Data_102_Final_Project.ipynb

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  "metadata": {
    "colab": {
      "name": "Data 102 Final Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SarahAncheta/data102/blob/main/Data_102_Final_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4XG7MHYFJ_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9963c986-3bc0-4575-a488-158ecd25d9ec"
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ioaeXNnFzeD"
      },
      "source": [
        "#@title set up mount symlink\n",
        "\n",
        "DRIVE_PATH = '/content/gdrive/My\\ Drive/data102_final_project'\n",
        "DRIVE_PYTHON_PATH = DRIVE_PATH.replace('\\\\', '')\n",
        "if not os.path.exists(DRIVE_PYTHON_PATH):\n",
        "  %mkdir $DRIVE_PATH\n",
        "\n",
        "## the space in `My Drive` causes some issues,\n",
        "## make a symlink to avoid this\n",
        "SYM_PATH = '/content/data102_final_project'\n",
        "if not os.path.exists(SYM_PATH):\n",
        "  !ln -s $DRIVE_PATH $SYM_PATH"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59YYaBEyWjTX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4e284d3-ac79-41b0-d18c-fb1d860aa5de"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "import statsmodels.api as sm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "from sklearn import linear_model\n",
        "import math\n",
        "import io"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nzx90AVD8xx5"
      },
      "source": [
        "### **Datasets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hsiIq4nW2dH",
        "outputId": "9509adeb-fbff-4ef0-96e2-2e288a5cf434"
      },
      "source": [
        "%cd data102_final_project"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/data102_final_project\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "dpDfAaU6k2Qd",
        "outputId": "b929ced8-774d-49e8-ea99-efc6e200d8c6"
      },
      "source": [
        "funding = pd.read_csv('candidate_summary_2018.csv')\n",
        "funding"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8TX05052/?...</td>\n",
              "      <td>ASHBY, KEN</td>\n",
              "      <td>H8TX05052</td>\n",
              "      <td>H</td>\n",
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              "      <td>75218.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.fec.gov/data/candidate/S6OH00288/?...</td>\n",
              "      <td>ECKHART, DON ELIJAH</td>\n",
              "      <td>S6OH00288</td>\n",
              "      <td>S</td>\n",
              "      <td>OH</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>,</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>43119.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://www.fec.gov/data/candidate/S8CA00499/?...</td>\n",
              "      <td>ZIESING, MICHAEL VINCENT</td>\n",
              "      <td>S8CA00499</td>\n",
              "      <td>S</td>\n",
              "      <td>CA</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1220 ALOHA ST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RED BLUFF</td>\n",
              "      <td>CA</td>\n",
              "      <td>96080.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6NJ13191/?...</td>\n",
              "      <td>SIRES, ALBIO</td>\n",
              "      <td>H6NJ13191</td>\n",
              "      <td>H</td>\n",
              "      <td>NJ</td>\n",
              "      <td>8.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>INCUMBENT</td>\n",
              "      <td>460108.57</td>\n",
              "      <td>596546.56</td>\n",
              "      <td>158849.09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>6050 BOULEVARD EAST</td>\n",
              "      <td>APT 6B</td>\n",
              "      <td>WEST NEW YORK</td>\n",
              "      <td>NJ</td>\n",
              "      <td>7093.0</td>\n",
              "      <td>161321.54</td>\n",
              "      <td>1848.84</td>\n",
              "      <td>163170.38</td>\n",
              "      <td>296686.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>459857.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>251.52</td>\n",
              "      <td>437542.51</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>98504.05</td>\n",
              "      <td>459357.05</td>\n",
              "      <td>437542.51</td>\n",
              "      <td>290287.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6MI03172/?...</td>\n",
              "      <td>SMITH, DOUGLAS MARK MR. JR.</td>\n",
              "      <td>H6MI03172</td>\n",
              "      <td>H</td>\n",
              "      <td>MI</td>\n",
              "      <td>3.0</td>\n",
              "      <td>IND</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1410 SCOTT CREEK DR.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BELMONT</td>\n",
              "      <td>MI</td>\n",
              "      <td>49306.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>3873.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3794</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6OH04082/?...</td>\n",
              "      <td>JORDAN, JAMES D.</td>\n",
              "      <td>H6OH04082</td>\n",
              "      <td>H</td>\n",
              "      <td>OH</td>\n",
              "      <td>4.0</td>\n",
              "      <td>REP</td>\n",
              "      <td>INCUMBENT</td>\n",
              "      <td>1241417.36</td>\n",
              "      <td>1809464.13</td>\n",
              "      <td>745640.94</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1709 SOUTH STATE ROUTE 560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>URBANA</td>\n",
              "      <td>OH</td>\n",
              "      <td>43078.0</td>\n",
              "      <td>560189.00</td>\n",
              "      <td>350028.82</td>\n",
              "      <td>910217.82</td>\n",
              "      <td>306389.77</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1221607.59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>72.15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19737.62</td>\n",
              "      <td>1599257.13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11057.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>11207.0</td>\n",
              "      <td>164000.00</td>\n",
              "      <td>1210400.59</td>\n",
              "      <td>1599184.98</td>\n",
              "      <td>1313687.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3795</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8NY27176/?...</td>\n",
              "      <td>MCMURRAY, NATHAN DOUGLAS</td>\n",
              "      <td>H8NY27176</td>\n",
              "      <td>H</td>\n",
              "      <td>NY</td>\n",
              "      <td>27.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>OPEN</td>\n",
              "      <td>1387653.64</td>\n",
              "      <td>1364110.33</td>\n",
              "      <td>23543.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1041 FOXCROFT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GRAND ISLAND</td>\n",
              "      <td>NY</td>\n",
              "      <td>14072.0</td>\n",
              "      <td>629591.23</td>\n",
              "      <td>527391.94</td>\n",
              "      <td>1156983.17</td>\n",
              "      <td>151065.00</td>\n",
              "      <td>48163.31</td>\n",
              "      <td>1900.00</td>\n",
              "      <td>1358111.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.00</td>\n",
              "      <td>587.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10954.41</td>\n",
              "      <td>1342096.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>3252.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3252.5</td>\n",
              "      <td>761.11</td>\n",
              "      <td>1354858.98</td>\n",
              "      <td>1341508.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3796</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8IN06152/?...</td>\n",
              "      <td>LAKE, JEANNINE LEE</td>\n",
              "      <td>H8IN06152</td>\n",
              "      <td>H</td>\n",
              "      <td>IN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>OPEN</td>\n",
              "      <td>100644.90</td>\n",
              "      <td>101754.84</td>\n",
              "      <td>50.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>5300 NORTH CO. ROAD 500 WEST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MUNICE</td>\n",
              "      <td>IN</td>\n",
              "      <td>47304.0</td>\n",
              "      <td>37970.87</td>\n",
              "      <td>51484.23</td>\n",
              "      <td>89455.10</td>\n",
              "      <td>3415.29</td>\n",
              "      <td>370.04</td>\n",
              "      <td>898.27</td>\n",
              "      <td>94138.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6216.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6216.66</td>\n",
              "      <td>289.54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>99183.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2371.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2371.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91767.70</td>\n",
              "      <td>98894.30</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>07/04/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3797</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8MD01177/?...</td>\n",
              "      <td>GALBRAITH, ALLISON MS.</td>\n",
              "      <td>H8MD01177</td>\n",
              "      <td>H</td>\n",
              "      <td>MD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>110517.66</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>319.98</td>\n",
              "      <td>350.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>PO BOX 25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FALLSTON</td>\n",
              "      <td>MD</td>\n",
              "      <td>21047.0</td>\n",
              "      <td>33834.61</td>\n",
              "      <td>76231.05</td>\n",
              "      <td>110065.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>102.00</td>\n",
              "      <td>110167.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>350.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>350.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>110167.66</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>04/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3798</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8CO06294/?...</td>\n",
              "      <td>KULIKOWSKI, JAIMIE BLACKLIGHT POLISH J</td>\n",
              "      <td>H8CO06294</td>\n",
              "      <td>H</td>\n",
              "      <td>CO</td>\n",
              "      <td>6.0</td>\n",
              "      <td>REP</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>60500.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1191 S YOSEMITE WAY</td>\n",
              "      <td>UNIT 45</td>\n",
              "      <td>DENVER</td>\n",
              "      <td>CO</td>\n",
              "      <td>80247.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>07/01/2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3799 rows × 50 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             Link_Image  ... Coverage_Start_Date\n",
              "0     https://www.fec.gov/data/candidate/H8TX05052/?...  ...                 NaN\n",
              "1     https://www.fec.gov/data/candidate/S6OH00288/?...  ...                 NaN\n",
              "2     https://www.fec.gov/data/candidate/S8CA00499/?...  ...                 NaN\n",
              "3     https://www.fec.gov/data/candidate/H6NJ13191/?...  ...          01/01/2017\n",
              "4     https://www.fec.gov/data/candidate/H6MI03172/?...  ...          01/01/2017\n",
              "...                                                 ...  ...                 ...\n",
              "3794  https://www.fec.gov/data/candidate/H6OH04082/?...  ...          01/01/2017\n",
              "3795  https://www.fec.gov/data/candidate/H8NY27176/?...  ...          01/01/2018\n",
              "3796  https://www.fec.gov/data/candidate/H8IN06152/?...  ...          07/04/2017\n",
              "3797  https://www.fec.gov/data/candidate/H8MD01177/?...  ...          04/01/2017\n",
              "3798  https://www.fec.gov/data/candidate/H8CO06294/?...  ...          07/01/2018\n",
              "\n",
              "[3799 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FxvkyKRR8Ye",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "76d696ff-ba3d-4636-8d77-b94e3b7317da"
      },
      "source": [
        "rep = pd.read_csv('rep_candidates.csv', encoding='cp1252')\n",
        "rep"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Candidate</th>\n",
              "      <th>State</th>\n",
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              "      <th>Office Type</th>\n",
              "      <th>Race Type</th>\n",
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              "      <th>Trump Endorsed?</th>\n",
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              "      <th>Great America Endorsed?</th>\n",
              "      <th>NRA Endorsed?</th>\n",
              "      <th>Right to Life Endorsed?</th>\n",
              "      <th>Susan B. Anthony Endorsed?</th>\n",
              "      <th>Club for Growth Endorsed?</th>\n",
              "      <th>Koch Support?</th>\n",
              "      <th>House Freedom Support?</th>\n",
              "      <th>Tea Party Endorsed?</th>\n",
              "      <th>Main Street Endorsed?</th>\n",
              "      <th>Chamber Endorsed?</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mike Dunleavy</td>\n",
              "      <td>AK</td>\n",
              "      <td>Governor of Alaska</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>None</td>\n",
              "      <td>On the Ballot</td>\n",
              "      <td>61.8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Michael Sheldon</td>\n",
              "      <td>AK</td>\n",
              "      <td>Governor of Alaska</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2.2</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mead Treadwell</td>\n",
              "      <td>AK</td>\n",
              "      <td>Governor of Alaska</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>31.9</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Darin Colbry</td>\n",
              "      <td>AK</td>\n",
              "      <td>Governor of Alaska</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>0.6</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thomas Gordon</td>\n",
              "      <td>AK</td>\n",
              "      <td>Governor of Alaska</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>1.3</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>769</th>\n",
              "      <td>Bill Dahlin</td>\n",
              "      <td>WY</td>\n",
              "      <td>Governor of Wyoming</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>1.5</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>770</th>\n",
              "      <td>Harriet Hageman</td>\n",
              "      <td>WY</td>\n",
              "      <td>Governor of Wyoming</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>21.5</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771</th>\n",
              "      <td>Sam Galeotos</td>\n",
              "      <td>WY</td>\n",
              "      <td>Governor of Wyoming</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>12.5</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>772</th>\n",
              "      <td>Foster Friess</td>\n",
