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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from helpers import (\n",
    "    get_data_path_for_config,\n",
    "    get_combined_df,\n",
    "    save_final_df_as_jsonl,\n",
    "    handle_slug_column_mappings,\n",
    "    set_home_type,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG_NAME = \"days_on_market\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing Metro_med_listings_price_cut_amt_uc_sfr_month.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfr_week.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_month.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfr_week.csv\n",
      "processing Metro_med_doz_pending_uc_sfrcondo_month.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfr_sm_month.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_mean_days_to_close_uc_sfrcondo_week.csv\n",
      "processing Metro_mean_days_to_close_uc_sfrcondo_month.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfr_week.csv\n",
      "processing Metro_median_days_to_close_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfr_sm_week.csv\n",
      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfrcondo_week.csv\n",
      "processing Metro_med_doz_pending_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_mean_days_to_close_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_week.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfr_week.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_month.csv\n",
      "processing Metro_mean_doz_pending_uc_sfrcondo_week.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_week.csv\n",
      "processing Metro_median_days_to_close_uc_sfrcondo_week.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfr_sm_month.csv\n",
      "processing Metro_mean_doz_pending_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfr_sm_month.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_median_days_to_close_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfr_month.csv\n",
      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_week.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_week.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_mean_days_to_close_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfr_sm_week.csv\n",
      "processing Metro_mean_doz_pending_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfr_sm_week.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_month.csv\n",
      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_med_doz_pending_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfr_month.csv\n",
      "processing Metro_med_doz_pending_uc_sfrcondo_week.csv\n",
      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfr_sm_month.csv\n",
      "processing Metro_median_days_to_close_uc_sfrcondo_month.csv\n",
      "processing Metro_perc_listings_price_cut_uc_sfr_sm_week.csv\n",
      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_month.csv\n",
      "processing Metro_mean_listings_price_cut_amt_uc_sfr_month.csv\n",
      "processing Metro_mean_doz_pending_uc_sfrcondo_month.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Percent Listings Price Cut</th>\n",
       "      <th>Mean Listings Price Cut Amount</th>\n",
       "      <th>Percent Listings Price Cut (Smoothed)</th>\n",
       "      <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
       "      <th>Median Days on Pending (Smoothed)</th>\n",
       "      <th>Median Days on Pending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2018-01-06</td>\n",
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       "      <td>13508.368375</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>2018-01-13</td>\n",
       "      <td>0.049042</td>\n",
       "      <td>14114.788383</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>2018-01-20</td>\n",
       "      <td>0.044740</td>\n",
       "      <td>14326.128956</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>2018-01-27</td>\n",
       "      <td>0.047930</td>\n",
       "      <td>13998.585612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13998.585612</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>0.047622</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>586709</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2024-01-13</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <th>586712</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>2024-01-27</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586713</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>2024-02-03</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586714 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",
       "586709    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "586710    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "586711    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "586712    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "586713    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "\n",
       "              Date  Percent Listings Price Cut  \\\n",
       "0       2018-01-06                         NaN   \n",
       "1       2018-01-13                    0.049042   \n",
       "2       2018-01-20                    0.044740   \n",
       "3       2018-01-27                    0.047930   \n",
       "4       2018-02-03                    0.047622   \n",
       "...            ...                         ...   \n",
       "586709  2024-01-06                    0.094017   \n",
       "586710  2024-01-13                    0.070175   \n",
       "586711  2024-01-20                    0.043478   \n",
       "586712  2024-01-27                    0.036697   \n",
       "586713  2024-02-03                    0.077670   \n",
       "\n",
       "        Mean Listings Price Cut Amount  Percent Listings Price Cut (Smoothed)  \\\n",
       "0                         13508.368375                                    NaN   \n",
       "1                         14114.788383                                    NaN   \n",
       "2                         14326.128956                                    NaN   \n",
       "3                         13998.585612                                    NaN   \n",
       "4                         14120.035549                               0.047622   \n",
       "...                                ...                                    ...   \n",
       "586709                             NaN                               0.037378   \n",
       "586710                             NaN                               0.043203   \n",
       "586711                             NaN                               0.054073   \n",
       "586712                             NaN                               0.061092   \n",
       "586713                             NaN                               0.057005   \n",
       "\n",
       "        Mean Listings Price Cut Amount (Smoothed)  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                    13998.585612   \n",
       "4                                    14120.035549   \n",
       "...                                           ...   \n",
       "586709                                        NaN   \n",
       "586710                                        NaN   \n",
       "586711                                        NaN   \n",
       "586712                                        NaN   \n",
       "586713                                        NaN   \n",
       "\n",
       "        Median Days on Pending (Smoothed)  Median Days on Pending  \n",
       "0                                     NaN                     NaN  \n",
       "1                                     NaN                     NaN  \n",
       "2                                     NaN                     NaN  \n",
       "3                                     NaN                     NaN  \n",
       "4                                     NaN                     NaN  \n",
       "...                                   ...                     ...  \n",
       "586709                                NaN                     NaN  \n",
       "586710                                NaN                     NaN  \n",
       "586711                                NaN                     NaN  \n",
       "586712                                NaN                     NaN  \n",
       "586713                                NaN                     NaN  \n",
       "\n",
       "[586714 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frames = []\n",
    "\n",
    "exclude_columns = [\n",
    "    \"RegionID\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "slug_column_mappings = {\n",
    "    \"_mean_listings_price_cut_amt_\": \"Mean Listings Price Cut Amount\",\n",
    "    \"_med_doz_pending_\": \"Median Days on Pending\",\n",
    "    \"_median_days_to_pending_\": \"Median Days to Close\",\n",
    "    \"_perc_listings_price_cut_\": \"Percent Listings Price Cut\",\n",
    "}\n",
    "\n",
    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
    "\n",
    "for filename in os.listdir(data_dir_path):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        print(\"processing \" + filename)\n",
    "        # skip month files for now since they are redundant\n",
    "        if \"month\" in filename:\n",
    "            continue\n",
    "\n",
    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
    "\n",
    "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
    "        cur_df = set_home_type(cur_df, filename)\n",
    "\n",
    "        data_frames = handle_slug_column_mappings(\n",
    "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
    "        )\n",
    "\n",
    "\n",
    "combined_df = get_combined_df(\n",
    "    data_frames,\n",
    "    [\n",
    "        \"RegionID\",\n",
    "        \"SizeRank\",\n",
    "        \"RegionName\",\n",
    "        \"RegionType\",\n",
    "        \"StateName\",\n",
    "        \"Home Type\",\n",
    "        \"Date\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Percent Listings Price Cut</th>\n",
       "      <th>Mean Listings Price Cut Amount</th>\n",
       "      <th>Percent Listings Price Cut (Smoothed)</th>\n",
       "      <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
       "      <th>Median Days on Pending (Smoothed)</th>\n",
       "      <th>Median Days on Pending</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>2018-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13508.368375</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>2018-01-13</td>\n",
       "      <td>0.049042</td>\n",
       "      <td>14114.788383</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>2018-01-20</td>\n",
       "      <td>0.044740</td>\n",
       "      <td>14326.128956</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>2018-01-27</td>\n",
       "      <td>0.047930</td>\n",
       "      <td>13998.585612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13998.585612</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>0.047622</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</td>\n",
       "      <td>14120.035549</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>586709</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>0.094017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586710</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>2024-01-13</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586711</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>2024-01-20</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586712</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>2024-01-27</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586713</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>2024-02-03</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586714 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",
       "586709     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "586710     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "586711     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "586712     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "586713     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "\n",
       "             Date  Percent Listings Price Cut  Mean Listings Price Cut Amount  \\\n",
       "0      2018-01-06                         NaN                    13508.368375   \n",
       "1      2018-01-13                    0.049042                    14114.788383   \n",
       "2      2018-01-20                    0.044740                    14326.128956   \n",
       "3      2018-01-27                    0.047930                    13998.585612   \n",
       "4      2018-02-03                    0.047622                    14120.035549   \n",
       "...           ...                         ...                             ...   \n",
       "586709 2024-01-06                    0.094017                             NaN   \n",
       "586710 2024-01-13                    0.070175                             NaN   \n",
       "586711 2024-01-20                    0.043478                             NaN   \n",
       "586712 2024-01-27                    0.036697                             NaN   \n",
       "586713 2024-02-03                    0.077670                             NaN   \n",
       "\n",
       "        Percent Listings Price Cut (Smoothed)  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         NaN   \n",
       "4                                    0.047622   \n",
       "...                                       ...   \n",
       "586709                               0.037378   \n",
       "586710                               0.043203   \n",
       "586711                               0.054073   \n",
       "586712                               0.061092   \n",
       "586713                               0.057005   \n",
       "\n",
       "        Mean Listings Price Cut Amount (Smoothed)  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "3                                    13998.585612   \n",
       "4                                    14120.035549   \n",
       "...                                           ...   \n",
       "586709                                        NaN   \n",
       "586710                                        NaN   \n",
       "586711                                        NaN   \n",
       "586712                                        NaN   \n",
       "586713                                        NaN   \n",
       "\n",
       "        Median Days on Pending (Smoothed)  Median Days on Pending  \n",
       "0                                     NaN                     NaN  \n",
       "1                                     NaN                     NaN  \n",
       "2                                     NaN                     NaN  \n",
       "3                                     NaN                     NaN  \n",
       "4                                     NaN                     NaN  \n",
       "...                                   ...                     ...  \n",
       "586709                                NaN                     NaN  \n",
       "586710                                NaN                     NaN  \n",
       "586711                                NaN                     NaN  \n",
       "586712                                NaN                     NaN  \n",
       "586713                                NaN                     NaN  \n",
       "\n",
       "[586714 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adjust column names\n",
    "final_df = combined_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[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
   ]
  }
 ],
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