Redfin_Market_Data_Zip_Code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Redfin Market Data - Zip Code.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNvSjFSogRo/WZRFd6z7K/V",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/analyticsariel/projects/blob/master/Redfin_Market_Data_Zip_Code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYRys7lpU-iF"
      },
      "source": [
        "# Redfin Market Data - Zip Code\n",
        "\n",
        "## Overview\n",
        "| Detail Tag            | Information                                                                                        |\n",
        "|-----------------------|----------------------------------------------------------------------------------------------------|\n",
        "| Originally Created By | Ariel Herrera arielherrera@analyticsariel.com |\n",
        "| External References   | API |\n",
        "| Input Datasets        | Source name |\n",
        "| Output Datasets       | Source name |\n",
        "| Input Data Source     | Pandas DataFrame |\n",
        "| Output Data Source    | Pandas DataFrame |\n",
        "\n",
        "## History\n",
        "| Date         | Developed By  | Reason                                                |\n",
        "|--------------|---------------|-------------------------------------------------------|\n",
        "| 1st Jun 2021 | Ariel Herrera | Create notebook. |\n",
        "\n",
        "## Getting Started\n",
        "1. Copy this notebook -> File -> Save a Copy in Drive\n",
        "2. Directions\n",
        "\n",
        "## Useful Resources\n",
        "- [Google Colab Cheat Sheet](https://towardsdatascience.com/cheat-sheet-for-google-colab-63853778c093)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKgiM1tbUnbI"
      },
      "source": [
        "## <font color=\"blue\">Install Packages</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7V2OU_iGUppW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZgrtjtLUi6a"
      },
      "source": [
        "## <font color=\"blue\">Imports</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vWKHxJDUjOJ"
      },
      "source": [
        "from google.colab import drive, files # specific to Google Colab\n",
        "from datetime import datetime\n",
        "import pandas as pd\n",
        "import time\n",
        "import warnings\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkdXQZp8UqcC"
      },
      "source": [
        "## <font color=\"blue\">Functions</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5p76TDONUsXe"
      },
      "source": [
        ""
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9B1YSUk9UsoZ"
      },
      "source": [
        "## <font color=\"blue\">Locals & Constants</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0x9BC1nUqO6"
      },
      "source": [
        ""
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGCaqKj1UvIo"
      },
      "source": [
        "## <font color=\"blue\">Data</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jT3eZXJ_UwaS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "e7af0da3-ed3b-48d0-cd7d-825abcd164ab"
      },
      "source": [
        "# url link from - https://www.redfin.com/news/data-center/\n",
        "url = 'https://redfin-public-data.s3.us-west-2.amazonaws.com/redfin_market_tracker/zip_code_market_tracker.tsv000.gz'\n",
        "\n",
        "# start time to read file\n",
        "startTime = time.time()\n",
        "df = pd.read_csv(url, compression='gzip', sep='\\t', on_bad_lines='skip')\n",
        "# end time \n",
        "executionTime = (time.time() - startTime)\n",
        "print('Execution time in minutes: ' + str(round(executionTime / 60, 2)))\n",
        "print('Num of rows:', len(df))\n",
        "print('Num of cols:', len(df.columns))\n",
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time in minutes: 2.29\n",
            "Num of rows: 5884029\n",
            "Num of cols: 58\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  period_begin  period_end  period_duration region_type  region_type_id  \\\n",
              "0   2013-02-01  2013-04-30               90    zip code               2   \n",
              "1   2015-09-01  2015-11-30               90    zip code               2   \n",
              "2   2016-01-01  2016-03-31               90    zip code               2   \n",
              "3   2017-04-01  2017-06-30               90    zip code               2   \n",
              "4   2015-01-01  2015-03-31               90    zip code               2   \n",
              "\n",
              "   table_id is_seasonally_adjusted           region  city          state  \\\n",
              "0     10714                      f  Zip Code: 26757   NaN  West Virginia   \n",
              "1     35915                      f  Zip Code: 84093   NaN           Utah   \n",
              "2     34773                      f  Zip Code: 80238   NaN       Colorado   \n",
              "3      1373                      f  Zip Code: 04210   NaN          Maine   \n",
              "4     23258                      f  Zip Code: 54603   NaN      Wisconsin   \n",
              "\n",
              "  state_code              property_type  property_type_id  median_sale_price  \\\n",
              "0         WV  Single Family Residential                 6            95000.0   \n",
              "1         UT                  Townhouse                13           299270.0   \n",
              "2         CO                  Townhouse                13           431250.0   \n",
              "3         ME                Condo/Co-op                 3           180000.0   \n",
              "4         WI                Condo/Co-op                 3           195000.0   \n",
              "\n",
              "   median_sale_price_mom  median_sale_price_yoy  median_list_price  \\\n",
              "0               0.032609              -0.208333           135000.0   \n",
              "1              -0.077749               0.088255           295000.0   \n",
              "2               0.140873               0.051913           415422.5   \n",
              "3              -0.088608              -0.100000           117400.0   \n",
              "4                    NaN                    NaN           199800.0   \n",
              "\n",
              "   median_list_price_mom  median_list_price_yoy  median_ppsf  median_ppsf_mom  \\\n",
              "0               0.040864              -0.068323    83.333333         0.218966   \n",
              "1               0.000000                    NaN   118.406430         0.069277   \n",
              "2               0.000120               0.118513   201.993317        -0.113819   \n",
              "3              -0.190345              -0.079216   112.781955        -0.063703   \n",
              "4                    NaN                    NaN   165.254237              NaN   \n",
              "\n",
              "   median_ppsf_yoy  median_list_ppsf  median_list_ppsf_mom  \\\n",
              "0        -0.136719        110.644861              0.165324   \n",
              "1        -0.039831        120.010225             -0.040325   \n",
              "2         0.249501        225.313886              0.000000   \n",
              "3         0.221940         91.640723             -0.159899   \n",
              "4              NaN        145.475728                   NaN   \n",
              "\n",
              "   median_list_ppsf_yoy  homes_sold  homes_sold_mom  homes_sold_yoy  \\\n",
              "0              0.181424        25.0        0.315789        0.470588   \n",
              "1                   NaN         4.0        1.000000        3.000000   \n",
              "2              0.305622         6.0        0.200000       -0.250000   \n",
              "3             -0.017776         7.0        0.750000        0.400000   \n",
              "4                   NaN         1.0             NaN             NaN   \n",
              "\n",
              "   pending_sales  pending_sales_mom  pending_sales_yoy  new_listings  \\\n",
              "0            2.0          -0.333333                0.0          17.0   \n",
              "1            NaN                NaN                NaN           NaN   \n",
              "2            6.0           0.200000                0.5          20.0   \n",
              "3            4.0           0.000000                1.0          10.0   \n",
              "4            1.0                NaN                NaN           4.0   \n",
              "\n",
              "   new_listings_mom  new_listings_yoy  inventory  inventory_mom  \\\n",
              "0         -0.392857         -0.638298       54.0      -0.129032   \n",
              "1               NaN               NaN        NaN            NaN   \n",
              "2          0.428571          1.222222        7.0       0.000000   \n",
              "3          0.111111          0.000000        3.0      -0.500000   \n",
              "4               NaN               NaN        4.0            NaN   \n",
              "\n",
              "   inventory_yoy  months_of_supply  months_of_supply_mom  \\\n",
              "0      -0.325000               NaN                   NaN   \n",
              "1            NaN               NaN                   NaN   \n",
              "2       1.333333               NaN                   NaN   \n",
              "3      -0.571429               NaN                   NaN   \n",
              "4            NaN               NaN                   NaN   \n",
              "\n",
              "   months_of_supply_yoy  median_dom  median_dom_mom  median_dom_yoy  \\\n",
              "0                   NaN       105.0            -9.0           -19.0   \n",
              "1                   NaN        40.5             3.5          -177.5   \n",
              "2                   NaN         5.0             0.0           -17.5   \n",
              "3                   NaN        19.0             9.0             6.0   \n",
              "4                   NaN       333.0             NaN             NaN   \n",
              "\n",
              "   avg_sale_to_list  avg_sale_to_list_mom  avg_sale_to_list_yoy  \\\n",
              "0          0.957351             -0.003772              0.028875   \n",
              "1          0.977453             -0.024910             -0.013896   \n",
              "2          1.009202              0.009876              0.020281   \n",
              "3          0.949244             -0.004520             -0.008079   \n",
              "4          0.975488                   NaN                   NaN   \n",
              "\n",
              "   sold_above_list  sold_above_list_mom  sold_above_list_yoy  price_drops  \\\n",
              "0         0.160000             0.054737             0.160000          NaN   \n",
              "1         0.250000            -0.250000             0.250000          NaN   \n",
              "2         0.500000             0.300000             0.250000          NaN   \n",
              "3         0.142857             0.142857             0.142857          NaN   \n",
              "4         0.000000                  NaN                  NaN          NaN   \n",
              "\n",
              "   price_drops_mom  price_drops_yoy  off_market_in_two_weeks  \\\n",
              "0              NaN              NaN                 0.000000   \n",
              "1              NaN              NaN                      NaN   \n",
              "2              NaN              NaN                 0.666667   \n",
              "3              NaN              NaN                 0.750000   \n",
              "4              NaN              NaN                 1.000000   \n",
              "\n",
              "   off_market_in_two_weeks_mom  off_market_in_two_weeks_yoy  \\\n",
              "0                     0.000000                     0.000000   \n",
              "1                          NaN                          NaN   \n",
              "2                     0.266667                    -0.083333   \n",
              "3                     0.750000                     0.250000   \n",
              "4                          NaN                          NaN   \n",
              "\n",
              "  parent_metro_region  parent_metro_region_metro_code         last_updated  \n",
              "0      Winchester, VA                           49020  2022-06-12 14:28:57  \n",
              "1  Salt Lake City, UT                           41620  2022-06-12 14:28:57  \n",
              "2          Denver, CO                           19740  2022-06-12 14:28:57  \n",
              "3        Lewiston, ME                           30340  2022-06-12 14:28:57  \n",
              "4       La Crosse, WI                           29100  2022-06-12 14:28:57  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>period_begin</th>\n",
              "      <th>period_end</th>\n",
              "      <th>period_duration</th>\n",
              "      <th>region_type</th>\n",
