fix: update python files to use set homes

processors/days_on_market.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,12 +13,13 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,247 +32,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        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",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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>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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <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",
-       "    </tr>\n",
-       "    <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",
-       "    </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": [
-       "        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"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "processing Metro_med_listings_price_cut_amt_uc_sfr_month.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfr_week.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_month.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfr_week.csv\n",
+      "processing Metro_med_doz_pending_uc_sfrcondo_month.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfr_sm_month.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_mean_days_to_close_uc_sfrcondo_week.csv\n",
+      "processing Metro_mean_days_to_close_uc_sfrcondo_month.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfr_week.csv\n",
+      "processing Metro_median_days_to_close_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfr_sm_week.csv\n",
+      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfrcondo_week.csv\n",
+      "processing Metro_med_doz_pending_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_mean_days_to_close_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_week.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfr_week.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_month.csv\n",
+      "processing Metro_mean_doz_pending_uc_sfrcondo_week.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_week.csv\n",
+      "processing Metro_median_days_to_close_uc_sfrcondo_week.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfr_sm_month.csv\n",
+      "processing Metro_mean_doz_pending_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfr_sm_month.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_median_days_to_close_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfr_month.csv\n",
+      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_week.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_week.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_mean_days_to_close_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfr_sm_week.csv\n",
+      "processing Metro_mean_doz_pending_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfr_sm_week.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfrcondo_month.csv\n",
+      "processing Metro_med_listings_price_cut_amt_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_med_doz_pending_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_med_listings_price_cut_perc_uc_sfrcondo_sm_week.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfr_month.csv\n",
+      "processing Metro_med_doz_pending_uc_sfrcondo_week.csv\n",
+      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_sm_month.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfr_sm_month.csv\n",
+      "processing Metro_median_days_to_close_uc_sfrcondo_month.csv\n",
+      "processing Metro_perc_listings_price_cut_uc_sfr_sm_week.csv\n",
+      "processing Metro_mean_listings_price_cut_perc_uc_sfrcondo_month.csv\n",
+      "processing Metro_mean_listings_price_cut_amt_uc_sfr_month.csv\n",
+      "processing Metro_mean_doz_pending_uc_sfrcondo_month.csv\n"
+     ]
     }
    ],
    "source": [
@@ -303,12 +122,8 @@
     "\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",
+    "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
+    "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
     "        data_frames = handle_slug_column_mappings(\n",
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
@@ -333,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -364,10 +179,10 @@
        "      <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>Mean Listings Price Cut Amount (Smoothed)</th>\n",
        "      <th>Median Days on Pending (Smoothed)</th>\n",
        "      <th>Median Days on Pending</th>\n",
        "    </tr>\n",
@@ -383,11 +198,11 @@
        "      <td>SFR</td>\n",
        "      <td>2018-01-06</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>13508.368375</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -398,12 +213,12 @@
        "      <td>NaN</td>\n",
        "      <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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -414,12 +229,12 @@
        "      <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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -430,10 +245,10 @@
        "      <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>13998.585612</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
@@ -446,10 +261,10 @@
        "      <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>14120.035549</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
@@ -476,14 +291,14 @@
        "      <td>Winfield, KS</td>\n",
        "      <td>msa</td>\n",
        "      <td>KS</td>\n",
-       "      <td>all homes (SFR + Condo)</td>\n",
+       "      <td>all homes</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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>586710</th>\n",
@@ -492,14 +307,14 @@
        "      <td>Winfield, KS</td>\n",
        "      <td>msa</td>\n",
        "      <td>KS</td>\n",
-       "      <td>all homes (SFR + Condo)</td>\n",
+       "      <td>all homes</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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>586711</th>\n",
@@ -508,14 +323,14 @@
        "      <td>Winfield, KS</td>\n",
        "      <td>msa</td>\n",
        "      <td>KS</td>\n",
-       "      <td>all homes (SFR + Condo)</td>\n",
+       "      <td>all homes</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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>586712</th>\n",
@@ -524,14 +339,14 @@
        "      <td>Winfield, KS</td>\n",
        "      <td>msa</td>\n",
        "      <td>KS</td>\n",
-       "      <td>all homes (SFR + Condo)</td>\n",
+       "      <td>all homes</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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>586713</th>\n",
@@ -540,14 +355,14 @@
        "      <td>Winfield, KS</td>\n",
        "      <td>msa</td>\n",
        "      <td>KS</td>\n",
-       "      <td>all homes (SFR + Condo)</td>\n",
+       "      <td>all homes</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",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -555,57 +370,57 @@
        "</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",
+       "        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",
+       "586709     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "586710     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "586711     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "586712     845172        769   Winfield, KS         msa    KS  all homes   \n",
+       "586713     845172        769   Winfield, KS         msa    KS  all homes   \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",
+       "             Date  Percent Listings Price Cut  Mean Listings Price Cut Amount  \\\n",
+       "0      2018-01-06                         NaN                    13508.368375   \n",
+       "1      2018-01-13                    0.049042                    14114.788383   \n",
+       "2      2018-01-20                    0.044740                    14326.128956   \n",
+       "3      2018-01-27                    0.047930                    13998.585612   \n",
+       "4      2018-02-03                    0.047622                    14120.035549   \n",
+       "...           ...                         ...                             ...   \n",
+       "586709 2024-01-06                    0.094017                             NaN   \n",
+       "586710 2024-01-13                    0.070175                             NaN   \n",
+       "586711 2024-01-20                    0.043478                             NaN   \n",
+       "586712 2024-01-27                    0.036697                             NaN   \n",
+       "586713 2024-02-03                    0.077670                             NaN   \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",
+       "        Percent Listings Price Cut (Smoothed)  \\\n",
+       "0                                         NaN   \n",
+       "1                                         NaN   \n",
+       "2                                         NaN   \n",
+       "3                                         NaN   \n",
+       "4                                    0.047622   \n",
+       "...                                       ...   \n",
+       "586709                               0.037378   \n",
+       "586710                               0.043203   \n",
+       "586711                               0.054073   \n",
+       "586712                               0.061092   \n",
+       "586713                               0.057005   \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",
+       "        Mean Listings Price Cut Amount (Smoothed)  \\\n",
+       "0                                             NaN   \n",
+       "1                                             NaN   \n",
+       "2                                             NaN   \n",
+       "3                                    13998.585612   \n",
+       "4                                    14120.035549   \n",
+       "...                                           ...   \n",
+       "586709                                        NaN   \n",
+       "586710                                        NaN   \n",
+       "586711                                        NaN   \n",
+       "586712                                        NaN   \n",
+       "586713                                        NaN   \n",
        "\n",
        "        Median Days on Pending (Smoothed)  Median Days on Pending  \n",
        "0                                     NaN                     NaN  \n",
@@ -623,7 +438,7 @@
        "[586714 rows x 13 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -640,6 +455,8 @@
     "    }\n",
     ")\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df"
    ]
   },

