{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG_NAME = \"home_values\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing City_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_sfrcondo_tier_0.67_1.0_sm_sa_month.csv\n",
      "processing County_zhvi_uc_sfrcondo_tier_0.67_1.0_sm_sa_month.csv\n",
      "processing Metro_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_uc_sfrcondo_tier_0.67_1.0_sm_sa_month.csv\n",
      "processing Zip_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_uc_sfr_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_uc_sfrcondo_tier_0.0_0.33_sm_sa_month.csv\n",
      "processing Metro_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_month.csv\n",
      "processing Zip_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_uc_sfrcondo_tier_0.0_0.33_sm_sa_month.csv\n",
      "processing State_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_sfrcondo_tier_0.0_0.33_sm_sa_month.csv\n",
      "processing City_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_bdrmcnt_5_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing City_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_uc_sfrcondo_tier_0.67_1.0_sm_sa_month.csv\n",
      "processing Zip_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing State_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Neighborhood_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing County_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_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>Bedroom Count</th>\n",
       "      <th>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-01-31</td>\n",
       "      <td>81310.639504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-02-29</td>\n",
       "      <td>80419.761984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-03-31</td>\n",
       "      <td>80480.449461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-04-30</td>\n",
       "      <td>79799.206525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-05-31</td>\n",
       "      <td>79666.469861</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117907</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>486974.735908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117908</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>485847.539614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117909</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>484223.885775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117910</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>481522.403338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117911</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>nan</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>481181.718200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117912 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RegionID  SizeRank RegionName RegionType StateName Bedroom Count  \\\n",
       "0              3        48     Alaska      state       nan     1-Bedroom   \n",
       "1              3        48     Alaska      state       nan     1-Bedroom   \n",
       "2              3        48     Alaska      state       nan     1-Bedroom   \n",
       "3              3        48     Alaska      state       nan     1-Bedroom   \n",
       "4              3        48     Alaska      state       nan     1-Bedroom   \n",
       "...          ...       ...        ...        ...       ...           ...   \n",
       "117907        62        51    Wyoming      state       nan  All Bedrooms   \n",
       "117908        62        51    Wyoming      state       nan  All Bedrooms   \n",
       "117909        62        51    Wyoming      state       nan  All Bedrooms   \n",
       "117910        62        51    Wyoming      state       nan  All Bedrooms   \n",
       "117911        62        51    Wyoming      state       nan  All Bedrooms   \n",
       "\n",
       "          Home Type        Date  \\\n",
       "0         all homes  2000-01-31   \n",
       "1         all homes  2000-02-29   \n",
       "2         all homes  2000-03-31   \n",
       "3         all homes  2000-04-30   \n",
       "4         all homes  2000-05-31   \n",
       "...             ...         ...   \n",
       "117907  condo/co-op  2023-09-30   \n",
       "117908  condo/co-op  2023-10-31   \n",
       "117909  condo/co-op  2023-11-30   \n",
       "117910  condo/co-op  2023-12-31   \n",
       "117911  condo/co-op  2024-01-31   \n",
       "\n",
       "        Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                         81310.639504   \n",
       "1                                         80419.761984   \n",
       "2                                         80480.449461   \n",
       "3                                         79799.206525   \n",
       "4                                         79666.469861   \n",
       "...                                                ...   \n",
       "117907                                   486974.735908   \n",
       "117908                                   485847.539614   \n",
       "117909                                   484223.885775   \n",
       "117910                                   481522.403338   \n",
       "117911                                   481181.718200   \n",
       "\n",
       "        Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "117907                                                NaN   \n",
