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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"DATA_DIR = \"../data\"\n",
"PROCESSED_DIR = \"../processed/\"\n",
"FACET_DIR = \"for_sale_listings/\"\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": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processing Metro_new_pending_uc_sfrcondo_sm_month.csv\n",
"processing Metro_invt_fs_uc_sfrcondo_week.csv\n",
"processing Metro_mlp_uc_sfrcondo_week.csv\n",
"processing Metro_invt_fs_uc_sfr_month.csv\n",
"processing Metro_mlp_uc_sfr_sm_month.csv\n",
"processing Metro_new_pending_uc_sfrcondo_month.csv\n",
"processing Metro_mlp_uc_sfrcondo_sm_week.csv\n",
"processing Metro_invt_fs_uc_sfrcondo_month.csv\n",
"processing Metro_mlp_uc_sfr_sm_week.csv\n",
"processing Metro_mlp_uc_sfrcondo_month.csv\n",
"processing Metro_new_pending_uc_sfrcondo_sm_week.csv\n",
"processing Metro_invt_fs_uc_sfr_sm_week.csv\n",
"processing Metro_invt_fs_uc_sfr_sm_month.csv\n",
"processing Metro_mlp_uc_sfr_month.csv\n",
"processing Metro_new_listings_uc_sfrcondo_week.csv\n",
"processing Metro_mlp_uc_sfrcondo_sm_month.csv\n",
"processing Metro_invt_fs_uc_sfrcondo_sm_week.csv\n",
"processing Metro_new_listings_uc_sfrcondo_sm_week.csv\n",
"processing Metro_new_listings_uc_sfrcondo_month.csv\n",
"processing Metro_new_pending_uc_sfrcondo_week.csv\n",
"processing Metro_invt_fs_uc_sfr_week.csv\n",
"processing Metro_new_listings_uc_sfrcondo_sm_month.csv\n",
"processing Metro_mlp_uc_sfr_week.csv\n",
"processing Metro_invt_fs_uc_sfrcondo_sm_month.csv\n"
]
},
{
"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>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>New Pending (Smoothed)</th>\n",
" <th>Median Listing Price</th>\n",
" <th>Median Listing Price (Smoothed)</th>\n",
" <th>New Pending</th>\n",
" <th>New Listings</th>\n",
" <th>New Listings (Smoothed)</th>\n",
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" <td>SFR</td>\n",
" <td>2018-01-13</td>\n",
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" <td>Winfield, KS</td>\n",
" <td>msa</td>\n",
" <td>KS</td>\n",
" <td>all homes</td>\n",
" <td>2023-12-16</td>\n",
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" <td>133938.0</td>\n",
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" <td>2023-12-31</td>\n",
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"<p>693661 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" RegionID SizeRank RegionName RegionType StateName Home Type \\\n",
"0 102001 0 United States country NaN SFR \n",
"1 102001 0 United States country NaN SFR \n",
"2 102001 0 United States country NaN SFR \n",
"3 102001 0 United States country NaN SFR \n",
"4 102001 0 United States country NaN SFR \n",
"... ... ... ... ... ... ... \n",
"693656 845172 769 Winfield, KS msa KS all homes \n",
"693657 845172 769 Winfield, KS msa KS all homes \n",
"693658 845172 769 Winfield, KS msa KS all homes \n",
"693659 845172 769 Winfield, KS msa KS all homes \n",
"693660 845172 769 Winfield, KS msa KS all homes \n",
"\n",
" Date New Pending (Smoothed) Median Listing Price \\\n",
"0 2018-01-13 NaN 259000.0 \n",
"1 2018-01-20 NaN 259900.0 \n",
"2 2018-01-27 NaN 259900.0 \n",
"3 2018-01-31 NaN 254900.0 \n",
"4 2018-02-03 NaN 260000.0 \n",
"... ... ... ... \n",
"693656 2023-12-16 NaN 133938.0 \n",
"693657 2023-12-23 NaN 126463.0 \n",
"693658 2023-12-30 NaN 123225.0 \n",
"693659 2023-12-31 24.0 136233.0 \n",
"693660 2024-01-06 NaN 121488.0 \n",
"\n",
" Median Listing Price (Smoothed) New Pending New Listings \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 259700.0 NaN NaN \n",
"... ... ... ... \n",
"693656 133938.0 NaN NaN \n",
"693657 126463.0 NaN NaN \n",
"693658 123225.0 NaN NaN \n",
"693659 136233.0 24.0 28.0 \n",
"693660 121488.0 NaN NaN \n",
"\n",
" New Listings (Smoothed) \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"693656 NaN \n",
"693657 NaN \n",
"693658 NaN \n",
"693659 28.0 \n",
"693660 NaN \n",
"\n",
"[693661 rows x 13 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
"\n",
"exclude_columns = [\n",
" \"RegionID\",\n",
" \"SizeRank\",\n",
" \"RegionName\",\n",
" \"RegionType\",\n",
" \"StateName\",\n",
" \"Home Type\",\n",
"]\n",
"\n",
"data_frames = []\n",
"\n",
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
" if filename.endswith(\".csv\"):\n",
" print(\"processing \" + filename)\n",
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
"\n",
" # ignore monthly data for now since it is redundant\n",
" if \"monthly\" 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",
"\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",
"\n",
" if \"_mlp_\" in filename:\n",
" cur_df = pd.melt(\n",
" cur_df,\n",
" id_vars=exclude_columns,\n",
" value_vars=columns_to_pivot,\n",
" var_name=\"Date\",\n",
" value_name=(\n",
" \"Median Listing Price\"\n",
" if not smoothed\n",
" else \"Median Listing Price (Smoothed)\"\n",
" ),\n",
" )\n",
" data_frames.append(cur_df)\n",
"\n",
" elif \"_new_listings_\" in filename:\n",
" cur_df = pd.melt(\n",
" cur_df,\n",
" id_vars=exclude_columns,\n",
" value_vars=columns_to_pivot,\n",
" var_name=\"Date\",\n",
" value_name=(\n",
" \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
" ),\n",
" )\n",
" data_frames.append(cur_df)\n",
"\n",
" elif \"new_pending\" in filename:\n",
" cur_df = pd.melt(\n",
" cur_df,\n",
" id_vars=exclude_columns,\n",
" value_vars=columns_to_pivot,\n",
" var_name=\"Date\",\n",
" value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
" )\n",
" data_frames.append(cur_df)\n",
"\n",
"matching_cols = [\n",
" \"RegionID\",\n",
" \"Date\",\n",
" \"SizeRank\",\n",
" \"RegionName\",\n",
" \"RegionType\",\n",
" \"StateName\",\n",
" \"Home Type\",\n",
"]\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",
" suffixes=(\"\", \"_\" + str(i)),\n",
" how=\"outer\",\n",
" )\n",
" elif len(data_frames) == 1:\n",
" combined_df = data_frames[0]\n",
"\n",
" return combined_df\n",
"\n",
"\n",
"combined_df = get_combined_df(data_frames)\n",
"\n",
"\n",
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
"columns_to_coalesce = [\n",
" \"Median Listing Price\",\n",
" \"Median Listing Price (Smoothed)\",\n",
" \"New Listings\",\n",
" \"New Listings (Smoothed)\",\n",
" \"New Pending (Smoothed)\",\n",
" \"New Pending\",\n",
"]\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": 6,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Region ID</th>\n",
" <th>Size Rank</th>\n",
" <th>Region</th>\n",
" <th>Region Type</th>\n",
" <th>State</th>\n",
" <th>Home Type</th>\n",
" <th>Date</th>\n",
" <th>New Pending (Smoothed)</th>\n",
" <th>Median Listing Price</th>\n",
" <th>Median Listing Price (Smoothed)</th>\n",
" <th>New Pending</th>\n",
" <th>New Listings</th>\n",
" <th>New Listings (Smoothed)</th>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <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>2018-01-20</td>\n",
" <td>NaN</td>\n",
" <td>259900.0</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>2018-01-27</td>\n",
" <td>NaN</td>\n",
" <td>259900.0</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>2018-01-31</td>\n",
" <td>NaN</td>\n",
" <td>254900.0</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>2018-02-03</td>\n",
" <td>NaN</td>\n",
" <td>260000.0</td>\n",
" <td>259700.0</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",
" </tr>\n",
" <tr>\n",
" <th>693656</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</td>\n",
" <td>2023-12-16</td>\n",
" <td>NaN</td>\n",
" <td>133938.0</td>\n",
" <td>133938.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693657</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</td>\n",
" <td>2023-12-23</td>\n",
" <td>NaN</td>\n",
" <td>126463.0</td>\n",
" <td>126463.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693658</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</td>\n",
" <td>2023-12-30</td>\n",
" <td>NaN</td>\n",
" <td>123225.0</td>\n",
" <td>123225.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693659</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</td>\n",
" <td>2023-12-31</td>\n",
" <td>24.0</td>\n",
" <td>136233.0</td>\n",
" <td>136233.0</td>\n",
" <td>24.0</td>\n",
" <td>28.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693660</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</td>\n",
" <td>2024-01-06</td>\n",
" <td>NaN</td>\n",
" <td>121488.0</td>\n",
" <td>121488.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>693661 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Region ID Size Rank Region Region Type State Home Type \\\n",
"0 102001 0 United States country NaN SFR \n",
"1 102001 0 United States country NaN SFR \n",
"2 102001 0 United States country NaN SFR \n",
"3 102001 0 United States country NaN SFR \n",
"4 102001 0 United States country NaN SFR \n",
"... ... ... ... ... ... ... \n",
"693656 845172 769 Winfield, KS msa KS all homes \n",
"693657 845172 769 Winfield, KS msa KS all homes \n",
"693658 845172 769 Winfield, KS msa KS all homes \n",
"693659 845172 769 Winfield, KS msa KS all homes \n",
"693660 845172 769 Winfield, KS msa KS all homes \n",
"\n",
" Date New Pending (Smoothed) Median Listing Price \\\n",
"0 2018-01-13 NaN 259000.0 \n",
"1 2018-01-20 NaN 259900.0 \n",
"2 2018-01-27 NaN 259900.0 \n",
"3 2018-01-31 NaN 254900.0 \n",
"4 2018-02-03 NaN 260000.0 \n",
"... ... ... ... \n",
"693656 2023-12-16 NaN 133938.0 \n",
"693657 2023-12-23 NaN 126463.0 \n",
"693658 2023-12-30 NaN 123225.0 \n",
"693659 2023-12-31 24.0 136233.0 \n",
"693660 2024-01-06 NaN 121488.0 \n",
"\n",
" Median Listing Price (Smoothed) New Pending New Listings \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 259700.0 NaN NaN \n",
"... ... ... ... \n",
"693656 133938.0 NaN NaN \n",
"693657 126463.0 NaN NaN \n",
"693658 123225.0 NaN NaN \n",
"693659 136233.0 24.0 28.0 \n",
"693660 121488.0 NaN NaN \n",
"\n",
" New Listings (Smoothed) \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"693656 NaN \n",
"693657 NaN \n",
"693658 NaN \n",
"693659 28.0 \n",
"693660 NaN \n",
"\n",
"[693661 rows x 13 columns]"
]
},
"execution_count": 6,
"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": 7,
"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
}
|