processors/days_on_market.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[6]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[7]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "days_on_market/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[8]:
+
+
+data_frames = []
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_mean_listings_price_cut_amt_": "Mean Listings Price Cut Amount",
+    "_med_doz_pending_": "Median Days on Pending",
+    "_median_days_to_pending_": "Median Days to Close",
+    "_perc_listings_price_cut_": "Percent Listings Price Cut",
+}
+
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        # skip month files for now since they are redundant
+        if "month" in filename:
+            continue
+
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+        cur_df = set_home_type(cur_df, filename)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/for_sale_listings.ipynb

@@ -13,6 +13,7 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
@@ -371,12 +372,7 @@
     "        if \"month\" 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",
+    "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
     "        data_frames = handle_slug_column_mappings(\n",
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
@@ -695,6 +691,8 @@
     "    }\n",
     ")\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df"
    ]
   },

processors/for_sale_listings.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "for_sale_listings/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_mlp_": "Median Listing Price",
+    "_new_listings_": "New Listings",
+    "new_pending": "New Pending",
+}
+
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        # ignore monthly data for now since it is redundant
+        if "month" in filename:
+            continue
+
+        cur_df = set_home_type(cur_df, filename)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/helpers.py

@@ -19,6 +19,21 @@ def coalesce_columns(
     return combined_df
 
 
+def set_home_type(cur_df, filename):
+    if "_sfrcondo_" in filename:
+        cur_df["Home Type"] = "all homes"
+    if "_sfrcondomfr_" in filename:
+        cur_df["Home Type"] = "all homes plus multifamily"
+    elif "_sfr_" in filename:
+        cur_df["Home Type"] = "SFR"
+    elif "_condo_" in filename:
+        cur_df["Home Type"] = "condo/co-op"
+    elif "_mfr_" in filename:
+        cur_df["Home Type"] = "multifamily"
+
+    return cur_df
+
+
 def get_combined_df(data_frames, on):
     combined_df = None
     if len(data_frames) > 1:
@@ -72,7 +87,7 @@ def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
         os.makedirs(FULL_PROCESSED_DIR_PATH)
 
     final_df.to_json(
-        FULL_PROCESSED_DIR_PATH + "final5.jsonl", orient="records", lines=True
+        FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
     )
 
 

processors/home_values.ipynb

@@ -13,6 +13,7 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
@@ -436,12 +437,7 @@
     "        else:\n",
     "            cur_df[\"Bedroom Count\"] = \"All Bedrooms\"\n",
     "\n",
-    "        if \"_uc_sfr_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"SFR\"\n",
-    "        elif \"_uc_sfrcondo_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"all homes (SFR/condo)\"\n",
-    "        elif \"_uc_condo_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"condo\"\n",
+    "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
     "        cur_df[\"StateName\"] = cur_df[\"StateName\"].astype(str)\n",
     "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
@@ -1401,6 +1397,8 @@
     "    }\n",
     ")\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df"
    ]
   },

processors/home_values.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "home_values/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[5]:
+
+
+data_frames = []
+
+slug_column_mappings = {
+    "_tier_0.0_0.33_": "Bottom Tier ZHVI",
+    "_tier_0.33_0.67_": "Mid Tier ZHVI",
+    "_tier_0.67_1.0_": "Top Tier ZHVI",
+    "": "ZHVI",
+}
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        exclude_columns = [
+            "RegionID",
+            "SizeRank",
+            "RegionName",
+            "RegionType",
+            "StateName",
+            "Bedroom Count",
+            "Home Type",
+        ]
+
+        if "Zip" in filename:
+            continue
+        if "Neighborhood" in filename:
+            continue
+        if "City" in filename:
+            continue
+        if "Metro" in filename:
+            continue
+        if "County" in filename:
+            continue
+
+        if "City" in filename:
+            exclude_columns = exclude_columns + ["State", "Metro", "CountyName"]
+        elif "Zip" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "City",
+                "Metro",
+                "CountyName",
+            ]
+        elif "County" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "Metro",
+                "StateCodeFIPS",
+                "MunicipalCodeFIPS",
+            ]
+        elif "Neighborhood" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "City",
+                "Metro",
+                "CountyName",
+            ]
+
+        if "_bdrmcnt_1_" in filename:
+            cur_df["Bedroom Count"] = "1-Bedroom"
+        elif "_bdrmcnt_2_" in filename:
+            cur_df["Bedroom Count"] = "2-Bedrooms"
+        elif "_bdrmcnt_3_" in filename:
+            cur_df["Bedroom Count"] = "3-Bedrooms"
+        elif "_bdrmcnt_4_" in filename:
+            cur_df["Bedroom Count"] = "4-Bedrooms"
+        elif "_bdrmcnt_5_" in filename:
+            cur_df["Bedroom Count"] = "5+-Bedrooms"
+        else:
+            cur_df["Bedroom Count"] = "All Bedrooms"
+
+        cur_df = set_home_type(cur_df, filename)
+
+        cur_df["StateName"] = cur_df["StateName"].astype(str)
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Bedroom Count",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[11]:
+
+
+final_df = combined_df
+
+for index, row in final_df.iterrows():
+    if row["RegionType"] == "city":
+        final_df.at[index, "City"] = row["RegionName"]
+    elif row["RegionType"] == "county":
+        final_df.at[index, "County"] = row["RegionName"]
+    if row["RegionType"] == "state":
+        final_df.at[index, "StateName"] = row["RegionName"]
+
+# coalesce State and StateName columns
+# final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
+# final_df["County"] = final_df["County"].combine_first(final_df["CountyName"])
+
+# final_df = final_df.drop(
+#     columns=[
+#         "StateName",
+#         # "CountyName"
+#     ]
+# )
+final_df
+
+
+# In[12]:
+
+
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateCodeFIPS": "State Code FIPS",
+        "StateName": "State",
+        "MunicipalCodeFIPS": "Municipal Code FIPS",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[13]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/home_values_forecasts.ipynb

