Preliminary_Analysis.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "example_colab.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "USEXN3DJjumD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf54920-2723-4b47-ab39-ba1f2ffeb019"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A209bQmrkE6y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b9954c5-26ae-4c10-8bc2-32b328444cbb"
      },
      "source": [
        "!pip install geopandas\n",
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting geopandas\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 4.2 MB/s \n",
            "\u001b[?25hCollecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 40.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.7.1)\n",
            "Collecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.20-cp37-cp37m-manylinux1_x86_64.whl (15.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 15.4 MB 39 kB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.1.5)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.2.0)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.5.30)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2018.9)\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.20 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGAx8IDgj0hE"
      },
      "source": [
        "ROOT_FOLDER = '/content/drive/MyDrive/'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pe60itG5j3uR"
      },
      "source": [
        "# data is now our pandas DataFrame containing all of the data from our csv file\n",
        "df = pd.read_csv(ROOT_FOLDER + \"eviction_with_census_tract.csv\",index_col=0)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2JIVVWxKfnt"
      },
      "source": [
        "# Clean Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnB251qQj76Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9dae2294-567e-4fea-bc8d-28ff0313bccf"
      },
      "source": [
        "# .head() just gives us a preview of the top rows of our dataframe, we can pass in an integer to\n",
        "#  tell it how many rows we would like to see, but the default is 5\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>DataID</th>\n",
              "      <th>FEIN</th>\n",
              "      <th>Owner_Groups_All</th>\n",
              "      <th>Only_Groups</th>\n",
              "      <th>index_right</th>\n",
              "      <th>STATEFP</th>\n",
              "      <th>COUNTYFP</th>\n",
              "      <th>TRACTCE</th>\n",
              "      <th>AFFGEOID</th>\n",
              "      <th>GEOID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>LSAD</th>\n",
              "      <th>ALAND</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>MAD_ADDR_POST</td>\n",
              "      <td>M</td>\n",
              "      <td>92.63</td>\n",
              "      <td>A</td>\n",
              "      <td>159 BROOKLINE ROAD 8, TOWNSEND, MA, 01469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PointAddress</td>\n",
              "      <td>159  Brookline Road 5</td>\n",
              "      <td>Townsend</td>\n",
              "      <td>MA</td>\n",
              "      <td>1469.0</td>\n",
              "      <td>2048SU000012</td>\n",
              "      <td>ayer district</td>\n",
              "      <td>2020-11-10</td>\n",
              "      <td>Summary Process - Residential (c239)</td>\n",
              "      <td>Townsend</td>\n",
              "      <td>MA</td>\n",
              "      <td>159  Brookline Road 5</td>\n",
              "      <td>1469.0</td>\n",
              "      <td>821985</td>\n",
              "      <td>299</td>\n",
              "      <td>Townsend</td>\n",
              "      <td>M_185312_938643</td>\n",
              "      <td>FEE</td>\n",
              "      <td>38</td>\n",
              "      <td>38 9 0</td>\n",
              "      <td>1</td>\n",
              "      <td>83300.0</td>\n",
              "      <td>563900.0</td>\n",
              "      <td>3700.0</td>\n",
              "      <td>650900.0</td>\n",
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              "      <td>4458.0</td>\n",
              "      <td>4458.0</td>\n",
              "      <td>301</td>\n",
              "      <td>301</td>\n",
              "      <td>...</td>\n",
              "      <td>15 SCALES LANE</td>\n",
              "      <td>TOWNSEND</td>\n",
              "      <td>MA</td>\n",
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              "      <td>2019</td>\n",
              "      <td>1.000173e+05</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>POINT (185305.2413596544 938650.9030090374)</td>\n",
              "      <td>KING DANA</td>\n",
              "      <td>KING DANA</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KING DANA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1171.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>2.501730e+10</td>\n",
              "      <td>3011.01</td>\n",
              "      <td>CT</td>\n",
              "      <td>44272760.0</td>\n",
              "      <td>54203.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MAD_ADDR_POST</td>\n",
              "      <td>M</td>\n",
              "      <td>97.89</td>\n",
              "      <td>A</td>\n",
              "      <td>21 SCHOOL STREET 3, ATTLEBORO, MA, 02703</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PointAddress</td>\n",
              "      <td>21 School Street Apt 3</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>MA</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>2034SU000044</td>\n",
              "      <td>attleboro district</td>\n",
              "      <td>2020-11-13</td>\n",
              "      <td>Summary Process - Residential (c239)</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>MA</td>\n",
              "      <td>21 School Street Apt 3</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>51329</td>\n",
              "      <td>16</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>M_218112_854353</td>\n",
              "      <td>FEE</td>\n",
              "      <td>39</td>\n",
              "      <td>39_181</td>\n",
              "      <td>1</td>\n",
              "      <td>70100.0</td>\n",
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              "      <td>1110</td>\n",
              "      <td>1110</td>\n",
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              "      <td>SHARON</td>\n",
              "      <td>MA</td>\n",
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              "      <td>2019</td>\n",
              "      <td>2.578051e+03</td>\n",
              "      <td>2.437888</td>\n",
              "      <td>98.803573</td>\n",
              "      <td>71.631631</td>\n",
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              "      <td>1.184443e+06</td>\n",
              "      <td>40.572792</td>\n",
              "      <td>5.493047e+06</td>\n",
              "      <td>3.637660</td>\n",
              "      <td>236.643156</td>\n",
              "      <td>171.563992</td>\n",
              "      <td>65.079165</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>239.509129</td>\n",
              "      <td>65.749524</td>\n",
              "      <td>POINT (218112.614739474 854353.7222426279)</td>\n",
              "      <td>TRUXTON RONALD D AND AHUVA F</td>\n",
              "      <td>TRUXTON RONALD D AND AHUVA F</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TRUXTON RONALD D AND AHUVA F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>389.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>631600.0</td>\n",
              "      <td>1400000US25005631600</td>\n",
              "      <td>2.500563e+10</td>\n",
              "      <td>6316.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>1571486.0</td>\n",
              "      <td>23449.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>MAD_ADDR_POST</td>\n",
              "      <td>M</td>\n",
              "      <td>100.00</td>\n",
              "      <td>M</td>\n",
              "      <td>171 ROUTE 6A, SANDWICH, MA, 02563</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PointAddress</td>\n",
              "      <td>171 ROUTE 6A</td>\n",
              "      <td>SANDWICH</td>\n",
              "      <td>MA</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>2025SU000057</td>\n",
              "      <td>barnstable district</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>Summary Process - Residential (c239)</td>\n",
              "      <td>Sandwich</td>\n",
