{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from helpers import (\n",
    "    get_combined_df,\n",
    "    save_final_df_as_jsonl,\n",
    "    handle_slug_column_mappings,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data\"\n",
    "PROCESSED_DIR = \"../processed/\"\n",
    "FACET_DIR = \"days_on_market/\"\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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Home Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
       "      <th>Percent Listings Price Cut</th>\n",
       "      <th>Mean Listings Price Cut Amount</th>\n",
       "      <th>Percent Listings Price Cut (Smoothed)</th>\n",
       "      <th>Median Days on Pending (Smoothed)</th>\n",
       "      <th>Median Days on Pending</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>2018-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13508.368375</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>United States</td>\n",
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       "      <td>SFR</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049042</td>\n",
       "      <td>14114.788383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>SFR</td>\n",
       "      <td>2018-01-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044740</td>\n",
       "      <td>14326.128956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2018-01-27</td>\n",
       "      <td>13998.585612</td>\n",
       "      <td>0.047930</td>\n",
       "      <td>13998.585612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>SFR</td>\n",
       "      <td>2018-02-03</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</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",
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       "    <tr>\n",
       "      <th>586709</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>586710</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586711</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>586712</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586713</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 (SFR + Condo)</td>\n",
       "      <td>2024-02-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586714 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RegionID  ...  Median Days on Pending\n",
       "0         102001  ...                     NaN\n",
       "1         102001  ...                     NaN\n",
       "2         102001  ...                     NaN\n",
       "3         102001  ...                     NaN\n",
       "4         102001  ...                     NaN\n",
       "...          ...  ...                     ...\n",
       "586709    845172  ...                     NaN\n",
       "586710    845172  ...                     NaN\n",
       "586711    845172  ...                     NaN\n",
       "586712    845172  ...                     NaN\n",
       "586713    845172  ...                     NaN\n",
       "\n",
       "[586714 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frames = []\n",
    "\n",
    "exclude_columns = [\n",
    "    \"RegionID\",\n",
    "    \"SizeRank\",\n",
    "    \"RegionName\",\n",
    "    \"RegionType\",\n",
    "    \"StateName\",\n",
    "    \"Home Type\",\n",
    "]\n",
    "\n",
    "slug_column_mappings = {\n",
    "    \"_mean_listings_price_cut_amt_\": \"Mean Listings Price Cut Amount\",\n",
    "    \"_med_doz_pending_\": \"Median Days on Pending\",\n",
    "    \"_median_days_to_pending_\": \"Median Days to Close\",\n",
    "    \"_perc_listings_price_cut_\": \"Percent Listings Price Cut\",\n",
    "}\n",
    "\n",
    "\n",
    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        print(\"processing \" + filename)\n",
    "        # skip month files for now since they are redundant\n",
    "        if \"month\" in filename:\n",
    "            continue\n",
    "\n",
    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
    "\n",
    "        if \"_uc_sfrcondo_\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"all homes (SFR + Condo)\"\n",
    "            # change column type to string\n",
    "            cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
    "        elif \"_uc_sfr_\" in filename:\n",
    "            cur_df[\"Home Type\"] = \"SFR\"\n",
    "\n",
    "        data_frames = handle_slug_column_mappings(\n",
    "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
    "        )\n",
    "\n",
    "\n",
    "combined_df = get_combined_df(\n",
    "    data_frames,\n",
    "    [\n",
    "        \"RegionID\",\n",
    "        \"SizeRank\",\n",
    "        \"RegionName\",\n",
    "        \"RegionType\",\n",
    "        \"StateName\",\n",
    "        \"Home Type\",\n",
    "        \"Date\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <th>Date</th>\n",
       "      <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
       "      <th>Percent Listings Price Cut</th>\n",
       "      <th>Mean Listings Price Cut Amount</th>\n",
       "      <th>Percent Listings Price Cut (Smoothed)</th>\n",
       "      <th>Median Days on Pending (Smoothed)</th>\n",
       "      <th>Median Days on Pending</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",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102001</td>\n",
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       "      <td>United States</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</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>SFR</td>\n",
       "      <td>2018-02-03</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</td>\n",
