       0                      9  \n",
       "6                       0                      7  \n",
       "7                       0                      3  \n",
       "8                       0                      3  \n",
       "9                      29                      0  \n",
       "10                      0                     16  \n",
       "11                      0                      4  \n",
       "12                      4                      0  \n",
       "13                      0                     20  \n",
       "14                     11                      0  \n",
       "15                      6                      0  \n",
       "16                      6                      0  \n",
       "17                      8                      0  \n",
       "18                      8                      0  \n",
       "19                      1                      3  \n",
       "20                      0                     10  \n",
       "21                      0                     11  \n",
       "22                      0                     16  \n",
       "23                      0                     10  \n",
       "24                      6                      0  \n",
       "25                     10                      0  \n",
       "26                      3                      0  \n",
       "27                      4                      1  \n",
       "28                      0                      6  \n",
       "29                      0                      4  \n",
       "30                      0                     14  \n",
       "31                      0                      5  \n",
       "32                      0                     29  \n",
       "33                     15                      0  \n",
       "34                      3                      0  \n",
       "35                     18                      0  \n",
       "36                      7                      0  \n",
       "37                      0                      7  \n",
       "38                      0                     20  \n",
       "39                      0                      4  \n",
       "40                      9                      0  \n",
       "41                      3                      0  \n",
       "42                     11                      0  \n",
       "43                     38                      0  \n",
       "44                      6                      0  \n",
       "45                      0                      3  \n",
       "46                      0                     13  \n",
       "47                      0                     12  \n",
       "48                      5                      0  \n",
       "49                      0                     10  \n",
       "50                      3                      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c93b71",
   "metadata": {},
   "source": [
    "# Scrape Latest State-Level Polling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afa64e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_latest_poll(url, state):\n",
    "    \"\"\"\n",
    "    url: a url which accepts <state> as an argument\n",
    "    state: the state which to get polling data for\n",
    "    \"\"\"\n",
    "    response = requests.get(url)\n",
    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
    "\n",
    "    # Find the latest poll\n",
    "    latest_polls = soup.find_all('tr', class_='visible-row')\n",
    "    \n",
    "    #if no polls, return\n",
    "    if not latest_polls:\n",
    "        return None\n",
    "\n",
    "    #return format\n",
    "    results_dict = {\n",
    "        'State': [],\n",
    "        'Date': [],\n",
    "        'Sample Size': [],\n",
    "        'Pollster': [],\n",
    "        'Result': []\n",
    "    }\n",
    "    \n",
    "    #populate return structure\n",
    "    for latest_poll in latest_polls:\n",
    "        # Extract date, sample size, pollster, sample type, and results\n",
    "        date = latest_poll.find('td', class_='dates hide-mobile').get_text(strip=True)\n",
    "        sample_size = latest_poll.find('td', class_='sample hide-mobile').get_text(strip=True)\n",
    "        pollster = latest_poll.find('div', class_='pollster-name').get_text(strip=True)\n",
    "        results = latest_poll.find('div', class_='mobile-answers-container').get_text(strip=True)\n",
    "\n",
    "        results_dict['State'].append(STATES_TO_FULL_NAMES[state])\n",
    "        results_dict['Date'].append(date)\n",
    "        results_dict['Sample Size'].append(sample_size)\n",
    "        results_dict['Pollster'].append(pollster)\n",
