US Income Tax Audit Algorithms.ipynb

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      "source": [
        "# Topic: US Income Tax Audit Algorithms\n",
        "Team members: Shuyi Zhu, Scott McCoy, Agnes(Tzuhua) Huang, Roozbeh Jafari\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7yp0C6ibmbo"
      },
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "random.seed(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J43Ck0zndccZ"
      },
      "source": [
        "# Data Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZvh0vYhf3Uk"
      },
      "source": [
        "Datasets:\n",
        "* Audit Rates by County from ProPublica \n",
        "* Tax Return Data by County from IRS \n",
        "* Demographic Data by County from Kaggle\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlRrueIgdLqa"
      },
      "source": [
        "## Audit Rates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ed5KyyBhvZ2"
      },
      "source": [
        "* Audit rates (per 1000 returns) gathered by ProPublica\n",
        "* https://github.com/propublica/auditData"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuqgdvISbqAL"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/propublica/auditData/master/data/cleaned/auditsData_2019.04.03.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df"
      ],
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>31840.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>8.165829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>87970.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>7.650335</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3136</th>\n",
              "      <td>56037</td>\n",
              "      <td>Sweetwater County, Wyoming</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>84960.0</td>\n",
              "      <td>578.0</td>\n",
              "      <td>6.803202</td>\n",
              "      <td>6.803202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3137</th>\n",
              "      <td>56039</td>\n",
              "      <td>Teton County, Wyoming</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>57890.0</td>\n",
              "      <td>447.0</td>\n",
              "      <td>7.721541</td>\n",
              "      <td>7.721541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3138</th>\n",
              "      <td>56041</td>\n",
              "      <td>Uinta County, Wyoming</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>37540.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>7.085775</td>\n",
              "      <td>7.085775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3139</th>\n",
              "      <td>56043</td>\n",
              "      <td>Washakie County, Wyoming</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>15830.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>6.948831</td>\n",
              "      <td>6.948831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3140</th>\n",
              "      <td>56045</td>\n",
              "      <td>Weston County, Wyoming</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>13390.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7.244212</td>\n",
              "      <td>7.244212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3141 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       fips                        name  ... audit_rate  audit_rate_trunk\n",
              "0      1001     Autauga County, Alabama  ...   7.575917          7.575917\n",
              "1      1003     Baldwin County, Alabama  ...   7.815388          7.815388\n",
              "2      1005     Barbour County, Alabama  ...   9.127789          9.127789\n",
              "3      1007        Bibb County, Alabama  ...   8.165829          8.165829\n",
              "4      1009      Blount County, Alabama  ...   7.650335          7.650335\n",
              "...     ...                         ...  ...        ...               ...\n",
              "3136  56037  Sweetwater County, Wyoming  ...   6.803202          6.803202\n",
              "3137  56039       Teton County, Wyoming  ...   7.721541          7.721541\n",
              "3138  56041       Uinta County, Wyoming  ...   7.085775          7.085775\n",
              "3139  56043    Washakie County, Wyoming  ...   6.948831          6.948831\n",
              "3140  56045      Weston County, Wyoming  ...   7.244212          7.244212\n",
              "\n",
              "[3141 rows x 7 columns]"
            ]
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      "source": [
        "df[df['audit_rate'] != df['audit_rate_trunk']]\n",
        "# audit rate trunk - floor of 6 for visualization purposes\n",
        "# only 63 counties - outliers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>2013</td>\n",
              "      <td>Aleutians East Borough, Alaska</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>3960.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>5.808081</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>2060</td>\n",
              "      <td>Bristol Bay Borough, Alaska</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>2140.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.271028</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2068</td>\n",
              "      <td>Denali Borough, Alaska</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>4620.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>5.627706</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2230</td>\n",
              "      <td>Skagway Municipality, Alaska</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>3050.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>5.901639</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2282</td>\n",
              "      <td>Yakutat City and Borough, Alaska</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.586207</td>\n",
              "      <td>6.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2718</th>\n",
              "      <td>48393</td>\n",
              "      <td>Roberts County, Texas</td>\n",
              "      <td>Texas</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2737</th>\n",
              "      <td>48431</td>\n",
              "      <td>Sterling County, Texas</td>\n",
              "      <td>Texas</td>\n",
              "      <td>2360.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.237288</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2743</th>\n",
              "      <td>48443</td>\n",
              "      <td>Terrell County, Texas</td>\n",
              "      <td>Texas</td>\n",
              "      <td>1550.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.870968</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2780</th>\n",
              "      <td>49009</td>\n",
              "      <td>Daggett County, Utah</td>\n",
              "      <td>Utah</td>\n",
              "      <td>1710.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.339181</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2791</th>\n",
              "      <td>49031</td>\n",
              "      <td>Piute County, Utah</td>\n",
              "      <td>Utah</td>\n",
              "      <td>2060.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.339806</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       fips                              name  ... audit_rate  audit_rate_trunk\n",
              "67     2013    Aleutians East Borough, Alaska  ...   5.808081               6.0\n",
              "71     2060       Bristol Bay Borough, Alaska  ...   3.271028               6.0\n",
              "72     2068            Denali Borough, Alaska  ...   5.627706               6.0\n",
              "89     2230      Skagway Municipality, Alaska  ...   5.901639               6.0\n",
              "94     2282  Yakutat City and Borough, Alaska  ...   2.586207               6.0\n",
              "...     ...                               ...  ...        ...               ...\n",
              "2718  48393             Roberts County, Texas  ...   3.750000               6.0\n",
              "2737  48431            Sterling County, Texas  ...   4.237288               6.0\n",
              "2743  48443             Terrell County, Texas  ...   3.870968               6.0\n",
              "2780  49009              Daggett County, Utah  ...   2.339181               6.0\n",
              "2791  49031                Piute County, Utah  ...   5.339806               6.0\n",
              "\n",
              "[63 rows x 7 columns]"
            ]
          },
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55c35XcFb1aP"
      },
      "source": [
        "# splitting 'fips' code into state and county columns for matching purposes\n",
        "df['st_code'] = df['fips'].astype('str').str[:-3].astype('int')\n",
        "df['cty_code'] = df['fips'].astype('str').str[-3:].astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3hfDoXnxZK6"
      },
      "source": [
        "## Income"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDt7IhV1693R"
      },
      "source": [
        "Demographic Data by County from Kaggle:\n",
        "\n",
        "https://www.kaggle.com/muonneutrino/us-census-demographic-data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAODevhpxbXS"
      },
      "source": [
        "dfi = pd.read_csv('https://raw.githubusercontent.com/Roozbeh-jafari/840.Data-Ethics-US-Taxpayers-Algorithms/main/DataSets/acs2015_county_data.csv?token=ARFHYYA2GCAOYQ6SAD3UFY3AWACHA')\n",
        "# dfi = pd.read_csv('acs2015_county_data.csv')\n",
        "# dfi = pd.read_csv('/content/acs2015_county_data.csv')\n",
        "# dfi = pd.read_csv('../Datasets/acs2015_county_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "vXPDbffmxbBs",
        "outputId": "5ec0c945-f6c2-4d2d-c362-89c9cebf72e5"
      },
      "source": [
        "dfi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CensusId</th>\n",
              "      <th>State</th>\n",
              "      <th>County</th>\n",
              "      <th>TotalPop</th>\n",
              "      <th>Men</th>\n",
              "      <th>Women</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Citizen</th>\n",
              "      <th>Income</th>\n",
              "      <th>IncomeErr</th>\n",
              "      <th>IncomePerCap</th>\n",
              "      <th>IncomePerCapErr</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>ChildPoverty</th>\n",
              "      <th>Professional</th>\n",
              "      <th>Service</th>\n",
              "      <th>Office</th>\n",
              "      <th>Construction</th>\n",
              "      <th>Production</th>\n",
              "      <th>Drive</th>\n",
              "      <th>Carpool</th>\n",
              "      <th>Transit</th>\n",
              "      <th>Walk</th>\n",
              "      <th>OtherTransp</th>\n",
              "      <th>WorkAtHome</th>\n",
              "      <th>MeanCommute</th>\n",
              "      <th>Employed</th>\n",
              "      <th>PrivateWork</th>\n",
              "      <th>PublicWork</th>\n",
              "      <th>SelfEmployed</th>\n",
              "      <th>FamilyWork</th>\n",
              "      <th>Unemployment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga</td>\n",
              "      <td>55221</td>\n",
              "      <td>26745</td>\n",
              "      <td>28476</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40725</td>\n",
              "      <td>51281.0</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24974</td>\n",
              "      <td>1080</td>\n",
              "      <td>12.9</td>\n",
              "      <td>18.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>87.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>26.5</td>\n",
              "      <td>23986</td>\n",
              "      <td>73.6</td>\n",
              "      <td>20.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin</td>\n",
              "      <td>195121</td>\n",
              "      <td>95314</td>\n",
              "      <td>99807</td>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147695</td>\n",
              "      <td>50254.0</td>\n",
              "      <td>1263.0</td>\n",
              "      <td>27317</td>\n",
              "      <td>711</td>\n",
              "      <td>13.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>27.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>84.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>26.4</td>\n",
              "      <td>85953</td>\n",
              "      <td>81.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour</td>\n",
              "      <td>26932</td>\n",
              "      <td>14497</td>\n",
              "      <td>12435</td>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20714</td>\n",
              "      <td>32964.0</td>\n",
              "      <td>2973.0</td>\n",
              "      <td>16824</td>\n",
              "      <td>798</td>\n",
              "      <td>26.7</td>\n",
              "      <td>45.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>23.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>83.8</td>\n",
              "      <td>10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>24.1</td>\n",
              "      <td>8597</td>\n",
              "      <td>71.8</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb</td>\n",
              "      <td>22604</td>\n",
              "      <td>12073</td>\n",
              "      <td>10531</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17495</td>\n",
              "      <td>38678.0</td>\n",
              "      <td>3995.0</td>\n",
              "      <td>18431</td>\n",
              "      <td>1618</td>\n",
              "      <td>16.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>21.5</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>83.2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>28.8</td>\n",
              "      <td>8294</td>\n",
              "      <td>76.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount</td>\n",
              "      <td>57710</td>\n",
              "      <td>28512</td>\n",
              "      <td>29198</td>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42345</td>\n",
              "      <td>45813.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>20532</td>\n",
              "      <td>708</td>\n",
              "      <td>16.7</td>\n",
              "      <td>27.2</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>19.9</td>\n",
              "      <td>84.9</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>22189</td>\n",
              "      <td>82.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.7</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3215</th>\n",
              "      <td>72145</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Vega Baja</td>\n",
              "      <td>56858</td>\n",
              "      <td>27379</td>\n",
              "      <td>29479</td>\n",
              "      <td>96.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43656</td>\n",
              "      <td>16948.0</td>\n",
              "      <td>1234.0</td>\n",
              "      <td>9102</td>\n",
              "      <td>538</td>\n",
              "      <td>48.5</td>\n",
              "      <td>56.1</td>\n",
              "      <td>30.1</td>\n",
              "      <td>21.6</td>\n",
              "      <td>24.7</td>\n",
              "      <td>9.2</td>\n",
              "      <td>14.3</td>\n",
              "      <td>89.1</td>\n",
              "      <td>7.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>13660</td>\n",
              "      <td>78.3</td>\n",
              "      <td>17.6</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3216</th>\n",
              "      <td>72147</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Vieques</td>\n",
              "      <td>9130</td>\n",
              "      <td>4585</td>\n",
              "      <td>4545</td>\n",
              "      <td>96.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7085</td>\n",
              "      <td>18104.0</td>\n",
              "      <td>3771.0</td>\n",
              "      <td>8821</td>\n",
              "      <td>939</td>\n",
              "      <td>39.1</td>\n",
              "      <td>58.1</td>\n",
              "      <td>18.4</td>\n",
              "      <td>38.2</td>\n",
              "      <td>18.5</td>\n",
              "      <td>15.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>69.1</td>\n",
              "      <td>17.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2860</td>\n",
              "      <td>44.5</td>\n",
              "      <td>41.6</td>\n",
              "      <td>13.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3217</th>\n",
              "      <td>72149</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Villalba</td>\n",
              "      <td>24685</td>\n",
              "      <td>12086</td>\n",
              "      <td>12599</td>\n",
              "      <td>99.7</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>18458</td>\n",
              "      <td>17818.0</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>8420</td>\n",
              "      <td>486</td>\n",
              "      <td>54.0</td>\n",
              "      <td>68.3</td>\n",
              "      <td>23.6</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.8</td>\n",
              "      <td>13.9</td>\n",
              "      <td>19.7</td>\n",
              "      <td>82.0</td>\n",
              "      <td>11.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>26.9</td>\n",
              "      <td>6795</td>\n",
              "      <td>59.2</td>\n",
              "      <td>27.5</td>\n",
              "      <td>13.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3218</th>\n",
              "      <td>72151</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Yabucoa</td>\n",
              "      <td>36279</td>\n",
              "      <td>17648</td>\n",
              "      <td>18631</td>\n",
              "      <td>99.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27924</td>\n",
              "      <td>15627.0</td>\n",
              "      <td>1836.0</td>\n",
              "      <td>7960</td>\n",
              "      <td>512</td>\n",
              "      <td>52.3</td>\n",
              "      <td>62.1</td>\n",
              "      <td>25.9</td>\n",
              "      <td>25.9</td>\n",
              "      <td>20.7</td>\n",
              "      <td>12.8</td>\n",
              "      <td>14.7</td>\n",
              "      <td>86.0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>29.5</td>\n",
              "      <td>8083</td>\n",
              "      <td>65.1</td>\n",
              "      <td>27.6</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3219</th>\n",
              "      <td>72153</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Yauco</td>\n",
              "      <td>39474</td>\n",
              "      <td>19047</td>\n",
              "      <td>20427</td>\n",
              "      <td>99.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30661</td>\n",
              "      <td>14708.0</td>\n",
              "      <td>1245.0</td>\n",
              "      <td>7743</td>\n",
              "      <td>474</td>\n",
              "      <td>51.7</td>\n",
              "      <td>56.3</td>\n",
              "      <td>28.1</td>\n",
              "      <td>20.5</td>\n",
              "      <td>26.7</td>\n",
              "      <td>14.2</td>\n",
              "      <td>10.5</td>\n",
              "      <td>84.8</td>\n",
              "      <td>8.5</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>24.6</td>\n",
              "      <td>8923</td>\n",
              "      <td>68.0</td>\n",
              "      <td>27.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3220 rows × 37 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CensusId        State     County  ...  SelfEmployed  FamilyWork  Unemployment\n",
              "0         1001      Alabama    Autauga  ...           5.5         0.0           7.6\n",
              "1         1003      Alabama    Baldwin  ...           5.8         0.4           7.5\n",
              "2         1005      Alabama    Barbour  ...           7.3         0.1          17.6\n",
              "3         1007      Alabama       Bibb  ...           6.7         0.4           8.3\n",
              "4         1009      Alabama     Blount  ...           4.2         0.4           7.7\n",
              "...        ...          ...        ...  ...           ...         ...           ...\n",
              "3215     72145  Puerto Rico  Vega Baja  ...           4.1         0.0          15.2\n",
              "3216     72147  Puerto Rico    Vieques  ...          13.6         0.3          12.2\n",
              "3217     72149  Puerto Rico   Villalba  ...          13.1         0.2          25.9\n",
              "3218     72151  Puerto Rico    Yabucoa  ...           7.3         0.0          24.3\n",
              "3219     72153  Puerto Rico      Yauco  ...           4.4         0.0          27.1\n",
              "\n",
              "[3220 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnxaddI8QNfa"
      },
      "source": [
        "## Tax Return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21GB5tW9o9fq"
      },
      "source": [
        "Tax return data by US county\n",
        "\n",
        "https://www.irs.gov/statistics/compliance-presence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "id5nO6JMQPC9",
        "outputId": "e24685da-b916-4d0f-f16a-31ba141288a5"
      },
      "source": [
        "dft = pd.read_csv('https://raw.githubusercontent.com/Roozbeh-jafari/840.Data-Ethics-US-Taxpayers-Algorithms/main/DataSets/tax-return-2015.csv?token=ARFHYYG36GHMQAHWGRLTBI3AWACJY')\n",
        "# dft = pd.read_csv('tax-return-2015.csv')\n",
        "# dft = pd.read_csv('/content/tax-return-2015.csv')\n",
        "# dft = pd.read_csv('../Datasets/tax-return-2015.csv')\n",
        "dft"
      ],
      "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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State FIPS code [1]</th>\n",
              "      <th>State</th>\n",
              "      <th>County FIPS code [1]</th>\n",
              "      <th>County name</th>\n",
              "      <th>Number of returns</th>\n",
              "      <th>Number of single returns</th>\n",
              "      <th>Number of joint returns</th>\n",
              "      <th>Number of head of household returns</th>\n",
              "      <th>Number with paid preparer's signature</th>\n",
              "      <th>Number of exemptions</th>\n",
              "      <th>Number of dependents</th>\n",
              "      <th>Number of volunteer prepared returns [2]</th>\n",
              "      <th>Number of volunteer income tax assistance (VITA) prepared returns</th>\n",
              "      <th>Number of tax counseling for the elderly (TCE) prepared returns</th>\n",
              "      <th>Number of volunteer prepared \\nreturns with\\nEarned Income Credit</th>\n",
              "      <th>Number of refund anticipation loan returns</th>\n",
              "      <th>Number of refund anticipation check returns</th>\n",
              "      <th>Number of elderly returns [3]</th>\n",
              "      <th>Adjusted gross income (AGI) [4]</th>\n",
              "      <th>Total income num</th>\n",
              "      <th>Total income amt</th>\n",
              "      <th>Salaries and wages in AGI num</th>\n",
              "      <th>Salaries and wages in AGI amt</th>\n",
              "      <th>Taxable interest num</th>\n",
              "      <th>Taxable interest amt</th>\n",
              "      <th>Ordinary dividends num</th>\n",
              "      <th>Ordinary dividends amt</th>\n",
              "      <th>Qualified dividends [5] num</th>\n",