       "117908                                                NaN   \n",
       "117909                                                NaN   \n",
       "117910                                                NaN   \n",
       "117911                                                NaN   \n",
       "\n",
       "        Top Tier ZHVI (Smoothed) (Seasonally Adjusted)  \n",
       "0                                                  NaN  \n",
       "1                                                  NaN  \n",
       "2                                                  NaN  \n",
       "3                                                  NaN  \n",
       "4                                                  NaN  \n",
       "...                                                ...  \n",
       "117907                                             NaN  \n",
       "117908                                             NaN  \n",
       "117909                                             NaN  \n",
       "117910                                             NaN  \n",
       "117911                                             NaN  \n",
       "\n",
       "[117912 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frames = []\n",
    "\n",
    "slug_column_mappings = {\n",
    "    \"_tier_0.0_0.33_\": \"Bottom Tier ZHVI\",\n",
    "    \"_tier_0.33_0.67_\": \"Mid Tier ZHVI\",\n",
    "    \"_tier_0.67_1.0_\": \"Top Tier ZHVI\",\n",
    "    \"\": \"ZHVI\",\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",
    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
    "        exclude_columns = [\n",
    "            \"RegionID\",\n",
    "            \"SizeRank\",\n",
    "            \"RegionName\",\n",
    "            \"RegionType\",\n",
    "            \"StateName\",\n",
    "            \"Bedroom Count\",\n",
    "            \"Home Type\",\n",
    "        ]\n",
    "\n",
    "        if \"Zip\" in filename:\n",
    "            continue\n",
    "        if \"Neighborhood\" in filename:\n",
    "            continue\n",
    "        if \"City\" in filename:\n",
    "            continue\n",
    "        if \"Metro\" in filename:\n",
    "            continue\n",
    "        if \"County\" in filename:\n",
    "            continue\n",
    "\n",
    "        if \"City\" in filename:\n",
    "            exclude_columns = exclude_columns + [\"State\", \"Metro\", \"CountyName\"]\n",
    "        elif \"Zip\" in filename:\n",
    "            exclude_columns = exclude_columns + [\n",
    "                \"State\",\n",
    "                \"City\",\n",
    "                \"Metro\",\n",
    "                \"CountyName\",\n",
    "            ]\n",
    "        elif \"County\" in filename:\n",
    "            exclude_columns = exclude_columns + [\n",
    "                \"State\",\n",
    "                \"Metro\",\n",
    "                \"StateCodeFIPS\",\n",
    "                \"MunicipalCodeFIPS\",\n",
    "            ]\n",
    "        elif \"Neighborhood\" in filename:\n",
    "            exclude_columns = exclude_columns + [\n",
    "                \"State\",\n",
    "                \"City\",\n",
    "                \"Metro\",\n",
    "                \"CountyName\",\n",
    "            ]\n",
    "\n",
    "        if \"_bdrmcnt_1_\" in filename:\n",
    "            cur_df[\"Bedroom Count\"] = \"1-Bedroom\"\n",
    "        elif \"_bdrmcnt_2_\" in filename:\n",
    "            cur_df[\"Bedroom Count\"] = \"2-Bedrooms\"\n",
    "        elif \"_bdrmcnt_3_\" in filename:\n",
    "            cur_df[\"Bedroom Count\"] = \"3-Bedrooms\"\n",
    "        elif \"_bdrmcnt_4_\" in filename:\n",
    "            cur_df[\"Bedroom Count\"] = \"4-Bedrooms\"\n",
    "        elif \"_bdrmcnt_5_\" in filename:\n",
    "            cur_df[\"Bedroom Count\"] = \"5+-Bedrooms\"\n",
    "        else:\n",
    "            cur_df[\"Bedroom Count\"] = \"All Bedrooms\"\n",
    "\n",
    "        cur_df = set_home_type(cur_df, filename)\n",
    "\n",
    "        cur_df[\"StateName\"] = cur_df[\"StateName\"].astype(str)\n",
    "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\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",
    "        \"Bedroom Count\",\n",
    "        \"Home Type\",\n",
    "        \"Date\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Bedroom Count</th>\n",
       "      <th>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-01-31</td>\n",
       "      <td>81310.639504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-02-29</td>\n",
       "      <td>80419.761984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-03-31</td>\n",
       "      <td>80480.449461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-04-30</td>\n",
       "      <td>79799.206525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-05-31</td>\n",
       "      <td>79666.469861</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117907</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>486974.735908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117908</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>485847.539614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117909</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>484223.885775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117910</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>481522.403338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117911</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>481181.718200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117912 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RegionID  SizeRank RegionName RegionType StateName Bedroom Count  \\\n",