    "        results_dict['Result'].append(results)\n",
    "        \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ef4480d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_poll_info(state, democratic_candidate='Harris'):\n",
    "    # URL of the webpage\n",
    "    url = f\"https://projects.fivethirtyeight.com/polls/{state}/\"\n",
    "    \n",
    "    #get all the latest polls\n",
    "    latest_polls = get_latest_poll(url, state)\n",
    "\n",
    "    if latest_polls:\n",
    "        #put in pandas dataframe\n",
    "        df = pd.DataFrame(latest_polls)\n",
    "        \n",
    "        #only keep if the poll was between Trump and democratic_candidate\n",
    "        df = df[df.Result.apply(lambda x: 'Trump' in x and democratic_candidate in x)]\n",
    "        \n",
    "        #sample size formatting\n",
    "        df['Sample Size'] = df['Sample Size'].apply(lambda x: x.replace(',', ''))\n",
    "\n",
    "        #if there is at least one poll between Trump and democratic_candidate...\n",
    "        if len(df) >= 1:\n",
    "            #get the latest poll (they're sorted by time by default)\n",
    "            df = df.iloc[[0]]\n",
    "            \n",
    "            #get the fraction of the poll that was for democratic_candidate and Trump\n",
    "            if democratic_candidate == 'Harris':\n",
    "                df[f'{democratic_candidate} Poll Frac'] = df.Result.apply(lambda x: int(re.findall(r'Harris(\\d+)', x)[0]) / 100)\n",
    "            elif democratic_candidate == 'Biden':\n",
    "                df[f'{democratic_candidate} Poll Frac'] = df.Result.apply(lambda x: int(re.findall(r'Biden(\\d+)', x)[0]) / 100)\n",
    "            df['Trump Poll Frac'] = df.Result.apply(lambda x: int(re.findall(r'Trump(\\d+)', x)[0]) / 100)\n",
    "            df = df[[c for c in df.columns if c not in ['Result']]]\n",
    "            return df\n",
    "        else:\n",
    "            return None\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "125b585d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alabama:\n",
      "No Trump/Harris polling data\n",
      "alaska:\n",
      "No Trump/Harris polling data\n",
      "arkansas:\n",
      "No Trump/Harris polling data\n",
      "colorado:\n",
      "No Trump/Harris polling data\n",
      "connecticut:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "delaware:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "hawaii:\n",
      "No Trump/Harris polling data\n",
      "idaho:\n",
      "No Trump/Harris polling data\n",
      "illinois:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "indiana:\n",
      "No Trump/Harris polling data\n",
      "iowa:\n",
      "No Trump/Harris polling data\n",
      "kansas:\n",
      "No Trump/Harris polling data\n",
      "kentucky:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "louisiana:\n",
      "No Trump/Harris polling data\n",
      "maryland:\n",
      "No Trump/Harris polling data\n",
      "massachusetts:\n",
      "No Trump/Harris polling data\n",
      "mississippi:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "missouri:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "nebraska:\n",
      "No Trump/Harris polling data\n",
      "new-jersey:\n",
      "No Trump/Harris polling data\n",
      "north-dakota:\n",
      "No Trump/Harris polling data\n",
      "oklahoma:\n",
      "No Trump/Harris polling data\n",
      "rhode-island:\n",
      "No Trump/Harris polling data\n",
      "south-carolina:\n",
      "No Trump/Harris polling data\n",
      "No Trump/Biden polling data\n",
      "south-dakota:\n",
      "No Trump/Harris polling data\n",
      "tennessee:\n",
      "No Trump/Harris polling data\n",
      "texas:\n",
      "No Trump/Harris polling data\n",
      "utah:\n",
      "No Trump/Harris polling data\n",
      "vermont:\n",
      "No Trump/Harris polling data\n",
      "west-virginia:\n",
      "No Trump/Harris polling data\n",
      "wyoming:\n",
      "No Trump/Harris polling data\n",
      "district-of-columbia:\n",
      "No Trump/Harris polling data\n"
     ]
    }
   ],
   "source": [
    "#get the latest Harris-Trump poll for each state\n",
    "state_info = []\n",
    "for state in US_STATES:\n",
    "    poll_info = get_poll_info(state, democratic_candidate='Harris')\n",
    "    if poll_info is None:\n",
    "        print(f'{state}:')\n",
    "        print(f'No Trump/Harris polling data')\n",
    "        poll_info = get_poll_info(state, democratic_candidate='Biden')\n",
    "        if poll_info is None:\n",
    "            print(f'No Trump/Biden polling data')\n",
    "    state_info.append(poll_info)\n",