@@ -398,6 +398,7 @@
     "        else:\n",
     "            # print('Raw')\n",
     "            cur_df.columns = list(cur_df.columns[:-3]) + cols\n",
+    "\n",
     "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
     "\n",
     "        data_frames.append(cur_df)\n",
@@ -461,6 +462,8 @@
     "        state = regionName.split(\", \")[1]\n",
     "        final_df.at[index, \"State\"] = state\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df"
    ]
   },

processors/home_values_forecasts.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import get_combined_df, save_final_df_as_jsonl
+
+
+# In[2]:
+
+
+DATA_DIR = "../data/"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "home_values_forecasts/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        cols = ["Month Over Month %", "Quarter Over Quarter %", "Year Over Year %"]
+        if filename.endswith("sm_sa_month.csv"):
+            # print('Smoothed')
+            cur_df.columns = list(cur_df.columns[:-3]) + [
+                x + " (Smoothed) (Seasonally Adjusted)" for x in cols
+            ]
+        else:
+            # print('Raw')
+            cur_df.columns = list(cur_df.columns[:-3]) + cols
+
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+
+        data_frames.append(cur_df)
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "RegionType",
+        "SizeRank",
+        "StateName",
+        "BaseDate",
+    ],
+)
+
+combined_df
+
+
+# In[1]:
+
+
+# Adjust columns
+final_df = combined_df
+final_df = combined_df.drop("StateName", axis=1)
+final_df = final_df.rename(
+    columns={
+        "CountyName": "County",
+        "BaseDate": "Date",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+    }
+)
+
+# iterate over rows of final_df and populate State and City columns if the regionType is msa
+for index, row in final_df.iterrows():
+    if row["Region Type"] == "msa":
+        regionName = row["Region"]
+        # final_df.at[index, 'Metro'] = regionName
+
+        city = regionName.split(", ")[0]
+        final_df.at[index, "City"] = city
+
+        state = regionName.split(", ")[1]
+        final_df.at[index, "State"] = state
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[9]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/new_construction.ipynb

@@ -13,6 +13,7 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
@@ -289,12 +290,7 @@
     "        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",
+    "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
     "        data_frames = handle_slug_column_mappings(\n",
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
@@ -551,6 +547,8 @@
     "    }\n",
     ")\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df.sort_values(by=[\"Region ID\", \"Home Type\", \"Date\"])"
    ]
   },

processors/new_construction.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "new_construction/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_median_sale_price_per_sqft": "Median Sale Price per Sqft",
+    "_median_sale_price": "Median Sale Price",
+    "sales_count": "Sales Count",
+}
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        cur_df = set_home_type(cur_df, filename)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+final_df = combined_df
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df.sort_values(by=["Region ID", "Home Type", "Date"])
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/rentals.ipynb

