fix: update region types, dates, and home types to match each other

README.md

@@ -39,7 +39,7 @@ dataset_info:
  class_label:
  names:
  '0': SFR
- '1': all homes (SFR + Condo)
+ '1': all homes
  - name: Date
  dtype: string
  id: Date
@@ -257,7 +257,7 @@ dataset_info:
  names:
  '0': SFR
  '1': all homes
- '2': condo/co-op only
+ '2': condo/co-op
  - name: Date
  dtype: string
  id: Date

checker.ipynb

@@ -12,7 +12,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 30,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [
  {
@@ -388,7 +388,7 @@
  "[255024 rows x 18 columns]"
  ]
  },
- "execution_count": 30,
+ "execution_count": 2,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -463,95 +463,18 @@
  },
  {
  "cell_type": "code",
- "execution_count": 34,
+ "execution_count": 8,
  "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/misikoff/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
  "from datasets import load_dataset"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 35,
+ "execution_count": 19,
  "metadata": {},
  "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "home_values_forecasts\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 15.9MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 19.4MB/s]\n",
- "Downloading data: 100%|██████████| 14.1M/14.1M [00:01<00:00, 11.0MB/s]\n",
- "Generating train split: 31854 examples [00:01, 27592.53 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "new_construction\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 7.13MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 10.4MB/s]\n",
- "Downloading data: 100%|██████████| 10.9M/10.9M [00:01<00:00, 5.63MB/s]\n",
- "Generating train split: 49487 examples [00:01, 42744.19 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "for_sale_listings\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 5.22MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 4.72MB/s]\n",
- "Downloading data: 100%|██████████| 180M/180M [00:06<00:00, 28.9MB/s] \n",
- "Generating train split: 578653 examples [00:16, 34651.20 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "rentals\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 6.18MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.3MB/s]\n",
- "Downloading data: 100%|██████████| 446M/446M [00:11<00:00, 38.2MB/s] \n",
- "Generating train split: 1258740 examples [00:28, 44189.96 examples/s]\n"
- ]
- },
  {
  "name": "stdout",
  "output_type": "stream",
@@ -563,44 +486,10 @@
  "name": "stderr",
  "output_type": "stream",
  "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.5MB/s]\n",
- "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 34.2MB/s] \n",
- "Generating train split: 255024 examples [00:09, 26686.54 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "home_values\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 2.18MB/s]\n",
- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
- "Downloading data: 100%|██████████| 42.1M/42.1M [00:01<00:00, 32.4MB/s]\n",
- "Generating train split: 117912 examples [00:03, 37382.19 examples/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "days_on_market\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
- "Downloading readme: 100%|████████���█| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
- "Downloading data: 100%|██████████| 233M/233M [00:06<00:00, 34.9MB/s] \n",
- "Generating train split: 586714 examples [00:17, 34104.98 examples/s]\n"
+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 14.2MB/s]\n",
+ "Downloading readme: 100%|██████████| 21.7k/21.7k [00:00<00:00, 3.80MB/s]\n",
+ "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 32.2MB/s] \n",
+ "Generating train split: 100%|██████████| 255024/255024 [00:10<00:00, 24068.33 examples/s]\n"
  ]
  }
  ],
@@ -608,13 +497,13 @@
  "dataset_dict = {}\n",
  "\n",
  "configs = [\n",
- " \"home_values_forecasts\",\n",
- " \"new_construction\",\n",
- " \"for_sale_listings\",\n",
- " \"rentals\",\n",
+ " # \"home_values_forecasts\",\n",
+ " # \"new_construction\",\n",
+ " # \"for_sale_listings\",\n",
+ " # \"rentals\",\n",
  " \"sales\",\n",
- " \"home_values\",\n",
- " \"days_on_market\",\n",
+ " # \"home_values\",\n",
+ " # \"days_on_market\",\n",
  "]\n",
  "for config in configs:\n",
  " print(config)\n",
@@ -623,19 +512,106 @@
  " config,\n",
  " trust_remote_code=True,\n",
  " download_mode=\"force_redownload\",\n",
- " cache_dir=\"~/desktop/cache\",\n",
+ " cache_dir=\"./cache\",\n",
  " )"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": null,
+ "execution_count": 40,
  "metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "ArrowInvalid",
+ "evalue": "Not a Feather V1 or Arrow IPC file",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mArrowInvalid\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[40], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyarrow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpa\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_feather\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 6\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m df\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pandas/io/feather_format.py:124\u001b[0m, in \u001b[0;36mread_feather\u001b[0;34m(path, columns, use_threads, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_handle(\n\u001b[1;32m 121\u001b[0m path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m, storage_options\u001b[38;5;241m=\u001b[39mstorage_options, is_text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 122\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_backend \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m using_pyarrow_string_dtype():\n\u001b[0;32m--> 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfeather\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_feather\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 125\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandles\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m feather\u001b[38;5;241m.\u001b[39mread_table(\n\u001b[1;32m 129\u001b[0m handles\u001b[38;5;241m.\u001b[39mhandle, columns\u001b[38;5;241m=\u001b[39mcolumns, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m(use_threads)\n\u001b[1;32m 130\u001b[0m )\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_backend \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy_nullable\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/feather.py:226\u001b[0m, in \u001b[0;36mread_feather\u001b[0;34m(source, columns, use_threads, memory_map, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_feather\u001b[39m(source, columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 200\u001b[0m memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 201\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;124;03m Read a pandas.DataFrame from Feather format. To read as pyarrow.Table use\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;124;03m feather.read_table.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;124;03m The contents of the Feather file as a pandas.DataFrame\u001b[39;00m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[43mread_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 227\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemory_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto_pandas(use_threads\u001b[38;5;241m=\u001b[39muse_threads, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/feather.py:252\u001b[0m, in \u001b[0;36mread_table\u001b[0;34m(source, columns, memory_map, use_threads)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_table\u001b[39m(source, columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 232\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;124;03m Read a pyarrow.Table from Feather format\u001b[39;00m\n\u001b[1;32m 234\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;124;03m The contents of the Feather file as a pyarrow.Table\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 252\u001b[0m reader \u001b[38;5;241m=\u001b[39m \u001b[43m_feather\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFeatherReader\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_memory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemory_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reader\u001b[38;5;241m.\u001b[39mread()\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/_feather.pyx:79\u001b[0m, in \u001b[0;36mpyarrow._feather.FeatherReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/error.pxi:154\u001b[0m, in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/error.pxi:91\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mArrowInvalid\u001b[0m: Not a Feather V1 or Arrow IPC file"
+ ]
+ }
+ ],
  "source": [
+ "import pyarrow as pa\n",
+ "\n",
+ "\n",
  "df = pd.read_feather(\n",
  " \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
- ")"
+ ")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Creating parquet from Arrow format: 100%|██████████| 256/256 [00:00<00:00, 738.39ba/s]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "27088039"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset_dict[config][\"train\"].to_parquet(\"test-sales.parquet\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Region ID': '102001',\n",
+ " 'Size Rank': 0,\n",
+ " 'Region': 'United States',\n",
+ " 'Region Type': 0,\n",
+ " 'State': None,\n",
+ " 'Home Type': 0,\n",
+ " 'Date': datetime.datetime(2008, 2, 2, 0, 0),\n",
+ " 'Mean Sale to List Ratio (Smoothed)': None,\n",
+ " 'Median Sale to List Ratio': None,\n",
+ " 'Median Sale Price': 172000.0,\n",
+ " 'Median Sale Price (Smoothed) (Seasonally Adjusted)': None,\n",
+ " 'Median Sale Price (Smoothed)': None,\n",
+ " 'Median Sale to List Ratio (Smoothed)': None,\n",
+ " '% Sold Below List': None,\n",
+ " '% Sold Below List (Smoothed)': None,\n",
+ " '% Sold Above List': None,\n",
+ " '% Sold Above List (Smoothed)': None,\n",
+ " 'Mean Sale to List Ratio': None}"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gen = iter(dataset_dict[config][\"train\"])\n",
+ "next(gen)"
  ]
  }
  ],