    "state_df = pd.concat(state_info).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9bf1599c",
   "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>State</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sample Size</th>\n",
       "      <th>Pollster</th>\n",
       "      <th>Biden Poll Frac</th>\n",
       "      <th>Trump Poll Frac</th>\n",
       "      <th>Harris Poll Frac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Feb. 29-March 3</td>\n",
       "      <td>179</td>\n",
       "      <td>Florida Atlantic University PolCom Lab/Mainstr...</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>April 16-23</td>\n",
       "      <td>250</td>\n",
       "      <td>The Bullfinch Group</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.49</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Aug. 6-8</td>\n",
       "      <td>1092</td>\n",
       "      <td>Trafalgar Group</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Oct. 1-4</td>\n",
       "      <td>435</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>July 25-27</td>\n",
       "      <td>1904</td>\n",
       "      <td>Capitol Weekly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>June 17-24</td>\n",
       "      <td>800</td>\n",
       "      <td>Global Strategy Group</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Florida</td>\n",
       "      <td>July 31-Aug. 3</td>\n",
       "      <td>976</td>\n",
       "      <td>Redfield &amp; Wilton Strategies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>July 31-Aug. 3</td>\n",
       "      <td>1128</td>\n",
       "      <td>Redfield &amp; Wilton Strategies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>April 16-23</td>\n",
       "      <td>250</td>\n",
       "      <td>The Bullfinch Group</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>Oct. 1-4</td>\n",
       "      <td>490</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>March 2-5</td>\n",
       "      <td>1000</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>July 8-9</td>\n",
       "      <td>600</td>\n",
       "      <td>Cygnal</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>Oct. 1-4</td>\n",
       "      <td>487</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>April 22-26</td>\n",
       "      <td>800</td>\n",
       "      <td>Faucheux Strategies</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Maine</td>\n",
       "      <td>July 23-25</td>\n",
       "      <td>1445</td>\n",
       "      <td>University of New Hampshire Survey Center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>June 19-20</td>\n",
       "      <td>635</td>\n",
       "      <td>Public Policy Polling</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>July 16-18</td>\n",
       "      <td>500</td>\n",
       "      <td>Suffolk University</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Aug. 6-8</td>\n",
       "      <td>800</td>\n",
       "      <td>InsiderAdvantage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>July 31-Aug. 3</td>\n",
       "      <td>538</td>\n",
       "      <td>Redfield &amp; Wilton Strategies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Montana</td>\n",
       "      <td>Aug. 5-6</td>\n",
       "      <td>1000</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>July 8-11</td>\n",
       "      <td>300</td>\n",
       "      <td>Torchlight Strategies</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>Aug. 6-8</td>\n",
       "      <td>1087</td>\n",
       "      <td>Trafalgar Group</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>July 26-28</td>\n",
       "      <td>1000</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>June 26-27</td>\n",
       "      <td>810</td>\n",
       "      <td>co/efficient</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>July 31-Aug. 3</td>\n",
       "      <td>493</td>\n",
       "      <td>Redfield &amp; Wilton Strategies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>New York</td>\n",
       "      <td>July 28-Aug. 1</td>\n",
       "      <td>1199</td>\n",
       "      <td>Siena College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Aug. 5-9</td>\n",
       "      <td>802</td>\n",
       "      <td>YouGov Blue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>June 15-19</td>\n",
       "      <td>500</td>\n",
       "      <td>Public Opinion Strategies</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>July 23-28</td>\n",
       "      <td>600</td>\n",
       "      <td>Fabrizio, Lee &amp; Associates/Impact Research</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>April 11-12</td>\n",
       "      <td>615</td>\n",
       "      <td>Cygnal</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>July 24-26</td>\n",
