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Datasets:
misikoff
/
zillow

Tasks:
Tabular Regression
Time Series Forecasting
Languages:
English
License:
Dataset card Files
xet
Community
misikoff commited on
Commit
afb4bd3
·
1 Parent(s): 05fb971

fix: update checker and remove old files

Browse files
Files changed (8) hide show
  1. checker.ipynb +140 -491
  2. processed/days_on_market/final.jsonl +0 -3
  3. processed/for_sale_listings/final.jsonl +0 -3
  4. processed/home_values/final.jsonl +0 -3
  5. processed/home_values_forecasts/final.jsonl +0 -3
  6. processed/new_construction/final.jsonl +0 -3
  7. processed/rentals/final.jsonl +0 -3
  8. processed/sales/final.jsonl +0 -3
checker.ipynb CHANGED
@@ -2,479 +2,107 @@
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  "cells": [
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  {
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  "cell_type": "code",
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- "execution_count": 1,
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "# import json as pandas\n",
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- "import pandas as pd"
 
 
 
 
 
 
11
  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 2,
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  "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "<div>\n",
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- "<style scoped>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " }\n",
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- "\n",
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- " .dataframe thead th {\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <thead>\n",
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- " <tr style=\"text-align: right;\">\n",
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- " <th></th>\n",
39
- " <th>Region ID</th>\n",
40
- " <th>Size Rank</th>\n",
41
- " <th>Region</th>\n",
42
- " <th>Region Type</th>\n",
43
- " <th>State</th>\n",
44
- " <th>Home Type</th>\n",
45
- " <th>Date</th>\n",
46
- " <th>Median Sale to List Ratio</th>\n",
47
- " <th>Median Sale Price</th>\n",
48
- " <th>Median Sale Price (Smoothed) (Seasonally Adjusted)</th>\n",
49
- " <th>Median Sale Price (Smoothed)</th>\n",
50
- " <th>% Sold Below List (Smoothed)</th>\n",
51
- " <th>Median Sale to List Ratio (Smoothed)</th>\n",
52
- " <th>% Sold Above List</th>\n",
53
- " <th>Mean Sale to List Ratio (Smoothed)</th>\n",
54
- " <th>Mean Sale to List Ratio</th>\n",
55
- " <th>% Sold Below List</th>\n",
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- " <th>% Sold Above List (Smoothed)</th>\n",
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- " </tr>\n",
58
- " </thead>\n",
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- " <tbody>\n",
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- " <tr>\n",
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- " <th>0</th>\n",
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- " <td>102001</td>\n",
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- " <td>0</td>\n",
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- " <td>United States</td>\n",
65
- " <td>country</td>\n",
66
- " <td>None</td>\n",
67
- " <td>SFR</td>\n",
68
- " <td>2008-02-02</td>\n",
69
- " <td>NaN</td>\n",
70
- " <td>172000.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>1</th>\n",
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- " <td>102001</td>\n",
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- " <td>0</td>\n",
85
- " <td>United States</td>\n",
86
- " <td>country</td>\n",
87
- " <td>None</td>\n",
88
- " <td>SFR</td>\n",
89
- " <td>2008-02-09</td>\n",
90
- " <td>NaN</td>\n",