processors/days_on_market.ipynb

@@ -91,6 +91,297 @@
  "processing Metro_mean_listings_price_cut_amt_uc_sfr_month.csv\n",
  "processing Metro_mean_doz_pending_uc_sfrcondo_month.csv\n"
  ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " vertical-align: middle;\n",
+ " }\n",
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+ " .dataframe tbody tr th {\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>Percent Listings Price Cut</th>\n",
+ " <th>Mean Listings Price Cut Amount</th>\n",
+ " <th>Percent Listings Price Cut (Smoothed)</th>\n",
+ " <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
+ " <th>Median Days on Pending (Smoothed)</th>\n",
+ " <th>Median Days on Pending</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>2018-01-06</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13508.368375</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <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>2018-01-13</td>\n",
+ " <td>0.049042</td>\n",
+ " <td>14114.788383</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>2018-01-20</td>\n",
+ " <td>0.044740</td>\n",
+ " <td>14326.128956</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>2018-01-27</td>\n",
+ " <td>0.047930</td>\n",
+ " <td>13998.585612</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13998.585612</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>0.047622</td>\n",
+ " <td>14120.035549</td>\n",
+ " <td>0.047622</td>\n",
+ " <td>14120.035549</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>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</td>\n",
+ " <td>2024-01-06</td>\n",
+ " <td>0.094017</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.037378</td>\n",
+ " <td>NaN</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</td>\n",
+ " <td>2024-01-13</td>\n",
+ " <td>0.070175</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.043203</td>\n",
+ " <td>NaN</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</td>\n",
+ " <td>2024-01-20</td>\n",
+ " <td>0.043478</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.054073</td>\n",
+ " <td>NaN</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</td>\n",
+ " <td>2024-01-27</td>\n",
+ " <td>0.036697</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.061092</td>\n",
+ " <td>NaN</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</td>\n",
+ " <td>2024-02-03</td>\n",
+ " <td>0.077670</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.057005</td>\n",
+ " <td>NaN</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 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",
+ "586709 845172 769 Winfield, KS msa KS all homes \n",
+ "586710 845172 769 Winfield, KS msa KS all homes \n",
+ "586711 845172 769 Winfield, KS msa KS all homes \n",
+ "586712 845172 769 Winfield, KS msa KS all homes \n",
+ "586713 845172 769 Winfield, KS msa KS all homes \n",
+ "\n",
+ " Date Percent Listings Price Cut \\\n",
+ "0 2018-01-06 NaN \n",
+ "1 2018-01-13 0.049042 \n",
+ "2 2018-01-20 0.044740 \n",
+ "3 2018-01-27 0.047930 \n",
+ "4 2018-02-03 0.047622 \n",
+ "... ... ... \n",
+ "586709 2024-01-06 0.094017 \n",
+ "586710 2024-01-13 0.070175 \n",
+ "586711 2024-01-20 0.043478 \n",
+ "586712 2024-01-27 0.036697 \n",
+ "586713 2024-02-03 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",
+ " Mean Listings Price Cut Amount (Smoothed) \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 13998.585612 \n",
+ "4 14120.035549 \n",
+ "... ... \n",
+ "586709 NaN \n",
+ "586710 NaN \n",
+ "586711 NaN \n",
+ "586712 NaN \n",
+ "586713 NaN \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": 8,
+ "metadata": {},
+ "output_type": "execute_result"
  }
  ],
  "source": [
@@ -148,7 +439,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 9,
  "metadata": {},
  "outputs": [
  {
@@ -438,7 +729,7 @@
  "[586714 rows x 13 columns]"
  ]
  },
- "execution_count": 4,
+ "execution_count": 9,
  "metadata": {},
  "output_type": "execute_result"
  }

