{
 "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 = \"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": 47,
   "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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       "      <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>Sale Price</th>\n",
       "      <th>Sale Price per Sqft</th>\n",
       "      <th>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>309000.0</td>\n",
       "      <td>137.412316</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>314596.0</td>\n",
       "      <td>140.504620</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>388250.0</td>\n",
       "      <td>238.300000</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>309072.5</td>\n",
       "      <td>137.199170</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>NaN</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>2023-09-30</td>\n",
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       "      <td>all homes</td>\n",
       "      <td>2023-10-31</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>SFR</td>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2023-11-30</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  Sale Price  Sale Price per Sqft    Count  \n",
       "0                   SFR  2018-01-31    309000.0           137.412316  33940.0  \n",
       "1             all homes  2018-01-31    314596.0           140.504620  37135.0  \n",
       "2      condo/co-op only  2018-01-31    388250.0           238.300000   3195.0  \n",
       "3                   SFR  2018-02-28    309072.5           137.199170  33304.0  \n",
       "4             all homes  2018-02-28    314608.0           140.304966  36493.0  \n",
       "...                 ...         ...         ...                  ...      ...  \n",
       "49482         all homes  2023-09-30         NaN                  NaN     26.0  \n",
       "49483               SFR  2023-10-31         NaN                  NaN     24.0  \n",
       "49484         all homes  2023-10-31         NaN                  NaN     24.0  \n",
       "49485               SFR  2023-11-30         NaN                  NaN     16.0  \n",
       "49486         all homes  2023-11-30         NaN                  NaN     16.0  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frames = []\n",
    "\n",
    "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
    "\n",
    "exclude_columns = [\n",
    "    \"RegionID\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    # \"Value Type\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "price_data_frames = []\n",
    "price_per_sqft_data_frames = []\n",
    "count_data_frames = []\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 \"sale_price_per_sqft\" in filename:\n",
    "            # cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
    "            # Perform pivot\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=\"Sale Price per Sqft\",\n",
    "            )\n",
    "            price_per_sqft_data_frames.append(cur_df)\n",
    "\n",
    "        elif \"sale_price_uc\" in filename:\n",
    "            # cur_df[\"Value Type\"] = \"Sale Price\"\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=\"Sale Price\",\n",
    "            )\n",
    "            price_data_frames.append(cur_df)\n",
    "\n",
    "        elif \"count\" in filename:\n",
    "            # cur_df[\"Value Type\"] = \"Count\"\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=\"Count\",\n",
    "            )\n",
    "            count_data_frames.append(cur_df)\n",
    "\n",
    "\n",
    "combined_price = pd.concat(price_data_frames)\n",
    "combined_price_per = pd.concat(price_per_sqft_data_frames)\n",
    "combined_count = pd.concat(count_data_frames)\n",
    "\n",
    "matching_cols = [\n",
    "    \"RegionID\",\n",
    "    \"Date\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    # \"Value Type\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "combined_df = pd.merge(\n",
    "    combined_price,\n",
    "    combined_price_per,\n",
    "    on=matching_cols,\n",
    "    how=\"outer\",\n",
    ")\n",
    "combined_df = pd.merge(\n",
    "    combined_df,\n",
    "    combined_count,\n",
    "    on=matching_cols,\n",
    "    how=\"outer\",\n",
    ")\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "      <th>Sale Price</th>\n",
       "      <th>Sale Price per Sqft</th>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>country</td>\n",
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       "      <th>2</th>\n",
       "      <td>102001</td>\n",
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       "      <td>country</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",
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       "      <td>2018-02-28</td>\n",
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       "      <td>137.199170</td>\n",
       "      <td>33304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</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>2023-09-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
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       "      <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>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  Sale Price  Sale Price per Sqft    Count  \n",
       "0                   SFR  2018-01-31    309000.0           137.412316  33940.0  \n",
       "1             all homes  2018-01-31    314596.0           140.504620  37135.0  \n",
       "2      condo/co-op only  2018-01-31    388250.0           238.300000   3195.0  \n",
       "3                   SFR  2018-02-28    309072.5           137.199170  33304.0  \n",
       "4             all homes  2018-02-28    314608.0           140.304966  36493.0  \n",
       "...                 ...         ...         ...                  ...      ...  \n",
       "49482         all homes  2023-09-30         NaN                  NaN     26.0  \n",
       "49483               SFR  2023-10-31         NaN                  NaN     24.0  \n",
       "49484         all homes  2023-10-31         NaN                  NaN     24.0  \n",
       "49485               SFR  2023-11-30         NaN                  NaN     16.0  \n",
       "49486         all homes  2023-11-30         NaN                  NaN     16.0  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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