{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data\"\n",
    "PROCESSED_DIR = \"../processed/\"\n",
    "FACET_DIR = \"new_constructions/\"\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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing Metro_new_con_sales_count_raw_uc_condo_month.csv\n",
      "processing Metro_new_con_median_sale_price_per_sqft_uc_sfr_month.csv\n",
      "processing Metro_new_con_sales_count_raw_uc_sfr_month.csv\n",
      "processing Metro_new_con_median_sale_price_uc_sfrcondo_month.csv\n",
      "processing Metro_new_con_median_sale_price_per_sqft_uc_condo_month.csv\n",
      "processing Metro_new_con_sales_count_raw_uc_sfrcondo_month.csv\n",
      "processing Metro_new_con_median_sale_price_uc_condo_month.csv\n",
      "processing Metro_new_con_median_sale_price_uc_sfr_month.csv\n",
      "processing Metro_new_con_median_sale_price_per_sqft_uc_sfrcondo_month.csv\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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 Sale Price per Sqft</th>\n",
       "      <th>Median Sale Price</th>\n",
       "      <th>Sales Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>137.412316</td>\n",
       "      <td>309000.0</td>\n",
       "      <td>33940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>140.504620</td>\n",
       "      <td>314596.0</td>\n",
       "      <td>37135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>condo/co-op only</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>238.300000</td>\n",
       "      <td>388250.0</td>\n",
       "      <td>3195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>137.199170</td>\n",
       "      <td>309072.5</td>\n",
       "      <td>33304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2018-02-28</td>\n",
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       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-10-31</td>\n",
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       "      <td>845162</td>\n",
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       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>49486</th>\n",
       "      <td>845162</td>\n",
       "      <td>535</td>\n",
       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-11-30</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>49487 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionID  SizeRank     RegionName RegionType StateName  \\\n",
       "0        102001         0  United States    country       NaN   \n",
       "1        102001         0  United States    country       NaN   \n",
       "2        102001         0  United States    country       NaN   \n",
       "3        102001         0  United States    country       NaN   \n",
       "4        102001         0  United States    country       NaN   \n",
       "...         ...       ...            ...        ...       ...   \n",
       "49482    845162       535   Granbury, TX        msa        TX   \n",
       "49483    845162       535   Granbury, TX        msa        TX   \n",
       "49484    845162       535   Granbury, TX        msa        TX   \n",
       "49485    845162       535   Granbury, TX        msa        TX   \n",
       "49486    845162       535   Granbury, TX        msa        TX   \n",
       "\n",
       "              Home Type        Date  Median Sale Price per Sqft  \\\n",
       "0                   SFR  2018-01-31                  137.412316   \n",
       "1             all homes  2018-01-31                  140.504620   \n",
       "2      condo/co-op only  2018-01-31                  238.300000   \n",
       "3                   SFR  2018-02-28                  137.199170   \n",
       "4             all homes  2018-02-28                  140.304966   \n",
       "...                 ...         ...                         ...   \n",
       "49482         all homes  2023-09-30                         NaN   \n",
       "49483               SFR  2023-10-31                         NaN   \n",
       "49484         all homes  2023-10-31                         NaN   \n",
       "49485               SFR  2023-11-30                         NaN   \n",
       "49486         all homes  2023-11-30                         NaN   \n",
       "\n",
       "       Median Sale Price  Sales Count  \n",
       "0               309000.0      33940.0  \n",
       "1               314596.0      37135.0  \n",
       "2               388250.0       3195.0  \n",
       "3               309072.5      33304.0  \n",
       "4               314608.0      36493.0  \n",
       "...                  ...          ...  \n",
       "49482                NaN         26.0  \n",
       "49483                NaN         24.0  \n",
       "49484                NaN         24.0  \n",
       "49485                NaN         16.0  \n",
       "49486                NaN         16.0  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 61,
     "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 = {\"median_sale_price_per_sqft\": [], \"median_sale_price\": [], \"sales_count\": []}\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",
    "        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",
    "        if \"median_sale_price_per_sqft\" 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=\"Median Sale Price per Sqft\",\n",
    "            )\n",
    "            batches[\"median_sale_price_per_sqft\"].append(cur_df)\n",
    "\n",
    "        elif \"median_sale_price\" 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=\"Median Sale Price\",\n",
    "            )\n",
    "            batches[\"median_sale_price\"].append(cur_df)\n",
    "\n",
    "        elif \"sales_count\" 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=\"Sales Count\",\n",
    "            )\n",
    "            batches[\"sales_count\"].append(cur_df)\n",
    "\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",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Region ID</th>\n",
       "      <th>Size Rank</th>\n",
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       "      <th>Region Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Home Type</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>102001</td>\n",
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       "      <td>37135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102001</td>\n",
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       "      <td>3195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2018-02-28</td>\n",
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       "      <td>309072.5</td>\n",
       "      <td>33304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
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       "      <td>all homes</td>\n",
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       "      <td>845162</td>\n",
       "      <td>535</td>\n",
       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>845162</td>\n",
       "      <td>535</td>\n",
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       "      <td>TX</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>845162</td>\n",
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       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>49485</th>\n",
       "      <td>845162</td>\n",
       "      <td>535</td>\n",
       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>SFR</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49486</th>\n",
       "      <td>845162</td>\n",
       "      <td>535</td>\n",
       "      <td>Granbury, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>all homes</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49487 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region ID  Size Rank         Region Region Type State  \\\n",
       "0         102001          0  United States     country   NaN   \n",
       "1         102001          0  United States     country   NaN   \n",
       "2         102001          0  United States     country   NaN   \n",
       "3         102001          0  United States     country   NaN   \n",
       "4         102001          0  United States     country   NaN   \n",
       "...          ...        ...            ...         ...   ...   \n",
       "49482     845162        535   Granbury, TX         msa    TX   \n",
       "49483     845162        535   Granbury, TX         msa    TX   \n",
       "49484     845162        535   Granbury, TX         msa    TX   \n",
       "49485     845162        535   Granbury, TX         msa    TX   \n",
       "49486     845162        535   Granbury, TX         msa    TX   \n",
       "\n",
       "              Home Type        Date  Median Sale Price per Sqft  \\\n",
       "0                   SFR  2018-01-31                  137.412316   \n",
       "1             all homes  2018-01-31                  140.504620   \n",
       "2      condo/co-op only  2018-01-31                  238.300000   \n",
       "3                   SFR  2018-02-28                  137.199170   \n",
       "4             all homes  2018-02-28                  140.304966   \n",
       "...                 ...         ...                         ...   \n",
       "49482         all homes  2023-09-30                         NaN   \n",
       "49483               SFR  2023-10-31                         NaN   \n",
       "49484         all homes  2023-10-31                         NaN   \n",
       "49485               SFR  2023-11-30                         NaN   \n",
       "49486         all homes  2023-11-30                         NaN   \n",
       "\n",
       "       Median Sale Price  Sales Count  \n",
       "0               309000.0      33940.0  \n",
       "1               314596.0      37135.0  \n",
       "2               388250.0       3195.0  \n",
       "3               309072.5      33304.0  \n",
       "4               314608.0      36493.0  \n",
       "...                  ...          ...  \n",
       "49482                NaN         26.0  \n",
       "49483                NaN         24.0  \n",
       "49484                NaN         24.0  \n",
       "49485                NaN         16.0  \n",
       "49486                NaN         16.0  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "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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