processors/for_sale_listings.ipynb

@@ -632,18 +632,18 @@
  "578651 845172 769 Winfield, KS msa KS all homes \n",
  "578652 845172 769 Winfield, KS msa KS all homes \n",
  "\n",
- " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
- "0 2018-01-13 259000.0 NaN \n",
- "1 2018-01-20 259900.0 NaN \n",
- "2 2018-01-27 259900.0 NaN \n",
- "3 2018-02-03 260000.0 259700.0 \n",
- "4 2018-02-10 264900.0 261175.0 \n",
- "... ... ... ... \n",
- "578648 2023-12-09 134950.0 138913.0 \n",
- "578649 2023-12-16 120000.0 133938.0 \n",
- "578650 2023-12-23 111000.0 126463.0 \n",
- "578651 2023-12-30 126950.0 123225.0 \n",
- "578652 2024-01-06 128000.0 121488.0 \n",
+ " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
+ "0 2018-01-13 259000.0 NaN \n",
+ "1 2018-01-20 259900.0 NaN \n",
+ "2 2018-01-27 259900.0 NaN \n",
+ "3 2018-02-03 260000.0 259700.0 \n",
+ "4 2018-02-10 264900.0 261175.0 \n",
+ "... ... ... ... \n",
+ "578648 2023-12-09 134950.0 138913.0 \n",
+ "578649 2023-12-16 120000.0 133938.0 \n",
+ "578650 2023-12-23 111000.0 126463.0 \n",
+ "578651 2023-12-30 126950.0 123225.0 \n",
+ "578652 2024-01-06 128000.0 121488.0 \n",
  "\n",
  " New Pending (Smoothed) New Listings New Listings (Smoothed) \\\n",
  "0 NaN NaN NaN \n",

processors/home_values.ipynb

@@ -32,7 +32,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [
  {
@@ -140,7 +140,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>1-Bedroom</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-01-31</td>\n",
  " <td>81310.639504</td>\n",
  " <td>NaN</td>\n",
@@ -154,7 +154,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>1-Bedroom</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-02-29</td>\n",
  " <td>80419.761984</td>\n",
  " <td>NaN</td>\n",
@@ -168,7 +168,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>1-Bedroom</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-03-31</td>\n",
  " <td>80480.449461</td>\n",
  " <td>NaN</td>\n",
@@ -182,7 +182,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>1-Bedroom</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-04-30</td>\n",
  " <td>79799.206525</td>\n",
  " <td>NaN</td>\n",
@@ -196,7 +196,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>1-Bedroom</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-05-31</td>\n",
  " <td>79666.469861</td>\n",
  " <td>NaN</td>\n",
@@ -224,7 +224,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-09-30</td>\n",
  " <td>486974.735908</td>\n",
  " <td>NaN</td>\n",
@@ -238,7 +238,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-10-31</td>\n",
  " <td>485847.539614</td>\n",
  " <td>NaN</td>\n",
@@ -252,7 +252,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-11-30</td>\n",
  " <td>484223.885775</td>\n",
  " <td>NaN</td>\n",
@@ -266,7 +266,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-12-31</td>\n",
  " <td>481522.403338</td>\n",
  " <td>NaN</td>\n",
@@ -280,7 +280,7 @@
  " <td>state</td>\n",
  " <td>nan</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2024-01-31</td>\n",
  " <td>481181.718200</td>\n",
  " <td>NaN</td>\n",
@@ -305,18 +305,18 @@
  "117910 62 51 Wyoming state nan All Bedrooms \n",
  "117911 62 51 Wyoming state nan All Bedrooms \n",
  "\n",
- " Home Type Date \\\n",
- "0 all homes (SFR/condo) 2000-01-31 \n",
- "1 all homes (SFR/condo) 2000-02-29 \n",
- "2 all homes (SFR/condo) 2000-03-31 \n",
- "3 all homes (SFR/condo) 2000-04-30 \n",
- "4 all homes (SFR/condo) 2000-05-31 \n",
- "... ... ... \n",
- "117907 condo 2023-09-30 \n",
- "117908 condo 2023-10-31 \n",
- "117909 condo 2023-11-30 \n",
- "117910 condo 2023-12-31 \n",
- "117911 condo 2024-01-31 \n",
+ " Home Type Date \\\n",
+ "0 all homes 2000-01-31 \n",
+ "1 all homes 2000-02-29 \n",
+ "2 all homes 2000-03-31 \n",
+ "3 all homes 2000-04-30 \n",
+ "4 all homes 2000-05-31 \n",
+ "... ... ... \n",
+ "117907 condo/co-op 2023-09-30 \n",
+ "117908 condo/co-op 2023-10-31 \n",
+ "117909 condo/co-op 2023-11-30 \n",
+ "117910 condo/co-op 2023-12-31 \n",
+ "117911 condo/co-op 2024-01-31 \n",
  "\n",
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
  "0 81310.639504 \n",
@@ -360,7 +360,7 @@
  "[117912 rows x 11 columns]"
  ]
  },
- "execution_count": 5,
+ "execution_count": 3,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -466,7 +466,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 11,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [
  {
@@ -499,15 +499,8 @@
  " <th>Home Type</th>\n",
  " <th>Date</th>\n",
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2</th>\n",
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6</th>\n",
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
  " </tr>\n",
  " </thead>\n",
  " <tbody>\n",
@@ -518,19 +511,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-01-31</td>\n",
+ " <td>81310.639504</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>81310.639504</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>1</th>\n",
@@ -539,19 +525,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-02-29</td>\n",
+ " <td>80419.761984</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>80419.761984</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>2</th>\n",
@@ -560,19 +539,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-03-31</td>\n",
+ " <td>80480.449461</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>80480.449461</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>3</th>\n",
@@ -581,19 +553,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-04-30</td>\n",
+ " <td>79799.206525</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>79799.206525</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>4</th>\n",
@@ -602,19 +567,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-05-31</td>\n",
+ " <td>79666.469861</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>79666.469861</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>...</th>\n",
@@ -629,13 +587,6 @@
  " <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>117907</th>\n",
@@ -645,18 +596,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-09-30</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>486974.735908</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117908</th>\n",
@@ -666,18 +610,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-10-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>485847.539614</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117909</th>\n",
@@ -687,18 +624,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>484223.885775</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117910</th>\n",
@@ -708,18 +638,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-12-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>481522.403338</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117911</th>\n",
@@ -729,31 +652,24 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2024-01-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>481181.718200</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " </tbody>\n",
  "</table>\n",
- "<p>117912 rows × 18 columns</p>\n",
+ "<p>117912 rows × 11 columns</p>\n",
  "</div>"
  ],
  "text/plain": [
  " RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
- "0 3 48 Alaska state Alaska 1-Bedrooms \n",
- "1 3 48 Alaska state Alaska 1-Bedrooms \n",
- "2 3 48 Alaska state Alaska 1-Bedrooms \n",
- "3 3 48 Alaska state Alaska 1-Bedrooms \n",
- "4 3 48 Alaska state Alaska 1-Bedrooms \n",
+ "0 3 48 Alaska state Alaska 1-Bedroom \n",
+ "1 3 48 Alaska state Alaska 1-Bedroom \n",
+ "2 3 48 Alaska state Alaska 1-Bedroom \n",
+ "3 3 48 Alaska state Alaska 1-Bedroom \n",
+ "4 3 48 Alaska state Alaska 1-Bedroom \n",
  "... ... ... ... ... ... ... \n",
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
@@ -761,57 +677,31 @@
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
  "\n",
- " Home Type Date \\\n",
- "0 all homes (SFR/condo) 2000-01-31 \n",
- "1 all homes (SFR/condo) 2000-02-29 \n",
- "2 all homes (SFR/condo) 2000-03-31 \n",
- "3 all homes (SFR/condo) 2000-04-30 \n",
- "4 all homes (SFR/condo) 2000-05-31 \n",
- "... ... ... \n",
- "117907 condo 2023-09-30 \n",
- "117908 condo 2023-10-31 \n",
- "117909 condo 2023-11-30 \n",
- "117910 condo 2023-12-31 \n",
- "117911 condo 2024-01-31 \n",
+ " Home Type Date \\\n",
+ "0 all homes 2000-01-31 \n",
+ "1 all homes 2000-02-29 \n",
+ "2 all homes 2000-03-31 \n",
+ "3 all homes 2000-04-30 \n",
+ "4 all homes 2000-05-31 \n",
+ "... ... ... \n",
+ "117907 condo/co-op 2023-09-30 \n",
+ "117908 condo/co-op 2023-10-31 \n",
+ "117909 condo/co-op 2023-11-30 \n",
+ "117910 condo/co-op 2023-12-31 \n",
+ "117911 condo/co-op 2024-01-31 \n",
  "\n",
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
+ "0 81310.639504 \n",
+ "1 80419.761984 \n",
+ "2 80480.449461 \n",
+ "3 79799.206525 \n",
+ "4 79666.469861 \n",
  "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
+ "117907 486974.735908 \n",
+ "117908 485847.539614 \n",
+ "117909 484223.885775 \n",
+ "117910 481522.403338 \n",
+ "117911 481181.718200 \n",
  "\n",
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
  "0 NaN \n",
@@ -826,88 +716,23 @@
  "117910 NaN \n",
  "117911 NaN \n",
  "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 486974.735908 \n",
- "117908 485847.539614 \n",
- "117909 484223.885775 \n",
- "117910 481522.403338 \n",
- "117911 481181.718200 \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
- "0 81310.639504 \n",
- "1 80419.761984 \n",
- "2 80480.449461 \n",
- "3 79799.206525 \n",
- "4 79666.469861 \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
+ " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "117907 NaN \n",
+ "117908 NaN \n",
+ "117909 NaN \n",
+ "117910 NaN \n",
+ "117911 NaN \n",
  "\n",
- "[117912 rows x 18 columns]"
+ "[117912 rows x 11 columns]"
  ]
  },
- "execution_count": 11,
+ "execution_count": 4,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -938,7 +763,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 12,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [
  {
@@ -971,15 +796,8 @@
  " <th>Home Type</th>\n",
  " <th>Date</th>\n",
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2</th>\n",
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6</th>\n",
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
  " </tr>\n",
  " </thead>\n",
  " <tbody>\n",
@@ -990,19 +808,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-01-31</td>\n",
+ " <td>81310.639504</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>81310.639504</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>1</th>\n",
@@ -1011,19 +822,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-02-29</td>\n",
+ " <td>80419.761984</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>80419.761984</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>2</th>\n",
@@ -1032,19 +836,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-03-31</td>\n",
+ " <td>80480.449461</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>80480.449461</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>3</th>\n",
@@ -1053,19 +850,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-04-30</td>\n",
+ " <td>79799.206525</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>79799.206525</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>4</th>\n",
@@ -1074,19 +864,12 @@
  " <td>Alaska</td>\n",
  " <td>state</td>\n",
  " <td>Alaska</td>\n",
- " <td>1-Bedrooms</td>\n",
- " <td>all homes (SFR/condo)</td>\n",
+ " <td>1-Bedroom</td>\n",
+ " <td>all homes</td>\n",
  " <td>2000-05-31</td>\n",
+ " <td>79666.469861</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>79666.469861</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>...</th>\n",
@@ -1101,13 +884,6 @@
  " <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>117907</th>\n",
@@ -1117,18 +893,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-09-30</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>486974.735908</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117908</th>\n",
@@ -1138,18 +907,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-10-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>485847.539614</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117909</th>\n",
@@ -1159,18 +921,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-11-30</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>484223.885775</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117910</th>\n",
@@ -1180,18 +935,11 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2023-12-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>481522.403338</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " <tr>\n",
  " <th>117911</th>\n",
@@ -1201,31 +949,24 @@
  " <td>state</td>\n",
  " <td>Wyoming</td>\n",
  " <td>All Bedrooms</td>\n",
- " <td>condo</td>\n",
+ " <td>condo/co-op</td>\n",
  " <td>2024-01-31</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " <td>481181.718200</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
  " </tr>\n",
  " </tbody>\n",
  "</table>\n",
- "<p>117912 rows × 18 columns</p>\n",
+ "<p>117912 rows × 11 columns</p>\n",
  "</div>"
  ],
  "text/plain": [
  " Region ID Size Rank Region Region Type State Bedroom Count \\\n",
- "0 3 48 Alaska state Alaska 1-Bedrooms \n",
- "1 3 48 Alaska state Alaska 1-Bedrooms \n",
- "2 3 48 Alaska state Alaska 1-Bedrooms \n",
- "3 3 48 Alaska state Alaska 1-Bedrooms \n",
- "4 3 48 Alaska state Alaska 1-Bedrooms \n",
+ "0 3 48 Alaska state Alaska 1-Bedroom \n",
+ "1 3 48 Alaska state Alaska 1-Bedroom \n",
+ "2 3 48 Alaska state Alaska 1-Bedroom \n",
+ "3 3 48 Alaska state Alaska 1-Bedroom \n",
+ "4 3 48 Alaska state Alaska 1-Bedroom \n",
  "... ... ... ... ... ... ... \n",
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
@@ -1233,57 +974,31 @@
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
  "\n",
- " Home Type Date \\\n",
- "0 all homes (SFR/condo) 2000-01-31 \n",
- "1 all homes (SFR/condo) 2000-02-29 \n",
- "2 all homes (SFR/condo) 2000-03-31 \n",
- "3 all homes (SFR/condo) 2000-04-30 \n",
- "4 all homes (SFR/condo) 2000-05-31 \n",
- "... ... ... \n",
- "117907 condo 2023-09-30 \n",
- "117908 condo 2023-10-31 \n",
- "117909 condo 2023-11-30 \n",
- "117910 condo 2023-12-31 \n",
- "117911 condo 2024-01-31 \n",
+ " Home Type Date \\\n",
+ "0 all homes 2000-01-31 \n",
+ "1 all homes 2000-02-29 \n",
+ "2 all homes 2000-03-31 \n",
+ "3 all homes 2000-04-30 \n",
+ "4 all homes 2000-05-31 \n",
+ "... ... ... \n",
+ "117907 condo/co-op 2023-09-30 \n",
+ "117908 condo/co-op 2023-10-31 \n",
+ "117909 condo/co-op 2023-11-30 \n",
+ "117910 condo/co-op 2023-12-31 \n",
+ "117911 condo/co-op 2024-01-31 \n",
  "\n",
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
+ "0 81310.639504 \n",
+ "1 80419.761984 \n",
+ "2 80480.449461 \n",
+ "3 79799.206525 \n",
+ "4 79666.469861 \n",
  "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
+ "117907 486974.735908 \n",
+ "117908 485847.539614 \n",
+ "117909 484223.885775 \n",
+ "117910 481522.403338 \n",
+ "117911 481181.718200 \n",
  "\n",
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
  "0 NaN \n",
@@ -1298,88 +1013,23 @@
  "117910 NaN \n",
  "117911 NaN \n",
  "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4 \\\n",
- "0 NaN \n",
- "1 NaN \n",
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- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
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- "117909 NaN \n",
- "117910 NaN \n",
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- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
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- "4 NaN \n",
- "... ... \n",
- "117907 486974.735908 \n",
- "117908 485847.539614 \n",
- "117909 484223.885775 \n",
- "117910 481522.403338 \n",
- "117911 481181.718200 \n",
- "\n",
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- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
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- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
- "4 NaN \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8 \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 NaN \n",
- "3 NaN \n",
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- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
- "\n",
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
- "0 81310.639504 \n",
- "1 80419.761984 \n",
- "2 80480.449461 \n",
- "3 79799.206525 \n",
- "4 79666.469861 \n",
- "... ... \n",
- "117907 NaN \n",
- "117908 NaN \n",
- "117909 NaN \n",
- "117910 NaN \n",
- "117911 NaN \n",
+ " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "117907 NaN \n",
+ "117908 NaN \n",
+ "117909 NaN \n",
+ "117910 NaN \n",
+ "117911 NaN \n",
  "\n",
- "[117912 rows x 18 columns]"
+ "[117912 rows x 11 columns]"
  ]
  },
- "execution_count": 12,
+ "execution_count": 5,
  "metadata": {},
  "output_type": "execute_result"
  }

