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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import json as pandas\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Region ID</th>\n",
" <th>Size Rank</th>\n",
" <th>Region</th>\n",
" <th>Region Type</th>\n",
" <th>State</th>\n",
" <th>Home Type</th>\n",
" <th>Date</th>\n",
" <th>Median Sale to List Ratio</th>\n",
" <th>Median Sale Price</th>\n",
" <th>Median Sale Price (Smoothed) (Seasonally Adjusted)</th>\n",
" <th>Median Sale Price (Smoothed)</th>\n",
" <th>% Sold Below List (Smoothed)</th>\n",
" <th>Median Sale to List Ratio (Smoothed)</th>\n",
" <th>% Sold Above List</th>\n",
" <th>Mean Sale to List Ratio (Smoothed)</th>\n",
" <th>Mean Sale to List Ratio</th>\n",
" <th>% Sold Below List</th>\n",
" <th>% Sold Above List (Smoothed)</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>None</td>\n",
" <td>SFR</td>\n",
" <td>2008-02-02</td>\n",
" <td>NaN</td>\n",
" <td>172000.0</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",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>102001</td>\n",
" <td>0</td>\n",
" <td>United States</td>\n",
" <td>country</td>\n",
" <td>None</td>\n",
" <td>SFR</td>\n",
" <td>2008-02-09</td>\n",
" <td>NaN</td>\n",
" <td>165400.0</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",
" </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>None</td>\n",
" <td>SFR</td>\n",
" <td>2008-02-16</td>\n",
" <td>NaN</td>\n",
" <td>168000.0</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",
" </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>None</td>\n",
" <td>SFR</td>\n",
" <td>2008-02-23</td>\n",
" <td>NaN</td>\n",
" <td>167600.0</td>\n",
" <td>NaN</td>\n",
" <td>167600.0</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",
" </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>None</td>\n",
" <td>SFR</td>\n",
" <td>2008-03-01</td>\n",
" <td>NaN</td>\n",
" <td>168100.0</td>\n",
" <td>NaN</td>\n",
" <td>168100.0</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",
" </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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255019</th>\n",
" <td>845160</td>\n",
" <td>198</td>\n",
" <td>Prescott Valley, AZ</td>\n",
" <td>msa</td>\n",
" <td>AZ</td>\n",
" <td>all homes</td>\n",
" <td>2023-11-11</td>\n",
" <td>0.985132</td>\n",
" <td>515000.0</td>\n",
" <td>480020.0</td>\n",
" <td>480020.0</td>\n",
" <td>0.651221</td>\n",
" <td>0.982460</td>\n",
" <td>0.080000</td>\n",
" <td>0.978546</td>\n",
" <td>0.983288</td>\n",
" <td>0.680000</td>\n",
" <td>0.119711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255020</th>\n",
" <td>845160</td>\n",
" <td>198</td>\n",
" <td>Prescott Valley, AZ</td>\n",
" <td>msa</td>\n",
" <td>AZ</td>\n",
" <td>all homes</td>\n",
" <td>2023-11-18</td>\n",
" <td>0.972559</td>\n",
" <td>510000.0</td>\n",
" <td>476901.0</td>\n",
" <td>476901.0</td>\n",
" <td>0.659583</td>\n",
" <td>0.980362</td>\n",
" <td>0.142857</td>\n",
" <td>0.972912</td>\n",
" <td>0.958341</td>\n",
" <td>0.625000</td>\n",
" <td>0.120214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255021</th>\n",
" <td>845160</td>\n",
" <td>198</td>\n",
" <td>Prescott Valley, AZ</td>\n",
" <td>msa</td>\n",
" <td>AZ</td>\n",
" <td>all homes</td>\n",
" <td>2023-11-25</td>\n",
" <td>0.979644</td>\n",
" <td>484500.0</td>\n",
" <td>496540.0</td>\n",
" <td>496540.0</td>\n",
" <td>0.669387</td>\n",
" <td>0.979179</td>\n",
" <td>0.088235</td>\n",
" <td>0.971177</td>\n",
" <td>0.973797</td>\n",
" <td>0.705882</td>\n",
" <td>0.107185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255022</th>\n",
" <td>845160</td>\n",
" <td>198</td>\n",
" <td>Prescott Valley, AZ</td>\n",
" <td>msa</td>\n",
" <td>AZ</td>\n",
" <td>all homes</td>\n",
" <td>2023-12-02</td>\n",
" <td>0.978261</td>\n",
" <td>538000.0</td>\n",
" <td>510491.0</td>\n",
" <td>510491.0</td>\n",
" <td>0.678777</td>\n",
" <td>0.978899</td>\n",
" <td>0.126761</td>\n",
" <td>0.970576</td>\n",
" <td>0.966876</td>\n",
" <td>0.704225</td>\n",
" <td>0.109463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255023</th>\n",
" <td>845160</td>\n",
" <td>198</td>\n",
" <td>Prescott Valley, AZ</td>\n",
" <td>msa</td>\n",
" <td>AZ</td>\n",
" <td>all homes</td>\n",
" <td>2023-12-09</td>\n",
