fix: widen df a little

processed/new_constructions/final.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c8ad5debe54338029e9969fc0a604ef71af2452582832a74715352da749b5d16
-size 16977897
+oid sha256:4cbd1713a383959a9c43afb152cf8dd169b584b809b1c940c9b48ae8b8d8a8e6
+size 9865188

processors/process_new_constructions.ipynb

@@ -2,7 +2,7 @@
  "cells": [
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -12,7 +12,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -25,7 +25,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 8,
+ "execution_count": 47,
  "metadata": {},
  "outputs": [
  {
@@ -69,10 +69,11 @@
  " <th>RegionName</th>\n",
  " <th>RegionType</th>\n",
  " <th>StateName</th>\n",
- " <th>Value Type</th>\n",
  " <th>Home Type</th>\n",
  " <th>Date</th>\n",
- " <th>Value</th>\n",
+ " <th>Sale Price</th>\n",
+ " <th>Sale Price per Sqft</th>\n",
+ " <th>Count</th>\n",
  " </tr>\n",
  " </thead>\n",
  " <tbody>\n",
@@ -83,58 +84,63 @@
  " <td>United States</td>\n",
  " <td>country</td>\n",
  " <td>NaN</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
+ " <td>SFR</td>\n",
  " <td>2018-01-31</td>\n",
- " <td>3195.000000</td>\n",
+ " <td>309000.0</td>\n",
+ " <td>137.412316</td>\n",
+ " <td>33940.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>1</th>\n",
- " <td>394913</td>\n",
- " <td>1</td>\n",
- " <td>New York, NY</td>\n",
- " <td>msa</td>\n",
- " <td>NY</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\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>137.000000</td>\n",
+ " <td>314596.0</td>\n",
+ " <td>140.504620</td>\n",
+ " <td>37135.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>2</th>\n",
- " <td>753899</td>\n",
- " <td>2</td>\n",
- " <td>Los Angeles, CA</td>\n",
- " <td>msa</td>\n",
- " <td>CA</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\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>148.000000</td>\n",
+ " <td>388250.0</td>\n",
+ " <td>238.300000</td>\n",
+ " <td>3195.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>3</th>\n",
- " <td>395209</td>\n",
- " <td>6</td>\n",
- " <td>Washington, DC</td>\n",
- " <td>msa</td>\n",
- " <td>VA</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
- " <td>2018-01-31</td>\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>309072.5</td>\n",
+ " <td>137.199170</td>\n",
+ " <td>33304.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>4</th>\n",
- " <td>394856</td>\n",
- " <td>8</td>\n",
- " <td>Miami, FL</td>\n",
- " <td>msa</td>\n",
- " <td>FL</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
- " <td>2018-01-31</td>\n",
- " <td>195.000000</td>\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>314608.0</td>\n",
+ " <td>140.304966</td>\n",
+ " <td>36493.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>...</th>\n",
@@ -147,103 +153,109 @@
  " <td>...</td>\n",
  " <td>...</td>\n",
  " <td>...</td>\n",
+ " <td>...</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <th>11071</th>\n",
- " <td>753912</td>\n",
- " <td>398</td>\n",
- " <td>Pinehurst, NC</td>\n",
+ " <th>49482</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>NC</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>179.300292</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>11072</th>\n",
- " <td>395086</td>\n",
- " <td>402</td>\n",
- " <td>Sevierville, TN</td>\n",
+ " <th>49483</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>TN</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>389.559797</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>11073</th>\n",
- " <td>394682</td>\n",
- " <td>449</td>\n",
- " <td>Hinesville, GA</td>\n",
+ " <th>49484</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>GA</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>143.916102</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",
  " </tr>\n",
  " <tr>\n",
- " <th>11074</th>\n",
- " <td>394674</td>\n",
- " <td>467</td>\n",
- " <td>Heber, UT</td>\n",
+ " <th>49485</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>UT</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
+ " <td>TX</td>\n",
