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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
864476a
·
1 Parent(s): 416395d

fix: widen df a little

Browse files
Files changed (3) hide show
  1. processed/new_constructions/final.jsonl +2 -2
  2. processors/process_new_constructions.ipynb +320 -240
  3. zillow.py +8 -2
processed/new_constructions/final.jsonl CHANGED
@@ -1,3 +1,3 @@
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+ size 9865188
processors/process_new_constructions.ipynb CHANGED
@@ -2,7 +2,7 @@
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  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 6,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -12,7 +12,7 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 7,
16
  "metadata": {},
17
  "outputs": [],
18
  "source": [
@@ -25,7 +25,7 @@
25
  },
26
  {
27
  "cell_type": "code",
28
- "execution_count": 8,
29
  "metadata": {},
30
  "outputs": [
31
  {
@@ -69,10 +69,11 @@
69
  " <th>RegionName</th>\n",
70
  " <th>RegionType</th>\n",
71
  " <th>StateName</th>\n",
72
- " <th>Value Type</th>\n",
73
  " <th>Home Type</th>\n",
74
  " <th>Date</th>\n",
75
- " <th>Value</th>\n",
 
 
76
  " </tr>\n",
77
  " </thead>\n",
78
  " <tbody>\n",
@@ -83,58 +84,63 @@
83
  " <td>United States</td>\n",
84
  " <td>country</td>\n",
85
  " <td>NaN</td>\n",
86
- " <td>Count</td>\n",
87
- " <td>Condo</td>\n",
88
  " <td>2018-01-31</td>\n",
89
- " <td>3195.000000</td>\n",
 
 
90
  " </tr>\n",
91
  " <tr>\n",
92
  " <th>1</th>\n",
93
- " <td>394913</td>\n",
94
- " <td>1</td>\n",
95
- " <td>New York, NY</td>\n",
96
- " <td>msa</td>\n",
97
- " <td>NY</td>\n",
98
- " <td>Count</td>\n",
99
- " <td>Condo</td>\n",
100
  " <td>2018-01-31</td>\n",
101
- " <td>137.000000</td>\n",
 
 
102
  " </tr>\n",
103
  " <tr>\n",
104
  " <th>2</th>\n",
105
- " <td>753899</td>\n",
106
- " <td>2</td>\n",
107
- " <td>Los Angeles, CA</td>\n",
108
- " <td>msa</td>\n",
109
- " <td>CA</td>\n",
110
- " <td>Count</td>\n",
111
- " <td>Condo</td>\n",
112
  " <td>2018-01-31</td>\n",
113
- " <td>148.000000</td>\n",
 
 
114
  " </tr>\n",
115
  " <tr>\n",
116
  " <th>3</th>\n",
117
- " <td>395209</td>\n",
118
- " <td>6</td>\n",
119
- " <td>Washington, DC</td>\n",
120
- " <td>msa</td>\n",
121
- " <td>VA</td>\n",
122
- " <td>Count</td>\n",
123
- " <td>Condo</td>\n",
124
- " <td>2018-01-31</td>\n",
125
  " <td>NaN</td>\n",
 
 
 
 
 
126
  " </tr>\n",
127
  " <tr>\n",
128
  " <th>4</th>\n",
129
- " <td>394856</td>\n",
130
- " <td>8</td>\n",
131
- " <td>Miami, FL</td>\n",
132
- " <td>msa</td>\n",
133
- " <td>FL</td>\n",
134
- " <td>Count</td>\n",
135
- " <td>Condo</td>\n",
136
- " <td>2018-01-31</td>\n",
137
- " <td>195.000000</td>\n",
 
138
  " </tr>\n",
139
  " <tr>\n",
140
  " <th>...</th>\n",
@@ -147,103 +153,109 @@
147
  " <td>...</td>\n",
148
  " <td>...</td>\n",
149
  " <td>...</td>\n",
 
150
  " </tr>\n",
151
  " <tr>\n",
152
- " <th>11071</th>\n",
153
- " <td>753912</td>\n",
154
- " <td>398</td>\n",
155
- " <td>Pinehurst, NC</td>\n",
156
  " <td>msa</td>\n",
157
- " <td>NC</td>\n",
158
- " <td>Sale Price Per Sqft</td>\n",
159
- " <td>SFR/Condo</td>\n",
160
- " <td>2023-11-30</td>\n",
161
- " <td>179.300292</td>\n",
 
162
  " </tr>\n",
163
  " <tr>\n",
164
- " <th>11072</th>\n",
165
- " <td>395086</td>\n",
166
- " <td>402</td>\n",
167
- " <td>Sevierville, TN</td>\n",
168
  " <td>msa</td>\n",
169
- " <td>TN</td>\n",
170
- " <td>Sale Price Per Sqft</td>\n",
171
- " <td>SFR/Condo</td>\n",
172
- " <td>2023-11-30</td>\n",
173
- " <td>389.559797</td>\n",
 
174
  " </tr>\n",
175
  " <tr>\n",
176
- " <th>11073</th>\n",
177
- " <td>394682</td>\n",
178
- " <td>449</td>\n",
179
- " <td>Hinesville, GA</td>\n",
180
  " <td>msa</td>\n",
181
- " <td>GA</td>\n",
182
- " <td>Sale Price Per Sqft</td>\n",
183
- " <td>SFR/Condo</td>\n",
184
- " <td>2023-11-30</td>\n",
185
- " <td>143.916102</td>\n",
 