       "      <td>700</td>\n",
       "      <td>Hoffman Research Group</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Aug. 6-9</td>\n",
       "      <td>693</td>\n",
       "      <td>The New York Times/Siena College</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>June 5-14</td>\n",
       "      <td>1450</td>\n",
       "      <td>Embold Research</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>May 10-13</td>\n",
       "      <td>500</td>\n",
       "      <td>Mason-Dixon Polling &amp; Strategy</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>June 20-July 1</td>\n",
       "      <td>962</td>\n",
       "      <td>Targoz Market Research</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Texas</td>\n",
       "      <td>June 25-July 18</td>\n",
       "      <td>400</td>\n",
       "      <td>ActiVote</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Utah</td>\n",
       "      <td>June 4-7</td>\n",
       "      <td>857</td>\n",
       "      <td>HarrisX</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.49</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>Feb. 29-March 3</td>\n",
       "      <td>111</td>\n",
       "      <td>Florida Atlantic University PolCom Lab/Mainstr...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>July 14-15</td>\n",
       "      <td>265</td>\n",
       "      <td>Florida Atlantic University PolCom Lab/Mainstr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Washington</td>\n",
       "      <td>July 24-25</td>\n",
       "      <td>581</td>\n",
       "      <td>Public Policy Polling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>June 4</td>\n",
       "      <td>464</td>\n",
       "      <td>Kaplan Strategies</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Aug. 6-8</td>\n",
       "      <td>800</td>\n",
       "      <td>InsiderAdvantage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Oct. 1-4</td>\n",
       "      <td>478</td>\n",
       "      <td>Emerson College</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>District Of Columbia</td>\n",
       "      <td>Oct. 20-Nov. 2</td>\n",
       "      <td>495</td>\n",
       "      <td>SurveyMonkey</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State             Date Sample Size  \\\n",
       "0                Alabama  Feb. 29-March 3         179   \n",
       "1                 Alaska      April 16-23         250   \n",
       "2                Arizona         Aug. 6-8        1092   \n",
       "3               Arkansas         Oct. 1-4         435   \n",
       "4             California       July 25-27        1904   \n",
       "5               Colorado       June 17-24         800   \n",
       "6                Florida   July 31-Aug. 3         976   \n",
       "7                Georgia   July 31-Aug. 3        1128   \n",
       "8                 Hawaii      April 16-23         250   \n",
       "9                  Idaho         Oct. 1-4         490   \n",
       "10               Indiana        March 2-5        1000   \n",
       "11                  Iowa         July 8-9         600   \n",
       "12                Kansas         Oct. 1-4         487   \n",
       "13             Louisiana      April 22-26         800   \n",
       "14                 Maine       July 23-25        1445   \n",
       "15              Maryland       June 19-20         635   \n",
       "16         Massachusetts       July 16-18         500   \n",
       "17              Michigan         Aug. 6-8         800   \n",
       "18             Minnesota   July 31-Aug. 3         538   \n",
       "19               Montana         Aug. 5-6        1000   \n",
       "20              Nebraska        July 8-11         300   \n",
       "21                Nevada         Aug. 6-8        1087   \n",
       "22         New Hampshire       July 26-28        1000   \n",
       "23            New Jersey       June 26-27         810   \n",
       "24            New Mexico   July 31-Aug. 3         493   \n",
       "25              New York   July 28-Aug. 1        1199   \n",
       "26        North Carolina         Aug. 5-9         802   \n",
       "27          North Dakota       June 15-19         500   \n",
       "28                  Ohio       July 23-28         600   \n",
       "29              Oklahoma      April 11-12         615   \n",
       "30                Oregon       July 24-26         700   \n",
       "31          Pennsylvania         Aug. 6-9         693   \n",
       "32          Rhode Island        June 5-14        1450   \n",
       "33          South Dakota        May 10-13         500   \n",
       "34             Tennessee   June 20-July 1         962   \n",
