SocialWeb_12_18.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Set pandas display options\n",
    "pd.options.display.max_colwidth = 10000\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bk/vn3d52k94dlggw29lr3_w3wr0000gn/T/ipykernel_85645/1596480348.py:3: DtypeWarning:\n",
      "\n",
      "Columns (0,3,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load tweets into dataframe. Drop the state column.\n",
    "file = 'cleantweets.csv'\n",
    "df = pd.read_csv(file, sep=';').dropna(subset=['state'])\n",
    "df['text'] = df['text'].str.lower()\n",
    "\n",
    "# Drop the tweets that are not formatted correctly (15 tweets dropped)\n",
    "df = df.loc[~(df['id'].str.isdigit() == False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of tweets in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet total: 9455538\n"
     ]
    }
   ],
   "source": [
    "# Total tweets in dataframe\n",
    "print(f\"Tweet total: {len(df)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign a tweet to a candidate, both, or neither.\n",
    "df['candidate'] = np.where(\n",
    "        (df['text'].str.contains('trump') == True) & (df['text'].str.contains('biden') == True), \n",
    "        'Trump_Biden', 'NA')\n",
    "df['candidate'] = np.where(\n",
    "        (df['text'].str.contains('trump') == True) & (df['text'].str.contains('biden') == False), \n",
    "        'Trump', df['candidate'])\n",
    "df['candidate'] = np.where(\n",
    "        (df['text'].str.contains('biden') == True) & (df['text'].str.contains('trump') == False), \n",
    "        'Biden', df['candidate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tweets relating to each candidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trump/Biden tweets: 802761\n",
      "Trump tweets: 3710694\n",
      "Biden tweets: 1889374\n",
      "Unrelated tweets: 3052709\n"
     ]
    }
   ],
   "source": [
    "# Print the tweets for each candidate.\n",
    "print(f\"Trump/Biden tweets: {len(df[df['candidate'] == 'Trump_Biden'])}\")\n",
    "print(f\"Trump tweets: {len(df[df['candidate'] == 'Trump'])}\")\n",
    "print(f\"Biden tweets: {len(df[df['candidate'] == 'Biden'])}\")\n",
    "print(f\"Unrelated tweets: {len(df[df['candidate'] == 'NA'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tweets for Biden and Trump from each state.\n",
    "\n",
    "df2 = df.loc[(df['candidate'] != 'NA') & (df['candidate'] != 'Trump_Biden')]\n",
    "state_df = df2.groupby(['state']).size().sort_values(ascending=False).rename('Tweets_per_state').reset_index()\n",
    "state_df = state_df.merge(df2.groupby(['state', 'candidate']).size().rename('Tweets_per_candidate').reset_index())\n",
    "state_df = state_df[~(state_df['candidate'] == 'NA')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the tweets for each candidate in the top 10 states with the most tweets overall.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 8))\n",
    "sns.barplot(data=state_df.head(30),x='state', y='Tweets_per_candidate',hue='candidate')\n",
    "plt.title('Trump has more tweets in top 10 states with the most overall tweets', fontsize=28)\n",
    "plt.legend(loc=1, prop={'size': 20})\n",
    "ax.set_ylabel('Tweets', fontsize=20)\n",
    "ax.set_xlabel('State', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vader sentiment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentimentVerdict(sentiment):\n",
    "    if sentiment['compound'] >= 0.05:\n",
    "        return \"Positive\"\n",
    "    elif sentiment['compound'] <= -0.05:\n",
    "        return \"Negative\"\n",
    "    else:\n",
    "        return \"Neutral\"\n",
    "\n",
    "df2['sentiment_scores'] = df2['text'].apply(lambda x: sid.polarity_scores(x))\n",
    "df2['sentiment'] = df2['sentiment_scores'].apply(lambda x: sentimentVerdict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a small dataframe for plotting sentiment\n",
    "sent_df = df2.groupby(['candidate', 'sentiment']).size().rename('total_sent_per_candidate').reset_index()\n",
    "sent_df = sent_df.merge(df2.groupby(['candidate']).size().rename('total_tweets_per_candidate').reset_index())\n",
    "sent_df['sent_percent'] = sent_df['total_sent_per_candidate'] / sent_df['total_tweets_per_candidate'] * 100\n",
    "\n",
    "# Plot the sentiment percentage for each candidate.\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "sns.barplot(data=sent_df, x='sentiment', y='sent_percent' ,hue='candidate')\n",
    "plt.title('Positive tweets are more common for both candidates', fontsize=15)\n",
    "plt.legend(loc=0, prop={'size': 10})\n",
    "ax.set_ylabel('Sentiment%', fontsize=15)\n",
    "ax.set_xlabel('Sentiment', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment % pos neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a datafarame for predicting election results based on sentiment analysis.\n",
    "overview_df = pd.DataFrame()\n",
    "\n",
    "# Get the unique states\n",
    "overview_df['state'] = df2.state.unique()\n",
    "\n",
    "#Total number of tweets\n",
    "overview_df = overview_df.merge(df2.groupby('state')['id'].count().reset_index(name=\"total_tweets\"), on='state', how='left')\n",
    "\n",
    "#Number of tweets for Trump\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Trump') &\n",
    "                                 (df2['sentiment'] == 'Positive')].groupby('state')['id'].count().reset_index(name=\"Trump_P\"), on='state', how='left')\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Trump') &\n",
    "                                 (df2['sentiment'] == 'Negative')].groupby('state')['id'].count().reset_index(name=\"Trump_N\"), on='state', how='left')\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Trump') &\n",
    "                                 (df2['sentiment'] == 'Neutral')].groupby('state')['id'].count().reset_index(name=\"Trump_Na\"), on='state', how='left')\n",
    "#Number of tweets for Biden\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Biden') &\n",
    "                                 (df2['sentiment'] == 'Positive')].groupby('state')['id'].count().reset_index(name=\"Biden_P\"), on='state', how='left')\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Biden') &\n",
    "                                 (df2['sentiment'] == 'Negative')].groupby('state')['id'].count().reset_index(name=\"Biden_N\"), on='state', how='left')\n",
    "overview_df = overview_df.merge(df2[(df2['candidate'] == 'Biden') &\n",
    "                                 (df2['sentiment'] == 'Neutral')].groupby('state')['id'].count().reset_index(name=\"Biden_Na\"), on='state', how='left')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Positive tweets perc of total number of tweets\n",
    "overview_df['Trump_P_perc_total'] = overview_df['Trump_P'] / overview_df['total_tweets']\n",
    "overview_df['Biden_P_perc_total'] = overview_df['Biden_P'] / overview_df['total_tweets']\n",
    "\n",
    "# Positive tweets perc of number of tweets of each candidate\n",
    "overview_df['Trump_P_perc_only'] = overview_df['Trump_P'] / (overview_df['Trump_P'] + overview_df['Trump_N'] + overview_df['Trump_Na'])\n",
    "overview_df['Biden_P_perc_only'] = overview_df['Biden_P'] / (overview_df['Biden_P'] + overview_df['Biden_N'] + overview_df['Biden_Na'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# States codes for the map figure\n",
    "stateMapping = {}\n",
    "states = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New York', 'New Mexico', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n",
    "stateCodes = ['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV', 'WI', 'WY']\n",
    "stateMapping['state']= states\n",
    "stateMapping['state_code'] = stateCodes \n",
    "overview_df = overview_df.merge(pd.DataFrame.from_dict(stateMapping), on='state', how='left')\n"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2020 Election Real Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read in the final results of the election.\n",
    "real_result =  pd.read_csv('finalResult.csv', lineterminator='\\n')\n",
    "real_result = real_result.drop(['State'], axis=1)\n",
    "real_result = real_result.rename(columns={'newST': 'state_code'})\n",
    "real_result['winner'] = real_result['won'].str.replace(\"T\", \"Trump\").replace(\"B\", \"Biden\")\n",
    "\n",
    "# Make Biden's color blue.\n",
    "d = {0: 2, 2: 0}\n",
    "real_result = real_result.rename(d).sort_index()\n",
    "\n",
    "#Drop the unnecessary row\n",
    "real_result = real_result.drop([52])\n",
    "\n",
    "# Join actual election results with overview_df.\n",
    "overview_df = overview_df.merge(real_result[[\"state_code\", \"winner\"]], on='state_code', how='left')"
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   "source": [
    "# Print the election map showing the actual results of the 2020 US presidential election.\n",
    "fig = px.choropleth(real_result, \n",
    "                    color='winner', \n",
    "                    locations=\"state_code\", \n",
    "                    locationmode=\"USA-states\", \n",
    "                    scope=\"usa\",\n",
    "                    title=f\"2020 Actual U.S. Presidential Election Map\")\n",
    "\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1: Predicted election accuracy for winner candidate with highest respective positive tweets to each candidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
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    "# Plot the results of the election based on method 1: Winner is candidate with highest respective positive tweets.\n",
    "overview_df['predicted_winner'] = np.where(overview_df['Trump_P_perc_only'] > overview_df['Biden_P_perc_only'], 'Trump', 'Biden')\n",
    "\n",
    "# Get the predicted election accutacy.\n",
    "overview_df[\"correct\"] = np.where(overview_df[\"predicted_winner\"] == overview_df[\"winner\"], 1, 0)\n",
    "accuracy = np.sum(overview_df[\"correct\"]) / len(overview_df[\"correct\"])\n",
    "\n",
    "fig = px.choropleth(\n",
    "    overview_df, \n",
    "    color='predicted_winner', \n",
    "    locations='state_code', \n",
    "    locationmode=\"USA-states\", \n",
    "    scope=\"usa\",\n",
    "    title=f\"2020 Predicted U.S. Presidential Election Map <br><sup>Accuracy {round(accuracy,2)*100}%</sup>\")\n",
    "\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2:  Predicted election accuracy for winner candidate with highest positive and highest of opposite candidate negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
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    "# Plot the results of the election based on method 2:\n",
    "overview_df['predicted_winner'] = np.where(overview_df[\"Trump_P\"] + overview_df[\"Biden_N\"] > \n",
    "                                           overview_df[\"Biden_P\"] + overview_df[\"Trump_N\"], 'Trump', 'Biden')\n",
    "overview_df[\"correct\"] = np.where(overview_df[\"predicted_winner\"] == overview_df[\"winner\"], 1, 0)\n",
    "accuracy = np.sum(overview_df[\"correct\"]) / len(overview_df[\"correct\"])\n",
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    "                    color='predicted_winner', \n",
    "                    locations='state_code', \n",
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    "                    scope=\"usa\", \n",
    "                    title=f\"2020 Predicted U.S. Presidential Election Map <br><sup>Accuracy {round(accuracy,2)* 100}%</sup>\")\n",
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    "### Method 3: Predicted election accuracy for winner candidate with lowest respective negative tweets"
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    "# Plot the results of the election based on method 3:\n",
    "\n",
    "# Negative tweets perc of total number of tweets\n",
    "overview_df['Trump_N_perc_total'] = overview_df['Trump_N'] / overview_df['total_tweets']\n",
    "overview_df['Biden_N_perc_total'] = overview_df['Biden_N'] / overview_df['total_tweets']\n",
    "\n",
    "# Negative tweets perc of numver of tweets of each candidate\n",
    "overview_df['Trump_N_perc_only'] = overview_df['Trump_N'] / (overview_df['Trump_P'] + overview_df['Trump_N'] + overview_df['Trump_Na'])\n",
    "overview_df['Biden_N_perc_only'] = overview_df['Biden_N'] / (overview_df['Biden_P'] + overview_df['Biden_N'] + overview_df['Biden_Na'])\n",
    "\n",
    "overview_df['predicted_winner'] = np.where(overview_df[\"Trump_N_perc_only\"] > \n",
    "                                            overview_df[\"Biden_N_perc_only\"], 'Biden', 'Trump')\n",
    "\n",
    "\n",
    "overview_df[\"correct\"] = np.where(overview_df[\"predicted_winner\"] == overview_df[\"winner\"], 1, 0)\n",
    "accuracy = np.sum(overview_df[\"correct\"]) / len(overview_df[\"correct\"])\n",
    "\n",
    "\n",
    "fig = px.choropleth(overview_df, \n",
    "                    color='predicted_winner', \n",
    "                    locations='state_code', \n",
    "                    locationmode=\"USA-states\", \n",
    "                    scope=\"usa\", \n",
    "                    title=f\"2020 Predicted U.S. Presidential Election Map <br><sup>Accuracy {round(accuracy,1)*100}%</sup>\")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real elections with swing states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
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   "source": [
    "#we consider it a swing state if during the real election there was a difference of less than 5 %\n",
    "#Arizona -> biden, florida -> Trump, Georiga -> Biden, Michigan -> Biden, Nevada -> Biden, North carolina -> Trump\n",
    "#, Pennysilvina _ biden, wisconsin -> Biden\n",
    "real_result['Swing state to'] = real_result['winner']\n",
    "real_result.loc[real_result['state_code'] == 'AZ' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'GA' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'MI' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'NV' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'PA' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'WI' ,['Swing state to']] = 'Swing state to Biden'\n",
    "real_result.loc[real_result['state_code'] == 'FL' ,['Swing state to']] = 'Swing state to Trump'\n",
    "real_result.loc[real_result['state_code'] == 'NC' ,['Swing state to']] = 'Swing state to Trump'"
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    "fig = px.choropleth(locations=real_result['state_code'], \n",
    "                    locationmode=\"USA-states\",\n",
    "                    color=real_result['Swing state to'],\n",
    "                    color_discrete_map={'Trump':'rgb(200, 0, 0)', 'Swing state to Trump': 'rgb(300, 40, 40)', 'Biden':'rgb(0, 0, 200)', 'Swing state to Biden': 'rgb(40, 40, 300)'},scope=\"usa\",\n",
    "                    title=f\"2020 Actual U.S. Presidential Election Map (Swing States)\")\n",
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    "fig.show()"
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  {
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   "source": [
    "# Our results with swing states"
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   "source": [
    "Method 1 has the highest accuracy among the others. So, we are going to continue with the result of Method 1 from now on."
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  {
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   "execution_count": 104,
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    "overview_df['predicted_winner'] = np.where(overview_df['Trump_P_perc_only'] > overview_df['Biden_P_perc_only'], 'Trump', 'Biden')\n",
    "overview_df['predicted_winner_swing'] = overview_df['predicted_winner']\n",
    "overview_df['predicted_winner_swing'].loc[indices_trump] = 'Swing state to Trump'\n",
    "overview_df['predicted_winner_swing'].loc[indices_biden] = 'Swing state to Biden'\n"
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Actual U.S. Presidential Election Map (Swing States)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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   "source": [
    "fig = px.choropleth(locations=overview_df['state_code'], \n",
    "                    locationmode=\"USA-states\",\n",
    "                    color=overview_df['predicted_winner_swing'],\n",
    "                    color_discrete_map={'Trump':'rgb(200, 0, 0)', 'Swing state to Trump': 'rgb(300, 40, 40)', 'Biden':'rgb(0, 0, 200)', 'Swing state to Biden': 'rgb(40, 40, 300)'},scope=\"usa\",\n",
    "                    title=f\"2020 Predicted U.S. Presidential Election Map (Swing States)\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results in our election"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>total_tweets</th>\n",
       "      <th>Trump_P</th>\n",
       "      <th>Trump_N</th>\n",
       "      <th>Trump_Na</th>\n",
       "      <th>Biden_P</th>\n",
       "      <th>Biden_N</th>\n",
       "      <th>Biden_Na</th>\n",
       "      <th>Trump_P_perc_total</th>\n",
       "      <th>Biden_P_perc_total</th>\n",
       "      <th>...</th>\n",
       "      <th>state_code</th>\n",
       "      <th>winner</th>\n",
       "      <th>predicted_winner</th>\n",
       "      <th>correct</th>\n",
       "      <th>Trump_N_perc_total</th>\n",
       "      <th>Biden_N_perc_total</th>\n",
       "      <th>Trump_N_perc_only</th>\n",
       "      <th>Biden_N_perc_only</th>\n",
       "      <th>predicted_winner_swing</th>\n",
       "      <th>Electoral votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>196670</td>\n",
       "      <td>52686</td>\n",
       "      <td>44664</td>\n",
       "      <td>32826</td>\n",
       "      <td>27436</td>\n",
       "      <td>16235</td>\n",
       "      <td>22823</td>\n",
       "      <td>0.267890</td>\n",
       "      <td>0.139503</td>\n",
       "      <td>...</td>\n",
       "      <td>PA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.227101</td>\n",
       "      <td>0.082549</td>\n",
       "      <td>0.343105</td>\n",
       "      <td>0.244157</td>\n",
       "      <td>Biden</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>125193</td>\n",
       "      <td>33783</td>\n",
       "      <td>28596</td>\n",
       "      <td>20680</td>\n",
       "      <td>17168</td>\n",
       "      <td>10393</td>\n",
       "      <td>14573</td>\n",
       "      <td>0.269847</td>\n",
       "      <td>0.137132</td>\n",
       "      <td>...</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.228415</td>\n",
       "      <td>0.083016</td>\n",
       "      <td>0.344285</td>\n",
       "      <td>0.246665</td>\n",
       "      <td>Swing state to Biden</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>153582</td>\n",
       "      <td>40696</td>\n",
       "      <td>36362</td>\n",
       "      <td>25681</td>\n",
       "      <td>20913</td>\n",
       "      <td>12336</td>\n",
       "      <td>17594</td>\n",
       "      <td>0.264979</td>\n",
       "      <td>0.136168</td>\n",
       "      <td>...</td>\n",
       "      <td>MI</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.236760</td>\n",
       "      <td>0.080322</td>\n",
       "      <td>0.353926</td>\n",
       "      <td>0.242629</td>\n",
       "      <td>Biden</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>67763</td>\n",
       "      <td>18979</td>\n",
       "      <td>15187</td>\n",
       "      <td>11123</td>\n",
       "      <td>8968</td>\n",
       "      <td>5719</td>\n",
       "      <td>7787</td>\n",
       "      <td>0.280079</td>\n",
       "      <td>0.132344</td>\n",
       "      <td>...</td>\n",
       "      <td>NV</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.224119</td>\n",
       "      <td>0.084397</td>\n",
       "      <td>0.335335</td>\n",
       "      <td>0.254472</td>\n",
       "      <td>Trump</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Florida</td>\n",
       "      <td>522636</td>\n",
       "      <td>153555</td>\n",
       "      <td>111233</td>\n",
       "      <td>87552</td>\n",
       "      <td>65108</td>\n",
       "      <td>45540</td>\n",
       "      <td>59648</td>\n",
       "      <td>0.293809</td>\n",
       "      <td>0.124576</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.212831</td>\n",
       "      <td>0.087135</td>\n",
       "      <td>0.315698</td>\n",
       "      <td>0.267417</td>\n",
       "      <td>Trump</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>California</td>\n",
       "      <td>762340</td>\n",
       "      <td>198997</td>\n",
       "      <td>180620</td>\n",
       "      <td>125280</td>\n",
       "      <td>109793</td>\n",
       "      <td>58759</td>\n",
       "      <td>88891</td>\n",
       "      <td>0.261034</td>\n",
       "      <td>0.144021</td>\n",
       "      <td>...</td>\n",
       "      <td>CA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.236928</td>\n",
       "      <td>0.077077</td>\n",
       "      <td>0.357736</td>\n",
       "      <td>0.228241</td>\n",
       "      <td>Biden</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>92046</td>\n",
       "      <td>23529</td>\n",
       "      <td>20696</td>\n",
       "      <td>15253</td>\n",
       "      <td>14396</td>\n",
       "      <td>6969</td>\n",
       "      <td>11203</td>\n",
       "      <td>0.255622</td>\n",
       "      <td>0.156400</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.224844</td>\n",
       "      <td>0.075712</td>\n",
       "      <td>0.347961</td>\n",
       "      <td>0.213983</td>\n",
       "      <td>Biden</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>70770</td>\n",
       "      <td>20602</td>\n",
       "      <td>14844</td>\n",
       "      <td>11572</td>\n",
       "      <td>9040</td>\n",
       "      <td>6371</td>\n",
       "      <td>8341</td>\n",
       "      <td>0.291112</td>\n",
       "      <td>0.127738</td>\n",
       "      <td>...</td>\n",
       "      <td>SC</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209750</td>\n",
       "      <td>0.090024</td>\n",
       "      <td>0.315709</td>\n",
       "      <td>0.268230</td>\n",
       "      <td>Trump</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New York</td>\n",
       "      <td>389320</td>\n",
       "      <td>101574</td>\n",
       "      <td>91989</td>\n",
       "      <td>64100</td>\n",
       "      <td>56063</td>\n",
       "      <td>30460</td>\n",
       "      <td>45134</td>\n",
       "      <td>0.260901</td>\n",
       "      <td>0.144002</td>\n",
       "      <td>...</td>\n",
       "      <td>NM</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.236281</td>\n",
       "      <td>0.078239</td>\n",
       "      <td>0.357013</td>\n",
       "      <td>0.231359</td>\n",
       "      <td>Biden</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>155901</td>\n",
       "      <td>43142</td>\n",
       "      <td>32916</td>\n",
       "      <td>24933</td>\n",
       "      <td>21838</td>\n",
       "      <td>13548</td>\n",
       "      <td>19524</td>\n",
       "      <td>0.276727</td>\n",
       "      <td>0.140076</td>\n",
       "      <td>...</td>\n",
       "      <td>GA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.211134</td>\n",
       "      <td>0.086901</td>\n",
       "      <td>0.325930</td>\n",
       "      <td>0.246731</td>\n",
       "      <td>Trump</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>29285</td>\n",
       "      <td>8492</td>\n",
       "      <td>6784</td>\n",
       "      <td>4684</td>\n",
       "      <td>3698</td>\n",
       "      <td>2493</td>\n",
       "      <td>3134</td>\n",
       "      <td>0.289978</td>\n",
       "      <td>0.126276</td>\n",
       "      <td>...</td>\n",
       "      <td>HI</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231654</td>\n",
       "      <td>0.085129</td>\n",
       "      <td>0.339880</td>\n",
       "      <td>0.267346</td>\n",
       "      <td>Trump</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>160701</td>\n",
       "      <td>44876</td>\n",
       "      <td>35994</td>\n",
       "      <td>26804</td>\n",
       "      <td>20994</td>\n",
       "      <td>13768</td>\n",
       "      <td>18265</td>\n",
       "      <td>0.279252</td>\n",
       "      <td>0.130640</td>\n",
       "      <td>...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Trump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.223981</td>\n",
       "      <td>0.085675</td>\n",
       "      <td>0.334287</td>\n",
       "      <td>0.259641</td>\n",
       "      <td>Trump</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Texas</td>\n",