       "0              3        48     Alaska      state    Alaska     1-Bedroom   \n",
       "1              3        48     Alaska      state    Alaska     1-Bedroom   \n",
       "2              3        48     Alaska      state    Alaska     1-Bedroom   \n",
       "3              3        48     Alaska      state    Alaska     1-Bedroom   \n",
       "4              3        48     Alaska      state    Alaska     1-Bedroom   \n",
       "...          ...       ...        ...        ...       ...           ...   \n",
       "117907        62        51    Wyoming      state   Wyoming  All Bedrooms   \n",
       "117908        62        51    Wyoming      state   Wyoming  All Bedrooms   \n",
       "117909        62        51    Wyoming      state   Wyoming  All Bedrooms   \n",
       "117910        62        51    Wyoming      state   Wyoming  All Bedrooms   \n",
       "117911        62        51    Wyoming      state   Wyoming  All Bedrooms   \n",
       "\n",
       "          Home Type        Date  \\\n",
       "0         all homes  2000-01-31   \n",
       "1         all homes  2000-02-29   \n",
       "2         all homes  2000-03-31   \n",
       "3         all homes  2000-04-30   \n",
       "4         all homes  2000-05-31   \n",
       "...             ...         ...   \n",
       "117907  condo/co-op  2023-09-30   \n",
       "117908  condo/co-op  2023-10-31   \n",
       "117909  condo/co-op  2023-11-30   \n",
       "117910  condo/co-op  2023-12-31   \n",
       "117911  condo/co-op  2024-01-31   \n",
       "\n",
       "        Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                         81310.639504   \n",
       "1                                         80419.761984   \n",
       "2                                         80480.449461   \n",
       "3                                         79799.206525   \n",
       "4                                         79666.469861   \n",
       "...                                                ...   \n",
       "117907                                   486974.735908   \n",
       "117908                                   485847.539614   \n",
       "117909                                   484223.885775   \n",
       "117910                                   481522.403338   \n",
       "117911                                   481181.718200   \n",
       "\n",
       "        Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "117907                                                NaN   \n",
       "117908                                                NaN   \n",
       "117909                                                NaN   \n",
       "117910                                                NaN   \n",
       "117911                                                NaN   \n",
       "\n",
       "        Top Tier ZHVI (Smoothed) (Seasonally Adjusted)  \n",
       "0                                                  NaN  \n",
       "1                                                  NaN  \n",
       "2                                                  NaN  \n",
       "3                                                  NaN  \n",
       "4                                                  NaN  \n",
       "...                                                ...  \n",
       "117907                                             NaN  \n",
       "117908                                             NaN  \n",
       "117909                                             NaN  \n",
       "117910                                             NaN  \n",
       "117911                                             NaN  \n",
       "\n",
       "[117912 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = combined_df\n",
    "\n",
    "for index, row in final_df.iterrows():\n",
    "    if row[\"RegionType\"] == \"city\":\n",
    "        final_df.at[index, \"City\"] = row[\"RegionName\"]\n",
    "    elif row[\"RegionType\"] == \"county\":\n",
    "        final_df.at[index, \"County\"] = row[\"RegionName\"]\n",
    "    if row[\"RegionType\"] == \"state\":\n",
    "        final_df.at[index, \"StateName\"] = row[\"RegionName\"]\n",
    "\n",
    "# coalesce State and StateName columns\n",
    "# final_df[\"State\"] = final_df[\"State\"].combine_first(final_df[\"StateName\"])\n",
    "# final_df[\"County\"] = final_df[\"County\"].combine_first(final_df[\"CountyName\"])\n",
    "\n",
    "# final_df = final_df.drop(\n",
    "#     columns=[\n",
    "#         \"StateName\",\n",
    "#         # \"CountyName\"\n",
    "#     ]\n",
    "# )\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Bedroom Count</th>\n",
       "      <th>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "      <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-01-31</td>\n",
       "      <td>81310.639504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-02-29</td>\n",
       "      <td>80419.761984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-03-31</td>\n",
       "      <td>80480.449461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-04-30</td>\n",
       "      <td>79799.206525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>state</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>1-Bedroom</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2000-05-31</td>\n",
       "      <td>79666.469861</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117907</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>486974.735908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117908</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>485847.539614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117909</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>484223.885775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117910</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>481522.403338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117911</th>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>state</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>All Bedrooms</td>\n",
       "      <td>condo/co-op</td>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>481181.718200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117912 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Region ID  Size Rank   Region Region Type    State Bedroom Count  \\\n",
       "0               3         48   Alaska       state   Alaska     1-Bedroom   \n",
       "1               3         48   Alaska       state   Alaska     1-Bedroom   \n",
       "2               3         48   Alaska       state   Alaska     1-Bedroom   \n",
       "3               3         48   Alaska       state   Alaska     1-Bedroom   \n",
       "4               3         48   Alaska       state   Alaska     1-Bedroom   \n",
       "...           ...        ...      ...         ...      ...           ...   \n",
       "117907         62         51  Wyoming       state  Wyoming  All Bedrooms   \n",
       "117908         62         51  Wyoming       state  Wyoming  All Bedrooms   \n",
       "117909         62         51  Wyoming       state  Wyoming  All Bedrooms   \n",
       "117910         62         51  Wyoming       state  Wyoming  All Bedrooms   \n",
       "117911         62         51  Wyoming       state  Wyoming  All Bedrooms   \n",
       "\n",
       "          Home Type       Date  \\\n",
       "0         all homes 2000-01-31   \n",
       "1         all homes 2000-02-29   \n",
       "2         all homes 2000-03-31   \n",
       "3         all homes 2000-04-30   \n",
       "4         all homes 2000-05-31   \n",
       "...             ...        ...   \n",
       "117907  condo/co-op 2023-09-30   \n",
       "117908  condo/co-op 2023-10-31   \n",
       "117909  condo/co-op 2023-11-30   \n",
       "117910  condo/co-op 2023-12-31   \n",
       "117911  condo/co-op 2024-01-31   \n",
       "\n",
       "        Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                         81310.639504   \n",
       "1                                         80419.761984   \n",
       "2                                         80480.449461   \n",
       "3                                         79799.206525   \n",
       "4                                         79666.469861   \n",
       "...                                                ...   \n",
       "117907                                   486974.735908   \n",
       "117908                                   485847.539614   \n",
       "117909                                   484223.885775   \n",
       "117910                                   481522.403338   \n",
       "117911                                   481181.718200   \n",
       "\n",
       "        Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "117907                                                NaN   \n",
       "117908                                                NaN   \n",
       "117909                                                NaN   \n",
       "117910                                                NaN   \n",
       "117911                                                NaN   \n",
       "\n",
       "        Top Tier ZHVI (Smoothed) (Seasonally Adjusted)  \n",
       "0                                                  NaN  \n",
       "1                                                  NaN  \n",
       "2                                                  NaN  \n",
       "3                                                  NaN  \n",
       "4                                                  NaN  \n",
       "...                                                ...  \n",
       "117907                                             NaN  \n",
       "117908                                             NaN  \n",
       "117909                                             NaN  \n",
       "117910                                             NaN  \n",
       "117911                                             NaN  \n",
       "\n",
       "[117912 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df.rename(\n",
    "    columns={\n",
    "        \"RegionID\": \"Region ID\",\n",
    "        \"SizeRank\": \"Size Rank\",\n",
    "        \"RegionName\": \"Region\",\n",
    "        \"RegionType\": \"Region Type\",\n",
    "        \"StateCodeFIPS\": \"State Code FIPS\",\n",
    "        \"StateName\": \"State\",\n",
    "        \"MunicipalCodeFIPS\": \"Municipal Code FIPS\",\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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
   ]
  }
 ],
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