processors/home_values_forecasts.ipynb

@@ -322,43 +322,43 @@
  "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
  "\n",
  " BaseDate Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
- "0 2023-12-31 NaN \n",
- "1 2023-12-31 NaN \n",
- "2 2023-12-31 0.4 \n",
- "3 2023-12-31 0.2 \n",
- "4 2023-12-31 NaN \n",
- "... ... ... \n",
- "31849 2023-12-31 NaN \n",
- "31850 2023-12-31 NaN \n",
- "31851 2023-12-31 NaN \n",
- "31852 2023-12-31 NaN \n",
- "31853 2023-12-31 NaN \n",
+ "0 2023-12-31 NaN \n",
+ "1 2023-12-31 NaN \n",
+ "2 2023-12-31 0.4 \n",
+ "3 2023-12-31 0.2 \n",
+ "4 2023-12-31 NaN \n",
+ "... ... ... \n",
+ "31849 2023-12-31 NaN \n",
+ "31850 2023-12-31 NaN \n",
+ "31851 2023-12-31 NaN \n",
+ "31852 2023-12-31 NaN \n",
+ "31853 2023-12-31 NaN \n",
  "\n",
  " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
- "0 NaN \n",
- "1 NaN \n",
- "2 0.9 \n",
- "3 0.7 \n",
- "4 NaN \n",
- "... ... \n",
- "31849 NaN \n",
- "31850 NaN \n",
- "31851 NaN \n",
- "31852 NaN \n",
- "31853 NaN \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 0.9 \n",
+ "3 0.7 \n",
+ "4 NaN \n",
+ "... ... \n",
+ "31849 NaN \n",
+ "31850 NaN \n",
+ "31851 NaN \n",
+ "31852 NaN \n",
+ "31853 NaN \n",
  "\n",
  " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
- "0 NaN -0.7 \n",
- "1 NaN -0.7 \n",
- "2 3.2 -0.6 \n",
- "3 2.7 -0.6 \n",
- "4 NaN -0.7 \n",
- "... ... ... \n",
- "31849 NaN -0.7 \n",
- "31850 NaN -0.7 \n",
- "31851 NaN -1.0 \n",
- "31852 NaN -0.4 \n",
- "31853 NaN -0.9 \n",
+ "0 NaN -0.7 \n",
+ "1 NaN -0.7 \n",
+ "2 3.2 -0.6 \n",
+ "3 2.7 -0.6 \n",
+ "4 NaN -0.7 \n",
+ "... ... ... \n",
+ "31849 NaN -0.7 \n",
+ "31850 NaN -0.7 \n",
+ "31851 NaN -1.0 \n",
+ "31852 NaN -0.4 \n",
+ "31853 NaN -0.9 \n",
  "\n",
  " Quarter Over Quarter % Year Over Year % \n",
  "0 -0.9 0.6 \n",
@@ -420,19 +420,336 @@
  },
  {
  "cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [
  {
- "ename": "NameError",
- "evalue": "name 'combined_df' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Adjust columns\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m final_df \u001b[38;5;241m=\u001b[39m \u001b[43mcombined_df\u001b[49m\n\u001b[1;32m 3\u001b[0m final_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39mdrop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStateName\u001b[39m\u001b[38;5;124m\"\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m final_df \u001b[38;5;241m=\u001b[39m final_df\u001b[38;5;241m.\u001b[39mrename(\n\u001b[1;32m 5\u001b[0m columns\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCountyName\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCounty\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m }\n\u001b[1;32m 12\u001b[0m )\n",
- "\u001b[0;31mNameError\u001b[0m: name 'combined_df' is not defined"
- ]
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+ " <td>Springfield, MA</td>\n",
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+ " <td>2.7</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>2.9</td>\n",
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+ " <th>4</th>\n",
+ " <td>58198</td>\n",
+ " <td>8838</td>\n",
+ " <td>1003</td>\n",
+ " <td>zip</td>\n",
+ " <td>MA</td>\n",
+ " <td>Amherst</td>\n",
+ " <td>Springfield, MA</td>\n",
+ " <td>Hampshire County</td>\n",
+ " <td>2023-12-31</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>-0.7</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>AR</td>\n",
+ " <td>Jonesboro</td>\n",
+ " <td>Jonesboro, AR</td>\n",
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+ " <td>2023-12-31</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>834213</td>\n",
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+ " <td>NY</td>\n",
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+ " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
+ " <td>Queens County</td>\n",
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+ " <td>845914</td>\n",
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+ " <td>AZ</td>\n",
+ " <td>Tempe</td>\n",
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+ " <td>Maricopa County</td>\n",
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+ " <td>5.4</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>31854 rows × 15 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Region ID Size Rank Region Region Type State City \\\n",
+ "0 58001 30490 501 zip NY Holtsville \n",
+ "1 58002 30490 544 zip NY Holtsville \n",
+ "2 58196 7440 1001 zip MA Agawam \n",
+ "3 58197 3911 1002 zip MA Amherst \n",
+ "4 58198 8838 1003 zip MA Amherst \n",
+ "... ... ... ... ... ... ... \n",
+ "31849 827279 7779 72405 zip AR Jonesboro \n",
+ "31850 834213 30490 11437 zip NY New York \n",
+ "31851 845914 6361 85288 zip AZ Tempe \n",
+ "31852 847854 39992 20598 zip VA Arlington \n",
+ "31853 847855 30490 34249 zip FL Sarasota \n",
+ "\n",
+ " Metro County \\\n",
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+ "2 Springfield, MA Hampden County \n",
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+ "... ... ... \n",
+ "31849 Jonesboro, AR Craighead County \n",
+ "31850 New York-Newark-Jersey City, NY-NJ-PA Queens County \n",
+ "31851 Phoenix-Mesa-Chandler, AZ Maricopa County \n",
+ "31852 Washington-Arlington-Alexandria, DC-VA-MD-WV Arlington County \n",
+ "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
+ "\n",
+ " Date Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
+ "0 2023-12-31 NaN \n",
+ "1 2023-12-31 NaN \n",
+ "2 2023-12-31 0.4 \n",
+ "3 2023-12-31 0.2 \n",
+ "4 2023-12-31 NaN \n",
+ "... ... ... \n",
+ "31849 2023-12-31 NaN \n",
+ "31850 2023-12-31 NaN \n",
+ "31851 2023-12-31 NaN \n",
+ "31852 2023-12-31 NaN \n",
+ "31853 2023-12-31 NaN \n",
+ "\n",
+ " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 0.9 \n",
+ "3 0.7 \n",
+ "4 NaN \n",
+ "... ... \n",
+ "31849 NaN \n",
+ "31850 NaN \n",
+ "31851 NaN \n",
+ "31852 NaN \n",
+ "31853 NaN \n",
+ "\n",
+ " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
+ "0 NaN -0.7 \n",
+ "1 NaN -0.7 \n",
+ "2 3.2 -0.6 \n",
+ "3 2.7 -0.6 \n",
+ "4 NaN -0.7 \n",
+ "... ... ... \n",
+ "31849 NaN -0.7 \n",
+ "31850 NaN -0.7 \n",
+ "31851 NaN -1.0 \n",
+ "31852 NaN -0.4 \n",
+ "31853 NaN -0.9 \n",
+ "\n",
+ " Quarter Over Quarter % Year Over Year % \n",
+ "0 -0.9 0.6 \n",
+ "1 -0.9 0.6 \n",
+ "2 0.0 3.0 \n",
+ "3 0.0 2.9 \n",
+ "4 0.0 3.4 \n",
+ "... ... ... \n",
+ "31849 0.0 2.5 \n",
+ "31850 -0.9 0.6 \n",
+ "31851 0.0 4.5 \n",
+ "31852 0.9 1.2 \n",
+ "31853 -0.1 5.4 \n",
+ "\n",
+ "[31854 rows x 15 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
  }
  ],
  "source": [