              "      <td>WY</td>\n",
              "      <td>Governor of Wyoming</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>25.6</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>Taylor Haynes</td>\n",
              "      <td>WY</td>\n",
              "      <td>Governor of Wyoming</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8/21/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>5.6</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>774 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Candidate State  ... Chamber Endorsed? No Labels Support?\n",
              "0      Mike Dunleavy    AK  ...               NaN                NaN\n",
              "1    Michael Sheldon    AK  ...               NaN                NaN\n",
              "2     Mead Treadwell    AK  ...               NaN                NaN\n",
              "3       Darin Colbry    AK  ...               NaN                NaN\n",
              "4      Thomas Gordon    AK  ...               NaN                NaN\n",
              "..               ...   ...  ...               ...                ...\n",
              "769      Bill Dahlin    WY  ...               NaN                NaN\n",
              "770  Harriet Hageman    WY  ...               NaN                NaN\n",
              "771     Sam Galeotos    WY  ...               NaN                NaN\n",
              "772    Foster Friess    WY  ...               NaN                NaN\n",
              "773    Taylor Haynes    WY  ...               NaN                NaN\n",
              "\n",
              "[774 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16MoILYSWhPD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "outputId": "b978259d-2888-4a5f-a817-1db646a5efa7"
      },
      "source": [
        "dem = pd.read_csv('dem_candidates.csv', encoding='cp1252')\n",
        "dem"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Candidate</th>\n",
              "      <th>State</th>\n",
              "      <th>District</th>\n",
              "      <th>Office Type</th>\n",
              "      <th>Race Type</th>\n",
              "      <th>Race Primary Election Date</th>\n",
              "      <th>Primary Status</th>\n",
              "      <th>Primary Runoff Status</th>\n",
              "      <th>General Status</th>\n",
              "      <th>Partisan Lean</th>\n",
              "      <th>Primary %</th>\n",
              "      <th>Won Primary</th>\n",
              "      <th>Race</th>\n",
              "      <th>Veteran?</th>\n",
              "      <th>LGBTQ?</th>\n",
              "      <th>Elected Official?</th>\n",
              "      <th>Self-Funder?</th>\n",
              "      <th>STEM?</th>\n",
              "      <th>Obama Alum?</th>\n",
              "      <th>Party Support?</th>\n",
              "      <th>Emily Endorsed?</th>\n",
              "      <th>Guns Sense Candidate?</th>\n",
              "      <th>Biden Endorsed?</th>\n",
              "      <th>Warren Endorsed?</th>\n",
              "      <th>Sanders Endorsed?</th>\n",
              "      <th>Our Revolution Endorsed?</th>\n",
              "      <th>Justice Dems Endorsed?</th>\n",
              "      <th>PCCC Endorsed?</th>\n",
              "      <th>Indivisible Endorsed?</th>\n",
              "      <th>WFP Endorsed?</th>\n",
              "      <th>VoteVets Endorsed?</th>\n",
              "      <th>No Labels Support?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Anthony White (Alabama)</td>\n",
              "      <td>AL</td>\n",
              "      <td>Governor of Alabama</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6/5/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-28.879999</td>\n",
              "      <td>3.420000</td>\n",
              "      <td>No</td>\n",
              "      <td>Nonwhite</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Christopher Countryman</td>\n",
              "      <td>AL</td>\n",
              "      <td>Governor of Alabama</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6/5/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-28.879999</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Doug \"New Blue\" Smith</td>\n",
              "      <td>AL</td>\n",
              "      <td>Governor of Alabama</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6/5/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-28.879999</td>\n",
              "      <td>3.270000</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>James C. Fields</td>\n",
              "      <td>AL</td>\n",
              "      <td>Governor of Alabama</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6/5/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-28.879999</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>No</td>\n",
              "      <td>Nonwhite</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sue Bell Cobb</td>\n",
              "      <td>AL</td>\n",
              "      <td>Governor of Alabama</td>\n",
              "      <td>Governor</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6/5/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-28.879999</td>\n",
              "      <td>28.980000</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>806</th>\n",
              "      <td>Talley Sergent</td>\n",
              "      <td>WV</td>\n",
              "      <td>U.S. House West Virginia District 2</td>\n",
              "      <td>Representative</td>\n",
              "      <td>Regular</td>\n",
              "      <td>5/8/18</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>None</td>\n",
              "      <td>On the Ballot</td>\n",
              "      <td>-35.330002</td>\n",
              "      <td>62.570000</td>\n",
              "      <td>Yes</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>807</th>\n",
              "      <td>Janice Hagerman</td>\n",
              "      <td>WV</td>\n",
              "      <td>U.S. House West Virginia District 3</td>\n",
              "      <td>Representative</td>\n",
              "      <td>Regular</td>\n",
              "      <td>5/8/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-47.480000</td>\n",
              "      <td>7.240000</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808</th>\n",
              "      <td>Paul Davis</td>\n",
              "      <td>WV</td>\n",
              "      <td>U.S. House West Virginia District 3</td>\n",
              "      <td>Representative</td>\n",
              "      <td>Regular</td>\n",
              "      <td>5/8/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-47.480000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>809</th>\n",
              "      <td>Richard Ojeda</td>\n",
              "      <td>WV</td>\n",
              "      <td>U.S. House West Virginia District 3</td>\n",
              "      <td>Representative</td>\n",
              "      <td>Regular</td>\n",
              "      <td>5/8/18</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>None</td>\n",
              "      <td>On the Ballot</td>\n",
              "      <td>-47.480000</td>\n",
              "      <td>52.160000</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Nonwhite</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>810</th>\n",
              "      <td>Shirley Love</td>\n",
              "      <td>WV</td>\n",
              "      <td>U.S. House West Virginia District 3</td>\n",
              "      <td>Representative</td>\n",
              "      <td>Regular</td>\n",
              "      <td>5/8/18</td>\n",
              "      <td>Lost</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>-47.480000</td>\n",
              "      <td>24.639999</td>\n",
              "      <td>No</td>\n",
              "      <td>White</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>811 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Candidate State  ... VoteVets Endorsed? No Labels Support?\n",
              "0    Anthony White (Alabama)    AL  ...                NaN                NaN\n",
              "1     Christopher Countryman    AL  ...                NaN                NaN\n",
              "2      Doug \"New Blue\" Smith    AL  ...                NaN                NaN\n",
              "3            James C. Fields    AL  ...                NaN                NaN\n",
              "4              Sue Bell Cobb    AL  ...                NaN                NaN\n",
              "..                       ...   ...  ...                ...                ...\n",
              "806           Talley Sergent    WV  ...                 No                NaN\n",
              "807          Janice Hagerman    WV  ...                 No                NaN\n",
              "808               Paul Davis    WV  ...                 No                NaN\n",
              "809            Richard Ojeda    WV  ...                Yes                NaN\n",
              "810             Shirley Love    WV  ...                 No                NaN\n",
              "\n",
              "[811 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mef5NVsH8Yd3"
      },
      "source": [
        "### **Question 1 (Bayesian Inference)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2U0ZawO8d7p"
      },
      "source": [
        "Question 1 (Bayesian Inference): Can we predict whether an individual running for Senate is trying to take a position that is currently open, filled, or filled by the individual in question based solely off of funding and net expenditure? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGwGDXXClrTB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "a46e17d5-8c0e-40f9-cf32-ffab18e9419f"
      },
      "source": [
        "#Cleaning dataset\n",
        "#Removing all irrelevant columns\n",
        "funding = funding.fillna(method='ffill')\n",
        "cleaned = funding[['Cand_Incumbent_Challenger_Open_Seat', 'Total_Contribution', 'Operating_Expenditure']]\n",
        "cleaned.head()\n",
        "#binarizing Challenger, Incumbent, Open as 1, 2, 3 respectively\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'CHALLENGER', 'Cand_Incumbent_Challenger_Open_Seat'] = 1\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'INCUMBENT', 'Cand_Incumbent_Challenger_Open_Seat'] = 2\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'OPEN', 'Cand_Incumbent_Challenger_Open_Seat'] = 3\n",
        "cleaned.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Cand_Incumbent_Challenger_Open_Seat</th>\n",
              "      <th>Total_Contribution</th>\n",
              "      <th>Operating_Expenditure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>459857.05</td>\n",
              "      <td>437542.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cand_Incumbent_Challenger_Open_Seat  ...  Operating_Expenditure\n",
              "0                                   1  ...                   0.00\n",
              "1                                   1  ...                   0.00\n",
              "2                                   1  ...                   0.00\n",
              "3                                   2  ...              437542.51\n",
              "4                                   1  ...                3873.60\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r57Q6j32l0VS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50e56cbb-9138-4afd-af5b-b09464fdfcec"
      },
      "source": [
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X = cleaned[['Total_Contribution','Operating_Expenditure']].to_numpy()\n",
        "y = cleaned['Cand_Incumbent_Challenger_Open_Seat'].to_numpy()\n",
        "y = y.astype('int')\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=109) # 70% training and 30% test\n",
        "\n",
        "#Import Gaussian Naive Bayes model\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "gnb = GaussianNB()\n",
        "\n",
        "#Train the model using the training sets\n",
        "gnb.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = gnb.predict(X_test)\n",
        "\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.28596491228070176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VkpwwYxdrXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "5a02bdfd-af90-463f-cff5-5cdfcacf666d"
      },
      "source": [
        "#Cleaning dataset\n",
        "#Removing all irrelevant columns\n",
        "funding = funding.fillna(method='ffill')\n",
        "cleaned = funding[['Cand_Incumbent_Challenger_Open_Seat', 'Total_Contribution', 'Operating_Expenditure']]\n",
        "cleaned.head()\n",
        "#dropping all candidates that have a Total_Contribution or Operating_Expenditure of 0\n",
        "#we are only looking to see if strength of contribution can predict a seat filled, open, or filled by candidate in question\n",
        "cleaned = cleaned.loc[~((cleaned['Total_Contribution'] == 0))]\n",
        "cleaned = cleaned.loc[~((cleaned['Operating_Expenditure'] == 0))]\n",
        "#binarizing Challenger, Incumbent, Open as 1, 2, 3 respectively\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'CHALLENGER', 'Cand_Incumbent_Challenger_Open_Seat'] = 1\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'INCUMBENT', 'Cand_Incumbent_Challenger_Open_Seat'] = 2\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'OPEN', 'Cand_Incumbent_Challenger_Open_Seat'] = 3\n",
        "cleaned.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cand_Incumbent_Challenger_Open_Seat</th>\n",
              "      <th>Total_Contribution</th>\n",
              "      <th>Operating_Expenditure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>459857.05</td>\n",
              "      <td>437542.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>4365.06</td>\n",
              "      <td>7357.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>20583.09</td>\n",
              "      <td>19670.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>3547064.51</td>\n",
              "      <td>3946213.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>10258.00</td>\n",
              "      <td>14448.81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cand_Incumbent_Challenger_Open_Seat  ...  Operating_Expenditure\n",
              "3                                   2  ...              437542.51\n",
              "6                                   1  ...                7357.50\n",
              "7                                   1  ...               19670.32\n",
              "8                                   3  ...             3946213.62\n",
              "9                                   1  ...               14448.81\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2GcPpGgdwUo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f02fe7d-f867-4d53-9072-0f77ca8ccbf2"
      },
      "source": [
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X = cleaned[['Total_Contribution','Operating_Expenditure']].to_numpy()\n",
        "y = cleaned['Cand_Incumbent_Challenger_Open_Seat'].to_numpy()\n",
        "y = y.astype('int')\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=109) # 70% training and 30% test\n",
        "\n",
        "#Import Gaussian Naive Bayes model\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "gnb = GaussianNB()\n",
        "\n",
        "#Train the model using the training sets\n",
        "gnb.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = gnb.predict(X_test)\n",
        "\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.2991573033707865\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFrK6ZBwdw9w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "5841d647-dacb-4a35-cc6b-e6a3e70caf32"
      },
      "source": [
        "#Cleaning dataset\n",
        "#Removing all irrelevant columns\n",
