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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 = \"new_constructions/\"\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": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processing Metro_new_con_sales_count_raw_uc_condo_month.csv\n",
"processing Metro_new_con_median_sale_price_per_sqft_uc_sfr_month.csv\n",
"processing Metro_new_con_sales_count_raw_uc_sfr_month.csv\n",
"processing Metro_new_con_median_sale_price_uc_sfrcondo_month.csv\n",
"processing Metro_new_con_median_sale_price_per_sqft_uc_condo_month.csv\n",
"processing Metro_new_con_sales_count_raw_uc_sfrcondo_month.csv\n",
"processing Metro_new_con_median_sale_price_uc_condo_month.csv\n",
"processing Metro_new_con_median_sale_price_uc_sfr_month.csv\n",
"processing Metro_new_con_median_sale_price_per_sqft_uc_sfrcondo_month.csv\n"
]
},
{
"data": {
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" vertical-align: top;\n",
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"</style>\n",
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" <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>Median Sale Price per Sqft</th>\n",
" <th>Median Sale Price</th>\n",
" <th>Sales Count</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>2018-01-31</td>\n",
" <td>137.412316</td>\n",
" <td>309000.0</td>\n",
" <td>33940.0</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>all homes</td>\n",
" <td>2018-01-31</td>\n",
" <td>140.504620</td>\n",
" <td>314596.0</td>\n",
" <td>37135.0</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>condo/co-op only</td>\n",
" <td>2018-01-31</td>\n",
" <td>238.300000</td>\n",
" <td>388250.0</td>\n",
" <td>3195.0</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>2018-02-28</td>\n",
" <td>137.199170</td>\n",
" <td>309072.5</td>\n",
" <td>33304.0</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>all homes</td>\n",
" <td>2018-02-28</td>\n",
" <td>140.304966</td>\n",
" <td>314608.0</td>\n",
" <td>36493.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>TX</td>\n",
" <td>all homes</td>\n",
" <td>2023-09-30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49483</th>\n",
" <td>845162</td>\n",
" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>SFR</td>\n",
" <td>2023-10-31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49484</th>\n",
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" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>all homes</td>\n",
" <td>2023-10-31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
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" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>SFR</td>\n",
" <td>2023-11-30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>49486</th>\n",
" <td>845162</td>\n",
" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>all homes</td>\n",
" <td>2023-11-30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49487 rows × 10 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",
"49482 845162 535 Granbury, TX msa TX \n",
"49483 845162 535 Granbury, TX msa TX \n",
"49484 845162 535 Granbury, TX msa TX \n",
"49485 845162 535 Granbury, TX msa TX \n",
"49486 845162 535 Granbury, TX msa TX \n",
"\n",
" Home Type Date Median Sale Price per Sqft \\\n",
"0 SFR 2018-01-31 137.412316 \n",
"1 all homes 2018-01-31 140.504620 \n",
"2 condo/co-op only 2018-01-31 238.300000 \n",
"3 SFR 2018-02-28 137.199170 \n",
"4 all homes 2018-02-28 140.304966 \n",
"... ... ... ... \n",
"49482 all homes 2023-09-30 NaN \n",
"49483 SFR 2023-10-31 NaN \n",
"49484 all homes 2023-10-31 NaN \n",
"49485 SFR 2023-11-30 NaN \n",
"49486 all homes 2023-11-30 NaN \n",
"\n",
" Median Sale Price Sales Count \n",
"0 309000.0 33940.0 \n",
"1 314596.0 37135.0 \n",
"2 388250.0 3195.0 \n",
"3 309072.5 33304.0 \n",
"4 314608.0 36493.0 \n",
"... ... ... \n",
"49482 NaN 26.0 \n",
"49483 NaN 24.0 \n",
"49484 NaN 24.0 \n",
"49485 NaN 16.0 \n",
"49486 NaN 16.0 \n",
"\n",
"[49487 rows x 10 columns]"
]
},
"execution_count": 56,
"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",
"batches = {\"median_sale_price_per_sqft\": [], \"median_sale_price\": [], \"sales_count\": []}\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",