       "      <td>14120.035549</td>\n",
       "      <td>0.047622</td>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "      <td>KS</td>\n",
       "      <td>all homes (SFR + Condo)</td>\n",
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       "      <td>845172</td>\n",
       "      <td>769</td>\n",
       "      <td>Winfield, KS</td>\n",
       "      <td>msa</td>\n",
       "      <td>KS</td>\n",
       "      <td>all homes (SFR + Condo)</td>\n",
       "      <td>2024-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>586711</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586712</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 (SFR + Condo)</td>\n",
       "      <td>2024-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586713</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 (SFR + Condo)</td>\n",
       "      <td>2024-02-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586714 rows × 13 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",
       "586709     845172        769   Winfield, KS         msa    KS   \n",
       "586710     845172        769   Winfield, KS         msa    KS   \n",
       "586711     845172        769   Winfield, KS         msa    KS   \n",
       "586712     845172        769   Winfield, KS         msa    KS   \n",
       "586713     845172        769   Winfield, KS         msa    KS   \n",
       "\n",
       "                      Home Type        Date  \\\n",
       "0                           SFR  2018-01-06   \n",
       "1                           SFR  2018-01-13   \n",
       "2                           SFR  2018-01-20   \n",
       "3                           SFR  2018-01-27   \n",
       "4                           SFR  2018-02-03   \n",
       "...                         ...         ...   \n",
       "586709  all homes (SFR + Condo)  2024-01-06   \n",
       "586710  all homes (SFR + Condo)  2024-01-13   \n",
       "586711  all homes (SFR + Condo)  2024-01-20   \n",
       "586712  all homes (SFR + Condo)  2024-01-27   \n",
       "586713  all homes (SFR + Condo)  2024-02-03   \n",
       "\n",
       "        Mean Listings Price Cut Amount (Smoothed)  Percent Listings Price Cut  \\\n",
       "0                                             NaN                         NaN   \n",
       "1                                             NaN                    0.049042   \n",
       "2                                             NaN                    0.044740   \n",
       "3                                    13998.585612                    0.047930   \n",
       "4                                    14120.035549                    0.047622   \n",
       "...                                           ...                         ...   \n",
       "586709                                        NaN                    0.094017   \n",
       "586710                                        NaN                    0.070175   \n",
       "586711                                        NaN                    0.043478   \n",
       "586712                                        NaN                    0.036697   \n",
       "586713                                        NaN                    0.077670   \n",
       "\n",
       "        Mean Listings Price Cut Amount  Percent Listings Price Cut (Smoothed)  \\\n",
       "0                         13508.368375                                    NaN   \n",
       "1                         14114.788383                                    NaN   \n",
       "2                         14326.128956                                    NaN   \n",
       "3                         13998.585612                                    NaN   \n",
       "4                         14120.035549                               0.047622   \n",
       "...                                ...                                    ...   \n",
       "586709                             NaN                               0.037378   \n",
       "586710                             NaN                               0.043203   \n",
       "586711                             NaN                               0.054073   \n",
       "586712                             NaN                               0.061092   \n",
       "586713                             NaN                               0.057005   \n",
       "\n",
       "        Median Days on Pending (Smoothed)  Median Days on Pending  \n",
       "0                                     NaN                     NaN  \n",
       "1                                     NaN                     NaN  \n",
       "2                                     NaN                     NaN  \n",
       "3                                     NaN                     NaN  \n",
       "4                                     NaN                     NaN  \n",
       "...                                   ...                     ...  \n",
       "586709                                NaN                     NaN  \n",
       "586710                                NaN                     NaN  \n",
       "586711                                NaN                     NaN  \n",
       "586712                                NaN                     NaN  \n",
       "586713                                NaN                     NaN  \n",
       "\n",
       "[586714 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adjust column names\n",
    "final_df = combined_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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
   ]
  }
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