              "      <th>region_type_id</th>\n",
              "      <th>table_id</th>\n",
              "      <th>is_seasonally_adjusted</th>\n",
              "      <th>region</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>state_code</th>\n",
              "      <th>property_type</th>\n",
              "      <th>property_type_id</th>\n",
              "      <th>median_sale_price</th>\n",
              "      <th>median_sale_price_mom</th>\n",
              "      <th>median_sale_price_yoy</th>\n",
              "      <th>median_list_price</th>\n",
              "      <th>median_list_price_mom</th>\n",
              "      <th>median_list_price_yoy</th>\n",
              "      <th>median_ppsf</th>\n",
              "      <th>median_ppsf_mom</th>\n",
              "      <th>median_ppsf_yoy</th>\n",
              "      <th>median_list_ppsf</th>\n",
              "      <th>median_list_ppsf_mom</th>\n",
              "      <th>median_list_ppsf_yoy</th>\n",
              "      <th>homes_sold</th>\n",
              "      <th>homes_sold_mom</th>\n",
              "      <th>homes_sold_yoy</th>\n",
              "      <th>pending_sales</th>\n",
              "      <th>pending_sales_mom</th>\n",
              "      <th>pending_sales_yoy</th>\n",
              "      <th>new_listings</th>\n",
              "      <th>new_listings_mom</th>\n",
              "      <th>new_listings_yoy</th>\n",
              "      <th>inventory</th>\n",
              "      <th>inventory_mom</th>\n",
              "      <th>inventory_yoy</th>\n",
              "      <th>months_of_supply</th>\n",
              "      <th>months_of_supply_mom</th>\n",
              "      <th>months_of_supply_yoy</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "      <th>avg_sale_to_list</th>\n",
              "      <th>avg_sale_to_list_mom</th>\n",
              "      <th>avg_sale_to_list_yoy</th>\n",
              "      <th>sold_above_list</th>\n",
              "      <th>sold_above_list_mom</th>\n",
              "      <th>sold_above_list_yoy</th>\n",
              "      <th>price_drops</th>\n",
              "      <th>price_drops_mom</th>\n",
              "      <th>price_drops_yoy</th>\n",
              "      <th>off_market_in_two_weeks</th>\n",
              "      <th>off_market_in_two_weeks_mom</th>\n",
              "      <th>off_market_in_two_weeks_yoy</th>\n",
              "      <th>parent_metro_region</th>\n",
              "      <th>parent_metro_region_metro_code</th>\n",
              "      <th>last_updated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-02-01</td>\n",
              "      <td>2013-04-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>10714</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 26757</td>\n",
              "      <td>NaN</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>WV</td>\n",
              "      <td>Single Family Residential</td>\n",
              "      <td>6</td>\n",
              "      <td>95000.0</td>\n",
              "      <td>0.032609</td>\n",
              "      <td>-0.208333</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>0.040864</td>\n",
              "      <td>-0.068323</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>0.218966</td>\n",
              "      <td>-0.136719</td>\n",
              "      <td>110.644861</td>\n",
              "      <td>0.165324</td>\n",
              "      <td>0.181424</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.470588</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>-0.392857</td>\n",
              "      <td>-0.638298</td>\n",
              "      <td>54.0</td>\n",
              "      <td>-0.129032</td>\n",
              "      <td>-0.325000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>105.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-19.0</td>\n",
              "      <td>0.957351</td>\n",
              "      <td>-0.003772</td>\n",
              "      <td>0.028875</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.054737</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Winchester, VA</td>\n",
              "      <td>49020</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-09-01</td>\n",
              "      <td>2015-11-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>35915</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 84093</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Utah</td>\n",
              "      <td>UT</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>299270.0</td>\n",
              "      <td>-0.077749</td>\n",
              "      <td>0.088255</td>\n",
              "      <td>295000.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>118.406430</td>\n",
              "      <td>0.069277</td>\n",
              "      <td>-0.039831</td>\n",
              "      <td>120.010225</td>\n",
              "      <td>-0.040325</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</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>40.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>-177.5</td>\n",
              "      <td>0.977453</td>\n",
              "      <td>-0.024910</td>\n",
              "      <td>-0.013896</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.250000</td>\n",
              "      <td>0.250000</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>Salt Lake City, UT</td>\n",
              "      <td>41620</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>2016-03-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>34773</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 80238</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>CO</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>431250.0</td>\n",
              "      <td>0.140873</td>\n",
              "      <td>0.051913</td>\n",
              "      <td>415422.5</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.118513</td>\n",
              "      <td>201.993317</td>\n",
              "      <td>-0.113819</td>\n",
              "      <td>0.249501</td>\n",
              "      <td>225.313886</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.305622</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>-0.250000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-17.5</td>\n",
              "      <td>1.009202</td>\n",
              "      <td>0.009876</td>\n",
              "      <td>0.020281</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>Denver, CO</td>\n",
              "      <td>19740</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-04-01</td>\n",
              "      <td>2017-06-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>1373</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 04210</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maine</td>\n",
              "      <td>ME</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>-0.088608</td>\n",
              "      <td>-0.100000</td>\n",
              "      <td>117400.0</td>\n",
              "      <td>-0.190345</td>\n",
              "      <td>-0.079216</td>\n",
              "      <td>112.781955</td>\n",
              "      <td>-0.063703</td>\n",
              "      <td>0.221940</td>\n",
              "      <td>91.640723</td>\n",
              "      <td>-0.159899</td>\n",
              "      <td>-0.017776</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.949244</td>\n",
              "      <td>-0.004520</td>\n",
              "      <td>-0.008079</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>Lewiston, ME</td>\n",
              "      <td>30340</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-01</td>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>23258</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 54603</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>WI</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>195000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>199800.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.254237</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>145.475728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>333.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.975488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>La Crosse, WI</td>\n",
              "      <td>29100</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-924a4ef9-9b09-4f8b-82a0-547a4ad3c2e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-924a4ef9-9b09-4f8b-82a0-547a4ad3c2e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-924a4ef9-9b09-4f8b-82a0-547a4ad3c2e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAk6Sl0bUxcx"
      },
      "source": [
        "## <font color=\"blue\">Transformations</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyrWZ__PUxM7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "9cd7bba8-dbb1-46b6-920f-011b53ea1203"
      },
      "source": [
        "# filter on state code\n",
        "state_code = 'FL'\n",
        "df_filter = df.loc[df['state_code'] == state_code]\n",
        "print('Num of rows:', len(df_filter))\n",
        "df_filter.head(1)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of rows: 386584\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  period_begin  period_end  period_duration region_type  region_type_id  \\\n",
              "5   2019-01-01  2019-03-31               90    zip code               2   \n",
              "\n",
              "   table_id is_seasonally_adjusted           region  city    state state_code  \\\n",
              "5     14776                      f  Zip Code: 34994   NaN  Florida         FL   \n",
              "\n",
              "  property_type  property_type_id  median_sale_price  median_sale_price_mom  \\\n",
              "5   Condo/Co-op                 3           115500.0              -0.017021   \n",
              "\n",
              "   median_sale_price_yoy  median_list_price  median_list_price_mom  \\\n",
              "5              -0.024905           129500.0               0.027778   \n",
              "\n",
              "   median_list_price_yoy  median_ppsf  median_ppsf_mom  median_ppsf_yoy  \\\n",
              "5              -0.007663   111.434109        -0.038593         0.018897   \n",
              "\n",
              "   median_list_ppsf  median_list_ppsf_mom  median_list_ppsf_yoy  homes_sold  \\\n",
              "5             125.0              0.001278             -0.020153        64.0   \n",
              "\n",
              "   homes_sold_mom  homes_sold_yoy  pending_sales  pending_sales_mom  \\\n",
              "5        0.103448       -0.157895           20.0           0.052632   \n",
              "\n",
              "   pending_sales_yoy  new_listings  new_listings_mom  new_listings_yoy  \\\n",
              "5          -0.354839          93.0         -0.010638         -0.311111   \n",
              "\n",
              "   inventory  inventory_mom  inventory_yoy  months_of_supply  \\\n",
              "5       84.0      -0.096774      -0.333333               NaN   \n",
              "\n",
              "   months_of_supply_mom  months_of_supply_yoy  median_dom  median_dom_mom  \\\n",
              "5                   NaN                   NaN        50.0            -3.0   \n",
              "\n",
              "   median_dom_yoy  avg_sale_to_list  avg_sale_to_list_mom  \\\n",
              "5            -9.5          0.943252             -0.000173   \n",
              "\n",
              "   avg_sale_to_list_yoy  sold_above_list  sold_above_list_mom  \\\n",
              "5             -0.005195          0.09375             0.007543   \n",
              "\n",
              "   sold_above_list_yoy  price_drops  price_drops_mom  price_drops_yoy  \\\n",
              "5             0.054276          NaN              NaN              NaN   \n",
              "\n",
              "   off_market_in_two_weeks  off_market_in_two_weeks_mom  \\\n",
              "5                      0.1                    -0.005263   \n",
              "\n",
              "   off_market_in_two_weeks_yoy parent_metro_region  \\\n",
              "5                    -0.254839  Port St. Lucie, FL   \n",
              "\n",
              "   parent_metro_region_metro_code         last_updated  \n",
              "5                           38940  2022-06-12 14:28:57  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5089ef22-2f6b-4fc3-b966-6fd4b8ea68ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>period_begin</th>\n",
              "      <th>period_end</th>\n",
              "      <th>period_duration</th>\n",
              "      <th>region_type</th>\n",
              "      <th>region_type_id</th>\n",
              "      <th>table_id</th>\n",
              "      <th>is_seasonally_adjusted</th>\n",
              "      <th>region</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>state_code</th>\n",
              "      <th>property_type</th>\n",
              "      <th>property_type_id</th>\n",
              "      <th>median_sale_price</th>\n",