" <td>0.981498</td>\n",
" <td>485000.0</td>\n",
" <td>503423.0</td>\n",
" <td>503423.0</td>\n",
" <td>0.658777</td>\n",
" <td>0.977990</td>\n",
" <td>0.100000</td>\n",
" <td>0.970073</td>\n",
" <td>0.981278</td>\n",
" <td>0.600000</td>\n",
" <td>0.114463</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>255024 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" Region ID Size Rank Region Region Type State \\\n",
"0 102001 0 United States country None \n",
"1 102001 0 United States country None \n",
"2 102001 0 United States country None \n",
"3 102001 0 United States country None \n",
"4 102001 0 United States country None \n",
"... ... ... ... ... ... \n",
"255019 845160 198 Prescott Valley, AZ msa AZ \n",
"255020 845160 198 Prescott Valley, AZ msa AZ \n",
"255021 845160 198 Prescott Valley, AZ msa AZ \n",
"255022 845160 198 Prescott Valley, AZ msa AZ \n",
"255023 845160 198 Prescott Valley, AZ msa AZ \n",
"\n",
" Home Type Date Median Sale to List Ratio Median Sale Price \\\n",
"0 SFR 2008-02-02 NaN 172000.0 \n",
"1 SFR 2008-02-09 NaN 165400.0 \n",
"2 SFR 2008-02-16 NaN 168000.0 \n",
"3 SFR 2008-02-23 NaN 167600.0 \n",
"4 SFR 2008-03-01 NaN 168100.0 \n",
"... ... ... ... ... \n",
"255019 all homes 2023-11-11 0.985132 515000.0 \n",
"255020 all homes 2023-11-18 0.972559 510000.0 \n",
"255021 all homes 2023-11-25 0.979644 484500.0 \n",
"255022 all homes 2023-12-02 0.978261 538000.0 \n",
"255023 all homes 2023-12-09 0.981498 485000.0 \n",
"\n",
" Median Sale Price (Smoothed) (Seasonally Adjusted) \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"255019 480020.0 \n",
"255020 476901.0 \n",
"255021 496540.0 \n",
"255022 510491.0 \n",
"255023 503423.0 \n",
"\n",
" Median Sale Price (Smoothed) % Sold Below List (Smoothed) \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 167600.0 NaN \n",
"4 168100.0 NaN \n",
"... ... ... \n",
"255019 480020.0 0.651221 \n",
"255020 476901.0 0.659583 \n",
"255021 496540.0 0.669387 \n",
"255022 510491.0 0.678777 \n",
"255023 503423.0 0.658777 \n",
"\n",
" Median Sale to List Ratio (Smoothed) % Sold Above List \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"255019 0.982460 0.080000 \n",
"255020 0.980362 0.142857 \n",
"255021 0.979179 0.088235 \n",
"255022 0.978899 0.126761 \n",
"255023 0.977990 0.100000 \n",
"\n",
" Mean Sale to List Ratio (Smoothed) Mean Sale to List Ratio \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"255019 0.978546 0.983288 \n",
"255020 0.972912 0.958341 \n",
"255021 0.971177 0.973797 \n",
"255022 0.970576 0.966876 \n",
"255023 0.970073 0.981278 \n",
"\n",
" % Sold Below List % Sold Above List (Smoothed) \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"255019 0.680000 0.119711 \n",
"255020 0.625000 0.120214 \n",
"255021 0.705882 0.107185 \n",
"255022 0.704225 0.109463 \n",
"255023 0.600000 0.114463 \n",
"\n",
"[255024 rows x 18 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read the data\n",
"x = pd.read_json(\"processed/sales/final5.jsonl\", lines=True)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['country', 'msa'], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get unique values for column\n",
"x[\"Region Type\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['SFR', 'all homes'], dtype=object)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[\"Home Type\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['1-Bedroom', '2-Bedrooms', '3-Bedrooms', '4-Bedrooms',\n",
" '5+-Bedrooms', 'All Bedrooms'], dtype=object)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[\"Bedroom Count\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sales\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"dataset_dict = {}\n",
"\n",
"configs = [\n",
" # \"home_values_forecasts\",\n",
" # \"new_construction\",\n",
" # \"for_sale_listings\",\n",
" # \"rentals\",\n",
" \"sales\",\n",
" # \"home_values\",\n",
" # \"days_on_market\",\n",
"]\n",
"for config in configs:\n",
" print(config)\n",
" dataset_dict[config] = load_dataset(\n",
" \"misikoff/zillow\",\n",
" config,\n",
" trust_remote_code=True,\n",
" download_mode=\"force_redownload\",\n",
" cache_dir=\"./cache\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sta663",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|