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- " <td>165400.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
102
- " <tr>\n",
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- " <th>2</th>\n",
104
- " <td>102001</td>\n",
105
- " <td>0</td>\n",
106
- " <td>United States</td>\n",
107
- " <td>country</td>\n",
108
- " <td>None</td>\n",
109
- " <td>SFR</td>\n",
110
- " <td>2008-02-16</td>\n",
111
- " <td>NaN</td>\n",
112
- " <td>168000.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
123
- " <tr>\n",
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- " <th>3</th>\n",
125
- " <td>102001</td>\n",
126
- " <td>0</td>\n",
127
- " <td>United States</td>\n",
128
- " <td>country</td>\n",
129
- " <td>None</td>\n",
130
- " <td>SFR</td>\n",
131
- " <td>2008-02-23</td>\n",
132
- " <td>NaN</td>\n",
133
- " <td>167600.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>167600.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>4</th>\n",
146
- " <td>102001</td>\n",
147
- " <td>0</td>\n",
148
- " <td>United States</td>\n",
149
- " <td>country</td>\n",
150
- " <td>None</td>\n",
151
- " <td>SFR</td>\n",
152
- " <td>2008-03-01</td>\n",
153
- " <td>NaN</td>\n",
154
- " <td>168100.0</td>\n",
155
- " <td>NaN</td>\n",
156
- " <td>168100.0</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>...</th>\n",
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- " <td>...</td>\n",
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- " <td>...</td>\n",
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- " <td>...</td>\n",
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- " <td>...</td>\n",
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- " <td>...</td>\n",
172
- " <td>...</td>\n",
173
- " <td>...</td>\n",
174
- " <td>...</td>\n",
175
- " <td>...</td>\n",
176
- " <td>...</td>\n",
177
- " <td>...</td>\n",
178
- " <td>...</td>\n",
179
- " <td>...</td>\n",
180
- " <td>...</td>\n",
181
- " <td>...</td>\n",
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- " <td>...</td>\n",
183
- " <td>...</td>\n",
184
- " <td>...</td>\n",
185
- " </tr>\n",
186
- " <tr>\n",
187
- " <th>255019</th>\n",
188
- " <td>845160</td>\n",
189
- " <td>198</td>\n",
190
- " <td>Prescott Valley, AZ</td>\n",
191
- " <td>msa</td>\n",
192
- " <td>AZ</td>\n",
193
- " <td>all homes</td>\n",
194
- " <td>2023-11-11</td>\n",
195
- " <td>0.985132</td>\n",
196
- " <td>515000.0</td>\n",
197
- " <td>480020.0</td>\n",
198
- " <td>480020.0</td>\n",
199
- " <td>0.651221</td>\n",
200
- " <td>0.982460</td>\n",
201
- " <td>0.080000</td>\n",
202
- " <td>0.978546</td>\n",
203
- " <td>0.983288</td>\n",
204
- " <td>0.680000</td>\n",
205
- " <td>0.119711</td>\n",
206
- " </tr>\n",
207
- " <tr>\n",
208
- " <th>255020</th>\n",
209
- " <td>845160</td>\n",
210
- " <td>198</td>\n",
211
- " <td>Prescott Valley, AZ</td>\n",
212
- " <td>msa</td>\n",
213
- " <td>AZ</td>\n",
214
- " <td>all homes</td>\n",
215
- " <td>2023-11-18</td>\n",
216
- " <td>0.972559</td>\n",
217
- " <td>510000.0</td>\n",
218
- " <td>476901.0</td>\n",
219
- " <td>476901.0</td>\n",
220
- " <td>0.659583</td>\n",
221
- " <td>0.980362</td>\n",
222
- " <td>0.142857</td>\n",
223
- " <td>0.972912</td>\n",
224
- " <td>0.958341</td>\n",
225
- " <td>0.625000</td>\n",
226
- " <td>0.120214</td>\n",
227
- " </tr>\n",
228
- " <tr>\n",
229
- " <th>255021</th>\n",
230
- " <td>845160</td>\n",
231
- " <td>198</td>\n",
232
- " <td>Prescott Valley, AZ</td>\n",
233
- " <td>msa</td>\n",
234
- " <td>AZ</td>\n",
235
- " <td>all homes</td>\n",
236
- " <td>2023-11-25</td>\n",
237
- " <td>0.979644</td>\n",
238
- " <td>484500.0</td>\n",
239
- " <td>496540.0</td>\n",
240
- " <td>496540.0</td>\n",
241
- " <td>0.669387</td>\n",
242
- " <td>0.979179</td>\n",
243