186
  " </tr>\n",
187
  " <tr>\n",
188
- " <th>11074</th>\n",
189
- " <td>394674</td>\n",
190
- " <td>467</td>\n",
191
- " <td>Heber, UT</td>\n",
192
  " <td>msa</td>\n",
193
- " <td>UT</td>\n",
194
- " <td>Sale Price Per Sqft</td>\n",
195
- " <td>SFR/Condo</td>\n",
196
  " <td>2023-11-30</td>\n",
197
- " <td>567.030369</td>\n",
 
 
198
  " </tr>\n",
199
  " <tr>\n",
200
- " <th>11075</th>\n",
201
- " <td>753893</td>\n",
202
- " <td>477</td>\n",
203
- " <td>Jefferson, GA</td>\n",
204
  " <td>msa</td>\n",
205
- " <td>GA</td>\n",
206
- " <td>Sale Price Per Sqft</td>\n",
207
- " <td>SFR/Condo</td>\n",
208
  " <td>2023-11-30</td>\n",
209
- " <td>193.031691</td>\n",
 
 
210
  " </tr>\n",
211
  " </tbody>\n",
212
  "</table>\n",
213
- "<p>95850 rows × 9 columns</p>\n",
214
  "</div>"
215
  ],
216
  "text/plain": [
217
- " RegionID SizeRank RegionName RegionType StateName \\\n",
218
- "0 102001 0 United States country NaN \n",
219
- "1 394913 1 New York, NY msa NY \n",
220
- "2 753899 2 Los Angeles, CA msa CA \n",
221
- "3 395209 6 Washington, DC msa VA \n",
222
- "4 394856 8 Miami, FL msa FL \n",
223
- "... ... ... ... ... ... \n",
224
- "11071 753912 398 Pinehurst, NC msa NC \n",
225
- "11072 395086 402 Sevierville, TN msa TN \n",
226
- "11073 394682 449 Hinesville, GA msa GA \n",
227
- "11074 394674 467 Heber, UT msa UT \n",
228
- "11075 753893 477 Jefferson, GA msa GA \n",
229
  "\n",
230
- " Value Type Home Type Date Value \n",
231
- "0 Count Condo 2018-01-31 3195.000000 \n",
232
- "1 Count Condo 2018-01-31 137.000000 \n",
233
- "2 Count Condo 2018-01-31 148.000000 \n",
234
- "3 Count Condo 2018-01-31 NaN \n",
235
- "4 Count Condo 2018-01-31 195.000000 \n",
236
- "... ... ... ... ... \n",
237
- "11071 Sale Price Per Sqft SFR/Condo 2023-11-30 179.300292 \n",
238
- "11072 Sale Price Per Sqft SFR/Condo 2023-11-30 389.559797 \n",
239
- "11073 Sale Price Per Sqft SFR/Condo 2023-11-30 143.916102 \n",
240
- "11074 Sale Price Per Sqft SFR/Condo 2023-11-30 567.030369 \n",
241
- "11075 Sale Price Per Sqft SFR/Condo 2023-11-30 193.031691 \n",
242
  "\n",
243
- "[95850 rows x 9 columns]"
244
  ]
245
  },
246
- "execution_count": 8,
247
  "metadata": {},
248
  "output_type": "execute_result"
249
  }
@@ -259,49 +271,98 @@
259
  " \"RegionName\",\n",
260
  " \"RegionType\",\n",
261
  " \"StateName\",\n",
262
- " \"Value Type\",\n",
263
  " \"Home Type\",\n",
264
  "]\n",
265
  "\n",
 
 
 
 
266
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
267
  " if filename.endswith(\".csv\"):\n",
268
  " print(\"processing \" + filename)\n",
269
  " cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
270
- " if \"sale_price_per_sqft\" in filename:\n",
271
- " cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
272
- " elif \"sale_price_uc\" in filename:\n",
273
- " cur_df[\"Value Type\"] = \"Sale Price\"\n",
274
- " elif \"count\" in filename:\n",
275
- " cur_df[\"Value Type\"] = \"Count\"\n",
276
  "\n",
277
  " if \"sfrcondo\" in filename:\n",
278
- " cur_df[\"Home Type\"] = \"SFR/Condo\"\n",
279
  " elif \"sfr\" in filename:\n",
280
  " cur_df[\"Home Type\"] = \"SFR\"\n",
281
  " elif \"condo\" in filename:\n",
282
- " cur_df[\"Home Type\"] = \"Condo\"\n",
283
  "\n",
284
  " # Identify columns to pivot\n",
285
  " columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
286
  "\n",
287
- " # Perform pivot\n",
288
- " cur_df = pd.melt(\n",
289
- " cur_df,\n",
290
- " id_vars=exclude_columns,\n",
291
- " value_vars=columns_to_pivot,\n",
292
- " var_name=\"Date\",\n",
293
- " value_name=\"Value\",\n",
294
- " )\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
295
  "\n",
296
- " data_frames.append(cur_df)\n",
 
 
 
 
 
 
 
 
 
 
 