+ " <td>SFR</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>567.030369</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.0</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <th>11075</th>\n",
- " <td>753893</td>\n",
- " <td>477</td>\n",
- " <td>Jefferson, GA</td>\n",
+ " <th>49486</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>GA</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
+ " <td>TX</td>\n",
+ " <td>all homes</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>193.031691</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.0</td>\n",
  " </tr>\n",
  " </tbody>\n",
  "</table>\n",
- "<p>95850 rows × 9 columns</p>\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 394913 1 New York, NY msa NY \n",
- "2 753899 2 Los Angeles, CA msa CA \n",
- "3 395209 6 Washington, DC msa VA \n",
- "4 394856 8 Miami, FL msa FL \n",
- "... ... ... ... ... ... \n",
- "11071 753912 398 Pinehurst, NC msa NC \n",
- "11072 395086 402 Sevierville, TN msa TN \n",
- "11073 394682 449 Hinesville, GA msa GA \n",
- "11074 394674 467 Heber, UT msa UT \n",
- "11075 753893 477 Jefferson, GA msa GA \n",
+ " 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",
- " Value Type Home Type Date Value \n",
- "0 Count Condo 2018-01-31 3195.000000 \n",
- "1 Count Condo 2018-01-31 137.000000 \n",
- "2 Count Condo 2018-01-31 148.000000 \n",
- "3 Count Condo 2018-01-31 NaN \n",
- "4 Count Condo 2018-01-31 195.000000 \n",
- "... ... ... ... ... \n",
- "11071 Sale Price Per Sqft SFR/Condo 2023-11-30 179.300292 \n",
- "11072 Sale Price Per Sqft SFR/Condo 2023-11-30 389.559797 \n",
- "11073 Sale Price Per Sqft SFR/Condo 2023-11-30 143.916102 \n",
- "11074 Sale Price Per Sqft SFR/Condo 2023-11-30 567.030369 \n",
- "11075 Sale Price Per Sqft SFR/Condo 2023-11-30 193.031691 \n",
+ " Home Type Date Sale Price Sale Price per Sqft Count \n",
+ "0 SFR 2018-01-31 309000.0 137.412316 33940.0 \n",
+ "1 all homes 2018-01-31 314596.0 140.504620 37135.0 \n",
+ "2 condo/co-op only 2018-01-31 388250.0 238.300000 3195.0 \n",
+ "3 SFR 2018-02-28 309072.5 137.199170 33304.0 \n",
+ "4 all homes 2018-02-28 314608.0 140.304966 36493.0 \n",
+ "... ... ... ... ... ... \n",
+ "49482 all homes 2023-09-30 NaN NaN 26.0 \n",
+ "49483 SFR 2023-10-31 NaN NaN 24.0 \n",
+ "49484 all homes 2023-10-31 NaN NaN 24.0 \n",
+ "49485 SFR 2023-11-30 NaN NaN 16.0 \n",
+ "49486 all homes 2023-11-30 NaN NaN 16.0 \n",
  "\n",
- "[95850 rows x 9 columns]"
+ "[49487 rows x 10 columns]"
  ]
  },
- "execution_count": 8,
+ "execution_count": 47,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -259,49 +271,98 @@
  " \"RegionName\",\n",
  " \"RegionType\",\n",
  " \"StateName\",\n",
- " \"Value Type\",\n",
+ " # \"Value Type\",\n",
  " \"Home Type\",\n",
  "]\n",
  "\n",
+ "price_data_frames = []\n",
+ "price_per_sqft_data_frames = []\n",
+ "count_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",
- " if \"sale_price_per_sqft\" in filename:\n",
- " cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
- " elif \"sale_price_uc\" in filename:\n",
- " cur_df[\"Value Type\"] = \"Sale Price\"\n",
- " elif \"count\" in filename:\n",
- " cur_df[\"Value Type\"] = \"Count\"\n",
  "\n",
  " if \"sfrcondo\" in filename:\n",
- " cur_df[\"Home Type\"] = \"SFR/Condo\"\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\"\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",
- " # Perform pivot\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=\"Value\",\n",
- " )\n",
+ " if \"sale_price_per_sqft\" in filename:\n",
+ " # cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
+ " # Perform pivot\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=\"Sale Price per Sqft\",\n",
+ " )\n",
+ " price_per_sqft_data_frames.append(cur_df)\n",
+ "\n",
+ " elif \"sale_price_uc\" in filename:\n",
+ " # cur_df[\"Value Type\"] = \"Sale Price\"\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=\"Sale Price\",\n",
+ " )\n",
+ " price_data_frames.append(cur_df)\n",
+ "\n",
+ " elif \"count\" in filename:\n",
+ " # cur_df[\"Value Type\"] = \"Count\"\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=\"Count\",\n",
+ " )\n",
+ " count_data_frames.append(cur_df)\n",
+ "\n",
+ "\n",
+ "combined_price = pd.concat(price_data_frames)\n",
