{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data\"\n",
    "PROCESSED_DIR = \"../processed/\"\n",
    "FACET_DIR = \"for_sale_listings/\"\n",
    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing Metro_new_pending_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_month.csv\n",
      "processing Metro_mlp_uc_sfr_sm_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_month.csv\n",
      "processing Metro_mlp_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_month.csv\n",
      "processing Metro_mlp_uc_sfr_sm_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_sm_month.csv\n",
      "processing Metro_mlp_uc_sfr_month.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_mlp_uc_sfr_week.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_sm_month.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>RegionID</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>RegionType</th>\n",
       "      <th>StateName</th>\n",
       "      <th>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>New Pending (Smoothed)</th>\n",
       "      <th>Median Listing Price</th>\n",
       "      <th>Median Listing Price (Smoothed)</th>\n",
       "      <th>New Pending</th>\n",
       "      <th>New Listings</th>\n",
       "      <th>New Listings (Smoothed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-02-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>259700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>693656</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133938.0</td>\n",
       "      <td>133938.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693657</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126463.0</td>\n",
       "      <td>126463.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693658</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123225.0</td>\n",
       "      <td>123225.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>693659</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>24.0</td>\n",
       "      <td>136233.0</td>\n",
       "      <td>136233.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693660</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121488.0</td>\n",
       "      <td>121488.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>693661 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RegionID  SizeRank     RegionName RegionType StateName  Home Type  \\\n",
       "0         102001         0  United States    country       NaN        SFR   \n",
       "1         102001         0  United States    country       NaN        SFR   \n",
       "2         102001         0  United States    country       NaN        SFR   \n",
       "3         102001         0  United States    country       NaN        SFR   \n",
       "4         102001         0  United States    country       NaN        SFR   \n",
       "...          ...       ...            ...        ...       ...        ...   \n",
       "693656    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "693657    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "693658    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "693659    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "693660    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "\n",
       "              Date  New Pending (Smoothed)  Median Listing Price  \\\n",
       "0       2018-01-13                     NaN              259000.0   \n",
       "1       2018-01-20                     NaN              259900.0   \n",
       "2       2018-01-27                     NaN              259900.0   \n",
       "3       2018-01-31                     NaN              254900.0   \n",
       "4       2018-02-03                     NaN              260000.0   \n",
       "...            ...                     ...                   ...   \n",
       "693656  2023-12-16                     NaN              133938.0   \n",
       "693657  2023-12-23                     NaN              126463.0   \n",
       "693658  2023-12-30                     NaN              123225.0   \n",
       "693659  2023-12-31                    24.0              136233.0   \n",
       "693660  2024-01-06                     NaN              121488.0   \n",
       "\n",
       "        Median Listing Price (Smoothed)  New Pending  New Listings  \\\n",
       "0                                   NaN          NaN           NaN   \n",
       "1                                   NaN          NaN           NaN   \n",
       "2                                   NaN          NaN           NaN   \n",
       "3                                   NaN          NaN           NaN   \n",
       "4                              259700.0          NaN           NaN   \n",
       "...                                 ...          ...           ...   \n",
       "693656                         133938.0          NaN           NaN   \n",
       "693657                         126463.0          NaN           NaN   \n",
       "693658                         123225.0          NaN           NaN   \n",
       "693659                         136233.0         24.0          28.0   \n",
       "693660                         121488.0          NaN           NaN   \n",
       "\n",
       "        New Listings (Smoothed)  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "...                         ...  \n",
       "693656                      NaN  \n",
       "693657                      NaN  \n",
       "693658                      NaN  \n",
       "693659                     28.0  \n",
       "693660                      NaN  \n",
       "\n",
       "[693661 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
    "\n",
    "exclude_columns = [\n",
    "    \"RegionID\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "data_frames = []\n",
    "\n",
    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        print(\"processing \" + filename)\n",
    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
    "\n",
    "        # ignore monthly data for now since it is redundant\n",
    "        if \"monthly\" in filename:\n",
    "            continue\n",
    "\n",
    "        if \"sfrcondo\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"all homes\"\n",
    "        elif \"sfr\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"SFR\"\n",
    "        elif \"condo\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
    "\n",
    "        # Identify columns to pivot\n",
    "        columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
    "\n",
    "        smoothed = \"_sm_\" in filename\n",
    "\n",
    "        if \"_mlp_\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=(\n",
    "                    \"Median Listing Price\"\n",
    "                    if not smoothed\n",
    "                    else \"Median Listing Price (Smoothed)\"\n",
    "                ),\n",
    "            )\n",
    "            data_frames.append(cur_df)\n",
    "\n",
    "        elif \"_new_listings_\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=(\n",
    "                    \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
    "                ),\n",
    "            )\n",
    "            data_frames.append(cur_df)\n",
    "\n",
    "        elif \"new_pending\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
    "            )\n",
    "            data_frames.append(cur_df)\n",
    "\n",
    "matching_cols = [\n",
    "    \"RegionID\",\n",
    "    \"Date\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "\n",
    "def get_combined_df(data_frames):\n",
    "    combined_df = None\n",
    "    if len(data_frames) > 1:\n",
    "        # iterate over dataframes and merge or concat\n",
    "        combined_df = data_frames[0]\n",
    "        for i in range(1, len(data_frames)):\n",
    "            cur_df = data_frames[i]\n",
    "            combined_df = pd.merge(\n",
    "                combined_df,\n",
    "                cur_df,\n",
    "                on=[\n",
    "                    \"RegionID\",\n",
    "                    \"SizeRank\",\n",
    "                    \"RegionName\",\n",
    "                    \"RegionType\",\n",
    "                    \"StateName\",\n",
    "                    \"Home Type\",\n",
    "                    \"Date\",\n",
    "                ],\n",
    "                suffixes=(\"\", \"_\" + str(i)),\n",
    "                how=\"outer\",\n",
    "            )\n",
    "    elif len(data_frames) == 1:\n",
    "        combined_df = data_frames[0]\n",
    "\n",
    "    return combined_df\n",
    "\n",
    "\n",
    "combined_df = get_combined_df(data_frames)\n",
    "\n",
    "\n",
    "# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
    "columns_to_coalesce = [\n",
    "    \"Median Listing Price\",\n",
    "    \"Median Listing Price (Smoothed)\",\n",
    "    \"New Listings\",\n",
    "    \"New Listings (Smoothed)\",\n",
    "    \"New Pending (Smoothed)\",\n",
    "    \"New Pending\",\n",
    "]\n",
    "\n",
    "for index, row in combined_df.iterrows():\n",
    "    for col in combined_df.columns:\n",
    "        for column_to_coalesce in columns_to_coalesce:\n",
    "            if column_to_coalesce in col and \"_\" in col:\n",
    "                if not pd.isna(row[col]):\n",
    "                    combined_df.at[index, column_to_coalesce] = row[col]\n",
    "\n",
    "# remove columns with underscores\n",
    "combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region ID</th>\n",
       "      <th>Size Rank</th>\n",
       "      <th>Region</th>\n",
       "      <th>Region Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>New Pending (Smoothed)</th>\n",
       "      <th>Median Listing Price</th>\n",
       "      <th>Median Listing Price (Smoothed)</th>\n",
       "      <th>New Pending</th>\n",
       "      <th>New Listings</th>\n",
       "      <th>New Listings (Smoothed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-02-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>259700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693656</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133938.0</td>\n",
       "      <td>133938.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693657</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126463.0</td>\n",
       "      <td>126463.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693658</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123225.0</td>\n",
       "      <td>123225.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693659</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>24.0</td>\n",
       "      <td>136233.0</td>\n",
       "      <td>136233.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693660</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121488.0</td>\n",
       "      <td>121488.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>693661 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Region ID  Size Rank         Region Region Type State  Home Type  \\\n",
       "0          102001          0  United States     country   NaN        SFR   \n",
       "1          102001          0  United States     country   NaN        SFR   \n",
       "2          102001          0  United States     country   NaN        SFR   \n",
       "3          102001          0  United States     country   NaN        SFR   \n",
       "4          102001          0  United States     country   NaN        SFR   \n",
       "...           ...        ...            ...         ...   ...        ...   \n",
       "693656     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "693657     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "693658     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "693659     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "693660     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "\n",
       "              Date  New Pending (Smoothed)  Median Listing Price  \\\n",
       "0       2018-01-13                     NaN              259000.0   \n",
       "1       2018-01-20                     NaN              259900.0   \n",
       "2       2018-01-27                     NaN              259900.0   \n",
       "3       2018-01-31                     NaN              254900.0   \n",
       "4       2018-02-03                     NaN              260000.0   \n",
       "...            ...                     ...                   ...   \n",
       "693656  2023-12-16                     NaN              133938.0   \n",
       "693657  2023-12-23                     NaN              126463.0   \n",
       "693658  2023-12-30                     NaN              123225.0   \n",
       "693659  2023-12-31                    24.0              136233.0   \n",
       "693660  2024-01-06                     NaN              121488.0   \n",
       "\n",
       "        Median Listing Price (Smoothed)  New Pending  New Listings  \\\n",
       "0                                   NaN          NaN           NaN   \n",
       "1                                   NaN          NaN           NaN   \n",
       "2                                   NaN          NaN           NaN   \n",
       "3                                   NaN          NaN           NaN   \n",
       "4                              259700.0          NaN           NaN   \n",
       "...                                 ...          ...           ...   \n",
       "693656                         133938.0          NaN           NaN   \n",
       "693657                         126463.0          NaN           NaN   \n",
       "693658                         123225.0          NaN           NaN   \n",
       "693659                         136233.0         24.0          28.0   \n",
       "693660                         121488.0          NaN           NaN   \n",
       "\n",
       "        New Listings (Smoothed)  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "...                         ...  \n",
       "693656                      NaN  \n",
       "693657                      NaN  \n",
       "693658                      NaN  \n",
       "693659                     28.0  \n",
       "693660                      NaN  \n",
       "\n",
       "[693661 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = combined_df\n",
    "final_df = final_df.rename(\n",
    "    columns={\n",
    "        \"RegionID\": \"Region ID\",\n",
    "        \"SizeRank\": \"Size Rank\",\n",
    "        \"RegionName\": \"Region\",\n",
    "        \"RegionType\": \"Region Type\",\n",
    "        \"StateName\": \"State\",\n",
    "    }\n",
    ")\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
    "    os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
    "\n",
    "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
   ]
  }
 ],
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