297
  "\n",
298
- "combined_df = pd.concat(data_frames)\n",
299
  "combined_df"
300
  ]
301
  },
302
  {
303
  "cell_type": "code",
304
- "execution_count": 9,
305
  "metadata": {},
306
  "outputs": [
307
  {
@@ -330,10 +391,11 @@
330
  " <th>Region</th>\n",
331
  " <th>Region Type</th>\n",
332
  " <th>State</th>\n",
333
- " <th>Value Type</th>\n",
334
  " <th>Home Type</th>\n",
335
  " <th>Date</th>\n",
336
- " <th>Value</th>\n",
 
 
337
  " </tr>\n",
338
  " </thead>\n",
339
  " <tbody>\n",
@@ -344,58 +406,63 @@
344
  " <td>United States</td>\n",
345
  " <td>country</td>\n",
346
  " <td>NaN</td>\n",
347
- " <td>Count</td>\n",
348
- " <td>Condo</td>\n",
349
  " <td>2018-01-31</td>\n",
350
- " <td>3195.000000</td>\n",
 
 
351
  " </tr>\n",
352
  " <tr>\n",
353
  " <th>1</th>\n",
354
- " <td>394913</td>\n",
355
- " <td>1</td>\n",
356
- " <td>New York, NY</td>\n",
357
- " <td>msa</td>\n",
358
- " <td>NY</td>\n",
359
- " <td>Count</td>\n",
360
- " <td>Condo</td>\n",
361
  " <td>2018-01-31</td>\n",
362
- " <td>137.000000</td>\n",
 
 
363
  " </tr>\n",
364
  " <tr>\n",
365
  " <th>2</th>\n",
366
- " <td>753899</td>\n",
367
- " <td>2</td>\n",
368
- " <td>Los Angeles, CA</td>\n",
369
- " <td>msa</td>\n",
370
- " <td>CA</td>\n",
371
- " <td>Count</td>\n",
372
- " <td>Condo</td>\n",
373
  " <td>2018-01-31</td>\n",
374
- " <td>148.000000</td>\n",
 
 
375
  " </tr>\n",
376
  " <tr>\n",
377
  " <th>3</th>\n",
378
- " <td>395209</td>\n",
379
- " <td>6</td>\n",
380
- " <td>Washington, DC</td>\n",
381
- " <td>msa</td>\n",
382
- " <td>VA</td>\n",
383
- " <td>Count</td>\n",
384
- " <td>Condo</td>\n",
385
- " <td>2018-01-31</td>\n",
386
  " <td>NaN</td>\n",
 
 
 
 
 
387
  " </tr>\n",
388
  " <tr>\n",
389
  " <th>4</th>\n",
390
- " <td>394856</td>\n",
391
- " <td>8</td>\n",
392
- " <td>Miami, FL</td>\n",
393
- " <td>msa</td>\n",
394
- " <td>FL</td>\n",
395
- " <td>Count</td>\n",
396
- " <td>Condo</td>\n",
397
- " <td>2018-01-31</td>\n",
398
- " <td>195.000000</td>\n",
 
399
  " </tr>\n",
400
  " <tr>\n",
401
  " <th>...</th>\n",
@@ -408,103 +475,109 @@
408
  " <td>...</td>\n",
409
  " <td>...</td>\n",
410
  " <td>...</td>\n",
 
411
  " </tr>\n",
412
  " <tr>\n",
413
- " <th>11071</th>\n",
414
- " <td>753912</td>\n",
415
- " <td>398</td>\n",
416
- " <td>Pinehurst, NC</td>\n",
417
  " <td>msa</td>\n",
418
- " <td>NC</td>\n",
419
- " <td>Sale Price Per Sqft</td>\n",
420
- " <td>SFR/Condo</td>\n",
421
- " <td>2023-11-30</td>\n",
422
- " <td>179.300292</td>\n",
 
423
  " </tr>\n",
424
  " <tr>\n",
425
- " <th>11072</th>\n",
426
- " <td>395086</td>\n",
427
- " <td>402</td>\n",
428
- " <td>Sevierville, TN</td>\n",
429
  " <td>msa</td>\n",
430
- " <td>TN</td>\n",
431
- " <td>Sale Price Per Sqft</td>\n",
432
- " <td>SFR/Condo</td>\n",
433
- " <td>2023-11-30</td>\n",
434
- " <td>389.559797</td>\n",
 
435
  " </tr>\n",
436
  " <tr>\n",
437
- " <th>11073</th>\n",
438
- " <td>394682</td>\n",
439
- " <td>449</td>\n",
440
- " <td>Hinesville, GA</td>\n",
441
  " <td>msa</td>\n",
442
- " <td>GA</td>\n",
443
- " <td>Sale Price Per Sqft</td>\n",
444
- " <td>SFR/Condo</td>\n",
445
- " <td>2023-11-30</td>\n",
446
- " <td>143.916102</td>\n",
 
447
  " </tr>\n",
448
  " <tr>\n",
449
- " <th>11074</th>\n",
450
- " <td>394674</td>\n",
451
- " <td>467</td>\n",
452
- " <td>Heber, UT</td>\n",
453
  " <td>msa</td>\n",
454
- " <td>UT</td>\n",
455
- " <td>Sale Price Per Sqft</td>\n",
456
- " <td>SFR/Condo</td>\n",
457
  " <td>2023-11-30</td>\n",
458
- " <td>567.030369</td>\n",
 