+ "combined_price_per = pd.concat(price_per_sqft_data_frames)\n",
+ "combined_count = pd.concat(count_data_frames)\n",
+ "\n",
+ "matching_cols = [\n",
+ " \"RegionID\",\n",
+ " \"Date\",\n",
+ " \"SizeRank\",\n",
+ " \"RegionName\",\n",
+ " \"RegionType\",\n",
+ " \"StateName\",\n",
+ " # \"Value Type\",\n",
+ " \"Home Type\",\n",
+ "]\n",
  "\n",
- " data_frames.append(cur_df)\n",
+ "combined_df = pd.merge(\n",
+ " combined_price,\n",
+ " combined_price_per,\n",
+ " on=matching_cols,\n",
+ " how=\"outer\",\n",
+ ")\n",
+ "combined_df = pd.merge(\n",
+ " combined_df,\n",
+ " combined_count,\n",
+ " on=matching_cols,\n",
+ " how=\"outer\",\n",
+ ")\n",
  "\n",
- "combined_df = pd.concat(data_frames)\n",
  "combined_df"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 9,
+ "execution_count": 48,
  "metadata": {},
  "outputs": [
  {
@@ -330,10 +391,11 @@
  " <th>Region</th>\n",
  " <th>Region Type</th>\n",
  " <th>State</th>\n",
- " <th>Value Type</th>\n",
  " <th>Home Type</th>\n",
  " <th>Date</th>\n",
- " <th>Value</th>\n",
+ " <th>Sale Price</th>\n",
+ " <th>Sale Price per Sqft</th>\n",
+ " <th>Count</th>\n",
  " </tr>\n",
  " </thead>\n",
  " <tbody>\n",
@@ -344,58 +406,63 @@
  " <td>United States</td>\n",
  " <td>country</td>\n",
  " <td>NaN</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
+ " <td>SFR</td>\n",
  " <td>2018-01-31</td>\n",
- " <td>3195.000000</td>\n",
+ " <td>309000.0</td>\n",
+ " <td>137.412316</td>\n",
+ " <td>33940.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>1</th>\n",
- " <td>394913</td>\n",
- " <td>1</td>\n",
- " <td>New York, NY</td>\n",
- " <td>msa</td>\n",
- " <td>NY</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\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>137.000000</td>\n",
+ " <td>314596.0</td>\n",
+ " <td>140.504620</td>\n",
+ " <td>37135.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>2</th>\n",
- " <td>753899</td>\n",
- " <td>2</td>\n",
- " <td>Los Angeles, CA</td>\n",
- " <td>msa</td>\n",
- " <td>CA</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\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>148.000000</td>\n",
+ " <td>388250.0</td>\n",
+ " <td>238.300000</td>\n",
+ " <td>3195.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>3</th>\n",
- " <td>395209</td>\n",
- " <td>6</td>\n",
- " <td>Washington, DC</td>\n",
- " <td>msa</td>\n",
- " <td>VA</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
- " <td>2018-01-31</td>\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>309072.5</td>\n",
+ " <td>137.199170</td>\n",
+ " <td>33304.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>4</th>\n",
- " <td>394856</td>\n",
- " <td>8</td>\n",
- " <td>Miami, FL</td>\n",
- " <td>msa</td>\n",
- " <td>FL</td>\n",
- " <td>Count</td>\n",
- " <td>Condo</td>\n",
- " <td>2018-01-31</td>\n",
- " <td>195.000000</td>\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>314608.0</td>\n",
+ " <td>140.304966</td>\n",
+ " <td>36493.0</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>...</th>\n",
@@ -408,103 +475,109 @@
  " <td>...</td>\n",
  " <td>...</td>\n",
  " <td>...</td>\n",
+ " <td>...</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <th>11071</th>\n",
- " <td>753912</td>\n",
- " <td>398</td>\n",
- " <td>Pinehurst, NC</td>\n",
+ " <th>49482</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>NC</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>179.300292</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>11072</th>\n",
- " <td>395086</td>\n",
- " <td>402</td>\n",
- " <td>Sevierville, TN</td>\n",
+ " <th>49483</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>TN</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>389.559797</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>11073</th>\n",
- " <td>394682</td>\n",
- " <td>449</td>\n",
- " <td>Hinesville, GA</td>\n",
+ " <th>49484</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>GA</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
- " <td>2023-11-30</td>\n",
- " <td>143.916102</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",
  " </tr>\n",
  " <tr>\n",
- " <th>11074</th>\n",
- " <td>394674</td>\n",