- " <td>0.088235</td>\n",
244
- " <td>0.971177</td>\n",
245
- " <td>0.973797</td>\n",
246
- " <td>0.705882</td>\n",
247
- " <td>0.107185</td>\n",
248
- " </tr>\n",
249
- " <tr>\n",
250
- " <th>255022</th>\n",
251
- " <td>845160</td>\n",
252
- " <td>198</td>\n",
253
- " <td>Prescott Valley, AZ</td>\n",
254
- " <td>msa</td>\n",
255
- " <td>AZ</td>\n",
256
- " <td>all homes</td>\n",
257
- " <td>2023-12-02</td>\n",
258
- " <td>0.978261</td>\n",
259
- " <td>538000.0</td>\n",
260
- " <td>510491.0</td>\n",
261
- " <td>510491.0</td>\n",
262
- " <td>0.678777</td>\n",
263
- " <td>0.978899</td>\n",
264
- " <td>0.126761</td>\n",
265
- " <td>0.970576</td>\n",
266
- " <td>0.966876</td>\n",
267
- " <td>0.704225</td>\n",
268
- " <td>0.109463</td>\n",
269
- " </tr>\n",
270
- " <tr>\n",
271
- " <th>255023</th>\n",
272
- " <td>845160</td>\n",
273
- " <td>198</td>\n",
274
- " <td>Prescott Valley, AZ</td>\n",
275
- " <td>msa</td>\n",
276
- " <td>AZ</td>\n",
277
- " <td>all homes</td>\n",
278
- " <td>2023-12-09</td>\n",
279
- " <td>0.981498</td>\n",
280
- " <td>485000.0</td>\n",
281
- " <td>503423.0</td>\n",
282
- " <td>503423.0</td>\n",
283
- " <td>0.658777</td>\n",
284
- " <td>0.977990</td>\n",
285
- " <td>0.100000</td>\n",
286
- " <td>0.970073</td>\n",
287
- " <td>0.981278</td>\n",
288
- " <td>0.600000</td>\n",
289
- " <td>0.114463</td>\n",
290
- " </tr>\n",
291
- " </tbody>\n",
292
- "</table>\n",
293
- "<p>255024 rows × 18 columns</p>\n",
294
- "</div>"
295
- ],
296
- "text/plain": [
297
- " Region ID Size Rank Region Region Type State \\\n",
298
- "0 102001 0 United States country None \n",
299
- "1 102001 0 United States country None \n",
300
- "2 102001 0 United States country None \n",
301
- "3 102001 0 United States country None \n",
302
- "4 102001 0 United States country None \n",
303
- "... ... ... ... ... ... \n",
304
- "255019 845160 198 Prescott Valley, AZ msa AZ \n",
305
- "255020 845160 198 Prescott Valley, AZ msa AZ \n",
306
- "255021 845160 198 Prescott Valley, AZ msa AZ \n",
307
- "255022 845160 198 Prescott Valley, AZ msa AZ \n",
308
- "255023 845160 198 Prescott Valley, AZ msa AZ \n",
309
- "\n",
310
- " Home Type Date Median Sale to List Ratio Median Sale Price \\\n",
311
- "0 SFR 2008-02-02 NaN 172000.0 \n",
312
- "1 SFR 2008-02-09 NaN 165400.0 \n",
313
- "2 SFR 2008-02-16 NaN 168000.0 \n",
314
- "3 SFR 2008-02-23 NaN 167600.0 \n",
315
- "4 SFR 2008-03-01 NaN 168100.0 \n",
316
- "... ... ... ... ... \n",
317
- "255019 all homes 2023-11-11 0.985132 515000.0 \n",
318
- "255020 all homes 2023-11-18 0.972559 510000.0 \n",
319
- "255021 all homes 2023-11-25 0.979644 484500.0 \n",
320
- "255022 all homes 2023-12-02 0.978261 538000.0 \n",
321
- "255023 all homes 2023-12-09 0.981498 485000.0 \n",
322
- "\n",
323
- " Median Sale Price (Smoothed) (Seasonally Adjusted) \\\n",
324
- "0 NaN \n",
325
- "1 NaN \n",
326
- "2 NaN \n",
327
- "3 NaN \n",
328
- "4 NaN \n",
329
- "... ... \n",
330
- "255019 480020.0 \n",
331
- "255020 476901.0 \n",
332
- "255021 496540.0 \n",
333
- "255022 510491.0 \n",
334
- "255023 503423.0 \n",
335
- "\n",
336
- " Median Sale Price (Smoothed) % Sold Below List (Smoothed) \\\n",
337
- "0 NaN NaN \n",
338
- "1 NaN NaN \n",
339
- "2 NaN NaN \n",
340
- "3 167600.0 NaN \n",
341
- "4 168100.0 NaN \n",
342
- "... ... ... \n",
343
- "255019 480020.0 0.651221 \n",
344
- "255020 476901.0 0.659583 \n",
345
- "255021 496540.0 0.669387 \n",
346
- "255022 510491.0 0.678777 \n",
347
- "255023 503423.0 0.658777 \n",
348
- "\n",
349
- " Median Sale to List Ratio (Smoothed) % Sold Above List \\\n",
350
- "0 NaN NaN \n",
351
- "1 NaN NaN \n",
352
- "2 NaN NaN \n",
353
- "3 NaN NaN \n",
354
- "4 NaN NaN \n",
355
- "... ... ... \n",
356