       "35                 Texas  June 25-July 18         400   \n",
       "36                  Utah         June 4-7         857   \n",
       "37               Vermont  Feb. 29-March 3         111   \n",
       "38              Virginia       July 14-15         265   \n",
       "39            Washington       July 24-25         581   \n",
       "40         West Virginia           June 4         464   \n",
       "41             Wisconsin         Aug. 6-8         800   \n",
       "42               Wyoming         Oct. 1-4         478   \n",
       "43  District Of Columbia   Oct. 20-Nov. 2         495   \n",
       "\n",
       "                                             Pollster  Biden Poll Frac  \\\n",
       "0   Florida Atlantic University PolCom Lab/Mainstr...             0.38   \n",
       "1                                 The Bullfinch Group             0.26   \n",
       "2                                     Trafalgar Group              NaN   \n",
       "3                                     Emerson College             0.24   \n",
       "4                                      Capitol Weekly              NaN   \n",
       "5                               Global Strategy Group             0.50   \n",
       "6                        Redfield & Wilton Strategies              NaN   \n",
       "7                        Redfield & Wilton Strategies              NaN   \n",
       "8                                 The Bullfinch Group             0.42   \n",
       "9                                     Emerson College             0.26   \n",
       "10                                    Emerson College             0.34   \n",
       "11                                             Cygnal             0.39   \n",
       "12                                    Emerson College             0.31   \n",
       "13                                Faucheux Strategies             0.33   \n",
       "14          University of New Hampshire Survey Center              NaN   \n",
       "15                              Public Policy Polling             0.56   \n",
       "16                                 Suffolk University             0.47   \n",
       "17                                   InsiderAdvantage              NaN   \n",
       "18                       Redfield & Wilton Strategies              NaN   \n",
       "19                                    Emerson College              NaN   \n",
       "20                              Torchlight Strategies             0.42   \n",
       "21                                    Trafalgar Group              NaN   \n",
       "22                                    Emerson College              NaN   \n",
       "23                                       co/efficient             0.40   \n",
       "24                       Redfield & Wilton Strategies              NaN   \n",
       "25                                      Siena College              NaN   \n",
       "26                                        YouGov Blue              NaN   \n",
       "27                          Public Opinion Strategies             0.28   \n",
       "28         Fabrizio, Lee & Associates/Impact Research              NaN   \n",
       "29                                             Cygnal             0.30   \n",
       "30                             Hoffman Research Group              NaN   \n",
       "31                   The New York Times/Siena College              NaN   \n",
       "32                                    Embold Research             0.40   \n",
       "33                     Mason-Dixon Polling & Strategy             0.31   \n",
       "34                             Targoz Market Research             0.28   \n",
       "35                                           ActiVote             0.46   \n",
       "36                                            HarrisX             0.20   \n",
       "37  Florida Atlantic University PolCom Lab/Mainstr...             0.59   \n",
       "38  Florida Atlantic University PolCom Lab/Mainstr...              NaN   \n",
       "39                              Public Policy Polling              NaN   \n",
       "40                                  Kaplan Strategies             0.28   \n",
       "41                                   InsiderAdvantage              NaN   \n",
       "42                                    Emerson College             0.15   \n",
       "43                                       SurveyMonkey             0.94   \n",
       "\n",
       "    Trump Poll Frac  Harris Poll Frac  \n",
       "0              0.57               NaN  \n",
       "1              0.49               NaN  \n",
       "2              0.48              0.47  \n",