- " <td>467</td>\n",
- " <td>Heber, UT</td>\n",
+ " <th>49485</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>UT</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
+ " <td>TX</td>\n",
+ " <td>SFR</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>567.030369</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.0</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <th>11075</th>\n",
- " <td>753893</td>\n",
- " <td>477</td>\n",
- " <td>Jefferson, GA</td>\n",
+ " <th>49486</th>\n",
+ " <td>845162</td>\n",
+ " <td>535</td>\n",
+ " <td>Granbury, TX</td>\n",
  " <td>msa</td>\n",
- " <td>GA</td>\n",
- " <td>Sale Price Per Sqft</td>\n",
- " <td>SFR/Condo</td>\n",
+ " <td>TX</td>\n",
+ " <td>all homes</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>193.031691</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.0</td>\n",
  " </tr>\n",
  " </tbody>\n",
  "</table>\n",
- "<p>95850 rows × 9 columns</p>\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 394913 1 New York, NY msa NY \n",
- "2 753899 2 Los Angeles, CA msa CA \n",
- "3 395209 6 Washington, DC msa VA \n",
- "4 394856 8 Miami, FL msa FL \n",
- "... ... ... ... ... ... \n",
- "11071 753912 398 Pinehurst, NC msa NC \n",
- "11072 395086 402 Sevierville, TN msa TN \n",
- "11073 394682 449 Hinesville, GA msa GA \n",
- "11074 394674 467 Heber, UT msa UT \n",
- "11075 753893 477 Jefferson, GA msa GA \n",
+ " 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",
- " Value Type Home Type Date Value \n",
- "0 Count Condo 2018-01-31 3195.000000 \n",
- "1 Count Condo 2018-01-31 137.000000 \n",
- "2 Count Condo 2018-01-31 148.000000 \n",
- "3 Count Condo 2018-01-31 NaN \n",
- "4 Count Condo 2018-01-31 195.000000 \n",
- "... ... ... ... ... \n",
- "11071 Sale Price Per Sqft SFR/Condo 2023-11-30 179.300292 \n",
- "11072 Sale Price Per Sqft SFR/Condo 2023-11-30 389.559797 \n",
- "11073 Sale Price Per Sqft SFR/Condo 2023-11-30 143.916102 \n",
- "11074 Sale Price Per Sqft SFR/Condo 2023-11-30 567.030369 \n",
- "11075 Sale Price Per Sqft SFR/Condo 2023-11-30 193.031691 \n",
+ " Home Type Date Sale Price Sale Price per Sqft Count \n",
+ "0 SFR 2018-01-31 309000.0 137.412316 33940.0 \n",
+ "1 all homes 2018-01-31 314596.0 140.504620 37135.0 \n",
+ "2 condo/co-op only 2018-01-31 388250.0 238.300000 3195.0 \n",
+ "3 SFR 2018-02-28 309072.5 137.199170 33304.0 \n",
+ "4 all homes 2018-02-28 314608.0 140.304966 36493.0 \n",
+ "... ... ... ... ... ... \n",
+ "49482 all homes 2023-09-30 NaN NaN 26.0 \n",
+ "49483 SFR 2023-10-31 NaN NaN 24.0 \n",
+ "49484 all homes 2023-10-31 NaN NaN 24.0 \n",
+ "49485 SFR 2023-11-30 NaN NaN 16.0 \n",
+ "49486 all homes 2023-11-30 NaN NaN 16.0 \n",
  "\n",
- "[95850 rows x 9 columns]"
+ "[49487 rows x 10 columns]"
  ]
  },
- "execution_count": 9,
+ "execution_count": 48,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -526,7 +599,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 10,
+ "execution_count": 49,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -535,6 +608,13 @@
  "\n",
  "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
  ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
  }
  ],
  "metadata": {

zillow.py

@@ -133,7 +133,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
  "Value Type": datasets.Value(dtype="string", id="Value Type"),
  "Home Type": datasets.Value(dtype="string", id="Home Type"),
  "Date": datasets.Value(dtype="string", id="Date"),
- "Value": datasets.Value(dtype="float32", id="Value"),
+ "Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
+ "Sale Price per Sqft": datasets.Value(
+ dtype="float32", id="Sale Price per Sqft"
+ ),
+ "Count": datasets.Value(dtype="int32", id="Count"),
  # These are the features of your dataset like images, labels ...
  }
  )
@@ -253,7 +257,9 @@ class NewDataset(datasets.GeneratorBasedBuilder):
  "Value Type": data["Value Type"],
  "Home Type": data["Home Type"],
  "Date": data["Date"],
- "Value": data["Value"],
+ "Sale Price": data["Sale Price"],
+ "Sale Price per Sqft": data["Sale Price per Sqft"],
+ "Count": data["Count"],
  # "answer": "" if split == "test" else data["answer"],
  }
  # else: