{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data\"\n",
    "PROCESSED_DIR = \"../processed/\"\n",
    "FACET_DIR = \"for_sale_listings/\"\n",
    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing Metro_new_pending_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_month.csv\n",
      "processing Metro_mlp_uc_sfr_sm_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_month.csv\n",
      "processing Metro_mlp_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_month.csv\n",
      "processing Metro_mlp_uc_sfr_sm_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_sm_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_sm_month.csv\n",
      "processing Metro_mlp_uc_sfr_month.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_week.csv\n",
      "processing Metro_mlp_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_sm_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_month.csv\n",
      "processing Metro_new_pending_uc_sfrcondo_week.csv\n",
      "processing Metro_invt_fs_uc_sfr_week.csv\n",
      "processing Metro_new_listings_uc_sfrcondo_sm_month.csv\n",
      "processing Metro_mlp_uc_sfr_week.csv\n",
      "processing Metro_invt_fs_uc_sfrcondo_sm_month.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "        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>Median Listing Price</th>\n",
       "      <th>Median Listing Price (Smoothed)</th>\n",
       "      <th>New Listings</th>\n",
       "      <th>New Listings (Smoothed)</th>\n",
       "      <th>New Pending (Smoothed)</th>\n",
       "      <th>New Pending</th>\n",
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       "  <tbody>\n",
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       "      <td>all homes</td>\n",
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       "      <td>259000.0</td>\n",
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       "      <td>all homes</td>\n",
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       "      <td>71177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>all homes</td>\n",
       "      <td>2018-01-20</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72625.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38281.0</td>\n",
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       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-31</td>\n",
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       "      <td>SFR</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2398146</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
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       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
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       "      <td>all homes</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
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       "      <td>all homes</td>\n",
       "      <td>2024-01-06</td>\n",
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       "      <td>121488.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2398149 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         RegionID  SizeRank     RegionName RegionType StateName  Home Type  \\\n",
       "0          102001         0  United States    country       NaN  all homes   \n",
       "1          102001         0  United States    country       NaN        SFR   \n",
       "2          102001         0  United States    country       NaN  all homes   \n",
       "3          102001         0  United States    country       NaN        SFR   \n",
       "4          102001         0  United States    country       NaN  all homes   \n",
       "...           ...       ...            ...        ...       ...        ...   \n",
       "2398144    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "2398145    845172       769   Winfield, KS        msa        KS        SFR   \n",
       "2398146    845172       769   Winfield, KS        msa        KS        SFR   \n",
       "2398147    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "2398148    845172       769   Winfield, KS        msa        KS  all homes   \n",
       "\n",
       "               Date  Median Listing Price  Median Listing Price (Smoothed)  \\\n",
       "0        2018-01-06                   NaN                              NaN   \n",
       "1        2018-01-13              259000.0                              NaN   \n",
       "2        2018-01-13              259900.0                              NaN   \n",
       "3        2018-01-20              259900.0                              NaN   \n",
       "4        2018-01-20              259900.0                              NaN   \n",
       "...             ...                   ...                              ...   \n",
       "2398144  2023-12-31                   NaN                         136233.0   \n",
       "2398145  2024-01-06                   NaN                         131088.0   \n",
       "2398146  2024-01-06              135450.0                              NaN   \n",
       "2398147  2024-01-06              128000.0                              NaN   \n",
       "2398148  2024-01-06                   NaN                         121488.0   \n",
       "\n",
       "         New Listings  New Listings (Smoothed)  New Pending (Smoothed)  \\\n",
       "0                 NaN                      NaN                     NaN   \n",
       "1                 NaN                      NaN                     NaN   \n",
       "2             71177.0                      NaN                     NaN   \n",
       "3                 NaN                      NaN                     NaN   \n",
       "4             72625.0                      NaN                     NaN   \n",
       "...               ...                      ...                     ...   \n",
       "2398144           NaN                     28.0                     NaN   \n",
       "2398145           NaN                      NaN                     NaN   \n",
       "2398146           NaN                      NaN                     NaN   \n",
       "2398147           NaN                      NaN                     NaN   \n",
       "2398148           NaN                      NaN                     NaN   \n",
       "\n",
       "         New Pending  \n",
       "0            24766.0  \n",
       "1                NaN  \n",
       "2            35229.0  \n",
       "3                NaN  \n",
       "4            38281.0  \n",
       "...              ...  \n",
       "2398144         24.0  \n",
       "2398145          NaN  \n",
       "2398146          NaN  \n",
       "2398147          NaN  \n",
       "2398148          NaN  \n",
       "\n",
       "[2398149 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
    "\n",
    "exclude_columns = [\n",
    "    \"RegionID\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "batches = {\"mlp\": [], \"new_listings\": [], \"new_pending\": []}\n",
    "\n",
    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        print(\"processing \" + filename)\n",
    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
    "\n",
    "        # ignore monthly data for now since it is redundant\n",
    "        if \"monthly\" in filename:\n",
    "            continue\n",
    "\n",
    "        if \"sfrcondo\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"all homes\"\n",
    "        elif \"sfr\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"SFR\"\n",
    "        elif \"condo\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
    "\n",
    "        # Identify columns to pivot\n",
    "        columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
    "\n",
    "        smoothed = \"_sm_\" in filename\n",
    "\n",
    "        if \"_mlp_\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=(\n",
    "                    \"Median Listing Price\"\n",
    "                    if not smoothed\n",
    "                    else \"Median Listing Price (Smoothed)\"\n",
    "                ),\n",
    "            )\n",
    "            batches[\"mlp\"].append(cur_df)\n",
    "\n",
    "        elif \"_new_listings_\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=(\n",
    "                    \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
    "                ),\n",
    "            )\n",
    "            batches[\"new_listings\"].append(cur_df)\n",
    "\n",
    "        elif \"new_pending\" in filename:\n",
    "            cur_df = pd.melt(\n",
    "                cur_df,\n",
    "                id_vars=exclude_columns,\n",
    "                value_vars=columns_to_pivot,\n",
    "                var_name=\"Date\",\n",
    "                value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
    "            )\n",
    "            batches[\"new_pending\"].append(cur_df)\n",
    "\n",
    "matching_cols = [\n",
    "    \"RegionID\",\n",
    "    \"Date\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
    "\n",
    "if len(combined_batches) > 0:\n",
    "    combined_df = combined_batches[0]\n",
    "    for batch in combined_batches[1:]:\n",
    "        combined_df = pd.merge(\n",
    "            combined_df,\n",
    "            batch,\n",
    "            on=matching_cols,\n",
    "            how=\"outer\",\n",
    "        )\n",
    "\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>1</th>\n",
       "      <td>102001</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102001</td>\n",
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       "      <td>all homes</td>\n",
       "      <td>2018-01-13</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>71177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
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       "      <td>SFR</td>\n",
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       "      <td>NaN</td>\n",
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       "    <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>all homes</td>\n",
       "      <td>2018-01-20</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72625.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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>2398144</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136233.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398145</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131088.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398146</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>135450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398147</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>128000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398148</th>\n",
       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121488.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2398149 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  all homes   \n",
       "1           102001          0  United States     country   NaN        SFR   \n",
       "2           102001          0  United States     country   NaN  all homes   \n",
       "3           102001          0  United States     country   NaN        SFR   \n",
       "4           102001          0  United States     country   NaN  all homes   \n",
       "...            ...        ...            ...         ...   ...        ...   \n",
       "2398144     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "2398145     845172        769   Winfield, KS         msa    KS        SFR   \n",
       "2398146     845172        769   Winfield, KS         msa    KS        SFR   \n",
       "2398147     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "2398148     845172        769   Winfield, KS         msa    KS  all homes   \n",
       "\n",
       "               Date  Median Listing Price  Median Listing Price (Smoothed)  \\\n",
       "0        2018-01-06                   NaN                              NaN   \n",
       "1        2018-01-13              259000.0                              NaN   \n",
       "2        2018-01-13              259900.0                              NaN   \n",
       "3        2018-01-20              259900.0                              NaN   \n",
       "4        2018-01-20              259900.0                              NaN   \n",
       "...             ...                   ...                              ...   \n",
       "2398144  2023-12-31                   NaN                         136233.0   \n",
       "2398145  2024-01-06                   NaN                         131088.0   \n",
       "2398146  2024-01-06              135450.0                              NaN   \n",
       "2398147  2024-01-06              128000.0                              NaN   \n",
       "2398148  2024-01-06                   NaN                         121488.0   \n",
       "\n",
       "         New Listings  New Listings (Smoothed)  New Pending (Smoothed)  \\\n",
       "0                 NaN                      NaN                     NaN   \n",
       "1                 NaN                      NaN                     NaN   \n",
       "2             71177.0                      NaN                     NaN   \n",
       "3                 NaN                      NaN                     NaN   \n",
       "4             72625.0                      NaN                     NaN   \n",
       "...               ...                      ...                     ...   \n",
       "2398144           NaN                     28.0                     NaN   \n",
       "2398145           NaN                      NaN                     NaN   \n",
       "2398146           NaN                      NaN                     NaN   \n",
       "2398147           NaN                      NaN                     NaN   \n",
       "2398148           NaN                      NaN                     NaN   \n",
       "\n",
       "         New Pending  \n",
       "0            24766.0  \n",
       "1                NaN  \n",
       "2            35229.0  \n",
       "3                NaN  \n",
       "4            38281.0  \n",
       "...              ...  \n",
       "2398144         24.0  \n",
       "2398145          NaN  \n",
       "2398146          NaN  \n",
       "2398147          NaN  \n",
       "2398148          NaN  \n",
       "\n",
       "[2398149 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = combined_df\n",
    "final_df = final_df.rename(\n",
    "    columns={\n",
    "        \"RegionID\": \"Region ID\",\n",
    "        \"SizeRank\": \"Size Rank\",\n",
    "        \"RegionName\": \"Region\",\n",
    "        \"RegionType\": \"Region Type\",\n",
    "        \"StateName\": \"State\",\n",
    "    }\n",
    ")\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
    "    os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
    "\n",
    "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
   ]
  }
 ],
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