              "      <td>MA</td>\n",
              "      <td>171 Route 6A, Room 1 (Upstairs)</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>2022847</td>\n",
              "      <td>261</td>\n",
              "      <td>Sandwich</td>\n",
              "      <td>F_932826_2736632</td>\n",
              "      <td>FEE</td>\n",
              "      <td>66</td>\n",
              "      <td>66-030-</td>\n",
              "      <td>1</td>\n",
              "      <td>116000.0</td>\n",
              "      <td>133400.0</td>\n",
              "      <td>5300.0</td>\n",
              "      <td>254700.0</td>\n",
              "      <td>300000.0</td>\n",
              "      <td>20041006.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1346.0</td>\n",
              "      <td>101</td>\n",
              "      <td>101</td>\n",
              "      <td>...</td>\n",
              "      <td>171 ROUTE 6A</td>\n",
              "      <td>SANDWICH</td>\n",
              "      <td>MA</td>\n",
              "      <td>02563</td>\n",
              "      <td>2011</td>\n",
              "      <td>1.233307e+04</td>\n",
              "      <td>0.109137</td>\n",
              "      <td>18.775621</td>\n",
              "      <td>10.928205</td>\n",
              "      <td>7.847415</td>\n",
              "      <td>4.097081e+05</td>\n",
              "      <td>99.108470</td>\n",
              "      <td>8.995918e+05</td>\n",
              "      <td>1.195690</td>\n",
              "      <td>215.127232</td>\n",
              "      <td>125.213147</td>\n",
              "      <td>89.914085</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1145.778535</td>\n",
              "      <td>171.797499</td>\n",
              "      <td>POINT (284312.402709826 834112.9964528072)</td>\n",
              "      <td>DELLEA SUSAN A</td>\n",
              "      <td>DELLEA SUSAN A</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DELLEA SUSAN A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13300.0</td>\n",
              "      <td>1400000US25001013300</td>\n",
              "      <td>2.500101e+10</td>\n",
              "      <td>133.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>12813860.0</td>\n",
              "      <td>1665619.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>MAD_ADDR_POST</td>\n",
              "      <td>M</td>\n",
              "      <td>90.53</td>\n",
              "      <td>A</td>\n",
              "      <td>10 THACHER BROOK CIRCLE 8, ATTLEBORO, MA, 02703</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PointAddress</td>\n",
              "      <td>10 Thacher Brook Circle  Apt 3</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>MA</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>2034SU000043</td>\n",
              "      <td>attleboro district</td>\n",
              "      <td>2020-11-12</td>\n",
              "      <td>Summary Process - Residential (c239)</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>MA</td>\n",
              "      <td>10 Thacher Brook Circle  Apt 3</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>58944</td>\n",
              "      <td>16</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>M_218630_853510</td>\n",
              "      <td>TAX</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2722000.0</td>\n",
              "      <td>13018200.0</td>\n",
              "      <td>431300.0</td>\n",
              "      <td>16171500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19850101.0</td>\n",
              "      <td>12204.0</td>\n",
              "      <td>11110.0</td>\n",
              "      <td>000</td>\n",
              "      <td>000</td>\n",
              "      <td>...</td>\n",
              "      <td>7 WELLS AVE SUITE 14</td>\n",
              "      <td>NEWTON</td>\n",
              "      <td>MA</td>\n",
              "      <td>02459</td>\n",
              "      <td>2019</td>\n",
              "      <td>1.202846e+06</td>\n",
              "      <td>0.010146</td>\n",
              "      <td>26.128661</td>\n",
              "      <td>7.213033</td>\n",
              "      <td>18.915628</td>\n",
              "      <td>9.857484e+04</td>\n",
              "      <td>1066.715831</td>\n",
              "      <td>5.856367e+05</td>\n",
              "      <td>4.941036</td>\n",
              "      <td>29198.248811</td>\n",
              "      <td>8060.417943</td>\n",
              "      <td>21137.830868</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>111748.020917</td>\n",
              "      <td>2501.159490</td>\n",
              "      <td>POINT (218724.8575637158 853790.9962384583)</td>\n",
              "      <td>ETON THAMES</td>\n",
              "      <td>ETON THAMES ASSOCIATION LIMITED</td>\n",
              "      <td>True</td>\n",
              "      <td>ETON THAMES ASSOCIATES LIMITED PARTNERSHIP</td>\n",
              "      <td>00ec9c</td>\n",
              "      <td>L86003423</td>\n",
              "      <td>00ec9c</td>\n",
              "      <td>NaN</td>\n",
              "      <td>390.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>631800.0</td>\n",
              "      <td>1400000US25005631800</td>\n",
              "      <td>2.500563e+10</td>\n",
              "      <td>6318.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>12001536.0</td>\n",
              "      <td>305704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>MAD_ADDR_POST</td>\n",
              "      <td>M</td>\n",
              "      <td>100.00</td>\n",
              "      <td>A</td>\n",
              "      <td>75 CHARLES STREET 2, HYANNIS, MA, 02601</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PointAddress</td>\n",
              "      <td>75 Charles Street #2</td>\n",
              "      <td>Hyannis</td>\n",
              "      <td>MA</td>\n",
              "      <td>2601.0</td>\n",
              "      <td>2025SU000065</td>\n",
              "      <td>barnstable district</td>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>Summary Process - Residential (c239)</td>\n",
              "      <td>Hyannis</td>\n",
              "      <td>MA</td>\n",
              "      <td>75 Charles Street #2</td>\n",
              "      <td>2601.0</td>\n",
              "      <td>84778</td>\n",
              "      <td>20</td>\n",
              "      <td>Barnstable</td>\n",
              "      <td>F_987792_2702297</td>\n",
              "      <td>FEE</td>\n",
              "      <td>309</td>\n",
              "      <td>309_256</td>\n",
              "      <td>1</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>147900.0</td>\n",
              "      <td>36300.0</td>\n",
              "      <td>274200.0</td>\n",
              "      <td>865000.0</td>\n",
              "      <td>20070403.0</td>\n",
              "      <td>3260.0</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>1110</td>\n",
              "      <td>1110</td>\n",
              "      <td>...</td>\n",
              "      <td>474 CRAIGVILLE BEACH ROAD</td>\n",
              "      <td>HYANNIS</td>\n",
              "      <td>MA</td>\n",
              "      <td>02647</td>\n",
              "      <td>2018</td>\n",
              "      <td>9.049301e+03</td>\n",
              "      <td>0.360249</td>\n",
              "      <td>40.872368</td>\n",
              "      <td>11.540806</td>\n",
              "      <td>29.331562</td>\n",
              "      <td>4.332268e+05</td>\n",
              "      <td>45.368098</td>\n",
              "      <td>1.319898e+06</td>\n",
              "      <td>2.046667</td>\n",
              "      <td>343.616939</td>\n",
              "      <td>97.024385</td>\n",
              "      <td>246.592554</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>840.707280</td>\n",
              "      <td>121.604951</td>\n",
              "      <td>POINT (301074.556216199 823658.6264352691)</td>\n",
              "      <td>HOSTETTER ADAM J AND DANIEL C JR</td>\n",
              "      <td>HOSTETTER ADAM J AND DANIEL C JR TRUST</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>HOSTETTER ADAM J AND DANIEL C JR TRUST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>118.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15300.0</td>\n",
              "      <td>1400000US25001015300</td>\n",
              "      <td>2.500102e+10</td>\n",
              "      <td>153.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>13212205.0</td>\n",
              "      <td>531166.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>7918</th>\n",
              "      <td>7918</td>\n",
              "      <td>0</td>\n",
              "      <td>7918</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U</td>\n",
              "      <td>0.00</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>248 Meadow Drive</td>\n",
              "      <td>Raynham</td>\n",
              "      <td>MA</td>\n",
              "      <td>2767.0</td>\n",
              "      <td>21H83SP00216TA</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-19</td>\n",
              "      <td>Efiled SP Summons and Complaint - Cause</td>\n",
              "      <td>Raynham</td>\n",
              "      <td>MA</td>\n",
              "      <td>248 Meadow Drive</td>\n",
              "      <td>2767.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>POINT (-inf -inf)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7919</th>\n",
              "      <td>7919</td>\n",
              "      <td>0</td>\n",
              "      <td>7919</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U</td>\n",
              "      <td>0.00</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>202 Meadow Drive</td>\n",
              "      <td>Raynham</td>\n",
              "      <td>MA</td>\n",
              "      <td>2767.0</td>\n",
              "      <td>21H83SP00215TA</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-19</td>\n",
              "      <td>Efiled SP Summons and Complaint - Cause</td>\n",
              "      <td>Raynham</td>\n",
              "      <td>MA</td>\n",
              "      <td>202 Meadow Drive</td>\n",
              "      <td>2767.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>POINT (-inf -inf)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7920</th>\n",
              "      <td>7920</td>\n",
              "      <td>0</td>\n",
              "      <td>7920</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U</td>\n",
              "      <td>0.00</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27 Melville Street Apt 1</td>\n",
              "      <td>Fall River</td>\n",
              "      <td>MA</td>\n",
              "      <td>2724.0</td>\n",
              "      <td>21H83SP00228FR</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>Efiled SP Summons and Complaint - Non-payment ...</td>\n",
              "      <td>Fall River</td>\n",
              "      <td>MA</td>\n",
              "      <td>27 Melville Street Apt 1</td>\n",
              "      <td>2724.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>POINT (-inf -inf)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7921</th>\n",
              "      <td>7921</td>\n",
              "      <td>0</td>\n",
              "      <td>7921</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U</td>\n",
              "      <td>0.00</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>617 Summer Street 3rd Floor</td>\n",
              "      <td>New Bedford</td>\n",
              "      <td>MA</td>\n",
              "      <td>2740.0</td>\n",
              "      <td>21H83SP00240NB</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-21</td>\n",
              "      <td>Efiled SP Summons and Complaint - Cause</td>\n",
              "      <td>New Bedford</td>\n",
              "      <td>MA</td>\n",
              "      <td>617 Summer Street 3rd Floor</td>\n",
              "      <td>2740.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>POINT (-inf -inf)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7922</th>\n",
              "      <td>7922</td>\n",
              "      <td>0</td>\n",
              "      <td>7922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U</td>\n",
              "      <td>0.00</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50 Main Street 4108</td>\n",
              "      <td>North Easton</td>\n",
              "      <td>MA</td>\n",
              "      <td>2356.0</td>\n",
              "      <td>21H83SP00261TA</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-22</td>\n",
              "      <td>Efiled SP Summons and Complaint - Non-payment ...</td>\n",
              "      <td>North Easton</td>\n",
              "      <td>MA</td>\n",
              "      <td>50 Main Street 4108</td>\n",
              "      <td>2356.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>POINT (-inf -inf)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5498 rows × 92 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0.1  Join_Count  TARGET_FID  ... LSAD       ALAND     AWATER\n",
              "0                0           1           0  ...   CT  44272760.0    54203.0\n",
              "1                1           1           1  ...   CT   1571486.0    23449.0\n",
              "2                2           1           2  ...   CT  12813860.0  1665619.0\n",
              "3                3           1           3  ...   CT  12001536.0   305704.0\n",
              "4                4           1           4  ...   CT  13212205.0   531166.0\n",
              "...            ...         ...         ...  ...  ...         ...        ...\n",
              "7918          7918           0        7918  ...  NaN         NaN        NaN\n",
              "7919          7919           0        7919  ...  NaN         NaN        NaN\n",
              "7920          7920           0        7920  ...  NaN         NaN        NaN\n",
              "7921          7921           0        7921  ...  NaN         NaN        NaN\n",
              "7922          7922           0        7922  ...  NaN         NaN        NaN\n",
              "\n",
              "[5498 rows x 92 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fj0TQOJ0O3o_",
        "outputId": "95fa9afb-6228-47c4-b5b1-f2be714fe37e"
      },
      "source": [
        "#Identify  not useful or duplicated columns and which column is important\n",
        "#show columns\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0.1', 'Join_Count', 'TARGET_FID', 'Loc_name', 'Status',\n",
              "       'Score', 'Match_type', 'Match_addr', 'User_fld', 'Addr_type',\n",
              "       'ARC_Street', 'ARC_City', 'ARC_State', 'ARC_ZIP', 'case_numbe',\n",
              "       'court_divi', 'file_date', 'initiating', 'property_a', 'property_1',\n",
              "       'property_2', 'property_3', 'mapc_id', 'muni_id', 'muni', 'parloc_id',\n",
              "       'poly_typ', 'map_num', 'mappar_id', 'loc_id_cnt', 'land_value',\n",
              "       'bldg_value', 'othr_value', 'total_valu', 'ls_price', 'ls_date',\n",
              "       'bldg_area', 'res_area', 'luc_1', 'luc_2', 'luc_adj_1', 'luc_adj_2',\n",
              "       'num_units', 'units_est', 'units_src', 'num_rooms', 'yr_built',\n",
              "       'site_addr', 'addr_str', 'addr_num', 'addr_zip', 'owner_name',\n",
              "       'owner_addr', 'owner_city', 'owner_stat', 'owner_zip', 'fy',\n",
              "       'lot_areaft', 'far', 'pct_imperv', 'pct_bldg', 'pct_pave', 'landv_pac',\n",
              "       'bldgv_psf', 'totv_pac', 'bldlnd_rat', 'sqm_imperv', 'sqm_bldg',\n",
              "       'sqm_pave', 'realesttyp', 'temp', 'st_area_sh', 'st_length_',\n",
              "       'geometry', 'CoreName', 'CleanName', 'Corp', 'EntityName', 'DataID',\n",
              "       'FEIN', 'Owner_Groups_All', 'Only_Groups', 'index_right', 'STATEFP',\n",
              "       'COUNTYFP', 'TRACTCE', 'AFFGEOID', 'GEOID', 'NAME', 'LSAD', 'ALAND',\n",
              "       'AWATER'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4PyPItTeT7a"
      },
      "source": [
        "#create a dataframe with useful columns\n",
        "df_imp = df.filter(['muni','owner_name', 'geometry', 'Corp', 'ARC_Street', 'ARC_City', 'poly_typ',\n",
        "       'ARC_State', 'ARC_ZIP', 'case_numbe', 'court_divi', 'file_date', 'num_rooms', 'yr_built', 'ls_price',\n",
        "       'total_valu', 'ls_date', 'property_3', 'STATEFP',\n",
        "       'COUNTYFP', 'TRACTCE', 'AFFGEOID', 'GEOID', 'NAME', 'LSAD', 'ALAND',\n",
        "       'AWATER', 'Initiation', 'realesttyp'], axis=1)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FH9UYLrHU1Pn"
      },
      "source": [
        "#remove NaN values from column muni \n",
        "df_imp = df_imp[df_imp['muni']. notna()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "v4govxUAfJPG",
        "outputId": "a9e51b76-d661-406c-cf16-0a2490237f05"
      },
      "source": [
        "df_imp"
      ],
      "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>muni</th>\n",
              "      <th>owner_name</th>\n",
              "      <th>geometry</th>\n",
              "      <th>Corp</th>\n",
              "      <th>ARC_Street</th>\n",
              "      <th>ARC_City</th>\n",
              "      <th>poly_typ</th>\n",
              "      <th>ARC_State</th>\n",
              "      <th>ARC_ZIP</th>\n",
              "      <th>case_numbe</th>\n",
              "      <th>court_divi</th>\n",
              "      <th>file_date</th>\n",
              "      <th>num_rooms</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>ls_price</th>\n",
              "      <th>total_valu</th>\n",
              "      <th>ls_date</th>\n",
              "      <th>property_3</th>\n",
              "      <th>STATEFP</th>\n",
              "      <th>COUNTYFP</th>\n",
              "      <th>TRACTCE</th>\n",
              "      <th>AFFGEOID</th>\n",
              "      <th>GEOID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>LSAD</th>\n",
              "      <th>ALAND</th>\n",
              "      <th>AWATER</th>\n",
              "      <th>realesttyp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Townsend</td>\n",
              "      <td>KING, DANA</td>\n",
              "      <td>POINT (185305.2413596544 938650.9030090374)</td>\n",
              "      <td>False</td>\n",
              "      <td>159  Brookline Road 5</td>\n",
              "      <td>Townsend</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>1469.0</td>\n",
              "      <td>2048SU000012</td>\n",
              "      <td>ayer district</td>\n",
              "      <td>2020-11-10</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1930</td>\n",
              "      <td>195500.0</td>\n",
              "      <td>650900.0</td>\n",
              "      <td>20090831.0</td>\n",
              "      <td>1469.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>301101.0</td>\n",
              "      <td>1400000US25017301101</td>\n",
              "      <td>2.501730e+10</td>\n",
              "      <td>3011.01</td>\n",
              "      <td>CT</td>\n",
              "      <td>44272760.0</td>\n",
              "      <td>54203.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Attleboro</td>\n",
              "      <td>TRUXTON RONALD D &amp; AHUVA F</td>\n",
              "      <td>POINT (218112.614739474 854353.7222426279)</td>\n",
              "      <td>False</td>\n",
              "      <td>21 School Street Apt 3</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>2034SU000044</td>\n",
              "      <td>attleboro district</td>\n",
              "      <td>2020-11-13</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1910</td>\n",
              "      <td>350500.0</td>\n",
              "      <td>325100.0</td>\n",
              "      <td>20101018.0</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>631600.0</td>\n",
              "      <td>1400000US25005631600</td>\n",
              "      <td>2.500563e+10</td>\n",
              "      <td>6316.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>1571486.0</td>\n",
              "      <td>23449.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sandwich</td>\n",
              "      <td>DELLEA, SUSAN A</td>\n",
              "      <td>POINT (284312.402709826 834112.9964528072)</td>\n",
              "      <td>False</td>\n",
              "      <td>171 ROUTE 6A</td>\n",
              "      <td>SANDWICH</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>2025SU000057</td>\n",
              "      <td>barnstable district</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1850</td>\n",
              "      <td>300000.0</td>\n",
              "      <td>254700.0</td>\n",
              "      <td>20041006.0</td>\n",
              "      <td>2563.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13300.0</td>\n",
              "      <td>1400000US25001013300</td>\n",
              "      <td>2.500101e+10</td>\n",
              "      <td>133.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>12813860.0</td>\n",
              "      <td>1665619.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Attleboro</td>\n",
              "      <td>ETON THAMES ASSOC LTD PARTNERS</td>\n",
              "      <td>POINT (218724.8575637158 853790.9962384583)</td>\n",
              "      <td>True</td>\n",
              "      <td>10 Thacher Brook Circle  Apt 3</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>TAX</td>\n",
              "      <td>MA</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>2034SU000043</td>\n",
              "      <td>attleboro district</td>\n",
              "      <td>2020-11-12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1967</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16171500.0</td>\n",
              "      <td>19850101.0</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>631800.0</td>\n",
              "      <td>1400000US25005631800</td>\n",
              "      <td>2.500563e+10</td>\n",
              "      <td>6318.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>12001536.0</td>\n",
              "      <td>305704.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barnstable</td>\n",
              "      <td>HOSTETTER, ADAM J &amp; DANIEL C JR TRS</td>\n",
              "      <td>POINT (301074.556216199 823658.6264352691)</td>\n",
              "      <td>False</td>\n",
              "      <td>75 Charles Street #2</td>\n",
              "      <td>Hyannis</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>2601.0</td>\n",
              "      <td>2025SU000065</td>\n",
              "      <td>barnstable district</td>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1930</td>\n",
              "      <td>865000.0</td>\n",
              "      <td>274200.0</td>\n",
              "      <td>20070403.0</td>\n",
              "      <td>2601.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15300.0</td>\n",
              "      <td>1400000US25001015300</td>\n",
              "      <td>2.500102e+10</td>\n",
              "      <td>153.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>13212205.0</td>\n",
              "      <td>531166.0</td>\n",
              "      <td>3.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>7908</th>\n",
              "      <td>Woburn</td>\n",
              "      <td>TUDOR GLEN VILLAGE INC</td>\n",
              "      <td>POINT (226147.9846521748 914257.011558391)</td>\n",
              "      <td>True</td>\n",
              "      <td>111 Locust Street TH 111</td>\n",
              "      <td>Woburn</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>1801.0</td>\n",
              "      <td>2153SU000008</td>\n",
              "      <td>woburn district</td>\n",
              "      <td>2021-01-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1981</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29471000.0</td>\n",
              "      <td>19810821.0</td>\n",
              "      <td>1801.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>333100.0</td>\n",
              "      <td>1400000US25017333100</td>\n",
              "      <td>2.501733e+10</td>\n",
              "      <td>3331.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>7097763.0</td>\n",
              "      <td>34957.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7911</th>\n",
              "      <td>Attleboro</td>\n",
              "      <td>EASTLANDE PARK CORP</td>\n",
              "      <td>POINT (212133.6071519015 849637.3732836959)</td>\n",
              "      <td>True</td>\n",
              "      <td>1346 Newport Avenue E-2</td>\n",
              "      <td>Attleboro</td>\n",
              "      <td>TAX</td>\n",
              "      <td>MA</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>21H83SP00255TA</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>1739000.0</td>\n",
              "      <td>19860129.0</td>\n",
              "      <td>2703.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>631100.0</td>\n",
              "      <td>1400000US25005631100</td>\n",
              "      <td>2.500563e+10</td>\n",
              "      <td>6311.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>8193706.0</td>\n",
              "      <td>152007.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7912</th>\n",
              "      <td>Woburn</td>\n",
              "      <td>TUDOR GLEN VILLAGE INC</td>\n",
              "      <td>POINT (226147.9846521748 914257.011558391)</td>\n",
              "      <td>True</td>\n",
              "      <td>111 Locust  Street TH 15</td>\n",
              "      <td>Woburn</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>1801.0</td>\n",
              "      <td>2153SU000007</td>\n",
              "      <td>woburn district</td>\n",
              "      <td>2021-01-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1981</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29471000.0</td>\n",
              "      <td>19810821.0</td>\n",
              "      <td>1801.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>333100.0</td>\n",
              "      <td>1400000US25017333100</td>\n",
              "      <td>2.501733e+10</td>\n",
              "      <td>3331.00</td>\n",
              "      <td>CT</td>\n",
              "      <td>7097763.0</td>\n",
              "      <td>34957.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7913</th>\n",
              "      <td>Taunton</td>\n",
              "      <td>FARRELL BRUCE E TRS</td>\n",
              "      <td>POINT (232043.8016217993 845744.3341158029)</td>\n",
              "      <td>False</td>\n",
              "      <td>1521 Somerset Avenue Rear 2nd f</td>\n",
              "      <td>Taunton</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>2780.0</td>\n",
              "      <td>21H83SP00251TA</td>\n",
              "      <td>southeast</td>\n",
              "      <td>2021-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>343900.0</td>\n",
              "      <td>20161027.0</td>\n",
              "      <td>2780.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>613902.0</td>\n",
              "      <td>1400000US25005613902</td>\n",
              "      <td>2.500561e+10</td>\n",
              "      <td>6139.02</td>\n",
              "      <td>CT</td>\n",
              "      <td>8045315.0</td>\n",
              "      <td>245448.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7914</th>\n",
              "      <td>Wrentham</td>\n",
              "      <td>SIAKOTOS  MICHAEL N TRUSTEE</td>\n",
              "      <td>POINT (209188.4793092531 864826.7085488176)</td>\n",
              "      <td>False</td>\n",
              "      <td>1085 West Street 5</td>\n",
              "      <td>Wrentham</td>\n",
              "      <td>FEE</td>\n",
              "      <td>MA</td>\n",
              "      <td>2093.0</td>\n",
              "      <td>2157SU000001</td>\n",
              "      <td>wrentham district</td>\n",
              "      <td>2021-01-19</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>565500.0</td>\n",
              "      <td>19961223.0</td>\n",
              "      <td>2093.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>441202.0</td>\n",
              "      <td>1400000US25021441202</td>\n",
              "      <td>2.502144e+10</td>\n",
              "      <td>4412.02</td>\n",
              "      <td>CT</td>\n",
              "      <td>23379379.0</td>\n",
              "      <td>277134.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5016 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            muni                           owner_name  ...     AWATER realesttyp\n",
              "0       Townsend                           KING, DANA  ...    54203.0        8.0\n",
              "1      Attleboro           TRUXTON RONALD D & AHUVA F  ...    23449.0        3.0\n",
              "2       Sandwich                      DELLEA, SUSAN A  ...  1665619.0        1.0\n",
              "3      Attleboro       ETON THAMES ASSOC LTD PARTNERS  ...   305704.0        0.0\n",
              "4     Barnstable  HOSTETTER, ADAM J & DANIEL C JR TRS  ...   531166.0        3.0\n",
              "...          ...                                  ...  ...        ...        ...\n",
              "7908      Woburn               TUDOR GLEN VILLAGE INC  ...    34957.0        4.0\n",
              "7911   Attleboro                  EASTLANDE PARK CORP  ...   152007.0        3.0\n",
              "7912      Woburn               TUDOR GLEN VILLAGE INC  ...    34957.0        4.0\n",
              "7913     Taunton                  FARRELL BRUCE E TRS  ...   245448.0        3.0\n",
              "7914    Wrentham          SIAKOTOS  MICHAEL N TRUSTEE  ...   277134.0        3.0\n",
              "\n",
              "[5016 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NMww-3vHBAS5",
        "outputId": "23240869-6dbb-4285-e276-1ed924c933b2"
      },
      "source": [
        "#look at means table\n",
        "df_imp.describe().round(1)"
      ],
      "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>ARC_ZIP</th>\n",
              "      <th>num_rooms</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>ls_price</th>\n",
              "      <th>total_valu</th>\n",
              "      <th>ls_date</th>\n",
              "      <th>property_3</th>\n",
              "      <th>STATEFP</th>\n",
              "      <th>COUNTYFP</th>\n",
              "      <th>TRACTCE</th>\n",
              "      <th>GEOID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>ALAND</th>\n",
              "      <th>AWATER</th>\n",
              "      <th>realesttyp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4980.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5.016000e+03</td>\n",
              "      <td>5.016000e+03</td>\n",
              "      <td>4394.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5.016000e+03</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>5016.0</td>\n",
              "      <td>4982.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1982.2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1860.0</td>\n",
              "      <td>7.117300e+06</td>\n",
              "      <td>2.049381e+07</td>\n",
              "      <td>20041195.1</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.2</td>\n",
              "      <td>493663.0</td>\n",
              "      <td>2.501769e+10</td>\n",
              "      <td>4936.6</td>\n",
              "      <td>6991977.8</td>\n",
              "      <td>673691.7</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>566.3</td>\n",
              "      <td>121.0</td>\n",
              "      <td>379.4</td>\n",
              "      <td>4.522842e+07</td>\n",
              "      <td>2.365568e+08</td>\n",
              "      <td>143714.1</td>\n",
              "      <td>588.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.8</td>\n",
              "      <td>253252.5</td>\n",
              "      <td>8.766662e+06</td>\n",
              "      <td>2532.5</td>\n",
              "      <td>17811921.0</td>\n",
              "      <td>3152151.2</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1001.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.000000e+02</td>\n",
              "      <td>-1.000000e+00</td>\n",
              "      <td>18851213.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.500101e+10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>76783.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1605.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.464000e+05</td>\n",
              "      <td>20000601.0</td>\n",
              "      <td>1605.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>311400.0</td>\n",
              "      <td>2.500921e+10</td>\n",
              "      <td>3114.0</td>\n",
              "      <td>838713.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1960.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>8.000000e+04</td>\n",
              "      <td>5.719000e+05</td>\n",
              "      <td>20070702.5</td>\n",
              "      <td>1952.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>541101.5</td>\n",
              "      <td>2.501738e+10</td>\n",
              "      <td>5411.0</td>\n",
              "      <td>1813518.0</td>\n",
              "      <td>55271.5</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2332.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>5.766250e+05</td>\n",
              "      <td>5.371800e+06</td>\n",
              "      <td>20131113.2</td>\n",
              "      <td>2330.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>730401.0</td>\n",
              "      <td>2.502516e+10</td>\n",
              "      <td>7304.0</td>\n",
              "      <td>6145036.0</td>\n",
              "      <td>348565.8</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21245.0</td>\n",
              "      <td>3604.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>1.598200e+09</td>\n",
              "      <td>4.580550e+09</td>\n",
              "      <td>21030308.0</td>\n",
              "      <td>21245.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>985600.0</td>\n",
              "      <td>2.502776e+10</td>\n",
              "      <td>9856.0</td>\n",
              "      <td>503627514.0</td>\n",
              "      <td>56061159.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ARC_ZIP  num_rooms  yr_built  ...        ALAND      AWATER  realesttyp\n",
              "count   4980.0     5016.0    5016.0  ...       5016.0      5016.0      4982.0\n",
              "mean    1982.2       32.0    1860.0  ...    6991977.8    673691.7         3.4\n",
              "std      566.3      121.0     379.4  ...   17811921.0   3152151.2         2.2\n",
              "min     1001.0       -1.0       0.0  ...      76783.0         0.0         0.0\n",
              "25%     1605.0        0.0    1900.0  ...     838713.0         0.0         2.0\n",
              "50%     1960.0        8.0    1920.0  ...    1813518.0     55271.5         3.0\n",
              "75%     2332.0       18.0    1971.0  ...    6145036.0    348565.8         4.0\n",
              "max    21245.0     3604.0    2017.0  ...  503627514.0  56061159.0        12.0\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IojMem4zkr4A"
      },
      "source": [
        "Since the following columns seems to contain meaningless data less than or equal to 0. I will discard those and look at the mean again, along with the distributions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Flh-AZHcHytG",
        "outputId": "b79ad39c-b19a-463b-f14c-bd3809d7e7a4"
      },
      "source": [
        "df['num_rooms'][df['num_rooms'] > 0].describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3032.0\n",
              "mean       53.0\n",
              "std       152.1\n",
              "min         1.0\n",
              "25%        10.0\n",
              "50%        15.0\n",
              "75%        28.2\n",
              "max      3604.0\n",
              "Name: num_rooms, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "D71EbnO_LrW6",
        "outputId": "cdeadafc-99bc-4776-e4bd-97067b40c6b1"
      },
      "source": [
        "df['num_rooms'][df['num_rooms'] > 0][df['num_rooms'] < 100].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd79c7590>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0iZHCQZ4Mfld",
        "outputId": "5dafb91b-ef25-43a8-cc11-6f9e63178a52"
      },
      "source": [
        "df['yr_built'][df['yr_built'] > 0][df['yr_built'] < 2017].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd799df50>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RSrrpSUSagN8H/hB4DvgSTiB5ZJ5ztojIPhHpEpGbEzxfLiIPuM/vFJH2uOducdP3ici74tI/KSK7ROQlEfmWiFTMvq4xxWR0sTWOukqOnpjM+CTA2HIjczdV2VyOYpVsH8f3gF8CVcBvqur7VPUBVf1ToGaOc/zAXcAVQAdwjYh0zMp2PTCkqhuAO4E73HM7gKuBc4AtwFdFxC8ibcDHgM2qei7gd/MZU7SCk4sLHKsaKpiYmmZkIrOTAE/WOE4fVQXOelVgs8eLUbI1jn9T1Q5V/T+qehSc2gKAqm6e45yLgC5V7VbVMLAN2Dorz1bgPvf4IeAycfaf3ApsU9WQqh4EutzrAZQAlSJSghPIjiR5D8YUpMU2Vc0srz6S2X6OuZYbiWl1Z4/bJMDik2zg+LsEaU8ucE4bcDjucY+bljCPqkaAYaBprnNVtRf4B+BV4CgwrKo/TvTiInKDiHSKSGcgEFigqMbkr+AihuNC3FyODE8CnGu5kZjY7HFrqio+8wYOEVkhIhfifMM/X0QucH/ehvNtP6tEpBGnNrIOWAVUi8gHE+VV1btVdbOqbm5paclmMY3JqJnAUZZu4IjtPZ7ZwDHfHA6w2ePFbKFP4rtwOsRXA1+MSx8F/nKBc3uBNXGPV7tpifL0uE1P9cDAPOf+L+CgqgbAmV8CvBH4xgJlMaZgBScjVJX58aewbWw8ryYB9gdDNM/RMQ7O7PHWunKrcRSheQOHqt4H3Cciv62q30nx2k8DG0VkHc4f/atx5oLE2w5ch9PsdSXwqKqqiGwH/lNEvohTs9gIPAVEgUtEpAqYAC4DOlMslzEFZSyc3pLqMV5NAuwPhlnfmnBszIzW2nL6rMZRdOb9NIrIB1X1G0C7iHxq9vOq+sUEp8Wei4jITcDDOKOf7lXVXSJyG9CpqtuBe4D7RaQLGMQdIeXmexDYDUSAG1V1GtgpIg8Bz7rpzwF3p3zXxhSQ0clI2h3jMSsbMjsJcGa5kXlqHOCMrNrfF8zY65r8sNCnsdr9Pf/Xijmo6g5gx6y0W+OOJ4Gr5jj3duD2BOl/Dfx1OuUxphClu4lTvJX1Few9NpqhEsUtNzJPHwc4gePxrv6Mva7JDws1Vf2r+/tvs1McY8xsYxkJHJU8tjeAquKMeF+chZYbiWmtK2d0MsJEeJrKstS3vjX5KdkJgF8QkToRKRWRn4pIYK7RTMaYzBqdTG8vjngr6zM7CXCh5UZiWmNDcq2fo6gkO4/jclUdAd6Ls1bVBuDPvSqUMeaksXAkrb044sXmchzJUD/HQsuNxCyfmQRoI6uKSbKBI/apfQ/wbVUd9qg8xphZghmoccRmjx/L0MiqhZYbiZlZdsRmjxeVZD+NPxCRvThDYD8iIi2AfRKMyYKx0PTiR1W5gSNTNY6FlhuJmZk9Pmo1jmKS7LLqN+NMtNusqlPAGKevO2WMybBQZJrwdHTRneOxSYDHM1XjWGC5kZi6yhLKSny2L0eRSeXTeDbOfI74c76e4fIYY+IsdmXcmBK/j+aaco5l6A/4QsuNxIiIs4WsBY6iktSnUUTuB9YDzwOxXVkUCxzGeGos5Px3W2zgAKef41iGOqkDwYUn/8Usty1ki06yn8bNQIdmeicYY8y8RkNTQPor48ZbXlfBqwPji74OOJ3jm5bXJv26e4+NZOR1TX5IdlTVS8AKLwtijDldrMZRu8jOcYAVdRUZaapSVfqD4QXncMS01NpCh8Um2U9jM7BbRJ4CZj4Bqvo+T0pljAEgmMEax4r6CoYnppicmqaiNP1Z3CMT7nIjyTZV1VUwGoowHo5QlebS8Ca/JPsu/o2XhTDGJBac6eNY/HIdsTkVx4YnaW+uXiD33AJBp9ay0ByOk697cu/x9mYLHMUg2eG4P8eZMV7qHj+Ns0KtMcZDIxNOjaOuonTR11pRl5kNnWJzMpKtccSWHbGRVcUj2bWq/ghnT/B/dZPagO97VShjjGM4FjgqMxA46jOzB3h/MAw4c0OSMbPsiE0CLBrJdo7fCLwJGAFQ1f1Aq1eFMsY4RianKCvxLapPImamqWqRgSOQ5AKHMa11sb3HrcZRLJINHCFVDcceuJMAbWiuMR4bmZiiPgO1DYDailJqyksWvV5VYDREqV+SLlddRQlVZf6M70BocifZwPFzEflLoFJE3gl8G/hv74pljAFnBFNdBobixmRiFnd/MERzTXnS+3qICKsaKukZyswcEpN7yQaOm4EA8CLwxzi7+v2VV4UyxjiGM1jjgNjs8cXXOJLtGI9pa6ik90Tmtq41uZXsqKooTmf4R1X1SlX9t2RmkYvIFhHZJyJdInJzgufLReQB9/mdItIe99wtbvo+EXlXXHqDiDwkIntFZI+IXJrMPRhTiEYmpzLSMR6zvK5i0Qsd9geTW6cqXltjJb1DFjiKxbyBQxx/IyL9wD5gn7v7363zneee6wfuAq4AOoBrRKRjVrbrgSFV3QDcCdzhntsBXA2cA2wBvupeD+BLwI9U9Wzg9cCe5G7VmMKT8RpHXQV9oyGi0fS7KAOjoaQ7xmPaGioZGp9iPJyZHQhNbi1U4/gkzmiqN6jqMlVdBlwMvElEPrnAuRcBXara7Xasb+P0pdi3Ave5xw8Bl4nTcLoV2KaqIVU9CHQBF4lIPfBW4B4AVQ2r6omk7tSYAjQyMZWRORwxK+oriESV/rH0hsZGo8rAWDjlpqrVjc4OhFbrKA4LBY7fA65x/3gDoKrdwAeBDy1wbhtwOO5xj5uWMI+qRoBhoGmec9fh9LX8u4g8JyJfE5H0p8Aak8dUlZHJCHWVmewcdyfjDacXOIbGw0xHNelZ4zFtDU7g6LF+jqKwUOAoVdX+2YmqGgAy9zUoeSXABcC/qOr5OBtKndZ3AiAiN4hIp4h0BgKBbJbRmIwYC08zHdWMN1VB+nM5YufFtqJNVpvVOIrKQoEjnOZzAL3AmrjHq920hHncuSH1wMA85/YAPaq6001/CCeQnEZV71bVzaq6uaWlZYGiGpN/hjO43EjMzN7j6QYOt2M9VnNJVmttBSU+sZFVRWKhwPF6ERlJ8DMKvHaBc58GNorIOhEpw+ns3j4rz3bgOvf4SuBRd7TWduBqd9TVOmAj8JSqHgMOi8hZ7jmXAbuTulNjCkxsnapM1jiaa8rx+yTtkVWxSXwr6ytTOs/vE1Y2VFiNo0jM23iqqmmvc6CqERG5CXgY8AP3quouEbkN6FTV7Tid3PeLSBcwiBNccPM9iBMUIsCNqhrbefBPgW+6wagb+IN0y2hMPhvJ4DpVMX6f0LKILWSPDU8610ixcxxgTWMVrw7aJMBi4Okax6q6A2eyYHzarXHHk8BVc5x7O3B7gvTncXYkNKaoDXtQ4wBYXl+R9rIjx0Ymaa11ai2pam+uZseLR9N6XZNfkp05bozJspFJZ85DJvs4AFYuYifAY8OTKfdvxKxrqubE+BRDYwt1j5p8Z4HDmDzlVY1jRX36s8ePDk+wMsURVTHr3M2jDg6MpXW+yR8WOIzJU7E+jpoMLnIIJ7dyHQulNotbVTk6PJnyUNyY2K6Dh/otcBQ6CxzG5KnhiSlqy0vS6k+YT2xDp1Sbq5x9w6fTrnGsXVaFTyxwFAMLHMbkqaHxMI3Vqc3QTsbJ2eOpBY7jac7hiCkr8bG6sYqDAzayqtBZ4DAmTw2OeRM40p09nu4cjnjtzdUc7A+mfb7JDxY4jMlTQ+NhmrwIHGnOHu9xJ++takivxgFwZnM1BwNji1qd1+SeBQ5j8tRgMExjVeYDR1VZCbUVJSk3VR0eGqfEJ4uqcZy1opax8LQtPVLgLHAYk6cGx8Msq/ZmLdEVaczleHVwnNWNlYvqrD97RS0Ae46OpH0Nk3sWOIzJQ+PhCJNTUZZVp760RzKcLWRTW1r98OA4a5ZVLep1Ny13AsfeY6OLuo7JLQscxuShQXd2tVc1jnS2kH11cJy1iwwc1eUlnNFUxd5jVuMoZBY4jMlDQ2PO5D/Pahx1FQSCIaaT7KQemZzixPjUogMHOM1Ve49ajaOQWeAwJg8NuFu7elbjqK9gOqr0B5NrrjrsrmqbmcBRx8GBMSbC0wtnNnnJAocxeWho3Gmq8mJUFTgLHcLJuRkLiQWOxfZxAHSsqkMVdh8dXvS1TG5Y4DAmDw0EncDR5GHnOJD08uqvZjBwnL+2AYBnXzmx6GuZ3LDAYUweGhgLU+ITajO8wGHMzLIjSQ7JPdg/TkNVaUZW6m2trWDNskqefXVo0dcyuWGBw5g81DcSoqW2HF+GFziMaaouo9QvSc/l6OobZWNrTcZe/8K1jTzzyhDOTtGm0FjgMCYP9Y1O0prmYoLJ8PmE1trkhuSqKvv7gmxorc3Y61/Yvoy+0RAHE6yU2zcyyce3PccNX++kbzS9fUOMtzwNHCKyRUT2iUiXiNyc4PlyEXnAfX6niLTHPXeLm75PRN416zy/iDwnIj/wsvzG5ErfSIjWNPb1TsXyuuT2Hh8YC3NifCqjNY63bWoB4NG9faekhyLT/P6/P82PXjrGo3v7+Pi3nrdaSR7yLHCIiB+4C7gC6ACuEZGOWdmuB4ZUdQNwJ3CHe24HcDVwDrAF+Kp7vZiPA3u8KrsxudY3Oul54HBmjy8cOPYfd1az3bg8c4FjzbIqNrTW8Ni+UwPHPY8fZPfREb5y7QV85j2v4cnuAesLyUNe1jguArpUtVtVw8A2YOusPFuB+9zjh4DLRETc9G2qGlLVg0CXez1EZDXwHuBrHpbdmJwJR6IMjU/RWutdUxWcnD2+0Df6rj5nst7GDDZVAVz2mlZ2dg8SGHXmkvSemOCff9rF5R3LeWfHcn5n8xpqykt46JmejL6uWTwvA0cbcDjucY+bljCPqkaAYaBpgXP/CfgLIJr5IhuTewF3Ut7yOm9rHKsbqxgLTzM0PjVvvv19QWrLSzJenqsuXEMkqnxz5yuoKrd+/yUAPvtep2GiuryEt25q5tG9fdZclWcKqnNcRN4L9KnqM0nkvUFEOkWkMxAIZKF0xmRGn9t81Opx4GhvcuZkHBqYfyvXl4+Psr61BqcxIHM2tNZwecdy/u/PD/Dxbc/z0719fOqdm06ZK/K2s1o5PhLi5eO2+VM+8TJw9AJr4h6vdtMS5hGREqAeGJjn3DcB7xORQzhNX+8QkW8kenFVvVtVN6vq5paWlsXfjTFZ0uc23XjdVHVGUzUAr8wTOKJRZdeREc5ZVedJGT73/nNZu6yK7b8+wrUXr+X6N6875fk3tC8DsH6OPOPN7CLH08BGEVmH80f/auDaWXm2A9cBTwJXAo+qqorIduA/ReSLwCpgI/CUqj4J3AIgIm8DPq2qH/TwHozJuuNZqnGsWVaJT5zJfXN5ZXCc0ckIr1td70kZltdVsONjb2F0MpJwm9z2pioaq0p59pUhrrlorSdlMKnzLHCoakREbgIeBvzAvaq6S0RuAzpVdTtwD3C/iHQBgzjBBTffg8BuIALcqKq2IppZEnqHJigr8dHs0XIjMeUlflY1VM5b43ihx1kW5LVtDZ6Vo8Tvm3NvdRHh/LWNPHfYlifJJ17WOFDVHcCOWWm3xh1PAlfNce7twO3zXPtnwM8yUU5j8knPiQnaGio9mzUer72pmkMDc9c4XuodprzEl9GhuKk6f00Dj+7tY3RyitoKb1YLNqkpqM5xY5aCniEncGTDuuZqugPBOUctvdAzTMeqOkr9uftT8ZqVTv/K/j7rIM8XFjiMyTO9QxOsbsxO4DhrRS2jkxF6T0yc9tx0VHmpd5jXtnnTv5Gs2HazL9t2s3nDAocxeWRyapr+YChrNY7XrHT3AE+wI9/uIyOMhafZ7I5sypXVjZVUlvrZd9wCR76wwGFMHukZcr75r16WrRqH0wy05+jpe4D/qnsAgEvW5TZw+HzCpuU1vGyBI29Y4DAmj8SajNoaFr9hUjJqyktYu6yKvQmagX7VPcCZzdWertKbrE3La9l3zPo48oUFDmPyyCF3mfEzmrITOAA6VtbxQu+pw11DkWl2HhzkkvVNWSvHfM5aUUt/MMTgWDjXRTFY4DAmrxwIBKkpL/F8Zdx4m9sbOTw4wdHhkx3kTxwYIBiK8M7XLM9aOeazMdZBbs1VecECh8NPaCgAAA+mSURBVDF5pDswxpkt1RlfF2o+F69zahU7uwdn0n686zjVZX4uzZMaxwZ3L5AuG5KbFyxwGJNHugNBzmyuzuprdqyqo7mmjEd2HweckV0/fOkobz+7lYpS/wJnZ8fKugqqyvwcCFjgyAcWOIzJE+PhCEeGJ1nfkt1Z2n6fsOXcFTy6t48T42G+91wvJ8anuDaP1oby+YQzW6o5EJh/JV+THRY4jMkT3e4fxTOzHDgAPnjJGUxGpvn0t1/gH3+8j/PXNuRNM1XM+pYaDlhTVV6wwGFMntjnDok9a0X2A8fZK+r4yG+s5yd7jqMKd/z267Laz5KMDS019J6YYDwcyXVRljxPFzk0xiRv99ERKkp9rGvOzYKCf7HlbN533ipW1FXQUJV4tdpcWu92kHcHxjg3x8ugLHVW4zAmT+w+MsJZK+rwZ2FV3LmcvaIuL4MGMNP3Yx3kuWeBw5g8oKrsPjpCx0pvdtorBu3NVfgE6+fIAxY4jMkDvScmGJ6YosNddNCcrrzEz9plVTayKg9Y4DAmDzz7qrPkx3lrGnNckvy2vqXGmqrygAUOY/LAs68MUVnqn1nm3CS2vrWG7v4xpqOJN54y2WGBw5g80PnKIOetaaAkhzvtFYINLTWEI1F6hube7tZ4z9NPqYhsEZF9ItIlIjcneL5cRB5wn98pIu1xz93ipu8TkXe5aWtE5DER2S0iu0Tk416W35hsGAtF2HN0lM3t1ky1kPWtznIs1lyVW54FDhHxA3cBVwAdwDUi0jEr2/XAkKpuAO4E7nDP7QCuBs4BtgBfda8XAf5MVTuAS4AbE1zTmILy68MnmI4qF5xhgWMhM0Ny+6yDPJe8rHFcBHSpareqhoFtwNZZebYC97nHDwGXiTNddSuwTVVDqnoQ6AIuUtWjqvosgKqOAnuANg/vwRjPPdk9gE/ggrUWOBbSUFVGc02Z1ThyzMvA0QYcjnvcw+l/5GfyqGoEGAaakjnXbdY6H9iZ6MVF5AYR6RSRzkAgkPZNGOO1X+7v5/VrGqivLM11UQrCmS01trx6jhVkT5yI1ADfAT6hqqdvlgyo6t2qullVN7e0tGS3gMYkaXh8ihd6TvCWjfYZTZYNyc09LwNHL7Am7vFqNy1hHhEpAeqBgfnOFZFSnKDxTVX9riclNyZLnjjQT1ThLRubc12UgrGhtYah8SnbRjaHvAwcTwMbRWSdiJThdHZvn5VnO3Cde3wl8Kiqqpt+tTvqah2wEXjK7f+4B9ijql/0sOzGZMUv9geoKS/hvDUNuS5KwVjf4oyssuaq3PEscLh9FjcBD+N0Yj+oqrtE5DYReZ+b7R6gSUS6gE8BN7vn7gIeBHYDPwJuVNVp4E3A7wHvEJHn3Z93e3UPxngpGlUe2d3Hb5zVQqnN30iaLXaYe54uq66qO4Ads9JujTueBK6a49zbgdtnpT0O5NcmAcak6bnDQ/QHQ1zesTzXRSkobQ2VVJT6bLHDHLKvOcbkyI93HafUL7z97NZcF6Wg+HzCmc3WQZ5LFjiMyQFV5eFdx7h0fTN1FTYMN1XrW2vossCRMxY4jMmBPUdHOTQwbs1UadrQUkPP0ASTU9O5LsqSZIHDmBz43nM9lPqF97x2Za6LUpDWt1ajCgf7bemRXLDAYUyWRaajfP/5I7z9rFYaq/Nzm9Z8FxtZZUNyc8MChzFZ9suufgKjIT5wgS2zlq51zdX4BPZb4MgJCxzGZNl9Txyipbacd5xt/Rvpqij1s7G1lhd7TuS6KEuSBQ5jsuhAIMjP9gX44MVnUFZi//0W47w1DTx3+ATOYhMmm+yTa0wWfe2X3ZT5fVx78dpcF6Xgnb+2gRPjUxwasN0As80ChzFZ0h0I8mBnD9devJaW2vJcF6fgnbfWWd/ruVeHclySpccChzFZEI0qn/neS1SW+rnpHRtyXZyisLG1loaqUp44MJDroiw5FjiMyYJ/frSLJ7sH+Kv3vIbmGqttZILfJ7xpfTOP7++3fo4ss8BhjIeCoQi3/89u7vzJy3zggjZ+9w1rFj7JJO0tG5s5NjJp8zmyzNPVcY1ZavpGJnnq0CC/PnyCF3qGebF3mPHwNL93yRnc+psdOFvKmEx56yZn58SHdx1j4/LaHJdm6bDAYcwiRaPKz18OcM/jB3m8qx+AshIfHSvruPLC1fzW+W2cv7Yxx6UsTqsaKrmofRnff/4IN759gwXmLLHAYUyaRian+K/nevn3Jw7RHRhjRV0Ff/bOTbx1Uwsdq+psc6Ysef/5bfzl917k1z3DtpNilljgMCaBwGiIZ14Z4sR4mPHwNNNRZVqV6agSikTZ1TvM/zvQz+RUlNevrudLV5/Hu1+70oJFDrz39Sv5/A/3cNdjXfzbhzbnujhLggUOs2RMRxWfkLA5Y3hiiqcODvLEgX6e6Bpg3/HRea91Zks1V164mqsuXMPrVtdbE0kO1VWUcv2bz+TOn7zMkwcGuHR9U66LVPQ8DRwisgX4EuAHvqaqn5/1fDnwdeBCYAD4XVU95D53C3A9MA18TFUfTuaaxgAcH5nkqYODdB4a5KlDQ3QHgoQiUcpKfCyrKqOhqpTGqjKiqgRGQ3S7y3OXl/h4Q/sytp6/iovXNbGyvoKqMj9+n+D3CT5xflvNIr/84VvW8V/P9/KJB57jwT++lDOaqnNdpKImXo1/FhE/8DLwTqAHeBq4RlV3x+X5KPA6Vf0TEbka+C1V/V0R6QC+BVwErAJ+AmxyT5v3mols3rxZOzs7M3p/5lTTUWVqOur+KJHpKOL+kfWL4PMx80f35O/E3/7B6XCORJWoOr+no4qqogpRVRRmjgeCYY6PTHIgEOTF3mGee/UErw46y1BUlfm5YG0jHavqqC4rYTwcYXAszND4FCfGw/hEWFZdxrltdVxwRiMXrG2kotSfxX85kym7j4xw7dd+BcCNb9vA5ecsZ01jFT6f1QbTJSLPqOpp7X9e1jguArpUtdstwDZgKxD/R34r8Dfu8UPAV8T5S7IV2KaqIeCgiHS51yOJa2bMH973NIcGxk+ZXKSnHZw8TJRPT8mnp6cliNux6yin59O41JNpia63UL55XiOF+51yA0a63z98wkwwAScARaLpf5lZWV/B61bX86FLz+AN7cusk3oJ6VhVx3c+8kY+870XuX3HHm7fsYcyv4+6ylLK/EJZiQ9/ikEknSbIfAtTP/jYmykvyeyXIS8DRxtwOO5xD3DxXHlUNSIiw0CTm/6rWefGNi9Y6JoAiMgNwA0Aa9emt6DcGU3VJ//B4z4NscP4D9XJtOTynXo9mefcBfIluGAsLf4DfDJtoXynf+znO7fU76PU7zTdlPiFMr+PUr/zH1TdzuRpJe5YiUaV6Sgnj9WpWaBQ4ndqKH6fD7+Pmd8+EffHKaO4v30Cy6rKaK2rYM2ySlprK04rv1k61rfUsO2GS+kOBHmye4BXB8cZnYwQjkQJR6JMp/INJ43vL5rOSR4TD0JZ0XaOq+rdwN3gNFWlc43Pvrcjo2UyxmTHmS01nOnuEmgyz8s6fC8Qv77CajctYR4RKQHqcTrJ5zo3mWsaY4zxkJeB42lgo4isE5Ey4Gpg+6w824Hr3OMrgUfVaWDfDlwtIuUisg7YCDyV5DWNMcZ4yLOmKrfP4ibgYZyhs/eq6i4RuQ3oVNXtwD3A/W7n9yBOIMDN9yBOp3cEuFFVpwESXdOrezDGGHM6z4bj5hMbjmuMMambaziujVM0xhiTEgscxhhjUmKBwxhjTEoscBhjjEnJkugcF5EA8EoWXqoZ6M/C62SL3U/+K7Z7svvJL2eoasvsxCUROLJFRDoTjUAoVHY/+a/Y7snupzBYU5UxxpiUWOAwxhiTEgscmXV3rguQYXY/+a/Y7snupwBYH4cxxpiUWI3DGGNMSixwGGOMSYkFjnmIyL0i0iciL81K/1MR2Ssiu0TkC3Hpt4hIl4jsE5F3xaVvcdO6ROTmbN5DvET3IyIPiMjz7s8hEXk+7rm8vh+3LInu6TwR+ZV7T50icpGbLiLyZbfcL4jIBXHnXCci+92f6xK9VjbMcT+vF5EnReRFEflvEamLey6v3yMRWSMij4nIbvf/y8fd9GUi8oj77/2IiDS66Xn9Hs1zP1e5j6MisnnWOXn9HqVFVe1njh/grcAFwEtxaW8HfgKUu49b3d8dwK+BcmAdcABn6Xe/e3wmUObm6ciX+5n1/D8CtxbK/czzHv0YuMI9fjfws7jjH+LslnsJsNNNXwZ0u78b3ePGPLqfp4HfcI8/DHyuUN4jYCVwgXtcC7zslvsLwM1u+s3AHYXwHs1zP68BzgJ+BmyOy5/371E6P1bjmIeq/gJnn5B4HwE+r6ohN0+fm74V2KaqIVU9CHQBF7k/XararaphYJubN+vmuB/A+aYH/A7wLTcp7+8H5rwnBWLfyuuBI+7xVuDr6vgV0CAiK4F3AY+o6qCqDgGPAFu8L/3p5rifTcAv3ONHgN92j/P+PVLVo6r6rHs8CuwB2tzy3Odmuw94v3uc1+/RXPejqntUdV+CU/L+PUqHBY7UbQLeIiI7ReTnIvIGN70NOByXr8dNmys937wFOK6q+93HhXw/nwD+XkQOA/8A3OKmF+o97eLkH5WrOLl9ckHdj4i0A+cDO4HlqnrUfeoYsNw9Lph7mnU/cymY+0mFBY7UleBUly8B/hx40P22Xuiu4WRto9B9BPikqq4BPomz02Qh+zDwURF5Bqd5JJzj8qRMRGqA7wCfUNWR+OfUadMpqHkB893PUmCBI3U9wHfdqvRTQBRnIbNeTn4TBFjtps2VnjdEpAT4APBAXHLB3g/OPvbfdY+/jdMsAAV6T6q6V1UvV9ULcYL7AfepgrgfESnF+SP7TVWNvS/H3SYo3N+xJt+8v6c57mcueX8/acl1J0u+/wDtnNpR+SfAbe7xJpzqpgDncGonWDdOB1iJe7yOk51g5+TL/bhpW4Cfz0oriPuZ4z3aA7zNPb4MeMY9fg+ndrw+5aYvAw7idLo2usfL8uh+YgMwfMDXgQ8Xynvk/lt/HfinWel/z6md418ohPdorvuJe/5nnNo5nvfvUVr/DrkuQD7/4Hy7OwpM4dQ0rnff5G8ALwHPAu+Iy/8ZnG+D+3BH9bjp78YZfXEA+Ew+3Y+b/h/AnyTIn9f3M8979GbgGfc/407gQjevAHe55X5x1n/wD+N0XHYBf5Bn9/Nx99/7ZeDzuCs+FMJ75L4XCrwAPO/+vBtoAn4K7McZpbisEN6jee7nt9z3KwQcBx4ulPconR9bcsQYY0xKrI/DGGNMSixwGGOMSYkFDmOMMSmxwGGMMSYlFjiMMcakxAKHMcaYlFjgMMYYk5L/D40TI/g1Qv7KAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5lGQiW3IJl2",
        "outputId": "630b401f-5edc-4205-8255-373e7dfedfbb"
      },
      "source": [
        "df['yr_built'][df['yr_built'] > 0].describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4818.0\n",
              "mean     1936.4\n",
              "std        42.5\n",
              "min      1725.0\n",
              "25%      1900.0\n",
              "50%      1925.0\n",
              "75%      1971.0\n",
              "max      2017.0\n",
              "Name: yr_built, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRaWWjuSIRBb",
        "outputId": "ca397f79-71bf-413a-8049-ff2c6c250483"
      },
      "source": [
        "df['ls_price'][df['ls_price'] > 10].describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3.084000e+03\n",
              "mean     1.157600e+07\n",
              "std      5.723535e+07\n",
              "min      2.000000e+01\n",
              "25%      1.247500e+05\n",
              "50%      3.313050e+05\n",
              "75%      3.050076e+06\n",
              "max      1.598200e+09\n",
              "Name: ls_price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "N_qLa-l_Myum",
        "outputId": "ed4c0bd0-9360-4ab5-e473-85565478e139"
      },
      "source": [
        "df['ls_price'][df['ls_price'] > 10][df['ls_price'] < 400].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd793f310>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixirzbbTIm5N",
        "outputId": "a49ec375-080d-41b8-dc75-19947671123c"
      },
      "source": [
        "df['property_3'][df['property_3'] > 0].describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     5448.0\n",
              "mean      1986.4\n",
              "std        560.2\n",
              "min       1001.0\n",
              "25%       1605.0\n",
              "50%       2019.0\n",
              "75%       2339.0\n",
              "max      21245.0\n",
              "Name: property_3, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "SNNFl0AGNm8g",
        "outputId": "24b6b8a7-4438-4806-ca40-48c6ed86cb24"
      },
      "source": [
        "df['property_3'][df['property_3'] > 0][df['property_3'] < 20000].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd7373490>"
            ]
          },
          "metadata": {},
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkGWwUHdKJF3",
        "outputId": "95d20c23-7201-4d01-ca6d-6ca7f246332b"
      },
      "source": [
        "df['realesttyp'][df['realesttyp'] > 0].describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4648.0\n",
              "mean        3.6\n",
              "std         2.1\n",
              "min         1.0\n",
              "25%         2.0\n",
              "50%         4.0\n",
              "75%         4.0\n",
              "max        12.0\n",
              "Name: realesttyp, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KSz0wntKmkL",
        "outputId": "91d09dd5-92a5-4adb-ec6a-17fd0908bdc8"
      },
      "source": [
        "df['realesttyp'][df['realesttyp'] > 0].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "FcOeZokOOS8B",
        "outputId": "3a71037d-8c33-4ff3-d3af-0edfec0ba5c0"
      },
      "source": [
        "df['realesttyp'][df['realesttyp'] > 0].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd72c5550>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "HG1wN5viO32T",
        "outputId": "422e8575-9ac1-4dd6-87ec-eaeeb076d69e"
      },
      "source": [
        "df['ls_date'][df['ls_date'] > 20210000.0][df['ls_date'] < 20210000.0].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcd7116450>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "JPjssNquTvPV",
        "outputId": "30a71846-6f16-4870-91bf-6993d2580ac7"
      },
      "source": [
        "ax = df_imp['num_rooms'][df_imp['num_rooms'] < 10].plot.kde()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsed2yeOiTN4",
        "outputId": "bd7ebe55-d9ef-4849-8fc4-68b775616242"
      },
      "source": [
        "df['num_rooms'][df['num_rooms'] < 10].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0.0000    2459\n",
              " 8.0000     168\n",
              " 6.0000     147\n",
              " 7.0000     103\n",
              " 5.0000      87\n",
              " 9.0000      67\n",
              " 4.0000      34\n",
              " 3.0000      15\n",
              " 2.0000      15\n",
              "-0.9999       7\n",
              " 1.0000       5\n",
              "Name: num_rooms, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WX1cb4tDhrdU",
        "outputId": "e759b2db-ecfa-42ed-8030-24947abfbb91"
      },
      "source": [
        "df_imp['num_rooms'][df_imp['num_rooms'] > 2000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3212    3604.0\n",
              "3228    3604.0\n",
              "Name: num_rooms, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "auGS-xOwlDr6"
      },
      "source": [
        "This is where I will value count so I can see the number of occurences of each value present in the following df columns. This will help me see what are most frequence values in each."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhdR4k45E22m",
        "outputId": "b4bc166a-bffa-4db9-eb35-c8adc1ae27bb"
      },
      "source": [
        "df['ls_price'][df['ls_price'] < 2].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0.0000      1837\n",
              " 1.0000       495\n",
              "-0.9999         7\n",
              "-100.0000       3\n",
              "Name: ls_price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2W3byc4yyzs7",
        "outputId": "6a6c571b-8451-491b-ff0a-504216e5e924"
      },
      "source": [
        "df['initiating'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Efiled SP Summons and Complaint - Non-payment of Rent                          2876\n",
              "Summary Process - Residential (c239)                                            941\n",
              "SP Summons and Complaint - Non-payment of Rent                                  819\n",
              "Efiled SP Summons and Complaint - Cause                                         342\n",
              "Efiled SP Summons and Complaint - No Cause                                      224\n",
              "SP Summons and Complaint - No Cause                                             102\n",
              "SP Summons and Complaint - Cause                                                 74\n",
              "SP Transfer - Non-payment of Rent                                                44\n",
              "Efiled SP Summons and Complaint - Foreclosure                                    38\n",
              "SP Transfer- No Cause                                                            17\n",
              "SP Transfer - Cause                                                              12\n",
              "Public Housing Tenant Illegal Activity Declaratory Judgment (c139 &#167;19)       3\n",
              "Summary Process Residential Non-payment of Rent                                   2\n",
              "SP Summons and Complaint - Foreclosure                                            2\n",
              "Case transferred from another court or has prior manual docket - no fee due       1\n",
              "SP Transfer - Foreclosure                                                         1\n",
              "Name: initiating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkrdnNOfZTSc",
        "outputId": "84fedd9f-aca5-4680-f880-8354db66082f"
      },
      "source": [
        "print(df_imp['file_date'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020-12-21    393\n",
            "2020-11-16    391\n",
            "2020-12-14    343\n",
            "2020-12-07    305\n",
            "2020-11-30    263\n",
            "             ... \n",
            "2020-12-05      1\n",
            "2020-11-26      1\n",
            "2020-12-12      1\n",
            "2021-01-09      1\n",
            "2020-12-26      1\n",
            "Name: file_date, Length: 64, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRJLYtnZZfuv",
        "outputId": "ef1debee-01a8-4b14-f22e-43702df88ca1"
      },
      "source": [
        "df_imp['num_rooms'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0      1977\n",
              "12.0      232\n",
              "15.0      231\n",
              "18.0      196\n",
              "10.0      171\n",
              "         ... \n",
              "58.0        1\n",
              "216.0       1\n",
              "76.0        1\n",
              "992.0       1\n",
              "75.0        1\n",
              "Name: num_rooms, Length: 228, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVu_bhBURu0L",
        "outputId": "9730bf5c-debc-4353-9de4-683130c83832"
      },
      "source": [
        "#not exact duplicats but very similar\n",
        "np.where(df[\"ARC_Street\"] == df[\"property_2\"], True, False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False, ...,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CC26LyLllWX-"
      },
      "source": [
        "Following code is to check if there are exact duplicate columns in the dataframe. There was only one: propert_1.\n",
        "There are some others that are almost the same. I explained what they are in the report, the preliminary analysis section."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TXug3a0Ss1q",
        "outputId": "8fb4a639-9d27-467f-c335-6339a72e7387"
      },
      "source": [
        "# import pandas library\n",
        "import pandas as pd\n",
        "\n",
        "# This function take a dataframe\n",
        "# as a parameter and returning list\n",
        "# of column names whose contents\n",
        "# are duplicates.\n",
        "def getDuplicateColumns(df):\n",
        "\n",
        "\t# Create an empty set\n",
        "\tduplicateColumnNames = set()\n",
        "\t\n",
        "\t# Iterate through all the columns\n",
        "\t\n",
        "\n",
        "\t# of dataframe\n",
        "\tfor x in range(df.shape[1]):\n",
        "\t\t\n",
        "\t\t# Take column at xth index.\n",
        "\t\tcol = df.iloc[:, x]\n",
        "\t\t\n",
        "\t\t# Iterate through all the columns in\n",
        "\t\t# DataFrame from (x + 1)th index to\n",
        "\t\t# last index\n",
        "\t\tfor y in range(x + 1, df.shape[1]):\n",
        "\t\t\t\n",
        "\t\t\t# Take column at yth index.\n",
        "\t\t\totherCol = df.iloc[:, y]\n",
        "\t\t\t\n",
        "\t\t\t# Check if two columns at x & y\n",
        "\t\t\t# index are equal or not,\n",
        "\t\t\t# if equal then adding\n",
        "\t\t\t# to the set\n",
        "\t\t\tif col.equals(otherCol):\n",
        "\t\t\t\tduplicateColumnNames.add(df.columns.values[y])\n",
        "\t\t\t\t\n",
        "\t# Return list of unique column names\n",
        "\t# whose contents are duplicates.\n",
        "\treturn list(duplicateColumnNames)\n",
        "\n",
        "# Driver code\n",
        "if __name__ == \"__main__\" :\n",
        "\n",
        "\n",
        "\t# Get list of duplicate columns\n",
        "\tduplicateColNames = getDuplicateColumns(df)\n",
        "\n",
        "\tprint('Duplicate Columns are :')\n",
        "\t\t\n",
        "\t# Iterate through duplicate\n",
        "\t# column names\n",
        "\tfor column in duplicateColNames :\n",
        "\t  print('Column Name : ', column)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate Columns are :\n",
            "Column Name :  property_1\n"
          ]
        }
      ]
    }
  ]
}