              "      <th>Qualified dividends [5] amt</th>\n",
              "      <th>State and local income tax refunds\\n num</th>\n",
              "      <th>State and local income tax refunds\\n amt</th>\n",
              "      <th>Business or profession\\nnet income (less loss) num</th>\n",
              "      <th>Business or profession\\nnet income (less loss) amt</th>\n",
              "      <th>Net capital gain (less loss) in AGI num</th>\n",
              "      <th>Net capital gain (less loss) in AGI amt</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions num</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions amt</th>\n",
              "      <th>Pensions and annuities in AGI num</th>\n",
              "      <th>Pensions and annuities in AGI amt</th>\n",
              "      <th>Number of \\nfarm returns num</th>\n",
              "      <th>...</th>\n",
              "      <th>Nonrefundable education credit num</th>\n",
              "      <th>Nonrefundable education credit amt</th>\n",
              "      <th>Retirement savings contribution credit num</th>\n",
              "      <th>Retirement savings contribution credit amt</th>\n",
              "      <th>Child tax credit num</th>\n",
              "      <th>Child tax credit amt</th>\n",
              "      <th>Residential energy tax credit num</th>\n",
              "      <th>Residential energy tax credit amt</th>\n",
              "      <th>Self-employment tax num</th>\n",
              "      <th>Self-employment tax amt</th>\n",
              "      <th>Total premium tax credit num</th>\n",
              "      <th>Total premium tax credit amt</th>\n",
              "      <th>Advance premium tax credit num</th>\n",
              "      <th>Advance premium tax credit amt</th>\n",
              "      <th>Health care individual responsibility payment num</th>\n",
              "      <th>Health care individual responsibility payment amt</th>\n",
              "      <th>Total tax payments num</th>\n",
              "      <th>Total tax payments amt</th>\n",
              "      <th>Earned income credit [7] num</th>\n",
              "      <th>Earned income credit [7] amt</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] num</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] amt</th>\n",
              "      <th>Additional child tax credit num</th>\n",
              "      <th>Additional child tax credit amt</th>\n",
              "      <th>Refundable education credit num</th>\n",
              "      <th>Refundable education credit amt</th>\n",
              "      <th>Net premium tax credit num</th>\n",
              "      <th>Net premium tax credit amt</th>\n",
              "      <th>Income tax [9] num</th>\n",
              "      <th>Income tax [9] amt</th>\n",
              "      <th>Total tax liability [10] num</th>\n",
              "      <th>Total tax liability [10] amt</th>\n",
              "      <th>Additional Medicare tax num</th>\n",
              "      <th>Additional Medicare tax amt</th>\n",
              "      <th>Net investment income tax num</th>\n",
              "      <th>Net investment income tax amt</th>\n",
              "      <th>Tax due at time of filing [11] num</th>\n",
              "      <th>Tax due at time of filing [11] amt</th>\n",
              "      <th>Overpayments refunded [12] num</th>\n",
              "      <th>Overpayments refunded [12] amt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>US</td>\n",
              "      <td>0</td>\n",
              "      <td>Total [13]</td>\n",
              "      <td>148965910</td>\n",
              "      <td>70209250</td>\n",
              "      <td>53923170</td>\n",
              "      <td>21890640</td>\n",
              "      <td>80046180</td>\n",
              "      <td>289089330</td>\n",
              "      <td>95564540</td>\n",
              "      <td>3549610</td>\n",
              "      <td>1855800</td>\n",
              "      <td>1693980</td>\n",
              "      <td>526520</td>\n",
              "      <td>457940</td>\n",
              "      <td>21365980</td>\n",
              "      <td>34715360</td>\n",
              "      <td>10074264203</td>\n",
              "      <td>148463360</td>\n",
              "      <td>10223004480</td>\n",
              "      <td>123272570</td>\n",
              "      <td>7008525735</td>\n",
              "      <td>42221420</td>\n",
              "      <td>89362337</td>\n",
              "      <td>27438590</td>\n",
              "      <td>246436572</td>\n",
              "      <td>25603510</td>\n",
              "      <td>191255367</td>\n",
              "      <td>20154420</td>\n",
              "      <td>30623936</td>\n",
              "      <td>24704120</td>\n",
              "      <td>335562990</td>\n",
              "      <td>24187930</td>\n",
              "      <td>667720309</td>\n",
              "      <td>13973180</td>\n",
              "      <td>249199152</td>\n",
              "      <td>27891270</td>\n",
              "      <td>680105287</td>\n",
              "      <td>1853030</td>\n",
              "      <td>...</td>\n",
              "      <td>9671340</td>\n",
              "      <td>10242220</td>\n",
              "      <td>7969270</td>\n",
              "      <td>1395349</td>\n",
              "      <td>22262740</td>\n",
              "      <td>26867439</td>\n",
              "      <td>2594560</td>\n",
              "      <td>2128417</td>\n",
              "      <td>19385880</td>\n",
              "      <td>59225710</td>\n",
              "      <td>5166550</td>\n",
              "      <td>18878404</td>\n",
              "      <td>5836460</td>\n",
              "      <td>20882039</td>\n",
              "      <td>6653270</td>\n",
              "      <td>3064758</td>\n",
              "      <td>140778140</td>\n",
              "      <td>1766105893</td>\n",
              "      <td>27972300</td>\n",
              "      <td>68008777</td>\n",
              "      <td>23959360</td>\n",
              "      <td>58451691</td>\n",
              "      <td>19400100</td>\n",
              "      <td>26045489</td>\n",
              "      <td>9635000</td>\n",
              "      <td>8567079</td>\n",
              "      <td>2403200</td>\n",
              "      <td>1596320</td>\n",
              "      <td>102005440</td>\n",
              "      <td>1432106443</td>\n",
              "      <td>112137600</td>\n",
              "      <td>1522112987</td>\n",
              "      <td>3475390</td>\n",
              "      <td>7607693</td>\n",
              "      <td>3781620</td>\n",
              "      <td>21033338</td>\n",
              "      <td>28883270</td>\n",
              "      <td>159391261</td>\n",
              "      <td>112176640</td>\n",
              "      <td>350257176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>2053620</td>\n",
              "      <td>834300</td>\n",
              "      <td>763210</td>\n",
              "      <td>415730</td>\n",
              "      <td>1172420</td>\n",
              "      <td>4166050</td>\n",
              "      <td>1467040</td>\n",
              "      <td>45570</td>\n",
              "      <td>28830</td>\n",
              "      <td>16760</td>\n",
              "      <td>7720</td>\n",
              "      <td>15400</td>\n",
              "      <td>457080</td>\n",
              "      <td>477210</td>\n",
              "      <td>111789331</td>\n",
              "      <td>2046690</td>\n",
              "      <td>113236900</td>\n",
              "      <td>1710150</td>\n",
              "      <td>80323944</td>\n",
              "      <td>502130</td>\n",
              "      <td>763242</td>\n",
              "      <td>268410</td>\n",
              "      <td>1724024</td>\n",
              "      <td>246330</td>\n",
              "      <td>1351071</td>\n",
              "      <td>318950</td>\n",
              "      <td>340193</td>\n",
              "      <td>345310</td>\n",
              "      <td>3128573</td>\n",
              "      <td>228700</td>\n",
              "      <td>4203762</td>\n",
              "      <td>171570</td>\n",
              "      <td>2797936</td>\n",
              "      <td>421830</td>\n",
              "      <td>10091228</td>\n",
              "      <td>45200</td>\n",
              "      <td>...</td>\n",
              "      <td>145900</td>\n",
              "      <td>154923</td>\n",
              "      <td>122430</td>\n",
              "      <td>22009</td>\n",
              "      <td>331830</td>\n",
              "      <td>386740</td>\n",
              "      <td>39130</td>\n",
              "      <td>19239</td>\n",
              "      <td>251070</td>\n",
              "      <td>638724</td>\n",
              "      <td>79620</td>\n",
              "      <td>275503</td>\n",
              "      <td>86890</td>\n",
              "      <td>295407</td>\n",
              "      <td>70350</td>\n",
              "      <td>29736</td>\n",
              "      <td>1960840</td>\n",
              "      <td>17604731</td>\n",
              "      <td>521420</td>\n",
              "      <td>1444531</td>\n",
              "      <td>467990</td>\n",
              "      <td>1279982</td>\n",
              "      <td>360840</td>\n",
              "      <td>481943</td>\n",
              "      <td>173090</td>\n",
              "      <td>163859</td>\n",
              "      <td>39440</td>\n",
              "      <td>21778</td>\n",
              "      <td>1303380</td>\n",
              "      <td>13179616</td>\n",
              "      <td>1458720</td>\n",
              "      <td>14075850</td>\n",
              "      <td>24800</td>\n",
              "      <td>43315</td>\n",
              "      <td>30150</td>\n",
              "      <td>111100</td>\n",
              "      <td>361940</td>\n",
              "      <td>1717754</td>\n",
              "      <td>1607070</td>\n",
              "      <td>4845454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>1</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>23960</td>\n",
              "      <td>9130</td>\n",
              "      <td>10020</td>\n",
              "      <td>4290</td>\n",
              "      <td>10960</td>\n",
              "      <td>50220</td>\n",
              "      <td>17800</td>\n",
              "      <td>510</td>\n",
              "      <td>130</td>\n",
              "      <td>370</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>5150</td>\n",
              "      <td>5160</td>\n",
              "      <td>1291011</td>\n",
              "      <td>23880</td>\n",
              "      <td>1304385</td>\n",
              "      <td>20360</td>\n",
              "      <td>979232</td>\n",
              "      <td>7210</td>\n",
              "      <td>5850</td>\n",
              "      <td>3030</td>\n",
              "      <td>9391</td>\n",
              "      <td>2740</td>\n",
              "      <td>7109</td>\n",
              "      <td>3770</td>\n",
              "      <td>3235</td>\n",
              "      <td>3490</td>\n",
              "      <td>28135</td>\n",
              "      <td>2520</td>\n",
              "      <td>17134</td>\n",
              "      <td>1930</td>\n",
              "      <td>25374</td>\n",
              "      <td>5520</td>\n",
              "      <td>147466</td>\n",
              "      <td>380</td>\n",
              "      <td>...</td>\n",
              "      <td>1790</td>\n",
              "      <td>2078</td>\n",
              "      <td>1380</td>\n",
              "      <td>245</td>\n",
              "      <td>4780</td>\n",
              "      <td>6155</td>\n",
              "      <td>410</td>\n",
              "      <td>143</td>\n",
              "      <td>2550</td>\n",
              "      <td>6138</td>\n",
              "      <td>780</td>\n",
              "      <td>2376</td>\n",
              "      <td>840</td>\n",
              "      <td>2604</td>\n",
              "      <td>710</td>\n",
              "      <td>323</td>\n",
              "      <td>23120</td>\n",
              "      <td>175903</td>\n",
              "      <td>5240</td>\n",
              "      <td>14098</td>\n",
              "      <td>4650</td>\n",
              "      <td>12521</td>\n",
              "      <td>3630</td>\n",
              "      <td>4716</td>\n",
              "      <td>1870</td>\n",
              "      <td>1743</td>\n",
              "      <td>390</td>\n",
              "      <td>195</td>\n",
              "      <td>16400</td>\n",
              "      <td>126990</td>\n",
              "      <td>17800</td>\n",
              "      <td>135207</td>\n",
              "      <td>120</td>\n",
              "      <td>115</td>\n",
              "      <td>170</td>\n",
              "      <td>387</td>\n",
              "      <td>4310</td>\n",
              "      <td>14233</td>\n",
              "      <td>18910</td>\n",
              "      <td>53521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>3</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>93140</td>\n",
              "      <td>38310</td>\n",
              "      <td>39800</td>\n",
              "      <td>12900</td>\n",
              "      <td>46970</td>\n",
              "      <td>183680</td>\n",
              "      <td>56550</td>\n",
              "      <td>1700</td>\n",
              "      <td>350</td>\n",
              "      <td>1350</td>\n",
              "      <td>90</td>\n",
              "      <td>300</td>\n",
              "      <td>15100</td>\n",
              "      <td>27460</td>\n",
              "      <td>5585227</td>\n",
              "      <td>92800</td>\n",
              "      <td>5671834</td>\n",
              "      <td>72260</td>\n",
              "      <td>3646995</td>\n",
              "      <td>25980</td>\n",
              "      <td>45974</td>\n",
              "      <td>17040</td>\n",
              "      <td>105811</td>\n",
              "      <td>15600</td>\n",
              "      <td>79788</td>\n",
              "      <td>13570</td>\n",
              "      <td>15614</td>\n",
              "      <td>17480</td>\n",
              "      <td>208501</td>\n",
              "      <td>15770</td>\n",
              "      <td>243919</td>\n",
              "      <td>11320</td>\n",
              "      <td>211185</td>\n",
              "      <td>21820</td>\n",
              "      <td>563755</td>\n",
              "      <td>1140</td>\n",
              "      <td>...</td>\n",
              "      <td>5100</td>\n",
              "      <td>5504</td>\n",
              "      <td>4280</td>\n",
              "      <td>787</td>\n",
              "      <td>14220</td>\n",
              "      <td>17377</td>\n",
              "      <td>1410</td>\n",
              "      <td>798</td>\n",
              "      <td>13680</td>\n",
              "      <td>37978</td>\n",
              "      <td>4420</td>\n",
              "      <td>15978</td>\n",
              "      <td>4920</td>\n",
              "      <td>17275</td>\n",
              "      <td>3530</td>\n",
              "      <td>1644</td>\n",
              "      <td>86980</td>\n",
              "      <td>848360</td>\n",
              "      <td>17840</td>\n",
              "      <td>45725</td>\n",
              "      <td>15280</td>\n",
              "      <td>39092</td>\n",
              "      <td>12080</td>\n",
              "      <td>15810</td>\n",
              "      <td>5630</td>\n",
              "      <td>5139</td>\n",
              "      <td>2230</td>\n",
              "      <td>1481</td>\n",
              "      <td>62980</td>\n",
              "      <td>711964</td>\n",
              "      <td>69840</td>\n",
              "      <td>763095</td>\n",
              "      <td>1450</td>\n",
              "      <td>2138</td>\n",
              "      <td>1840</td>\n",
              "      <td>5128</td>\n",
              "      <td>21340</td>\n",
              "      <td>119349</td>\n",
              "      <td>65910</td>\n",
              "      <td>184209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>5</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>9590</td>\n",
              "      <td>3560</td>\n",
              "      <td>3020</td>\n",
              "      <td>2860</td>\n",
              "      <td>6590</td>\n",
              "      <td>19710</td>\n",
              "      <td>7540</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240</td>\n",
              "      <td>2860</td>\n",
              "      <td>2410</td>\n",
              "      <td>376661</td>\n",
              "      <td>9540</td>\n",
              "      <td>381264</td>\n",
              "      <td>7860</td>\n",
              "      <td>271680</td>\n",
              "      <td>2110</td>\n",
              "      <td>2548</td>\n",
              "      <td>1040</td>\n",
              "      <td>5639</td>\n",
              "      <td>950</td>\n",
              "      <td>4741</td>\n",
              "      <td>910</td>\n",
              "      <td>805</td>\n",
              "      <td>1650</td>\n",
              "      <td>9281</td>\n",
              "      <td>890</td>\n",
              "      <td>8629</td>\n",
              "      <td>720</td>\n",
              "      <td>10959</td>\n",
              "      <td>1790</td>\n",
              "      <td>38421</td>\n",
              "      <td>430</td>\n",
              "      <td>...</td>\n",
              "      <td>500</td>\n",
              "      <td>524</td>\n",
              "      <td>550</td>\n",
              "      <td>98</td>\n",
              "      <td>1480</td>\n",
              "      <td>1431</td>\n",
              "      <td>130</td>\n",
              "      <td>64</td>\n",
              "      <td>1260</td>\n",
              "      <td>2406</td>\n",
              "      <td>370</td>\n",
              "      <td>1252</td>\n",
              "      <td>390</td>\n",
              "      <td>1358</td>\n",
              "      <td>370</td>\n",
              "      <td>139</td>\n",
              "      <td>9100</td>\n",
              "      <td>54654</td>\n",
              "      <td>3360</td>\n",
              "      <td>9888</td>\n",
              "      <td>3080</td>\n",
              "      <td>8715</td>\n",
              "      <td>2430</td>\n",
              "      <td>3265</td>\n",
              "      <td>720</td>\n",
              "      <td>690</td>\n",
              "      <td>170</td>\n",
              "      <td>72</td>\n",
              "      <td>5110</td>\n",
              "      <td>32266</td>\n",
              "      <td>6130</td>\n",
              "      <td>35494</td>\n",
              "      <td>20</td>\n",
              "      <td>32</td>\n",
              "      <td>50</td>\n",
              "      <td>181</td>\n",
              "      <td>1340</td>\n",
              "      <td>4631</td>\n",
              "      <td>7830</td>\n",
              "      <td>24181</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",
              "      <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>3187</th>\n",
              "      <td>56</td>\n",
              "      <td>WY</td>\n",
              "      <td>37</td>\n",
              "      <td>Sweetwater County</td>\n",
              "      <td>20480</td>\n",
              "      <td>8820</td>\n",
              "      <td>8820</td>\n",
              "      <td>2440</td>\n",
              "      <td>10550</td>\n",
              "      <td>43190</td>\n",
              "      <td>15100</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>3440</td>\n",
              "      <td>4190</td>\n",
              "      <td>1454663</td>\n",
              "      <td>20420</td>\n",
              "      <td>1466609</td>\n",
              "      <td>18210</td>\n",
              "      <td>1143570</td>\n",
              "      <td>5630</td>\n",
              "      <td>6143</td>\n",
              "      <td>3260</td>\n",
              "      <td>13902</td>\n",
              "      <td>3000</td>\n",
              "      <td>10430</td>\n",
              "      <td>280</td>\n",
              "      <td>187</td>\n",
              "      <td>2050</td>\n",
              "      <td>25836</td>\n",
              "      <td>2680</td>\n",
              "      <td>33575</td>\n",
              "      <td>1660</td>\n",
              "      <td>27831</td>\n",
              "      <td>4030</td>\n",
              "      <td>97112</td>\n",
              "      <td>190</td>\n",
              "      <td>...</td>\n",
              "      <td>1000</td>\n",
              "      <td>802</td>\n",
              "      <td>720</td>\n",
              "      <td>119</td>\n",
              "      <td>4290</td>\n",
              "      <td>6400</td>\n",
              "      <td>340</td>\n",
              "      <td>90</td>\n",
              "      <td>1610</td>\n",
              "      <td>4576</td>\n",
              "      <td>450</td>\n",
              "      <td>2729</td>\n",
              "      <td>500</td>\n",
              "      <td>2997</td>\n",
              "      <td>1290</td>\n",
              "      <td>751</td>\n",
              "      <td>19880</td>\n",
              "      <td>236650</td>\n",
              "      <td>2560</td>\n",
              "      <td>5692</td>\n",
              "      <td>2230</td>\n",
              "      <td>5165</td>\n",
              "      <td>2060</td>\n",
              "      <td>2931</td>\n",
              "      <td>800</td>\n",
              "      <td>556</td>\n",
              "      <td>230</td>\n",
              "      <td>156</td>\n",
              "      <td>15770</td>\n",
              "      <td>189538</td>\n",
              "      <td>16550</td>\n",
              "      <td>198517</td>\n",
              "      <td>230</td>\n",
              "      <td>274</td>\n",
              "      <td>310</td>\n",
              "      <td>1109</td>\n",
              "      <td>3390</td>\n",
              "      <td>16251</td>\n",
              "      <td>16440</td>\n",
              "      <td>50305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3188</th>\n",
              "      <td>56</td>\n",
              "      <td>WY</td>\n",
              "      <td>39</td>\n",
              "      <td>Teton County</td>\n",
              "      <td>14310</td>\n",
              "      <td>8120</td>\n",
              "      <td>4780</td>\n",
              "      <td>1120</td>\n",
              "      <td>8080</td>\n",
              "      <td>26350</td>\n",
              "      <td>8110</td>\n",
              "      <td>380</td>\n",
              "      <td>380</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>800</td>\n",
              "      <td>3000</td>\n",
              "      <td>3553907</td>\n",
              "      <td>14290</td>\n",
              "      <td>3604863</td>\n",
              "      <td>11860</td>\n",
              "      <td>899016</td>\n",
              "      <td>5310</td>\n",
              "      <td>169269</td>\n",
              "      <td>4760</td>\n",
              "      <td>535447</td>\n",
              "      <td>4590</td>\n",
              "      <td>443480</td>\n",
              "      <td>490</td>\n",
              "      <td>7167</td>\n",
              "      <td>3630</td>\n",
              "      <td>81004</td>\n",
              "      <td>4700</td>\n",
              "      <td>1547983</td>\n",
              "      <td>1290</td>\n",
              "      <td>57224</td>\n",
              "      <td>1470</td>\n",
              "      <td>48977</td>\n",
              "      <td>150</td>\n",
              "      <td>...</td>\n",
              "      <td>550</td>\n",
              "      <td>499</td>\n",
              "      <td>500</td>\n",
              "      <td>102</td>\n",
              "      <td>1780</td>\n",
              "      <td>2233</td>\n",
