{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data/\"\n",
    "PROCESSED_DIR = \"../processed/\"\n",
    "FACET_DIR = \"home_value_forecasts/\"\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing Zip_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
      "processing Zip_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_month.csv\n",
      "processing Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_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>BaseDate</th>\n",
       "      <th>Month Over Month % (Smoothed)</th>\n",
       "      <th>Quarter Over Quarter % (Smoothed)</th>\n",
       "      <th>Year Over Year % (Smoothed)</th>\n",
       "      <th>Month Over Month % (Raw)</th>\n",
       "      <th>Quarter Over Quarter % (Raw)</th>\n",
       "      <th>Year Over Year % (Raw)</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</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>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.7</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>394913</td>\n",
       "      <td>1</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>msa</td>\n",
       "      <td>NY</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.6</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>753899</td>\n",
       "      <td>2</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>msa</td>\n",
       "      <td>CA</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</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>394463</td>\n",
       "      <td>3</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>msa</td>\n",
       "      <td>IL</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.4</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>394514</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>82097</td>\n",
       "      <td>39992</td>\n",
       "      <td>55087</td>\n",
       "      <td>zip</td>\n",
       "      <td>MN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>MN</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Faribault-Northfield, MN</td>\n",
       "      <td>Rice County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>85325</td>\n",
       "      <td>39992</td>\n",
       "      <td>62093</td>\n",
       "      <td>zip</td>\n",
       "      <td>IL</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Louis, MO-IL</td>\n",
       "      <td>Macoupin County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>92085</td>\n",
       "      <td>39992</td>\n",
       "      <td>77661</td>\n",
       "      <td>zip</td>\n",
       "      <td>TX</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>Chambers County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20165</th>\n",
       "      <td>92811</td>\n",
       "      <td>39992</td>\n",
       "      <td>79078</td>\n",
       "      <td>zip</td>\n",
       "      <td>TX</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borger, TX</td>\n",
       "      <td>Hutchinson County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20166</th>\n",
       "      <td>98183</td>\n",
       "      <td>39992</td>\n",
       "      <td>95419</td>\n",
       "      <td>zip</td>\n",
       "      <td>CA</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>CA</td>\n",
       "      <td>Camp Meeker</td>\n",
       "      <td>Santa Rosa-Petaluma, CA</td>\n",
       "      <td>Sonoma County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21062 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionID  SizeRank       RegionName RegionType StateName    BaseDate  \\\n",
       "0        102001         0    United States    country       NaN  2023-12-31   \n",
       "1        394913         1     New York, NY        msa        NY  2023-12-31   \n",
       "2        753899         2  Los Angeles, CA        msa        CA  2023-12-31   \n",
       "3        394463         3      Chicago, IL        msa        IL  2023-12-31   \n",
       "4        394514         4       Dallas, TX        msa        TX  2023-12-31   \n",
       "...         ...       ...              ...        ...       ...         ...   \n",
       "20162     82097     39992            55087        zip        MN  2023-12-31   \n",
       "20163     85325     39992            62093        zip        IL  2023-12-31   \n",
       "20164     92085     39992            77661        zip        TX  2023-12-31   \n",
       "20165     92811     39992            79078        zip        TX  2023-12-31   \n",
       "20166     98183     39992            95419        zip        CA  2023-12-31   \n",
       "\n",
       "       Month Over Month % (Smoothed)  Quarter Over Quarter % (Smoothed)  \\\n",
       "0                                0.1                                0.4   \n",
       "1                                0.2                                0.2   \n",
       "2                               -0.1                               -1.8   \n",
       "3                                0.1                                0.4   \n",
       "4                               -0.1                                0.0   \n",
       "...                              ...                                ...   \n",
       "20162                            0.1                                0.7   \n",
       "20163                            0.9                                0.4   \n",
       "20164                           -0.5                                0.3   \n",
       "20165                           -1.2                               -1.1   \n",
       "20166                           -0.5                               -0.2   \n",
       "\n",
       "       Year Over Year % (Smoothed)  Month Over Month % (Raw)  \\\n",
       "0                              3.5                      -0.5   \n",
       "1                              1.0                      -0.7   \n",
       "2                              0.7                      -0.6   \n",
       "3                              1.6                      -0.8   \n",
       "4                              3.2                      -0.6   \n",
       "...                            ...                       ...   \n",
       "20162                          1.8                      -0.9   \n",
       "20163                          3.7                      -0.7   \n",
       "20164                         -0.6                      -0.4   \n",
       "20165                         -3.1                      -1.7   \n",
       "20166                          0.0                      -0.5   \n",
       "\n",
       "       Quarter Over Quarter % (Raw)  Year Over Year % (Raw) State  \\\n",
       "0                               0.4                     3.7   NaN   \n",
       "1                              -0.9                     0.6   NaN   \n",
       "2                               0.8                     1.4   NaN   \n",
       "3                              -0.2                     1.4   NaN   \n",
       "4                               0.9                     3.6   NaN   \n",
       "...                             ...                     ...   ...   \n",
       "20162                          -0.2                     2.6    MN   \n",
       "20163                           0.4                     2.3    IL   \n",
       "20164                           0.0                     1.2    TX   \n",
       "20165                          -2.6                    -1.9    TX   \n",
       "20166                           0.6                    -0.4    CA   \n",
       "\n",
       "              City                                 Metro         CountyName  \n",
       "0              NaN                                   NaN                NaN  \n",
       "1              NaN                                   NaN                NaN  \n",
       "2              NaN                                   NaN                NaN  \n",
       "3              NaN                                   NaN                NaN  \n",
       "4              NaN                                   NaN                NaN  \n",
       "...            ...                                   ...                ...  \n",
       "20162       Warsaw              Faribault-Northfield, MN        Rice County  \n",
       "20163          NaN                      St. Louis, MO-IL    Macoupin County  \n",
       "20164          NaN  Houston-The Woodlands-Sugar Land, TX    Chambers County  \n",
       "20165          NaN                            Borger, TX  Hutchinson County  \n",
       "20166  Camp Meeker               Santa Rosa-Petaluma, CA      Sonoma County  \n",
       "\n",
       "[21062 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metro_data_frames = []\n",
    "zip_data_frames = []\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",
    "        cols = [\"Month Over Month %\", \"Quarter Over Quarter %\", \"Year Over Year %\"]\n",
    "        if filename.endswith(\"sm_sa_month.csv\"):\n",
    "            # print('Smoothed')\n",
    "            cur_df.columns = list(cur_df.columns[:-3]) + [\n",
    "                x + \" (Smoothed)\" for x in cols\n",
    "            ]\n",
    "        else:\n",
    "            # print('Raw')\n",
    "            cur_df.columns = list(cur_df.columns[:-3]) + [x + \" (Raw)\" for x in cols]\n",
    "\n",
    "        if filename.startswith(\"Metro\"):\n",
    "            # print('Metro')\n",
    "            metro_data_frames.append(cur_df)\n",
    "\n",
    "        elif filename.startswith(\"Zip\"):\n",
    "            # print('Zip')\n",
    "            zip_data_frames.append(cur_df)\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 them\n",
    "        final_df = data_frames[0]\n",
    "        for i in range(1, len(data_frames)):\n",
    "            cur_df = data_frames[i]\n",
    "            cols = list(cur_df.columns[-3:])\n",
    "            cols.append(\"RegionID\")\n",
    "            combined_df = pd.merge(final_df, cur_df[cols], on=\"RegionID\")\n",
    "    elif len(data_frames) == 1:\n",
    "        combined_df = data_frames[0]\n",
    "\n",
    "    return combined_df\n",
    "\n",
    "\n",
    "combined_metro_dfs = get_combined_df(metro_data_frames)\n",
    "combined_zip_dfs = get_combined_df(zip_data_frames)\n",
    "\n",
    "combined_df = pd.concat([combined_metro_dfs, combined_zip_dfs])\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>RegionType</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Metro</th>\n",
       "      <th>County</th>\n",
       "      <th>BaseDate</th>\n",
       "      <th>Month Over Month % (Smoothed)</th>\n",
       "      <th>Quarter Over Quarter % (Smoothed)</th>\n",
       "      <th>Year Over Year % (Smoothed)</th>\n",
       "      <th>Month Over Month % (Raw)</th>\n",
       "      <th>Quarter Over Quarter % (Raw)</th>\n",
       "      <th>Year Over Year % (Raw)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>394913</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>msa</td>\n",
       "      <td>1</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>753899</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>msa</td>\n",
       "      <td>2</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394463</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>msa</td>\n",
       "      <td>3</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>394514</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>4</td>\n",
       "      <td>TX</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>82097</td>\n",
       "      <td>55087</td>\n",
       "      <td>zip</td>\n",
       "      <td>39992</td>\n",
       "      <td>MN</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Faribault-Northfield, MN</td>\n",
       "      <td>Rice County</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>85325</td>\n",
       "      <td>62093</td>\n",
       "      <td>zip</td>\n",
       "      <td>39992</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Louis, MO-IL</td>\n",
       "      <td>Macoupin County</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>92085</td>\n",
       "      <td>77661</td>\n",
       "      <td>zip</td>\n",
       "      <td>39992</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
       "      <td>Chambers County</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20165</th>\n",
       "      <td>92811</td>\n",
       "      <td>79078</td>\n",
       "      <td>zip</td>\n",
       "      <td>39992</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borger, TX</td>\n",
       "      <td>Hutchinson County</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20166</th>\n",
       "      <td>98183</td>\n",
       "      <td>95419</td>\n",
       "      <td>zip</td>\n",
       "      <td>39992</td>\n",
       "      <td>CA</td>\n",
       "      <td>Camp Meeker</td>\n",
       "      <td>Santa Rosa-Petaluma, CA</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21062 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionID       RegionName RegionType  SizeRank State         City  \\\n",
       "0        102001    United States    country         0   NaN          NaN   \n",
       "1        394913     New York, NY        msa         1    NY     New York   \n",
       "2        753899  Los Angeles, CA        msa         2    CA  Los Angeles   \n",
       "3        394463      Chicago, IL        msa         3    IL      Chicago   \n",
       "4        394514       Dallas, TX        msa         4    TX       Dallas   \n",
       "...         ...              ...        ...       ...   ...          ...   \n",
       "20162     82097            55087        zip     39992    MN       Warsaw   \n",
       "20163     85325            62093        zip     39992    IL          NaN   \n",
       "20164     92085            77661        zip     39992    TX          NaN   \n",
       "20165     92811            79078        zip     39992    TX          NaN   \n",
       "20166     98183            95419        zip     39992    CA  Camp Meeker   \n",
       "\n",
       "                                      Metro             County    BaseDate  \\\n",
       "0                                       NaN                NaN  2023-12-31   \n",
       "1                                       NaN                NaN  2023-12-31   \n",
       "2                                       NaN                NaN  2023-12-31   \n",
       "3                                       NaN                NaN  2023-12-31   \n",
       "4                                       NaN                NaN  2023-12-31   \n",
       "...                                     ...                ...         ...   \n",
       "20162              Faribault-Northfield, MN        Rice County  2023-12-31   \n",
       "20163                      St. Louis, MO-IL    Macoupin County  2023-12-31   \n",
       "20164  Houston-The Woodlands-Sugar Land, TX    Chambers County  2023-12-31   \n",
       "20165                            Borger, TX  Hutchinson County  2023-12-31   \n",
       "20166               Santa Rosa-Petaluma, CA      Sonoma County  2023-12-31   \n",
       "\n",
       "       Month Over Month % (Smoothed)  Quarter Over Quarter % (Smoothed)  \\\n",
       "0                                0.1                                0.4   \n",
       "1                                0.2                                0.2   \n",
       "2                               -0.1                               -1.8   \n",
       "3                                0.1                                0.4   \n",
       "4                               -0.1                                0.0   \n",
       "...                              ...                                ...   \n",
       "20162                            0.1                                0.7   \n",
       "20163                            0.9                                0.4   \n",
       "20164                           -0.5                                0.3   \n",
       "20165                           -1.2                               -1.1   \n",
       "20166                           -0.5                               -0.2   \n",
       "\n",
       "       Year Over Year % (Smoothed)  Month Over Month % (Raw)  \\\n",
       "0                              3.5                      -0.5   \n",
       "1                              1.0                      -0.7   \n",
       "2                              0.7                      -0.6   \n",
       "3                              1.6                      -0.8   \n",
       "4                              3.2                      -0.6   \n",
       "...                            ...                       ...   \n",
       "20162                          1.8                      -0.9   \n",
       "20163                          3.7                      -0.7   \n",
       "20164                         -0.6                      -0.4   \n",
       "20165                         -3.1                      -1.7   \n",
       "20166                          0.0                      -0.5   \n",
       "\n",
       "       Quarter Over Quarter % (Raw)  Year Over Year % (Raw)  \n",
       "0                               0.4                     3.7  \n",
       "1                              -0.9                     0.6  \n",
       "2                               0.8                     1.4  \n",
       "3                              -0.2                     1.4  \n",
       "4                               0.9                     3.6  \n",
       "...                             ...                     ...  \n",
       "20162                          -0.2                     2.6  \n",
       "20163                           0.4                     2.3  \n",
       "20164                           0.0                     1.2  \n",
       "20165                          -2.6                    -1.9  \n",
       "20166                           0.6                    -0.4  \n",
       "\n",
       "[21062 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(combined_df.columns)\n",
    "result_cols = [x for x in cols if \"%\" in x]\n",
    "\n",
    "all_cols = [\n",
    "    \"RegionID\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"SizeRank\",\n",
    "    \"StateName\",\n",
    "    \"State\",\n",
    "    \"City\",\n",
    "    \"Metro\",\n",
    "    \"CountyName\",\n",
    "    \"BaseDate\",\n",
    "] + result_cols\n",
    "\n",
    "final_df = combined_df[all_cols]\n",
    "final_df = final_df.drop(\"StateName\", axis=1)\n",
    "final_df = final_df.rename(columns={\"CountyName\": \"County\"})\n",
    "\n",
    "# iterate over rows of final_df and populate State and City columns if the regionType is msa\n",
    "for index, row in final_df.iterrows():\n",
    "    if row[\"RegionType\"] == \"msa\":\n",
    "        regionName = row[\"RegionName\"]\n",
    "        # final_df.at[index, 'Metro'] = regionName\n",
    "\n",
    "        city = regionName.split(\", \")[0]\n",
    "        final_df.at[index, \"City\"] = city\n",
    "\n",
    "        state = regionName.split(\", \")[1]\n",
    "        final_df.at[index, \"State\"] = state\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}