        "funding = funding.fillna(method='ffill')\n",
        "cleaned = funding[['Cand_Incumbent_Challenger_Open_Seat', 'Total_Contribution', 'Operating_Expenditure', 'Individual_Itemized_Contribution', 'Individual_Unitemized_Contribution', 'Individual_Contribution', 'Other_Committee_Contribution', 'Party_Committee_Contribution', 'Cand_Contribution']]\n",
        "cleaned.head()\n",
        "#dropping all candidates that have a Total_Contribution or Operating_Expenditure of 0\n",
        "#we are only looking to see if strength of contribution can predict a seat filled, open, or filled by candidate in question\n",
        "cleaned = cleaned.loc[~((cleaned['Total_Contribution'] == 0))]\n",
        "cleaned = cleaned.loc[~((cleaned['Operating_Expenditure'] == 0))]\n",
        "#binarizing Challenger, Incumbent, Open as 1, 2, 3 respectively\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'CHALLENGER', 'Cand_Incumbent_Challenger_Open_Seat'] = 1\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'INCUMBENT', 'Cand_Incumbent_Challenger_Open_Seat'] = 2\n",
        "cleaned.loc[cleaned['Cand_Incumbent_Challenger_Open_Seat'] == 'OPEN', 'Cand_Incumbent_Challenger_Open_Seat'] = 3\n",
        "cleaned.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cand_Incumbent_Challenger_Open_Seat</th>\n",
              "      <th>Total_Contribution</th>\n",
              "      <th>Operating_Expenditure</th>\n",
              "      <th>Individual_Itemized_Contribution</th>\n",
              "      <th>Individual_Unitemized_Contribution</th>\n",
              "      <th>Individual_Contribution</th>\n",
              "      <th>Other_Committee_Contribution</th>\n",
              "      <th>Party_Committee_Contribution</th>\n",
              "      <th>Cand_Contribution</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>459857.05</td>\n",
              "      <td>437542.51</td>\n",
              "      <td>161321.54</td>\n",
              "      <td>1848.84</td>\n",
              "      <td>163170.38</td>\n",
              "      <td>296686.67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>4365.06</td>\n",
              "      <td>7357.50</td>\n",
              "      <td>481.21</td>\n",
              "      <td>3883.85</td>\n",
              "      <td>4365.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>20583.09</td>\n",
              "      <td>19670.32</td>\n",
              "      <td>8831.10</td>\n",
              "      <td>10735.39</td>\n",
              "      <td>19566.49</td>\n",
              "      <td>200.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>816.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>3547064.51</td>\n",
              "      <td>3946213.62</td>\n",
              "      <td>2311197.01</td>\n",
              "      <td>672129.40</td>\n",
              "      <td>2983326.41</td>\n",
              "      <td>550232.50</td>\n",
              "      <td>5150.0</td>\n",
              "      <td>8355.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>10258.00</td>\n",
              "      <td>14448.81</td>\n",
              "      <td>6425.00</td>\n",
              "      <td>3658.00</td>\n",
              "      <td>10083.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cand_Incumbent_Challenger_Open_Seat  ...  Cand_Contribution\n",
              "3                                   2  ...                0.0\n",
              "6                                   1  ...                0.0\n",
              "7                                   1  ...              816.6\n",
              "8                                   3  ...             8355.6\n",
              "9                                   1  ...              175.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOScdaHhd0wp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92c293da-18d0-4b69-dbb8-2fa7f073d6bb"
      },
      "source": [
        "\n",
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X = cleaned[['Total_Contribution', 'Operating_Expenditure', 'Individual_Itemized_Contribution', 'Individual_Unitemized_Contribution', 'Individual_Contribution', 'Other_Committee_Contribution', 'Party_Committee_Contribution', 'Cand_Contribution']].to_numpy()\n",
        "y = cleaned['Cand_Incumbent_Challenger_Open_Seat'].to_numpy()\n",
        "y = y.astype('int')\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=109) # 70% training and 30% test\n",
        "\n",
        "#Import Gaussian Naive Bayes model\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "gnb = GaussianNB()\n",
        "\n",
        "#Train the model using the training sets\n",
        "gnb.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = gnb.predict(X_test)\n",
        "\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.4058988764044944\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY6G6I__6miJ"
      },
      "source": [
        "### **Question 2 (Causal Inference)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejnGX2QZVVph"
      },
      "source": [
        "Question 2 (Causal Inference): How do endorsements effect who you vote for? What endorsements have more weight than others?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXRXl0QJCVUZ"
      },
      "source": [
        "#data cleaning\n",
        "dem['Primary Status'] = dem['Primary Status'].replace('Lost',0).replace('Advanced',1) # outcome of election\n",
        "dem = dem.replace('Yes',1).replace('No',0).fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXUK2YuNtG0l"
      },
      "source": [
        "rep['Primary Status'] = rep['Primary Status'].replace('Lost',0).replace('Advanced',1).replace('On the Ballot',1) # outcome of election\n",
        "rep = rep.replace('Yes',1).replace('No',0).fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4kU2gr-otKs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "f2039971-a40c-48b1-a37f-b2406d985b83"
      },
      "source": [
        "Y = dem['Primary Status'].dropna()\n",
        "Z = dem.iloc[:,19:32] #all endorsements\n",
        "Z = sm.add_constant(Z)\n",
        "\n",
        "model = sm.OLS(Y,Z)\n",
        "results = model.fit()\n",
        "results.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>Primary Status</td>  <th>  R-squared:         </th> <td>   0.161</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.147</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.77</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 11 May 2021</td> <th>  Prob (F-statistic):</th> <td>1.39e-23</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:18:22</td>     <th>  Log-Likelihood:    </th> <td> -465.55</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   811</td>      <th>  AIC:               </th> <td>   959.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   797</td>      <th>  BIC:               </th> <td>   1025.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    13</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                    <td>    0.2311</td> <td>    0.019</td> <td>   12.345</td> <td> 0.000</td> <td>    0.194</td> <td>    0.268</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Party Support?</th>           <td>    0.4680</td> <td>    0.085</td> <td>    5.479</td> <td> 0.000</td> <td>    0.300</td> <td>    0.636</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Emily Endorsed?</th>          <td>    0.2230</td> <td>    0.067</td> <td>    3.307</td> <td> 0.001</td> <td>    0.091</td> <td>    0.355</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Guns Sense Candidate?</th>    <td>    0.0969</td> <td>    0.037</td> <td>    2.632</td> <td> 0.009</td> <td>    0.025</td> <td>    0.169</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Biden Endorsed?</th>          <td>    0.2463</td> <td>    0.151</td> <td>    1.627</td> <td> 0.104</td> <td>   -0.051</td> <td>    0.543</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Warren Endorsed? </th>        <td>    0.3149</td> <td>    0.207</td> <td>    1.523</td> <td> 0.128</td> <td>   -0.091</td> <td>    0.721</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sanders Endorsed?</th>        <td>    0.1430</td> <td>    0.162</td> <td>    0.885</td> <td> 0.377</td> <td>   -0.174</td> <td>    0.460</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Our Revolution Endorsed?</th> <td>   -0.0023</td> <td>    0.056</td> <td>   -0.042</td> <td> 0.967</td> <td>   -0.113</td> <td>    0.109</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Justice Dems Endorsed?</th>   <td>    0.0689</td> <td>    0.071</td> <td>    0.976</td> <td> 0.329</td> <td>   -0.070</td> <td>    0.208</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PCCC Endorsed?</th>           <td>   -0.0032</td> <td>    0.130</td> <td>   -0.025</td> <td> 0.980</td> <td>   -0.259</td> <td>    0.253</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Indivisible Endorsed?</th>    <td>    0.3370</td> <td>    0.067</td> <td>    4.993</td> <td> 0.000</td> <td>    0.204</td> <td>    0.469</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WFP Endorsed?</th>            <td>    0.1603</td> <td>    0.087</td> <td>    1.846</td> <td> 0.065</td> <td>   -0.010</td> <td>    0.331</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>VoteVets Endorsed?</th>       <td>    0.0084</td> <td>    0.089</td> <td>    0.093</td> <td> 0.926</td> <td>   -0.167</td> <td>    0.184</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No Labels Support?</th>       <td>   -0.2353</td> <td>    0.310</td> <td>   -0.759</td> <td> 0.448</td> <td>   -0.844</td> <td>    0.373</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>143.721</td> <th>  Durbin-Watson:     </th> <td>   2.092</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 120.923</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.856</td>  <th>  Prob(JB):          </th> <td>5.52e-27</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.196</td>  <th>  Cond. No.          </th> <td>    21.4</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:         Primary Status   R-squared:                       0.161\n",
              "Model:                            OLS   Adj. R-squared:                  0.147\n",
              "Method:                 Least Squares   F-statistic:                     11.77\n",
              "Date:                Tue, 11 May 2021   Prob (F-statistic):           1.39e-23\n",
              "Time:                        04:18:22   Log-Likelihood:                -465.55\n",
              "No. Observations:                 811   AIC:                             959.1\n",
              "Df Residuals:                     797   BIC:                             1025.\n",
              "Df Model:                          13                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "============================================================================================\n",
              "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
              "--------------------------------------------------------------------------------------------\n",
              "const                        0.2311      0.019     12.345      0.000       0.194       0.268\n",
              "Party Support?               0.4680      0.085      5.479      0.000       0.300       0.636\n",
              "Emily Endorsed?              0.2230      0.067      3.307      0.001       0.091       0.355\n",
              "Guns Sense Candidate?        0.0969      0.037      2.632      0.009       0.025       0.169\n",
              "Biden Endorsed?              0.2463      0.151      1.627      0.104      -0.051       0.543\n",
              "Warren Endorsed?             0.3149      0.207      1.523      0.128      -0.091       0.721\n",
              "Sanders Endorsed?            0.1430      0.162      0.885      0.377      -0.174       0.460\n",
              "Our Revolution Endorsed?    -0.0023      0.056     -0.042      0.967      -0.113       0.109\n",
              "Justice Dems Endorsed?       0.0689      0.071      0.976      0.329      -0.070       0.208\n",
              "PCCC Endorsed?              -0.0032      0.130     -0.025      0.980      -0.259       0.253\n",
              "Indivisible Endorsed?        0.3370      0.067      4.993      0.000       0.204       0.469\n",
              "WFP Endorsed?                0.1603      0.087      1.846      0.065      -0.010       0.331\n",
              "VoteVets Endorsed?           0.0084      0.089      0.093      0.926      -0.167       0.184\n",
              "No Labels Support?          -0.2353      0.310     -0.759      0.448      -0.844       0.373\n",
              "==============================================================================\n",
              "Omnibus:                      143.721   Durbin-Watson:                   2.092\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              120.923\n",
              "Skew:                           0.856   Prob(JB):                     5.52e-27\n",
              "Kurtosis:                       2.196   Cond. No.                         21.4\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfLiqUKutUfC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "outputId": "7aa1e201-cb82-4d9c-cc4c-7b109ebfa279"
      },
      "source": [
        "Y = rep['Primary Status'].dropna()\n",
        "Z = rep.iloc[:,11:25] #all endorsements\n",
        "Z = sm.add_constant(Z)\n",
        "\n",
        "model = sm.OLS(Y,Z)\n",
        "results = model.fit()\n",
        "results.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>Primary Status</td>  <th>  R-squared:         </th> <td>   0.096</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.079</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.740</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 11 May 2021</td> <th>  Prob (F-statistic):</th> <td>1.02e-10</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:18:22</td>     <th>  Log-Likelihood:    </th> <td> -477.24</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   774</td>      <th>  AIC:               </th> <td>   984.5</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   759</td>      <th>  BIC:               </th> <td>   1054.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                      <td>    0.2781</td> <td>    0.018</td> <td>   15.424</td> <td> 0.000</td> <td>    0.243</td> <td>    0.313</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Rep Party Support?</th>         <td>    0.2329</td> <td>    0.064</td> <td>    3.662</td> <td> 0.000</td> <td>    0.108</td> <td>    0.358</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Trump Endorsed?</th>            <td>    0.3135</td> <td>    0.135</td> <td>    2.328</td> <td> 0.020</td> <td>    0.049</td> <td>    0.578</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Bannon Endorsed?</th>           <td>    0.1512</td> <td>    0.276</td> <td>    0.548</td> <td> 0.584</td> <td>   -0.391</td> <td>    0.693</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Great America Endorsed?</th>    <td>   -0.1060</td> <td>    0.292</td> <td>   -0.363</td> <td> 0.717</td> <td>   -0.679</td> <td>    0.467</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>NRA Endorsed?</th>              <td>    0.2214</td> <td>    0.128</td> <td>    1.730</td> <td> 0.084</td> <td>   -0.030</td> <td>    0.473</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Right to Life Endorsed?</th>    <td>    0.0449</td> <td>    0.058</td> <td>    0.774</td> <td> 0.439</td> <td>   -0.069</td> <td>    0.159</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Susan B. Anthony Endorsed?</th> <td>    0.0056</td> <td>    0.104</td> <td>    0.054</td> <td> 0.957</td> <td>   -0.198</td> <td>    0.209</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Club for Growth Endorsed?</th>  <td>    0.0959</td> <td>    0.132</td> <td>    0.724</td> <td> 0.469</td> <td>   -0.164</td> <td>    0.356</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Koch Support?</th>              <td>    0.3452</td> <td>    0.117</td> <td>    2.951</td> <td> 0.003</td> <td>    0.116</td> <td>    0.575</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>House Freedom Support?</th>     <td>   -0.0097</td> <td>    0.147</td> <td>   -0.066</td> <td> 0.947</td> <td>   -0.299</td> <td>    0.279</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Tea Party Endorsed?</th>        <td>    0.1943</td> <td>    0.129</td> <td>    1.511</td> <td> 0.131</td> <td>   -0.058</td> <td>    0.447</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Main Street Endorsed?</th>      <td>    0.1711</td> <td>    0.115</td> <td>    1.486</td> <td> 0.138</td> <td>   -0.055</td> <td>    0.397</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Chamber Endorsed?</th>          <td>    0.0501</td> <td>    0.171</td> <td>    0.294</td> <td> 0.769</td> <td>   -0.285</td> <td>    0.385</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No Labels Support?</th>         <td>    0.1055</td> <td>    0.322</td> <td>    0.328</td> <td> 0.743</td> <td>   -0.526</td> <td>    0.737</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>421.964</td> <th>  Durbin-Watson:     </th> <td>   1.592</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 121.821</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.787</td>  <th>  Prob(JB):          </th> <td>3.52e-27</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 1.860</td>  <th>  Cond. No.          </th> <td>    23.4</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:         Primary Status   R-squared:                       0.096\n",
              "Model:                            OLS   Adj. R-squared:                  0.079\n",
              "Method:                 Least Squares   F-statistic:                     5.740\n",
              "Date:                Tue, 11 May 2021   Prob (F-statistic):           1.02e-10\n",
              "Time:                        04:18:22   Log-Likelihood:                -477.24\n",
              "No. Observations:                 774   AIC:                             984.5\n",
              "Df Residuals:                     759   BIC:                             1054.\n",
              "Df Model:                          14                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================================\n",
              "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "----------------------------------------------------------------------------------------------\n",
              "const                          0.2781      0.018     15.424      0.000       0.243       0.313\n",
              "Rep Party Support?             0.2329      0.064      3.662      0.000       0.108       0.358\n",
              "Trump Endorsed?                0.3135      0.135      2.328      0.020       0.049       0.578\n",
              "Bannon Endorsed?               0.1512      0.276      0.548      0.584      -0.391       0.693\n",
              "Great America Endorsed?       -0.1060      0.292     -0.363      0.717      -0.679       0.467\n",
              "NRA Endorsed?                  0.2214      0.128      1.730      0.084      -0.030       0.473\n",
              "Right to Life Endorsed?        0.0449      0.058      0.774      0.439      -0.069       0.159\n",
              "Susan B. Anthony Endorsed?     0.0056      0.104      0.054      0.957      -0.198       0.209\n",
              "Club for Growth Endorsed?      0.0959      0.132      0.724      0.469      -0.164       0.356\n",
              "Koch Support?                  0.3452      0.117      2.951      0.003       0.116       0.575\n",
              "House Freedom Support?        -0.0097      0.147     -0.066      0.947      -0.299       0.279\n",
              "Tea Party Endorsed?            0.1943      0.129      1.511      0.131      -0.058       0.447\n",
              "Main Street Endorsed?          0.1711      0.115      1.486      0.138      -0.055       0.397\n",
              "Chamber Endorsed?              0.0501      0.171      0.294      0.769      -0.285       0.385\n",
              "No Labels Support?             0.1055      0.322      0.328      0.743      -0.526       0.737\n",
              "==============================================================================\n",
              "Omnibus:                      421.964   Durbin-Watson:                   1.592\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              121.821\n",
              "Skew:                           0.787   Prob(JB):                     3.52e-27\n",
              "Kurtosis:                       1.860   Cond. No.                         23.4\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lk3PLBIqmGNn"
      },
      "source": [
        "### Naive Estimator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tafc--Ptf_uM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a207c48-e1c0-4e5d-daad-e574bf1854d3"
      },
      "source": [
        "# ATE for dem\n",
        "endorsements = dem[['Party Support?',\n",
        "            'Biden Endorsed?',\n",
        " 'Warren Endorsed? ',\n",
        " 'Sanders Endorsed?',\n",
        " 'Emily Endorsed?',\n",
        " 'Guns Sense Candidate?',\n",
        " 'Indivisible Endorsed?',]]\n",
        "\n",
        "# endorsements = dem[['Party Support?',\n",
        "#  'Biden Endorsed?',\n",
        "#  'Warren Endorsed? ',\n",
        "#  'Sanders Endorsed?',]]\n",
        "\n",
        "# endorsements = dem[['Party Support?']]\n",
        "\n",
        "not_endorsed = endorsements[endorsements.sum(axis=1) == 0]\n",
        "\n",
        "control = dem.loc[not_endorsed.index] \n",
        "treat = dem.loc[~dem.index.isin(control.index)]\n",
        "\n",
        "treatment = pd.Series(np.zeros(len(dem))).rename('treatment')\n",
        "treatment.loc[treat.index] = 1\n",
        "dem['treatment'] = treatment #treatment 1 = endorsed by any candidate\n",
        "\n",
        "ATE = np.mean(treat['Primary Status']) - np.mean(control['Primary Status'])\n",
        "ATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.31374077395323174"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvSWMQOtNTRR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de747de8-f1a6-46cf-f55b-4fc14810fc33"
      },
      "source": [
        "len(control), len(treat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(772, 39)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMB3oDHckgOI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "e05cec9a-4e17-4060-c1a3-80641ccb766e"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "Y = dem['Primary Status'].dropna()\n",
        "Z = dem['treatment'].dropna()\n",
        "Z = sm.add_constant(Z)\n",
        "\n",
        "model = sm.OLS(Y,Z)\n",
        "results = model.fit()\n",
        "results.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>Primary Status</td>  <th>  R-squared:         </th> <td>   0.089</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.088</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   78.87</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 11 May 2021</td> <th>  Prob (F-statistic):</th> <td>4.22e-18</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:18:22</td>     <th>  Log-Likelihood:    </th> <td> -499.03</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   811</td>      <th>  AIC:               </th> <td>   1002.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   809</td>      <th>  BIC:               </th> <td>   1011.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>     <td>    0.2953</td> <td>    0.016</td> <td>   18.306</td> <td> 0.000</td> <td>    0.264</td> <td>    0.327</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>treatment</th> <td>    0.6534</td> <td>    0.074</td> <td>    8.881</td> <td> 0.000</td> <td>    0.509</td> <td>    0.798</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>371.518</td> <th>  Durbin-Watson:     </th> <td>   2.179</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 145.460</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.880</td>  <th>  Prob(JB):          </th> <td>2.59e-32</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 1.902</td>  <th>  Cond. No.          </th> <td>    4.69</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:         Primary Status   R-squared:                       0.089\n",
              "Model:                            OLS   Adj. R-squared:                  0.088\n",
              "Method:                 Least Squares   F-statistic:                     78.87\n",
              "Date:                Tue, 11 May 2021   Prob (F-statistic):           4.22e-18\n",
              "Time:                        04:18:22   Log-Likelihood:                -499.03\n",
              "No. Observations:                 811   AIC:                             1002.\n",
              "Df Residuals:                     809   BIC:                             1011.\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "const          0.2953      0.016     18.306      0.000       0.264       0.327\n",
              "treatment      0.6534      0.074      8.881      0.000       0.509       0.798\n",
              "==============================================================================\n",
              "Omnibus:                      371.518   Durbin-Watson:                   2.179\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              145.460\n",
              "Skew:                           0.880   Prob(JB):                     2.59e-32\n",
              "Kurtosis:                       1.902   Cond. No.                         4.69\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3yJy8rWwETD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afd9405e-17fa-4a77-a9df-4d679dc94024"
      },
      "source": [
        "# ATE for rep\n",
        "endorsements = rep[['Rep Party Support?']]\n",
        "#  'Trump Endorsed?',\n",
        "#  'Koch Support?',\n",
        "#  'NRA Endorsed?',]]\n",
        "\n",
        "not_endorsed = endorsements[endorsements.sum(axis=1) == 0]\n",
        "\n",
        "control = rep.loc[not_endorsed.index] \n",
        "treat = rep.loc[~rep.index.isin(control.index)]\n",
        "\n",
        "treatment = pd.Series(np.zeros(len(rep))).rename('treatment')\n",
        "treatment.loc[treat.index] = 1\n",
        "rep['treatment'] = treatment #treatment 1 = endorsed by any candidate\n",
        "\n",
        "ATE = np.mean(treat['Primary Status']) - np.mean(control['Primary Status'])\n",
        "ATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.31103074141048825"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQW62xIfwEbX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "938014fb-7e57-413e-91b9-b72453db55c7"
      },
      "source": [
        "Y = rep['Primary Status'].dropna()\n",
        "Z = rep['treatment'].dropna()\n",
        "Z = sm.add_constant(Z)\n",
        "\n",
        "model = sm.OLS(Y,Z)\n",
        "results = model.fit()\n",
        "results.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>Primary Status</td>  <th>  R-squared:         </th> <td>   0.077</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.076</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   64.74</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 11 May 2021</td> <th>  Prob (F-statistic):</th> <td>3.21e-15</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:18:22</td>     <th>  Log-Likelihood:    </th> <td> -485.01</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   774</td>      <th>  AIC:               </th> <td>   974.0</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   772</td>      <th>  BIC:               </th> <td>   983.3</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>     <td>    0.2849</td> <td>    0.017</td> <td>   16.397</td> <td> 0.000</td> <td>    0.251</td> <td>    0.319</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>treatment</th> <td>    0.4033</td> <td>    0.050</td> <td>    8.046</td> <td> 0.000</td> <td>    0.305</td> <td>    0.502</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>378.098</td> <th>  Durbin-Watson:     </th> <td>   1.617</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 108.733</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.726</td>  <th>  Prob(JB):          </th> <td>2.45e-24</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 1.877</td>  <th>  Cond. No.          </th> <td>    3.13</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:         Primary Status   R-squared:                       0.077\n",
              "Model:                            OLS   Adj. R-squared:                  0.076\n",
              "Method:                 Least Squares   F-statistic:                     64.74\n",
              "Date:                Tue, 11 May 2021   Prob (F-statistic):           3.21e-15\n",
              "Time:                        04:18:22   Log-Likelihood:                -485.01\n",
              "No. Observations:                 774   AIC:                             974.0\n",
              "Df Residuals:                     772   BIC:                             983.3\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "const          0.2849      0.017     16.397      0.000       0.251       0.319\n",
              "treatment      0.4033      0.050      8.046      0.000       0.305       0.502\n",
              "==============================================================================\n",
              "Omnibus:                      378.098   Durbin-Watson:                   1.617\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              108.733\n",
              "Skew:                           0.726   Prob(JB):                     2.45e-24\n",
              "Kurtosis:                       1.877   Cond. No.                         3.13\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pORVztQXkdbh"
      },
      "source": [
        "### With Confounder\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPiyYuXtj9BE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fab82fca-47cc-4a97-9c29-ab91d726e8af"
      },
      "source": [
        "# dem\n",
        "X = (dem[['Obama Alum?','Partisan Lean']].replace(\"Yes\",1).replace(\"No\",0)).dropna()\n",
        "Z = dem['treatment'].loc[X.index]\n",
        "Y = dem['Primary Status'].loc[X.index]\n",
        "\n",
        "Y = Y.to_numpy()\n",
        "X = X.to_numpy()\n",
        "Z = Z.to_numpy()\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression as LR\n",
        "lr = LR(penalty='none', max_iter=200, random_state=0)\n",
        "fit = lr.fit(X,Z)\n",
        "\n",
        "def estimate_treatment_effect(fit,X,Y,Z):\n",
        "  e_x = fit.predict_proba(X)[:,1]\n",
        "  ATE = np.mean((Z*Y)/e_x - (1-Z)*Y/(1-e_x))\n",
        "  return ATE\n",
        "\n",
        "estimate_treatment_effect(fit,X,Y,Z)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3341156228498388"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vw8_S9bZ2cH6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "942ee852-7219-4682-b3aa-35f7a7ebb7d5"
      },
      "source": [
        "Y = dem['Primary Status'].dropna()\n",
        "Z = dem['treatment'].dropna()\n",
        "\n",
        "n_1 = np.sum(Z)\n",
        "n_0 = len(Z) - n_1\n",
        "ATE = np.sum(Y*Z)/n_1 - np.sum(Y*(1-Z))/n_0\n",
        "ATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6533811611531819"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ASbafNc3UTf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d3e69329-4ce6-45d4-c75f-989f5840bd92"
      },
      "source": [
        "Y = dem['Primary Status'].dropna()\n",
