{
"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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" }\n",
"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RegionID</th>\n",
" <th>SizeRank</th>\n",
" <th>RegionName</th>\n",
" <th>RegionType</th>\n",
" <th>StateName</th>\n",
" <th>Home Type</th>\n",
" <th>Date</th>\n",
" <th>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",
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" <tr>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\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",
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" <td>0</td>\n",
" <td>United States</td>\n",
" <td>country</td>\n",
" <td>NaN</td>\n",
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" <td>13998.585612</td>\n",
" <td>0.047930</td>\n",
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" <td>NaN</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",
" </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",
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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",
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" <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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"</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",
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" <tbody>\n",
" <tr>\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",
" <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",
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" <tr>\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",
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" <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",
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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>2024-01-27</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",
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" <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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
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