@@ -13,6 +13,7 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
@@ -368,56 +369,51 @@
     "            \"Home Type\",\n",
     "        ]\n",
     "\n",
-    "        if \"_sfrcondomfr_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"all homes plus multifamily\"\n",
-    "            # change column type to string\n",
-    "            cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
-    "            if \"City\" in filename:\n",
-    "                exclude_columns = [\n",
-    "                    \"RegionID\",\n",
-    "                    \"SizeRank\",\n",
-    "                    \"RegionName\",\n",
-    "                    \"RegionType\",\n",
-    "                    \"StateName\",\n",
-    "                    \"Home Type\",\n",
-    "                    # City Specific\n",
-    "                    \"State\",\n",
-    "                    \"Metro\",\n",
-    "                    \"CountyName\",\n",
-    "                ]\n",
-    "            elif \"Zip\" in filename:\n",
-    "                exclude_columns = [\n",
-    "                    \"RegionID\",\n",
-    "                    \"SizeRank\",\n",
-    "                    \"RegionName\",\n",
-    "                    \"RegionType\",\n",
-    "                    \"StateName\",\n",
-    "                    \"Home Type\",\n",
-    "                    # Zip Specific\n",
-    "                    \"State\",\n",
-    "                    \"City\",\n",
-    "                    \"Metro\",\n",
-    "                    \"CountyName\",\n",
-    "                ]\n",
-    "            elif \"County\" in filename:\n",
-    "                exclude_columns = [\n",
-    "                    \"RegionID\",\n",
-    "                    \"SizeRank\",\n",
-    "                    \"RegionName\",\n",
-    "                    \"RegionType\",\n",
-    "                    \"StateName\",\n",
-    "                    \"Home Type\",\n",
-    "                    # County Specific\n",
-    "                    \"State\",\n",
-    "                    \"Metro\",\n",
-    "                    \"StateCodeFIPS\",\n",
-    "                    \"MunicipalCodeFIPS\",\n",
-    "                ]\n",
+    "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
     "\n",
-    "        elif \"_sfr_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"SFR\"\n",
-    "        elif \"_mfr_\" in filename:\n",
-    "            cur_df[\"Home Type\"] = \"multifamily\"\n",
+    "        cur_df = set_home_type(cur_df, filename)\n",
+    "\n",
+    "        if \"City\" in filename:\n",
+    "            exclude_columns = [\n",
+    "                \"RegionID\",\n",
+    "                \"SizeRank\",\n",
+    "                \"RegionName\",\n",
+    "                \"RegionType\",\n",
+    "                \"StateName\",\n",
+    "                \"Home Type\",\n",
+    "                # City Specific\n",
+    "                \"State\",\n",
+    "                \"Metro\",\n",
+    "                \"CountyName\",\n",
+    "            ]\n",
+    "        elif \"Zip\" in filename:\n",
+    "            exclude_columns = [\n",
+    "                \"RegionID\",\n",
+    "                \"SizeRank\",\n",
+    "                \"RegionName\",\n",
+    "                \"RegionType\",\n",
+    "                \"StateName\",\n",
+    "                \"Home Type\",\n",
+    "                # Zip Specific\n",
+    "                \"State\",\n",
+    "                \"City\",\n",
+    "                \"Metro\",\n",
+    "                \"CountyName\",\n",
+    "            ]\n",
+    "        elif \"County\" in filename:\n",
+    "            exclude_columns = [\n",
+    "                \"RegionID\",\n",
+    "                \"SizeRank\",\n",
+    "                \"RegionName\",\n",
+    "                \"RegionType\",\n",
+    "                \"StateName\",\n",
+    "                \"Home Type\",\n",
+    "                # County Specific\n",
+    "                \"State\",\n",
+    "                \"Metro\",\n",
+    "                \"StateCodeFIPS\",\n",
+    "                \"MunicipalCodeFIPS\",\n",
+    "            ]\n",
     "\n",
     "        data_frames = handle_slug_column_mappings(\n",
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
@@ -1075,6 +1071,8 @@
     "    }\n",
     ")\n",
     "\n",
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
     "final_df"
    ]
   },