       "3              0.57               NaN  \n",
       "4              0.35              0.59  \n",
       "5              0.40               NaN  \n",
       "6              0.47              0.41  \n",
       "7              0.46              0.44  \n",
       "8              0.34               NaN  \n",
       "9              0.55               NaN  \n",
       "10             0.55               NaN  \n",
       "11             0.51               NaN  \n",
       "12             0.47               NaN  \n",
       "13             0.48               NaN  \n",
       "14             0.45              0.54  \n",
       "15             0.30               NaN  \n",
       "16             0.29               NaN  \n",
       "17             0.47              0.49  \n",
       "18             0.41              0.46  \n",
       "19             0.54              0.39  \n",
       "20             0.42               NaN  \n",
       "21             0.48              0.45  \n",
       "22             0.41              0.48  \n",
       "23             0.41               NaN  \n",
       "24             0.37              0.44  \n",
       "25             0.37              0.49  \n",
       "26             0.46              0.46  \n",
       "27             0.62               NaN  \n",
       "28             0.52              0.42  \n",
       "29             0.61               NaN  \n",
       "30             0.40              0.45  \n",
       "31             0.44              0.46  \n",
       "32             0.33               NaN  \n",
       "33             0.50               NaN  \n",
       "34             0.58               NaN  \n",
       "35             0.54               NaN  \n",
       "36             0.49               NaN  \n",
       "37             0.28               NaN  \n",
       "38             0.45              0.41  \n",
       "39             0.38              0.52  \n",
       "40             0.55               NaN  \n",
       "41             0.49              0.48  \n",
       "42             0.68               NaN  \n",
       "43             0.05               NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5417747",
   "metadata": {},
   "source": [
    "# Join the 2020 Election Data to the Latest State-Level Polling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7fd7cc59",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = voting_df.merge(\n",
    "    state_df,\n",
    "    on='State',\n",
    "    how='outer'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "56f45091",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined['Sample Size'] = joined['Sample Size'].apply(lambda x: float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f10d591c",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined['exists_trump_harris_poll'] = joined.apply(lambda row: np.isnan(row['Trump Poll Frac']) == False and np.isnan(row['Harris Poll Frac']) == False, 1)\n",
    "joined['exists_trump_biden_poll'] = joined.apply(lambda row: np.isnan(row['Trump Poll Frac']) == False and np.isnan(row['Biden Poll Frac']) == False, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06f25fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = joined.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6bb3f34f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#get the number of votes for each candidate in 2020\n",
    "joined['N Votes'] = joined['Trump Votes'] + joined['Biden Votes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "697dfeb8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#get the fraction of votes for each candidate in 2020\n",
    "joined['Biden Vote Frac'] = joined['Biden Votes'] / joined['N Votes']\n",
    "joined['Trump Vote Frac'] = joined['Trump Votes'] / joined['N Votes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c2965e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = joined.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c9d3c0db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rebalance the polls to assume it's a race between just Trump and Harris or Trump and Biden\n",
    "joined['Poll Sum Frac'] = joined.apply(lambda row: np.nansum([row['Harris Poll Frac'], row['Trump Poll Frac'], row['Biden Poll Frac']]), 1)\n",
    "joined['Harris Poll Frac'] = joined['Harris Poll Frac'] / joined['Poll Sum Frac']\n",
    "joined['Trump Poll Frac'] = joined['Trump Poll Frac'] / joined['Poll Sum Frac']\n",
    "joined['Biden Poll Frac'] = joined['Biden Poll Frac'] / joined['Poll Sum Frac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba4c6c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from latest data here:\n",
    "#https://projects.fivethirtyeight.com/polls/president-general/2024/national/\n",