              "      <th>median_sale_price_mom</th>\n",
              "      <th>median_sale_price_yoy</th>\n",
              "      <th>median_list_price</th>\n",
              "      <th>median_list_price_mom</th>\n",
              "      <th>median_list_price_yoy</th>\n",
              "      <th>median_ppsf</th>\n",
              "      <th>median_ppsf_mom</th>\n",
              "      <th>median_ppsf_yoy</th>\n",
              "      <th>median_list_ppsf</th>\n",
              "      <th>median_list_ppsf_mom</th>\n",
              "      <th>median_list_ppsf_yoy</th>\n",
              "      <th>homes_sold</th>\n",
              "      <th>homes_sold_mom</th>\n",
              "      <th>homes_sold_yoy</th>\n",
              "      <th>pending_sales</th>\n",
              "      <th>pending_sales_mom</th>\n",
              "      <th>pending_sales_yoy</th>\n",
              "      <th>new_listings</th>\n",
              "      <th>new_listings_mom</th>\n",
              "      <th>new_listings_yoy</th>\n",
              "      <th>inventory</th>\n",
              "      <th>inventory_mom</th>\n",
              "      <th>inventory_yoy</th>\n",
              "      <th>months_of_supply</th>\n",
              "      <th>months_of_supply_mom</th>\n",
              "      <th>months_of_supply_yoy</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "      <th>avg_sale_to_list</th>\n",
              "      <th>avg_sale_to_list_mom</th>\n",
              "      <th>avg_sale_to_list_yoy</th>\n",
              "      <th>sold_above_list</th>\n",
              "      <th>sold_above_list_mom</th>\n",
              "      <th>sold_above_list_yoy</th>\n",
              "      <th>price_drops</th>\n",
              "      <th>price_drops_mom</th>\n",
              "      <th>price_drops_yoy</th>\n",
              "      <th>off_market_in_two_weeks</th>\n",
              "      <th>off_market_in_two_weeks_mom</th>\n",
              "      <th>off_market_in_two_weeks_yoy</th>\n",
              "      <th>parent_metro_region</th>\n",
              "      <th>parent_metro_region_metro_code</th>\n",
              "      <th>last_updated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>2019-03-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14776</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 34994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>115500.0</td>\n",
              "      <td>-0.017021</td>\n",
              "      <td>-0.024905</td>\n",
              "      <td>129500.0</td>\n",
              "      <td>0.027778</td>\n",
              "      <td>-0.007663</td>\n",
              "      <td>111.434109</td>\n",
              "      <td>-0.038593</td>\n",
              "      <td>0.018897</td>\n",
              "      <td>125.0</td>\n",
              "      <td>0.001278</td>\n",
              "      <td>-0.020153</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.103448</td>\n",
              "      <td>-0.157895</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>-0.354839</td>\n",
              "      <td>93.0</td>\n",
              "      <td>-0.010638</td>\n",
              "      <td>-0.311111</td>\n",
              "      <td>84.0</td>\n",
              "      <td>-0.096774</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-9.5</td>\n",
              "      <td>0.943252</td>\n",
              "      <td>-0.000173</td>\n",
              "      <td>-0.005195</td>\n",
              "      <td>0.09375</td>\n",
              "      <td>0.007543</td>\n",
              "      <td>0.054276</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.005263</td>\n",
              "      <td>-0.254839</td>\n",
              "      <td>Port St. Lucie, FL</td>\n",
              "      <td>38940</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filter.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "KgGwc4xofl8R",
        "outputId": "9149ee32-c8f3-4e68-a91a-532ff8469943"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       period_duration  region_type_id       table_id  city  property_type_id  \\\n",
              "count         386584.0        386584.0  386584.000000   0.0     386584.000000   \n",
              "mean              90.0             2.0   14401.738595   NaN          4.522937   \n",
              "std                0.0             0.0    2932.434518   NaN          4.678744   \n",
              "min               90.0             2.0   13309.000000   NaN         -1.000000   \n",
              "25%               90.0             2.0   13789.000000   NaN         -1.000000   \n",
              "50%               90.0             2.0   14107.000000   NaN          4.000000   \n",
              "75%               90.0             2.0   14457.000000   NaN          6.000000   \n",
              "max               90.0             2.0   41994.000000   NaN         13.000000   \n",
              "\n",
              "       median_sale_price  median_sale_price_mom  median_sale_price_yoy  \\\n",
              "count       3.865840e+05          375070.000000          354181.000000   \n",
              "mean        2.485784e+05               0.028650               0.223037   \n",
              "std         2.755073e+05               0.951993              26.950052   \n",
              "min         1.000000e+00              -0.999977              -0.999846   \n",
              "25%         1.290000e+05              -0.021739               0.006944   \n",
              "50%         1.981500e+05               0.000000               0.104938   \n",
              "75%         2.950000e+05               0.043478               0.236667   \n",
              "max         1.550000e+07             499.500000           15999.000000   \n",
              "\n",
              "       median_list_price  median_list_price_mom  median_list_price_yoy  \\\n",
              "count       3.695950e+05          357695.000000          341551.000000   \n",
              "mean        2.769843e+05               0.033566               0.160217   \n",
              "std         3.352239e+05               1.339819               1.485152   \n",
              "min         5.500000e+02              -0.997876              -0.996839   \n",
              "25%         1.430000e+05              -0.024081               0.002466   \n",
              "50%         2.180000e+05               0.000334               0.095248   \n",
              "75%         3.200000e+05               0.044544               0.217239   \n",
              "max         4.506500e+07             309.537304             365.666667   \n",
              "\n",
              "         median_ppsf  median_ppsf_mom  median_ppsf_yoy  median_list_ppsf  \\\n",
              "count  386395.000000    374896.000000    354017.000000     369525.000000   \n",
              "mean      148.259141         0.030682         0.207479        164.909370   \n",
              "std       259.905190         2.450966        20.047660        854.991414   \n",
              "min         0.000751        -0.999977        -0.999895          0.379310   \n",
              "25%        88.902488        -0.011790         0.025857         98.524268   \n",
              "50%       125.874126         0.002891         0.098984        135.819747   \n",
              "75%       175.362459         0.032692         0.206923        189.221550   \n",
              "max    110000.000000       750.167760     11531.467532     481981.823877   \n",
              "\n",
              "       median_list_ppsf_mom  median_list_ppsf_yoy     homes_sold  \\\n",
              "count         357635.000000         341497.000000  386584.000000   \n",
              "mean               0.115408              0.164534      61.708069   \n",
              "std               51.770820             15.321325      78.563830   \n",
              "min               -0.999916             -0.999956       1.000000   \n",
              "25%               -0.012506              0.024943       5.000000   \n",
              "50%                0.004334              0.090763      28.000000   \n",
              "75%                0.032156              0.188895      93.000000   \n",
              "max            30918.478629           8892.526139     917.000000   \n",
              "\n",
              "       homes_sold_mom  homes_sold_yoy  pending_sales  pending_sales_mom  \\\n",
              "count   375070.000000   354181.000000  316374.000000      287859.000000   \n",
              "mean         0.050315        0.270425      18.340205           0.180661   \n",
              "std          0.378792        3.095896      21.476741           0.806736   \n",
              "min         -0.909091       -0.985714       1.000000          -0.960784   \n",
              "25%         -0.092308       -0.138889       3.000000          -0.239437   \n",
              "50%          0.000000        0.030675      10.000000           0.000000   \n",
              "75%          0.115942        0.295455      26.000000           0.333333   \n",
              "max         31.500000      388.000000     280.000000          24.500000   \n",
              "\n",
              "       pending_sales_yoy   new_listings  new_listings_mom  new_listings_yoy  \\\n",
              "count      279464.000000  356849.000000     344684.000000     323062.000000   \n",
              "mean            0.392120      76.121275          0.039112          0.178116   \n",
              "std             1.400496      91.512000          0.338705          1.092806   \n",
              "min            -0.978261       1.000000         -0.962963         -0.965517   \n",
              "25%            -0.166667       8.000000         -0.096774         -0.141732   \n",
              "50%             0.090909      38.000000          0.000000          0.018750   \n",
              "75%             0.562500     117.000000          0.111111          0.250000   \n",
              "max           121.000000     949.000000         24.000000         91.000000   \n",
              "\n",
              "           inventory  inventory_mom  inventory_yoy  months_of_supply  \\\n",
              "count  353000.000000  341523.000000  321726.000000               0.0   \n",
              "mean       82.882167       0.023553       0.027931               NaN   \n",
              "std       113.389546       0.312170       0.765429               NaN   \n",
              "min         1.000000      -0.958333      -0.982143               NaN   \n",
              "25%         9.000000      -0.083333      -0.272727               NaN   \n",
              "50%        39.000000       0.000000      -0.070175               NaN   \n",
              "75%       117.000000       0.072165       0.133758               NaN   \n",
              "max      1797.000000      22.000000      52.000000               NaN   \n",
              "\n",
              "       months_of_supply_mom  months_of_supply_yoy     median_dom  \\\n",
              "count                   0.0                   0.0  376605.000000   \n",
              "mean                    NaN                   NaN      94.903525   \n",
              "std                     NaN                   NaN     195.004104   \n",
              "min                     NaN                   NaN       1.000000   \n",
              "25%                     NaN                   NaN      39.000000   \n",
              "50%                     NaN                   NaN      64.500000   \n",
              "75%                     NaN                   NaN     102.000000   \n",
              "max                     NaN                   NaN    8220.000000   \n",
              "\n",
              "       median_dom_mom  median_dom_yoy  avg_sale_to_list  avg_sale_to_list_mom  \\\n",
              "count   364695.000000   338237.000000     386238.000000         374724.000000   \n",
              "mean        -0.954071      -13.199811          0.962556              0.000457   \n",
              "std        125.340271      209.017558          0.043712              0.025909   \n",
              "min      -7264.000000    -8152.000000          0.500000             -1.065897   \n",
              "25%         -7.000000      -30.500000          0.948438             -0.004195   \n",
              "50%          0.000000       -8.000000          0.966644              0.000000   \n",
              "75%          5.500000       10.000000          0.981624              0.005213   \n",
              "max       8099.000000     8094.000000          1.963333              0.991597   \n",
              "\n",
              "       avg_sale_to_list_yoy  sold_above_list  sold_above_list_mom  \\\n",
              "count         353807.000000    386584.000000        375070.000000   \n",
              "mean               0.004886         0.153578             0.001984   \n",
              "std                0.047809         0.176456             0.102426   \n",
              "min               -0.965909         0.000000            -1.000000   \n",
              "25%               -0.007514         0.000000            -0.015315   \n",
              "50%                0.004130         0.115385             0.000000   \n",
              "75%                0.017783         0.214286             0.019715   \n",
              "max                1.033554         1.000000             1.000000   \n",
              "\n",
              "       sold_above_list_yoy  price_drops  price_drops_mom  price_drops_yoy  \\\n",
              "count        354181.000000          0.0              0.0              0.0   \n",
              "mean              0.017541          NaN              NaN              NaN   \n",
              "std               0.191574          NaN              NaN              NaN   \n",
              "min              -1.000000          NaN              NaN              NaN   \n",
              "25%              -0.038503          NaN              NaN              NaN   \n",
              "50%               0.000000          NaN              NaN              NaN   \n",
              "75%               0.068182          NaN              NaN              NaN   \n",
              "max               1.000000          NaN              NaN              NaN   \n",
              "\n",
              "       off_market_in_two_weeks  off_market_in_two_weeks_mom  \\\n",
              "count            316374.000000                287859.000000   \n",
              "mean                  0.289652                     0.003040   \n",
              "std                   0.271135                     0.265677   \n",
              "min                   0.000000                    -1.500000   \n",
              "25%                   0.000000                    -0.094118   \n",
              "50%                   0.250000                     0.000000   \n",
              "75%                   0.453125                     0.100000   \n",
              "max                   2.000000                     1.500000   \n",
              "\n",
              "       off_market_in_two_weeks_yoy  parent_metro_region_metro_code  \n",
              "count                279464.000000                   386584.000000  \n",
              "mean                      0.039075                    34960.707820  \n",
              "std                       0.274532                     9082.778223  \n",
              "min                      -1.250000                    11580.000000  \n",
              "25%                      -0.068421                    27260.000000  \n",
              "50%                       0.001201                    36740.000000  \n",
              "75%                       0.159091                    45220.000000  \n",
              "max                       1.000000                    48424.000000  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>period_duration</th>\n",
              "      <th>region_type_id</th>\n",
              "      <th>table_id</th>\n",
              "      <th>city</th>\n",
              "      <th>property_type_id</th>\n",
              "      <th>median_sale_price</th>\n",
              "      <th>median_sale_price_mom</th>\n",
              "      <th>median_sale_price_yoy</th>\n",
              "      <th>median_list_price</th>\n",
              "      <th>median_list_price_mom</th>\n",
              "      <th>median_list_price_yoy</th>\n",
              "      <th>median_ppsf</th>\n",
              "      <th>median_ppsf_mom</th>\n",
              "      <th>median_ppsf_yoy</th>\n",
              "      <th>median_list_ppsf</th>\n",
              "      <th>median_list_ppsf_mom</th>\n",
              "      <th>median_list_ppsf_yoy</th>\n",
              "      <th>homes_sold</th>\n",
              "      <th>homes_sold_mom</th>\n",
              "      <th>homes_sold_yoy</th>\n",
              "      <th>pending_sales</th>\n",
              "      <th>pending_sales_mom</th>\n",
              "      <th>pending_sales_yoy</th>\n",
              "      <th>new_listings</th>\n",
              "      <th>new_listings_mom</th>\n",
              "      <th>new_listings_yoy</th>\n",
              "      <th>inventory</th>\n",
              "      <th>inventory_mom</th>\n",
              "      <th>inventory_yoy</th>\n",
              "      <th>months_of_supply</th>\n",
              "      <th>months_of_supply_mom</th>\n",
              "      <th>months_of_supply_yoy</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "      <th>avg_sale_to_list</th>\n",
              "      <th>avg_sale_to_list_mom</th>\n",
              "      <th>avg_sale_to_list_yoy</th>\n",
              "      <th>sold_above_list</th>\n",
              "      <th>sold_above_list_mom</th>\n",
              "      <th>sold_above_list_yoy</th>\n",
              "      <th>price_drops</th>\n",
              "      <th>price_drops_mom</th>\n",
              "      <th>price_drops_yoy</th>\n",
              "      <th>off_market_in_two_weeks</th>\n",
              "      <th>off_market_in_two_weeks_mom</th>\n",
              "      <th>off_market_in_two_weeks_yoy</th>\n",
              "      <th>parent_metro_region_metro_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>386584.0</td>\n",
              "      <td>386584.0</td>\n",
              "      <td>386584.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>386584.000000</td>\n",
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              "      <td>3.695950e+05</td>\n",
              "      <td>357695.000000</td>\n",
              "      <td>341551.000000</td>\n",
              "      <td>386395.000000</td>\n",
              "      <td>374896.000000</td>\n",
              "      <td>354017.000000</td>\n",
              "      <td>369525.000000</td>\n",
              "      <td>357635.000000</td>\n",
              "      <td>341497.000000</td>\n",
              "      <td>386584.000000</td>\n",
              "      <td>375070.000000</td>\n",
              "      <td>354181.000000</td>\n",
              "      <td>316374.000000</td>\n",
              "      <td>287859.000000</td>\n",
              "      <td>279464.000000</td>\n",
              "      <td>356849.000000</td>\n",
              "      <td>344684.000000</td>\n",
              "      <td>323062.000000</td>\n",
              "      <td>353000.000000</td>\n",
              "      <td>341523.000000</td>\n",
              "      <td>321726.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>376605.000000</td>\n",
              "      <td>364695.000000</td>\n",
              "      <td>338237.000000</td>\n",
              "      <td>386238.000000</td>\n",
              "      <td>374724.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14401.738595</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.522937</td>\n",
              "      <td>2.485784e+05</td>\n",
              "      <td>0.028650</td>\n",
              "      <td>0.223037</td>\n",
              "      <td>2.769843e+05</td>\n",
              "      <td>0.033566</td>\n",
              "      <td>0.160217</td>\n",
              "      <td>148.259141</td>\n",
              "      <td>0.030682</td>\n",
              "      <td>0.207479</td>\n",
              "      <td>164.909370</td>\n",
              "      <td>0.115408</td>\n",
              "      <td>0.164534</td>\n",
              "      <td>61.708069</td>\n",
              "      <td>0.050315</td>\n",
              "      <td>0.270425</td>\n",
              "      <td>18.340205</td>\n",
              "      <td>0.180661</td>\n",
              "      <td>0.392120</td>\n",
              "      <td>76.121275</td>\n",
              "      <td>0.039112</td>\n",
              "      <td>0.178116</td>\n",
              "      <td>82.882167</td>\n",
              "      <td>0.023553</td>\n",
              "      <td>0.027931</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94.903525</td>\n",
              "      <td>-0.954071</td>\n",
              "      <td>-13.199811</td>\n",
              "      <td>0.962556</td>\n",
              "      <td>0.000457</td>\n",
              "      <td>0.004886</td>\n",
              "      <td>0.153578</td>\n",
              "      <td>0.001984</td>\n",
              "      <td>0.017541</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.289652</td>\n",
              "      <td>0.003040</td>\n",
              "      <td>0.039075</td>\n",
              "      <td>34960.707820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2932.434518</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.678744</td>\n",
              "      <td>2.755073e+05</td>\n",
              "      <td>0.951993</td>\n",
              "      <td>26.950052</td>\n",
              "      <td>3.352239e+05</td>\n",
              "      <td>1.339819</td>\n",
              "      <td>1.485152</td>\n",
              "      <td>259.905190</td>\n",
              "      <td>2.450966</td>\n",
              "      <td>20.047660</td>\n",
              "      <td>854.991414</td>\n",
              "      <td>51.770820</td>\n",
              "      <td>15.321325</td>\n",
              "      <td>78.563830</td>\n",
              "      <td>0.378792</td>\n",
              "      <td>3.095896</td>\n",
              "      <td>21.476741</td>\n",
              "      <td>0.806736</td>\n",
              "      <td>1.400496</td>\n",
              "      <td>91.512000</td>\n",
              "      <td>0.338705</td>\n",
              "      <td>1.092806</td>\n",
              "      <td>113.389546</td>\n",
              "      <td>0.312170</td>\n",
              "      <td>0.765429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>195.004104</td>\n",
              "      <td>125.340271</td>\n",
              "      <td>209.017558</td>\n",
              "      <td>0.043712</td>\n",
              "      <td>0.025909</td>\n",
              "      <td>0.047809</td>\n",
              "      <td>0.176456</td>\n",
              "      <td>0.102426</td>\n",
              "      <td>0.191574</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.271135</td>\n",
              "      <td>0.265677</td>\n",
              "      <td>0.274532</td>\n",
              "      <td>9082.778223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13309.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-0.999977</td>\n",
              "      <td>-0.999846</td>\n",
              "      <td>5.500000e+02</td>\n",
              "      <td>-0.997876</td>\n",
              "      <td>-0.996839</td>\n",
              "      <td>0.000751</td>\n",
              "      <td>-0.999977</td>\n",
              "      <td>-0.999895</td>\n",
              "      <td>0.379310</td>\n",
              "      <td>-0.999916</td>\n",
              "      <td>-0.999956</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.909091</td>\n",
              "      <td>-0.985714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.960784</td>\n",
              "      <td>-0.978261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.962963</td>\n",
              "      <td>-0.965517</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.958333</td>\n",
              "      <td>-0.982143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-7264.000000</td>\n",
              "      <td>-8152.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-1.065897</td>\n",
              "      <td>-0.965909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.500000</td>\n",
              "      <td>-1.250000</td>\n",
              "      <td>11580.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13789.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.290000e+05</td>\n",
              "      <td>-0.021739</td>\n",
              "      <td>0.006944</td>\n",
              "      <td>1.430000e+05</td>\n",
              "      <td>-0.024081</td>\n",
              "      <td>0.002466</td>\n",
              "      <td>88.902488</td>\n",
              "      <td>-0.011790</td>\n",
              "      <td>0.025857</td>\n",
              "      <td>98.524268</td>\n",
              "      <td>-0.012506</td>\n",
              "      <td>0.024943</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>-0.092308</td>\n",
              "      <td>-0.138889</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-0.239437</td>\n",
              "      <td>-0.166667</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>-0.096774</td>\n",
              "      <td>-0.141732</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>-0.272727</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-30.500000</td>\n",
              "      <td>0.948438</td>\n",
              "      <td>-0.004195</td>\n",
              "      <td>-0.007514</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.015315</td>\n",
              "      <td>-0.038503</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.094118</td>\n",
              "      <td>-0.068421</td>\n",
              "      <td>27260.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14107.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.981500e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.104938</td>\n",
              "      <td>2.180000e+05</td>\n",
              "      <td>0.000334</td>\n",
              "      <td>0.095248</td>\n",
              "      <td>125.874126</td>\n",
              "      <td>0.002891</td>\n",
              "      <td>0.098984</td>\n",
              "      <td>135.819747</td>\n",
              "      <td>0.004334</td>\n",
              "      <td>0.090763</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030675</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018750</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.070175</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>0.966644</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004130</td>\n",
              "      <td>0.115385</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001201</td>\n",
              "      <td>36740.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14457.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.950000e+05</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.236667</td>\n",
              "      <td>3.200000e+05</td>\n",
              "      <td>0.044544</td>\n",
              "      <td>0.217239</td>\n",
              "      <td>175.362459</td>\n",
              "      <td>0.032692</td>\n",
              "      <td>0.206923</td>\n",
              "      <td>189.221550</td>\n",
              "      <td>0.032156</td>\n",
              "      <td>0.188895</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0.115942</td>\n",
              "      <td>0.295455</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>0.072165</td>\n",
              "      <td>0.133758</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.981624</td>\n",
              "      <td>0.005213</td>\n",
              "      <td>0.017783</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.019715</td>\n",
              "      <td>0.068182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.453125</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.159091</td>\n",
              "      <td>45220.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>41994.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.550000e+07</td>\n",
              "      <td>499.500000</td>\n",
              "      <td>15999.000000</td>\n",
              "      <td>4.506500e+07</td>\n",
              "      <td>309.537304</td>\n",
              "      <td>365.666667</td>\n",
              "      <td>110000.000000</td>\n",
              "      <td>750.167760</td>\n",
              "      <td>11531.467532</td>\n",
              "      <td>481981.823877</td>\n",
              "      <td>30918.478629</td>\n",
              "      <td>8892.526139</td>\n",
              "      <td>917.000000</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>388.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>949.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8220.000000</td>\n",
              "      <td>8099.000000</td>\n",
              "      <td>8094.000000</td>\n",
              "      <td>1.963333</td>\n",
              "      <td>0.991597</td>\n",
              "      <td>1.033554</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>48424.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec76ec4b-2188-4435-9649-51cdadd71f3e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec76ec4b-2188-4435-9649-51cdadd71f3e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ec76ec4b-2188-4435-9649-51cdadd71f3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sample Dataset"
      ],
      "metadata": {
        "id": "BSNId6G2h_Aw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "PROBLEM - Data takes a long time to load in Tableau\n",
        "RESOLUTION - Sample a subset of zip codes\n",
        "\"\"\"\n",
        "# get zip code county data\n",
        "geo_data_url = 'https://raw.githubusercontent.com/scpike/us-state-county-zip/master/geo-data.csv'\n",
        "df_geo = pd.read_csv(geo_data_url)\n",
        "print('Num of rows:', len(df_geo))\n",
        "print('Num of columns:', len(df_geo.columns))\n",
        "df_geo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "DBQ-JBLWiBie",
        "outputId": "d83c1e9d-38de-4633-d48a-f3b2952268c9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of rows: 33103\n",
            "Num of columns: 6\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   state_fips    state state_abbr zipcode      county        city\n",
              "0           1  Alabama         AL   35004   St. Clair       Acmar\n",
              "1           1  Alabama         AL   35005   Jefferson  Adamsville\n",
              "2           1  Alabama         AL   35006   Jefferson       Adger\n",
              "3           1  Alabama         AL   35007      Shelby    Keystone\n",
              "4           1  Alabama         AL   35010  Tallapoosa    New site"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43619ce1-4f1a-4336-b493-7b7170d132bb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_fips</th>\n",
              "      <th>state</th>\n",
              "      <th>state_abbr</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>county</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>AL</td>\n",
              "      <td>35004</td>\n",
              "      <td>St. Clair</td>\n",
              "      <td>Acmar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>AL</td>\n",
              "      <td>35005</td>\n",
              "      <td>Jefferson</td>\n",
              "      <td>Adamsville</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>AL</td>\n",
              "      <td>35006</td>\n",
              "      <td>Jefferson</td>\n",
              "      <td>Adger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>AL</td>\n",
              "      <td>35007</td>\n",
              "      <td>Shelby</td>\n",
              "      <td>Keystone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>AL</td>\n",
              "      <td>35010</td>\n",
              "      <td>Tallapoosa</td>\n",
              "      <td>New site</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43619ce1-4f1a-4336-b493-7b7170d132bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43619ce1-4f1a-4336-b493-7b7170d132bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43619ce1-4f1a-4336-b493-7b7170d132bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "display(Image('https://www.fdottampabay.com/assets/images/d7-service-area-map-new.png'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "fCKERDEojaL1",
        "outputId": "17a48ab4-37f3-4af4-9354-54a3d1b9ca91"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filter on single county\n",
        "df_geo_county = df_geo.loc[(df_geo['state_abbr'] == 'FL') &\n",
        "                           (df_geo['county'].isin(['Hillsborough', 'Pinellas', 'Pasco', 'Hernando', 'Citris']))]\n",
        "# create feature to check if zip code is valid\n",
        "df_geo_county['valid_zip_code'] = df_geo_county.apply(lambda x: x['zipcode'].isnumeric(), axis=1)\n",
        "print('Dataset:')\n",
        "print(df_geo_county.groupby(['valid_zip_code', 'county'])['zipcode'].count())\n",
        "print(' ')\n",
        "print('Invalid zip codes:', df_geo_county.loc[df_geo_county['valid_zip_code'] == False]['zipcode'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yR3wn7UiKUz",
        "outputId": "e1697189-4b71-48fc-ff77-2fc7abc93599"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset:\n",
            "valid_zip_code  county      \n",
            "False           Hernando         1\n",
            "                Hillsborough     2\n",
            "                Pasco            1\n",
            "                Pinellas         1\n",
            "True            Hernando         9\n",
            "                Hillsborough    45\n",
            "                Pasco           19\n",
            "                Pinellas        46\n",
            "Name: zipcode, dtype: int64\n",
            " \n",
            "Invalid zip codes: ['335HH' '336HH' '337HH' '346HH' '346XX']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filter on only valid zip codes\n",
        "df_geo_county_valid = df_geo_county.loc[df_geo_county['valid_zip_code'] == True]\n",
        "print('Num of VALID zip codes:', len(df_geo_county_valid))\n",
        "df_geo_county_valid.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "Cald-abQintC",
        "outputId": "c9c8d4fa-117d-4ceb-bc5c-2e73771d0de9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of VALID zip codes: 119\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      state_fips    state state_abbr zipcode        county     city  \\\n",
              "5129          12  Florida         FL   33510  Hillsborough  Brandon   \n",
              "\n",
              "      valid_zip_code  \n",
              "5129            True  "
            ],
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              "      <th>state_fips</th>\n",
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              "      <th>5129</th>\n",
              "      <td>12</td>\n",
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              "      <td>FL</td>\n",
              "      <td>33510</td>\n",
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              "          const element = document.querySelector('#df-22d04236-5ad0-4efe-9f52-6877bcce0e0d');\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get zip code from region field\n",
        "df_filter['zipcode'] = df_filter.apply(lambda x: x['region'].split(':')[1].strip(), axis=1)\n",
        "# merge market data and geo zip codes\n",
        "df_merge = pd.merge(\n",
        "    df_filter,\n",
        "    df_geo_county_valid[['zipcode', 'city', 'county']],\n",
        "    how='inner', # only return zip codes from BOTH tables\n",
        "    on=['zipcode'] # field to join on\n",
        ")\n",
        "print('Num of rows:', len(df_merge))\n",
        "df_merge.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "gYO5wjGCi0Dk",
        "outputId": "613f07b3-ffbb-448a-ba2d-54847f904e9e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of rows: 56173\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  period_begin  period_end  period_duration region_type  region_type_id  \\\n",
              "0   2016-05-01  2016-07-31               90    zip code               2   \n",
              "1   2017-08-01  2017-10-31               90    zip code               2   \n",
              "2   2021-02-01  2021-04-30               90    zip code               2   \n",
              "3   2015-02-01  2015-04-30               90    zip code               2   \n",
              "4   2020-08-01  2020-10-31               90    zip code               2   \n",
              "\n",
              "   table_id is_seasonally_adjusted           region  city_x    state  \\\n",
              "0     14383                      f  Zip Code: 33781     NaN  Florida   \n",
              "1     14383                      f  Zip Code: 33781     NaN  Florida   \n",
              "2     14383                      f  Zip Code: 33781     NaN  Florida   \n",
              "3     14383                      f  Zip Code: 33781     NaN  Florida   \n",
              "4     14383                      f  Zip Code: 33781     NaN  Florida   \n",
              "\n",
              "  state_code              property_type  property_type_id  median_sale_price  \\\n",
              "0         FL  Single Family Residential                 6           138000.0   \n",
              "1         FL                  Townhouse                13           185100.0   \n",
              "2         FL                Condo/Co-op                 3            84850.0   \n",
              "3         FL                Condo/Co-op                 3            34900.0   \n",
              "4         FL                  Townhouse                13           228750.0   \n",
              "\n",
              "   median_sale_price_mom  median_sale_price_yoy  median_list_price  \\\n",
              "0               0.061538               0.205240           159900.0   \n",
              "1              -0.002425               0.088824           204900.0   \n",
              "2              -0.030286               0.022289            87500.0   \n",
              "3              -0.008523              -0.002857            39900.0   \n",
              "4               0.063953               0.063953           239000.0   \n",
              "\n",
              "   median_list_price_mom  median_list_price_yoy  median_ppsf  median_ppsf_mom  \\\n",
              "0               0.066711               0.391645   115.571776         0.001622   \n",
              "1              -0.000488               0.122740   127.400000         0.009324   \n",
              "2               0.029412               0.535088   101.066561        -0.156275   \n",
              "3               0.081301               0.453552    32.461677         0.022727   \n",
              "4              -0.004167               0.059867   158.379671         0.024006   \n",
              "\n",
              "   median_ppsf_yoy  median_list_ppsf  median_list_ppsf_mom  \\\n",
              "0         0.272115        132.211538              0.051082   \n",
              "1         0.063131        134.325190              0.002698   \n",
              "2         0.101004         94.691943             -0.122395   \n",
              "3        -0.054554         35.978359              0.078378   \n",
              "4         0.049524        163.120567              0.006917   \n",
              "\n",
              "   median_list_ppsf_yoy  homes_sold  homes_sold_mom  homes_sold_yoy  \\\n",
              "0              0.287100        97.0       -0.058252        0.077778   \n",
              "1              0.045160        23.0        0.150000        0.095238   \n",
              "2              0.238886         8.0        0.142857        0.600000   \n",
              "3             -0.124478         7.0        0.750000        0.000000   \n",
              "4              0.072695        12.0       -0.368421       -0.076923   \n",
              "\n",
              "   pending_sales  pending_sales_mom  pending_sales_yoy  new_listings  \\\n",
              "0           21.0          -0.363636          -0.045455         101.0   \n",
              "1            9.0           0.500000           0.000000          18.0   \n",
              "2            2.0           1.000000          -0.333333           9.0   \n",
              "3            2.0          -0.333333           0.000000           9.0   \n",
              "4            3.0          -0.500000           0.000000          17.0   \n",
              "\n",
              "   new_listings_mom  new_listings_yoy  inventory  inventory_mom  \\\n",
              "0         -0.073394          0.041237       82.0       0.078947   \n",
              "1         -0.142857         -0.280000       10.0       0.000000   \n",
              "2          0.000000          0.285714        5.0      -0.166667   \n",
              "3         -0.307692          0.500000       11.0      -0.153846   \n",
              "4         -0.105263          0.062500        8.0       0.333333   \n",
              "\n",
              "   inventory_yoy  months_of_supply  months_of_supply_mom  \\\n",
              "0      -0.068182               NaN                   NaN   \n",
              "1      -0.333333               NaN                   NaN   \n",
              "2       0.250000               NaN                   NaN   \n",
              "3      -0.083333               NaN                   NaN   \n",
              "4       0.142857               NaN                   NaN   \n",
              "\n",
              "   months_of_supply_yoy  median_dom  median_dom_mom  median_dom_yoy  \\\n",
              "0                   NaN        48.0            -2.0            -2.0   \n",
              "1                   NaN        23.0             0.0           -27.0   \n",
              "2                   NaN        13.0           -35.0           -51.5   \n",
              "3                   NaN        52.0            19.0          -172.0   \n",
              "4                   NaN        19.0             0.0             5.0   \n",
              "\n",
              "   avg_sale_to_list  avg_sale_to_list_mom  avg_sale_to_list_yoy  \\\n",
              "0          0.993254              0.005869              0.008949   \n",
              "1          0.973702              0.007817             -0.009215   \n",
              "2          0.986780             -0.004834             -0.000821   \n",
              "3          0.933755              0.009664             -0.053506   \n",
              "4          0.986936              0.005513              0.021330   \n",
              "\n",
              "   sold_above_list  sold_above_list_mom  sold_above_list_yoy  price_drops  \\\n",
              "0         0.329897             0.009509             0.041008          NaN   \n",
              "1         0.173913             0.073913             0.078675          NaN   \n",
              "2         0.250000            -0.035714             0.250000          NaN   \n",
              "3         0.000000             0.000000            -0.142857          NaN   \n",
              "4         0.166667             0.061404             0.166667          NaN   \n",
              "\n",
              "   price_drops_mom  price_drops_yoy  off_market_in_two_weeks  \\\n",
              "0              NaN              NaN                 0.428571   \n",
              "1              NaN              NaN                 0.444444   \n",
              "2              NaN              NaN                 1.000000   \n",
              "3              NaN              NaN                 0.500000   \n",
              "4              NaN              NaN                 0.666667   \n",
              "\n",
              "   off_market_in_two_weeks_mom  off_market_in_two_weeks_yoy  \\\n",
              "0                     0.125541                     0.201299   \n",
              "1                     0.111111                     0.111111   \n",
              "2                     0.000000                     1.000000   \n",
              "3                     0.166667                     0.000000   \n",
              "4                     0.000000                    -0.333333   \n",
              "\n",
              "  parent_metro_region  parent_metro_region_metro_code         last_updated  \\\n",
              "0           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "1           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "2           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "3           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "4           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "\n",
              "  zipcode            city_y    county  \n",
              "0   33781  Saint petersburg  Pinellas  \n",
              "1   33781  Saint petersburg  Pinellas  \n",
              "2   33781  Saint petersburg  Pinellas  \n",
              "3   33781  Saint petersburg  Pinellas  \n",
              "4   33781  Saint petersburg  Pinellas  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bad2fb24-c9d7-451a-9ed1-76b1eb22b5c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>period_begin</th>\n",
              "      <th>period_end</th>\n",
              "      <th>period_duration</th>\n",
              "      <th>region_type</th>\n",
              "      <th>region_type_id</th>\n",
              "      <th>table_id</th>\n",
              "      <th>is_seasonally_adjusted</th>\n",
              "      <th>region</th>\n",
              "      <th>city_x</th>\n",
              "      <th>state</th>\n",
              "      <th>state_code</th>\n",
              "      <th>property_type</th>\n",
              "      <th>property_type_id</th>\n",
              "      <th>median_sale_price</th>\n",
              "      <th>median_sale_price_mom</th>\n",
              "      <th>median_sale_price_yoy</th>\n",
              "      <th>median_list_price</th>\n",
              "      <th>median_list_price_mom</th>\n",
              "      <th>median_list_price_yoy</th>\n",
              "      <th>median_ppsf</th>\n",
              "      <th>median_ppsf_mom</th>\n",
              "      <th>median_ppsf_yoy</th>\n",
              "      <th>median_list_ppsf</th>\n",
              "      <th>median_list_ppsf_mom</th>\n",
              "      <th>median_list_ppsf_yoy</th>\n",
              "      <th>homes_sold</th>\n",
              "      <th>homes_sold_mom</th>\n",
              "      <th>homes_sold_yoy</th>\n",
              "      <th>pending_sales</th>\n",
              "      <th>pending_sales_mom</th>\n",
              "      <th>pending_sales_yoy</th>\n",
              "      <th>new_listings</th>\n",
              "      <th>new_listings_mom</th>\n",
              "      <th>new_listings_yoy</th>\n",
              "      <th>inventory</th>\n",
              "      <th>inventory_mom</th>\n",
              "      <th>inventory_yoy</th>\n",
              "      <th>months_of_supply</th>\n",
              "      <th>months_of_supply_mom</th>\n",
              "      <th>months_of_supply_yoy</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "      <th>avg_sale_to_list</th>\n",
              "      <th>avg_sale_to_list_mom</th>\n",
              "      <th>avg_sale_to_list_yoy</th>\n",
              "      <th>sold_above_list</th>\n",
              "      <th>sold_above_list_mom</th>\n",
              "      <th>sold_above_list_yoy</th>\n",
              "      <th>price_drops</th>\n",
              "      <th>price_drops_mom</th>\n",
              "      <th>price_drops_yoy</th>\n",
              "      <th>off_market_in_two_weeks</th>\n",
              "      <th>off_market_in_two_weeks_mom</th>\n",
              "      <th>off_market_in_two_weeks_yoy</th>\n",
              "      <th>parent_metro_region</th>\n",
              "      <th>parent_metro_region_metro_code</th>\n",
              "      <th>last_updated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>city_y</th>\n",
              "      <th>county</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>2016-07-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Single Family Residential</td>\n",
              "      <td>6</td>\n",
              "      <td>138000.0</td>\n",
              "      <td>0.061538</td>\n",
              "      <td>0.205240</td>\n",
              "      <td>159900.0</td>\n",
              "      <td>0.066711</td>\n",
              "      <td>0.391645</td>\n",
              "      <td>115.571776</td>\n",
              "      <td>0.001622</td>\n",
              "      <td>0.272115</td>\n",
              "      <td>132.211538</td>\n",
              "      <td>0.051082</td>\n",
              "      <td>0.287100</td>\n",
              "      <td>97.0</td>\n",
              "      <td>-0.058252</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>21.0</td>\n",
              "      <td>-0.363636</td>\n",
              "      <td>-0.045455</td>\n",
              "      <td>101.0</td>\n",
              "      <td>-0.073394</td>\n",
              "      <td>0.041237</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.078947</td>\n",
              "      <td>-0.068182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.993254</td>\n",
              "      <td>0.005869</td>\n",
              "      <td>0.008949</td>\n",
              "      <td>0.329897</td>\n",
              "      <td>0.009509</td>\n",
              "      <td>0.041008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.125541</td>\n",
              "      <td>0.201299</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-08-01</td>\n",
              "      <td>2017-10-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>185100.0</td>\n",
              "      <td>-0.002425</td>\n",
              "      <td>0.088824</td>\n",
              "      <td>204900.0</td>\n",
              "      <td>-0.000488</td>\n",
              "      <td>0.122740</td>\n",
              "      <td>127.400000</td>\n",
              "      <td>0.009324</td>\n",
              "      <td>0.063131</td>\n",
              "      <td>134.325190</td>\n",
              "      <td>0.002698</td>\n",
              "      <td>0.045160</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.0</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>-0.280000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-27.0</td>\n",
              "      <td>0.973702</td>\n",
              "      <td>0.007817</td>\n",
              "      <td>-0.009215</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.073913</td>\n",
              "      <td>0.078675</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-02-01</td>\n",
              "      <td>2021-04-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>84850.0</td>\n",
              "      <td>-0.030286</td>\n",
              "      <td>0.022289</td>\n",
              "      <td>87500.0</td>\n",
              "      <td>0.029412</td>\n",
              "      <td>0.535088</td>\n",
              "      <td>101.066561</td>\n",
              "      <td>-0.156275</td>\n",
              "      <td>0.101004</td>\n",
              "      <td>94.691943</td>\n",
              "      <td>-0.122395</td>\n",
              "      <td>0.238886</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-0.166667</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>-35.0</td>\n",
              "      <td>-51.5</td>\n",
              "      <td>0.986780</td>\n",
              "      <td>-0.004834</td>\n",
              "      <td>-0.000821</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-02-01</td>\n",
              "      <td>2015-04-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>34900.0</td>\n",
              "      <td>-0.008523</td>\n",
              "      <td>-0.002857</td>\n",
              "      <td>39900.0</td>\n",
              "      <td>0.081301</td>\n",
              "      <td>0.453552</td>\n",
              "      <td>32.461677</td>\n",
              "      <td>0.022727</td>\n",
              "      <td>-0.054554</td>\n",
              "      <td>35.978359</td>\n",
              "      <td>0.078378</td>\n",
              "      <td>-0.124478</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.307692</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>-172.0</td>\n",
              "      <td>0.933755</td>\n",
              "      <td>0.009664</td>\n",
              "      <td>-0.053506</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>2020-10-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>228750.0</td>\n",
              "      <td>0.063953</td>\n",
              "      <td>0.063953</td>\n",
              "      <td>239000.0</td>\n",
              "      <td>-0.004167</td>\n",
              "      <td>0.059867</td>\n",
              "      <td>158.379671</td>\n",
              "      <td>0.024006</td>\n",
              "      <td>0.049524</td>\n",
              "      <td>163.120567</td>\n",
              "      <td>0.006917</td>\n",
              "      <td>0.072695</td>\n",
              "      <td>12.0</td>\n",
              "      <td>-0.368421</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>-0.105263</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.986936</td>\n",
              "      <td>0.005513</td>\n",
              "      <td>0.021330</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.061404</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bad2fb24-c9d7-451a-9ed1-76b1eb22b5c7')\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-bad2fb24-c9d7-451a-9ed1-76b1eb22b5c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bad2fb24-c9d7-451a-9ed1-76b1eb22b5c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### View Bad Data from Redfin"
      ],
      "metadata": {
        "id": "dblQX4_IB4UU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# choose a specific zip code / property type and view MoM change\n",
        "# TODO: Future auto loop through each MoM column and recalulate\n",
        "df_merge.loc[(df_merge['zipcode'] == '33510') & \n",
        "             (df_merge['property_type'] == 'Single Family Residential')]\\\n",
        "  .sort_values(by=['period_begin']).tail(2)\\\n",
        "  [['period_begin', 'median_dom', 'median_dom_mom', 'median_dom_yoy']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jGBDgfXXAoNN",
        "outputId": "b6113330-c5ae-4c82-c02e-aa18f274156b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      period_begin  median_dom  median_dom_mom  median_dom_yoy\n",
              "46424   2022-02-01         6.0            -1.0             0.0\n",
              "46786   2022-03-01         7.0             1.0             2.5"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>period_begin</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "    </tr>\n",
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              "      <td>7.0</td>\n",
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              "      <td>2.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c29b4cc-b9c2-4488-9a2e-835c57e47871 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c29b4cc-b9c2-4488-9a2e-835c57e47871');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test to correct the error\n",
        "df_test = df_merge.copy()\n",
        "df_test['median_dom_mom_correction'] = df_test.sort_values(by=['period_begin'])['median_dom'].pct_change()\n",
        "df_test.loc[(df_test['zipcode'] == '33510') & \n",
        "             (df_test['property_type'] == 'Single Family Residential')]\\\n",
        "  .sort_values(by=['period_begin']).tail(2)\\\n",
        "  [['period_begin', 'median_dom', 'median_dom_mom', 'median_dom_yoy', 'median_dom_mom_correction']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "aByYTpifBZiB",
        "outputId": "e6dcefd7-58a9-4a13-b383-1fe2c1b9591b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      period_begin  median_dom  median_dom_mom  median_dom_yoy  \\\n",
              "46424   2022-02-01         6.0            -1.0             0.0   \n",
              "46786   2022-03-01         7.0             1.0             2.5   \n",
              "\n",
              "       median_dom_mom_correction  \n",
              "46424                   0.000000  \n",
              "46786                   0.166667  "
            ],
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              "      <th>median_dom_mom_correction</th>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-9d48ede6-b351-4cb3-ae37-fdfb5214918d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d48ede6-b351-4cb3-ae37-fdfb5214918d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Add Features"
      ],
      "metadata": {
        "id": "yQjxIZ90CBHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# add features\n",
        "df_features = df_merge.copy()\n",
        "print('Max begin date:', df_features['period_begin'].max())\n",
        "print('Max end date:', df_features['period_end'].max())\n",
        "max_end_date = df_features['period_end'].max()\n",
        "\n",
        "# 1) flag for most recent date\n",
        "df_features['latest_period'] = df_features.apply(\n",
        "    lambda x: True if x['period_end'] == max_end_date else False, axis=1)\n",
        "\n",
        "# 2) remove Redfin city and keep Geo city\n",
        "df_features = df_features.drop(columns=['city_x'])\n",
        "df_features = df_features.rename(columns={'city_y': 'city'})\n",
        "\n",
        "# 3) fix median days on market (bad data)\n",
        "df_features['median_dom_mom'] = df_features.sort_values(by=['period_begin'])['median_dom'].pct_change()\n",
        "\n",
        "# view first 5 rows\n",
        "df_features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "wSOz1k_8lN1R",
        "outputId": "f6cca746-8a21-43f3-aada-f817c6eeba53"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max begin date: 2022-03-01\n",
            "Max end date: 2022-05-31\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  period_begin  period_end  period_duration region_type  region_type_id  \\\n",
              "0   2016-05-01  2016-07-31               90    zip code               2   \n",
              "1   2017-08-01  2017-10-31               90    zip code               2   \n",
              "2   2021-02-01  2021-04-30               90    zip code               2   \n",
              "3   2015-02-01  2015-04-30               90    zip code               2   \n",
              "4   2020-08-01  2020-10-31               90    zip code               2   \n",
              "\n",
              "   table_id is_seasonally_adjusted           region    state state_code  \\\n",
              "0     14383                      f  Zip Code: 33781  Florida         FL   \n",
              "1     14383                      f  Zip Code: 33781  Florida         FL   \n",
              "2     14383                      f  Zip Code: 33781  Florida         FL   \n",
              "3     14383                      f  Zip Code: 33781  Florida         FL   \n",
              "4     14383                      f  Zip Code: 33781  Florida         FL   \n",
              "\n",
              "               property_type  property_type_id  median_sale_price  \\\n",
              "0  Single Family Residential                 6           138000.0   \n",
              "1                  Townhouse                13           185100.0   \n",
              "2                Condo/Co-op                 3            84850.0   \n",
              "3                Condo/Co-op                 3            34900.0   \n",
              "4                  Townhouse                13           228750.0   \n",
              "\n",
              "   median_sale_price_mom  median_sale_price_yoy  median_list_price  \\\n",
              "0               0.061538               0.205240           159900.0   \n",
              "1              -0.002425               0.088824           204900.0   \n",
              "2              -0.030286               0.022289            87500.0   \n",
              "3              -0.008523              -0.002857            39900.0   \n",
              "4               0.063953               0.063953           239000.0   \n",
              "\n",
              "   median_list_price_mom  median_list_price_yoy  median_ppsf  median_ppsf_mom  \\\n",
              "0               0.066711               0.391645   115.571776         0.001622   \n",
              "1              -0.000488               0.122740   127.400000         0.009324   \n",
              "2               0.029412               0.535088   101.066561        -0.156275   \n",
              "3               0.081301               0.453552    32.461677         0.022727   \n",
              "4              -0.004167               0.059867   158.379671         0.024006   \n",
              "\n",
              "   median_ppsf_yoy  median_list_ppsf  median_list_ppsf_mom  \\\n",
              "0         0.272115        132.211538              0.051082   \n",
              "1         0.063131        134.325190              0.002698   \n",
              "2         0.101004         94.691943             -0.122395   \n",
              "3        -0.054554         35.978359              0.078378   \n",
              "4         0.049524        163.120567              0.006917   \n",
              "\n",
              "   median_list_ppsf_yoy  homes_sold  homes_sold_mom  homes_sold_yoy  \\\n",
              "0              0.287100        97.0       -0.058252        0.077778   \n",
              "1              0.045160        23.0        0.150000        0.095238   \n",
              "2              0.238886         8.0        0.142857        0.600000   \n",
              "3             -0.124478         7.0        0.750000        0.000000   \n",
              "4              0.072695        12.0       -0.368421       -0.076923   \n",
              "\n",
              "   pending_sales  pending_sales_mom  pending_sales_yoy  new_listings  \\\n",
              "0           21.0          -0.363636          -0.045455         101.0   \n",
              "1            9.0           0.500000           0.000000          18.0   \n",
              "2            2.0           1.000000          -0.333333           9.0   \n",
              "3            2.0          -0.333333           0.000000           9.0   \n",
              "4            3.0          -0.500000           0.000000          17.0   \n",
              "\n",
              "   new_listings_mom  new_listings_yoy  inventory  inventory_mom  \\\n",
              "0         -0.073394          0.041237       82.0       0.078947   \n",
              "1         -0.142857         -0.280000       10.0       0.000000   \n",
              "2          0.000000          0.285714        5.0      -0.166667   \n",
              "3         -0.307692          0.500000       11.0      -0.153846   \n",
              "4         -0.105263          0.062500        8.0       0.333333   \n",
              "\n",
              "   inventory_yoy  months_of_supply  months_of_supply_mom  \\\n",
              "0      -0.068182               NaN                   NaN   \n",
              "1      -0.333333               NaN                   NaN   \n",
              "2       0.250000               NaN                   NaN   \n",
              "3      -0.083333               NaN                   NaN   \n",
              "4       0.142857               NaN                   NaN   \n",
              "\n",
              "   months_of_supply_yoy  median_dom  median_dom_mom  median_dom_yoy  \\\n",
              "0                   NaN        48.0       -0.150442            -2.0   \n",
              "1                   NaN        23.0       -0.417722           -27.0   \n",
              "2                   NaN        13.0        1.600000           -51.5   \n",
              "3                   NaN        52.0        0.238095          -172.0   \n",
              "4                   NaN        19.0        1.375000             5.0   \n",
              "\n",
              "   avg_sale_to_list  avg_sale_to_list_mom  avg_sale_to_list_yoy  \\\n",
              "0          0.993254              0.005869              0.008949   \n",
              "1          0.973702              0.007817             -0.009215   \n",
              "2          0.986780             -0.004834             -0.000821   \n",
              "3          0.933755              0.009664             -0.053506   \n",
              "4          0.986936              0.005513              0.021330   \n",
              "\n",
              "   sold_above_list  sold_above_list_mom  sold_above_list_yoy  price_drops  \\\n",
              "0         0.329897             0.009509             0.041008          NaN   \n",
              "1         0.173913             0.073913             0.078675          NaN   \n",
              "2         0.250000            -0.035714             0.250000          NaN   \n",
              "3         0.000000             0.000000            -0.142857          NaN   \n",
              "4         0.166667             0.061404             0.166667          NaN   \n",
              "\n",
              "   price_drops_mom  price_drops_yoy  off_market_in_two_weeks  \\\n",
              "0              NaN              NaN                 0.428571   \n",
              "1              NaN              NaN                 0.444444   \n",
              "2              NaN              NaN                 1.000000   \n",
              "3              NaN              NaN                 0.500000   \n",
              "4              NaN              NaN                 0.666667   \n",
              "\n",
              "   off_market_in_two_weeks_mom  off_market_in_two_weeks_yoy  \\\n",
              "0                     0.125541                     0.201299   \n",
              "1                     0.111111                     0.111111   \n",
              "2                     0.000000                     1.000000   \n",
              "3                     0.166667                     0.000000   \n",
              "4                     0.000000                    -0.333333   \n",
              "\n",
              "  parent_metro_region  parent_metro_region_metro_code         last_updated  \\\n",
              "0           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "1           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "2           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "3           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "4           Tampa, FL                           45300  2022-06-12 14:28:57   \n",
              "\n",
              "  zipcode              city    county  latest_period  \n",
              "0   33781  Saint petersburg  Pinellas          False  \n",
              "1   33781  Saint petersburg  Pinellas          False  \n",
              "2   33781  Saint petersburg  Pinellas          False  \n",
              "3   33781  Saint petersburg  Pinellas          False  \n",
              "4   33781  Saint petersburg  Pinellas          False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c4ee0de-49a0-4ca5-b00a-ac60ebc6a5cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>period_begin</th>\n",
              "      <th>period_end</th>\n",
              "      <th>period_duration</th>\n",
              "      <th>region_type</th>\n",
              "      <th>region_type_id</th>\n",
              "      <th>table_id</th>\n",
              "      <th>is_seasonally_adjusted</th>\n",
              "      <th>region</th>\n",
              "      <th>state</th>\n",
              "      <th>state_code</th>\n",
              "      <th>property_type</th>\n",
              "      <th>property_type_id</th>\n",
              "      <th>median_sale_price</th>\n",
              "      <th>median_sale_price_mom</th>\n",
              "      <th>median_sale_price_yoy</th>\n",
              "      <th>median_list_price</th>\n",
              "      <th>median_list_price_mom</th>\n",
              "      <th>median_list_price_yoy</th>\n",
              "      <th>median_ppsf</th>\n",
              "      <th>median_ppsf_mom</th>\n",
              "      <th>median_ppsf_yoy</th>\n",
              "      <th>median_list_ppsf</th>\n",
              "      <th>median_list_ppsf_mom</th>\n",
              "      <th>median_list_ppsf_yoy</th>\n",
              "      <th>homes_sold</th>\n",
              "      <th>homes_sold_mom</th>\n",
              "      <th>homes_sold_yoy</th>\n",
              "      <th>pending_sales</th>\n",
              "      <th>pending_sales_mom</th>\n",
              "      <th>pending_sales_yoy</th>\n",
              "      <th>new_listings</th>\n",
              "      <th>new_listings_mom</th>\n",
              "      <th>new_listings_yoy</th>\n",
              "      <th>inventory</th>\n",
              "      <th>inventory_mom</th>\n",
              "      <th>inventory_yoy</th>\n",
              "      <th>months_of_supply</th>\n",
              "      <th>months_of_supply_mom</th>\n",
              "      <th>months_of_supply_yoy</th>\n",
              "      <th>median_dom</th>\n",
              "      <th>median_dom_mom</th>\n",
              "      <th>median_dom_yoy</th>\n",
              "      <th>avg_sale_to_list</th>\n",
              "      <th>avg_sale_to_list_mom</th>\n",
              "      <th>avg_sale_to_list_yoy</th>\n",
              "      <th>sold_above_list</th>\n",
              "      <th>sold_above_list_mom</th>\n",
              "      <th>sold_above_list_yoy</th>\n",
              "      <th>price_drops</th>\n",
              "      <th>price_drops_mom</th>\n",
              "      <th>price_drops_yoy</th>\n",
              "      <th>off_market_in_two_weeks</th>\n",
              "      <th>off_market_in_two_weeks_mom</th>\n",
              "      <th>off_market_in_two_weeks_yoy</th>\n",
              "      <th>parent_metro_region</th>\n",
              "      <th>parent_metro_region_metro_code</th>\n",
              "      <th>last_updated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>city</th>\n",
              "      <th>county</th>\n",
              "      <th>latest_period</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>2016-07-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Single Family Residential</td>\n",
              "      <td>6</td>\n",
              "      <td>138000.0</td>\n",
              "      <td>0.061538</td>\n",
              "      <td>0.205240</td>\n",
              "      <td>159900.0</td>\n",
              "      <td>0.066711</td>\n",
              "      <td>0.391645</td>\n",
              "      <td>115.571776</td>\n",
              "      <td>0.001622</td>\n",
              "      <td>0.272115</td>\n",
              "      <td>132.211538</td>\n",
              "      <td>0.051082</td>\n",
              "      <td>0.287100</td>\n",
              "      <td>97.0</td>\n",
              "      <td>-0.058252</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>21.0</td>\n",
              "      <td>-0.363636</td>\n",
              "      <td>-0.045455</td>\n",
              "      <td>101.0</td>\n",
              "      <td>-0.073394</td>\n",
              "      <td>0.041237</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.078947</td>\n",
              "      <td>-0.068182</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.0</td>\n",
              "      <td>-0.150442</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.993254</td>\n",
              "      <td>0.005869</td>\n",
              "      <td>0.008949</td>\n",
              "      <td>0.329897</td>\n",
              "      <td>0.009509</td>\n",
              "      <td>0.041008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.125541</td>\n",
              "      <td>0.201299</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-08-01</td>\n",
              "      <td>2017-10-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>185100.0</td>\n",
              "      <td>-0.002425</td>\n",
              "      <td>0.088824</td>\n",
              "      <td>204900.0</td>\n",
              "      <td>-0.000488</td>\n",
              "      <td>0.122740</td>\n",
              "      <td>127.400000</td>\n",
              "      <td>0.009324</td>\n",
              "      <td>0.063131</td>\n",
              "      <td>134.325190</td>\n",
              "      <td>0.002698</td>\n",
              "      <td>0.045160</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.0</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>-0.280000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>-0.417722</td>\n",
              "      <td>-27.0</td>\n",
              "      <td>0.973702</td>\n",
              "      <td>0.007817</td>\n",
              "      <td>-0.009215</td>\n",
              "      <td>0.173913</td>\n",
              "      <td>0.073913</td>\n",
              "      <td>0.078675</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-02-01</td>\n",
              "      <td>2021-04-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>84850.0</td>\n",
              "      <td>-0.030286</td>\n",
              "      <td>0.022289</td>\n",
              "      <td>87500.0</td>\n",
              "      <td>0.029412</td>\n",
              "      <td>0.535088</td>\n",
              "      <td>101.066561</td>\n",
              "      <td>-0.156275</td>\n",
              "      <td>0.101004</td>\n",
              "      <td>94.691943</td>\n",
              "      <td>-0.122395</td>\n",
              "      <td>0.238886</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-0.166667</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>-51.5</td>\n",
              "      <td>0.986780</td>\n",
              "      <td>-0.004834</td>\n",
              "      <td>-0.000821</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-02-01</td>\n",
              "      <td>2015-04-30</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Condo/Co-op</td>\n",
              "      <td>3</td>\n",
              "      <td>34900.0</td>\n",
              "      <td>-0.008523</td>\n",
              "      <td>-0.002857</td>\n",
              "      <td>39900.0</td>\n",
              "      <td>0.081301</td>\n",
              "      <td>0.453552</td>\n",
              "      <td>32.461677</td>\n",
              "      <td>0.022727</td>\n",
              "      <td>-0.054554</td>\n",
              "      <td>35.978359</td>\n",
              "      <td>0.078378</td>\n",
              "      <td>-0.124478</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-0.307692</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.238095</td>\n",
              "      <td>-172.0</td>\n",
              "      <td>0.933755</td>\n",
              "      <td>0.009664</td>\n",
              "      <td>-0.053506</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>2020-10-31</td>\n",
              "      <td>90</td>\n",
              "      <td>zip code</td>\n",
              "      <td>2</td>\n",
              "      <td>14383</td>\n",
              "      <td>f</td>\n",
              "      <td>Zip Code: 33781</td>\n",
              "      <td>Florida</td>\n",
              "      <td>FL</td>\n",
              "      <td>Townhouse</td>\n",
              "      <td>13</td>\n",
              "      <td>228750.0</td>\n",
              "      <td>0.063953</td>\n",
              "      <td>0.063953</td>\n",
              "      <td>239000.0</td>\n",
              "      <td>-0.004167</td>\n",
              "      <td>0.059867</td>\n",
              "      <td>158.379671</td>\n",
              "      <td>0.024006</td>\n",
              "      <td>0.049524</td>\n",
              "      <td>163.120567</td>\n",
              "      <td>0.006917</td>\n",
              "      <td>0.072695</td>\n",
              "      <td>12.0</td>\n",
              "      <td>-0.368421</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>-0.105263</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.986936</td>\n",
              "      <td>0.005513</td>\n",
              "      <td>0.021330</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.061404</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>Tampa, FL</td>\n",
              "      <td>45300</td>\n",
              "      <td>2022-06-12 14:28:57</td>\n",
              "      <td>33781</td>\n",
              "      <td>Saint petersburg</td>\n",
              "      <td>Pinellas</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c4ee0de-49a0-4ca5-b00a-ac60ebc6a5cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c4ee0de-49a0-4ca5-b00a-ac60ebc6a5cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c4ee0de-49a0-4ca5-b00a-ac60ebc6a5cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"blue\">Output</font>"
      ],
      "metadata": {
        "id": "G8X9_v9nfEk6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "date_str = datetime.today().strftime('%Y-%m')\n",
        "print('Current year/month:', date_str)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlCxgS0vfM7R",
        "outputId": "937f8308-98ea-4909-97a8-dab972342159"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current year/month: 2022-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download file\n",
        "df_features.to_csv('zip_realtor_{0}_{1}.csv'.format(state_code, date_str), index=False)\n",
        "files.download('zip_realtor_{0}_{1}.csv'.format(state_code, date_str))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "C6r45lNkfFav",
        "outputId": "5499226e-b232-4e30-c56b-30cd235cf18a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_8ee7fd23-076c-4cf6-85b9-62dc782518e6\", \"zip_realtor_FL_2022-06.csv\", 36720428)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WrYHVh9Ukid"
      },
      "source": [
        "# End Notebook"
      ]
    }
  ]
}