processors/new_construction.ipynb

@@ -514,18 +514,18 @@
  "49485 845162 535 Granbury, TX msa TX all homes \n",
  "49486 845162 535 Granbury, TX msa TX all homes \n",
  "\n",
- " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
- "0 2018-01-31 33940.0 137.412316 309000.0 \n",
- "1 2018-02-28 33304.0 137.199170 309072.5 \n",
- "2 2018-03-31 42641.0 139.520863 315488.0 \n",
- "3 2018-04-30 37588.0 139.778110 314990.0 \n",
- "4 2018-05-31 39933.0 143.317968 324500.0 \n",
- "... ... ... ... ... \n",
- "49482 2023-07-31 31.0 NaN NaN \n",
- "49483 2023-08-31 33.0 NaN NaN \n",
- "49484 2023-09-30 26.0 NaN NaN \n",
- "49485 2023-10-31 24.0 NaN NaN \n",
- "49486 2023-11-30 16.0 NaN NaN \n",
+ " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
+ "0 2018-01-31 33940.0 137.412316 309000.0 \n",
+ "1 2018-02-28 33304.0 137.199170 309072.5 \n",
+ "2 2018-03-31 42641.0 139.520863 315488.0 \n",
+ "3 2018-04-30 37588.0 139.778110 314990.0 \n",
+ "4 2018-05-31 39933.0 143.317968 324500.0 \n",
+ "... ... ... ... ... \n",
+ "49482 2023-07-31 31.0 NaN NaN \n",
+ "49483 2023-08-31 33.0 NaN NaN \n",
+ "49484 2023-09-30 26.0 NaN NaN \n",
+ "49485 2023-10-31 24.0 NaN NaN \n",
+ "49486 2023-11-30 16.0 NaN NaN \n",
  "\n",
  "[49487 rows x 10 columns]"
  ]

processors/rentals.ipynb

@@ -2,7 +2,7 @@
  "cells": [
  {
  "cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -19,7 +19,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -32,7 +32,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [
  {
@@ -346,7 +346,7 @@
  "[1258740 rows x 15 columns]"
  ]
  },
- "execution_count": 7,
+ "execution_count": 3,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -438,7 +438,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [
  {
@@ -740,7 +740,7 @@
  "[1258740 rows x 14 columns]"
  ]
  },
- "execution_count": 8,
+ "execution_count": 4,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -764,7 +764,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [
  {
@@ -915,7 +915,7 @@
  " <td>city</td>\n",
  " <td>all homes plus multifamily</td>\n",
  " <td>Camden County</td>\n",
- " <td>NaN</td>\n",
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
  " <td>2023-08-31</td>\n",
@@ -932,7 +932,7 @@
  " <td>city</td>\n",
  " <td>all homes plus multifamily</td>\n",
  " <td>Camden County</td>\n",
- " <td>NaN</td>\n",
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
  " <td>2023-09-30</td>\n",
@@ -949,7 +949,7 @@
  " <td>city</td>\n",
  " <td>all homes plus multifamily</td>\n",
  " <td>Camden County</td>\n",
- " <td>NaN</td>\n",
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
  " <td>2023-10-31</td>\n",
@@ -966,7 +966,7 @@
  " <td>city</td>\n",
  " <td>all homes plus multifamily</td>\n",
  " <td>Camden County</td>\n",
- " <td>NaN</td>\n",
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
  " <td>2023-11-30</td>\n",
@@ -983,7 +983,7 @@
  " <td>city</td>\n",
  " <td>all homes plus multifamily</td>\n",
  " <td>Camden County</td>\n",
- " <td>NaN</td>\n",
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
  " <td>NaN</td>\n",
  " <td>NaN</td>\n",
  " <td>2023-12-31</td>\n",
@@ -1011,31 +1011,44 @@
  "1258738 857850 713 Cherry Hill city \n",
  "1258739 857850 713 Cherry Hill city \n",
  "\n",
- " Home Type State Metro \\\n",
- "0 all homes plus multifamily Ada County Boise City, ID \n",
- "1 all homes plus multifamily Ada County Boise City, ID \n",
- "2 all homes plus multifamily Ada County Boise City, ID \n",
- "3 all homes plus multifamily Ada County Boise City, ID \n",
- "4 all homes plus multifamily Ada County Boise City, ID \n",
- "... ... ... ... \n",
- "1258735 all homes plus multifamily Camden County NaN \n",
- "1258736 all homes plus multifamily Camden County NaN \n",
- "1258737 all homes plus multifamily Camden County NaN \n",
- "1258738 all homes plus multifamily Camden County NaN \n",
- "1258739 all homes plus multifamily Camden County NaN \n",
+ " Home Type State \\\n",
+ "0 all homes plus multifamily Ada County \n",
+ "1 all homes plus multifamily Ada County \n",
+ "2 all homes plus multifamily Ada County \n",
+ "3 all homes plus multifamily Ada County \n",
+ "4 all homes plus multifamily Ada County \n",
+ "... ... ... \n",
+ "1258735 all homes plus multifamily Camden County \n",
+ "1258736 all homes plus multifamily Camden County \n",
+ "1258737 all homes plus multifamily Camden County \n",
+ "1258738 all homes plus multifamily Camden County \n",
+ "1258739 all homes plus multifamily Camden County \n",
+ "\n",
+ " Metro State Code FIPS \\\n",
+ "0 Boise City, ID 16.0 \n",
+ "1 Boise City, ID 16.0 \n",
+ "2 Boise City, ID 16.0 \n",
+ "3 Boise City, ID 16.0 \n",
+ "4 Boise City, ID 16.0 \n",
+ "... ... ... \n",
+ "1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
+ "1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
+ "1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
+ "1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
+ "1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
  "\n",
- " State Code FIPS Municipal Code FIPS Date Rent (Smoothed) \\\n",
- "0 16.0 1.0 2015-01-31 927.493763 \n",
- "1 16.0 1.0 2015-02-28 931.690623 \n",
- "2 16.0 1.0 2015-03-31 932.568601 \n",
- "3 16.0 1.0 2015-04-30 933.148134 \n",
- "4 16.0 1.0 2015-05-31 941.045724 \n",
- "... ... ... ... ... \n",
- "1258735 NaN NaN 2023-08-31 2291.604800 \n",
- "1258736 NaN NaN 2023-09-30 2296.188906 \n",
- "1258737 NaN NaN 2023-10-31 2292.270938 \n",
- "1258738 NaN NaN 2023-11-30 2253.417140 \n",
- "1258739 NaN NaN 2023-12-31 2280.830303 \n",
+ " Municipal Code FIPS Date Rent (Smoothed) \\\n",
+ "0 1.0 2015-01-31 927.493763 \n",
+ "1 1.0 2015-02-28 931.690623 \n",
+ "2 1.0 2015-03-31 932.568601 \n",
+ "3 1.0 2015-04-30 933.148134 \n",
+ "4 1.0 2015-05-31 941.045724 \n",
+ "... ... ... ... \n",
+ "1258735 NaN 2023-08-31 2291.604800 \n",
+ "1258736 NaN 2023-09-30 2296.188906 \n",
+ "1258737 NaN 2023-10-31 2292.270938 \n",
+ "1258738 NaN 2023-11-30 2253.417140 \n",
+ "1258739 NaN 2023-12-31 2280.830303 \n",
  "\n",
  " Rent (Smoothed) (Seasonally Adjusted) City County \n",
  "0 927.493763 NaN Ada County \n",
@@ -1053,7 +1066,7 @@
  "[1258740 rows x 14 columns]"
  ]
  },
- "execution_count": 6,
+ "execution_count": 5,
  "metadata": {},
  "output_type": "execute_result"
  }

processors/sales.ipynb

@@ -2,7 +2,7 @@
  "cells": [
  {
  "cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -19,7 +19,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -32,7 +32,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [
  {
@@ -442,7 +442,7 @@
  "[255024 rows x 18 columns]"
  ]
  },
- "execution_count": 4,
+ "execution_count": 3,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -502,7 +502,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 52,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [
  {
@@ -878,7 +878,7 @@
  "[255024 rows x 18 columns]"
  ]
  },
- "execution_count": 52,
+ "execution_count": 4,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -901,7 +901,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 53,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [
  {
@@ -1277,7 +1277,7 @@
  "[255024 rows x 18 columns]"
  ]
  },
- "execution_count": 53,
+ "execution_count": 5,
  "metadata": {},
  "output_type": "execute_result"
  }
@@ -1290,7 +1290,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 54,
+ "execution_count": 6,
  "metadata": {},
  "outputs": [],
  "source": [

test-sales.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:089f3958ace720a88adc9f6ea28689c9d8dd27a972b78a21a0883874cae8719d
+size 7524442

zillow.py

@@ -35,6 +35,16 @@ _HOMEPAGE = "https://www.zillow.com/research/data/"
 
 _LICENSE = "other"
 
+HOME_TYPES = [
+ "all homes",
+ "all homes plus multifamily",
+ "SFR",
+ "condo/co-op",
+ "multifamily",
+]
+
+REGION_TYPES = ["county", "city", "zip", "country", "msa"]
+
 
 class Zillow(datasets.GeneratorBasedBuilder):
  """Housing data in the United States provided by Zillow"""
@@ -89,13 +99,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
  "Region Type": datasets.ClassLabel(
- num_classes=3, names=["zip", "country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
  "City": datasets.Value(dtype="string", id="City"),
  "Metro": datasets.Value(dtype="string", id="Metro"),
  "County": datasets.Value(dtype="string", id="County"),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Month Over Month % (Smoothed) (Seasonally Adjusted)": datasets.Value(
  dtype="float32",
  id="Month Over Month % (Smoothed) (Seasonally Adjusted)",
@@ -126,13 +136,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
  "Region Type": datasets.ClassLabel(
- num_classes=2, names=["country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
  "Home Type": datasets.ClassLabel(
- num_classes=3, names=["SFR", "all homes", "condo/co-op only"]
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Median Sale Price": datasets.Value(
  dtype="float32", id="Median Sale Price"
  ),
@@ -149,13 +159,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
  "Region Type": datasets.ClassLabel(
- num_classes=2, names=["country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
  "Home Type": datasets.ClassLabel(
- num_classes=2, names=["SFR", "all homes"]
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Median Listing Price": datasets.Value(
  dtype="float32", id="Median Listing Price"
  ),
@@ -179,14 +189,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
  "Region Type": datasets.ClassLabel(
- num_classes=5, names=["county", "city", "zip", "country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
  "Home Type": datasets.ClassLabel(
- num_classes=3,
- names=["all homes plus multifamily", "SFR", "multifamily"],
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Rent (Smoothed)": datasets.Value(
  dtype="float32", id="Rent (Smoothed)"
  ),
@@ -202,12 +211,11 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
  "Region Type": datasets.ClassLabel(
- num_classes=2, names=["country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
  "Home Type": datasets.ClassLabel(
- num_classes=2,
- names=["SFR", "all homes"],
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
  "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Mean Sale to List Ratio (Smoothed)": datasets.Value(
@@ -252,10 +260,12 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
- "Region Type": datasets.ClassLabel(num_classes=1, names=["state"]),
+ "Region Type": datasets.ClassLabel(
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
+ ),
  "State": datasets.Value(dtype="string", id="State"),
  "Home Type": datasets.ClassLabel(
- num_classes=3, names=["all homes (SFR/condo)", "SFR", "condo"]
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
  "Bedroom Count": datasets.ClassLabel(
  num_classes=6,
@@ -268,7 +278,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "All Bedrooms",
  ],
  ),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
  dtype="float32",
  id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
@@ -289,16 +299,14 @@ class Zillow(datasets.GeneratorBasedBuilder):
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
  "Region": datasets.Value(dtype="string", id="Region"),
- # "Region Type": datasets.Value(dtype="string", id="Region Type"),
  "Region Type": datasets.ClassLabel(
- num_classes=2, names=["country", "msa"]
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
  ),
  "State": datasets.Value(dtype="string", id="State"),
- # "Home Type": datasets.Value(dtype="string", id="Home Type"),
  "Home Type": datasets.ClassLabel(
- num_classes=2, names=["SFR", "all homes (SFR + Condo)"]
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
  ),
- "Date": datasets.Value(dtype="string", id="Date"),
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
  "Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
  dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
  ),
@@ -336,7 +344,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
  )
 
  def _split_generators(self, dl_manager):
- file_path = os.path.join("processed", self.config.name, "final5.jsonl")
+ file_path = os.path.join("processed", self.config.name, "final.jsonl")
  file_train = dl_manager.download(file_path)
  # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
  # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))