    "HARRIS_NATIONAL_POLL_FRAC = 0.456\n",
    "TRUMP_NATIONAL_POLL_FRAC = 0.435"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05475e4",
   "metadata": {},
   "source": [
    "# Simulating 2024 Election Based on 2020 Election and Recent Polling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ede4d8cb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def simulate_election(df, weight_vote, weight_poll, baseline_uncertainty=None, return_alphas_betas=False):\n",
    "    \"\"\"\n",
    "    this function simulates an election using the joined dataframe df\n",
    "    weight_vote is our level of trust on the 2020 election being predictive of the 2024 election\n",
    "    weight_poll is our level of trust in the most recent polling data being predictive of the 2024 election\n",
    "    \"\"\"\n",
    "    \n",
    "    \"\"\"\n",
    "    in a Beta(alpha, beta) distribution:\n",
    "    - alpha is the number of \"successes\" + 1\n",
    "    - beta is the number of \"failures\" + 1\n",
    "    here we'll thus set:\n",
    "    - alpha as the weighted average number of voters who'd vote for Harris\n",
    "    - beta as the weighted average number of voters who'd vote for Trump\n",
    "    these weighted averages use the weight_vote and weight_poll we defined in the arguments\n",
    "    \"\"\"\n",
    "    \n",
    "    #get indices for each logical polling situation\n",
    "    exists_trump_harris_polling = df.exists_trump_harris_poll.values\n",
    "    exists_trump_biden_polling = df.exists_trump_biden_poll.values\n",
    "    not_exists_polling = (~exists_trump_harris_polling) & (~exists_trump_biden_polling)\n",
    "    \n",
    "    #if we're not encforcing uncertainty, use sample size as number of votes\n",
    "    if baseline_uncertainty is None:\n",
    "        n_votes_vals = df['N Votes']\n",
    "    #otherwise, set sample size to allow uncertainty to be set at the given value\n",
    "    #if no Trump/Harris poll for a state, but there is Trump/Biden poll, 1.5x the uncertainty\n",
    "    #if no poll for a state, 2x the uncertainty\n",
    "    else:\n",
    "        n_votes_vals = 1/(4*baseline_uncertainty**2) - 3\n",
    "        n_votes_missing_harris_polling_vals = 1/(4*(1.5*baseline_uncertainty)**2) - 3\n",
    "        n_votes_missing_polling_vals = 1/(4*(2*baseline_uncertainty)**2) - 3\n",
    "     \n",
    "    #posterior alphas and betas for the Beta distribution of p(Harris) winning\n",
    "    alphas = weight_vote * n_votes_vals * df['Biden Vote Frac'] + weight_poll * n_votes_vals * df['Harris Poll Frac'] + 1\n",
    "    \n",
    "    betas = weight_vote * n_votes_vals * df['Trump Vote Frac'] + weight_poll * n_votes_vals * df['Trump Poll Frac'] + 1\n",
    "    \n",
    "    #for states that do not have Trump/Harris polling but do have Trump/Biden polling\n",
    "    alphas[exists_trump_biden_polling] = weight_vote * n_votes_missing_harris_polling_vals * df.iloc[exists_trump_biden_polling]['Biden Vote Frac'] + weight_poll * n_votes_missing_harris_polling_vals * df.iloc[exists_trump_biden_polling]['Biden Poll Frac'] + 1\n",
    "    betas[exists_trump_biden_polling] = weight_vote * n_votes_missing_harris_polling_vals * df.iloc[exists_trump_biden_polling]['Trump Vote Frac'] + weight_poll * n_votes_missing_harris_polling_vals * df.iloc[exists_trump_biden_polling]['Trump Poll Frac'] + 1\n",
    "    \n",
    "    #for states that have no polling data at all\n",
    "    alphas[not_exists_polling] = weight_vote * n_votes_missing_polling_vals * df.iloc[not_exists_polling]['Biden Vote Frac'] + weight_poll * n_votes_missing_polling_vals * HARRIS_NATIONAL_POLL_FRAC + 1\n",
    "    betas[not_exists_polling] = weight_vote * n_votes_missing_polling_vals * df.iloc[not_exists_polling]['Trump Vote Frac'] + weight_poll * n_votes_missing_polling_vals * TRUMP_NATIONAL_POLL_FRAC + 1\n",
    "\n",
    "    #using these alphas and betas, simulate the probability that Harris would win\n",
    "    p_wins = [np.random.beta(a,b) for a,b in zip(alphas, betas)]\n",
    "    harris_wins = np.array([p > 0.5 for p in p_wins])\n",
    "    harris_evotes = df[harris_wins]['Electoral Votes'].sum()\n",
    "    trump_evotes = df[~harris_wins]['Electoral Votes'].sum()\n",
    "    \n",
    "    if return_alphas_betas:\n",
    "        return harris_evotes, trump_evotes, alphas, betas\n",
    "    return harris_evotes, trump_evotes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "93602eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of simulations\n",
    "n_sims = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1426979d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the baseline uncertainty (this should get lower as we get closer to election day)\n",