              "      <td>90</td>\n",
              "      <td>62</td>\n",
              "      <td>3130</td>\n",
              "      <td>14922</td>\n",
              "      <td>1430</td>\n",
              "      <td>6567</td>\n",
              "      <td>1670</td>\n",
              "      <td>7448</td>\n",
              "      <td>880</td>\n",
              "      <td>465</td>\n",
              "      <td>13530</td>\n",
              "      <td>988600</td>\n",
              "      <td>1140</td>\n",
              "      <td>1718</td>\n",
              "      <td>790</td>\n",
              "      <td>1360</td>\n",
              "      <td>960</td>\n",
              "      <td>1430</td>\n",
              "      <td>390</td>\n",
              "      <td>329</td>\n",
              "      <td>660</td>\n",
              "      <td>593</td>\n",
              "      <td>11080</td>\n",
              "      <td>771953</td>\n",
              "      <td>11860</td>\n",
              "      <td>806686</td>\n",
              "      <td>420</td>\n",
              "      <td>2709</td>\n",
              "      <td>1180</td>\n",
              "      <td>61744</td>\n",
              "      <td>4000</td>\n",
              "      <td>52433</td>\n",
              "      <td>8690</td>\n",
              "      <td>201951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3189</th>\n",
              "      <td>56</td>\n",
              "      <td>WY</td>\n",
              "      <td>41</td>\n",
              "      <td>Uinta County</td>\n",
              "      <td>9280</td>\n",
              "      <td>3660</td>\n",
              "      <td>4440</td>\n",
              "      <td>1070</td>\n",
              "      <td>5020</td>\n",
              "      <td>20670</td>\n",
              "      <td>7600</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1490</td>\n",
              "      <td>2180</td>\n",
              "      <td>589149</td>\n",
              "      <td>9240</td>\n",
              "      <td>595900</td>\n",
              "      <td>8050</td>\n",
              "      <td>442112</td>\n",
              "      <td>2230</td>\n",
              "      <td>3195</td>\n",
              "      <td>1350</td>\n",
              "      <td>6727</td>\n",
              "      <td>1240</td>\n",
              "      <td>5026</td>\n",
              "      <td>200</td>\n",
              "      <td>192</td>\n",
              "      <td>1330</td>\n",
              "      <td>10136</td>\n",
              "      <td>1230</td>\n",
              "      <td>19569</td>\n",
              "      <td>750</td>\n",
              "      <td>15386</td>\n",
              "      <td>1830</td>\n",
              "      <td>43867</td>\n",
              "      <td>360</td>\n",
              "      <td>...</td>\n",
              "      <td>490</td>\n",
              "      <td>452</td>\n",
              "      <td>410</td>\n",
              "      <td>62</td>\n",
              "      <td>2080</td>\n",
              "      <td>3329</td>\n",
              "      <td>110</td>\n",
              "      <td>29</td>\n",
              "      <td>940</td>\n",
              "      <td>2314</td>\n",
              "      <td>220</td>\n",
              "      <td>1571</td>\n",
              "      <td>250</td>\n",
              "      <td>1658</td>\n",
              "      <td>520</td>\n",
              "      <td>297</td>\n",
              "      <td>8890</td>\n",
              "      <td>88023</td>\n",
              "      <td>1370</td>\n",
              "      <td>3077</td>\n",
              "      <td>1190</td>\n",
              "      <td>2756</td>\n",
              "      <td>1100</td>\n",
              "      <td>1631</td>\n",
              "      <td>420</td>\n",
              "      <td>318</td>\n",
              "      <td>120</td>\n",
              "      <td>93</td>\n",
              "      <td>6630</td>\n",
              "      <td>67298</td>\n",
              "      <td>7090</td>\n",
              "      <td>71440</td>\n",
              "      <td>70</td>\n",
              "      <td>63</td>\n",
              "      <td>120</td>\n",
              "      <td>566</td>\n",
              "      <td>1590</td>\n",
              "      <td>8646</td>\n",
              "      <td>7330</td>\n",
              "      <td>22735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3190</th>\n",
              "      <td>56</td>\n",
              "      <td>WY</td>\n",
              "      <td>43</td>\n",
              "      <td>Washakie County</td>\n",
              "      <td>3770</td>\n",
              "      <td>1580</td>\n",
              "      <td>1800</td>\n",
              "      <td>360</td>\n",
              "      <td>1990</td>\n",
              "      <td>7610</td>\n",
              "      <td>2340</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>450</td>\n",
              "      <td>1230</td>\n",
              "      <td>246250</td>\n",
              "      <td>3760</td>\n",
              "      <td>251213</td>\n",
              "      <td>3080</td>\n",
              "      <td>151655</td>\n",
              "      <td>1410</td>\n",
              "      <td>2117</td>\n",
              "      <td>920</td>\n",
              "      <td>5051</td>\n",
              "      <td>840</td>\n",
              "      <td>3660</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>650</td>\n",
              "      <td>3810</td>\n",
              "      <td>750</td>\n",
              "      <td>9306</td>\n",
              "      <td>460</td>\n",
              "      <td>6592</td>\n",
              "      <td>810</td>\n",
              "      <td>17278</td>\n",
              "      <td>180</td>\n",
              "      <td>...</td>\n",
              "      <td>160</td>\n",
              "      <td>147</td>\n",
              "      <td>190</td>\n",
              "      <td>34</td>\n",
              "      <td>690</td>\n",
              "      <td>1010</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>540</td>\n",
              "      <td>1194</td>\n",
              "      <td>160</td>\n",
              "      <td>1071</td>\n",
              "      <td>160</td>\n",
              "      <td>1189</td>\n",
              "      <td>240</td>\n",
              "      <td>153</td>\n",
              "      <td>3510</td>\n",
              "      <td>44665</td>\n",
              "      <td>490</td>\n",
              "      <td>1194</td>\n",
              "      <td>420</td>\n",
              "      <td>1059</td>\n",
              "      <td>370</td>\n",
              "      <td>545</td>\n",
              "      <td>120</td>\n",
              "      <td>91</td>\n",
              "      <td>70</td>\n",
              "      <td>45</td>\n",
              "      <td>2650</td>\n",
              "      <td>35646</td>\n",
              "      <td>2870</td>\n",
              "      <td>37470</td>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "      <td>60</td>\n",
              "      <td>122</td>\n",
              "      <td>750</td>\n",
              "      <td>3035</td>\n",
              "      <td>2740</td>\n",
              "      <td>8604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3191</th>\n",
              "      <td>56</td>\n",
              "      <td>WY</td>\n",
              "      <td>45</td>\n",
              "      <td>Weston County</td>\n",
              "      <td>3290</td>\n",
              "      <td>1420</td>\n",
              "      <td>1550</td>\n",
              "      <td>310</td>\n",
              "      <td>2010</td>\n",
              "      <td>6440</td>\n",
              "      <td>1810</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>380</td>\n",
              "      <td>990</td>\n",
              "      <td>220131</td>\n",
              "      <td>3280</td>\n",
              "      <td>223641</td>\n",
              "      <td>2690</td>\n",
              "      <td>146771</td>\n",
              "      <td>1000</td>\n",
              "      <td>1179</td>\n",
              "      <td>580</td>\n",
              "      <td>2657</td>\n",
              "      <td>520</td>\n",
              "      <td>1978</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>630</td>\n",
              "      <td>4538</td>\n",
              "      <td>620</td>\n",
              "      <td>12431</td>\n",
              "      <td>340</td>\n",
              "      <td>5874</td>\n",
              "      <td>790</td>\n",
              "      <td>16625</td>\n",
              "      <td>250</td>\n",
              "      <td>...</td>\n",
              "      <td>100</td>\n",
              "      <td>87</td>\n",
              "      <td>140</td>\n",
              "      <td>24</td>\n",
              "      <td>540</td>\n",
              "      <td>808</td>\n",
              "      <td>40</td>\n",
              "      <td>8</td>\n",
              "      <td>470</td>\n",
              "      <td>1272</td>\n",
              "      <td>80</td>\n",
              "      <td>493</td>\n",
              "      <td>80</td>\n",
              "      <td>531</td>\n",
              "      <td>190</td>\n",
              "      <td>106</td>\n",
              "      <td>3090</td>\n",
              "      <td>36544</td>\n",
              "      <td>430</td>\n",
              "      <td>911</td>\n",
              "      <td>360</td>\n",
              "      <td>795</td>\n",
              "      <td>270</td>\n",
              "      <td>327</td>\n",
              "      <td>80</td>\n",
              "      <td>65</td>\n",
              "      <td>40</td>\n",
              "      <td>27</td>\n",
              "      <td>2440</td>\n",
              "      <td>29256</td>\n",
              "      <td>2580</td>\n",
              "      <td>31055</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>173</td>\n",
              "      <td>680</td>\n",
              "      <td>3629</td>\n",
              "      <td>2410</td>\n",
              "      <td>6858</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3192 rows × 131 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      State FIPS code [1]  ... Overpayments refunded [12] amt\n",
              "0                       0  ...                      350257176\n",
              "1                       1  ...                        4845454\n",
              "2                       1  ...                          53521\n",
              "3                       1  ...                         184209\n",
              "4                       1  ...                          24181\n",
              "...                   ...  ...                            ...\n",
              "3187                   56  ...                          50305\n",
              "3188                   56  ...                         201951\n",
              "3189                   56  ...                          22735\n",
              "3190                   56  ...                           8604\n",
              "3191                   56  ...                           6858\n",
              "\n",
              "[3192 rows x 131 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rENcG19AQPAU"
      },
      "source": [
        "# adding ratio columns\n",
        "dft['volunteer_preparer_percentage'] = dft['Number of volunteer prepared returns [2]'] / dft['Number of returns']\n",
        "dft['paid_preparer_percentage'] = dft['Number with paid preparer\\'s signature'] / dft['Number of returns']\n",
        "dft['wage_income_percentage'] = dft['Salaries and wages in AGI num'] / dft['Number of returns']\n",
        "dft['dividends_percentage'] = dft['Qualified dividends [5] num'] / dft['Number of returns']\n",
        "dft['cap_gain_percentage'] =  dft['Net capital gain (less loss) in AGI num'] / dft['Number of returns']\n",
        "dft['eic_percentage'] =  dft['Earned income credit [7] num'] / dft['Number of returns']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "id": "Ve7VLldrQO-A",
        "outputId": "beb85d5e-a246-45ca-8d5b-1c5d0dd1453c"
      },
      "source": [
        "dft[dft['Number of returns'] > 10000].sort_values('dividends_percentage', ascending = False)\n",
        "\n",
        "# counties w. > 10,000 returns sorted by percentage of returns with divend income (wealthy populations)"
      ],
      "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>State FIPS code [1]</th>\n",
              "      <th>State</th>\n",
              "      <th>County FIPS code [1]</th>\n",
              "      <th>County name</th>\n",
              "      <th>Number of returns</th>\n",
              "      <th>Number of single returns</th>\n",
              "      <th>Number of joint returns</th>\n",
              "      <th>Number of head of household returns</th>\n",
              "      <th>Number with paid preparer's signature</th>\n",
              "      <th>Number of exemptions</th>\n",
              "      <th>Number of dependents</th>\n",
              "      <th>Number of volunteer prepared returns [2]</th>\n",
              "      <th>Number of volunteer income tax assistance (VITA) prepared returns</th>\n",
              "      <th>Number of tax counseling for the elderly (TCE) prepared returns</th>\n",
              "      <th>Number of volunteer prepared \\nreturns with\\nEarned Income Credit</th>\n",
              "      <th>Number of refund anticipation loan returns</th>\n",
              "      <th>Number of refund anticipation check returns</th>\n",
              "      <th>Number of elderly returns [3]</th>\n",
              "      <th>Adjusted gross income (AGI) [4]</th>\n",
              "      <th>Total income num</th>\n",
              "      <th>Total income amt</th>\n",
              "      <th>Salaries and wages in AGI num</th>\n",
              "      <th>Salaries and wages in AGI amt</th>\n",
              "      <th>Taxable interest num</th>\n",
              "      <th>Taxable interest amt</th>\n",
              "      <th>Ordinary dividends num</th>\n",
              "      <th>Ordinary dividends amt</th>\n",
              "      <th>Qualified dividends [5] num</th>\n",
              "      <th>Qualified dividends [5] amt</th>\n",
              "      <th>State and local income tax refunds\\n num</th>\n",
              "      <th>State and local income tax refunds\\n amt</th>\n",
              "      <th>Business or profession\\nnet income (less loss) num</th>\n",
              "      <th>Business or profession\\nnet income (less loss) amt</th>\n",
              "      <th>Net capital gain (less loss) in AGI num</th>\n",
              "      <th>Net capital gain (less loss) in AGI amt</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions num</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions amt</th>\n",
              "      <th>Pensions and annuities in AGI num</th>\n",
              "      <th>Pensions and annuities in AGI amt</th>\n",
              "      <th>Number of \\nfarm returns num</th>\n",
              "      <th>...</th>\n",
              "      <th>Residential energy tax credit num</th>\n",
              "      <th>Residential energy tax credit amt</th>\n",
              "      <th>Self-employment tax num</th>\n",
              "      <th>Self-employment tax amt</th>\n",
              "      <th>Total premium tax credit num</th>\n",
              "      <th>Total premium tax credit amt</th>\n",
              "      <th>Advance premium tax credit num</th>\n",
              "      <th>Advance premium tax credit amt</th>\n",
              "      <th>Health care individual responsibility payment num</th>\n",
              "      <th>Health care individual responsibility payment amt</th>\n",
              "      <th>Total tax payments num</th>\n",
              "      <th>Total tax payments amt</th>\n",
              "      <th>Earned income credit [7] num</th>\n",
              "      <th>Earned income credit [7] amt</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] num</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] amt</th>\n",
              "      <th>Additional child tax credit num</th>\n",
              "      <th>Additional child tax credit amt</th>\n",
              "      <th>Refundable education credit num</th>\n",
              "      <th>Refundable education credit amt</th>\n",
              "      <th>Net premium tax credit num</th>\n",
              "      <th>Net premium tax credit amt</th>\n",
              "      <th>Income tax [9] num</th>\n",
              "      <th>Income tax [9] amt</th>\n",
              "      <th>Total tax liability [10] num</th>\n",
              "      <th>Total tax liability [10] amt</th>\n",
              "      <th>Additional Medicare tax num</th>\n",
              "      <th>Additional Medicare tax amt</th>\n",
              "      <th>Net investment income tax num</th>\n",
              "      <th>Net investment income tax amt</th>\n",
              "      <th>Tax due at time of filing [11] num</th>\n",
              "      <th>Tax due at time of filing [11] amt</th>\n",
              "      <th>Overpayments refunded [12] num</th>\n",
              "      <th>Overpayments refunded [12] amt</th>\n",
              "      <th>volunteer_preparer_percentage</th>\n",
              "      <th>paid_preparer_percentage</th>\n",
              "      <th>wage_income_percentage</th>\n",
              "      <th>dividends_percentage</th>\n",
              "      <th>cap_gain_percentage</th>\n",
              "      <th>eic_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1813</th>\n",
              "      <td>34</td>\n",
              "      <td>NJ</td>\n",
              "      <td>19</td>\n",
              "      <td>Hunterdon County</td>\n",
              "      <td>64620</td>\n",
              "      <td>29980</td>\n",
              "      <td>29410</td>\n",
              "      <td>4120</td>\n",
              "      <td>39990</td>\n",
              "      <td>122900</td>\n",
              "      <td>35850</td>\n",
              "      <td>1200</td>\n",
              "      <td>1060</td>\n",
              "      <td>130</td>\n",
              "      <td>220</td>\n",
              "      <td>0</td>\n",
              "      <td>3050</td>\n",
              "      <td>18680</td>\n",
              "      <td>7689706</td>\n",
              "      <td>64490</td>\n",
              "      <td>7835302</td>\n",
              "      <td>52340</td>\n",
              "      <td>5435316</td>\n",
              "      <td>29620</td>\n",
              "      <td>50326</td>\n",
              "      <td>26940</td>\n",
              "      <td>210596</td>\n",
              "      <td>25660</td>\n",
              "      <td>162584</td>\n",
              "      <td>17180</td>\n",
              "      <td>34002</td>\n",
              "      <td>11680</td>\n",
              "      <td>305709</td>\n",
              "      <td>22440</td>\n",
              "      <td>439415</td>\n",
              "      <td>9610</td>\n",
              "      <td>217142</td>\n",
              "      <td>14340</td>\n",
              "      <td>411313</td>\n",
              "      <td>1410</td>\n",
              "      <td>...</td>\n",
              "      <td>1830</td>\n",
              "      <td>1454</td>\n",
              "      <td>10060</td>\n",
              "      <td>50492</td>\n",
              "      <td>1580</td>\n",
              "      <td>7605</td>\n",
              "      <td>1840</td>\n",
              "      <td>8551</td>\n",
              "      <td>1620</td>\n",
              "      <td>894</td>\n",
              "      <td>60710</td>\n",
              "      <td>1495165</td>\n",
              "      <td>3440</td>\n",
              "      <td>5491</td>\n",
              "      <td>2510</td>\n",
              "      <td>4287</td>\n",
              "      <td>2010</td>\n",
              "      <td>2475</td>\n",
              "      <td>3310</td>\n",
              "      <td>3265</td>\n",
              "      <td>760</td>\n",
              "      <td>864</td>\n",
              "      <td>51350</td>\n",
              "      <td>1358257</td>\n",
              "      <td>53730</td>\n",
              "      <td>1435113</td>\n",
              "      <td>5920</td>\n",
              "      <td>11773</td>\n",
              "      <td>5370</td>\n",
              "      <td>12796</td>\n",
              "      <td>17050</td>\n",
              "      <td>137075</td>\n",
              "      <td>42330</td>\n",
              "      <td>146439</td>\n",
              "      <td>0.018570</td>\n",
              "      <td>0.618849</td>\n",
              "      <td>0.809966</td>\n",
              "      <td>0.397091</td>\n",
              "      <td>0.347261</td>\n",
              "      <td>0.053234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>12</td>\n",
              "      <td>FL</td>\n",
              "      <td>119</td>\n",
              "      <td>Sumter County</td>\n",
              "      <td>55010</td>\n",
              "      <td>20100</td>\n",
              "      <td>30600</td>\n",
              "      <td>3460</td>\n",
              "      <td>27050</td>\n",
              "      <td>97130</td>\n",
              "      <td>12340</td>\n",
              "      <td>6720</td>\n",
              "      <td>50</td>\n",
              "      <td>6670</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>3960</td>\n",
              "      <td>39270</td>\n",
              "      <td>4047803</td>\n",
              "      <td>54790</td>\n",
              "      <td>4097430</td>\n",
              "      <td>24610</td>\n",
              "      <td>969323</td>\n",
              "      <td>28690</td>\n",
              "      <td>69477</td>\n",
              "      <td>23290</td>\n",
              "      <td>191614</td>\n",
              "      <td>21790</td>\n",
              "      <td>140086</td>\n",
              "      <td>2320</td>\n",
              "      <td>4092</td>\n",
              "      <td>5980</td>\n",
              "      <td>64339</td>\n",
              "      <td>21050</td>\n",
              "      <td>309635</td>\n",
              "      <td>22910</td>\n",
              "      <td>504241</td>\n",
              "      <td>32140</td>\n",
              "      <td>1079604</td>\n",
              "      <td>640</td>\n",
              "      <td>...</td>\n",
              "      <td>1560</td>\n",
              "      <td>678</td>\n",
              "      <td>4510</td>\n",
              "      <td>11235</td>\n",
              "      <td>1960</td>\n",
              "      <td>9065</td>\n",
              "      <td>2240</td>\n",
              "      <td>10206</td>\n",
              "      <td>1310</td>\n",
              "      <td>646</td>\n",
              "      <td>49550</td>\n",
              "      <td>685964</td>\n",
              "      <td>5430</td>\n",
              "      <td>13000</td>\n",
              "      <td>4660</td>\n",
              "      <td>11362</td>\n",
              "      <td>3290</td>\n",
              "      <td>4564</td>\n",
              "      <td>940</td>\n",
              "      <td>813</td>\n",
              "      <td>810</td>\n",
              "      <td>600</td>\n",
              "      <td>40740</td>\n",
              "      <td>581465</td>\n",
              "      <td>42790</td>\n",
              "      <td>601857</td>\n",
              "      <td>350</td>\n",
              "      <td>672</td>\n",
              "      <td>1180</td>\n",
              "      <td>7398</td>\n",
              "      <td>14510</td>\n",
              "      <td>69364</td>\n",
              "      <td>34260</td>\n",
              "      <td>132747</td>\n",
              "      <td>0.122160</td>\n",
              "      <td>0.491729</td>\n",
              "      <td>0.447373</td>\n",
              "      <td>0.396110</td>\n",
              "      <td>0.382658</td>\n",
              "      <td>0.098709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>6</td>\n",
              "      <td>CA</td>\n",