processors/rentals.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[3]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "rentals/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[7]:
+
+
+data_frames = []
+
+slug_column_mappings = {"": "Rent"}
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        # print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        exclude_columns = [
+            "RegionID",
+            "SizeRank",
+            "RegionName",
+            "RegionType",
+            "StateName",
+            "Home Type",
+        ]
+
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+
+        cur_df = set_home_type(cur_df, filename)
+
+        if "City" in filename:
+            exclude_columns = [
+                "RegionID",
+                "SizeRank",
+                "RegionName",
+                "RegionType",
+                "StateName",
+                "Home Type",
+                # City Specific
+                "State",
+                "Metro",
+                "CountyName",
+            ]
+        elif "Zip" in filename:
+            exclude_columns = [
+                "RegionID",
+                "SizeRank",
+                "RegionName",
+                "RegionType",
+                "StateName",
+                "Home Type",
+                # Zip Specific
+                "State",
+                "City",
+                "Metro",
+                "CountyName",
+            ]
+        elif "County" in filename:
+            exclude_columns = [
+                "RegionID",
+                "SizeRank",
+                "RegionName",
+                "RegionType",
+                "StateName",
+                "Home Type",
+                # County Specific
+                "State",
+                "Metro",
+                "StateCodeFIPS",
+                "MunicipalCodeFIPS",
+            ]
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[8]:
+
+
+final_df = combined_df
+
+for index, row in final_df.iterrows():
+    if row["RegionType"] == "city":
+        final_df.at[index, "City"] = row["RegionName"]
+    elif row["RegionType"] == "county":
+        final_df.at[index, "County"] = row["RegionName"]
+
+# coalesce State and StateName columns
+final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
+final_df["State"] = final_df["County"].combine_first(final_df["CountyName"])
+
+final_df = final_df.drop(columns=["StateName", "CountyName"])
+final_df
+
+
+# In[6]:
+
+
+# Adjust column names
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateCodeFIPS": "State Code FIPS",
+        "MunicipalCodeFIPS": "Municipal Code FIPS",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[7]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/sales.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,12 +13,13 @@
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
+    "    set_home_type,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -441,7 +442,7 @@
        "[255024 rows x 18 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,10 +477,7 @@
     "\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",
+    "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
     "        data_frames = handle_slug_column_mappings(\n",
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
@@ -504,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -880,7 +878,7 @@
        "[255024 rows x 18 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -903,7 +901,396 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        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>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>Median Sale to List Ratio</th>\n",
+       "      <th>Median Sale Price</th>\n",
+       "      <th>Median Sale Price (Smoothed) (Seasonally Adjusted)</th>\n",
+       "      <th>Median Sale Price (Smoothed)</th>\n",
+       "      <th>% Sold Below List (Smoothed)</th>\n",
+       "      <th>Median Sale to List Ratio (Smoothed)</th>\n",
+       "      <th>% Sold Above List</th>\n",
+       "      <th>Mean Sale to List Ratio (Smoothed)</th>\n",
+       "      <th>Mean Sale to List Ratio</th>\n",
+       "      <th>% Sold Below List</th>\n",
+       "      <th>% Sold Above List (Smoothed)</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>2008-02-02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>172000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>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>SFR</td>\n",
+       "      <td>2008-02-09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>165400.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>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>2008-02-16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>168000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>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>2008-02-23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>167600.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>167600.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>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>2008-03-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>168100.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>168100.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>...</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",
+       "      <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>255019</th>\n",
+       "      <td>845160</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Prescott Valley, AZ</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-11-11</td>\n",
+       "      <td>0.985132</td>\n",
+       "      <td>515000.0</td>\n",
+       "      <td>480020.0</td>\n",
+       "      <td>480020.0</td>\n",
+       "      <td>0.651221</td>\n",
+       "      <td>0.982460</td>\n",
+       "      <td>0.080000</td>\n",
+       "      <td>0.978546</td>\n",
+       "      <td>0.983288</td>\n",
+       "      <td>0.680000</td>\n",
+       "      <td>0.119711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255020</th>\n",
+       "      <td>845160</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Prescott Valley, AZ</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-11-18</td>\n",
+       "      <td>0.972559</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>476901.0</td>\n",
+       "      <td>476901.0</td>\n",
+       "      <td>0.659583</td>\n",
+       "      <td>0.980362</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.972912</td>\n",
+       "      <td>0.958341</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.120214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255021</th>\n",
+       "      <td>845160</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Prescott Valley, AZ</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-11-25</td>\n",
+       "      <td>0.979644</td>\n",
+       "      <td>484500.0</td>\n",
+       "      <td>496540.0</td>\n",
+       "      <td>496540.0</td>\n",
+       "      <td>0.669387</td>\n",
+       "      <td>0.979179</td>\n",
+       "      <td>0.088235</td>\n",
+       "      <td>0.971177</td>\n",
+       "      <td>0.973797</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.107185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255022</th>\n",
+       "      <td>845160</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Prescott Valley, AZ</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-12-02</td>\n",
+       "      <td>0.978261</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>510491.0</td>\n",
+       "      <td>510491.0</td>\n",
+       "      <td>0.678777</td>\n",
+       "      <td>0.978899</td>\n",
+       "      <td>0.126761</td>\n",
+       "      <td>0.970576</td>\n",
+       "      <td>0.966876</td>\n",
+       "      <td>0.704225</td>\n",
+       "      <td>0.109463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255023</th>\n",
+       "      <td>845160</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Prescott Valley, AZ</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>all homes</td>\n",
+       "      <td>2023-12-09</td>\n",
+       "      <td>0.981498</td>\n",
+       "      <td>485000.0</td>\n",
+       "      <td>503423.0</td>\n",
+       "      <td>503423.0</td>\n",
+       "      <td>0.658777</td>\n",
+       "      <td>0.977990</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.970073</td>\n",
+       "      <td>0.981278</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.114463</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>255024 rows × 18 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",
+       "255019     845160        198  Prescott Valley, AZ         msa    AZ   \n",
+       "255020     845160        198  Prescott Valley, AZ         msa    AZ   \n",
+       "255021     845160        198  Prescott Valley, AZ         msa    AZ   \n",
+       "255022     845160        198  Prescott Valley, AZ         msa    AZ   \n",
+       "255023     845160        198  Prescott Valley, AZ         msa    AZ   \n",
+       "\n",
+       "        Home Type       Date  Median Sale to List Ratio  Median Sale Price  \\\n",
+       "0             SFR 2008-02-02                        NaN           172000.0   \n",
+       "1             SFR 2008-02-09                        NaN           165400.0   \n",
+       "2             SFR 2008-02-16                        NaN           168000.0   \n",
+       "3             SFR 2008-02-23                        NaN           167600.0   \n",
+       "4             SFR 2008-03-01                        NaN           168100.0   \n",
+       "...           ...        ...                        ...                ...   \n",
+       "255019  all homes 2023-11-11                   0.985132           515000.0   \n",
+       "255020  all homes 2023-11-18                   0.972559           510000.0   \n",
+       "255021  all homes 2023-11-25                   0.979644           484500.0   \n",
+       "255022  all homes 2023-12-02                   0.978261           538000.0   \n",
+       "255023  all homes 2023-12-09                   0.981498           485000.0   \n",
+       "\n",
+       "        Median Sale Price (Smoothed) (Seasonally Adjusted)  \\\n",
+       "0                                                     NaN    \n",
+       "1                                                     NaN    \n",
+       "2                                                     NaN    \n",
+       "3                                                     NaN    \n",
+       "4                                                     NaN    \n",
+       "...                                                   ...    \n",
+       "255019                                           480020.0    \n",
+       "255020                                           476901.0    \n",
+       "255021                                           496540.0    \n",
+       "255022                                           510491.0    \n",
+       "255023                                           503423.0    \n",
+       "\n",
+       "        Median Sale Price (Smoothed)  % Sold Below List (Smoothed)  \\\n",
+       "0                                NaN                           NaN   \n",
+       "1                                NaN                           NaN   \n",
+       "2                                NaN                           NaN   \n",
+       "3                           167600.0                           NaN   \n",
+       "4                           168100.0                           NaN   \n",
+       "...                              ...                           ...   \n",
+       "255019                      480020.0                      0.651221   \n",
+       "255020                      476901.0                      0.659583   \n",
+       "255021                      496540.0                      0.669387   \n",
+       "255022                      510491.0                      0.678777   \n",
+       "255023                      503423.0                      0.658777   \n",
+       "\n",
+       "        Median Sale to List Ratio (Smoothed)  % Sold Above List  \\\n",
+       "0                                        NaN                NaN   \n",
+       "1                                        NaN                NaN   \n",
+       "2                                        NaN                NaN   \n",
+       "3                                        NaN                NaN   \n",
+       "4                                        NaN                NaN   \n",
+       "...                                      ...                ...   \n",
+       "255019                              0.982460           0.080000   \n",
+       "255020                              0.980362           0.142857   \n",
+       "255021                              0.979179           0.088235   \n",
+       "255022                              0.978899           0.126761   \n",
+       "255023                              0.977990           0.100000   \n",
+       "\n",
+       "        Mean Sale to List Ratio (Smoothed)  Mean Sale to List Ratio  \\\n",
+       "0                                      NaN                      NaN   \n",
+       "1                                      NaN                      NaN   \n",
+       "2                                      NaN                      NaN   \n",
+       "3                                      NaN                      NaN   \n",
+       "4                                      NaN                      NaN   \n",
+       "...                                    ...                      ...   \n",
+       "255019                            0.978546                 0.983288   \n",
+       "255020                            0.972912                 0.958341   \n",
+       "255021                            0.971177                 0.973797   \n",
+       "255022                            0.970576                 0.966876   \n",
+       "255023                            0.970073                 0.981278   \n",
+       "\n",
+       "        % Sold Below List  % Sold Above List (Smoothed)  \n",
+       "0                     NaN                           NaN  \n",
+       "1                     NaN                           NaN  \n",
+       "2                     NaN                           NaN  \n",
+       "3                     NaN                           NaN  \n",
+       "4                     NaN                           NaN  \n",
+       "...                   ...                           ...  \n",
+       "255019           0.680000                      0.119711  \n",
+       "255020           0.625000                      0.120214  \n",
+       "255021           0.705882                      0.107185  \n",
+       "255022           0.704225                      0.109463  \n",
+       "255023           0.600000                      0.114463  \n",
+       "\n",
+       "[255024 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df[\"Date\"] = pd.to_datetime(final_df[\"Date\"], format=\"%Y-%m-%d\")\n",
+    "\n",
+    "final_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [

processors/sales.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+    set_home_type,
+)
+
+
+# In[3]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "sales/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[4]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_median_sale_to_list_": "Median Sale to List Ratio",
+    "_mean_sale_to_list_": "Mean Sale to List Ratio",
+    "_median_sale_price_": "Median Sale Price",
+    "_pct_sold_above_list_": "% Sold Above List",
+    "_pct_sold_below_list_": "% Sold Below List",
+    "_sales_count_now_": "Nowcast",
+}
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        # ignore monthly data for now since it is redundant
+        if "month" in filename:
+            continue
+
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        cur_df = set_home_type(cur_df, filename)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[52]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df["Date"] = pd.to_datetime(final_df["Date"])
+final_df.sort_values(by=["Region ID", "Home Type", "Date"])
+
+
+# In[53]:
+
+
+final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
+
+final_df
+
+
+# In[54]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+