" 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",
" if \"median_sale_price_per_sqft\" 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=\"Median Sale Price per Sqft\",\n",
" )\n",
" batches[\"median_sale_price_per_sqft\"].append(cur_df)\n",
"\n",
" elif \"median_sale_price\" 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=\"Median Sale Price\",\n",
" )\n",
" batches[\"median_sale_price\"].append(cur_df)\n",
"\n",
" elif \"sales_count\" 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=\"Sales Count\",\n",
" )\n",
" batches[\"sales_count\"].append(cur_df)\n",
"\n",
"\n",
"matching_cols = [\n",
" \"RegionID\",\n",
" \"Date\",\n",
" \"SizeRank\",\n",
" \"RegionName\",\n",
" \"RegionType\",\n",
" \"StateName\",\n",
" \"Home Type\",\n",
"]\n",
"\n",
"combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
"\n",
"if len(combined_batches) > 0:\n",
" combined_df = combined_batches[0]\n",
" for batch in combined_batches[1:]:\n",
" combined_df = pd.merge(\n",
" combined_df,\n",
" batch,\n",
" on=matching_cols,\n",
" how=\"outer\",\n",
" )\n",
"\n",
"combined_df"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Region ID</th>\n",
" <th>Size Rank</th>\n",
" <th>Region</th>\n",
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" <th>State</th>\n",
" <th>Home Type</th>\n",
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" <th>Median Sale Price per Sqft</th>\n",
" <th>Median Sale Price</th>\n",
" <th>Sales Count</th>\n",
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" <td>137.412316</td>\n",
" <td>309000.0</td>\n",
" <td>33940.0</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>all homes</td>\n",
" <td>2018-01-31</td>\n",
" <td>140.504620</td>\n",
" <td>314596.0</td>\n",
" <td>37135.0</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",
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" <td>2018-01-31</td>\n",
" <td>238.300000</td>\n",
" <td>388250.0</td>\n",
" <td>3195.0</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>2018-02-28</td>\n",
" <td>137.199170</td>\n",
" <td>309072.5</td>\n",
" <td>33304.0</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>all homes</td>\n",
" <td>2018-02-28</td>\n",
" <td>140.304966</td>\n",
" <td>314608.0</td>\n",
" <td>36493.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>all homes</td>\n",
" <td>2023-09-30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49483</th>\n",
" <td>845162</td>\n",
" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>SFR</td>\n",
" <td>2023-10-31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49484</th>\n",
" <td>845162</td>\n",
" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>all homes</td>\n",
" <td>2023-10-31</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.0</td>\n",
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" <th>49485</th>\n",
" <td>845162</td>\n",
" <td>535</td>\n",
" <td>Granbury, TX</td>\n",
" <td>msa</td>\n",
" <td>TX</td>\n",
" <td>SFR</td>\n",
" <td>2023-11-30</td>\n",
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"</table>\n",
"<p>49487 rows × 10 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",
"49482 845162 535 Granbury, TX msa TX \n",
"49483 845162 535 Granbury, TX msa TX \n",
"49484 845162 535 Granbury, TX msa TX \n",
"49485 845162 535 Granbury, TX msa TX \n",
"49486 845162 535 Granbury, TX msa TX \n",
"\n",
" Home Type Date Median Sale Price per Sqft \\\n",
"0 SFR 2018-01-31 137.412316 \n",
"1 all homes 2018-01-31 140.504620 \n",
"2 condo/co-op only 2018-01-31 238.300000 \n",
"3 SFR 2018-02-28 137.199170 \n",
"4 all homes 2018-02-28 140.304966 \n",
"... ... ... ... \n",
"49482 all homes 2023-09-30 NaN \n",
"49483 SFR 2023-10-31 NaN \n",
"49484 all homes 2023-10-31 NaN \n",
"49485 SFR 2023-11-30 NaN \n",
"49486 all homes 2023-11-30 NaN \n",
"\n",
" Median Sale Price Sales Count \n",
"0 309000.0 33940.0 \n",
"1 314596.0 37135.0 \n",
"2 388250.0 3195.0 \n",
"3 309072.5 33304.0 \n",
"4 314608.0 36493.0 \n",
"... ... ... \n",
"49482 NaN 26.0 \n",
"49483 NaN 24.0 \n",
"49484 NaN 24.0 \n",
"49485 NaN 16.0 \n",
"49486 NaN 16.0 \n",
"\n",
"[49487 rows x 10 columns]"
]
},
"execution_count": 57,
"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": 58,
"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
}
|