              "      <td>41</td>\n",
              "      <td>Marin County</td>\n",
              "      <td>132580</td>\n",
              "      <td>67110</td>\n",
              "      <td>51990</td>\n",
              "      <td>11250</td>\n",
              "      <td>85400</td>\n",
              "      <td>245630</td>\n",
              "      <td>69200</td>\n",
              "      <td>2670</td>\n",
              "      <td>760</td>\n",
              "      <td>1920</td>\n",
              "      <td>240</td>\n",
              "      <td>0</td>\n",
              "      <td>5650</td>\n",
              "      <td>45950</td>\n",
              "      <td>22455167</td>\n",
              "      <td>132120</td>\n",
              "      <td>23006100</td>\n",
              "      <td>92480</td>\n",
              "      <td>11778965</td>\n",
              "      <td>63780</td>\n",
              "      <td>273964</td>\n",
              "      <td>55050</td>\n",
              "      <td>1174705</td>\n",
              "      <td>52350</td>\n",
              "      <td>923269</td>\n",
              "      <td>29000</td>\n",
              "      <td>127648</td>\n",
              "      <td>37640</td>\n",
              "      <td>1435995</td>\n",
              "      <td>53720</td>\n",
              "      <td>3891232</td>\n",
              "      <td>21460</td>\n",
              "      <td>626341</td>\n",
              "      <td>25110</td>\n",
              "      <td>864938</td>\n",
              "      <td>370</td>\n",
              "      <td>...</td>\n",
              "      <td>2000</td>\n",
              "      <td>4797</td>\n",
              "      <td>33410</td>\n",
              "      <td>212290</td>\n",
              "      <td>5680</td>\n",
              "      <td>30083</td>\n",
              "      <td>6890</td>\n",
              "      <td>34803</td>\n",
              "      <td>3420</td>\n",
              "      <td>2033</td>\n",
              "      <td>119520</td>\n",
              "      <td>5220628</td>\n",
              "      <td>9000</td>\n",
              "      <td>14985</td>\n",
              "      <td>6010</td>\n",
              "      <td>10473</td>\n",
              "      <td>6620</td>\n",
              "      <td>8423</td>\n",
              "      <td>3920</td>\n",
              "      <td>3160</td>\n",
              "      <td>3010</td>\n",
              "      <td>3142</td>\n",
              "      <td>102240</td>\n",
              "      <td>4651959</td>\n",
              "      <td>111170</td>\n",
              "      <td>5007282</td>\n",
              "      <td>14360</td>\n",
              "      <td>45073</td>\n",
              "      <td>16270</td>\n",
              "      <td>136967</td>\n",
              "      <td>44780</td>\n",
              "      <td>502657</td>\n",
              "      <td>68740</td>\n",
              "      <td>430535</td>\n",
              "      <td>0.020139</td>\n",
              "      <td>0.644139</td>\n",
              "      <td>0.697541</td>\n",
              "      <td>0.394856</td>\n",
              "      <td>0.405189</td>\n",
              "      <td>0.067884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2902</th>\n",
              "      <td>51</td>\n",
              "      <td>VA</td>\n",
              "      <td>75</td>\n",
              "      <td>Goochland County</td>\n",
              "      <td>10870</td>\n",
              "      <td>4360</td>\n",
              "      <td>5410</td>\n",
              "      <td>800</td>\n",
              "      <td>5320</td>\n",
              "      <td>21040</td>\n",
              "      <td>5590</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>850</td>\n",
              "      <td>3590</td>\n",
              "      <td>1469342</td>\n",
              "      <td>10840</td>\n",
              "      <td>1494253</td>\n",
              "      <td>8630</td>\n",
              "      <td>874459</td>\n",
              "      <td>4530</td>\n",
              "      <td>16436</td>\n",
              "      <td>4140</td>\n",
              "      <td>78959</td>\n",
              "      <td>3960</td>\n",
              "      <td>66087</td>\n",
              "      <td>3020</td>\n",
              "      <td>6474</td>\n",
              "      <td>2030</td>\n",
              "      <td>43710</td>\n",
              "      <td>3560</td>\n",
              "      <td>137061</td>\n",
              "      <td>1780</td>\n",
              "      <td>50127</td>\n",
              "      <td>2730</td>\n",
              "      <td>76409</td>\n",
              "      <td>230</td>\n",
              "      <td>...</td>\n",
              "      <td>290</td>\n",
              "      <td>190</td>\n",
              "      <td>1750</td>\n",
              "      <td>7640</td>\n",
              "      <td>380</td>\n",
              "      <td>1404</td>\n",
              "      <td>460</td>\n",
              "      <td>1618</td>\n",
              "      <td>290</td>\n",
              "      <td>135</td>\n",
              "      <td>10220</td>\n",
              "      <td>324490</td>\n",
              "      <td>1080</td>\n",
              "      <td>2132</td>\n",
              "      <td>890</td>\n",
              "      <td>1795</td>\n",
              "      <td>650</td>\n",
              "      <td>794</td>\n",
              "      <td>510</td>\n",
              "      <td>460</td>\n",
              "      <td>190</td>\n",
              "      <td>163</td>\n",
              "      <td>8310</td>\n",
              "      <td>293644</td>\n",
              "      <td>8840</td>\n",
              "      <td>309636</td>\n",
              "      <td>670</td>\n",
              "      <td>2874</td>\n",
              "      <td>910</td>\n",
              "      <td>5759</td>\n",
              "      <td>2920</td>\n",
              "      <td>28795</td>\n",
              "      <td>6970</td>\n",
              "      <td>27771</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.489420</td>\n",
              "      <td>0.793928</td>\n",
              "      <td>0.364305</td>\n",
              "      <td>0.327507</td>\n",
              "      <td>0.099356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1817</th>\n",
              "      <td>34</td>\n",
              "      <td>NJ</td>\n",
              "      <td>27</td>\n",
              "      <td>Morris County</td>\n",
              "      <td>254130</td>\n",
              "      <td>120340</td>\n",
              "      <td>108490</td>\n",
              "      <td>20840</td>\n",
              "      <td>158580</td>\n",
              "      <td>486400</td>\n",
              "      <td>149700</td>\n",
              "      <td>4680</td>\n",
              "      <td>1950</td>\n",
              "      <td>2750</td>\n",
              "      <td>360</td>\n",
              "      <td>40</td>\n",
              "      <td>13230</td>\n",
              "      <td>66420</td>\n",
              "      <td>31615050</td>\n",
              "      <td>253560</td>\n",
              "      <td>32166997</td>\n",
              "      <td>209280</td>\n",
              "      <td>22492547</td>\n",
              "      <td>110440</td>\n",
              "      <td>349459</td>\n",
              "      <td>97250</td>\n",
              "      <td>938077</td>\n",
              "      <td>92470</td>\n",
              "      <td>717372</td>\n",
              "      <td>64070</td>\n",
              "      <td>168483</td>\n",
              "      <td>40370</td>\n",
              "      <td>1153220</td>\n",
              "      <td>83660</td>\n",
              "      <td>2057863</td>\n",
              "      <td>33910</td>\n",
              "      <td>755822</td>\n",
              "      <td>49720</td>\n",
              "      <td>1371852</td>\n",
              "      <td>500</td>\n",
              "      <td>...</td>\n",
              "      <td>5580</td>\n",
              "      <td>2726</td>\n",
              "      <td>36050</td>\n",
              "      <td>198922</td>\n",
              "      <td>5150</td>\n",
              "      <td>23866</td>\n",
              "      <td>6090</td>\n",
              "      <td>26077</td>\n",
              "      <td>7500</td>\n",
              "      <td>4165</td>\n",
              "      <td>238970</td>\n",
              "      <td>6606381</td>\n",
              "      <td>16920</td>\n",
              "      <td>31330</td>\n",
              "      <td>13140</td>\n",
              "      <td>25121</td>\n",
              "      <td>11090</td>\n",
              "      <td>13833</td>\n",
              "      <td>14380</td>\n",
              "      <td>13927</td>\n",
              "      <td>2580</td>\n",
              "      <td>2655</td>\n",
              "      <td>199890</td>\n",
              "      <td>5923380</td>\n",
              "      <td>209680</td>\n",
              "      <td>6254561</td>\n",
              "      <td>22560</td>\n",
              "      <td>61692</td>\n",
              "      <td>20660</td>\n",
              "      <td>72436</td>\n",
              "      <td>61980</td>\n",
              "      <td>546072</td>\n",
              "      <td>172120</td>\n",
              "      <td>685064</td>\n",
              "      <td>0.018416</td>\n",
              "      <td>0.624011</td>\n",
              "      <td>0.823516</td>\n",
              "      <td>0.363869</td>\n",
              "      <td>0.329202</td>\n",
              "      <td>0.066580</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",
              "      <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>100</th>\n",
              "      <td>4</td>\n",
              "      <td>AZ</td>\n",
              "      <td>1</td>\n",
              "      <td>Apache County</td>\n",
              "      <td>24580</td>\n",
              "      <td>9840</td>\n",
              "      <td>7090</td>\n",
              "      <td>7400</td>\n",
              "      <td>14170</td>\n",
              "      <td>58140</td>\n",
              "      <td>27220</td>\n",
              "      <td>1190</td>\n",
              "      <td>950</td>\n",
              "      <td>230</td>\n",
              "      <td>350</td>\n",
              "      <td>120</td>\n",
              "      <td>5870</td>\n",
              "      <td>5310</td>\n",
              "      <td>911110</td>\n",
              "      <td>24520</td>\n",
              "      <td>918371</td>\n",
              "      <td>21050</td>\n",
              "      <td>733026</td>\n",
              "      <td>1990</td>\n",
              "      <td>2401</td>\n",
              "      <td>880</td>\n",
              "      <td>3678</td>\n",
              "      <td>780</td>\n",
              "      <td>2751</td>\n",
              "      <td>1270</td>\n",
              "      <td>1553</td>\n",
              "      <td>3150</td>\n",
              "      <td>17794</td>\n",
              "      <td>910</td>\n",
              "      <td>7052</td>\n",
              "      <td>920</td>\n",
              "      <td>14224</td>\n",
              "      <td>4820</td>\n",
              "      <td>104837</td>\n",
              "      <td>1160</td>\n",
              "      <td>...</td>\n",
              "      <td>360</td>\n",
              "      <td>300</td>\n",
              "      <td>2280</td>\n",
              "      <td>3494</td>\n",
              "      <td>150</td>\n",
              "      <td>726</td>\n",
              "      <td>170</td>\n",
              "      <td>749</td>\n",
              "      <td>440</td>\n",
              "      <td>225</td>\n",
              "      <td>23610</td>\n",
              "      <td>137677</td>\n",
              "      <td>9950</td>\n",
              "      <td>27172</td>\n",
              "      <td>9210</td>\n",
              "      <td>24788</td>\n",
              "      <td>6920</td>\n",
              "      <td>9584</td>\n",
              "      <td>1250</td>\n",
              "      <td>961</td>\n",
              "      <td>90</td>\n",
              "      <td>81</td>\n",
              "      <td>12630</td>\n",
              "      <td>71154</td>\n",
              "      <td>14580</td>\n",
              "      <td>76195</td>\n",
              "      <td>50</td>\n",
              "      <td>66</td>\n",
              "      <td>60</td>\n",
              "      <td>147</td>\n",
              "      <td>2820</td>\n",
              "      <td>8037</td>\n",
              "      <td>21020</td>\n",
              "      <td>68871</td>\n",
              "      <td>0.048413</td>\n",
              "      <td>0.576485</td>\n",
              "      <td>0.856387</td>\n",
              "      <td>0.031733</td>\n",
              "      <td>0.037022</td>\n",
              "      <td>0.404801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>6</td>\n",
              "      <td>CA</td>\n",
              "      <td>25</td>\n",
              "      <td>Imperial County</td>\n",
              "      <td>78190</td>\n",
              "      <td>27940</td>\n",
              "      <td>30220</td>\n",
              "      <td>19270</td>\n",
              "      <td>52200</td>\n",
              "      <td>194960</td>\n",
              "      <td>89030</td>\n",
              "      <td>1190</td>\n",
              "      <td>1160</td>\n",
              "      <td>30</td>\n",
              "      <td>570</td>\n",
              "      <td>140</td>\n",
              "      <td>12810</td>\n",
              "      <td>12870</td>\n",
              "      <td>3115478</td>\n",
              "      <td>77830</td>\n",
              "      <td>3151402</td>\n",
              "      <td>69340</td>\n",
              "      <td>2420135</td>\n",
              "      <td>13660</td>\n",
              "      <td>10840</td>\n",
              "      <td>2770</td>\n",
              "      <td>15226</td>\n",
              "      <td>2440</td>\n",
              "      <td>11928</td>\n",
              "      <td>7140</td>\n",
              "      <td>9963</td>\n",
              "      <td>10760</td>\n",
              "      <td>125226</td>\n",
              "      <td>2600</td>\n",
              "      <td>37634</td>\n",
              "      <td>1950</td>\n",
              "      <td>27454</td>\n",
              "      <td>7560</td>\n",
              "      <td>189090</td>\n",
              "      <td>310</td>\n",
              "      <td>...</td>\n",
              "      <td>710</td>\n",
              "      <td>1147</td>\n",
              "      <td>9870</td>\n",
              "      <td>22267</td>\n",
              "      <td>3180</td>\n",
              "      <td>22812</td>\n",
              "      <td>3320</td>\n",
              "      <td>24107</td>\n",
              "      <td>7850</td>\n",
              "      <td>3782</td>\n",
              "      <td>74580</td>\n",
              "      <td>431541</td>\n",
              "      <td>32810</td>\n",
              "      <td>89368</td>\n",
              "      <td>29920</td>\n",
              "      <td>78510</td>\n",
              "      <td>23050</td>\n",
              "      <td>28807</td>\n",
              "      <td>4970</td>\n",
              "      <td>3772</td>\n",
              "      <td>1760</td>\n",
              "      <td>531</td>\n",
              "      <td>38350</td>\n",
              "      <td>262967</td>\n",
              "      <td>48530</td>\n",
              "      <td>293619</td>\n",
              "      <td>320</td>\n",
              "      <td>629</td>\n",
              "      <td>500</td>\n",
              "      <td>1694</td>\n",
              "      <td>12320</td>\n",
              "      <td>56405</td>\n",
              "      <td>63640</td>\n",
              "      <td>190644</td>\n",
              "      <td>0.015219</td>\n",
              "      <td>0.667605</td>\n",
              "      <td>0.886814</td>\n",
              "      <td>0.031206</td>\n",
              "      <td>0.033252</td>\n",
              "      <td>0.419619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1843</th>\n",
              "      <td>35</td>\n",
              "      <td>NM</td>\n",
              "      <td>31</td>\n",
              "      <td>McKinley County</td>\n",
              "      <td>32200</td>\n",
              "      <td>15290</td>\n",
              "      <td>7830</td>\n",
              "      <td>8830</td>\n",
              "      <td>19410</td>\n",
              "      <td>72840</td>\n",
              "      <td>33820</td>\n",
              "      <td>1160</td>\n",
              "      <td>1140</td>\n",
              "      <td>30</td>\n",
              "      <td>570</td>\n",
              "      <td>50</td>\n",
              "      <td>5250</td>\n",
              "      <td>5540</td>\n",
              "      <td>1052301</td>\n",
              "      <td>32040</td>\n",
              "      <td>1061322</td>\n",
              "      <td>27890</td>\n",
              "      <td>841594</td>\n",
              "      <td>2360</td>\n",
              "      <td>3853</td>\n",
              "      <td>1090</td>\n",
              "      <td>4694</td>\n",
              "      <td>970</td>\n",
              "      <td>3617</td>\n",
              "      <td>1350</td>\n",
              "      <td>1706</td>\n",
              "      <td>4050</td>\n",
              "      <td>31781</td>\n",
              "      <td>930</td>\n",
              "      <td>9130</td>\n",
              "      <td>840</td>\n",
              "      <td>10496</td>\n",
              "      <td>4420</td>\n",
              "      <td>89427</td>\n",
              "      <td>770</td>\n",
              "      <td>...</td>\n",
              "      <td>230</td>\n",
              "      <td>114</td>\n",
              "      <td>3250</td>\n",
              "      <td>5416</td>\n",
              "      <td>220</td>\n",
              "      <td>698</td>\n",
              "      <td>250</td>\n",
              "      <td>749</td>\n",
              "      <td>460</td>\n",
              "      <td>226</td>\n",
              "      <td>31190</td>\n",
              "      <td>171365</td>\n",
              "      <td>15100</td>\n",
              "      <td>40941</td>\n",
              "      <td>14040</td>\n",
              "      <td>37188</td>\n",
              "      <td>9930</td>\n",
              "      <td>12652</td>\n",
              "      <td>1290</td>\n",
              "      <td>951</td>\n",
              "      <td>80</td>\n",
              "      <td>60</td>\n",
              "      <td>14940</td>\n",
              "      <td>82081</td>\n",
              "      <td>17900</td>\n",
              "      <td>89054</td>\n",
              "      <td>80</td>\n",
              "      <td>70</td>\n",
              "      <td>140</td>\n",
              "      <td>367</td>\n",
              "      <td>2810</td>\n",
              "      <td>9519</td>\n",
              "      <td>28500</td>\n",
              "      <td>89464</td>\n",
              "      <td>0.036025</td>\n",
              "      <td>0.602795</td>\n",
              "      <td>0.866149</td>\n",
              "      <td>0.030124</td>\n",
              "      <td>0.028882</td>\n",
              "      <td>0.468944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2779</th>\n",
              "      <td>48</td>\n",
              "      <td>TX</td>\n",
              "      <td>427</td>\n",
              "      <td>Starr County</td>\n",
              "      <td>24430</td>\n",
              "      <td>8990</td>\n",
              "      <td>8400</td>\n",
              "      <td>6980</td>\n",
              "      <td>16260</td>\n",
              "      <td>58850</td>\n",
              "      <td>27180</td>\n",
              "      <td>290</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>110</td>\n",
              "      <td>8390</td>\n",
              "      <td>2840</td>\n",
              "      <td>778432</td>\n",
              "      <td>24390</td>\n",
              "      <td>785941</td>\n",
              "      <td>20930</td>\n",
              "      <td>663958</td>\n",
              "      <td>2570</td>\n",
              "      <td>1965</td>\n",
              "      <td>450</td>\n",
              "      <td>1463</td>\n",
              "      <td>380</td>\n",
              "      <td>921</td>\n",
              "      <td>200</td>\n",
              "      <td>146</td>\n",
              "      <td>6810</td>\n",
              "      <td>45689</td>\n",
              "      <td>380</td>\n",
              "      <td>3196</td>\n",
              "      <td>280</td>\n",
              "      <td>3956</td>\n",
              "      <td>1780</td>\n",
              "      <td>35331</td>\n",
              "      <td>1100</td>\n",
              "      <td>...</td>\n",
              "      <td>520</td>\n",
              "      <td>358</td>\n",
              "      <td>5300</td>\n",
              "      <td>8843</td>\n",
              "      <td>1360</td>\n",
              "      <td>5554</td>\n",
              "      <td>1420</td>\n",
              "      <td>5907</td>\n",
              "      <td>4750</td>\n",
              "      <td>2741</td>\n",
              "      <td>23840</td>\n",
              "      <td>133157</td>\n",
              "      <td>12150</td>\n",
              "      <td>40174</td>\n",
              "      <td>11050</td>\n",
              "      <td>32851</td>\n",
              "      <td>9250</td>\n",
              "      <td>12763</td>\n",
              "      <td>2080</td>\n",
              "      <td>1770</td>\n",
              "      <td>630</td>\n",
              "      <td>234</td>\n",
              "      <td>10340</td>\n",
              "      <td>41500</td>\n",
              "      <td>16720</td>\n",
              "      <td>53724</td>\n",
              "      <td>30</td>\n",
              "      <td>47</td>\n",
              "      <td>40</td>\n",
              "      <td>83</td>\n",
              "      <td>1970</td>\n",
              "      <td>6699</td>\n",
              "      <td>22160</td>\n",
              "      <td>85737</td>\n",
              "      <td>0.011871</td>\n",
              "      <td>0.665575</td>\n",
              "      <td>0.856734</td>\n",
              "      <td>0.015555</td>\n",
              "      <td>0.015555</td>\n",
              "      <td>0.497339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2727</th>\n",
              "      <td>48</td>\n",
              "      <td>TX</td>\n",
              "      <td>323</td>\n",
              "      <td>Maverick County</td>\n",
              "      <td>25190</td>\n",
              "      <td>8910</td>\n",
              "      <td>9700</td>\n",
              "      <td>6390</td>\n",
              "      <td>13410</td>\n",
              "      <td>61390</td>\n",
              "      <td>27950</td>\n",
              "      <td>1370</td>\n",
              "      <td>1370</td>\n",
              "      <td>0</td>\n",
              "      <td>630</td>\n",
              "      <td>130</td>\n",
              "      <td>6580</td>\n",
              "      <td>3520</td>\n",
              "      <td>890987</td>\n",
              "      <td>25140</td>\n",
              "      <td>898568</td>\n",
              "      <td>22510</td>\n",
              "      <td>746533</td>\n",
              "      <td>1710</td>\n",
              "      <td>1879</td>\n",
              "      <td>440</td>\n",
              "      <td>1614</td>\n",
              "      <td>360</td>\n",
              "      <td>1041</td>\n",
              "      <td>70</td>\n",
              "      <td>42</td>\n",
              "      <td>4590</td>\n",
              "      <td>45546</td>\n",
              "      <td>440</td>\n",
              "      <td>4578</td>\n",
              "      <td>350</td>\n",
              "      <td>4770</td>\n",
              "      <td>2110</td>\n",
              "      <td>37666</td>\n",
              "      <td>200</td>\n",
              "      <td>...</td>\n",
              "      <td>240</td>\n",
              "      <td>151</td>\n",
              "      <td>3950</td>\n",
              "      <td>7195</td>\n",
              "      <td>1060</td>\n",
              "      <td>4316</td>\n",
              "      <td>1110</td>\n",
              "      <td>4645</td>\n",
              "      <td>3920</td>\n",
              "      <td>2121</td>\n",
              "      <td>24510</td>\n",
              "      <td>139443</td>\n",
              "      <td>10530</td>\n",
              "      <td>32373</td>\n",
              "      <td>9470</td>\n",
              "      <td>27283</td>\n",
              "      <td>8030</td>\n",
              "      <td>11202</td>\n",
              "      <td>1690</td>\n",
              "      <td>1303</td>\n",
              "      <td>440</td>\n",
              "      <td>164</td>\n",
              "      <td>12160</td>\n",
              "      <td>56312</td>\n",
              "      <td>16530</td>\n",
              "      <td>66496</td>\n",
              "      <td>60</td>\n",
              "      <td>81</td>\n",
              "      <td>80</td>\n",
              "      <td>177</td>\n",
              "      <td>2490</td>\n",
              "      <td>7946</td>\n",
              "      <td>22340</td>\n",
              "      <td>77610</td>\n",
              "      <td>0.054387</td>\n",
              "      <td>0.532354</td>\n",
              "      <td>0.893609</td>\n",
              "      <td>0.014291</td>\n",
              "      <td>0.017467</td>\n",
              "      <td>0.418023</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1702 rows × 137 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      State FIPS code [1] State  ...  cap_gain_percentage eic_percentage\n",
              "1813                   34    NJ  ...             0.347261       0.053234\n",
              "389                    12    FL  ...             0.382658       0.098709\n",
              "212                     6    CA  ...             0.405189       0.067884\n",
              "2902                   51    VA  ...             0.327507       0.099356\n",
              "1817                   34    NJ  ...             0.329202       0.066580\n",
              "...                   ...   ...  ...                  ...            ...\n",
              "100                     4    AZ  ...             0.037022       0.404801\n",
              "204                     6    CA  ...             0.033252       0.419619\n",
              "1843                   35    NM  ...             0.028882       0.468944\n",
              "2779                   48    TX  ...             0.015555       0.497339\n",
              "2727                   48    TX  ...             0.017467       0.418023\n",
              "\n",
              "[1702 rows x 137 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-X6s_g_zUPH"
      },
      "source": [
        "## Merge"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "ye-0ZvO7xa_W",
        "outputId": "240eb849-b3aa-4eb6-a548-335016dd617a"
      },
      "source": [
        "# merging demographic with audit rates\n",
        "dfmm = df.merge(dfi, how = 'left', left_on='fips', right_on='CensusId').dropna()\n",
        "dfmm.head()\n",
        "\n",
        "# dfmm.to_csv('audit-demographics-merged.csv', index = False)\n",
        "# to save to csv"
      ],
      "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>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "      <th>st_code</th>\n",
              "      <th>cty_code</th>\n",
              "      <th>CensusId</th>\n",
              "      <th>State</th>\n",
              "      <th>County</th>\n",
              "      <th>TotalPop</th>\n",
              "      <th>Men</th>\n",
              "      <th>Women</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Citizen</th>\n",
              "      <th>Income</th>\n",
              "      <th>IncomeErr</th>\n",
              "      <th>IncomePerCap</th>\n",
              "      <th>IncomePerCapErr</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>ChildPoverty</th>\n",
              "      <th>Professional</th>\n",
              "      <th>Service</th>\n",
              "      <th>Office</th>\n",
              "      <th>Construction</th>\n",
              "      <th>Production</th>\n",
              "      <th>Drive</th>\n",
              "      <th>Carpool</th>\n",
              "      <th>Transit</th>\n",
              "      <th>Walk</th>\n",
              "      <th>OtherTransp</th>\n",
              "      <th>WorkAtHome</th>\n",
              "      <th>MeanCommute</th>\n",
              "      <th>Employed</th>\n",
              "      <th>PrivateWork</th>\n",
              "      <th>PublicWork</th>\n",
              "      <th>SelfEmployed</th>\n",
              "      <th>FamilyWork</th>\n",
              "      <th>Unemployment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga</td>\n",
              "      <td>55221.0</td>\n",
              "      <td>26745.0</td>\n",
              "      <td>28476.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40725.0</td>\n",
              "      <td>51281.0</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24974.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>18.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>87.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>26.5</td>\n",
              "      <td>23986.0</td>\n",
              "      <td>73.6</td>\n",
              "      <td>20.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin</td>\n",
              "      <td>195121.0</td>\n",
              "      <td>95314.0</td>\n",
              "      <td>99807.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147695.0</td>\n",
              "      <td>50254.0</td>\n",
              "      <td>1263.0</td>\n",
              "      <td>27317.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>27.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>84.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>26.4</td>\n",
              "      <td>85953.0</td>\n",
              "      <td>81.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour</td>\n",
              "      <td>26932.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>12435.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20714.0</td>\n",
              "      <td>32964.0</td>\n",
              "      <td>2973.0</td>\n",
              "      <td>16824.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>26.7</td>\n",
              "      <td>45.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>23.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>83.8</td>\n",
              "      <td>10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>24.1</td>\n",
              "      <td>8597.0</td>\n",
              "      <td>71.8</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>31840.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb</td>\n",
              "      <td>22604.0</td>\n",
              "      <td>12073.0</td>\n",
              "      <td>10531.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17495.0</td>\n",
              "      <td>38678.0</td>\n",
              "      <td>3995.0</td>\n",
              "      <td>18431.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>16.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>21.5</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>83.2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>28.8</td>\n",
              "      <td>8294.0</td>\n",
              "      <td>76.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>87970.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount</td>\n",
              "      <td>57710.0</td>\n",
              "      <td>28512.0</td>\n",
              "      <td>29198.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42345.0</td>\n",
              "      <td>45813.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>20532.0</td>\n",
              "      <td>708.0</td>\n",
              "      <td>16.7</td>\n",
              "      <td>27.2</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>19.9</td>\n",
              "      <td>84.9</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>22189.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fips                     name  ... FamilyWork  Unemployment\n",
              "0  1001  Autauga County, Alabama  ...        0.0           7.6\n",
              "1  1003  Baldwin County, Alabama  ...        0.4           7.5\n",
              "2  1005  Barbour County, Alabama  ...        0.1          17.6\n",
              "3  1007     Bibb County, Alabama  ...        0.4           8.3\n",
              "4  1009   Blount County, Alabama  ...        0.4           7.7\n",
              "\n",
              "[5 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "aomKeFLOQuJj",
        "outputId": "ff0bfb48-e60e-4fc1-810e-0b17f4176fb4"
      },
      "source": [
        "# merging tax return with audit rates\n",
        "dftm = dft.merge(df, how = 'left',left_on = ['State FIPS code [1]', 'County FIPS code [1]'], right_on= ['st_code', 'cty_code'])\n",
        "dftm.head()\n",
        "\n",
        "# dftm.to_csv('tax-return-merged.csv', index= False)   # saving to csv"
      ],
      "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>State FIPS code [1]</th>\n",
              "      <th>State</th>\n",
              "      <th>County FIPS code [1]</th>\n",
              "      <th>County name</th>\n",
              "      <th>Number of returns</th>\n",
              "      <th>Number of single returns</th>\n",
              "      <th>Number of joint returns</th>\n",
              "      <th>Number of head of household returns</th>\n",
              "      <th>Number with paid preparer's signature</th>\n",
              "      <th>Number of exemptions</th>\n",
              "      <th>Number of dependents</th>\n",
              "      <th>Number of volunteer prepared returns [2]</th>\n",
              "      <th>Number of volunteer income tax assistance (VITA) prepared returns</th>\n",
              "      <th>Number of tax counseling for the elderly (TCE) prepared returns</th>\n",
              "      <th>Number of volunteer prepared \\nreturns with\\nEarned Income Credit</th>\n",
              "      <th>Number of refund anticipation loan returns</th>\n",
              "      <th>Number of refund anticipation check returns</th>\n",
              "      <th>Number of elderly returns [3]</th>\n",
              "      <th>Adjusted gross income (AGI) [4]</th>\n",
              "      <th>Total income num</th>\n",
              "      <th>Total income amt</th>\n",
              "      <th>Salaries and wages in AGI num</th>\n",
              "      <th>Salaries and wages in AGI amt</th>\n",
              "      <th>Taxable interest num</th>\n",
              "      <th>Taxable interest amt</th>\n",
              "      <th>Ordinary dividends num</th>\n",
              "      <th>Ordinary dividends amt</th>\n",
              "      <th>Qualified dividends [5] num</th>\n",
              "      <th>Qualified dividends [5] amt</th>\n",
              "      <th>State and local income tax refunds\\n num</th>\n",
              "      <th>State and local income tax refunds\\n amt</th>\n",
              "      <th>Business or profession\\nnet income (less loss) num</th>\n",
              "      <th>Business or profession\\nnet income (less loss) amt</th>\n",
              "      <th>Net capital gain (less loss) in AGI num</th>\n",
              "      <th>Net capital gain (less loss) in AGI amt</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions num</th>\n",
              "      <th>Taxable individual retirement\\narrangement distributions amt</th>\n",
              "      <th>Pensions and annuities in AGI num</th>\n",
              "      <th>Pensions and annuities in AGI amt</th>\n",
              "      <th>Number of \\nfarm returns num</th>\n",
              "      <th>...</th>\n",
              "      <th>Health care individual responsibility payment amt</th>\n",
              "      <th>Total tax payments num</th>\n",
              "      <th>Total tax payments amt</th>\n",
              "      <th>Earned income credit [7] num</th>\n",
              "      <th>Earned income credit [7] amt</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] num</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] amt</th>\n",
              "      <th>Additional child tax credit num</th>\n",
              "      <th>Additional child tax credit amt</th>\n",
              "      <th>Refundable education credit num</th>\n",
              "      <th>Refundable education credit amt</th>\n",
              "      <th>Net premium tax credit num</th>\n",
              "      <th>Net premium tax credit amt</th>\n",
              "      <th>Income tax [9] num</th>\n",
              "      <th>Income tax [9] amt</th>\n",
              "      <th>Total tax liability [10] num</th>\n",
              "      <th>Total tax liability [10] amt</th>\n",
              "      <th>Additional Medicare tax num</th>\n",
              "      <th>Additional Medicare tax amt</th>\n",
              "      <th>Net investment income tax num</th>\n",
              "      <th>Net investment income tax amt</th>\n",
              "      <th>Tax due at time of filing [11] num</th>\n",
              "      <th>Tax due at time of filing [11] amt</th>\n",
              "      <th>Overpayments refunded [12] num</th>\n",
              "      <th>Overpayments refunded [12] amt</th>\n",
              "      <th>volunteer_preparer_percentage</th>\n",
              "      <th>paid_preparer_percentage</th>\n",
              "      <th>wage_income_percentage</th>\n",
              "      <th>dividends_percentage</th>\n",
              "      <th>cap_gain_percentage</th>\n",
              "      <th>eic_percentage</th>\n",
              "      <th>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "      <th>st_code</th>\n",
              "      <th>cty_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>US</td>\n",
              "      <td>0</td>\n",
              "      <td>Total [13]</td>\n",
              "      <td>148965910</td>\n",
              "      <td>70209250</td>\n",
              "      <td>53923170</td>\n",
              "      <td>21890640</td>\n",
              "      <td>80046180</td>\n",
              "      <td>289089330</td>\n",
              "      <td>95564540</td>\n",
              "      <td>3549610</td>\n",
              "      <td>1855800</td>\n",
              "      <td>1693980</td>\n",
              "      <td>526520</td>\n",
              "      <td>457940</td>\n",
              "      <td>21365980</td>\n",
              "      <td>34715360</td>\n",
              "      <td>10074264203</td>\n",
              "      <td>148463360</td>\n",
              "      <td>10223004480</td>\n",
              "      <td>123272570</td>\n",
              "      <td>7008525735</td>\n",
              "      <td>42221420</td>\n",
              "      <td>89362337</td>\n",
              "      <td>27438590</td>\n",
              "      <td>246436572</td>\n",
              "      <td>25603510</td>\n",
              "      <td>191255367</td>\n",
              "      <td>20154420</td>\n",
              "      <td>30623936</td>\n",
              "      <td>24704120</td>\n",
              "      <td>335562990</td>\n",
              "      <td>24187930</td>\n",
              "      <td>667720309</td>\n",
              "      <td>13973180</td>\n",
              "      <td>249199152</td>\n",
              "      <td>27891270</td>\n",
              "      <td>680105287</td>\n",
              "      <td>1853030</td>\n",
              "      <td>...</td>\n",
              "      <td>3064758</td>\n",
              "      <td>140778140</td>\n",
              "      <td>1766105893</td>\n",
              "      <td>27972300</td>\n",
              "      <td>68008777</td>\n",
              "      <td>23959360</td>\n",
              "      <td>58451691</td>\n",
              "      <td>19400100</td>\n",
              "      <td>26045489</td>\n",
              "      <td>9635000</td>\n",
              "      <td>8567079</td>\n",
              "      <td>2403200</td>\n",
              "      <td>1596320</td>\n",
              "      <td>102005440</td>\n",
              "      <td>1432106443</td>\n",
              "      <td>112137600</td>\n",
              "      <td>1522112987</td>\n",
              "      <td>3475390</td>\n",
              "      <td>7607693</td>\n",
              "      <td>3781620</td>\n",
              "      <td>21033338</td>\n",
              "      <td>28883270</td>\n",
              "      <td>159391261</td>\n",
              "      <td>112176640</td>\n",
              "      <td>350257176</td>\n",
              "      <td>0.023828</td>\n",
              "      <td>0.537346</td>\n",
              "      <td>0.827522</td>\n",
              "      <td>0.171875</td>\n",
              "      <td>0.162372</td>\n",
              "      <td>0.187777</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>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>2053620</td>\n",
              "      <td>834300</td>\n",
              "      <td>763210</td>\n",
              "      <td>415730</td>\n",
              "      <td>1172420</td>\n",
              "      <td>4166050</td>\n",
              "      <td>1467040</td>\n",
              "      <td>45570</td>\n",
              "      <td>28830</td>\n",
              "      <td>16760</td>\n",
              "      <td>7720</td>\n",
              "      <td>15400</td>\n",
              "      <td>457080</td>\n",
              "      <td>477210</td>\n",
              "      <td>111789331</td>\n",
              "      <td>2046690</td>\n",
              "      <td>113236900</td>\n",
              "      <td>1710150</td>\n",
              "      <td>80323944</td>\n",
              "      <td>502130</td>\n",
              "      <td>763242</td>\n",
              "      <td>268410</td>\n",
              "      <td>1724024</td>\n",
              "      <td>246330</td>\n",
              "      <td>1351071</td>\n",
              "      <td>318950</td>\n",
              "      <td>340193</td>\n",
              "      <td>345310</td>\n",
              "      <td>3128573</td>\n",
              "      <td>228700</td>\n",
              "      <td>4203762</td>\n",
              "      <td>171570</td>\n",
              "      <td>2797936</td>\n",
              "      <td>421830</td>\n",
              "      <td>10091228</td>\n",
              "      <td>45200</td>\n",
              "      <td>...</td>\n",
              "      <td>29736</td>\n",
              "      <td>1960840</td>\n",
              "      <td>17604731</td>\n",
              "      <td>521420</td>\n",
              "      <td>1444531</td>\n",
              "      <td>467990</td>\n",
              "      <td>1279982</td>\n",
              "      <td>360840</td>\n",
              "      <td>481943</td>\n",
              "      <td>173090</td>\n",
              "      <td>163859</td>\n",
              "      <td>39440</td>\n",
              "      <td>21778</td>\n",
              "      <td>1303380</td>\n",
              "      <td>13179616</td>\n",
              "      <td>1458720</td>\n",
              "      <td>14075850</td>\n",
              "      <td>24800</td>\n",
              "      <td>43315</td>\n",
              "      <td>30150</td>\n",
              "      <td>111100</td>\n",
              "      <td>361940</td>\n",
              "      <td>1717754</td>\n",
              "      <td>1607070</td>\n",
              "      <td>4845454</td>\n",
              "      <td>0.022190</td>\n",
              "      <td>0.570904</td>\n",
              "      <td>0.832749</td>\n",
              "      <td>0.119949</td>\n",
              "      <td>0.111364</td>\n",
              "      <td>0.253903</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>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>1</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>23960</td>\n",
              "      <td>9130</td>\n",
              "      <td>10020</td>\n",
              "      <td>4290</td>\n",
              "      <td>10960</td>\n",
              "      <td>50220</td>\n",
              "      <td>17800</td>\n",
              "      <td>510</td>\n",
              "      <td>130</td>\n",
              "      <td>370</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>5150</td>\n",
              "      <td>5160</td>\n",
              "      <td>1291011</td>\n",
              "      <td>23880</td>\n",
              "      <td>1304385</td>\n",
              "      <td>20360</td>\n",
              "      <td>979232</td>\n",
              "      <td>7210</td>\n",
              "      <td>5850</td>\n",
              "      <td>3030</td>\n",
              "      <td>9391</td>\n",
              "      <td>2740</td>\n",
              "      <td>7109</td>\n",
              "      <td>3770</td>\n",
              "      <td>3235</td>\n",
              "      <td>3490</td>\n",
              "      <td>28135</td>\n",
              "      <td>2520</td>\n",
              "      <td>17134</td>\n",
              "      <td>1930</td>\n",
              "      <td>25374</td>\n",
              "      <td>5520</td>\n",
              "      <td>147466</td>\n",
              "      <td>380</td>\n",
              "      <td>...</td>\n",
              "      <td>323</td>\n",
              "      <td>23120</td>\n",
              "      <td>175903</td>\n",
              "      <td>5240</td>\n",
              "      <td>14098</td>\n",
              "      <td>4650</td>\n",
              "      <td>12521</td>\n",
              "      <td>3630</td>\n",
              "      <td>4716</td>\n",
              "      <td>1870</td>\n",
              "      <td>1743</td>\n",
              "      <td>390</td>\n",
              "      <td>195</td>\n",
              "      <td>16400</td>\n",
              "      <td>126990</td>\n",
              "      <td>17800</td>\n",
              "      <td>135207</td>\n",
              "      <td>120</td>\n",
              "      <td>115</td>\n",
              "      <td>170</td>\n",
              "      <td>387</td>\n",
              "      <td>4310</td>\n",
              "      <td>14233</td>\n",
              "      <td>18910</td>\n",
              "      <td>53521</td>\n",
              "      <td>0.021285</td>\n",
              "      <td>0.457429</td>\n",
              "      <td>0.849750</td>\n",
              "      <td>0.114357</td>\n",
              "      <td>0.105175</td>\n",
              "      <td>0.218698</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>3</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>93140</td>\n",
              "      <td>38310</td>\n",
              "      <td>39800</td>\n",
              "      <td>12900</td>\n",
              "      <td>46970</td>\n",
              "      <td>183680</td>\n",
              "      <td>56550</td>\n",
              "      <td>1700</td>\n",
              "      <td>350</td>\n",
              "      <td>1350</td>\n",
              "      <td>90</td>\n",
              "      <td>300</td>\n",
              "      <td>15100</td>\n",
              "      <td>27460</td>\n",
              "      <td>5585227</td>\n",
              "      <td>92800</td>\n",
              "      <td>5671834</td>\n",
              "      <td>72260</td>\n",
              "      <td>3646995</td>\n",
              "      <td>25980</td>\n",
              "      <td>45974</td>\n",
              "      <td>17040</td>\n",
              "      <td>105811</td>\n",
              "      <td>15600</td>\n",
              "      <td>79788</td>\n",
              "      <td>13570</td>\n",
              "      <td>15614</td>\n",
              "      <td>17480</td>\n",
              "      <td>208501</td>\n",
              "      <td>15770</td>\n",
              "      <td>243919</td>\n",
              "      <td>11320</td>\n",
              "      <td>211185</td>\n",
              "      <td>21820</td>\n",
              "      <td>563755</td>\n",
              "      <td>1140</td>\n",
              "      <td>...</td>\n",
              "      <td>1644</td>\n",
              "      <td>86980</td>\n",
              "      <td>848360</td>\n",
              "      <td>17840</td>\n",
              "      <td>45725</td>\n",
              "      <td>15280</td>\n",
              "      <td>39092</td>\n",
              "      <td>12080</td>\n",
              "      <td>15810</td>\n",
              "      <td>5630</td>\n",
              "      <td>5139</td>\n",
              "      <td>2230</td>\n",
              "      <td>1481</td>\n",
              "      <td>62980</td>\n",
              "      <td>711964</td>\n",
              "      <td>69840</td>\n",
              "      <td>763095</td>\n",
              "      <td>1450</td>\n",
              "      <td>2138</td>\n",
              "      <td>1840</td>\n",
              "      <td>5128</td>\n",
              "      <td>21340</td>\n",
              "      <td>119349</td>\n",
              "      <td>65910</td>\n",
              "      <td>184209</td>\n",
              "      <td>0.018252</td>\n",
              "      <td>0.504295</td>\n",
              "      <td>0.775821</td>\n",
              "      <td>0.167490</td>\n",
              "      <td>0.169315</td>\n",
              "      <td>0.191540</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>AL</td>\n",
              "      <td>5</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>9590</td>\n",
              "      <td>3560</td>\n",
              "      <td>3020</td>\n",
              "      <td>2860</td>\n",
              "      <td>6590</td>\n",
              "      <td>19710</td>\n",
              "      <td>7540</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240</td>\n",
              "      <td>2860</td>\n",
              "      <td>2410</td>\n",
              "      <td>376661</td>\n",
              "      <td>9540</td>\n",
              "      <td>381264</td>\n",
              "      <td>7860</td>\n",
              "      <td>271680</td>\n",
              "      <td>2110</td>\n",
              "      <td>2548</td>\n",
              "      <td>1040</td>\n",
              "      <td>5639</td>\n",
              "      <td>950</td>\n",
              "      <td>4741</td>\n",
              "      <td>910</td>\n",
              "      <td>805</td>\n",
              "      <td>1650</td>\n",
              "      <td>9281</td>\n",
              "      <td>890</td>\n",
              "      <td>8629</td>\n",
              "      <td>720</td>\n",
              "      <td>10959</td>\n",
              "      <td>1790</td>\n",
              "      <td>38421</td>\n",
              "      <td>430</td>\n",
              "      <td>...</td>\n",
              "      <td>139</td>\n",
              "      <td>9100</td>\n",
              "      <td>54654</td>\n",
              "      <td>3360</td>\n",
              "      <td>9888</td>\n",
              "      <td>3080</td>\n",
              "      <td>8715</td>\n",
              "      <td>2430</td>\n",
              "      <td>3265</td>\n",
              "      <td>720</td>\n",
              "      <td>690</td>\n",
              "      <td>170</td>\n",
              "      <td>72</td>\n",
              "      <td>5110</td>\n",
              "      <td>32266</td>\n",
              "      <td>6130</td>\n",
              "      <td>35494</td>\n",
              "      <td>20</td>\n",
              "      <td>32</td>\n",
              "      <td>50</td>\n",
              "      <td>181</td>\n",
              "      <td>1340</td>\n",
              "      <td>4631</td>\n",
              "      <td>7830</td>\n",
              "      <td>24181</td>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.687174</td>\n",
              "      <td>0.819604</td>\n",
              "      <td>0.099062</td>\n",
              "      <td>0.092805</td>\n",
              "      <td>0.350365</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 146 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   State FIPS code [1] State  ...  st_code cty_code\n",
              "0                    0    US  ...      NaN      NaN\n",
              "1                    1    AL  ...      NaN      NaN\n",
              "2                    1    AL  ...      1.0      1.0\n",
              "3                    1    AL  ...      1.0      3.0\n",
              "4                    1    AL  ...      1.0      5.0\n",
              "\n",
              "[5 rows x 146 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "aSIvYS-Ej24l",
        "outputId": "2b6f4186-2607-4f93-d056-e741637f4a07"
      },
      "source": [
        "# merging demographic, tax return, and audit rates\n",
        "# dataframe with all variables of interest\n",
        "\n",
        "dfmt = df.merge(dfi, how = 'left', left_on='fips', right_on='CensusId')\n",
        "dfma = dfmt.merge(dft, how = 'left', left_on =['st_code', 'cty_code'], right_on = ['State FIPS code [1]', 'County FIPS code [1]'] )\n",
        "dfma.head()\n",
        "\n",
        "# dfma.to_csv('../Datasets/merged-all.csv', index=False)"
      ],
      "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>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "      <th>st_code</th>\n",
              "      <th>cty_code</th>\n",
              "      <th>CensusId</th>\n",
              "      <th>State_x</th>\n",
              "      <th>County</th>\n",
              "      <th>TotalPop</th>\n",
              "      <th>Men</th>\n",
              "      <th>Women</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Citizen</th>\n",
              "      <th>Income</th>\n",
              "      <th>IncomeErr</th>\n",
              "      <th>IncomePerCap</th>\n",
              "      <th>IncomePerCapErr</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>ChildPoverty</th>\n",
              "      <th>Professional</th>\n",
              "      <th>Service</th>\n",
              "      <th>Office</th>\n",
              "      <th>Construction</th>\n",
              "      <th>Production</th>\n",
              "      <th>Drive</th>\n",
              "      <th>Carpool</th>\n",
              "      <th>Transit</th>\n",
              "      <th>Walk</th>\n",
              "      <th>OtherTransp</th>\n",
              "      <th>WorkAtHome</th>\n",
              "      <th>MeanCommute</th>\n",
              "      <th>...</th>\n",
              "      <th>Residential energy tax credit num</th>\n",
              "      <th>Residential energy tax credit amt</th>\n",
              "      <th>Self-employment tax num</th>\n",
              "      <th>Self-employment tax amt</th>\n",
              "      <th>Total premium tax credit num</th>\n",
              "      <th>Total premium tax credit amt</th>\n",
              "      <th>Advance premium tax credit num</th>\n",
              "      <th>Advance premium tax credit amt</th>\n",
              "      <th>Health care individual responsibility payment num</th>\n",
              "      <th>Health care individual responsibility payment amt</th>\n",
              "      <th>Total tax payments num</th>\n",
              "      <th>Total tax payments amt</th>\n",
              "      <th>Earned income credit [7] num</th>\n",
              "      <th>Earned income credit [7] amt</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] num</th>\n",
              "      <th>Excess earned income credit \\n(refundable) [8] amt</th>\n",
              "      <th>Additional child tax credit num</th>\n",
              "      <th>Additional child tax credit amt</th>\n",
              "      <th>Refundable education credit num</th>\n",
              "      <th>Refundable education credit amt</th>\n",
              "      <th>Net premium tax credit num</th>\n",
              "      <th>Net premium tax credit amt</th>\n",
              "      <th>Income tax [9] num</th>\n",
              "      <th>Income tax [9] amt</th>\n",
              "      <th>Total tax liability [10] num</th>\n",
              "      <th>Total tax liability [10] amt</th>\n",
              "      <th>Additional Medicare tax num</th>\n",
              "      <th>Additional Medicare tax amt</th>\n",
              "      <th>Net investment income tax num</th>\n",
              "      <th>Net investment income tax amt</th>\n",
              "      <th>Tax due at time of filing [11] num</th>\n",
              "      <th>Tax due at time of filing [11] amt</th>\n",
              "      <th>Overpayments refunded [12] num</th>\n",
              "      <th>Overpayments refunded [12] amt</th>\n",
              "      <th>volunteer_preparer_percentage</th>\n",
              "      <th>paid_preparer_percentage</th>\n",
              "      <th>wage_income_percentage</th>\n",
              "      <th>dividends_percentage</th>\n",
              "      <th>cap_gain_percentage</th>\n",
              "      <th>eic_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga</td>\n",
              "      <td>55221.0</td>\n",
              "      <td>26745.0</td>\n",
              "      <td>28476.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40725.0</td>\n",
              "      <td>51281.0</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24974.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>18.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>87.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>26.5</td>\n",
              "      <td>...</td>\n",
              "      <td>410.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>2550.0</td>\n",
              "      <td>6138.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>2376.0</td>\n",
              "      <td>840.0</td>\n",
              "      <td>2604.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>323.0</td>\n",
              "      <td>23120.0</td>\n",
              "      <td>175903.0</td>\n",
              "      <td>5240.0</td>\n",
              "      <td>14098.0</td>\n",
              "      <td>4650.0</td>\n",
              "      <td>12521.0</td>\n",
              "      <td>3630.0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>1870.0</td>\n",
              "      <td>1743.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>16400.0</td>\n",
              "      <td>126990.0</td>\n",
              "      <td>17800.0</td>\n",
              "      <td>135207.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>387.0</td>\n",
              "      <td>4310.0</td>\n",
              "      <td>14233.0</td>\n",
              "      <td>18910.0</td>\n",
              "      <td>53521.0</td>\n",
              "      <td>0.021285</td>\n",
              "      <td>0.457429</td>\n",
              "      <td>0.849750</td>\n",
              "      <td>0.114357</td>\n",
              "      <td>0.105175</td>\n",
              "      <td>0.218698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin</td>\n",
              "      <td>195121.0</td>\n",
              "      <td>95314.0</td>\n",
              "      <td>99807.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147695.0</td>\n",
              "      <td>50254.0</td>\n",
              "      <td>1263.0</td>\n",
              "      <td>27317.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>27.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>84.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>26.4</td>\n",
              "      <td>...</td>\n",
              "      <td>1410.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>13680.0</td>\n",
              "      <td>37978.0</td>\n",
              "      <td>4420.0</td>\n",
              "      <td>15978.0</td>\n",
              "      <td>4920.0</td>\n",
              "      <td>17275.0</td>\n",
              "      <td>3530.0</td>\n",
              "      <td>1644.0</td>\n",
              "      <td>86980.0</td>\n",
              "      <td>848360.0</td>\n",
              "      <td>17840.0</td>\n",
              "      <td>45725.0</td>\n",
              "      <td>15280.0</td>\n",
              "      <td>39092.0</td>\n",
              "      <td>12080.0</td>\n",
              "      <td>15810.0</td>\n",
              "      <td>5630.0</td>\n",
              "      <td>5139.0</td>\n",
              "      <td>2230.0</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>62980.0</td>\n",
              "      <td>711964.0</td>\n",
              "      <td>69840.0</td>\n",
              "      <td>763095.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>2138.0</td>\n",
              "      <td>1840.0</td>\n",
              "      <td>5128.0</td>\n",
              "      <td>21340.0</td>\n",
              "      <td>119349.0</td>\n",
              "      <td>65910.0</td>\n",
              "      <td>184209.0</td>\n",
              "      <td>0.018252</td>\n",
              "      <td>0.504295</td>\n",
              "      <td>0.775821</td>\n",
              "      <td>0.167490</td>\n",
              "      <td>0.169315</td>\n",
              "      <td>0.191540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour</td>\n",
              "      <td>26932.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>12435.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20714.0</td>\n",
              "      <td>32964.0</td>\n",
              "      <td>2973.0</td>\n",
              "      <td>16824.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>26.7</td>\n",
              "      <td>45.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>23.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>83.8</td>\n",
              "      <td>10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>24.1</td>\n",
              "      <td>...</td>\n",
              "      <td>130.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>2406.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>1252.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>1358.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>9100.0</td>\n",
              "      <td>54654.0</td>\n",
              "      <td>3360.0</td>\n",
              "      <td>9888.0</td>\n",
              "      <td>3080.0</td>\n",
              "      <td>8715.0</td>\n",
              "      <td>2430.0</td>\n",
              "      <td>3265.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>690.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>5110.0</td>\n",
              "      <td>32266.0</td>\n",
              "      <td>6130.0</td>\n",
              "      <td>35494.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>1340.0</td>\n",
              "      <td>4631.0</td>\n",
              "      <td>7830.0</td>\n",
              "      <td>24181.0</td>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.687174</td>\n",
              "      <td>0.819604</td>\n",
              "      <td>0.099062</td>\n",
              "      <td>0.092805</td>\n",
              "      <td>0.350365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>31840.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb</td>\n",
              "      <td>22604.0</td>\n",
              "      <td>12073.0</td>\n",
              "      <td>10531.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17495.0</td>\n",
              "      <td>38678.0</td>\n",
              "      <td>3995.0</td>\n",
              "      <td>18431.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>16.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>21.5</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>83.2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>28.8</td>\n",
              "      <td>...</td>\n",
              "      <td>130.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>984.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>7640.0</td>\n",
              "      <td>50886.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>6367.0</td>\n",
              "      <td>2060.0</td>\n",
              "      <td>5736.0</td>\n",
              "      <td>1540.0</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>396.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>38383.0</td>\n",
              "      <td>5600.0</td>\n",
              "      <td>40622.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1180.0</td>\n",
              "      <td>6770.0</td>\n",
              "      <td>6560.0</td>\n",
              "      <td>19818.0</td>\n",
              "      <td>0.006281</td>\n",
              "      <td>0.586683</td>\n",
              "      <td>0.866834</td>\n",
              "      <td>0.055276</td>\n",
              "      <td>0.055276</td>\n",
              "      <td>0.285176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>87970.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount</td>\n",
              "      <td>57710.0</td>\n",
              "      <td>28512.0</td>\n",
              "      <td>29198.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42345.0</td>\n",
              "      <td>45813.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>20532.0</td>\n",
              "      <td>708.0</td>\n",
              "      <td>16.7</td>\n",
              "      <td>27.2</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>19.9</td>\n",
              "      <td>84.9</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>...</td>\n",
              "      <td>440.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>3110.0</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>940.0</td>\n",
              "      <td>4017.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>4248.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>21000.0</td>\n",
              "      <td>144382.0</td>\n",
              "      <td>4940.0</td>\n",
              "      <td>12659.0</td>\n",
              "      <td>4220.0</td>\n",
              "      <td>10645.0</td>\n",
              "      <td>3610.0</td>\n",
              "      <td>5017.0</td>\n",
              "      <td>1270.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>278.0</td>\n",
              "      <td>14410.0</td>\n",
              "      <td>96621.0</td>\n",
              "      <td>16260.0</td>\n",
              "      <td>105092.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>3610.0</td>\n",
              "      <td>12250.0</td>\n",
              "      <td>17590.0</td>\n",
              "      <td>49419.0</td>\n",
              "      <td>0.006812</td>\n",
              "      <td>0.638510</td>\n",
              "      <td>0.825613</td>\n",
              "      <td>0.084015</td>\n",
              "      <td>0.075386</td>\n",
              "      <td>0.224342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 183 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   fips                     name  ... cap_gain_percentage  eic_percentage\n",
              "0  1001  Autauga County, Alabama  ...            0.105175        0.218698\n",
              "1  1003  Baldwin County, Alabama  ...            0.169315        0.191540\n",
              "2  1005  Barbour County, Alabama  ...            0.092805        0.350365\n",
              "3  1007     Bibb County, Alabama  ...            0.055276        0.285176\n",
              "4  1009   Blount County, Alabama  ...            0.075386        0.224342\n",
              "\n",
              "[5 rows x 183 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtnsK37B002J"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlzsYM9QTk-u",
        "outputId": "b6a7454d-45ce-4cfb-ca8d-874778b9daf4"
      },
      "source": [
        "df.audit_rate.mean()\n",
        "# mean audit rate "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.730411024322167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NnKVxTTTnfj",
        "outputId": "9211e1fc-ad5d-488d-fd0b-db55f463c6af"
      },
      "source": [
        "df.audit_rate.std()\n",
        "# standard deviation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9803595789832831"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEpDnLV1DNYi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o81a_L-6RVoS"
      },
      "source": [
        "## Predicting Audit Rates on Demographics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "AUPYcMpC2zM2",
        "outputId": "ed3b9d88-eb64-4944-a6dc-ec340fc553c8"
      },
      "source": [
        "dfmm['Percentage_Male'] = dfmm['Men'] / (dfmm['Men'] + dfmm['Women'])\n",
        "dfmm['Percentage_Citizen'] = dfmm['Citizen'] / dfmm['TotalPop'] \n",
        "dfmm.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",
              "    }\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>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "      <th>st_code</th>\n",
              "      <th>cty_code</th>\n",
              "      <th>CensusId</th>\n",
              "      <th>State</th>\n",
              "      <th>County</th>\n",
              "      <th>TotalPop</th>\n",
              "      <th>Men</th>\n",
              "      <th>Women</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Citizen</th>\n",
              "      <th>Income</th>\n",
              "      <th>IncomeErr</th>\n",
              "      <th>IncomePerCap</th>\n",
              "      <th>IncomePerCapErr</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>ChildPoverty</th>\n",
              "      <th>Professional</th>\n",
              "      <th>Service</th>\n",
              "      <th>Office</th>\n",
              "      <th>Construction</th>\n",
              "      <th>Production</th>\n",
              "      <th>Drive</th>\n",
              "      <th>Carpool</th>\n",
              "      <th>Transit</th>\n",
              "      <th>Walk</th>\n",
              "      <th>OtherTransp</th>\n",
              "      <th>WorkAtHome</th>\n",
              "      <th>MeanCommute</th>\n",
              "      <th>Employed</th>\n",
              "      <th>PrivateWork</th>\n",
              "      <th>PublicWork</th>\n",
              "      <th>SelfEmployed</th>\n",
              "      <th>FamilyWork</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Citizen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga</td>\n",
              "      <td>55221.0</td>\n",
              "      <td>26745.0</td>\n",
              "      <td>28476.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40725.0</td>\n",
              "      <td>51281.0</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24974.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>18.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>87.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>26.5</td>\n",
              "      <td>23986.0</td>\n",
              "      <td>73.6</td>\n",
              "      <td>20.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.6</td>\n",
              "      <td>0.484327</td>\n",
              "      <td>0.737491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin</td>\n",
              "      <td>195121.0</td>\n",
              "      <td>95314.0</td>\n",
              "      <td>99807.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147695.0</td>\n",
              "      <td>50254.0</td>\n",
              "      <td>1263.0</td>\n",
              "      <td>27317.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>27.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>84.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>26.4</td>\n",
              "      <td>85953.0</td>\n",
              "      <td>81.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.488487</td>\n",
              "      <td>0.756941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour</td>\n",
              "      <td>26932.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>12435.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20714.0</td>\n",
              "      <td>32964.0</td>\n",
              "      <td>2973.0</td>\n",
              "      <td>16824.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>26.7</td>\n",
              "      <td>45.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>23.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>83.8</td>\n",
              "      <td>10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>24.1</td>\n",
              "      <td>8597.0</td>\n",
              "      <td>71.8</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0.538282</td>\n",
              "      <td>0.769122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>31840.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb</td>\n",
              "      <td>22604.0</td>\n",
              "      <td>12073.0</td>\n",
              "      <td>10531.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17495.0</td>\n",
              "      <td>38678.0</td>\n",
              "      <td>3995.0</td>\n",
              "      <td>18431.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>16.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>21.5</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>83.2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>28.8</td>\n",
              "      <td>8294.0</td>\n",
              "      <td>76.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.3</td>\n",
              "      <td>0.534109</td>\n",
              "      <td>0.773978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>87970.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount</td>\n",
              "      <td>57710.0</td>\n",
              "      <td>28512.0</td>\n",
              "      <td>29198.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42345.0</td>\n",
              "      <td>45813.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>20532.0</td>\n",
              "      <td>708.0</td>\n",
              "      <td>16.7</td>\n",
              "      <td>27.2</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>19.9</td>\n",
              "      <td>84.9</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>22189.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.7</td>\n",
              "      <td>0.494056</td>\n",
              "      <td>0.733755</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fips                     name  ... Percentage_Male  Percentage_Citizen\n",
              "0  1001  Autauga County, Alabama  ...        0.484327            0.737491\n",
              "1  1003  Baldwin County, Alabama  ...        0.488487            0.756941\n",
              "2  1005  Barbour County, Alabama  ...        0.538282            0.769122\n",
              "3  1007     Bibb County, Alabama  ...        0.534109            0.773978\n",
              "4  1009   Blount County, Alabama  ...        0.494056            0.733755\n",
              "\n",
              "[5 rows x 48 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuW88hq11zzx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rgoyo_zwxa5T",
        "outputId": "4b24057e-a9c3-4210-d30b-c7afd8b8e960"
      },
      "source": [
        "# Predicting Solely on Demographic Variables\n",
        "\n",
        "Xd = dfmm[[ 'Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific',  'Income']]\n",
        "\n",
        "Xd.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",
              "    }\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>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51281.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50254.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32964.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38678.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45813.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hispanic  White  Black  Native  Asian  Pacific   Income\n",
              "0       2.6   75.8   18.5     0.4    1.0      0.0  51281.0\n",
              "1       4.5   83.1    9.5     0.6    0.7      0.0  50254.0\n",
              "2       4.6   46.2   46.7     0.2    0.4      0.0  32964.0\n",
              "3       2.2   74.5   21.4     0.4    0.1      0.0  38678.0\n",
              "4       8.6   87.9    1.5     0.3    0.1      0.0  45813.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nELN9TVqxa2F",
        "outputId": "a6c0b995-216b-441a-c259-0a0709b86d51"
      },
      "source": [
        "yd = dfmm['audit_rate']\n",
        "yd.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7.575917\n",
              "1    7.815388\n",
              "2    9.127789\n",
              "3    8.165829\n",
              "4    7.650335\n",
              "Name: audit_rate, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUv9IN8KeBDp",
        "outputId": "360c3867-41fd-41ee-a74a-7b09c7f2bf2c"
      },
      "source": [
        "lmd = linear_model.LinearRegression()\n",
        "lmd.fit(Xd, yd)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "UXGzM6t4e_o8",
        "outputId": "1ea25412-adbf-455c-d156-5b1c30e4b27e"
      },
      "source": [
        "cframe = pd.DataFrame({'var': Xd.columns, 'coef': lmd.coef_})\n",
        "cframe"
      ],
      "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>var</th>\n",
              "      <th>coef</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hispanic</td>\n",
              "      <td>-0.006848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>White</td>\n",
              "      <td>-0.021091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Black</td>\n",
              "      <td>0.007953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Native</td>\n",
              "      <td>-0.004530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Asian</td>\n",
              "      <td>0.003075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Pacific</td>\n",
              "      <td>-0.095208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Income</td>\n",
              "      <td>-0.000032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        var      coef\n",
              "0  Hispanic -0.006848\n",
              "1     White -0.021091\n",
              "2     Black  0.007953\n",
              "3    Native -0.004530\n",
              "4     Asian  0.003075\n",
              "5   Pacific -0.095208\n",
              "6    Income -0.000032"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-_Fg9U5e0RK",
        "outputId": "849b55ff-c4f7-4759-c8cb-accf984c5f19"
      },
      "source": [
        "lmd.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-6.84770888e-03, -2.10911589e-02,  7.95333875e-03, -4.53002936e-03,\n",
              "        3.07467431e-03, -9.52078645e-02, -3.21104070e-05])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Agen7hnheA6T",
        "outputId": "57b86b7e-2b9d-4b53-a277-a1335629302c"
      },
      "source": [
        "scoresd = cross_val_score(lmd, Xd, yd, scoring = 'neg_root_mean_squared_error')\n",
        "scoresd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.77926079, -0.467718  , -0.87574846, -0.62537795, -0.8608938 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nq4qnlTIetan",
        "outputId": "50269a45-f2bf-4693-fe29-391d48478a26"
      },
      "source": [
        "scoresd.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.7217997986192111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZdh7T3q693f"
      },
      "source": [
        "We find that we are able to predict audit rates by county based solely on demographic characteristics like race and median income "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6G42nVezRbQJ"
      },
      "source": [
        "## Predicting Audit Rates on Tax Return Characteristics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cY-2WZaS50LT"
      },
      "source": [
        "### LR - all tax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rlqv86vV693h"
      },
      "source": [
        "We don't know how IRS calculates DIF, but we have the data used.\n",
        "\n",
        "We use this data to make a rough approximation for the score and see how well we can predict outcome."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOuA4JlUR_Ri"
      },
      "source": [
        "dftm.dropna(inplace= True)\n",
        "\n",
        "X = dftm.iloc[:, 4:131]\n",
        "y = dftm['audit_rate']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0jvqz_Ar60t",
        "outputId": "68b5669e-8ddf-4a5b-ce92-84d030eb4098"
      },
      "source": [
        "lm2 = linear_model.LinearRegression()\n",
        "lm2.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTAdrCIAr6xv",
        "outputId": "dff79b01-f8f0-46ce-80b0-f3bd631400da"
      },
      "source": [
        "scores2 = cross_val_score(lm2, X,y, cv = 10,  scoring= 'neg_root_mean_squared_error')\n",
        "scores2, scores2.mean()\n",
        "\n",
        "# Linear model is poor predictor with this dataset\n",
        "# Try non-linear model (decision tree next)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-2.05961464, -1.61901112, -0.55417922, -0.78217482, -0.88361248,\n",
              "        -1.70482932, -1.00815485, -0.91414916, -1.72274361, -0.67256148]),\n",
              " -1.192103071676139)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmWfRC7P_Ew9"
      },
      "source": [
        "### Decision Tree - all tax"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tl7pBzui_HJM",
        "outputId": "b0077199-c20a-4ad5-b7b5-c6dcb9ea2772"
      },
      "source": [
        "dt = DecisionTreeRegressor()\n",
        "dt.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=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, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elD507Sm_HG4",
        "outputId": "dec32620-ff61-4702-c4fb-9d1fd48c3679"
      },
      "source": [
        "scores3 = cross_val_score(dt, X,y, cv = 10,  scoring= 'neg_root_mean_squared_error')\n",
        "scores3, scores3.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.67507195, -0.61097602, -0.49241095, -0.52718882, -0.54254389,\n",
              "        -0.90712275, -0.50643461, -0.582797  , -0.81509094, -0.47588106]),\n",
              " -0.6135518006793913)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "dIF_YpmW_HAI",
        "outputId": "73eb89ba-8dec-4cf0-e1fa-c716b6c655ed"
      },
      "source": [
        "\n",
        "\n",
        "plt.figure(figsize = [10,10])\n",
        "tree.plot_tree(dt, max_depth=2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRcCIzh8693j"
      },
      "source": [
        "Decision tree model proves to be more effective than linerar model for this dataset. Predicts audit rates well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8bfF3_0FVq8"
      },
      "source": [
        "### Random Forest - all tax"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNar_rpwJytl",
        "outputId": "6fa82bfd-74e3-46f7-a6f8-b0ea43dd5213"
      },
      "source": [
        "rf = RandomForestRegressor()\n",
        "rf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGoSOcHOJytp",
        "outputId": "1b06fcea-6631-4f7e-95ab-382eab082c10"
      },
      "source": [
        "scores4 = cross_val_score(dt, X,y, cv = 10,  scoring= 'neg_root_mean_squared_error')\n",
        "scores4, scores4.mean()\n",
        "\n",
        "# RF regressor doesn't improve over simple decision tree"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.72503826, -0.58721583, -0.5070276 , -0.52788078, -0.51865097,\n",
              "        -0.92052774, -0.53240852, -0.5507789 , -0.99192245, -0.45302326]),\n",
              " -0.6314474311506348)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdKHBUUUUWdx"
      },
      "source": [
        "### Selected ratios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M71x0vj7693j"
      },
      "source": [
        "Next we see how well we can predict just on selected ratios, and see how features are correlated with outcomes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BORSXftZhv4",
        "outputId": "04eec4a3-d621-410b-e02b-9838b1a2fd81"
      },
      "source": [
        "dftm.dropna(inplace= True)\n",
        "dftm.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3138, 146)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "IZNs-xTEUYgW",
        "outputId": "8bfbf3b8-7f29-47eb-ce5b-ecc780da0071"
      },
      "source": [
        "X2 = dftm.iloc[: , 131:137]\n",
        "X2.drop(columns=['wage_income_percentage'], inplace=True)\n",
        "X2.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",
              "    }\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>volunteer_preparer_percentage</th>\n",
              "      <th>paid_preparer_percentage</th>\n",
              "      <th>dividends_percentage</th>\n",
              "      <th>cap_gain_percentage</th>\n",
              "      <th>eic_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.021285</td>\n",
              "      <td>0.457429</td>\n",
              "      <td>0.114357</td>\n",
              "      <td>0.105175</td>\n",
              "      <td>0.218698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.018252</td>\n",
              "      <td>0.504295</td>\n",
              "      <td>0.167490</td>\n",
              "      <td>0.169315</td>\n",
              "      <td>0.191540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.687174</td>\n",
              "      <td>0.099062</td>\n",
              "      <td>0.092805</td>\n",
              "      <td>0.350365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006281</td>\n",
              "      <td>0.586683</td>\n",
              "      <td>0.055276</td>\n",
              "      <td>0.055276</td>\n",
              "      <td>0.285176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.006812</td>\n",
              "      <td>0.638510</td>\n",
              "      <td>0.084015</td>\n",
              "      <td>0.075386</td>\n",
              "      <td>0.224342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   volunteer_preparer_percentage  ...  eic_percentage\n",
              "2                       0.021285  ...        0.218698\n",
              "3                       0.018252  ...        0.191540\n",
              "4                       0.003128  ...        0.350365\n",
              "5                       0.006281  ...        0.285176\n",
              "6                       0.006812  ...        0.224342\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVMIcxixUYd4"
      },
      "source": [
        "lm = linear_model.LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXeIEIoKUYa8",
        "outputId": "65580120-d290-46a1-a052-f5e69c8f1064"
      },
      "source": [
        "y = dftm['audit_rate']\n",
        "lm.fit(X2, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLXDS0NPaOQH",
        "outputId": "423d7db8-cbbf-419d-c810-5ea78b6c5b82"
      },
      "source": [
        "scores2 = cross_val_score(lm, X2, y, cv = 5, scoring= 'neg_root_mean_squared_error')\n",
        "scores2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.67255931, -0.32461396, -0.87430199, -0.52307603, -0.75999   ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3xQUWpWafFX",
        "outputId": "a757012e-d0c3-4b4b-90a2-695259124c0b"
      },
      "source": [
        "scores2.mean()\n",
        "# using just selected ratios is predictive, but significantly less than model that uses all characteristics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6309082584632681"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "G4-VO7_9a6BV",
        "outputId": "6a571a1c-f433-434b-ecb5-b1df0d8c7680"
      },
      "source": [
        "# Heatmap to show relationships between predictors and audit rates\n",
        "X22 = X2.copy()\n",
        "X22['audit_rate'] = y\n",
        "\n",
        "sns.heatmap(X22.corr(), cmap = 'coolwarm')\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 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5sFK4l_gY9l"
      },
      "source": [
        "# EDA "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUM2dI7lkkke"
      },
      "source": [
        "## Unemployment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "fBvKSfy8ii0d",
        "outputId": "8fa01a34-be90-4b98-a3a0-3d7633e863f2"
      },
      "source": [
        "sns.lmplot(x ='Unemployment', y = 'audit_rate', data = dfmm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7efc36ca21d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bt4WWeJLT7dk"
      },
      "source": [
        "Counties with higher unemployment rates have higher audit rates on average."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEDvV5Yc9AHx"
      },
      "source": [
        "## **Racial Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "mPEFvw_4693l",
        "outputId": "3f1a3ea0-d810-4d9a-9e6f-70e7f80b743e"
      },
      "source": [
        "dfag = pd.read_csv('https://raw.githubusercontent.com/Roozbeh-jafari/840.Data-Ethics-US-Taxpayers-Algorithms/main/DataSets/audit-demographics-merged.csv?token=ARFHYYEHJGDLYYHUMV6Y46LAWACOE')\n",
        "# dfag = pd.read_csv('/content/audit-demographics-merged.csv')\n",
        "dfag.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",
              "    }\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>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>Number_of_returns</th>\n",
              "      <th>estimated_exams</th>\n",
              "      <th>audit_rate</th>\n",
              "      <th>audit_rate_trunk</th>\n",
              "      <th>CensusId</th>\n",
              "      <th>State</th>\n",
              "      <th>County</th>\n",
              "      <th>TotalPop</th>\n",
              "      <th>Men</th>\n",
              "      <th>Women</th>\n",
              "      <th>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>Citizen</th>\n",
              "      <th>Income</th>\n",
              "      <th>IncomeErr</th>\n",
              "      <th>IncomePerCap</th>\n",
              "      <th>IncomePerCapErr</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>ChildPoverty</th>\n",
              "      <th>Professional</th>\n",
              "      <th>Service</th>\n",
              "      <th>Office</th>\n",
              "      <th>Construction</th>\n",
              "      <th>Production</th>\n",
              "      <th>Drive</th>\n",
              "      <th>Carpool</th>\n",
              "      <th>Transit</th>\n",
              "      <th>Walk</th>\n",
              "      <th>OtherTransp</th>\n",
              "      <th>WorkAtHome</th>\n",
              "      <th>MeanCommute</th>\n",
              "      <th>Employed</th>\n",
              "      <th>PrivateWork</th>\n",
              "      <th>PublicWork</th>\n",
              "      <th>SelfEmployed</th>\n",
              "      <th>FamilyWork</th>\n",
              "      <th>Unemployment</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Citizen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>95170.0</td>\n",
              "      <td>721.0</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>7.575917</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga</td>\n",
              "      <td>55221.0</td>\n",
              "      <td>26745.0</td>\n",
              "      <td>28476.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40725.0</td>\n",
              "      <td>51281.0</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24974.0</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>18.6</td>\n",
              "      <td>33.2</td>\n",
              "      <td>17.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>87.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>26.5</td>\n",
              "      <td>23986.0</td>\n",
              "      <td>73.6</td>\n",
              "      <td>20.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.6</td>\n",
              "      <td>0.484327</td>\n",
              "      <td>0.737491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>362490.0</td>\n",
              "      <td>2833.0</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>7.815388</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin</td>\n",
              "      <td>195121.0</td>\n",
              "      <td>95314.0</td>\n",
              "      <td>99807.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147695.0</td>\n",
              "      <td>50254.0</td>\n",
              "      <td>1263.0</td>\n",
              "      <td>27317.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>17.7</td>\n",
              "      <td>27.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.2</td>\n",
              "      <td>84.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>26.4</td>\n",
              "      <td>85953.0</td>\n",
              "      <td>81.5</td>\n",
              "      <td>12.3</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.488487</td>\n",
              "      <td>0.756941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>39440.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>9.127789</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour</td>\n",
              "      <td>26932.0</td>\n",
              "      <td>14497.0</td>\n",
              "      <td>12435.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20714.0</td>\n",
              "      <td>32964.0</td>\n",
              "      <td>2973.0</td>\n",
              "      <td>16824.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>26.7</td>\n",
              "      <td>45.3</td>\n",
              "      <td>26.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>23.1</td>\n",
              "      <td>10.8</td>\n",
              "      <td>23.1</td>\n",
              "      <td>83.8</td>\n",
              "      <td>10.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>24.1</td>\n",
              "      <td>8597.0</td>\n",
              "      <td>71.8</td>\n",
              "      <td>20.8</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0.538282</td>\n",
              "      <td>0.769122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>31840.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>8.165829</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb</td>\n",
              "      <td>22604.0</td>\n",
              "      <td>12073.0</td>\n",
              "      <td>10531.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17495.0</td>\n",
              "      <td>38678.0</td>\n",
              "      <td>3995.0</td>\n",
              "      <td>18431.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>16.8</td>\n",
              "      <td>27.9</td>\n",
              "      <td>21.5</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>83.2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>28.8</td>\n",
              "      <td>8294.0</td>\n",
              "      <td>76.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.3</td>\n",
              "      <td>0.534109</td>\n",
              "      <td>0.773978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>87970.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>7.650335</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount</td>\n",
              "      <td>57710.0</td>\n",
              "      <td>28512.0</td>\n",
              "      <td>29198.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42345.0</td>\n",
              "      <td>45813.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>20532.0</td>\n",
              "      <td>708.0</td>\n",
              "      <td>16.7</td>\n",
              "      <td>27.2</td>\n",
              "      <td>28.5</td>\n",
              "      <td>14.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>19.9</td>\n",
              "      <td>84.9</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>34.9</td>\n",
              "      <td>22189.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>13.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>7.7</td>\n",
              "      <td>0.494056</td>\n",
              "      <td>0.733755</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fips                     name  ... Percentage_Male  Percentage_Citizen\n",
              "0  1001  Autauga County, Alabama  ...        0.484327            0.737491\n",
              "1  1003  Baldwin County, Alabama  ...        0.488487            0.756941\n",
              "2  1005  Barbour County, Alabama  ...        0.538282            0.769122\n",
              "3  1007     Bibb County, Alabama  ...        0.534109            0.773978\n",
              "4  1009   Blount County, Alabama  ...        0.494056            0.733755\n",
              "\n",
              "[5 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhRkJI2e9Iy7",
        "outputId": "c0fbc519-f0e5-40e7-b07a-76e4d0ad774c"
      },
      "source": [
        "agg_race = dfag[['Hispanic', 'White','Black','Native', 'Asian', 'Pacific']]\n",
        "agg_race.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3138 entries, 0 to 3137\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Hispanic  3138 non-null   float64\n",
            " 1   White     3138 non-null   float64\n",
            " 2   Black     3138 non-null   float64\n",
            " 3   Native    3138 non-null   float64\n",
            " 4   Asian     3138 non-null   float64\n",
            " 5   Pacific   3138 non-null   float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 147.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pfL8hgZ9LyM",
        "outputId": "51da93e8-899e-49b2-f443-fb05b7a5d915"
      },
      "source": [
        "round(agg_race.mean(),2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hispanic     8.82\n",
              "White       77.36\n",
              "Black        8.89\n",
              "Native       1.71\n",
              "Asian        1.25\n",
              "Pacific      0.07\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "UKxVZRRM9OeP",
        "outputId": "31a2ce13-0653-41a4-cb8b-88825b0599cb"
      },
      "source": [
        "plt.bar( agg_race.columns, agg_race.mean())\n",
        "plt.title('Race Percentage across th US')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "PaMouobK9S2z",
        "outputId": "e70eebef-e2c3-4b9d-cf25-474f05f85d8f"
      },
      "source": [
        "agg_race['balck_white_ratio'] = round(agg_race.Black / agg_race.White * 100, 2)\n",
        "agg_race['audit_rate'] = dfag.audit_rate\n",
        "agg_race.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: 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",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: 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>Hispanic</th>\n",
              "      <th>White</th>\n",
              "      <th>Black</th>\n",
              "      <th>Native</th>\n",
              "      <th>Asian</th>\n",
              "      <th>Pacific</th>\n",
              "      <th>balck_white_ratio</th>\n",
              "      <th>audit_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.6</td>\n",
              "      <td>75.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.41</td>\n",
              "      <td>7.575917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.5</td>\n",
              "      <td>83.1</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.43</td>\n",
              "      <td>7.815388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.6</td>\n",
              "      <td>46.2</td>\n",
              "      <td>46.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.08</td>\n",
              "      <td>9.127789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.2</td>\n",
              "      <td>74.5</td>\n",
              "      <td>21.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.72</td>\n",
              "      <td>8.165829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.6</td>\n",
              "      <td>87.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.71</td>\n",
              "      <td>7.650335</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hispanic  White  Black  ...  Pacific  balck_white_ratio  audit_rate\n",
              "0       2.6   75.8   18.5  ...      0.0              24.41    7.575917\n",
              "1       4.5   83.1    9.5  ...      0.0              11.43    7.815388\n",
              "2       4.6   46.2   46.7  ...      0.0             101.08    9.127789\n",
              "3       2.2   74.5   21.4  ...      0.0              28.72    8.165829\n",
              "4       8.6   87.9    1.5  ...      0.0               1.71    7.650335\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "xJvCVGPP9_Sx",
        "outputId": "2d559340-31ee-487e-fb45-e9232e14b310"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "plt.plot(agg_race.balck_white_ratio, label='balck_white_ratio')\n",
        "plt.plot(agg_race.audit_rate * 100, label='Audit_rate')\n",
        "\n",
        "plt.xlabel('Counties', fontdict={'fontsize': 20})\n",
        "plt.ylabel('Ratio', fontdict={'fontsize': 20})\n",
        "\n",
        "plt.title('Black White Race Ratio Vs Audit Ratio', fontdict={'fontsize': 30})\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5pAyqSd7LRhQ"
      },
      "source": [
        "The graph shows a correlation between the black-white ratio and audit rate. The higher the black-white percentage, the higher the audit rate is."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4P8iW32-Chs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hbOQYpVlcru"
      },
      "source": [
        "## Gender Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71wmeSMUmZZS"
      },
      "source": [
        "dfag['MalePct'] = dfag['Men'] / dfag['TotalPop']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "qEsk8MIwl2BL",
        "outputId": "f1e03923-c5f6-4827-8171-1102ecd2bf56"
      },
      "source": [
        "x = dfag['MalePct']\n",
        "y = dfag['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Male Percentage')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Male Percentage V.S. Audit Rate')\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSAeFTVXHXdL"
      },
      "source": [
        "The scatter plot presents the relationship between audit rate and male percentage. In most of the counties, the male portion mostly range between 45 and 55 percent. The regression line indicate a weak linear relationship between these two variables. Gender doesn't significantly influence the audit rate. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbQq3NJkoeK4"
      },
      "source": [
        "Top 30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nzJkrvb6ogOb",
        "outputId": "934beaa2-ade0-4a4c-f5fa-587fc5a20ef2"
      },
      "source": [
        "df_top30 = dfag.sort_values('audit_rate', ascending=False)[['audit_rate', 'MalePct']].head(30)\n",
        "df_top30.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",
              "    }\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>audit_rate</th>\n",
              "      <th>MalePct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1425</th>\n",
              "      <td>11.790079</td>\n",
              "      <td>0.476180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1470</th>\n",
              "      <td>11.512648</td>\n",
              "      <td>0.469218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>11.444653</td>\n",
              "      <td>0.569524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1450</th>\n",
              "      <td>11.233596</td>\n",
              "      <td>0.479763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461</th>\n",
              "      <td>11.228534</td>\n",
              "      <td>0.445161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      audit_rate   MalePct\n",
              "1425   11.790079  0.476180\n",
              "1470   11.512648  0.469218\n",
              "1128   11.444653  0.569524\n",
              "1450   11.233596  0.479763\n",
              "1461   11.228534  0.445161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "2La0EePwpSRd",
        "outputId": "d7ca8ce9-9e07-4569-f955-f2eb8e223907"
      },
      "source": [
        "x = df_top30['MalePct']\n",
        "y = df_top30['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Male Percentage')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Top 30 Audited Counties - Male Percentage V.S. Audit Rate', fontsize=16)\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UUFHWWtpZTL"
      },
      "source": [
        "Bottom 30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Vni2yc5OpZTM",
        "outputId": "65d88d28-3069-4924-a231-27382abfd106"
      },
      "source": [
        "df_bottom30 = dfag.sort_values('audit_rate', ascending=True)[['audit_rate', 'MalePct']].head(30)\n",
        "df_bottom30.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",
              "    }\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>audit_rate</th>\n",
              "      <th>MalePct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2650</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.522124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2654</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.509363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2536</th>\n",
              "      <td>0.961538</td>\n",
              "      <td>0.513475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1689</th>\n",
              "      <td>1.503759</td>\n",
              "      <td>0.509753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1655</th>\n",
              "      <td>1.652893</td>\n",
              "      <td>0.486585</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      audit_rate   MalePct\n",
              "2650    0.000000  0.522124\n",
              "2654    0.000000  0.509363\n",
              "2536    0.961538  0.513475\n",
              "1689    1.503759  0.509753\n",
              "1655    1.652893  0.486585"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "UapGu1MqIGRB",
        "outputId": "24efa7cc-342a-4bc8-90cf-c97153041155"
      },
      "source": [
        "x = df_bottom30['MalePct']\n",
        "y = df_bottom30['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Male Percentage')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Bottom 30 Audited Counties - Male Percentage V.S. Audit Rate', fontsize=16)\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6NzmpDzHwoJ"
      },
      "source": [
        "If we look into the counties with the highest and lowest audit rates, respectively, gender doesn't demonstrate a strong correlation with audit rate as well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko1CLmuTsxc-"
      },
      "source": [
        "## Professional Rate Analysis\n",
        "Professional: percent employed in management, business, science, and arts\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "ovW49PDhsxdC",
        "outputId": "b4f67fe0-fc1f-4411-ce22-00ffee506e11"
      },
      "source": [
        "x = dfag['Professional']\n",
        "y = dfag['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Professional')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Professional Percentage V.S. Audit Rate', fontsize=16)\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHFCN9i3HOxC"
      },
      "source": [
        "The plot shows that professional percentage have a correlation with audit rate. According to the definition, the professional percentage indicates the percentage employed in the fields of specialty that requires advanced education or training.\n",
        "\n",
        "Counties with higher professional percentages, on average, have lower audit rates. We may infer that the higher the professional or educated level is, the counties are less likely to be audited."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udq40vJQsxdD"
      },
      "source": [
        "Top 30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "dN5ieiDNsxdD",
        "outputId": "7b08099b-ef35-4c30-eec6-4c01e1a63b23"
      },
      "source": [
        "df_top30 = dfag.sort_values('audit_rate', ascending=False)[['audit_rate', 'Professional']].head(30)\n",
        "df_top30.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",
              "    }\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>audit_rate</th>\n",
              "      <th>Professional</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1425</th>\n",
              "      <td>11.790079</td>\n",
              "      <td>24.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1470</th>\n",
              "      <td>11.512648</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>11.444653</td>\n",
              "      <td>27.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1450</th>\n",
              "      <td>11.233596</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461</th>\n",
              "      <td>11.228534</td>\n",
              "      <td>36.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      audit_rate  Professional\n",
              "1425   11.790079          24.7\n",
              "1470   11.512648          23.9\n",
              "1128   11.444653          27.9\n",
              "1450   11.233596          22.9\n",
              "1461   11.228534          36.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "k7y1uxvfsxdE",
        "outputId": "723c9e20-3369-4b60-9820-3d3ff52969b2"
      },
      "source": [
        "x = df_top30['Professional']\n",
        "y = df_top30['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Professional Percentage')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Top 30 Audited Counties - Professional Percentage V.S. Audit Rate', fontsize=16)\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fzVZNgJsxdE"
      },
      "source": [
        "Bottom 30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Twk-4QQRsxdF",
        "outputId": "6e991013-3b58-47c1-a283-2f590f033c97"
      },
      "source": [
        "df_bottom30 = dfag.sort_values('audit_rate', ascending=True)[['audit_rate', 'Professional']].head(30)\n",
        "df_bottom30.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",
              "    }\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>audit_rate</th>\n",
              "      <th>Professional</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2650</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2654</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>34.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2536</th>\n",
              "      <td>0.961538</td>\n",
              "      <td>52.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1689</th>\n",
              "      <td>1.503759</td>\n",
              "      <td>40.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1655</th>\n",
              "      <td>1.652893</td>\n",
              "      <td>44.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      audit_rate  Professional\n",
              "2650    0.000000          24.9\n",
              "2654    0.000000          34.3\n",
              "2536    0.961538          52.8\n",
              "1689    1.503759          40.2\n",
              "1655    1.652893          44.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "2giO4DD0sxdH",
        "outputId": "a57cd63e-ff88-43f1-a2c5-4a3cea4a8e65"
      },
      "source": [
        "x = df_bottom30['Professional']\n",
        "y = df_bottom30['audit_rate']\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.xlabel('Professional Percentage')\n",
        "plt.ylabel('Audit Rate')\n",
        "plt.title('Bottom 30 Audited Counties - Professional Percentage V.S. Audit Rate', fontsize=16)\n",
        "plt.scatter(x, y)\n",
        "\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "# a=slope, b=intercept\n",
        "plt.plot(x, a*x + b, color='orange')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-okhfMfQAzK"
      },
      "source": [
        "## Income Analysis\n",
        "[Link to tablaeu public](https://public.tableau.com/app/profile/shuyi.zhu/viz/840AuditRate-Income/D_AR)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "SoPoFVhOQHS3",
        "outputId": "3fcb2761-3525-432d-a85c-0f0266689188"
      },
      "source": [
        "%%HTML\n",
        "<div class='tableauPlaceholder' id='viz1622126085748' style='position: relative'><noscript><a href='#'><img alt='D_AR ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AR&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='840AuditRate-Income&#47;D_AR' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AR&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='zh-TW' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1622126085748');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else { vizElement.style.width='100%';vizElement.style.height='927px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "* Average income level below $50,000 shows the highest audit rate. \n",
        "* Lower income level shows a higher audit rate.\n",
        "* Individuals with higher poverty level is likely to have a higher audit rate.\n"
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        "[Link to tablaeu public](https://public.tableau.com/app/profile/shuyi.zhu/viz/840AuditRate-Income/D_AuditonCountyLevel)"
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        "%%HTML\n",
        "<div class='tableauPlaceholder' id='viz1622126652455' style='position: relative'><noscript><a href='#'><img alt='D_Audit on County Level ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AuditonCountyLevel&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='840AuditRate-Income&#47;D_AuditonCountyLevel' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AuditonCountyLevel&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='zh-TW' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1622126652455');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else { vizElement.style.width='100%';vizElement.style.height='927px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
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              "<div class='tableauPlaceholder' id='viz1622126652455' style='position: relative'><noscript><a href='#'><img alt='D_Audit on County Level ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AuditonCountyLevel&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='840AuditRate-Income&#47;D_AuditonCountyLevel' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;84&#47;840AuditRate-Income&#47;D_AuditonCountyLevel&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='zh-TW' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1622126652455');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.minWidth='420px';vizElement.style.maxWidth='650px';vizElement.style.width='100%';vizElement.style.minHeight='587px';vizElement.style.maxHeight='887px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else { vizElement.style.width='100%';vizElement.style.height='927px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
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        "* States in southern part show a lower average household income, yet, the average audit rate is higher. \n",
        "* Median household income in the United States was $56,516 in 2015.\n",
        "* Majority of the counties are below the median household income  but receive the highest audit rate. \n",
        "\n"
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    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Map - IRS Audit Rates by County\n",
        "[Link to tablaeu public](https://public.tableau.com/app/profile/scott1352/viz/IRSAuditRatesbyCounty/Dashboard2)"
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        "%%HTML\n",
        "<div class='tableauPlaceholder' id='viz1622126219533' style='position: relative'><noscript><a href='#'><img alt='Mean Values:Audit rate:           7.73Black pop %:       8.89Native pop %:     1.71White pop %:   77.34 Income:            46,833Dividends %:    14.96Cap Gains %:    15.01 ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;IR&#47;IRSAuditRatesbyCounty&#47;Dashboard2&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='IRSAuditRatesbyCounty&#47;Dashboard2' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;IR&#47;IRSAuditRatesbyCounty&#47;Dashboard2&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='zh-TW' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1622126219533');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.minWidth='1020px';vizElement.style.maxWidth='1250px';vizElement.style.width='100%';vizElement.style.minHeight='487px';vizElement.style.maxHeight='787px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.minWidth='1020px';vizElement.style.maxWidth='1250px';vizElement.style.width='100%';vizElement.style.minHeight='487px';vizElement.style.maxHeight='787px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else { vizElement.style.width='100%';vizElement.style.height='727px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
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              "<div class='tableauPlaceholder' id='viz1622126219533' style='position: relative'><noscript><a href='#'><img alt='Mean Values:Audit rate:           7.73Black pop %:       8.89Native pop %:     1.71White pop %:   77.34 Income:            46,833Dividends %:    14.96Cap Gains %:    15.01 ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;IR&#47;IRSAuditRatesbyCounty&#47;Dashboard2&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='IRSAuditRatesbyCounty&#47;Dashboard2' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;IR&#47;IRSAuditRatesbyCounty&#47;Dashboard2&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='zh-TW' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1622126219533');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.minWidth='1020px';vizElement.style.maxWidth='1250px';vizElement.style.width='100%';vizElement.style.minHeight='487px';vizElement.style.maxHeight='787px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.minWidth='1020px';vizElement.style.maxWidth='1250px';vizElement.style.width='100%';vizElement.style.minHeight='487px';vizElement.style.maxHeight='787px';vizElement.style.height=(divElement.offsetWidth*0.75)+'px';} else { vizElement.style.width='100%';vizElement.style.height='727px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
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      "source": [
        "According to the map, income tax filings in some counties were audited at a higher rate than the nation as a whole.\n",
        "\n"
      ]
    },
    {
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      "source": [
        "# Conclusions\n",
        "\n",
        "**Consequentialism:**\n",
        "\n",
        "The algorithm doesn't result in a good outcome on balance. Lower-income taxpayers face a higher chance of an audit. If the audit algorithm is unfair, it will result in certain groups of people will be treated unfairly in terms of time input in filing tax return documents.\n",
        "\n",
        "\n",
        "\n",
        "**Deontology:**\n",
        "\n",
        "From the perspective of deontology, the algorithm seems not consistent with moral rules. People have the rights to be treated equally; a unfair audit algorithm will against that right, especially when a high audit rate could be an indicator of less trust towards the specific minority.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlB4PP5VU4E7"
      },
      "source": [
        "# Recommendations\n",
        "**For individuals**\n",
        "* Programs to help low income taxpayers - VITA (Volunteer Income Tax Assistance)\n",
        "\n",
        "**For improving algorithmic fairness**\n",
        "* Change what algorithm optimizes \n",
        "* Probability of change vs amount of expected change \n",
        "\n",
        "**Tax Policy Changes**\n",
        "* Increase IRS enforcement budget\n",
        "* Make it easier to file taxes\n"
      ]
    }
  ]
}