    "BASELINE_UNCERTAINTY = 0.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ffa80798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.99\n",
      "0.02 0.98\n",
      "0.03 0.97\n",
      "0.04 0.96\n",
      "0.05 0.95\n",
      "0.06 0.94\n",
      "0.07 0.93\n",
      "0.08 0.92\n",
      "0.09 0.91\n",
      "0.1 0.9\n",
      "0.11 0.89\n",
      "0.12 0.88\n",
      "0.13 0.87\n",
      "0.14 0.86\n",
      "0.15 0.85\n",
      "0.16 0.84\n",
      "0.17 0.83\n",
      "0.18 0.82\n",
      "0.19 0.81\n",
      "0.2 0.8\n",
      "0.21 0.79\n",
      "0.22 0.78\n",
      "0.23 0.77\n",
      "0.24 0.76\n",
      "0.25 0.75\n",
      "0.26 0.74\n",
      "0.27 0.73\n",
      "0.28 0.72\n",
      "0.29 0.71\n",
      "0.3 0.7\n",
      "0.31 0.69\n",
      "0.32 0.68\n",
      "0.33 0.67\n",
      "0.34 0.66\n",
      "0.35 0.65\n",
      "0.36 0.64\n",
      "0.37 0.63\n",
      "0.38 0.62\n",
      "0.39 0.61\n",
      "0.4 0.6\n",
      "0.41 0.59\n",
      "0.42 0.58\n",
      "0.43 0.57\n",
      "0.44 0.56\n",
      "0.45 0.55\n",
      "0.46 0.54\n",
      "0.47 0.53\n",
      "0.48 0.52\n",
      "0.49 0.51\n",
      "0.5 0.5\n",
      "0.51 0.49\n",
      "0.52 0.48\n",
      "0.53 0.47\n",
      "0.54 0.46\n",
      "0.55 0.45\n",
      "0.56 0.44\n",
      "0.57 0.43\n",
      "0.58 0.42\n",
      "0.59 0.41\n",
      "0.6 0.4\n",
      "0.61 0.39\n",
      "0.62 0.38\n",
      "0.63 0.37\n",
      "0.64 0.36\n",
      "0.65 0.35\n",
      "0.66 0.34\n",
      "0.67 0.33\n",
      "0.68 0.32\n",
      "0.69 0.31\n",
      "0.7 0.3\n",
      "0.71 0.29\n",
      "0.72 0.28\n",
      "0.73 0.27\n",
      "0.74 0.26\n",
      "0.75 0.25\n",
      "0.76 0.24\n",
      "0.77 0.23\n",
      "0.78 0.22\n",
      "0.79 0.21\n",
      "0.8 0.2\n",
      "0.81 0.19\n",
      "0.82 0.18\n",
      "0.83 0.17\n",
      "0.84 0.16\n",
      "0.85 0.15\n",
      "0.86 0.14\n",
      "0.87 0.13\n",
      "0.88 0.12\n",
      "0.89 0.11\n",
      "0.9 0.1\n",
      "0.91 0.09\n",
      "0.92 0.08\n",
      "0.93 0.07\n",
      "0.94 0.06\n",
      "0.95 0.05\n",
      "0.96 0.04\n",
      "0.97 0.03\n",
      "0.98 0.02\n",
      "0.99 0.01\n",
      "1.0 0.0\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "#iterate over several choice of voting and polling weights\n",
    "for weight_vote in np.arange(0.01, 1.01, 0.01):\n",
    "    weight_vote = round(weight_vote, 10)\n",
    "    weight_poll = round(1-weight_vote, 10)\n",
    "    if weight_poll < 0:\n",
    "        continue\n",
    "    \n",
    "    print(weight_vote, weight_poll)\n",
    "    #do n_sims simulations\n",
    "    for _ in range(n_sims):\n",
    "        harris_evotes, trump_evotes = simulate_election(joined, weight_vote, weight_poll, BASELINE_UNCERTAINTY)\n",
    "        results.append([weight_vote, weight_poll, harris_evotes, trump_evotes])\n",
    "results = pd.DataFrame(columns=['weight_vote', 'weight_poll', 'harris_evotes', 'trump_evotes'], data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08f2a15a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#aggreate based on weight of polling data\n",
    "stats = results.groupby(\n",
    "    'weight_poll'\n",
    ").agg(\n",
    "    avg_harris_evotes = pd.NamedAgg('harris_evotes', np.mean),\n",
    "    dev_harris_evotes = pd.NamedAgg('harris_evotes', np.std),\n",
    "    avg_trump_evotes = pd.NamedAgg('trump_evotes', np.mean),\n",
    "    dev_trump_evotes = pd.NamedAgg('trump_evotes', np.std),\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "348b52f0",
   "metadata": {},
   "source": [
    "# Plot Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "784b90a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.errorbar(stats.weight_poll, stats.avg_harris_evotes, yerr=stats.dev_harris_evotes, color='cornflowerblue', linewidth=2, capsize=2)\n",
    "plt.errorbar(stats.weight_poll, stats.avg_trump_evotes, yerr=stats.dev_trump_evotes, color='firebrick', linewidth=2, capsize=2)\n",
    "plt.legend(['Harris', 'Trump'], fontsize=16, loc=1)\n",
    "plt.axhline(270, color='k', linestyle='--')\n",
    "plt.ylim(200, 340)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1), fontsize=18)\n",
    "plt.xlabel('Polling Trust Level', fontsize=22)\n",
    "plt.yticks(np.arange(200, 350, 20), fontsize=18)\n",
    "plt.ylabel('Electoral Votes', fontsize=22)\n",
    "plt.tight_layout()\n",
    "plt.savefig('election_simulations.png', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51d50c51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7852e375",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}