{
 "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": 38,
   "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",
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       "</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>Sales Count</th>\n",
       "      <th>Median Sale Price per Sqft</th>\n",
       "      <th>Median Sale Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2018-01-31</td>\n",
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       "      <td>137.412316</td>\n",
       "      <td>309000.0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>139.520863</td>\n",
       "      <td>315488.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
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       "      <td>country</td>\n",
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       "      <td>2018-04-30</td>\n",
       "      <td>37588.0</td>\n",
       "      <td>139.778110</td>\n",
       "      <td>314990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102001</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49482</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>2023-07-31</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>2023-09-30</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>24.0</td>\n",
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       "      <td>Granbury, TX</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>49487 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RegionID  SizeRank     RegionName RegionType StateName  Home Type  \\\n",
       "0        102001         0  United States    country       NaN        SFR   \n",
       "1        102001         0  United States    country       NaN        SFR   \n",
       "2        102001         0  United States    country       NaN        SFR   \n",
       "3        102001         0  United States    country       NaN        SFR   \n",
       "4        102001         0  United States    country       NaN        SFR   \n",
       "...         ...       ...            ...        ...       ...        ...   \n",
       "49482    845162       535   Granbury, TX        msa        TX  all homes   \n",
       "49483    845162       535   Granbury, TX        msa        TX  all homes   \n",
       "49484    845162       535   Granbury, TX        msa        TX  all homes   \n",
       "49485    845162       535   Granbury, TX        msa        TX  all homes   \n",
       "49486    845162       535   Granbury, TX        msa        TX  all homes   \n",
       "\n",
       "             Date  Sales Count  Median Sale Price per Sqft  Median Sale Price  \n",
       "0      2018-01-31      33940.0                  137.412316           309000.0  \n",
       "1      2018-02-28      33304.0                  137.199170           309072.5  \n",
       "2      2018-03-31      42641.0                  139.520863           315488.0  \n",
       "3      2018-04-30      37588.0                  139.778110           314990.0  \n",
       "4      2018-05-31      39933.0                  143.317968           324500.0  \n",
       "...           ...          ...                         ...                ...  \n",
       "49482  2023-07-31         31.0                         NaN                NaN  \n",
       "49483  2023-08-31         33.0                         NaN                NaN  \n",
       "49484  2023-09-30         26.0                         NaN                NaN  \n",
       "49485  2023-10-31         24.0                         NaN                NaN  \n",
       "49486  2023-11-30         16.0                         NaN                NaN  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "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",
    "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 \"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",
    "            data_frames.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",
    "            data_frames.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",
    "            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 or concat\n",
    "        combined_df = data_frames[0]\n",
    "        for i in range(1, len(data_frames)):\n",
    "            cur_df = data_frames[i]\n",
    "            combined_df = pd.merge(\n",
    "                combined_df,\n",
    "                cur_df,\n",
    "                on=[\n",
    "                    \"RegionID\",\n",
    "                    \"SizeRank\",\n",
    "                    \"RegionName\",\n",
    "                    \"RegionType\",\n",
    "                    \"StateName\",\n",
    "                    \"Home Type\",\n",
    "                    \"Date\",\n",
    "                ],\n",
    "                how=\"outer\",\n",
    "                suffixes=(\"\", \"_\" + str(i)),\n",
    "            )\n",
    "    elif len(data_frames) == 1:\n",
    "        combined_df = data_frames[0]\n",
    "\n",
    "    return combined_df\n",
    "\n",
    "\n",
    "combined_df = get_combined_df(data_frames)\n",
    "# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
    "columns_to_coalesce = [\"Sales Count\", \"Median Sale Price\", \"Median Sale Price per Sqft\"]\n",
    "\n",
    "for index, row in combined_df.iterrows():\n",
    "    for col in combined_df.columns:\n",
    "        for column_to_coalesce in columns_to_coalesce:\n",
    "            if column_to_coalesce in col and \"_\" in col:\n",
    "                if not pd.isna(row[col]):\n",
    "                    combined_df.at[index, column_to_coalesce] = row[col]\n",
    "\n",
    "# remove columns with underscores\n",
    "combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Region ID</th>\n",
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       "      <th>4</th>\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>2023-07-31</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2023-09-30</td>\n",
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       "      <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>2023-10-31</td>\n",
       "      <td>24.0</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",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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  Home Type  \\\n",
       "0         102001          0  United States     country   NaN        SFR   \n",
       "1         102001          0  United States     country   NaN        SFR   \n",
       "2         102001          0  United States     country   NaN        SFR   \n",
       "3         102001          0  United States     country   NaN        SFR   \n",
       "4         102001          0  United States     country   NaN        SFR   \n",
       "...          ...        ...            ...         ...   ...        ...   \n",
       "49482     845162        535   Granbury, TX         msa    TX  all homes   \n",
       "49483     845162        535   Granbury, TX         msa    TX  all homes   \n",
       "49484     845162        535   Granbury, TX         msa    TX  all homes   \n",
       "49485     845162        535   Granbury, TX         msa    TX  all homes   \n",
       "49486     845162        535   Granbury, TX         msa    TX  all homes   \n",
       "\n",
       "             Date  Sales Count  Median Sale Price per Sqft  Median Sale Price  \n",
       "0      2018-01-31      33940.0                  137.412316           309000.0  \n",
       "1      2018-02-28      33304.0                  137.199170           309072.5  \n",
       "2      2018-03-31      42641.0                  139.520863           315488.0  \n",
       "3      2018-04-30      37588.0                  139.778110           314990.0  \n",
       "4      2018-05-31      39933.0                  143.317968           324500.0  \n",
       "...           ...          ...                         ...                ...  \n",
       "49482  2023-07-31         31.0                         NaN                NaN  \n",
       "49483  2023-08-31         33.0                         NaN                NaN  \n",
       "49484  2023-09-30         26.0                         NaN                NaN  \n",
       "49485  2023-10-31         24.0                         NaN                NaN  \n",
       "49486  2023-11-30         16.0                         NaN                NaN  \n",
       "\n",
       "[49487 rows x 10 columns]"
      ]
     },
     "execution_count": 39,
     "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.sort_values(by=[\"Region ID\", \"Home Type\", \"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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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