- "255019 0.982460 0.080000 \n",
357
- "255020 0.980362 0.142857 \n",
358
- "255021 0.979179 0.088235 \n",
359
- "255022 0.978899 0.126761 \n",
360
- "255023 0.977990 0.100000 \n",
361
- "\n",
362
- " Mean Sale to List Ratio (Smoothed) Mean Sale to List Ratio \\\n",
363
- "0 NaN NaN \n",
364
- "1 NaN NaN \n",
365
- "2 NaN NaN \n",
366
- "3 NaN NaN \n",
367
- "4 NaN NaN \n",
368
- "... ... ... \n",
369
- "255019 0.978546 0.983288 \n",
370
- "255020 0.972912 0.958341 \n",
371
- "255021 0.971177 0.973797 \n",
372
- "255022 0.970576 0.966876 \n",
373
- "255023 0.970073 0.981278 \n",
374
- "\n",
375
- " % Sold Below List % Sold Above List (Smoothed) \n",
376
- "0 NaN NaN \n",
377
- "1 NaN NaN \n",
378
- "2 NaN NaN \n",
379
- "3 NaN NaN \n",
380
- "4 NaN NaN \n",
381
- "... ... ... \n",
382
- "255019 0.680000 0.119711 \n",
383
- "255020 0.625000 0.120214 \n",
384
- "255021 0.705882 0.107185 \n",
385
- "255022 0.704225 0.109463 \n",
386
- "255023 0.600000 0.114463 \n",
387
- "\n",
388
- "[255024 rows x 18 columns]"
389
- ]
390
- },
391
- "execution_count": 2,
392
- "metadata": {},
393
- "output_type": "execute_result"
394
- }
395
- ],
396
  "source": [
397
- "# read the data\n",
398
- "x = pd.read_json(\"processed/sales/final5.jsonl\", lines=True)\n",
399
- "x"
400
  ]
401
  },
402
  {
403
  "cell_type": "code",
404
- "execution_count": 33,
405
  "metadata": {},
406
  "outputs": [
407
  {
408
- "data": {
409
- "text/plain": [
410
- "array(['country', 'msa'], dtype=object)"
411
- ]
412
- },
413
- "execution_count": 33,
414
- "metadata": {},
415
- "output_type": "execute_result"
416
- }
417
- ],
418
- "source": [
419
- "# get unique values for column\n",
420
- "x[\"Region Type\"].unique()"
421
- ]
422
- },
423
- {
424
- "cell_type": "code",
425
- "execution_count": 32,
426
- "metadata": {},
427
- "outputs": [
428
  {
429
- "data": {
430
- "text/plain": [
431
- "array(['SFR', 'all homes'], dtype=object)"
432
- ]
433
- },
434
- "execution_count": 32,
435
- "metadata": {},
436
- "output_type": "execute_result"
437
- }
438
- ],
439
- "source": [
440
- "x[\"Home Type\"].unique()"
441
- ]
442
- },
443
- {
444
- "cell_type": "code",
445
- "execution_count": 15,
446
- "metadata": {},
447
- "outputs": [
448
  {
449
- "data": {
450
- "text/plain": [
451
- "array(['1-Bedroom', '2-Bedrooms', '3-Bedrooms', '4-Bedrooms',\n",
452
- " '5+-Bedrooms', 'All Bedrooms'], dtype=object)"
453
- ]
454
- },
455
- "execution_count": 15,
456
- "metadata": {},
457
- "output_type": "execute_result"
458
- }
459
- ],
460
- "source": [
461
- "x[\"Bedroom Count\"].unique()"
462
- ]
463
- },
464
- {
465
- "cell_type": "code",
466
- "execution_count": 8,
467
- "metadata": {},
468
- "outputs": [],
469
- "source": [
470
- "from datasets import load_dataset"
471
- ]
472
- },
473
- {
474
- "cell_type": "code",
475
- "execution_count": 19,
476
- "metadata": {},
477
- "outputs": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
478
  {
479
  "name": "stdout",
480
  "output_type": "stream",
@@ -486,10 +114,47 @@
486
  "name": "stderr",
487
  "output_type": "stream",
488
  "text": [
489
- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 14.2MB/s]\n",
490
- "Downloading readme: 100%|██████████| 21.7k/21.7k [00:00<00:00, 3.80MB/s]\n",
491
- "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 32.2MB/s] \n",
492
- "Generating train split: 100%|██████████| 255024/255024 [00:10<00:00, 24068.33 examples/s]\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
493
  ]
494
  }
495
  ],
@@ -497,13 +162,13 @@
497
  "dataset_dict = {}\n",
498
  "\n",
499
  "configs = [\n",
500
- " # \"home_values_forecasts\",\n",
501
- " # \"new_construction\",\n",
502
- " # \"for_sale_listings\",\n",
503
- " # \"rentals\",\n",
 