 
459
  " </tr>\n",
460
  " <tr>\n",
461
- " <th>11075</th>\n",
462
- " <td>753893</td>\n",
463
- " <td>477</td>\n",
464
- " <td>Jefferson, GA</td>\n",
465
  " <td>msa</td>\n",
466
- " <td>GA</td>\n",
467
- " <td>Sale Price Per Sqft</td>\n",
468
- " <td>SFR/Condo</td>\n",
469
  " <td>2023-11-30</td>\n",
470
- " <td>193.031691</td>\n",
 
 
471
  " </tr>\n",
472
  " </tbody>\n",
473
  "</table>\n",
474
- "<p>95850 rows × 9 columns</p>\n",
475
  "</div>"
476
  ],
477
  "text/plain": [
478
- " Region ID Size Rank Region Region Type State \\\n",
479
- "0 102001 0 United States country NaN \n",
480
- "1 394913 1 New York, NY msa NY \n",
481
- "2 753899 2 Los Angeles, CA msa CA \n",
482
- "3 395209 6 Washington, DC msa VA \n",
483
- "4 394856 8 Miami, FL msa FL \n",
484
- "... ... ... ... ... ... \n",
485
- "11071 753912 398 Pinehurst, NC msa NC \n",
486
- "11072 395086 402 Sevierville, TN msa TN \n",
487
- "11073 394682 449 Hinesville, GA msa GA \n",
488
- "11074 394674 467 Heber, UT msa UT \n",
489
- "11075 753893 477 Jefferson, GA msa GA \n",
490
  "\n",
491
- " Value Type Home Type Date Value \n",
492
- "0 Count Condo 2018-01-31 3195.000000 \n",
493
- "1 Count Condo 2018-01-31 137.000000 \n",
494
- "2 Count Condo 2018-01-31 148.000000 \n",
495
- "3 Count Condo 2018-01-31 NaN \n",
496
- "4 Count Condo 2018-01-31 195.000000 \n",
497
- "... ... ... ... ... \n",
498
- "11071 Sale Price Per Sqft SFR/Condo 2023-11-30 179.300292 \n",
499
- "11072 Sale Price Per Sqft SFR/Condo 2023-11-30 389.559797 \n",
500
- "11073 Sale Price Per Sqft SFR/Condo 2023-11-30 143.916102 \n",
501
- "11074 Sale Price Per Sqft SFR/Condo 2023-11-30 567.030369 \n",
502
- "11075 Sale Price Per Sqft SFR/Condo 2023-11-30 193.031691 \n",
503
  "\n",
504
- "[95850 rows x 9 columns]"
505
  ]
506
  },
507
- "execution_count": 9,
508
  "metadata": {},
509
  "output_type": "execute_result"
510
  }
@@ -526,7 +599,7 @@
526
  },
527
  {
528
  "cell_type": "code",
529
- "execution_count": 10,
530
  "metadata": {},
531
  "outputs": [],
532
  "source": [
@@ -535,6 +608,13 @@
535
  "\n",
536
  "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
537
  ]
 
 
 
 
 
 
 
538
  }
539
  ],
540
  "metadata": {
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
 
12
  },
13
  {
14
  "cell_type": "code",
15
+ "execution_count": 3,
16
  "metadata": {},
17
  "outputs": [],
18
  "source": [
 
25
  },
26
  {
27
  "cell_type": "code",
28
+ "execution_count": 47,
29
  "metadata": {},
30
  "outputs": [
31
  {
 
69
  " <th>RegionName</th>\n",
70
  " <th>RegionType</th>\n",
71
  " <th>StateName</th>\n",
 
72
  " <th>Home Type</th>\n",
73
  " <th>Date</th>\n",
74
+ " <th>Sale Price</th>\n",
75
+ " <th>Sale Price per Sqft</th>\n",
76
+ " <th>Count</th>\n",
77
  " </tr>\n",
78
  " </thead>\n",
79
  " <tbody>\n",
 
84
  " <td>United States</td>\n",
85
  " <td>country</td>\n",
86
  " <td>NaN</td>\n",
87
+ " <td>SFR</td>\n",
 
88
  " <td>2018-01-31</td>\n",
89
+ " <td>309000.0</td>\n",
90
+ " <td>137.412316</td>\n",
91
+ " <td>33940.0</td>\n",
92
  " </tr>\n",
93
  " <tr>\n",
94
  " <th>1</th>\n",
95
+ " <td>102001</td>\n",
96
+ " <td>0</td>\n",
97
+ " <td>United States</td>\n",
98
+ " <td>country</td>\n",
99
+ " <td>NaN</td>\n",
100
+ " <td>all homes</td>\n",
 
101
  " <td>2018-01-31</td>\n",
102
+ " <td>314596.0</td>\n",
103
+ " <td>140.504620</td>\n",
104
+ " <td>37135.0</td>\n",
105
  " </tr>\n",
106
  " <tr>\n",
107
  " <th>2</th>\n",
108
+ " <td>102001</td>\n",
109
+ " <td>0</td>\n",
110
+ " <td>United States</td>\n",
111
+ " <td>country</td>\n",
112
+ " <td>NaN</td>\n",
113
+ " <td>condo/co-op only</td>\n",
 
114
  " <td>2018-01-31</td>\n",
115
+ " <td>388250.0</td>\n",
116
+ " <td>238.300000</td>\n",
117
+ " <td>3195.0</td>\n",
118
  " </tr>\n",
119
  " <tr>\n",
120
  " <th>3</th>\n",
121
+ " <td>102001</td>\n",
122
+ " <td>0</td>\n",
123
+ " <td>United States</td>\n",
124
+ " <td>country</td>\n",
 
 
 
 
125
  " <td>NaN</td>\n",
126
+ " <td>SFR</td>\n",
127
+ " <td>2018-02-28</td>\n",
128
+ " <td>309072.5</td>\n",
129
+ " <td>137.199170</td>\n",
130
+ " <td>33304.0</td>\n",
131
  " </tr>\n",
132
  " <tr>\n",
133
  " <th>4</th>\n",
134
+ " <td>102001</td>\n",
135
+ " <td>0</td>\n",
136
+ " <td>United States</td>\n",
137
+ " <td>country</td>\n",
138
+ " <td>NaN</td>\n",
139
+ " <td>all homes</td>\n",
140
+ " <td>2018-02-28</td>\n",
141
+ " <td>314608.0</td>\n",
142
+ " <td>140.304966</td>\n",
143
+ " <td>36493.0</td>\n",
144
  " </tr>\n",
145
  " <tr>\n",
146
  " <th>...</th>\n",
 
153
  " <td>...</td>\n",
154
  " <td>...</td>\n",
155
  " <td>...</td>\n",
156
+ " <td>...</td>\n",
157
  " </tr>\n",
158
  " <tr>\n",
159
+ " <th>49482</th>\n",
160
+ " <td>845162</td>\n",
161
+ " <td>535</td>\n",
162
+ " <td>Granbury, TX</td>\n",
163
  " <td>msa</td>\n",
164
+ " <td>TX</td>\n",
165
+ " <td>all homes</td>\n",
166
+ " <td>2023-09-30</td>\n",
167
+ " <td>NaN</td>\n",
168
+ " <td>NaN</td>\n",
169
+ " <td>26.0</td>\n",
170
  " </tr>\n",
171
  " <tr>\n",
172
+ " <th>49483</th>\n",
173
+ " <td>845162</td>\n",
174
+ " <td>535</td>\n",
175
+ " <td>Granbury, TX</td>\n",
176
  " <td>msa</td>\n",
177
+ " <td>TX</td>\n",
178
+ " <td>SFR</td>\n",
179
+ " <td>2023-10-31</td>\n",
180
+ " <td>NaN</td>\n",
181
+ " <td>NaN</td>\n",
182
+ " <td>24.0</td>\n",
183
  " </tr>\n",
184
  " <tr>\n",
185
+ " <th>49484</th>\n",
186
+ " <td>845162</td>\n",
187
+ " <td>535</td>\n",
188
+ " <td>Granbury, TX</td>\n",
189
  " <td>msa</td>\n",
190
+ " <td>TX</td>\n",
191
+ " <td>all homes</td>\n",
192
+ " <td>2023-10-31</td>\n",
193
+ " <td>NaN</td>\n",
194
+ " <td>NaN</td>\n",
195
+ " <td>24.0</td>\n",
196
  " </tr>\n",
197
  " <tr>\n",
198
+ " <th>49485</th>\n",
199
+ " <td>845162</td>\n",
200
+ " <td>535</td>\n",
201
+ " <td>Granbury, TX</td>\n",
202
  " <td>msa</td>\n",
203
+ " <td>TX</td>\n",
204
+ " <td>SFR</td>\n",
 
205
  " <td>2023-11-30</td>\n",
206
+ " <td>NaN</td>\n",
207
+ " <td>NaN</td>\n",
208
+ " <td>16.0</td>\n",
209
  " </tr>\n",
210
  " <tr>\n",
211
+ " <th>49486</th>\n",
212
+ " <td>845162</td>\n",
213
+ " <td>535</td>\n",
214
+ " <td>Granbury, TX</td>\n",
215
  " <td>msa</td>\n",
216
+ " <td>TX</td>\n",
217
+ " <td>all homes</td>\n",
 
218
  " <td>2023-11-30</td>\n",
219
+ " <td>NaN</td>\n",
220
+ " <td>NaN</td>\n",
221
+ " <td>16.0</td>\n",
222
  " </tr>\n",
223
  " </tbody>\n",
224
  "</table>\n",
225
+ "<p>49487 rows × 10 columns</p>\n",
226
  "</div>"
227
  ],
228
  "text/plain": [
229
+ " RegionID SizeRank RegionName RegionType StateName \\\n",
230
+ "0 102001 0 United States country NaN \n",
231
+ "1 102001 0 United States country NaN \n",
232
+ "2 102001 0 United States country NaN \n",
233
+ "3 102001 0 United States country NaN \n",
234
+ "4 102001 0 United States country NaN \n",
235
+ "... ... ... ... ... ... \n",
236
+ "49482 845162 535 Granbury, TX msa TX \n",
237
+ "49483 845162 535 Granbury, TX msa TX \n",
238
+ "49484 845162 535 Granbury, TX msa TX \n",
239
+ "49485 845162 535 Granbury, TX msa TX \n",
240
+ "49486 845162 535 Granbury, TX msa TX \n",
241
  "\n",
242
+ " Home Type Date Sale Price Sale Price per Sqft Count \n",
243
+ "0 SFR 2018-01-31 309000.0 137.412316 33940.0 \n",
244
+ "1 all homes 2018-01-31 314596.0 140.504620 37135.0 \n",
245
+ "2 condo/co-op only 2018-01-31 388250.0 238.300000 3195.0 \n",
246
+ "3 SFR 2018-02-28 309072.5 137.199170 33304.0 \n",
247
+ "4 all homes 2018-02-28 314608.0 140.304966 36493.0 \n",
248
+ "... ... ... ... ... ... \n",
249
+ "49482 all homes 2023-09-30 NaN NaN 26.0 \n",
250
+ "49483 SFR 2023-10-31 NaN NaN 24.0 \n",
251
+ "49484 all homes 2023-10-31 NaN NaN 24.0 \n",
252
+ "49485 SFR 2023-11-30 NaN NaN 16.0 \n",
253
+ "49486 all homes 2023-11-30 NaN NaN 16.0 \n",
254
  "\n",
255
+ "[49487 rows x 10 columns]"
256
  ]
257
  },
258
+ "execution_count": 47,
259
  "metadata": {},
260
  "output_type": "execute_result"
261
  }
 
271
  " \"RegionName\",\n",
272
  " \"RegionType\",\n",
273
  " \"StateName\",\n",
274
+ " # \"Value Type\",\n",
275
  " \"Home Type\",\n",
276
  "]\n",
277
  "\n",
278
+ "price_data_frames = []\n",
279
+ "price_per_sqft_data_frames = []\n",
280
+ "count_data_frames = []\n",
281
+ "\n",
282
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
283
  " if filename.endswith(\".csv\"):\n",
284
  " print(\"processing \" + filename)\n",
285
  " cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
 
 
 
 
 
 
286
  "\n",
287
  " if \"sfrcondo\" in filename:\n",
288
+ " cur_df[\"Home Type\"] = \"all homes\"\n",
289
  " elif \"sfr\" in filename:\n",
290
  " cur_df[\"Home Type\"] = \"SFR\"\n",
291
  " elif \"condo\" in filename:\n",
292
+ " cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
293
  "\n",
294
  " # Identify columns to pivot\n",
295
  " columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
296
  "\n",
297
+ " if \"sale_price_per_sqft\" in filename:\n",
298
+ " # cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
299
+ " # Perform pivot\n",
300
+ " cur_df = pd.melt(\n",
301
+ " cur_df,\n",
302
+ " id_vars=exclude_columns,\n",
303
+ " value_vars=columns_to_pivot,\n",
304
+ " var_name=\"Date\",\n",
305
+ " value_name=\"Sale Price per Sqft\",\n",
306
+ " )\n",
307
+ " price_per_sqft_data_frames.append(cur_df)\n",
308
+ "\n",
309
+ " elif \"sale_price_uc\" in filename:\n",
310
+ " # cur_df[\"Value Type\"] = \"Sale Price\"\n",
311
+ " cur_df = pd.melt(\n",
312
+ " cur_df,\n",
313
+ " id_vars=exclude_columns,\n",
314
+ " value_vars=columns_to_pivot,\n",
315
+ " var_name=\"Date\",\n",
316
+ " value_name=\"Sale Price\",\n",
317
+ " )\n",
318
+ " price_data_frames.append(cur_df)\n",
319
+ "\n",
320
+ " elif \"count\" in filename:\n",
321
+ " # cur_df[\"Value Type\"] = \"Count\"\n",
322
+ " cur_df = pd.melt(\n",
323
+ " cur_df,\n",
324
+ " id_vars=exclude_columns,\n",
325
+ " value_vars=columns_to_pivot,\n",
326
+ " var_name=\"Date\",\n",
327
+ " value_name=\"Count\",\n",
328
+ " )\n",
329
+ " count_data_frames.append(cur_df)\n",
330
+ "\n",
331
+ "\n",
332
+ "combined_price = pd.concat(price_data_frames)\n",
333
+ "combined_price_per = pd.concat(price_per_sqft_data_frames)\n",
334
+ "combined_count = pd.concat(count_data_frames)\n",
335
+ "\n",
336
+ "matching_cols = [\n",
337
+ " \"RegionID\",\n",
338
+ " \"Date\",\n",
339
+ " \"SizeRank\",\n",
340
+ " \"RegionName\",\n",
341
+ " \"RegionType\",\n",
342
+ " \"StateName\",\n",
343
+ " # \"Value Type\",\n",
344
+ " \"Home Type\",\n",
345
+ "]\n",
346
  "\n",
347
+ "combined_df = pd.merge(\n",
348
+ " combined_price,\n",
349
+ " combined_price_per,\n",
350
+ " on=matching_cols,\n",
351
+ " how=\"outer\",\n",
352
+ ")\n",
353
+ "combined_df = pd.merge(\n",
354
+ " combined_df,\n",
355
+ " combined_count,\n",
356
+ " on=matching_cols,\n",
357
+ " how=\"outer\",\n",
358
+ ")\n",
359
  "\n",
 
360
  "combined_df"
361
  ]
362
  },
363
  {
364
  "cell_type": "code",
365
+ "execution_count": 48,
366
  "metadata": {},
367
  "outputs": [
368
  {
 
391
  " <th>Region</th>\n",
392
  " <th>Region Type</th>\n",
393
  " <th>State</th>\n",
 
394
  " <th>Home Type</th>\n",
395
  " <th>Date</th>\n",
396
+ " <th>Sale Price</th>\n",
397
+ " <th>Sale Price per Sqft</th>\n",
398
+ " <th>Count</th>\n",
399
  " </tr>\n",
400
  " </thead>\n",
401
  " <tbody>\n",
 
406
  " <td>United States</td>\n",
407
  " <td>country</td>\n",
408
  " <td>NaN</td>\n",
409
+ " <td>SFR</td>\n",
 
410
  " <td>2018-01-31</td>\n",
411
+ " <td>309000.0</td>\n",
412
+ " <td>137.412316</td>\n",
413
+ " <td>33940.0</td>\n",
414
  " </tr>\n",
415
  " <tr>\n",
416
  " <th>1</th>\n",
417
+ " <td>102001</td>\n",
418
+ " <td>0</td>\n",
419
+ " <td>United States</td>\n",
420
+ " <td>country</td>\n",
421
+ " <td>NaN</td>\n",
422
+ " <td>all homes</td>\n",
 
423
  " <td>2018-01-31</td>\n",
424
+ " <td>314596.0</td>\n",
425
+ " <td>140.504620</td>\n",
426
+ " <td>37135.0</td>\n",
427
  " </tr>\n",
428
  " <tr>\n",
429
  " <th>2</th>\n",
430
+ " <td>102001</td>\n",
431
+ " <td>0</td>\n",
432
+ " <td>United States</td>\n",
433
+ " <td>country</td>\n",
434
+ " <td>NaN</td>\n",
435
+ " <td>condo/co-op only</td>\n",
 
436
  " <td>2018-01-31</td>\n",
437
+ " <td>388250.0</td>\n",
438
+ " <td>238.300000</td>\n",
439
+ " <td>3195.0</td>\n",
440
  " </tr>\n",
441
  " <tr>\n",
442
  " <th>3</th>\n",
443
+ " <td>102001</td>\n",
444
+ " <td>0</td>\n",
445
+ " <td>United States</td>\n",
446
+ " <td>country</td>\n",
 
 
 
 
447
  " <td>NaN</td>\n",
448
+ " <td>SFR</td>\n",
449
+ " <td>2018-02-28</td>\n",
450
+ " <td>309072.5</td>\n",
451
+ " <td>137.199170</td>\n",
452
+ " <td>33304.0</td>\n",
453
  " </tr>\n",
454
  " <tr>\n",
455
  " <th>4</th>\n",
456
+ " <td>102001</td>\n",
457
+ " <td>0</td>\n",
458
+ " <td>United States</td>\n",
459
+ " <td>country</td>\n",
460
+ " <td>NaN</td>\n",
461
+ " <td>all homes</td>\n",
462
+ " <td>2018-02-28</td>\n",
463
+ " <td>314608.0</td>\n",
464
+ " <td>140.304966</td>\n",
465
+ " <td>36493.0</td>\n",
466
  " </tr>\n",
467
  " <tr>\n",
468
  " <th>...</th>\n",
 
475
  " <td>...</td>\n",
476
  " <td>...</td>\n",
477
  " <td>...</td>\n",
478
+ " <td>...</td>\n",
479
  " </tr>\n",
480
  " <tr>\n",
481
+ " <th>49482</th>\n",
482
+ " <td>845162</td>\n",
483
+ " <td>535</td>\n",
484
+ " <td>Granbury, TX</td>\n",
485
  " <td>msa</td>\n",
486
+ " <td>TX</td>\n",
487
+ " <td>all homes</td>\n",
488
+ " <td>2023-09-30</td>\n",
489
+ " <td>NaN</td>\n",
490
+ " <td>NaN</td>\n",
491
+ " <td>26.0</td>\n",
492
  " </tr>\n",
493
  " <tr>\n",
494
+ " <th>49483</th>\n",
495
+ " <td>845162</td>\n",
496
+ " <td>535</td>\n",
497
+ " <td>Granbury, TX</td>\n",
498
  " <td>msa</td>\n",
499
+ " <td>TX</td>\n",
500
+ " <td>SFR</td>\n",
501
+ " <td>2023-10-31</td>\n",
502
+ " <td>NaN</td>\n",
503
+ " <td>NaN</td>\n",
504
+ " <td>24.0</td>\n",
505
  " </tr>\n",
506
  " <tr>\n",
507
+ " <th>49484</th>\n",
508
+ " <td>845162</td>\n",
509
+ " <td>535</td>\n",
510
+ " <td>Granbury, TX</td>\n",
511
  " <td>msa</td>\n",
512
+ " <td>TX</td>\n",
513
+ " <td>all homes</td>\n",
514
+ " <td>2023-10-31</td>\n",
515
+ " <td>NaN</td>\n",
516
+ " <td>NaN</td>\n",
517
+ " <td>24.0</td>\n",
518
  " </tr>\n",
519
  " <tr>\n",
520
+ " <th>49485</th>\n",
521
+ " <td>845162</td>\n",
522
+ " <td>535</td>\n",
523
+ " <td>Granbury, TX</td>\n",
524
  " <td>msa</td>\n",
525
+ " <td>TX</td>\n",
526
+ " <td>SFR</td>\n",
 
527
  " <td>2023-11-30</td>\n",
528
+ " <td>NaN</td>\n",
529
+ " <td>NaN</td>\n",
530
+ " <td>16.0</td>\n",
531
  " </tr>\n",
532
  " <tr>\n",
533
+ " <th>49486</th>\n",
534
+ " <td>845162</td>\n",
535
+ " <td>535</td>\n",
536
+ " <td>Granbury, TX</td>\n",
537
  " <td>msa</td>\n",
538
+ " <td>TX</td>\n",
539
+ " <td>all homes</td>\n",
 
540
  " <td>2023-11-30</td>\n",
541
+ " <td>NaN</td>\n",
542
+ " <td>NaN</td>\n",
543
+ " <td>16.0</td>\n",
544
  " </tr>\n",
545
  " </tbody>\n",
546
  "</table>\n",
547
+ "<p>49487 rows × 10 columns</p>\n",
548
  "</div>"
549
  ],
550
  "text/plain": [
551
+ " Region ID Size Rank Region Region Type State \\\n",
552
+ "0 102001 0 United States country NaN \n",
553
+ "1 102001 0 United States country NaN \n",
554
+ "2 102001 0 United States country NaN \n",
555
+ "3 102001 0 United States country NaN \n",
556
+ "4 102001 0 United States country NaN \n",
557
+ "... ... ... ... ... ... \n",
558
+ "49482 845162 535 Granbury, TX msa TX \n",
559
+ "49483 845162 535 Granbury, TX msa TX \n",
560
+ "49484 845162 535 Granbury, TX msa TX \n",
561
+ "49485 845162 535 Granbury, TX msa TX \n",
562
+ "49486 845162 535 Granbury, TX msa TX \n",
563
  "\n",
564
+ " Home Type Date Sale Price Sale Price per Sqft Count \n",
565
+ "0 SFR 2018-01-31 309000.0 137.412316 33940.0 \n",
566
+ "1 all homes 2018-01-31 314596.0 140.504620 37135.0 \n",
567
+ "2 condo/co-op only 2018-01-31 388250.0 238.300000 3195.0 \n",
568
+ "3 SFR 2018-02-28 309072.5 137.199170 33304.0 \n",
569
+ "4 all homes 2018-02-28 314608.0 140.304966 36493.0 \n",
570
+ "... ... ... ... ... ... \n",
571
+ "49482 all homes 2023-09-30 NaN NaN 26.0 \n",
572
+ "49483 SFR 2023-10-31 NaN NaN 24.0 \n",
573
+ "49484 all homes 2023-10-31 NaN NaN 24.0 \n",
574
+ "49485 SFR 2023-11-30 NaN NaN 16.0 \n",
575
+ "49486 all homes 2023-11-30 NaN NaN 16.0 \n",
576
  "\n",
577
+ "[49487 rows x 10 columns]"
578
  ]
579
  },
580
+ "execution_count": 48,
581
  "metadata": {},
582
  "output_type": "execute_result"
583
  }
 
599
  },
600
  {
601
  "cell_type": "code",
602
+ "execution_count": 49,
603
  "metadata": {},
604
  "outputs": [],
605
  "source": [
 
608
  "\n",
609
  "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
610
  ]
611
+ },
612
+ {
613
+ "cell_type": "code",
614
+ "execution_count": null,
615
+ "metadata": {},
616
+ "outputs": [],
617
+ "source": []
618
  }
619
  ],
620
  "metadata": {
zillow.py CHANGED
@@ -133,7 +133,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
133
  "Value Type": datasets.Value(dtype="string", id="Value Type"),
134
  "Home Type": datasets.Value(dtype="string", id="Home Type"),
135
  "Date": datasets.Value(dtype="string", id="Date"),
136
- "Value": datasets.Value(dtype="float32", id="Value"),
 
 
 
 
137
  # These are the features of your dataset like images, labels ...
138
  }
139
  )
@@ -253,7 +257,9 @@ class NewDataset(datasets.GeneratorBasedBuilder):
253
  "Value Type": data["Value Type"],
254
  "Home Type": data["Home Type"],
255
  "Date": data["Date"],
256
- "Value": data["Value"],
 
 
257
  # "answer": "" if split == "test" else data["answer"],
258
  }
259
  # else:
 
133
  "Value Type": datasets.Value(dtype="string", id="Value Type"),
134
  "Home Type": datasets.Value(dtype="string", id="Home Type"),
135
  "Date": datasets.Value(dtype="string", id="Date"),
136
+ "Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
137
+ "Sale Price per Sqft": datasets.Value(
138
+ dtype="float32", id="Sale Price per Sqft"
139
+ ),
140
+ "Count": datasets.Value(dtype="int32", id="Count"),
141
  # These are the features of your dataset like images, labels ...
142
  }
143
  )
 
257
  "Value Type": data["Value Type"],
258
  "Home Type": data["Home Type"],
259
  "Date": data["Date"],
260
+ "Sale Price": data["Sale Price"],
261
+ "Sale Price per Sqft": data["Sale Price per Sqft"],
262
+ "Count": data["Count"],
263
  # "answer": "" if split == "test" else data["answer"],
264
  }
265
  # else: