{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # import json as pandas\n",
    "# import pandas as pd\n",
    "# # read the data\n",
    "# x = pd.read_json(\"processed/sales/final5.jsonl\", lines=True)\n",
    "# # x\n",
    "# x[\"Region Type\"].unique()\n",
    "# x[\"Home Type\"].unique()\n",
    "# x[\"Bedroom Count\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "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%|██████████| 26.9k/26.9k [00:00<00:00, 9.97MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 24.7MB/s]\n",
      "Downloading data: 100%|██████████| 14.1M/14.1M [00:00<00:00, 21.5MB/s]\n",
      "Generating train split: 100%|██████████| 31854/31854 [00:01<00:00, 26905.24 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 32/32 [00:00<00:00, 813.13ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_construction\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 16.8MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 28.7MB/s]\n",
      "Downloading data: 100%|██████████| 10.9M/10.9M [00:00<00:00, 21.7MB/s]\n",
      "Generating train split: 100%|██████████| 49487/49487 [00:01<00:00, 38197.59 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 50/50 [00:00<00:00, 1691.95ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for_sale_listings\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 2.19MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 19.1MB/s]\n",
      "Downloading data: 100%|██████████| 180M/180M [00:04<00:00, 37.8MB/s] \n",
      "Generating train split: 100%|██████████| 578653/578653 [00:18<00:00, 31984.31 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 579/579 [00:00<00:00, 1326.61ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rentals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 6.31MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 15.0MB/s]\n",
      "Downloading data: 100%|██████████| 447M/447M [00:13<00:00, 32.0MB/s] \n",
      "Generating train split: 100%|██████████| 1258740/1258740 [00:31<00:00, 40439.23 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 1259/1259 [00:00<00:00, 1671.78ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sales\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 16.1MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 14.9MB/s]\n",
      "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 34.1MB/s] \n",
      "Generating train split: 100%|██████████| 255024/255024 [00:10<00:00, 24278.38 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 256/256 [00:00<00:00, 862.81ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home_values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 11.3MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 12.2MB/s]\n",
      "Downloading data: 100%|██████████| 41.1M/41.1M [00:01<00:00, 34.2MB/s]\n",
      "Generating train split: 100%|██████████| 117912/117912 [00:03<00:00, 34804.14 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 118/118 [00:00<00:00, 1397.82ba/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "days_on_market\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 6.99MB/s]\n",
      "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 8.94MB/s]\n",
      "Downloading data: 100%|██████████| 229M/229M [00:06<00:00, 36.6MB/s] \n",
      "Generating train split: 100%|██████████| 586714/586714 [00:18<00:00, 31198.29 examples/s]\n",
      "Creating parquet from Arrow format: 100%|██████████| 587/587 [00:00<00:00, 1241.06ba/s]\n"
     ]
    }
   ],
   "source": [
    "dataset_dict = {}\n",
    "\n",
    "configs = [\n",
    "    \"days_on_market\",\n",
    "    \"for_sale_listings\",\n",
    "    \"home_values\",\n",
    "    \"home_values_forecasts\",\n",
    "    \"new_construction\",\n",
    "    \"rentals\",\n",
    "    \"sales\",\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",
    "    )\n",
    "    filename = path.join(\"parquet_files\", config + \".parquet\")\n",
    "    dataset_dict[config][\"train\"].to_parquet(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  }
 ],
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