 
504
  " \"sales\",\n",
505
- " # \"home_values\",\n",
506
- " # \"days_on_market\",\n",
507
  "]\n",
508
  "for config in configs:\n",
509
  " print(config)\n",
@@ -513,40 +178,24 @@
513
  " trust_remote_code=True,\n",
514
  " download_mode=\"force_redownload\",\n",
515
  " cache_dir=\"./cache\",\n",
516
- " )"
 
 
517
  ]
518
  },
519
  {
520
  "cell_type": "code",
521
- "execution_count": 40,
522
  "metadata": {},
523
- "outputs": [
524
- {
525
- "ename": "ArrowInvalid",
526
- "evalue": "Not a Feather V1 or Arrow IPC file",
527
- "output_type": "error",
528
- "traceback": [
529
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
530
- "\u001b[0;31mArrowInvalid\u001b[0m Traceback (most recent call last)",
531
- "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",
532
- "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",
533
- "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",
534
- "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",
535
- "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",
536
- "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",
537
- "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",
538
- "\u001b[0;31mArrowInvalid\u001b[0m: Not a Feather V1 or Arrow IPC file"
539
- ]
540
- }
541
- ],
542
  "source": [
543
- "import pyarrow as pa\n",
544
  "\n",
545
  "\n",
546
- "df = pd.read_feather(\n",
547
- " \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
548
- ")\n",
549
- "df"
550
  ]
551
  },
552
  {
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 14,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
9
+ "# # import json as pandas\n",
10
+ "# import pandas as pd\n",
11
+ "# # read the data\n",
12
+ "# x = pd.read_json(\"processed/sales/final5.jsonl\", lines=True)\n",
13
+ "# # x\n",
14
+ "# x[\"Region Type\"].unique()\n",
15
+ "# x[\"Home Type\"].unique()\n",
16
+ "# x[\"Bedroom Count\"].unique()"
17
  ]
18
  },
19
  {
20
  "cell_type": "code",
21
+ "execution_count": 17,
22
  "metadata": {},
23
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
24
  "source": [
25
+ "from datasets import load_dataset\n",
26
+ "from os import path"
 
27
  ]
28
  },
29
  {
30
  "cell_type": "code",
31
+ "execution_count": 19,
32
  "metadata": {},
33
  "outputs": [
34
  {
35
+ "name": "stdout",
36
+ "output_type": "stream",
37
+ "text": [
38
+ "home_values_forecasts\n"
39
+ ]
40
+ },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41
  {
42
+ "name": "stderr",
43
+ "output_type": "stream",
44
+ "text": [
45
+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 9.97MB/s]\n",
46
+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 24.7MB/s]\n",
47
+ "Downloading data: 100%|██████████| 14.1M/14.1M [00:00<00:00, 21.5MB/s]\n",
48
+ "Generating train split: 100%|██████████| 31854/31854 [00:01<00:00, 26905.24 examples/s]\n",
49
+ "Creating parquet from Arrow format: 100%|██████████| 32/32 [00:00<00:00, 813.13ba/s]\n"
50
+ ]
51
+ },
 
 
 
 
 
 
 
 
 
52
  {
53
+ "name": "stdout",
54
+ "output_type": "stream",
55
+ "text": [
56
+ "new_construction\n"
57
+ ]
58
+ },
59
+ {
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+ "name": "stderr",
61
+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 16.8MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 28.7MB/s]\n",
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+ "Downloading data: 100%|██████████| 10.9M/10.9M [00:00<00:00, 21.7MB/s]\n",
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+ "Generating train split: 100%|██████████| 49487/49487 [00:01<00:00, 38197.59 examples/s]\n",
67
+ "Creating parquet from Arrow format: 100%|██████████| 50/50 [00:00<00:00, 1691.95ba/s]\n"
68
+ ]
69
+ },
70
+ {
71
+ "name": "stdout",
72
+ "output_type": "stream",
73
+ "text": [
74
+ "for_sale_listings\n"
75
+ ]
76
+ },
77
+ {
78
+ "name": "stderr",
79
+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 2.19MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 19.1MB/s]\n",
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+ "Downloading data: 100%|██████████| 180M/180M [00:04<00:00, 37.8MB/s] \n",
84
+ "Generating train split: 100%|██████████| 578653/578653 [00:18<00:00, 31984.31 examples/s]\n",
85
+ "Creating parquet from Arrow format: 100%|██████████| 579/579 [00:00<00:00, 1326.61ba/s]\n"
86
+ ]
87
+ },
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+ {
89
+ "name": "stdout",
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+ "output_type": "stream",
91
+ "text": [
92
+ "rentals\n"
93
+ ]
94
+ },
95
+ {
96
+ "name": "stderr",
97
+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 6.31MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 15.0MB/s]\n",
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+ "Downloading data: 100%|██████████| 447M/447M [00:13<00:00, 32.0MB/s] \n",
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+ "Generating train split: 100%|██████████| 1258740/1258740 [00:31<00:00, 40439.23 examples/s]\n",
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+ "Creating parquet from Arrow format: 100%|██████████| 1259/1259 [00:00<00:00, 1671.78ba/s]\n"
104
+ ]
105
+ },
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  {
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  "name": "stdout",
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  "output_type": "stream",
 
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  "name": "stderr",
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  "output_type": "stream",
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  "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 16.1MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 14.9MB/s]\n",
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+ "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 34.1MB/s] \n",
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+ "Generating train split: 100%|██████████| 255024/255024 [00:10<00:00, 24278.38 examples/s]\n",
121
+ "Creating parquet from Arrow format: 100%|██████████| 256/256 [00:00<00:00, 862.81ba/s]\n"
122
+ ]
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+ },
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+ {
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+ "name": "stdout",
126
+ "output_type": "stream",
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+ "text": [
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+ "home_values\n"
129
+ ]
130
+ },
131
+ {
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+ "name": "stderr",
133
+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 11.3MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 12.2MB/s]\n",
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+ "Downloading data: 100%|██████████| 41.1M/41.1M [00:01<00:00, 34.2MB/s]\n",
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+ "Generating train split: 100%|██████████| 117912/117912 [00:03<00:00, 34804.14 examples/s]\n",
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+ "Creating parquet from Arrow format: 100%|██████████| 118/118 [00:00<00:00, 1397.82ba/s]\n"
140
+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "days_on_market\n"
147
+ ]
148
+ },
149
+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 26.9k/26.9k [00:00<00:00, 6.99MB/s]\n",
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+ "Downloading readme: 100%|██████████| 24.0k/24.0k [00:00<00:00, 8.94MB/s]\n",
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+ "Downloading data: 100%|██████████| 229M/229M [00:06<00:00, 36.6MB/s] \n",
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+ "Generating train split: 100%|██████████| 586714/586714 [00:18<00:00, 31198.29 examples/s]\n",
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+ "Creating parquet from Arrow format: 100%|██████████| 587/587 [00:00<00:00, 1241.06ba/s]\n"
158
  ]
159
  }
160
  ],
 
162
  "dataset_dict = {}\n",
163
  "\n",
164
  "configs = [\n",
165
+ " \"days_on_market\",\n",
166
+ " \"for_sale_listings\",\n",
167
+ " \"home_values\",\n",
168
+ " \"home_values_forecasts\",\n",
169
+ " \"new_construction\",\n",
170
+ " \"rentals\",\n",
171
  " \"sales\",\n",
 
 
172
  "]\n",
173
  "for config in configs:\n",
174
  " print(config)\n",
 
178
  " trust_remote_code=True,\n",
179
  " download_mode=\"force_redownload\",\n",
180
  " cache_dir=\"./cache\",\n",
181
+ " )\n",
182
+ " filename = path.join(\"parquet_files\", config + \".parquet\")\n",
183
+ " dataset_dict[config][\"train\"].to_parquet(filename)"
184
  ]
185
  },
186
  {
187
  "cell_type": "code",
188
+ "execution_count": 18,
189
  "metadata": {},
190
+ "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
191
  "source": [
192
+ "# import pyarrow as pa\n",
193
  "\n",
194
  "\n",
195
+ "# df = pd.read_feather(\n",
196
+ "# \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
197
+ "# )\n",
198
+ "# df"
199
  ]
200
  },
201
  {
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