       "      <td>560388</td>\n",
       "      <td>160156</td>\n",
       "      <td>116094</td>\n",
       "      <td>89407</td>\n",
       "      <td>75733</td>\n",
       "      <td>50497</td>\n",
       "      <td>68501</td>\n",
       "      <td>0.285795</td>\n",
       "      <td>0.135144</td>\n",
       "      <td>...</td>\n",
       "      <td>TX</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.207167</td>\n",
       "      <td>0.090111</td>\n",
       "      <td>0.317494</td>\n",
       "      <td>0.259317</td>\n",
       "      <td>Trump</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>80642</td>\n",
       "      <td>22646</td>\n",
       "      <td>17909</td>\n",
       "      <td>13105</td>\n",
       "      <td>10671</td>\n",
       "      <td>6861</td>\n",
       "      <td>9450</td>\n",
       "      <td>0.280821</td>\n",
       "      <td>0.132326</td>\n",
       "      <td>...</td>\n",
       "      <td>IN</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.222080</td>\n",
       "      <td>0.085080</td>\n",
       "      <td>0.333750</td>\n",
       "      <td>0.254281</td>\n",
       "      <td>Trump</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>119477</td>\n",
       "      <td>30054</td>\n",
       "      <td>29087</td>\n",
       "      <td>19511</td>\n",
       "      <td>17585</td>\n",
       "      <td>9337</td>\n",
       "      <td>13903</td>\n",
       "      <td>0.251546</td>\n",
       "      <td>0.147183</td>\n",
       "      <td>...</td>\n",
       "      <td>MA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.243453</td>\n",
       "      <td>0.078149</td>\n",
       "      <td>0.369819</td>\n",
       "      <td>0.228708</td>\n",
       "      <td>Biden</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>143369</td>\n",
       "      <td>38101</td>\n",
       "      <td>32550</td>\n",
       "      <td>23359</td>\n",
       "      <td>20419</td>\n",
       "      <td>12057</td>\n",
       "      <td>16883</td>\n",
       "      <td>0.265755</td>\n",
       "      <td>0.142423</td>\n",
       "      <td>...</td>\n",
       "      <td>VA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.227037</td>\n",
       "      <td>0.084098</td>\n",
       "      <td>0.346240</td>\n",
       "      <td>0.244272</td>\n",
       "      <td>Biden</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>163858</td>\n",
       "      <td>41427</td>\n",
       "      <td>38797</td>\n",
       "      <td>27261</td>\n",
       "      <td>23661</td>\n",
       "      <td>13101</td>\n",
       "      <td>19611</td>\n",
       "      <td>0.252823</td>\n",
       "      <td>0.144399</td>\n",
       "      <td>...</td>\n",
       "      <td>IL</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.236772</td>\n",
       "      <td>0.079953</td>\n",
       "      <td>0.360953</td>\n",
       "      <td>0.232398</td>\n",
       "      <td>Biden</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>113077</td>\n",
       "      <td>29491</td>\n",
       "      <td>27397</td>\n",
       "      <td>18423</td>\n",
       "      <td>15774</td>\n",
       "      <td>8955</td>\n",
       "      <td>13037</td>\n",
       "      <td>0.260805</td>\n",
       "      <td>0.139498</td>\n",
       "      <td>...</td>\n",
       "      <td>CO</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.242286</td>\n",
       "      <td>0.079194</td>\n",
       "      <td>0.363785</td>\n",
       "      <td>0.237118</td>\n",
       "      <td>Biden</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>104634</td>\n",
       "      <td>27305</td>\n",
       "      <td>26443</td>\n",
       "      <td>17493</td>\n",
       "      <td>14092</td>\n",
       "      <td>8075</td>\n",
       "      <td>11226</td>\n",
       "      <td>0.260957</td>\n",
       "      <td>0.134679</td>\n",
       "      <td>...</td>\n",
       "      <td>OR</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.252719</td>\n",
       "      <td>0.077174</td>\n",
       "      <td>0.371177</td>\n",
       "      <td>0.241817</td>\n",
       "      <td>Biden</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>56058</td>\n",
       "      <td>16519</td>\n",
       "      <td>11101</td>\n",
       "      <td>8978</td>\n",
       "      <td>7383</td>\n",
       "      <td>5210</td>\n",
       "      <td>6867</td>\n",
       "      <td>0.294677</td>\n",
       "      <td>0.131703</td>\n",
       "      <td>...</td>\n",
       "      <td>LA</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.198027</td>\n",
       "      <td>0.092939</td>\n",
       "      <td>0.303323</td>\n",
       "      <td>0.267729</td>\n",
       "      <td>Trump</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>72381</td>\n",
       "      <td>18853</td>\n",
       "      <td>16842</td>\n",
       "      <td>12072</td>\n",
       "      <td>10275</td>\n",
       "      <td>5831</td>\n",
       "      <td>8508</td>\n",
       "      <td>0.260469</td>\n",
       "      <td>0.141957</td>\n",
       "      <td>...</td>\n",
       "      <td>WI</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.232685</td>\n",
       "      <td>0.080560</td>\n",
       "      <td>0.352587</td>\n",
       "      <td>0.236898</td>\n",
       "      <td>Biden</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Utah</td>\n",
       "      <td>34336</td>\n",
       "      <td>9069</td>\n",
       "      <td>8268</td>\n",
       "      <td>5529</td>\n",
       "      <td>4774</td>\n",
       "      <td>2732</td>\n",
       "      <td>3964</td>\n",
       "      <td>0.264125</td>\n",
       "      <td>0.139038</td>\n",
       "      <td>...</td>\n",
       "      <td>UT</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.240797</td>\n",
       "      <td>0.079567</td>\n",
       "      <td>0.361585</td>\n",
       "      <td>0.238187</td>\n",
       "      <td>Biden</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>83859</td>\n",
       "      <td>21641</td>\n",
       "      <td>20008</td>\n",
       "      <td>13815</td>\n",
       "      <td>11943</td>\n",
       "      <td>6777</td>\n",
       "      <td>9675</td>\n",
       "      <td>0.258064</td>\n",
       "      <td>0.142418</td>\n",
       "      <td>...</td>\n",
       "      <td>MN</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.238591</td>\n",
       "      <td>0.080814</td>\n",
       "      <td>0.360738</td>\n",
       "      <td>0.238669</td>\n",
       "      <td>Biden</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>87538</td>\n",
       "      <td>22322</td>\n",
       "      <td>20657</td>\n",
       "      <td>14330</td>\n",
       "      <td>12668</td>\n",
       "      <td>7116</td>\n",
       "      <td>10445</td>\n",
       "      <td>0.254998</td>\n",
       "      <td>0.144714</td>\n",
       "      <td>...</td>\n",
       "      <td>MD</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.235978</td>\n",
       "      <td>0.081290</td>\n",
       "      <td>0.360449</td>\n",
       "      <td>0.235403</td>\n",
       "      <td>Biden</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>17127</td>\n",
       "      <td>5046</td>\n",
       "      <td>4013</td>\n",
       "      <td>2889</td>\n",
       "      <td>2015</td>\n",
       "      <td>1418</td>\n",
       "      <td>1746</td>\n",
       "      <td>0.294623</td>\n",
       "      <td>0.117650</td>\n",
       "      <td>...</td>\n",
       "      <td>AK</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.234308</td>\n",
       "      <td>0.082793</td>\n",
       "      <td>0.335872</td>\n",
       "      <td>0.273798</td>\n",
       "      <td>Trump</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>34703</td>\n",
       "      <td>9290</td>\n",
       "      <td>8009</td>\n",
       "      <td>5941</td>\n",
       "      <td>4677</td>\n",
       "      <td>2711</td>\n",
       "      <td>4075</td>\n",
       "      <td>0.267700</td>\n",
       "      <td>0.134772</td>\n",
       "      <td>...</td>\n",
       "      <td>IA</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.230787</td>\n",
       "      <td>0.078120</td>\n",
       "      <td>0.344621</td>\n",
       "      <td>0.236500</td>\n",
       "      <td>Biden</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>24502</td>\n",
       "      <td>7779</td>\n",
       "      <td>4972</td>\n",
       "      <td>4042</td>\n",
       "      <td>2851</td>\n",
       "      <td>2150</td>\n",
       "      <td>2708</td>\n",
       "      <td>0.317484</td>\n",
       "      <td>0.116358</td>\n",
       "      <td>...</td>\n",
       "      <td>MS</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.202922</td>\n",
       "      <td>0.087748</td>\n",
       "      <td>0.296076</td>\n",
       "      <td>0.278895</td>\n",
       "      <td>Trump</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>53499</td>\n",
       "      <td>15619</td>\n",
       "      <td>10739</td>\n",
       "      <td>8783</td>\n",
       "      <td>6847</td>\n",
       "      <td>5088</td>\n",
       "      <td>6423</td>\n",
       "      <td>0.291949</td>\n",
       "      <td>0.127984</td>\n",
       "      <td>...</td>\n",
       "      <td>OK</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.200733</td>\n",
       "      <td>0.095105</td>\n",
       "      <td>0.305597</td>\n",
       "      <td>0.277154</td>\n",
       "      <td>Trump</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>145268</td>\n",
       "      <td>39944</td>\n",
       "      <td>32726</td>\n",
       "      <td>23814</td>\n",
       "      <td>19720</td>\n",
       "      <td>11963</td>\n",
       "      <td>17101</td>\n",
       "      <td>0.274968</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>...</td>\n",
       "      <td>NC</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225280</td>\n",
       "      <td>0.082351</td>\n",
       "      <td>0.339186</td>\n",
       "      <td>0.245224</td>\n",
       "      <td>Trump</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>58027</td>\n",
       "      <td>16341</td>\n",
       "      <td>12296</td>\n",
       "      <td>9382</td>\n",
       "      <td>7762</td>\n",
       "      <td>5258</td>\n",
       "      <td>6988</td>\n",
       "      <td>0.281610</td>\n",
       "      <td>0.133765</td>\n",
       "      <td>...</td>\n",
       "      <td>KY</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.211901</td>\n",
       "      <td>0.090613</td>\n",
       "      <td>0.323417</td>\n",
       "      <td>0.262795</td>\n",
       "      <td>Trump</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>177339</td>\n",
       "      <td>48128</td>\n",
       "      <td>39849</td>\n",
       "      <td>29677</td>\n",
       "      <td>24187</td>\n",
       "      <td>14798</td>\n",
       "      <td>20700</td>\n",
       "      <td>0.271390</td>\n",
       "      <td>0.136388</td>\n",
       "      <td>...</td>\n",
       "      <td>OH</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.224705</td>\n",
       "      <td>0.083445</td>\n",
       "      <td>0.338697</td>\n",
       "      <td>0.247935</td>\n",
       "      <td>Swing state to Trump</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>64477</td>\n",
       "      <td>18215</td>\n",
       "      <td>14091</td>\n",
       "      <td>10614</td>\n",
       "      <td>8326</td>\n",
       "      <td>5757</td>\n",
       "      <td>7474</td>\n",
       "      <td>0.282504</td>\n",
       "      <td>0.129131</td>\n",
       "      <td>...</td>\n",
       "      <td>MO</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.218543</td>\n",
       "      <td>0.089288</td>\n",
       "      <td>0.328308</td>\n",
       "      <td>0.267059</td>\n",
       "      <td>Trump</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>9817</td>\n",
       "      <td>2700</td>\n",
       "      <td>2098</td>\n",
       "      <td>1589</td>\n",
       "      <td>1470</td>\n",
       "      <td>841</td>\n",
       "      <td>1119</td>\n",
       "      <td>0.275033</td>\n",
       "      <td>0.149740</td>\n",
       "      <td>...</td>\n",
       "      <td>SD</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.213711</td>\n",
       "      <td>0.085668</td>\n",
       "      <td>0.328480</td>\n",
       "      <td>0.245190</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>103691</td>\n",
       "      <td>31362</td>\n",
       "      <td>21059</td>\n",
       "      <td>17195</td>\n",
       "      <td>12772</td>\n",
       "      <td>9282</td>\n",
       "      <td>12021</td>\n",
       "      <td>0.302456</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>...</td>\n",
       "      <td>TN</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.203094</td>\n",
       "      <td>0.089516</td>\n",
       "      <td>0.302502</td>\n",
       "      <td>0.272399</td>\n",
       "      <td>Trump</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>25276</td>\n",
       "      <td>7217</td>\n",
       "      <td>5622</td>\n",
       "      <td>4156</td>\n",
       "      <td>3330</td>\n",
       "      <td>2064</td>\n",
       "      <td>2887</td>\n",
       "      <td>0.285528</td>\n",
       "      <td>0.131746</td>\n",
       "      <td>...</td>\n",
       "      <td>NE</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.222424</td>\n",
       "      <td>0.081658</td>\n",
       "      <td>0.330803</td>\n",
       "      <td>0.249245</td>\n",
       "      <td>Trump</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>63579</td>\n",
       "      <td>19229</td>\n",
       "      <td>12327</td>\n",
       "      <td>10335</td>\n",
       "      <td>7917</td>\n",
       "      <td>6145</td>\n",
       "      <td>7626</td>\n",
       "      <td>0.302443</td>\n",
       "      <td>0.124522</td>\n",
       "      <td>...</td>\n",
       "      <td>AL</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.193885</td>\n",
       "      <td>0.096651</td>\n",
       "      <td>0.294264</td>\n",
       "      <td>0.283336</td>\n",
       "      <td>Trump</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>48705</td>\n",
       "      <td>12780</td>\n",
       "      <td>10990</td>\n",
       "      <td>7873</td>\n",
       "      <td>6974</td>\n",
       "      <td>4225</td>\n",
       "      <td>5863</td>\n",
       "      <td>0.262396</td>\n",
       "      <td>0.143189</td>\n",
       "      <td>...</td>\n",
       "      <td>KS</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225644</td>\n",
       "      <td>0.086747</td>\n",
       "      <td>0.347312</td>\n",
       "      <td>0.247626</td>\n",
       "      <td>Swing state to Biden</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Montana</td>\n",
       "      <td>18102</td>\n",
       "      <td>5116</td>\n",
       "      <td>4035</td>\n",
       "      <td>3028</td>\n",
       "      <td>2360</td>\n",
       "      <td>1464</td>\n",
       "      <td>2099</td>\n",
       "      <td>0.282621</td>\n",
       "      <td>0.130372</td>\n",
       "      <td>...</td>\n",
       "      <td>MT</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.222904</td>\n",
       "      <td>0.080875</td>\n",
       "      <td>0.331308</td>\n",
       "      <td>0.247172</td>\n",
       "      <td>Trump</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>18595</td>\n",
       "      <td>4464</td>\n",
       "      <td>4350</td>\n",
       "      <td>3023</td>\n",
       "      <td>2783</td>\n",
       "      <td>1547</td>\n",
       "      <td>2428</td>\n",
       "      <td>0.240065</td>\n",
       "      <td>0.149664</td>\n",
       "      <td>...</td>\n",
       "      <td>DE</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.233934</td>\n",
       "      <td>0.083194</td>\n",
       "      <td>0.367492</td>\n",
       "      <td>0.228914</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>37377</td>\n",
       "      <td>10750</td>\n",
       "      <td>7829</td>\n",
       "      <td>6051</td>\n",
       "      <td>4857</td>\n",
       "      <td>3439</td>\n",
       "      <td>4451</td>\n",
       "      <td>0.287610</td>\n",
       "      <td>0.129946</td>\n",
       "      <td>...</td>\n",
       "      <td>AR</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209460</td>\n",
       "      <td>0.092008</td>\n",
       "      <td>0.317864</td>\n",
       "      <td>0.269789</td>\n",
       "      <td>Trump</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Washington</td>\n",
       "      <td>153095</td>\n",
       "      <td>38658</td>\n",
       "      <td>37986</td>\n",
       "      <td>25355</td>\n",
       "      <td>21987</td>\n",
       "      <td>12012</td>\n",
       "      <td>17097</td>\n",
       "      <td>0.252510</td>\n",
       "      <td>0.143617</td>\n",
       "      <td>...</td>\n",
       "      <td>WA</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.248120</td>\n",
       "      <td>0.078461</td>\n",
       "      <td>0.372415</td>\n",
       "      <td>0.235087</td>\n",
       "      <td>Biden</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>23206</td>\n",
       "      <td>6481</td>\n",
       "      <td>5106</td>\n",
       "      <td>3903</td>\n",
       "      <td>2988</td>\n",
       "      <td>2088</td>\n",
       "      <td>2640</td>\n",
       "      <td>0.279281</td>\n",
       "      <td>0.128760</td>\n",
       "      <td>...</td>\n",
       "      <td>ID</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.220029</td>\n",
       "      <td>0.089977</td>\n",
       "      <td>0.329632</td>\n",
       "      <td>0.270607</td>\n",
       "      <td>Trump</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>29500</td>\n",
       "      <td>7678</td>\n",
       "      <td>7119</td>\n",
       "      <td>4953</td>\n",
       "      <td>4044</td>\n",
       "      <td>2266</td>\n",
       "      <td>3440</td>\n",
       "      <td>0.260271</td>\n",
       "      <td>0.137085</td>\n",
       "      <td>...</td>\n",
       "      <td>NY</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.241322</td>\n",
       "      <td>0.076814</td>\n",
       "      <td>0.360456</td>\n",
       "      <td>0.232410</td>\n",
       "      <td>Biden</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>49638</td>\n",
       "      <td>12291</td>\n",
       "      <td>11810</td>\n",
       "      <td>8023</td>\n",
       "      <td>7392</td>\n",
       "      <td>3955</td>\n",
       "      <td>6167</td>\n",
       "      <td>0.247613</td>\n",
       "      <td>0.148918</td>\n",
       "      <td>...</td>\n",
       "      <td>CT</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.237923</td>\n",
       "      <td>0.079677</td>\n",
       "      <td>0.367638</td>\n",
       "      <td>0.225819</td>\n",
       "      <td>Biden</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>7540</td>\n",
       "      <td>2237</td>\n",
       "      <td>1637</td>\n",
       "      <td>1306</td>\n",
       "      <td>921</td>\n",
       "      <td>643</td>\n",
       "      <td>796</td>\n",
       "      <td>0.296684</td>\n",
       "      <td>0.122149</td>\n",
       "      <td>...</td>\n",
       "      <td>WY</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.217109</td>\n",
       "      <td>0.085279</td>\n",
       "      <td>0.316023</td>\n",
       "      <td>0.272458</td>\n",
       "      <td>Trump</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Maine</td>\n",
       "      <td>26817</td>\n",
       "      <td>7062</td>\n",
       "      <td>6821</td>\n",
       "      <td>4744</td>\n",
       "      <td>3446</td>\n",
       "      <td>2017</td>\n",
       "      <td>2727</td>\n",
       "      <td>0.263340</td>\n",
       "      <td>0.128501</td>\n",
       "      <td>...</td>\n",
       "      <td>ME</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.254354</td>\n",
       "      <td>0.075213</td>\n",
       "      <td>0.366189</td>\n",
       "      <td>0.246276</td>\n",
       "      <td>Biden</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>7635</td>\n",
       "      <td>2221</td>\n",
       "      <td>1592</td>\n",
       "      <td>1150</td>\n",
       "      <td>1032</td>\n",
       "      <td>672</td>\n",
       "      <td>968</td>\n",
       "      <td>0.290897</td>\n",
       "      <td>0.135167</td>\n",
       "      <td>...</td>\n",
       "      <td>WV</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Trump</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208513</td>\n",
       "      <td>0.088016</td>\n",
       "      <td>0.320774</td>\n",
       "      <td>0.251497</td>\n",
       "      <td>Trump</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>15276</td>\n",
       "      <td>4025</td>\n",
       "      <td>3624</td>\n",
       "      <td>2477</td>\n",
       "      <td>2113</td>\n",
       "      <td>1204</td>\n",
       "      <td>1833</td>\n",
       "      <td>0.263485</td>\n",
       "      <td>0.138322</td>\n",
       "      <td>...</td>\n",
       "      <td>RI</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.237235</td>\n",
       "      <td>0.078816</td>\n",
       "      <td>0.357891</td>\n",
       "      <td>0.233786</td>\n",
       "      <td>Biden</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>23263</td>\n",
       "      <td>6131</td>\n",
       "      <td>5375</td>\n",
       "      <td>3654</td>\n",
       "      <td>3398</td>\n",
       "      <td>1986</td>\n",
       "      <td>2719</td>\n",
       "      <td>0.263552</td>\n",
       "      <td>0.146069</td>\n",
       "      <td>...</td>\n",
       "      <td>NH</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231054</td>\n",
       "      <td>0.085372</td>\n",
       "      <td>0.354551</td>\n",
       "      <td>0.245094</td>\n",
       "      <td>Biden</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>6018</td>\n",
       "      <td>1637</td>\n",
       "      <td>1437</td>\n",
       "      <td>1128</td>\n",
       "      <td>724</td>\n",
       "      <td>470</td>\n",
       "      <td>622</td>\n",
       "      <td>0.272017</td>\n",
       "      <td>0.120306</td>\n",
       "      <td>...</td>\n",
       "      <td>ND</td>\n",
       "      <td>Trump</td>\n",
       "      <td>Biden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.238784</td>\n",
       "      <td>0.078099</td>\n",
       "      <td>0.341980</td>\n",
       "      <td>0.258811</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>12172</td>\n",
       "      <td>3047</td>\n",
       "      <td>3307</td>\n",
       "      <td>2038</td>\n",
       "      <td>1652</td>\n",
       "      <td>879</td>\n",
       "      <td>1249</td>\n",
       "      <td>0.250329</td>\n",
       "      <td>0.135721</td>\n",
       "      <td>...</td>\n",
       "      <td>VT</td>\n",
       "      <td>Biden</td>\n",
       "      <td>Biden</td>\n",
       "      <td>1</td>\n",
       "      <td>0.271689</td>\n",
       "      <td>0.072215</td>\n",
       "      <td>0.394066</td>\n",
       "      <td>0.232540</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  total_tweets  Trump_P  Trump_N  Trump_Na  Biden_P  \\\n",
       "0           Pennsylvania        196670    52686    44664     32826    27436   \n",
       "1             New Jersey        125193    33783    28596     20680    17168   \n",
       "2               Michigan        153582    40696    36362     25681    20913   \n",
       "3                 Nevada         67763    18979    15187     11123     8968   \n",
       "4                Florida        522636   153555   111233     87552    65108   \n",
       "5             California        762340   198997   180620    125280   109793   \n",
       "6   District of Columbia         92046    23529    20696     15253    14396   \n",
       "7         South Carolina         70770    20602    14844     11572     9040   \n",
       "8               New York        389320   101574    91989     64100    56063   \n",
       "9                Georgia        155901    43142    32916     24933    21838   \n",
       "10                Hawaii         29285     8492     6784      4684     3698   \n",
       "11               Arizona        160701    44876    35994     26804    20994   \n",
       "12                 Texas        560388   160156   116094     89407    75733   \n",
       "13               Indiana         80642    22646    17909     13105    10671   \n",
       "14         Massachusetts        119477    30054    29087     19511    17585   \n",
       "15              Virginia        143369    38101    32550     23359    20419   \n",
       "16              Illinois        163858    41427    38797     27261    23661   \n",
       "17              Colorado        113077    29491    27397     18423    15774   \n",
       "18                Oregon        104634    27305    26443     17493    14092   \n",
       "19             Louisiana         56058    16519    11101      8978     7383   \n",
       "20             Wisconsin         72381    18853    16842     12072    10275   \n",
       "21                  Utah         34336     9069     8268      5529     4774   \n",
       "22             Minnesota         83859    21641    20008     13815    11943   \n",
       "23              Maryland         87538    22322    20657     14330    12668   \n",
       "24                Alaska         17127     5046     4013      2889     2015   \n",
       "25                  Iowa         34703     9290     8009      5941     4677   \n",
       "26           Mississippi         24502     7779     4972      4042     2851   \n",
       "27              Oklahoma         53499    15619    10739      8783     6847   \n",
       "28        North Carolina        145268    39944    32726     23814    19720   \n",
       "29              Kentucky         58027    16341    12296      9382     7762   \n",
       "30                  Ohio        177339    48128    39849     29677    24187   \n",
       "31              Missouri         64477    18215    14091     10614     8326   \n",
       "32          South Dakota          9817     2700     2098      1589     1470   \n",
       "33             Tennessee        103691    31362    21059     17195    12772   \n",
       "34              Nebraska         25276     7217     5622      4156     3330   \n",
       "35               Alabama         63579    19229    12327     10335     7917   \n",
       "36                Kansas         48705    12780    10990      7873     6974   \n",
       "37               Montana         18102     5116     4035      3028     2360   \n",
       "38              Delaware         18595     4464     4350      3023     2783   \n",
       "39              Arkansas         37377    10750     7829      6051     4857   \n",
       "40            Washington        153095    38658    37986     25355    21987   \n",
       "41                 Idaho         23206     6481     5106      3903     2988   \n",
       "42            New Mexico         29500     7678     7119      4953     4044   \n",
       "43           Connecticut         49638    12291    11810      8023     7392   \n",
       "44               Wyoming          7540     2237     1637      1306      921   \n",
       "45                 Maine         26817     7062     6821      4744     3446   \n",
       "46         West Virginia          7635     2221     1592      1150     1032   \n",
       "47          Rhode Island         15276     4025     3624      2477     2113   \n",
       "48         New Hampshire         23263     6131     5375      3654     3398   \n",
       "49          North Dakota          6018     1637     1437      1128      724   \n",
       "50               Vermont         12172     3047     3307      2038     1652   \n",
       "\n",
       "    Biden_N  Biden_Na  Trump_P_perc_total  Biden_P_perc_total  ...  \\\n",
       "0     16235     22823            0.267890            0.139503  ...   \n",
       "1     10393     14573            0.269847            0.137132  ...   \n",
       "2     12336     17594            0.264979            0.136168  ...   \n",
       "3      5719      7787            0.280079            0.132344  ...   \n",
       "4     45540     59648            0.293809            0.124576  ...   \n",
       "5     58759     88891            0.261034            0.144021  ...   \n",
       "6      6969     11203            0.255622            0.156400  ...   \n",
       "7      6371      8341            0.291112            0.127738  ...   \n",
       "8     30460     45134            0.260901            0.144002  ...   \n",
       "9     13548     19524            0.276727            0.140076  ...   \n",
       "10     2493      3134            0.289978            0.126276  ...   \n",
       "11    13768     18265            0.279252            0.130640  ...   \n",
       "12    50497     68501            0.285795            0.135144  ...   \n",
       "13     6861      9450            0.280821            0.132326  ...   \n",
       "14     9337     13903            0.251546            0.147183  ...   \n",
       "15    12057     16883            0.265755            0.142423  ...   \n",
       "16    13101     19611            0.252823            0.144399  ...   \n",
       "17     8955     13037            0.260805            0.139498  ...   \n",
       "18     8075     11226            0.260957            0.134679  ...   \n",
       "19     5210      6867            0.294677            0.131703  ...   \n",
       "20     5831      8508            0.260469            0.141957  ...   \n",
       "21     2732      3964            0.264125            0.139038  ...   \n",
       "22     6777      9675            0.258064            0.142418  ...   \n",
       "23     7116     10445            0.254998            0.144714  ...   \n",
       "24     1418      1746            0.294623            0.117650  ...   \n",
       "25     2711      4075            0.267700            0.134772  ...   \n",
       "26     2150      2708            0.317484            0.116358  ...   \n",
       "27     5088      6423            0.291949            0.127984  ...   \n",
       "28    11963     17101            0.274968            0.135749  ...   \n",
       "29     5258      6988            0.281610            0.133765  ...   \n",
       "30    14798     20700            0.271390            0.136388  ...   \n",
       "31     5757      7474            0.282504            0.129131  ...   \n",
       "32      841      1119            0.275033            0.149740  ...   \n",
       "33     9282     12021            0.302456            0.123174  ...   \n",
       "34     2064      2887            0.285528            0.131746  ...   \n",
       "35     6145      7626            0.302443            0.124522  ...   \n",
       "36     4225      5863            0.262396            0.143189  ...   \n",
       "37     1464      2099            0.282621            0.130372  ...   \n",
       "38     1547      2428            0.240065            0.149664  ...   \n",
       "39     3439      4451            0.287610            0.129946  ...   \n",
       "40    12012     17097            0.252510            0.143617  ...   \n",
       "41     2088      2640            0.279281            0.128760  ...   \n",
       "42     2266      3440            0.260271            0.137085  ...   \n",
       "43     3955      6167            0.247613            0.148918  ...   \n",
       "44      643       796            0.296684            0.122149  ...   \n",
       "45     2017      2727            0.263340            0.128501  ...   \n",
       "46      672       968            0.290897            0.135167  ...   \n",
       "47     1204      1833            0.263485            0.138322  ...   \n",
       "48     1986      2719            0.263552            0.146069  ...   \n",
       "49      470       622            0.272017            0.120306  ...   \n",
       "50      879      1249            0.250329            0.135721  ...   \n",
       "\n",
       "    state_code  winner predicted_winner correct Trump_N_perc_total  \\\n",
       "0           PA   Biden            Biden       1           0.227101   \n",
       "1           NJ   Biden            Biden       1           0.228415   \n",
       "2           MI   Biden            Biden       1           0.236760   \n",
       "3           NV   Biden            Trump       1           0.224119   \n",
       "4           FL   Trump            Trump       0           0.212831   \n",
       "5           CA   Biden            Biden       1           0.236928   \n",
       "6          NaN     NaN            Biden       0           0.224844   \n",
       "7           SC   Trump            Trump       0           0.209750   \n",
       "8           NM   Biden            Biden       1           0.236281   \n",
       "9           GA   Biden            Trump       1           0.211134   \n",
       "10          HI   Biden            Trump       1           0.231654   \n",
       "11          AZ   Biden            Trump       1           0.223981   \n",
       "12          TX   Trump            Trump       0           0.207167   \n",
       "13          IN   Trump            Trump       0           0.222080   \n",
       "14          MA   Biden            Biden       1           0.243453   \n",
       "15          VA   Biden            Biden       1           0.227037   \n",
       "16          IL   Biden            Biden       1           0.236772   \n",
       "17          CO   Biden            Biden       1           0.242286   \n",
       "18          OR   Biden            Biden       1           0.252719   \n",
       "19          LA   Trump            Trump       0           0.198027   \n",
       "20          WI   Biden            Biden       1           0.232685   \n",
       "21          UT   Trump            Biden       0           0.240797   \n",
       "22          MN   Biden            Biden       1           0.238591   \n",
       "23          MD   Biden            Biden       1           0.235978   \n",
       "24          AK   Trump            Trump       0           0.234308   \n",
       "25          IA   Trump            Biden       0           0.230787   \n",
       "26          MS   Trump            Trump       0           0.202922   \n",
       "27          OK   Trump            Trump       0           0.200733   \n",
       "28          NC   Trump            Trump       0           0.225280   \n",
       "29          KY   Trump            Trump       0           0.211901   \n",
       "30          OH   Trump            Trump       0           0.224705   \n",
       "31          MO   Trump            Trump       0           0.218543   \n",
       "32          SD   Trump            Biden       0           0.213711   \n",
       "33          TN   Trump            Trump       0           0.203094   \n",
       "34          NE   Trump            Trump       0           0.222424   \n",
       "35          AL   Trump            Trump       0           0.193885   \n",
       "36          KS   Trump            Biden       0           0.225644   \n",
       "37          MT   Trump            Trump       0           0.222904   \n",
       "38          DE   Biden            Biden       1           0.233934   \n",
       "39          AR   Trump            Trump       0           0.209460   \n",
       "40          WA   Biden            Biden       1           0.248120   \n",
       "41          ID   Trump            Trump       0           0.220029   \n",
       "42          NY   Biden            Biden       1           0.241322   \n",
       "43          CT   Biden            Biden       1           0.237923   \n",
       "44          WY   Trump            Trump       0           0.217109   \n",
       "45          ME   Biden            Biden       1           0.254354   \n",
       "46          WV   Trump            Trump       0           0.208513   \n",
       "47          RI   Biden            Biden       1           0.237235   \n",
       "48          NH   Biden            Biden       1           0.231054   \n",
       "49          ND   Trump            Biden       0           0.238784   \n",
       "50          VT   Biden            Biden       1           0.271689   \n",
       "\n",
       "    Biden_N_perc_total  Trump_N_perc_only  Biden_N_perc_only  \\\n",
       "0             0.082549           0.343105           0.244157   \n",
       "1             0.083016           0.344285           0.246665   \n",
       "2             0.080322           0.353926           0.242629   \n",
       "3             0.084397           0.335335           0.254472   \n",
       "4             0.087135           0.315698           0.267417   \n",
       "5             0.077077           0.357736           0.228241   \n",
       "6             0.075712           0.347961           0.213983   \n",
       "7             0.090024           0.315709           0.268230   \n",
       "8             0.078239           0.357013           0.231359   \n",
       "9             0.086901           0.325930           0.246731   \n",
       "10            0.085129           0.339880           0.267346   \n",
       "11            0.085675           0.334287           0.259641   \n",
       "12            0.090111           0.317494           0.259317   \n",
       "13            0.085080           0.333750           0.254281   \n",
       "14            0.078149           0.369819           0.228708   \n",
       "15            0.084098           0.346240           0.244272   \n",
       "16            0.079953           0.360953           0.232398   \n",
       "17            0.079194           0.363785           0.237118   \n",
       "18            0.077174           0.371177           0.241817   \n",
       "19            0.092939           0.303323           0.267729   \n",
       "20            0.080560           0.352587           0.236898   \n",
       "21            0.079567           0.361585           0.238187   \n",
       "22            0.080814           0.360738           0.238669   \n",
       "23            0.081290           0.360449           0.235403   \n",
       "24            0.082793           0.335872           0.273798   \n",
       "25            0.078120           0.344621           0.236500   \n",
       "26            0.087748           0.296076           0.278895   \n",
       "27            0.095105           0.305597           0.277154   \n",
       "28            0.082351           0.339186           0.245224   \n",
       "29            0.090613           0.323417           0.262795   \n",
       "30            0.083445           0.338697           0.247935   \n",
       "31            0.089288           0.328308           0.267059   \n",
       "32            0.085668           0.328480           0.245190   \n",
       "33            0.089516           0.302502           0.272399   \n",
       "34            0.081658           0.330803           0.249245   \n",
       "35            0.096651           0.294264           0.283336   \n",
       "36            0.086747           0.347312           0.247626   \n",
       "37            0.080875           0.331308           0.247172   \n",
       "38            0.083194           0.367492           0.228914   \n",
       "39            0.092008           0.317864           0.269789   \n",
       "40            0.078461           0.372415           0.235087   \n",
       "41            0.089977           0.329632           0.270607   \n",
       "42            0.076814           0.360456           0.232410   \n",
       "43            0.079677           0.367638           0.225819   \n",
       "44            0.085279           0.316023           0.272458   \n",
       "45            0.075213           0.366189           0.246276   \n",
       "46            0.088016           0.320774           0.251497   \n",
       "47            0.078816           0.357891           0.233786   \n",
       "48            0.085372           0.354551           0.245094   \n",
       "49            0.078099           0.341980           0.258811   \n",
       "50            0.072215           0.394066           0.232540   \n",
       "\n",
       "    predicted_winner_swing  Electoral votes  \n",
       "0                    Biden             20.0  \n",
       "1     Swing state to Biden             14.0  \n",
       "2                    Biden             16.0  \n",
       "3                    Trump              6.0  \n",
       "4                    Trump             29.0  \n",
       "5                    Biden             55.0  \n",
       "6                    Biden              NaN  \n",
       "7                    Trump              9.0  \n",
       "8                    Biden              5.0  \n",
       "9                    Trump             16.0  \n",
       "10                   Trump              4.0  \n",
       "11                   Trump             11.0  \n",
       "12                   Trump             38.0  \n",
       "13                   Trump             11.0  \n",
       "14                   Biden             11.0  \n",
       "15                   Biden             13.0  \n",
       "16                   Biden             20.0  \n",
       "17                   Biden              9.0  \n",
       "18                   Biden              7.0  \n",
       "19                   Trump              8.0  \n",
       "20                   Biden             10.0  \n",
       "21                   Biden              6.0  \n",
       "22                   Biden             10.0  \n",
       "23                   Biden             10.0  \n",
       "24                   Trump              3.0  \n",
       "25                   Biden              6.0  \n",
       "26                   Trump              6.0  \n",
       "27                   Trump              7.0  \n",
       "28                   Trump             15.0  \n",
       "29                   Trump              8.0  \n",
       "30    Swing state to Trump             18.0  \n",
       "31                   Trump             10.0  \n",
       "32                   Biden              3.0  \n",
       "33                   Trump             11.0  \n",
       "34                   Trump              5.0  \n",
       "35                   Trump              9.0  \n",
       "36    Swing state to Biden              6.0  \n",
       "37                   Trump              3.0  \n",
       "38                   Biden              3.0  \n",
       "39                   Trump              6.0  \n",
       "40                   Biden             12.0  \n",
       "41                   Trump              4.0  \n",
       "42                   Biden             29.0  \n",
       "43                   Biden              7.0  \n",
       "44                   Trump              3.0  \n",
       "45                   Biden              4.0  \n",
       "46                   Trump              5.0  \n",
       "47                   Biden              4.0  \n",
       "48                   Biden              4.0  \n",
       "49                   Biden              3.0  \n",
       "50                   Biden              3.0  \n",
       "\n",
       "[51 rows x 22 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stateElectoral = {}\n",
    "\n",
    "states = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', \n",
    "          'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', \n",
    "          'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', \n",
    "          'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', \n",
    "          'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', \n",
    "          'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', \n",
    "          'New York', 'New Mexico', 'North Carolina', 'North Dakota', 'Ohio', \n",
    "          'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', \n",
    "          'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', \n",
    "          'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n",
    "\n",
    "Electoral_votes = [9, 3, 11, 6, 55, \n",
    "                   9, 7, 3, 29, 16, \n",
    "                   4, 4, 20, 11, 6, \n",
    "                   6, 8, 8, 4, 10, \n",
    "                   11, 16, 10, 6, 10, \n",
    "                   3, 5, 6, 4, 14, \n",
    "                   5, 29, 15, 3, 18, \n",
    "                   7, 7, 20, 4, 9, \n",
    "                   3, 11, 38, 6, 3, \n",
    "                   13, 12, 5, 10, 3]\n",
    "stateElectoral['state'] =  states\n",
    "\n",
    "stateElectoral['Electoral votes'] = Electoral_votes\n",
    "stateElectoral_df = pd.DataFrame.from_dict(stateElectoral)\n",
    "\n",
    "overview_df = overview_df.merge(pd.DataFrame.from_dict(stateElectoral), on='state', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Trump's predicted electoral votes: 245.0\n",
      " Biden's predicted electoral votes: 290.0\n"
     ]
    }
   ],
   "source": [
    "# Get the predicted number of electoral college votes for each candidate.\n",
    "pred_trump_votes = sum(overview_df[(overview_df['predicted_winner'] == 'Trump') & (overview_df['state'] != 'District of Columbia')]['Electoral votes'])\n",
    "pred_biden_votes = sum(overview_df[(overview_df['predicted_winner'] == 'Biden') & (overview_df['state'] != 'District of Columbia')]['Electoral votes'])\n",
    "print(f\" Trump's predicted electoral votes: {pred_trump_votes}\")\n",
    "print(f\" Biden's predicted electoral votes: {pred_biden_votes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden: 290 Trump: 245\n",
    "Biden wins. 3 votes are missing because D.C. is not included."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real result\n",
    "Biden: 306 Trump: 232"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trump_real = pd.DataFrame([232], columns=[\"Trump\"])\n",
    "df_biden_real = pd.DataFrame([306], columns=[\"Biden\"])\n",
    "\n",
    "\n",
    "\n",
    "ax = df_trump_real.plot.bar(color = 'r')\n",
    "df_biden_real.plot.bar(color = 'b', bottom = df_trump_real[\"Trump\"], ax=ax)\n",
    "plt.axhline(pred_biden_votes, color='black')\n",
    "plt.title(\"TODO Lauren (Title)\")\n",
    "plt.show()\n",
    "\n",
    "df_trump_pred = pd.DataFrame([pred_trump_votes], columns=[\"Trump\"])\n",
    "df_biden_pred = pd.DataFrame([pred_biden_votes], columns=[\"Biden\"])\n",
    "\n",
    "ax = df_tump_pred.plot.bar(color = 'r')\n",
    "df_biden_pred.plot.bar(color = 'b', bottom = df_trump_pred[\"Trump\"], ax=ax)\n",
    "plt.axhline(pred_biden_votes, color='black')\n",
    "plt.title(\"TODO Lauren (Title)\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.13"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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