        "Z = dem[['Obama Alum?','Partisan Lean','treatment']].dropna()\n",
        "# Z['Partisan Lean'] = stats.zscore(dem['Partisan Lean'])\n",
        "Z = sm.add_constant(Z)\n",
        "\n",
        "model = sm.OLS(Y,Z)\n",
        "results = model.fit()\n",
        "results.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>Primary Status</td>  <th>  R-squared:         </th> <td>   0.140</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.136</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   43.62</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 11 May 2021</td> <th>  Prob (F-statistic):</th> <td>3.97e-26</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:18:22</td>     <th>  Log-Likelihood:    </th> <td> -475.82</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   811</td>      <th>  AIC:               </th> <td>   959.6</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   807</td>      <th>  BIC:               </th> <td>   978.4</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td>    0.2200</td> <td>    0.019</td> <td>   11.431</td> <td> 0.000</td> <td>    0.182</td> <td>    0.258</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Obama Alum?</th>   <td>    0.1049</td> <td>    0.077</td> <td>    1.359</td> <td> 0.174</td> <td>   -0.047</td> <td>    0.256</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Partisan Lean</th> <td>   -0.0051</td> <td>    0.001</td> <td>   -6.874</td> <td> 0.000</td> <td>   -0.007</td> <td>   -0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>treatment</th>     <td>    0.6907</td> <td>    0.072</td> <td>    9.595</td> <td> 0.000</td> <td>    0.549</td> <td>    0.832</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>218.966</td> <th>  Durbin-Watson:     </th> <td>   2.273</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 120.854</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.811</td>  <th>  Prob(JB):          </th> <td>5.71e-27</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.028</td>  <th>  Cond. No.          </th> <td>    127.</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:         Primary Status   R-squared:                       0.140\n",
              "Model:                            OLS   Adj. R-squared:                  0.136\n",
              "Method:                 Least Squares   F-statistic:                     43.62\n",
              "Date:                Tue, 11 May 2021   Prob (F-statistic):           3.97e-26\n",
              "Time:                        04:18:22   Log-Likelihood:                -475.82\n",
              "No. Observations:                 811   AIC:                             959.6\n",
              "Df Residuals:                     807   BIC:                             978.4\n",
              "Df Model:                           3                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=================================================================================\n",
              "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const             0.2200      0.019     11.431      0.000       0.182       0.258\n",
              "Obama Alum?       0.1049      0.077      1.359      0.174      -0.047       0.256\n",
              "Partisan Lean    -0.0051      0.001     -6.874      0.000      -0.007      -0.004\n",
              "treatment         0.6907      0.072      9.595      0.000       0.549       0.832\n",
              "==============================================================================\n",
              "Omnibus:                      218.966   Durbin-Watson:                   2.273\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              120.854\n",
              "Skew:                           0.811   Prob(JB):                     5.71e-27\n",
              "Kurtosis:                       2.028   Cond. No.                         127.\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MA757Rp-0E4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0586f4f-0abe-42e8-afef-c46cdf1c9f17"
      },
      "source": [
        "# rep \n",
        "X = (dem[['Obama Alum?','Partisan Lean']].replace(\"Yes\",1).replace(\"No\",0)).dropna()\n",
        "Z = dem['treatment'].loc[X.index]\n",
        "Y = dem['Primary Status'].loc[X.index]\n",
        "\n",
        "Y = Y.to_numpy()\n",
        "X = X.to_numpy()\n",
        "Z = Z.to_numpy()\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression as LR\n",
        "lr = LR(penalty='none', max_iter=200, random_state=0)\n",
        "fit = lr.fit(X,Z)\n",
        "\n",
        "def estimate_treatment_effect(fit,X,Y,Z):\n",
        "  e_x = fit.predict_proba(X)[:,1]\n",
        "  ATE = np.mean((Z*Y)/e_x - (1-Z)*Y/(1-e_x))\n",
        "  return ATE\n",
        "\n",
        "estimate_treatment_effect(fit,X,Y,Z)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5942179239558727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dC0eO2GVlTnx"
      },
      "source": [
        "### Simpsons Paradox"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YA92ROT0ndmq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "9b0e6f6d-44c2-4234-c818-2a27fcad8d6a"
      },
      "source": [
        "# merging dem and rep\n",
        "df = rep[['Rep Party Support?','Primary Status']]\n",
        "df.columns = ['Party Support?','Primary Status']\n",
        "df['party'] = ['rep']*len(df)\n",
        "df2 = dem[['Party Support?','Primary Status']]\n",
        "df2['party'] = ['dem']*len(df2)\n",
        "all_candidates = pd.concat([df,df2])\n",
        "\n",
        "d = {'Party Support (Treatment)': [39/63,37/39,76/102], 'No Party Support (Control)': [219/711,228/772,447/1483]}\n",
        "pd.DataFrame(d,index=['Rep','Dem','Total'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>Party Support (Treatment)</th>\n",
              "      <th>No Party Support (Control)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rep</th>\n",
              "      <td>0.619048</td>\n",
              "      <td>0.308017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dem</th>\n",
              "      <td>0.948718</td>\n",
              "      <td>0.295337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total</th>\n",
              "      <td>0.745098</td>\n",
              "      <td>0.301416</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Party Support (Treatment)  No Party Support (Control)\n",
              "Rep                     0.619048                    0.308017\n",
              "Dem                     0.948718                    0.295337\n",
              "Total                   0.745098                    0.301416"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdbvrEYvcxGJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "647bd529-1399-4bb3-f304-117ebb92b73b"
      },
      "source": [
        "len(all_candidates[(all_candidates['Party Support?'] == 0) & \n",
        "              #  (all_candidates['Primary Status'] == 1) & \n",
        "               (all_candidates['party'] == 'dem')])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "772"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AmOCXjhtVdy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36f94259-3595-41bd-d35b-6347548dc3f1"
      },
      "source": [
        "len(all_candidates)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1585"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VAr3aoUZsOC"
      },
      "source": [
        "### **Question 2 (GLM and Nonparametric)**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS1ZXAqknS-B"
      },
      "source": [
        "### GLM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nS0qwDp7aGdy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97c879f4-5bc0-41d3-f7be-d22fc50b587b"
      },
      "source": [
        "list(dem)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Candidate',\n",
              " 'State',\n",
              " 'District',\n",
              " 'Office Type',\n",
              " 'Race Type',\n",
              " 'Race Primary Election Date',\n",
              " 'Primary Status',\n",
              " 'Primary Runoff Status',\n",
              " 'General Status',\n",
              " 'Partisan Lean',\n",
              " 'Primary %',\n",
              " 'Won Primary',\n",
              " 'Race',\n",
              " 'Veteran?',\n",
              " 'LGBTQ?',\n",
              " 'Elected Official?',\n",
              " 'Self-Funder?',\n",
              " 'STEM?',\n",
              " 'Obama Alum?',\n",
              " 'Party Support?',\n",
              " 'Emily Endorsed?',\n",
              " 'Guns Sense Candidate?',\n",
              " 'Biden Endorsed?',\n",
              " 'Warren Endorsed? ',\n",
              " 'Sanders Endorsed?',\n",
              " 'Our Revolution Endorsed?',\n",
              " 'Justice Dems Endorsed?',\n",
              " 'PCCC Endorsed?',\n",
              " 'Indivisible Endorsed?',\n",
              " 'WFP Endorsed?',\n",
              " 'VoteVets Endorsed?',\n",
              " 'No Labels Support?',\n",
              " 'treatment']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x15Y9Zn2aICA"
      },
      "source": [
        "##Cleaning data sets, choosing endorsements, primary status, and candidate, then replacing string values as specified above\n",
        "dem_clean = dem[['Candidate', 'Primary Status', 'Emily Endorsed?',\n",
        " 'Guns Sense Candidate?',\n",
        " 'Biden Endorsed?',\n",
        " 'Warren Endorsed? ',\n",
        " 'Sanders Endorsed?',\n",
        " 'Our Revolution Endorsed?',\n",
        " 'Justice Dems Endorsed?',\n",
        " 'PCCC Endorsed?',\n",
        " 'Indivisible Endorsed?',\n",
        " 'WFP Endorsed?',\n",
        " 'VoteVets Endorsed?',\n",
        " 'No Labels Support?' ]]\n",
        "dem_clean = dem_clean.fillna(0)\n",
        "dem_clean_binary = dem_clean.replace(['Advanced', 'Lost', 'Yes', 'No'], [1, 0, 1, -1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hp8yFLiUak7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c0b8d7b-dcca-4bcd-9eef-3e3a1b05f46f"
      },
      "source": [
        "list(rep)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Candidate',\n",
              " 'State',\n",
              " 'District',\n",
              " 'Office Type',\n",
              " 'Race Type',\n",
              " 'Race Primary Election Date',\n",
              " 'Primary Status',\n",
              " 'Primary Runoff Status',\n",
              " 'General Status',\n",
              " 'Primary %',\n",
              " 'Won Primary',\n",
              " 'Rep Party Support?',\n",
              " 'Trump Endorsed?',\n",
              " 'Bannon Endorsed?',\n",
              " 'Great America Endorsed?',\n",
              " 'NRA Endorsed?',\n",
              " 'Right to Life Endorsed?',\n",
              " 'Susan B. Anthony Endorsed?',\n",
              " 'Club for Growth Endorsed?',\n",
              " 'Koch Support?',\n",
              " 'House Freedom Support?',\n",
              " 'Tea Party Endorsed?',\n",
              " 'Main Street Endorsed?',\n",
              " 'Chamber Endorsed?',\n",
              " 'No Labels Support?',\n",
              " 'treatment']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqAXpzqltzgm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5e98fee-c7af-43e0-f4e5-5f8a0cb754f6"
      },
      "source": [
        "rep['Primary Status'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIFgxcHWbbqz"
      },
      "source": [
        "rep_clean = rep[['Candidate','Primary Status','Rep Party Support?',\n",
        " 'Trump Endorsed?',\n",
        " 'Bannon Endorsed?',\n",
        " 'Great America Endorsed?',\n",
        " 'NRA Endorsed?',\n",
        " 'Right to Life Endorsed?',\n",
        " 'Susan B. Anthony Endorsed?',\n",
        " 'Club for Growth Endorsed?',\n",
        " 'Koch Support?',\n",
        " 'House Freedom Support?',\n",
        " 'Tea Party Endorsed?',\n",
        " 'Main Street Endorsed?',\n",
        " 'Chamber Endorsed?',\n",
        " 'No Labels Support?']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9d5PXAgsb6XE"
      },
      "source": [
        " rep_clean = rep_clean.fillna(0)\n",
        " rep_clean_binary = rep_clean.replace(['Advanced', 'On the Ballot', 'Lost', 'Yes', 'No'], [1, 1, 0, 1, -1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQc54R4XgOo2"
      },
      "source": [
        "#Split into y, predicting primary status, and x, different endorsements for both data sets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCQaQwFskc6U"
      },
      "source": [
        "y_dem = dem_clean_binary[['Primary Status']]\n",
        "X_dem = dem_clean_binary[['Emily Endorsed?',\n",
        " 'Guns Sense Candidate?',\n",
        " 'Biden Endorsed?',\n",
        " 'Warren Endorsed? ',\n",
        " 'Sanders Endorsed?',\n",
        " 'Our Revolution Endorsed?',\n",
        " 'Justice Dems Endorsed?',\n",
        " 'PCCC Endorsed?',\n",
        " 'Indivisible Endorsed?',\n",
        " 'WFP Endorsed?',\n",
        " 'VoteVets Endorsed?',\n",
        " 'No Labels Support?' ]]\n",
        "\n",
        "y_rep = rep_clean_binary[['Primary Status']]\n",
        "X_rep = rep_clean_binary[['Rep Party Support?',\n",
        " 'Trump Endorsed?',\n",
        " 'Bannon Endorsed?',\n",
        " 'Great America Endorsed?',\n",
        " 'NRA Endorsed?',\n",
        " 'Right to Life Endorsed?',\n",
        " 'Susan B. Anthony Endorsed?',\n",
        " 'Club for Growth Endorsed?',\n",
        " 'Koch Support?',\n",
        " 'House Freedom Support?',\n",
        " 'Tea Party Endorsed?',\n",
        " 'Main Street Endorsed?',\n",
        " 'Chamber Endorsed?',\n",
        " 'No Labels Support?']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyySm0gIlo5z"
      },
      "source": [
        "Xdem_train, Xdem_test, ydem_train, ydem_test = train_test_split(\n",
        "    X_dem, y_dem, test_size=80, random_state=42\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u40VzgCQozdZ"
      },
      "source": [
        "Xrep_train, Xrep_test, yrep_train, yrep_test = train_test_split(\n",
        "    X_rep, y_rep, test_size=75, random_state=42\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWPYLpuBCzC7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32f2040c-3baa-47d7-f5f6-36d1ed776aa4"
      },
      "source": [
        "dem_log_reg = sm.Logit(ydem_train, Xdem_train).fit()\n",
        "print(dem_log_reg.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Maximum number of iterations has been exceeded.\n",
            "         Current function value: 0.664898\n",
            "         Iterations: 35\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:         Primary Status   No. Observations:                  731\n",
            "Model:                          Logit   Df Residuals:                      719\n",
            "Method:                           MLE   Df Model:                           11\n",
            "Date:                Tue, 11 May 2021   Pseudo R-squ.:                -0.06228\n",
            "Time:                        04:18:23   Log-Likelihood:                -486.04\n",
            "converged:                      False   LL-Null:                       -457.55\n",
            "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
            "============================================================================================\n",
            "                               coef    std err          z      P>|z|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------\n",
            "Emily Endorsed?              0.7416      0.357      2.076      0.038       0.041       1.442\n",
            "Guns Sense Candidate?       -0.3395      0.175     -1.940      0.052      -0.683       0.003\n",
            "Biden Endorsed?             27.7421   3.84e+05   7.22e-05      1.000   -7.53e+05    7.53e+05\n",
            "Warren Endorsed?            20.8785   2.23e+04      0.001      0.999   -4.37e+04    4.37e+04\n",
            "Sanders Endorsed?            0.6607      0.806      0.820      0.412      -0.918       2.240\n",
            "Our Revolution Endorsed?    -0.5006      0.283     -1.770      0.077      -1.055       0.054\n",
            "Justice Dems Endorsed?      -0.3727      0.361     -1.031      0.302      -1.081       0.336\n",
            "PCCC Endorsed?               0.1519      0.734      0.207      0.836      -1.287       1.590\n",
            "Indivisible Endorsed?        0.8735      0.355      2.462      0.014       0.178       1.569\n",
            "WFP Endorsed?                0.4639      0.429      1.081      0.280      -0.377       1.305\n",
            "VoteVets Endorsed?          -0.0354      0.455     -0.078      0.938      -0.927       0.856\n",
            "No Labels Support?         -22.7892   6.34e+04     -0.000      1.000   -1.24e+05    1.24e+05\n",
            "============================================================================================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW74vVCtIWm2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de95a163-69b8-4953-9c9d-d00fe087e48c"
      },
      "source": [
        "dem_yhat = dem_log_reg.predict(Xdem_test)\n",
        "dem_prediction = list(map(round, dem_yhat))\n",
        "print('Test accuracy = ', accuracy_score(ydem_test, dem_prediction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy =  0.7125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyAaabMmH_bP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97a39e68-270f-4780-c5db-8d1698235265"
      },
      "source": [
        "rep_log_reg = sm.Logit(yrep_train, Xrep_train).fit()\n",
        "print(rep_log_reg.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Maximum number of iterations has been exceeded.\n",
            "         Current function value: 0.667434\n",
            "         Iterations: 35\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:         Primary Status   No. Observations:                  699\n",
            "Model:                          Logit   Df Residuals:                      685\n",
            "Method:                           MLE   Df Model:                           13\n",
            "Date:                Tue, 11 May 2021   Pseudo R-squ.:                -0.04535\n",
            "Time:                        04:18:23   Log-Likelihood:                -466.54\n",
            "converged:                      False   LL-Null:                       -446.30\n",
            "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
            "==============================================================================================\n",
            "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------\n",
            "Rep Party Support?             0.2619      0.316      0.830      0.407      -0.357       0.881\n",
            "Trump Endorsed?                1.7546      0.892      1.968      0.049       0.007       3.502\n",
            "Bannon Endorsed?              17.1404   4575.287      0.004      0.997   -8950.257    8984.537\n",
            "Great America Endorsed?      -17.5243   4575.287     -0.004      0.997   -8984.922    8949.873\n",
            "NRA Endorsed?                  0.6586      0.696      0.947      0.344      -0.705       2.022\n",
            "Right to Life Endorsed?       -0.6392      0.284     -2.253      0.024      -1.195      -0.083\n",
            "Susan B. Anthony Endorsed?    -0.3573      0.535     -0.668      0.504      -1.406       0.691\n",
            "Club for Growth Endorsed?      0.3281      0.734      0.447      0.655      -1.110       1.767\n",
            "Koch Support?                  2.5542      1.069      2.389      0.017       0.459       4.649\n",
            "House Freedom Support?        -0.4898      0.780     -0.628      0.530      -2.019       1.039\n",
            "Tea Party Endorsed?            1.0024      0.764      1.311      0.190      -0.496       2.500\n",
            "Main Street Endorsed?          0.0246      0.597      0.041      0.967      -1.146       1.195\n",
            "Chamber Endorsed?             -0.3376      0.883     -0.382      0.702      -2.069       1.394\n",
            "No Labels Support?           -21.7605   5.31e+04     -0.000      1.000   -1.04e+05    1.04e+05\n",
            "==============================================================================================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j34L-tdrJcaj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d38cdfc5-7a65-4a18-e839-6d694929e036"
      },
      "source": [
        "rep_yhat = rep_log_reg.predict(Xrep_test)\n",
        "rep_prediction = list(map(round, rep_yhat))\n",
        "print('Test accuracy = ', accuracy_score(yrep_test, rep_prediction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy =  0.7066666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GisjiFYhPQZf"
      },
      "source": [
        "### Nonparametric\n",
        "\n",
        "Random Forest Model, depth 6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wurbTb8GPP90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e3ce642-298f-4f6c-c1d0-be24a6428915"
      },
      "source": [
        "dem_tree_model = RandomForestClassifier(n_estimators=100, max_depth=6)\n",
        "dem_tree_model.fit(Xdem_train, ydem_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=6, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bG_wcBsWA01"
      },
      "source": [
        "dem_probs = dem_tree_model.predict_proba(Xdem_test)[:, 1]\n",
        "demTree_prediction = dem_probs > 0.5\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfKDa6SRXaQK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf47c55-7e4e-42ed-b7eb-6744e89eb5f0"
      },
      "source": [
        "print(\"Accuracy\", (demTree_prediction == ydem_test['Primary Status']).mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.6625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vL2tFPWiXzCF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bf1d96b-5298-4244-a7c9-94d2f28ad9b6"
      },
      "source": [
        "rep_tree_model = RandomForestClassifier(n_estimators=100, max_depth=6)\n",
        "rep_tree_model.fit(Xrep_train, yrep_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=6, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ry9bKGmgX4ZS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c56afc7-2e72-4e23-b885-0b498625380f"
      },
      "source": [
        "rep_probs = rep_tree_model.predict_proba(Xrep_test)[:, 1]\n",
        "repTree_prediction = rep_probs > 0.5\n",
        "print(\"Accuracy\", (repTree_prediction == yrep_test['Primary Status']).mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.7333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIw8jrCSnqLq"
      },
      "source": [
        "### **EDA for Question 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BuXaVc82Ddh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "45870b88-d50e-4122-d5f8-611dd7211c48"
      },
      "source": [
        "plt.hist(dem['Primary %'])\n",
        "plt.xlabel('Democrat Primary %')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbVbO6nDpTKT"
      },
      "source": [
        "# assuming a multivariate Gaussian distribution\n",
        "mean = [0, 0]\n",
        "cov = [[1, 1], [1, 2]]\n",
        "x, y = np.random.multivariate_normal(mean, cov, 10000).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYPVEplsqeSe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "1b0e4d65-da7b-42df-9ca8-f532a69672dd"
      },
      "source": [
        "plt.hist2d(x, y, bins=30, cmap='Blues')\n",
        "cb = plt.colorbar()\n",
        "cb.set_label('counts in bin')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rP1qwtt1qfCC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "e904bd18-001f-49f3-949b-1ad6015bfd09"
      },
      "source": [
        "sns.distplot(dem['Primary %'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f56b47f4150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcZQOTvDr3A6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "b2457aa2-a872-49e0-a867-731f00fd2844"
      },
      "source": [
        "plt.hist(rep['Primary %'])\n",
        "plt.xlabel('Republican Primary %')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+1vXAZDvP/x04tvdzDPx74EvAvcDvAEf3dp6Baxg+U3iC4R3aBbOdVyAMdwD+KXAPw51EC963ww9IUodWcreMJGkWhrskdchwl6QOGe6S1CHDXZI6ZLhrySTZl+SuNsrf/0hyzBj28a1Z2q9M8vNt+sPjHEQuyc1tNNI/TvKHI/en77/eWOs4kCSrktzazsVrR9qvT/KS5ahJh5bhrqX03apaX8Mof98ALlyOIqrqn1bV/WPezRuq6hSGgZ9+a/+FSY4cRx0j396cy/nABxkG2LuobfuzwJ1V9f+WsiY9NxnuGpfP0wY9SvLSJJ9JcnuS/5Pk5a39yiQfTDKZ5E/auDIkeXOS90+/UJJPJTlzZP69bRzw7UlW7b/jdmW9oU2/Oskd7Sp7e2s7Pcnn2yBd/3f6yrvt97pW64NJfnMex3kL8KNt+28leXeSPwZ+fL86vpXkt1rdn2s13JzkK0l+rq0z0f773NEeP9Haz2zt24D7k/x6kotGjvfitHH+RzzBMNLi0cC+9kfhImA+x6QOGO5acm2s/bN4ejiIrcAvV9XfAf4V8IGR1ScYri7PAT6Y5IfmePkXApNV9QrgfwPvOkAdq4APAf+4XWW/ri36EvD3ahik698B/2lks/XALwB/G/iFJKNjfczkZxm+TThd221VdUpV3TpD3X/Q6n4c+I/ATwPnAr/e1tkL/HRVndZquHRk+9OAt1XVy4ArgDe1YzyCYZiG391vf7/HMDDXje34/iXwO1X1nTmOR52Y71s8aT6en+Quhiv2B4Ab22iWPwH8/jCECDBcTU67tqqeAh5M8hXg5XPs4yngY236dxkGnJrNGcAtVfUQQFVNj6v9YuCqJOsYhnZ43sg226vqMYAk9wM/wjOHYZ32kSTfBXYCv9za9jEM8DaTHwCfadP3AN+vqieS3MPwB45Wx/uTrG+v9bKR7b84chw7k3w9yakMw8XeWVVfH91ZO4Zz2nEcyzDK5LlJPsQwtMG7q+rzs9SqDhjuWkrfrar1SV4AfJahz/1KhjG618+yzf7jXxTDr0+Nvqs80NX8QsbP+A/ATVV1boYx8m8eWfb9kel9zP7/yBuqanK/tu9V1b5Z1n+inh7r46np/VTVUyP96L8CPMrwS0xHAN8b2f7b+73eh4E3A3+d4Ur+QP4tcDFDP/ytDKNOXgf8zBzbaQWzW0ZLrr31fyvwduA7wENJXgd/+TuRp4ys/rokRyR5KcOgUV9muBpe39rXMnTbTDuCp0cN/CcMYTWbLwA/leSktu/jWvuLeXoo1Tcv6CDH48XAnvZO5o0MPz03m08yjH/+dxn+kM6ovTs5sapuZuiDf4rhD+Lzl6hmPUcZ7hqLqrqTYYTD84E3ABe0Dxrv45k/h/g1hlEA/yfwz6vqe8AfAg8xDAF7KXDHyPrfBk7P8IPDr+Lp/uqZaphi+C3K69q+p7tzfhP4z0nu5Ln17vUDwKZW68t59tX6X6rhpyVvYujWmu3dAgxX7L/apq8B/gXDcNnvW5KK9ZzlqJBaNkmuZBjS9+Nzratnah+k3gG8rqoeXO569Nzjlbu0wrQvRu1g+PDXYNeMvHKXpA555S5JHTLcJalDhrskdchwl6QOGe6S1KH/D3s77QXsYX+oAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COoO5nSssFto",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "48f6f5e9-fa3f-44a5-8147-eea2a646be5b"
      },
      "source": [
        "sns.distplot(rep['Primary %'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f56b2700790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V3QbcgnctDNPM7D10nNx9Rzi3e7zr06SLCL+5pj/Dusbx09lrWbXroKvvZ4xpGvV9c4Sr6v6ahapaBIS7E5Jx05JtxYSHCkO7Ns3EhBFhIfztliF0bBPFlBkr2HvoeJO8rzHGPfUljopT3GaC0PGKatbsOcTAtHa1TmDolriYCKZNyuR4RTV3Tc/mWIWNtDKmOasvcZwtIiW1PEqBAU0RoGk8K3cdpLJaGdZEVxu+eiXF8pebBrExv4Qfv7XGRloZ04ydNHGoaqiqtqnlEauq1lTVjKgqy7YfIK19NJ3aRQckhov6dODnV/Tlg/UF/HnhloDEYIw5ff6sOW6asbV7DlN0pJzvDUoJaByTR3VlU0EpzyzcQs8Orbn67E4Bjce0LP4sj1yXm4d1boRIWjZXh9WIyBgR2SQiuSIytZbtkSIy09m+VETSnfJ4EflURI6IyLM19lnkHHO187DJFhvg7ZV7CAsR+qe0DWgcIsIT1/XnnPT2/GTWGtbsPhTQeIwx/nMtcYhIKPAcMBbIAG4SkYwa1SYDB1W1B/An4EmnvAz4FfCTOg4/UVUHOo99jR99y1JR5SFrzV4yOrUhKrzpOsXrEhkWyvO3DCExNpK7pmeTf9hGWhnTnLh5xTEUyFXVbc7Mum8C42rUGQe85jyfDYwWEVHVo6q6GG8CMadp0aZ9HDxWyaC0xp9e5FTFt47kpUnncKyimkkvL+PQMRukZ0xz4WbiSAF2+7ze45TVWkdVq4DDQEOG/LziNFP9SkRqXURCRKaISLaIZBcVFfkffQvy9so8ElpH0qND60CH8h96d4zlxduGsGP/Me54dTnHK6oDHZIxpgHcvXXYHRNVdQBwnvO4tbZKqvqiqmaqamZiYmKTBhhMDh2rYOE3hVw7sNMpL9TkpnO7J/DMTQNZvfsQP3x9BZXVnkCHZIyph5uJIw9I83md6pTVWsdZHKotUHyyg6pqnvOzFHgDb5OYqcO7a/OprFa+Nzg10KHUaUz/ZJ64dgCLNhXxs9lr7R4PY4Kcm8NxlwM9RaQr3gQxAbi5Rp0sYBLwNTAe+ORk07U7yaWdqu4XkXDgKuBjN4JvKd5euYc+HWPJ6NSG1UEygqmuIZOXZiQxZ1UehSVlXDkgmTpaIQEbMmlMILmWOFS1SkTuBxYAocDLqpojIo8D2aqaBbwEzBCRXOAA3uQCgIjsANoAESJyLXAZsBNY4CSNULxJ4+9ufYbmLu/QcVbtOsTPxvQOdCgNcmGvRI6VV/Hl1mLCQoTL+3U8afIwxgSGqzcAquo8vKsF+pY96vO8DLihjn3T6zjskMaKr6X7MKcAgDH9OjbaMRvjBqu6iAhXDEimyqN8vmU/IsJlGUmWPIwJMnbneAs2f30BvZNi6ZYYXKOpTkZEuPrsTqjCZ5uLEIFL+1ryMCaYWOJooYqPlLN8xwHuv6hHoEPxW4gI1wzshKIs2lREiAiX9E0KdFjGGIcljhbq442FeBQu7994zVRNKUSEcQNTUIVPvtmHR9WuPIwJEpY4Wqj56wtIi4smI7lNoEM5ZSEiXOtMyrhoUxEVVZ56R1sZY9xniaMFKimr5MvcYiad26XZf8mGiHDdoBQiw0L4cmsxFVWeb5OJMSYwLHG0QJ9+s4+Kag9jmmkzVU0nRltFhIXy6aZ9lFd5uPGcNNfXTDfG1M5+81qgBTkFJMZGBtWkhqdLRLg0I4kx/TqyLu8w98xYQVmlzW1lTCBY4mhhyiqr+fSbIi7vl0RIEM5NdbrO75XIuIGd+GTTPu54dTlHy239cmOamiWOFubzzUUcr6xmTL/kQIfimmFd43nq+2ezdPsBJk5bysGjNiW7MU3JEkcLMz+ngLbR4QzrFhfoUFx13aBU/jpxMBvySxj//FfkHbLFoIxpKpY4WpDKag8fbyjkkr5JZ0TH8eX9OjL9jqHsKyln/N++YkthaaBDMuaM0PK/Xc4gS7YVU1JW1WJGUzXE8G7xzLx7BFUeZfzzX7Ni58FAh2RMi2fDcVuQ+esLaBURynk9EwIdiutqTrY4aUQ6r3y5nQkvfs3NQzvTu2P9Nz7a1OzGnBq74mghqj3KgpxCLurdgajw0ECH0+TiYiKYcn43EltHMmPJTlbtsisPY9xiiaOFWLXrIPuPlDfbuakaQ2xUOHee1430hBhmrdjDF1vO7LXmjXGLJY4WYv76AiJCQ7io95m7vjpAVHgot49Ip3+nNnywvoD31+7FU/eiksaYU2CJowVQVRZsKGBkj3hio8IDHU7AhYWGMGFoZ0Z0i+fLrcW8sXQXFVWeQIdlTIthiaMF2JBfwu4Dx8+o0VT1CXEWhLpyQDIb80uYtngbpWWVgQ7LmBbB1cQhImNEZJOI5IrI1Fq2R4rITGf7UhFJd8rjReRTETkiIs/W2GeIiKxz9nlGmvv0r41gwfoCQgRb7KgWI3skMHFYFwpLynj+s63sKykLdEjGNHuuJQ4RCQWeA8YCGcBNIpJRo9pk4KCq9gD+BDzplJcBvwJ+Usuh/wbcBfR0HmMaP/rmZX5OAUO7xhHfOjLQoQSljE5tuOu8blRWK89/vpWtRUcCHZIxzZqbVxxDgVxV3aaqFcCbwLgadcYBrznPZwOjRURU9aiqLsabQL4lIslAG1VdoqoKTAeudfEzBL2tRUfYXHiEMf2smepkUtu34ocXdCc2KpxXv9xB9o4DgQ7JmGbLzcSRAuz2eb3HKau1jqpWAYeB+HqOuaeeYwIgIlNEJFtEsouKWu6wzAU5BQBcZomjXu1jIrjn/O50TYzh7VV5/PKdddZpbswpaLGd46r6oqpmqmpmYmLLHaK6YH0BZ6e1o1O76ECH0ixER4QyaUQ65/VM4B9LdnHz35ewr9T6PYzxh5uJIw9I83md6pTVWkdEwoC2QHE9x0yt55hnjLxDx1mz57A1U/kpNEQY2z+Zv9w0iJy9JVz9l8WstDvNjWkwNxPHcqCniHQVkQhgApBVo04WMMl5Ph74xOm7qJWq5gMlIjLcGU11GzC38UNvHj50mqku72ejqU7F1Wd34l8/PJeIsBBufOFr/v75Njweu1nQmPq4ljicPov7gQXARuAtVc0RkcdF5Bqn2ktAvIjkAg8D3w7ZFZEdwFPA7SKyx2dE1r3ANCAX2Ap84NZnCHbz1xfQOymWbomtAx1Ks5XRqQ3v3j+Ki3p34H/mbWTSK8us6cqYerg6O66qzgPm1Sh71Od5GXBDHfum11GeDfRvvCibp/1Hylm+4wD3X9Qj0KE0e+1aRfDCrUN4fekufvveBq54+gv+eMPZXNi7Q6BDMyYotdjO8ZZuQU4BHoWxA1ruErFNSUS4ZXgX3n1gFAmtI7n9leX8eu56W9PcmFpY4mim5q3Lp1tCDH06xgY6lBalV1Is79w3kh+MTGf6kp1c9qfPWbRpX6DDMiaoWOJohoqPlLNk2wHGDuiIzbjS+KLCQ/n11f2Yfc+5REeEcvsry3lo5moOHK0IdGjGBAVLHM3QhxsKqfYoV1gzlauGdGnP+w+O4sHRPXlv7V4ueeoz3ly2i2obeWXOcJY4mqF56/JJj29FRnL9y6Oa0xMZFsrDl/bivQfOo1tCDFPfXse45xbblCXmjGaJo5k5cLSCr7YWM3ZAsjVTNaHeHWOZdc8Inp4wkOIjFYx//mse/Ocq8g8fD3RoxjQ5SxzNzEcbCqj2KFdaM1WTExHGDUxh4Y8v4MGLe7Agp4CL/riIPy7YxBEbfWXOIJY4mpn31xXQOa4V/TpZM1WgtIoI4+HLevPxwxdwWUZHnv00lwv/71P+sWQnVdU2aaJp+Vy9AdA0roNHK/gqdz+Tz+tqzVSN4I2lu077GMO7xZPSLpoP1ufzy3fW8/TCLYzt15HeHWMb9G9087DOpx2DMU3NrjiakffX5VPlUa4+q1OgQzE+0uJacdd53bhlWGc8HmX6kp289OV29h6y/g/TMtkVRzMyZ1UevZJaWzNVEBIRMjq1pXfHNizbXszCb/bx3Ke5DExrx6UZSbRrFRHoEI1pNJY4momdxUdZsfMgPxvT25qpglhoiDCiewKDOrdn0aYivtq6n3V5hxnVI4HzeyUSFR4a6BCNOW2WOJqJd1btRQSuHVjrgocmyESFhzKmf0eGdYvjow2FLNpcxPIdBxjdN4lz0uMIDbHkb5ov6+NoBlSVOav2MLxrvK3018y0bxXB9zPTuPfC7nRoE0XWmr08s3ALG/NLOMnSM8YENbviaAZW7T7EjuJj3GtTqDdbqe1bceeornxTUMoH6wuYsWQnXRNiOCu1HQNS2wY6vDNaVbWHoiPlHK+sJgShsKSMDrGR1iR8EpY4moF3VuURGRbC2P62RGxzJiL0TW5Dr6RYlu84wMcbC7n62cXcMCSVn1/Rl/Yx1oHeVCqrPazZfYjVew6xY/9RfKcfe/GLbaS0i+bSjCRuG9HFFkqrhSWOIFdR5eHdNXu5NCOJ2KjwQIdjGkFoiDC8WzwD09pRWFrGS19sZ+E3+/jVVX25dmCK/aXrIo8qK3YeZOHGQkrKqoiPiWBUjwQ6tYsmJjKMao+SHt+KxbnFvLFsF699vYPrBqYw9Yo+dIiNCnT4QcMSR5BbuLGQg8cq+d5g6xRvaaLCQ3lkbF+uG5TCI2+v46GZa3h7ZR5PXNufLvExgQ6vxTlwtIK3snez68AxusS1YvyQNLonxnwnUd88rDO3j+xKUWk50xZv45XFO/hoYyG//95ZXHmWTfUDljiC3owlO0lpF80FvWwZ05boxN3r1w9OJa19KxbkFDD6/33G6L5JjOqR0KDRV3b3ef3W5x3mXyv3IALjB6cyqHO7eq/sEmMjeWRsX27MTOPHs9Zw3xsrWb4jnV9dlXHGj4pzdVSViIwRkU0ikisiU2vZHikiM53tS0Uk3WfbI075JhG53Kd8h4isE5HVIpLtZvyBlrvvCF9tLebmYZ3P+P+oLV2IeJuvfnRJL3p3jGVBTgF/XZRLnt19flpUlc83F/HGsl10iI3kgYt7MrhLe7+aA7sltuatu0dwx8iuvPrVDu6ekc3ximoXow5+riUOEQkFngPGAhnATSKSUaPaZOCgqvYA/gQ86eybAUwA+gFjgL86xzvhIlUdqKqZbsUfDF5fupPwUOH7mWmBDsU0kbbR4Uwc1oVbhnXhSHkVf1uUy4KcAipt8kS/VXuUrDV7mZ9TQP+Uttx5Xjfan+Id/OGhITx6dQa/HdePhd/s467p2ZRVnrnJw80rjqFArqpuU9UK4E1gXI0644DXnOezgdHi/VNgHPCmqpar6nYg1zneGeNYRRWzV+xhTP9kEmMjAx2OaWIZndrwo9G9GJTWns82F/HsJ7nsKj4a6LCajSqPh38u28XS7Qc4r2cCE85JIzz09L/ubh2Rzh/Hn82XW/ef0cnDzcSRAuz2eb3HKau1jqpWAYeB+Hr2VeBDEVkhIlPqenMRmSIi2SKSXVRUdFofJBDeXbOX0rIqbh3eJdChmACJjgjl+iGp3H5uOpXVHl74fBvvr91LRZVdfZxMtUd5c9luNuSXcOWAZMb2TyakEUeqXT8klSevP4vFufu5e8YKyqvOvOTRHO8cH6Wqg/E2gd0nIufXVklVX1TVTFXNTExMbNoIT5OqMmPJTnolteac9PaBDscEWK+kWP5rdE+Gdo3jy63FPPPJFrYVHQl0WEGp2qO8uXwXG/JLuOqsZEb2SHDlfb6fmcbvvzeAzzYX8ZNZa/GcYevQu5k48gDfxvlUp6zWOiISBrQFik+2r6qe+LkPmEMLbMJatfsQ6/NKuHV4FxvTbwCIDA9l3MAU7jyvKwDTFm9n7uo8ys/QppLaVFV7mJm9m5y93iuNc7u7kzROuPGczvz3mD68u2Yvf1iwydX3CjZuJo7lQE8R6SoiEXg7u7Nq1MkCJjnPxwOfqHcCnyxggjPqqivQE1gmIjEiEgsgIjHAZcB6Fz9DQLzw2VbaRodz3eDUQIdigky3hNY8eHFPRvVIYNn2Azy9cAufbW5+TbGNraraw49mrmZ93mGu6N/RtSuNmu65oBu3DO/M859tZcaSnU3ynsHAtcTh9FncDywANgJvqWqOiDwuItc41V4C4kUkF3gYmOrsmwO8BWwA5gP3qWo1kAQsFtURgpIAABEuSURBVJE1wDLgfVWd79ZnCITcfUf4cEMht43oQutIu83GfFdEWAhXDEjm7gu6Ex4WwqSXl/HTWWs4fKwy0KEFRFW1h4feWsN7a/MZ278jo3o2XdO0iPDY1f0Y3acDv567nk++KWyy9w4kORNm6MzMzNTs7OZxy8fPZq9h7uq9fDn1YhJaN+5oqsZYKtUEl8pqD/tKy3j+s23Ex0TwxLX9uazfmTOnWbVHefit1cxdvZepY/vQphGm5TmVGyqPVVTx/Re+ZnvRUWbdcy4ZLWSxNRFZUdttD82xc7zF2lV8jLdX5nHT0M6NnjRMyxQeGsJPL+/D3PtGEhcTwZQZK3jgn6soPlIe6NBcV+1RfjLL+4fWz8b05p4LugcsllYRYbw06Rxio8KZ/Npy9pWUBSyWpmCJI4g8vXALoSHCvRcG7hfANE/9U9qSdf8oHr60F/PX53Ppnz7n3TV7W+yaH5XVHh6auZo5q/L46eW9uffCwC85kNQmipduz+Tw8UrunN6y7y63xBEkcvcdYc6qPdw6vAsd2tgsnMZ/EWEhPDi6J+89cB5p7aN54J+r+MGry8ndVxro0BpVeVU1972+kqw1e/nvMX24L4jWqenXqS3PTBjEurzDPDRzdYsdpmuJI0j8bt5GWkWEcY9dbZjT1LtjLP/64bn88sq+rNh5kMv//AWPzl3PgaMVgQ7ttB2vqGbK9BV8uKGQx67O4IdB+PtySUYSv7iiL/NzCvi/D1vmMF0bthMEFm/Zz8Jv9jF1bB/r2zCNIiw0hDvP68Z1g1L488db+MeSnbyzKo8HR/fkluFdiAoPrf8gQWZfaRl3TV/B2j2H+MP1Z/H9c4J3DrfJo7qyff9R/rZoK13jY4I61lNhVxwBVlHl4fH3ckiLi+YHI9MDHY5pYeJbR/Lba/sz/0fnc3ZaO554fyPn/+FTXl68vVnNs7RhbwnXPvslmwtKeeGWIUH/RSwiPHZNP87rmcDP56zjy9z9gQ6pUVniCLAXP9/K5sIj/PqqfkSGNb+/Ak3z0Csplul3DOWNu4bRNSGGx9/bwKgnP2XaF9s4VlEV6PBO6v21+dzw/Fd4FGbdM6LZDDcODw3huYmD6Z7YminTs1mz+1CgQ2o0ljgCaGvREZ75JJcrByRzSUZSoMMxLZyIcG73BGbePYI3pwynV1Jrnnh/IyN+9wlPvLeBHfuDa/bd0rJKfvyWdwGlHkmxzL1/JP1T2gY6LL+0iQpn+uShxLWO4PZXlrWYgQqWOAKkosrDj95cTauIUH59dc1lSoxx1/Bu8bxx13Bm3zOCUT0TePWrHVz4x0Xc9vIyPtpQGPD1PxZv2c8Vz3zBnFV7ePDiHsy+ZwRJzXS0YVKbKP4xeRhhoSHcMm0Zuw8cC3RIp806xwPk/320iXV5h3n+liE2/NYETGZ6HJnpcRSWlPHmst28sWwnd03PJj4mgisGJDNuYCcGd25PSBOtQLmpoJTffbCRRZuK6BzXirfuHkFmelyTvLebusTHMP2Oodz4wtdMeHEJr985jPSE5ruuvE05EgDvr83nvjdWcvOwzvzvdQOa7H1tyhFTlxPTbFRWe/j0m33MXbOXhRsLKav0kNA6got6d2B03yRGdI+nbfTpT+vhy+NRlmwr5vVlu/hgXT6tI8N44OKe3HZuF7/7/Rrj/7iba7ivzzvMbS8vIzREeP3OYfRKinXtvRpDXVOOWOJoYuvzDnPD81/TNzmWf04Z3qQd4pY4jD/KK6vZWFDKNwUlbC4spazSgwDJbaPomhBDalwrOrWNJr51BLf4ueBYeVU1K3ce4qut+3l3zV52FB+jbXQ4E85J44cXdqfdKS7xGuyJA2BLYSkTpy2lstrDqz8Yytlp7Vx9v9NhiSMIEkfuviPc+MLXRIWHMue+c+kQ27RNVJY4zKmq9ig7DxxlW9FRtu8/yu4Dx6hy7oqOCA2hR4fWpMVFk9a+FfGtI2kdFUZsZBgiUF7lobzKQ1FpObuKj7LzwDE25pdQVukhRLzNZTcNTWNs/+TTvr+kOSQOgJ3FR5k4bSlFpeX8/voBXDcoOJdQqCtxWB9HE9lSWMqtLy1DBGZMHtrkScOY0xEaInRLaE23hNaAd03votJy9h4qI//wcaLCQ9lWdJTPNhdRVll7x3qIQHLbaLrEt+KmoZ0Z2T2Bod3iGmVG2+amS3wMc+8byX1vrOShmWvIySth6tg+hDXCuuhNwRJHE1i+4wB3Tc8mPDSEf9w5jG6JrQMdkjGnJSwkhOS20SS3jQbaf/tXuqpSVumhtLyS0rIqVCEqPITIsFDaRIfZvUo+4ltHMmPyMP7n/Y1MW7ydlbsO8ofxZ9GjQ3D3e4AlDlepKq9+tYP/eX8jqe2jmX7HMDrHtwp0WMa4RkSIjgglOiKUZvD9F3DhoSE8dk0/BnVux6+zcrji6cU8OLqHd5GuIL76sMThkl3Fx3hkzlq+zC1mdJ8OPHXjwEYfjWJMsGgufQvBatzAFEb2SODXWTn88cPNvL3SO6/Y1Wd3IrSJhkL7wzrHG9m+kjJe/Hwb07/eSXio8PMr+3Lz0M6IBP4f3zrHjalfoBPYwo2F/N+CTXxTUEr3xBjuvqA7Vw5IJiYAS0lb57iLKqs9fLGliLmr9/LBugKqPB6uH5zKjy/rTce21glujGm40X2TuKh3B+bnFPDnjzfzs9lr+U1WjnNDZgqZ6e0DPruxq4lDRMYATwOhwDRV/X2N7ZHAdGAIUAzcqKo7nG2PAJOBauBBVV3QkGM2hWqPsrmwlOydB1mx4wCLNhdx6Fgl7VqFc+M5aUwe1bVZ3xVqjAmskBDhigHJjO3fkeydB5mVvZv31+Yza8UeosJDGNo1nuHd4ujXqS0ZyW1IjG3a5RhcSxwiEgo8B1wK7AGWi0iWqm7wqTYZOKiqPURkAvAkcKOIZAATgH5AJ+BjEenl7FPfMRvN4i372XvoOEVHyikqLWfPwePsKD7KruJjVDhz+STGRnJBr0SuObsT5/VMJCIseDu0jDHNi4hwTnoc56TH8dg1/ViyrZjPN+/niy1F/GF+0bf14mIiSGsfTWr7VnRqF0VcTCRxMeHExXi/nxr7e8nNK46hQK6qbgMQkTeBcYDvl/w44DHn+WzgWfF2BowD3lTVcmC7iOQ6x6MBx2w0j7+Xw+bCIwC0jgwjuW0U3RJiGN23A72TYsnsEkdaXHRQ9F8YY1q2VhFhXNwniYv7eGfSPnyskg35JWzILyF3Xyl7Dh5nQ34JH28spLzq3/fSfPPbMY0ei5uJIwXY7fN6DzCsrjqqWiUih4F4p3xJjX1TnOf1HRMAEZkCTHFeHhGR017DMaf+KglAMK/YEuzxQfDHaPGdnmCPj4nBH6Nf8UU/eVrvVetcMi22c1xVXwRebMr3FJHs2kYgBItgjw+CP0aL7/QEe3wQ/DEGQ3xuNsjnAb7rO6Y6ZbXWEZEwoC3eTvK69m3IMY0xxrjIzcSxHOgpIl1FJAJvZ3dWjTpZwCTn+XjgE/XeWJIFTBCRSBHpCvQEljXwmMYYY1zkWlOV02dxP7AA79DZl1U1R0QeB7JVNQt4CZjhdH4fwJsIcOq9hbfTuwq4T1WrAWo7pluf4RQ0adPYKQj2+CD4Y7T4Tk+wxwfBH2PA4zsj7hw3xhjTeOymA2OMMX6xxGGMMcYvljgagYj8n4h8IyJrRWSOiLTz2faIiOSKyCYRuTyAMY5xYsgVkamBisMnnjQR+VRENohIjoj8l1MeJyIficgW52f7AMcZKiKrROQ953VXEVnqnMeZziCNQMbXTkRmO///NorIiGA6hyLykPPvu15E/ikiUYE8hyLysojsE5H1PmW1ni/xesaJc62IDA5gjEH1HWOJo3F8BPRX1bOAzcAjADWmThkD/NWZiqVJ+Uz/MhbIAG5yYgukKuDHqpoBDAfuc2KaCixU1Z7AQud1IP0XsNHn9ZPAn1S1B3AQ77Q5gfQ0MF9V+wBn4401KM6hiKQADwKZqtof74CWE1MLBeocvor3d9FXXedrLN4RnT3x3kz8twDGGFTfMZY4GoGqfqiqVc7LJXjvLwGfqVNUdTvgO3VKU/p2+hdVrQBOTNUSMKqar6orneeleL/wUpy4XnOqvQZcG5gIQURSgSuBac5rAS7GOz0OBD6+tsD5eEcnoqoVqnqIIDqHeEduRjv3abUC8gngOVTVz/GO4PRV1/kaB0xXryVAOxFJDkSMwfYdY4mj8d0BfOA8r23alZTv7OG+YImjViKSDgwClgJJqprvbCoAkgIUFsCfgZ8BJyb+iQcO+fwCB/o8dgWKgFec5rRpIhJDkJxDVc0D/gjswpswDgMrCK5zCHWfr2D9vQn4d4wljgYSkY+ddtqaj3E+dX6Btwnm9cBF2ryISGvgX8CPVLXEd5tzM2hAxouLyFXAPlVdEYj3b6AwYDDwN1UdBBylRrNUgM9he7x/EXfFO8t1DN9tggkqgTxfDREs3zEtdq6qxqaql5xsu4jcDlwFjNZ/3xwTLFOkBEsc/0FEwvEmjddV9W2nuFBEklU132kW2Beg8EYC14jIFUAU0AZvf0I7EQlz/mIO9HncA+xR1aXO69l4E0ewnMNLgO2qWgQgIm/jPa/BdA6h7vMVVL83wfQdY1ccjUC8i0v9DLhGVY/5bKpr6pSmFnRTtTj9BS8BG1X1KZ9NvtPQTALmNnVsAKr6iKqmqmo63vP1iapOBD7FOz1OQOMDUNUCYLeI9HaKRuOdbSEoziHeJqrhItLK+fc+EV/QnENHXecrC7jNGV01HDjs06TVpILuO0ZV7XGaD7wdUruB1c7jeZ9tvwC2ApuAsQGM8Qq8ozG2Ar8IgnM2Cm+TwFqf83YF3n6EhcAW4GMgLghivRB4z3neDe8vZi4wC4gMcGwDgWznPL4DtA+mcwj8BvgGWA/MACIDeQ6Bf+Ltb6nEe8U2ua7zBQje0YhbgXV4R4cFKsag+o6xKUeMMcb4xZqqjDHG+MUShzHGGL9Y4jDGGOMXSxzGGGP8YonDGGOMXyxxGHMSIlItIqudWQJmiUirOup91dSx+bz3SGfW1GwR6emUtRORD0XEfsdNo7P/VMac3HFVHaje2V0rgHt8NzqT96Gq5zb2G584dgP8GO89MD/i3/H9EvhfVfXUuZcxp8gShzEN9wXQQ0QuFJEvRCQL753QiMgR5+eFIvKZiMwVkW0i8nsRmSgiy0RknYh0d+pd7axJscqZBy3JKX9MRGaIyJfADBH5XEQGnghARBaLyNk14qrEO/NsK6DSeY80VV3k8vkwZyibq8qYBnD++h8LzHeKBuNdH2F7LdXPBvrinRp7GzBNVYeKd7GqB/BeGSwGhquqisideKeT+LGzfwYwSlWPi8gk4HbgRyLSC4hS1TU13u93wHTgOHAr3hlpf9kIH9uYWtkVhzEnFy0iq/FO67ELZ+0LYFkdSQNguXrXGynHOxXEh075OiDdeZ4KLBCRdcBP8S7Ec0KWqh53ns8CrnImhLwD7yI//0FVV6vqcFW9CO90Hvl4pwObKSL/OHE1Y0xjsSsOY07uuKoO9C3wztfH0ZPsU+7z3OPz2sO/f+f+AjylqlkiciHwmM8+3x5bVY+JyEd4pyf/PjCkrjd1JhL8Jd5JGf+C9yomHe8qfL84SbzG+MUShzGB0ZZ/T3896WQV8a5A+C7whaoePEm924B5qnrAGf3lcR61jgQz5lRZ4jAmMB4DZonIQeATvIsd1UpVV4hICfBKXXWcRHE7cJlT9BQwD+9IsJsbJ2RjvGx2XGOCnIh0AhYBfWx4rQkG1jluTBATkdvwrsX+C0saJljYFYcxxhi/2BWHMcYYv1jiMMYY4xdLHMYYY/xiicMYY4xfLHEYY4zxy/8H8XjEIqPn3t0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smsrnrVpsKZp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbd5c1a6-5d3e-4e9b-bbab-0f32233ada1a"
      },
      "source": [
        "# 0.0 is No and 1.0 is Yes\n",
        "dem['Biden Endorsed?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    801\n",
              "1.0     10\n",
              "Name: Biden Endorsed?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NgaXv3ttgUT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d708052-4bb1-401b-aa8d-e74a097426ad"
      },
      "source": [
        "# 0.0 is No and 1.0 is Yes\n",
        "dem['Sanders Endorsed?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    802\n",
              "1.0      9\n",
              "Name: Sanders Endorsed?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejOLL2mnt2uM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dccebca3-1cfa-49a2-9ac0-229ea10e866f"
      },
      "source": [
        "rep['Trump Endorsed?']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.0\n",
              "1      0.0\n",
              "2      0.0\n",
              "3      0.0\n",
              "4      0.0\n",
              "      ... \n",
              "769    0.0\n",
              "770    0.0\n",
              "771    0.0\n",
              "772    1.0\n",
              "773    0.0\n",
              "Name: Trump Endorsed?, Length: 774, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhOBdEg8t9EI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbbe492e-4ad0-4a9c-a80a-e8034c52579c"
      },
      "source": [
        "rep['Bannon Endorsed?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    766\n",
              "1.0      8\n",
              "Name: Bannon Endorsed?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXA_Jq9TuUh_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c94347ab-0846-44e6-ce80-33549f74c691"
      },
      "source": [
        "# 0.0 is No and 1.0 is Yes\n",
        "dem['Won Primary'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    572\n",
              "1.0    239\n",
              "Name: Won Primary, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hni6e-Pkuckw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bfa70cc-8df7-487e-9b3a-2c3e95d2a9e7"
      },
      "source": [
        "rep['Won Primary'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    529\n",
              "1    245\n",
              "Name: Won Primary, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fivMP6UQwwIX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbb406a4-b700-494f-d673-503a50796272"
      },
      "source": [
        "dem['Self-Funder?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    768\n",
              "1     43\n",
              "Name: Self-Funder?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGIBSZGp0SE5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c58a4f6-2aa7-4699-9aa9-8a572ef5b514"
      },
      "source": [
        "rep['House Freedom Support?'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    760\n",
              "1.0     14\n",
              "Name: House Freedom Support?, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SrURQnCTnvqy"
      },
      "source": [
        "### **EDA for Question 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G00m8jAE0Wo0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "8ea9b5b7-2553-4c27-833d-a193117687b1"
      },
      "source": [
        "data_summary = pd.read_csv('candidate_summary_2018.csv')\n",
        "data_summary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Link_Image</th>\n",
              "      <th>Cand_Name</th>\n",
              "      <th>Cand_Id</th>\n",
              "      <th>Cand_Office</th>\n",
              "      <th>Cand_Office_St</th>\n",
              "      <th>Cand_Office_Dist</th>\n",
              "      <th>Cand_Party_Affiliation</th>\n",
              "      <th>Cand_Incumbent_Challenger_Open_Seat</th>\n",
              "      <th>Total_Receipt</th>\n",
              "      <th>Total_Disbursement</th>\n",
              "      <th>Cash_On_Hand_COP</th>\n",
              "      <th>Debt_Owed_By_Committee</th>\n",
              "      <th>Coverage_End_Date</th>\n",
              "      <th>Cand_Street_1</th>\n",
              "      <th>Cand_Street_2</th>\n",
              "      <th>Cand_City</th>\n",
              "      <th>Cand_State</th>\n",
              "      <th>Cand_Zip</th>\n",
              "      <th>Individual_Itemized_Contribution</th>\n",
              "      <th>Individual_Unitemized_Contribution</th>\n",
              "      <th>Individual_Contribution</th>\n",
              "      <th>Other_Committee_Contribution</th>\n",
              "      <th>Party_Committee_Contribution</th>\n",
              "      <th>Cand_Contribution</th>\n",
              "      <th>Total_Contribution</th>\n",
              "      <th>Transfer_From_Other_Auth_Committee</th>\n",
              "      <th>Cand_Loan</th>\n",
              "      <th>Other_Loan</th>\n",
              "      <th>Total_Loan</th>\n",
              "      <th>Offsets_To_Operating_Expenditure</th>\n",
              "      <th>Offsets_To_Fundraising</th>\n",
              "      <th>Offsets_To_Leagal_Accounting</th>\n",
              "      <th>Other_Receipts</th>\n",
              "      <th>Operating_Expenditure</th>\n",
              "      <th>Exempt_Legal_Accounting_Disbursement</th>\n",
              "      <th>Fundraising_Disbursement</th>\n",
              "      <th>Transfer_To_Other_Auth_Committee</th>\n",
              "      <th>Cand_Loan_Repayment</th>\n",
              "      <th>Other_Loan_Repayment</th>\n",
              "      <th>Total_Loan_Repayment</th>\n",
              "      <th>Individual_Refund</th>\n",
              "      <th>Party_Committee_Refund</th>\n",
              "      <th>Other_Committee_Refund</th>\n",
              "      <th>Total_Contribution_Refund</th>\n",
              "      <th>Other_Disbursements</th>\n",
              "      <th>Net_Contribution</th>\n",
              "      <th>Net_Operating_Expenditure</th>\n",
              "      <th>Cash_On_Hand_BOP</th>\n",
              "      <th>Debt_Owe_To_Committee</th>\n",
              "      <th>Coverage_Start_Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8TX05052/?...</td>\n",
              "      <td>ASHBY, KEN</td>\n",
              "      <td>H8TX05052</td>\n",
              "      <td>H</td>\n",
              "      <td>TX</td>\n",
              "      <td>4.0</td>\n",
              "      <td>LIB</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11837 LOCHWOOD BOULEVARD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DALLAS</td>\n",
              "      <td>TX</td>\n",
              "      <td>75218.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.fec.gov/data/candidate/S6OH00288/?...</td>\n",
              "      <td>ECKHART, DON ELIJAH</td>\n",
              "      <td>S6OH00288</td>\n",
              "      <td>S</td>\n",
              "      <td>OH</td>\n",
              "      <td>0.0</td>\n",
              "      <td>REP</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>,</td>\n",
              "      <td>NaN</td>\n",
              "      <td>,</td>\n",
              "      <td>OH</td>\n",
              "      <td>43119.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://www.fec.gov/data/candidate/S8CA00499/?...</td>\n",
              "      <td>ZIESING, MICHAEL VINCENT</td>\n",
              "      <td>S8CA00499</td>\n",
              "      <td>S</td>\n",
              "      <td>CA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>GRE</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1220 ALOHA ST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RED BLUFF</td>\n",
              "      <td>CA</td>\n",
              "      <td>96080.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6NJ13191/?...</td>\n",
              "      <td>SIRES, ALBIO</td>\n",
              "      <td>H6NJ13191</td>\n",
              "      <td>H</td>\n",
              "      <td>NJ</td>\n",
              "      <td>8.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>INCUMBENT</td>\n",
              "      <td>460108.57</td>\n",
              "      <td>596546.56</td>\n",
              "      <td>158849.09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>6050 BOULEVARD EAST</td>\n",
              "      <td>APT 6B</td>\n",
              "      <td>WEST NEW YORK</td>\n",
              "      <td>NJ</td>\n",
              "      <td>7093.0</td>\n",
              "      <td>161321.54</td>\n",
              "      <td>1848.84</td>\n",
              "      <td>163170.38</td>\n",
              "      <td>296686.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>459857.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>251.52</td>\n",
              "      <td>437542.51</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>98504.05</td>\n",
              "      <td>459357.05</td>\n",
              "      <td>437542.51</td>\n",
              "      <td>290287.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6MI03172/?...</td>\n",
              "      <td>SMITH, DOUGLAS MARK MR. JR.</td>\n",
              "      <td>H6MI03172</td>\n",
              "      <td>H</td>\n",
              "      <td>MI</td>\n",
              "      <td>3.0</td>\n",
              "      <td>IND</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1410 SCOTT CREEK DR.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BELMONT</td>\n",
              "      <td>MI</td>\n",
              "      <td>49306.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>3873.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3794</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H6OH04082/?...</td>\n",
              "      <td>JORDAN, JAMES D.</td>\n",
              "      <td>H6OH04082</td>\n",
              "      <td>H</td>\n",
              "      <td>OH</td>\n",
              "      <td>4.0</td>\n",
              "      <td>REP</td>\n",
              "      <td>INCUMBENT</td>\n",
              "      <td>1241417.36</td>\n",
              "      <td>1809464.13</td>\n",
              "      <td>745640.94</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1709 SOUTH STATE ROUTE 560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>URBANA</td>\n",
              "      <td>OH</td>\n",
              "      <td>43078.0</td>\n",
              "      <td>560189.00</td>\n",
              "      <td>350028.82</td>\n",
              "      <td>910217.82</td>\n",
              "      <td>306389.77</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1221607.59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>72.15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19737.62</td>\n",
              "      <td>1599257.13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11057.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>11207.0</td>\n",
              "      <td>164000.00</td>\n",
              "      <td>1210400.59</td>\n",
              "      <td>1599184.98</td>\n",
              "      <td>1313687.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3795</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8NY27176/?...</td>\n",
              "      <td>MCMURRAY, NATHAN DOUGLAS</td>\n",
              "      <td>H8NY27176</td>\n",
              "      <td>H</td>\n",
              "      <td>NY</td>\n",
              "      <td>27.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>OPEN</td>\n",
              "      <td>1387653.64</td>\n",
              "      <td>1364110.33</td>\n",
              "      <td>23543.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1041 FOXCROFT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GRAND ISLAND</td>\n",
              "      <td>NY</td>\n",
              "      <td>14072.0</td>\n",
              "      <td>629591.23</td>\n",
              "      <td>527391.94</td>\n",
              "      <td>1156983.17</td>\n",
              "      <td>151065.00</td>\n",
              "      <td>48163.31</td>\n",
              "      <td>1900.00</td>\n",
              "      <td>1358111.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.00</td>\n",
              "      <td>587.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10954.41</td>\n",
              "      <td>1342096.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>3252.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3252.5</td>\n",
              "      <td>761.11</td>\n",
              "      <td>1354858.98</td>\n",
              "      <td>1341508.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>01/01/2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3796</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8IN06152/?...</td>\n",
              "      <td>LAKE, JEANNINE LEE</td>\n",
              "      <td>H8IN06152</td>\n",
              "      <td>H</td>\n",
              "      <td>IN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>OPEN</td>\n",
              "      <td>100644.90</td>\n",
              "      <td>101754.84</td>\n",
              "      <td>50.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>5300 NORTH CO. ROAD 500 WEST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MUNICE</td>\n",
              "      <td>IN</td>\n",
              "      <td>47304.0</td>\n",
              "      <td>37970.87</td>\n",
              "      <td>51484.23</td>\n",
              "      <td>89455.10</td>\n",
              "      <td>3415.29</td>\n",
              "      <td>370.04</td>\n",
              "      <td>898.27</td>\n",
              "      <td>94138.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6216.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6216.66</td>\n",
              "      <td>289.54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>99183.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2371.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2371.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91767.70</td>\n",
              "      <td>98894.30</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>07/04/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3797</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8MD01177/?...</td>\n",
              "      <td>GALBRAITH, ALLISON MS.</td>\n",
              "      <td>H8MD01177</td>\n",
              "      <td>H</td>\n",
              "      <td>MD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>DEM</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>110517.66</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>319.98</td>\n",
              "      <td>350.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>PO BOX 25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FALLSTON</td>\n",
              "      <td>MD</td>\n",
              "      <td>21047.0</td>\n",
              "      <td>33834.61</td>\n",
              "      <td>76231.05</td>\n",
              "      <td>110065.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>102.00</td>\n",
              "      <td>110167.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>350.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>350.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>110167.66</td>\n",
              "      <td>110197.68</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>04/01/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3798</th>\n",
              "      <td>https://www.fec.gov/data/candidate/H8CO06294/?...</td>\n",
              "      <td>KULIKOWSKI, JAIMIE BLACKLIGHT POLISH J</td>\n",
              "      <td>H8CO06294</td>\n",
              "      <td>H</td>\n",
              "      <td>CO</td>\n",
              "      <td>6.0</td>\n",
              "      <td>REP</td>\n",
              "      <td>CHALLENGER</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>60500.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12/31/2018</td>\n",
              "      <td>1191 S YOSEMITE WAY</td>\n",
              "      <td>UNIT 45</td>\n",
              "      <td>DENVER</td>\n",
              "      <td>CO</td>\n",
              "      <td>80247.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>119858.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>07/01/2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3799 rows × 50 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             Link_Image  ... Coverage_Start_Date\n",
              "0     https://www.fec.gov/data/candidate/H8TX05052/?...  ...                 NaN\n",
              "1     https://www.fec.gov/data/candidate/S6OH00288/?...  ...                 NaN\n",
              "2     https://www.fec.gov/data/candidate/S8CA00499/?...  ...                 NaN\n",
              "3     https://www.fec.gov/data/candidate/H6NJ13191/?...  ...          01/01/2017\n",
              "4     https://www.fec.gov/data/candidate/H6MI03172/?...  ...          01/01/2017\n",
              "...                                                 ...  ...                 ...\n",
              "3794  https://www.fec.gov/data/candidate/H6OH04082/?...  ...          01/01/2017\n",
              "3795  https://www.fec.gov/data/candidate/H8NY27176/?...  ...          01/01/2018\n",
              "3796  https://www.fec.gov/data/candidate/H8IN06152/?...  ...          07/04/2017\n",
              "3797  https://www.fec.gov/data/candidate/H8MD01177/?...  ...          04/01/2017\n",
              "3798  https://www.fec.gov/data/candidate/H8CO06294/?...  ...          07/01/2018\n",
              "\n",
              "[3799 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjamZ9xrbLSI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eba4c8b8-9e81-491f-df60-e21526692cc7"
      },
      "source": [
        "data_summary['Total_Contribution'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82898274.69"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDpXONkFfRW7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dafee3a-34b1-4574-88ee-86bec0a6d9c2"
      },
      "source": [
        "data_summary.loc[data_summary['Total_Contribution'] == 82898274.69, 'Cand_Name']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3652    SCOTT, RICK GOV\n",
              "Name: Cand_Name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHngBntkgBXr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84f1b799-9589-47e8-d0cb-47ef7d2f8334"
      },
      "source": [
        "data_summary['Total_Contribution'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "609429.6149460393"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XdtP-grggRD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e619fbdd-32b3-4073-e0e3-4171a44a9b23"
      },
      "source": [
        "count = 0\n",
        "for i in data_summary['Total_Contribution']:\n",
        "  if (data_summary['Total_Contribution'] == 0).any():\n",
        "    count += 1\n",
        "print(count)              "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3799\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIt3CZI8kJce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c37ef7aa-9c2a-4aca-8d0b-ca086fd817ec"
      },
      "source": [
        "data_summary['Cand_Incumbent_Challenger_Open_Seat'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CHALLENGER    2359\n",
              "OPEN          1015\n",
              "INCUMBENT      414\n",
              "Name: Cand_Incumbent_Challenger_Open_Seat, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ozk9LRJzm9OP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "16e25eec-e2f8-4178-e4e8-26ffb6f0ee52"
      },
      "source": [
        "plt.hist(data_summary['Total_Contribution'], bins=10, range=[0, 1.25])\n",
        "plt.xlabel('Net Funding')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cCUBeb1ngx9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "8bf999f1-d4a4-4fff-ee35-383347530433"
      },
      "source": [
        "sns.distplot(data_summary['Total_Contribution'], bins=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f56b2363d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D3AacUNXxYLaC7xbw9ZrcojGzrKxJD17SNuA04Nouz10gaVzS+MTExIqOM9lcpEUjeakCM8tK5QEv6Ujgc8CbI2L3/OcjYntEjEXE2Ojo6IqONVVW8COu4M3Mqg14SUMU4f7JiLiiymPBnBZNlwq+5oA3s8xUOYtGwIeB2yLiPVUdZ66pJVo0XqrAzHJSZQV/FvAa4BxJN5R/XlDh8RadReNpkmaWm8qmSUbEtwEt+cJVNLXILJpazRc6mVlekrqSdbEWTaNWo9lur/WQzMz6JsmA7zaLZqRRm3nezCwHSQX8QmvRdLZNOuDNLCNJBfxUqwV0b9EM12szPXozsxwkFfDTzcUreLdozCwnSQX85Mxqko+cvOMevJnlJqmAnznJOu+WfVBU8M12eKqkmWUjqYBfbDXJ4UYR+q7izSwXSQX87Dz4Li2a8sSrT7SaWS6SC/iaoNFtFk1Z1U82W2s9LDOzvkgq4Kdb7a7tGZgNeLdozCwXSQX8ZLPddQ48OODNLD9JBfxUq911mQKYXb7AAW9muUgq4Keb7a43+4DZm4BM+iSrmWUiqYCfarUZcg/ezAxILOCnWwtX8COeB29mmUkq4KeaC8+iGWoUc+O9oqSZ5SKpgF9sFk2jVqNekyt4M8tGUgG/2Dx46CwZ7AudzCwPSQX81CKzaMArSppZXtIK+KUqeN/VycwyklTATzdj0QreN/0ws5wkFfCLzYMHB7yZ5SWtgF+qB+/7sppZRtIKePfgzcxmVBbwkj4iaaekm6s6xnxFBf/Im310DDfqbtGYWTaqrOAvBZ5f4f4fYal58J4maWY5qSzgI+JbwANV7b+bxZYqgPIka6tNO3zjbTNLX9978JIukDQuaXxiYmLZ+2m3g2Y7FlyqAGbXhJ/2iVYzy0DfAz4itkfEWESMjY6OLns/ndkxS1Xw4BUlzSwPfQ/41TIT8Itd6FR3wJtZPtIJ+ObSFXynReOpkmaWgyqnSV4GfAc4WdK9ks6v6lgw21dffKmC4qYfDngzy0Gjqh1HxHlV7bubTgW/2EnWTUcUP+5DD08BG9ZiWGZmfZNVi2bzhmEAHtg3tSZjMjPrp3QCvodZNEP1GpuOaDjgzSwL6QR8c+kePMCWDcMOeDPLQjIBP90qrk5drIIH2LJhxAFvZllIJuB76cEDbNkwxO4DTV/NambJSyfgy5tpLzaLBooKHnyi1czSl07AN8sWTQ89eIBfOuDNLHHpBHwPs2hgNuB/4YA3s8SlE/A9zqLZMFxnuFHjgf0OeDNLWzIBP91jBS+JozcM88BeB7yZpS2ZgJ9dqmDhW/Z1PHrjCD998GHCN/4ws4QlE/AP7JtCgk3rhpZ87UmjR7JnssnOPZNrMDIzs/5IJuB37jnA0RtGlpwmCUXAA9wxsbfqYZmZ9U0yAX//7kmO3TTS02s3bxhmy4Zh7tjpgDezdCUT8Dv3HODYTUf0/PqTRjdw5659tNruw5tZmpIJ+EOp4KFo00w229z3y/0VjsrMrH+SCPhmq82uvZM8emPvFfzjH72RobrY8ZNfVjgyM7P+SSLgd+2dIoJDatGsG65z6tajuOGeB3lo/3SFozMz648kAv7+3QeAYn77oXj6SUcz3Qo+M35PFcMyM+urpAL+UCp4gOMftY5tR6/nkm/fyUMPu4o3s7SkEfDlBUuHcpK14wVPPp5de6f4r1+4dbWHZWbWV0kE/M7dB6gJjj7y0AN+6+b1vP5ZJ3H5jnu57Ls/qWB0Zmb9kUTA37/7AKMbR6jXll6Hpps3nvN4zv6Xo7ztipu4+Gs/8t2ezCwJiQT85CH33+cabtS45A/H+P3TTuC9X/shL7j4Gj4zfg+7D7gvb2aHr0a/B7Aa7t99gK2b1y/7/Z+6tmjNjD12M0eONPjyzT/nrZffyIWfu5HjHnUEjz16Aycds4F3/O4Tl1yO2MxsUFQa8JKeD1wM1IFLIuLdVRxnYs8kZzx284r3I4lfO34Tpxy3kXt++TC3/3w3//yL/Yzf/QDfueMXXHXL/bx8bCuv+PUTecyW9UjLawmZma2FygJeUh34G+C5wL3A9yT9fUSs6nSViODMf7GFU088atX2KYnHbFnPY7YU/ypotYMf79zDfQ8e4ANX38H7vnkHoxtH2Lp5HUeONDhypMGGkQYbhuvF13Lb6MYRjt00wuiRR1CrQbsNrQha7aAdxZ9WOw7aXiuXPN50xBCb1jUYadRX7ecys7xUWcE/DfhxRNwJIOnTwEuAVQ14SbzvVWes5i4foV4TJx+3iZOP28TpjzmKW3+2m/t++TB7DjTZtWeSA802U802k80WU802q7l+2UijxrrhOo1ajeG6kERE0A4Iyq8BzDye/dq5n4lUfE41QU2it394FK+rCVQ+Ln4hcdAvp4jiF2AQnXfNHLP4vjMGzTxmznMRs7/cABo1Ua+JRr1GvSbq6nw/5/1midm8YZgrX3/Wqu+3yoA/AZh7iei9wJnzXyTpAuCC8tu9km5fxTEcA+xaxf2lxJ/NwvzZLMyfTXcr/lz0hmW/9bELPdH3k6wRsR3YXsW+JY1HxFgV+z7c+bNZmD+bhfmz6W5QP5cqp4TcB5w45/ut5TYzM1sDVQb894DHS3qcpGHgXODvKzyemZnNUVmLJiKakv4j8A8U0yQ/EhG3VHW8BVTS+kmEP5uF+bNZmD+b7gbyc1GEb1lnZpYiX5ZpZpYoB7yZWaKSDXhJz5d0u6QfS7qw3+MZFJJOlPQNSbdKukXSm/o9pkEiqS7peklf6PdYBomkoyRdLukHkm6T9PR+j2lQSHpL+XfpZkmXSVr+yoerLMmAn7NMwu8ATwDOk/SE/o5qYDSBP4uIJwC/AbzBn81B3gTc1u9BDKCLgasi4hTgVPwZASDpBOBPgLGIeBLFhJJz+zuqWUkGPHOWSYiIKaCzTEL2IuJnEXFd+XgPxV/UE/o7qsEgaSvwQuCSfo9lkEh6FHA28GGAiJiKiAf7O6qB0gDWSWoA64Gf9nk8M1IN+G7LJDjE5pG0DTgNuLa/IxkYfwW8FfAdXw72OGAC+NuyfXWJpA39HtQgiIj7gL8EfgL8DHgoIr7S31HNSjXgbQmSjgQ+B7w5Inb3ezz9JulFwM6I2NHvsQygBnA68P6IOA3YB/i8FiBpM0V34HHArwAbJL26v6OalWrAe5mERUgaogj3T0bEFf0ez4A4C3ixpLspWnrnSPpEf4c0MO4F7o2Izr/0LqcIfIPfAu6KiImImAauAJ7R5zHNSDXgvUzCAlTcpeTDwG0R8Z5+j2dQRMTbImJrRGyj+P/l6xExMJVYP0XEz4F7JJ1cbnoOq7zs92HsJ8BvSFpf/t16DgN0Arrvq0lWYUCWSRhUZwGvAW6SdEO57e0R8aU+jskG3xuBT5YF053A6/o8noEQEddKuhy4jmKG2vUM0LIFXqrAzCxRqbZozMyy54A3M0uUA97MLFEOeDOzRDngzcz6RNJHJO2UdHMPr32vpBvKPz+UtORyEZ5FY2bWJ5LOBvYCHysXK+v1fW8ETouIf7vY61zB25qTdPScSuTnku6b8/3wvNe+WdL6Hvb5TUkL3tVe0pGSPijpDkk7ytefuczxv1bSryzy/Lsk/Vb5+G5JxxzCvrdJeuWc78ck/fVyxmmDLyK+BTwwd5ukkyRdVf5/eo2kU7q89TzgsqX2n+SFTjbYIuIXwFMBJF0E7I2Iv1zg5W8GPgHsX+FhLwHuAh4fEW1Jj6NYSno5XgvcTJdVAyXVI+Idyx4lbANeCXwKICLGgfEV7M8OP9uBfx8RPyqLkPcB53SelPRYirVvvr7UjlzB20CQ9JxypcKbyr7kiKQ/oVjA6RuSvlG+7v2SxssbLLyzx32fBJwJ/OeIaANExF0R8cXy+T8tb9Zws6Q3l9u2lTe2+FB5rK9IWifppcAYxVWdN5Tb7pb03yVdB7xM0qXl6zreWv5c35X0q+X+D3qNpL3lw3cDv1nu+y2SnqXy5iOStkj6vKQbJf2TpKeU2y8qP7NvSrqz/NzsMFQuAvgM4LPlleYfBI6f97JzgcsjorXU/hzwNgiOAC4FXhERT6b4l+V/iIi/pqiSnx0Rzy5f+58iYgx4CvCvOiG3hCcCN3T7CyHpDIrL7s+kuAHKH0s6rXz68cDfRMQTgQeBP4iIyykq6ldFxFMj4uHytb+IiNMj4tNdjv9Q+XP9L4oliRdzIXBNue/3znvuncD1EfEU4O3Ax+Y8dwrwPIp7IfyXckE5O/zUgAfL//6dP7827zXn0kN7prMzs36rU6zI98Py+49S3GCim5eXlfL1FMG90rtRPRO4MiL2RcReitUAf7N87q6I6KzXs4OifbKQv1vkucvmfF3Jre6eCXwcICK+DhwtaVP53BcjYjIidgE7gWNXcBzrk3Lp7rskvQyKxQElndp5vuzHbwa+08v+HPB22Cj75n8OPKesYr9IUf0v5RbgVBW3cjwUk3Met1j8nNW+RZ6LLo+blH//JNWA4flvOkSHMlYbEJIuowjrkyXdK+l84FXA+ZK+T/H/7ty70Z0LfDp6nP7ogLdB0AK2dfrTFKtdXl0+3gNsLB9vogjShyQdS3HP3SVFxB0UbZV3ShLM9NhfCFwD/J6K5V43AL9fblvM3DH14hVzvnYqr7uBM8rHLwY6LZXF9n0NxV9+JD0L2OWbtRzeIuK8iDg+IobK5ao/XJ4fen5EnBoRT4iId815/UUR0fPNVvxb3gbBAYo++GdV3Nfye8AHyue2A1dJ+mlEPFvS9cAPKG7J+H8P4Rh/BPxP4MeSHgZ2AX8REddJuhT4bvm6SyLiehW3M1zIpcAHyv300nLZLOlGiir7vHLbh4D/XVZpVzH7L4AbgVa5/VKKVlTHRcBHyn3tB/5ND8e2jPlCJzOzRLlFY2aWKLdoLCmSrgVG5m1+TUTc1I/xmPWTWzRmZolyi8bMLFEOeDOzRDngzcwS5YA3M0vU/weU6YtM6naWAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uG0s9dvtpdrk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}