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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_values(['Mean Listings Price Cut Amount', 'Median Days on Pending', 'Median Days to Close', 'Percent Listings Price Cut'])\n"
]
},
{
"data": {
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" <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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" <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>NaN</td>\n",
" <td>0.047930</td>\n",
" <td>13998.585612</td>\n",
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" </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",
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" <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",
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"</table>\n",
"<p>586714 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" RegionID SizeRank RegionName RegionType StateName \\\n",
"0 102001 0 United States country NaN \n",
"1 102001 0 United States country NaN \n",
"2 102001 0 United States country NaN \n",
"3 102001 0 United States country NaN \n",
"4 102001 0 United States country NaN \n",
"... ... ... ... ... ... \n",
"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 NaN 0.047930 \n",
"4 NaN 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 NaN \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": 6,
"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",
"def get_df(\n",
" df, exclude_columns, columns_to_pivot, col_name, smoothed, seasonally_adjusted\n",
"):\n",
" if smoothed:\n",
" col_name += \" (Smoothed)\"\n",
" if seasonally_adjusted:\n",
" col_name += \" (Seasonally Adjusted)\"\n",
"\n",
" df = pd.melt(\n",
" df,\n",
" id_vars=exclude_columns,\n",
" value_vars=columns_to_pivot,\n",
" var_name=\"Date\",\n",
" value_name=col_name,\n",
" )\n",
" return df\n",
"\n",
"\n",
"def get_combined_df(data_frames):\n",
" combined_df = None\n",
" if len(data_frames) > 1:\n",
" # iterate over dataframes and merge or concat\n",
" combined_df = data_frames[0]\n",
" for i in range(1, len(data_frames)):\n",
" cur_df = data_frames[i]\n",
" combined_df = pd.merge(\n",
" combined_df,\n",
" cur_df,\n",
" on=[\n",
" \"RegionID\",\n",
" \"SizeRank\",\n",
" \"RegionName\",\n",
" \"RegionType\",\n",
" \"StateName\",\n",
" \"Home Type\",\n",
" \"Date\",\n",
" ],\n",
" how=\"outer\",\n",
" suffixes=(\"\", \"_\" + str(i)),\n",
" )\n",
" elif len(data_frames) == 1:\n",
" combined_df = data_frames[0]\n",
"\n",
" return combined_df\n",
"\n",
"\n",
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
" if filename.endswith(\".csv\"):\n",
" # print(\"processing \" + filename)\n",
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
"\n",
" # skip month files for now since they are redundant\n",
" if \"month\" in filename:\n",
" continue\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",
" # Identify columns to pivot\n",
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
"\n",
" smoothed = \"_sm_\" in filename\n",
" seasonally_adjusted = \"_sa_\" in filename\n",
"\n",
" # iterate over slug column mappings and get df\n",
" for slug, col_name in slug_column_mappings.items():\n",
" if slug in filename:\n",
" cur_df = get_df(\n",
" cur_df,\n",
" exclude_columns,\n",
" columns_to_pivot,\n",
" col_name,\n",
" smoothed,\n",
" seasonally_adjusted,\n",
" )\n",
"\n",
" data_frames.append(cur_df)\n",
" break\n",
"\n",
"\n",
"combined_df = get_combined_df(data_frames)\n",
"\n",
"columns_to_coalesce = slug_column_mappings.values()\n",
"print(columns_to_coalesce)\n",
"\n",
"for index, row in combined_df.iterrows():\n",
" for col in combined_df.columns:\n",
" for column_to_coalesce in columns_to_coalesce:\n",
" if column_to_coalesce in col and \"_\" in col:\n",
" if not pd.isna(row[col]):\n",
" combined_df.at[index, column_to_coalesce] = row[col]\n",
"\n",
"# remove columns with underscores\n",
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
"\n",
"combined_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" <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": [
" 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 NaN 0.047930 \n",
"4 NaN 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 NaN \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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_df = combined_df\n",
"final_df = final_df.rename(\n",
" columns={\n",
" \"RegionID\": \"Region ID\",\n",
" \"SizeRank\": \"Size Rank\",\n",
" \"RegionName\": \"Region\",\n",
" \"RegionType\": \"Region Type\",\n",
" \"StateName\": \"State\",\n",
" }\n",
")\n",
"\n",
"final_df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
"\n",
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
]
}
],
"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
}
|