Hugging Face's logo

Datasets:
misikoff
/
zillow

Tasks:
Tabular Regression
Time Series Forecasting
Languages:
English
License:
Dataset card Files Community
misikoff commited on
Commit
a460052
•
1 Parent(s): 3355ad5

fix: update handling of merges

Browse files
Files changed (7) hide show
  1. processed/for_sale_listings/final.jsonl +2 -2
  2. processed/new_constructions/final.jsonl +2 -2
  3. processed/rentals/final.jsonl +2 -2
  4. processors/for_sale_listings.ipynb +246 -207
  5. processors/new_constructions.ipynb +163 -169
  6. processors/rentals.ipynb +250 -51
  7. tester.ipynb +6 -6
processed/for_sale_listings/final.jsonl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3e6c841353b6b109a2b7692c1daed5409e9ec37ab08ce6b0e70fa8b8c05495bd
3
- size 739103203
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f449b3db5edd6115f4be4da56992047d9fa50c24cbd85e5715f041bf8b1d5fda
3
+ size 215422186
processed/new_constructions/final.jsonl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:e0a487f1b425d392a62c6dd88655fe958c42a937d901efdd5dbc2ebf055826f3
3
- size 10854928
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:276b20bd2011faa1fb59f58892ef16b8bbfbb8111a10c7e8d4f433a9226bf3c5
3
+ size 10903095
processed/rentals/final.jsonl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:bb1ebabaee1148e8960e20e063dd5aebe6dddfdd4013911c4e3cbdd5002328cb
3
- size 20448052
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d30311278d0da51a72097cb296e419eeafeb47a63bcf169975900c6e3252fa29
3
+ size 20682905
processors/for_sale_listings.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 8,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -12,7 +12,7 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 9,
16
  "metadata": {},
17
  "outputs": [],
18
  "source": [
@@ -25,7 +25,7 @@
25
  },
26
  {
27
  "cell_type": "code",
28
- "execution_count": 10,
29
  "metadata": {},
30
  "outputs": [
31
  {
@@ -86,12 +86,12 @@
86
  " <th>StateName</th>\n",
87
  " <th>Home Type</th>\n",
88
  " <th>Date</th>\n",
 
89
  " <th>Median Listing Price</th>\n",
90
  " <th>Median Listing Price (Smoothed)</th>\n",
 
91
  " <th>New Listings</th>\n",
92
  " <th>New Listings (Smoothed)</th>\n",
93
- " <th>New Pending (Smoothed)</th>\n",
94
- " <th>New Pending</th>\n",
95
  " </tr>\n",
96
  " </thead>\n",
97
  " <tbody>\n",
@@ -102,14 +102,14 @@
102
  " <td>United States</td>\n",
103
  " <td>country</td>\n",
104
  " <td>NaN</td>\n",
105
- " <td>all homes</td>\n",
106
- " <td>2018-01-06</td>\n",
107
  " <td>NaN</td>\n",
 
108
  " <td>NaN</td>\n",
109
  " <td>NaN</td>\n",
110
  " <td>NaN</td>\n",
111
  " <td>NaN</td>\n",
112
- " <td>24766.0</td>\n",
113
  " </tr>\n",
114
  " <tr>\n",
115
  " <th>1</th>\n",
@@ -119,9 +119,9 @@
119
  " <td>country</td>\n",
120
  " <td>NaN</td>\n",
121
  " <td>SFR</td>\n",
122
- " <td>2018-01-13</td>\n",
123
- " <td>259000.0</td>\n",
124
  " <td>NaN</td>\n",
 
125
  " <td>NaN</td>\n",
126
  " <td>NaN</td>\n",
127
  " <td>NaN</td>\n",
@@ -134,14 +134,14 @@
134
  " <td>United States</td>\n",
135
  " <td>country</td>\n",
136
  " <td>NaN</td>\n",
137
- " <td>all homes</td>\n",
138
- " <td>2018-01-13</td>\n",
 
139
  " <td>259900.0</td>\n",
140
  " <td>NaN</td>\n",
141
- " <td>71177.0</td>\n",
142
  " <td>NaN</td>\n",
143
  " <td>NaN</td>\n",
144
- " <td>35229.0</td>\n",
145
  " </tr>\n",
146
  " <tr>\n",
147
  " <th>3</th>\n",
@@ -151,9 +151,9 @@
151
  " <td>country</td>\n",
152
  " <td>NaN</td>\n",
153
  " <td>SFR</td>\n",
154
- " <td>2018-01-20</td>\n",
155
- " <td>259900.0</td>\n",
156
  " <td>NaN</td>\n",
 
157
  " <td>NaN</td>\n",
158
  " <td>NaN</td>\n",
159
  " <td>NaN</td>\n",
@@ -166,14 +166,14 @@
166
  " <td>United States</td>\n",
167
  " <td>country</td>\n",
168
  " <td>NaN</td>\n",
169
- " <td>all homes</td>\n",
170
- " <td>2018-01-20</td>\n",
171
- " <td>259900.0</td>\n",
 
 
172
  " <td>NaN</td>\n",
173
- " <td>72625.0</td>\n",
174
  " <td>NaN</td>\n",
175
  " <td>NaN</td>\n",
176
- " <td>38281.0</td>\n",
177
  " </tr>\n",
178
  " <tr>\n",
179
  " <th>...</th>\n",
@@ -192,71 +192,71 @@
192
  " <td>...</td>\n",
193
  " </tr>\n",
194
  " <tr>\n",
195
- " <th>2398144</th>\n",
196
  " <td>845172</td>\n",
197
  " <td>769</td>\n",
198
  " <td>Winfield, KS</td>\n",
199
  " <td>msa</td>\n",
200
  " <td>KS</td>\n",
201
  " <td>all homes</td>\n",
202
- " <td>2023-12-31</td>\n",
 
 
 
203
  " <td>NaN</td>\n",
204
- " <td>136233.0</td>\n",
205
  " <td>NaN</td>\n",
206
- " <td>28.0</td>\n",
207
  " <td>NaN</td>\n",
208
- " <td>24.0</td>\n",
209
  " </tr>\n",
210
  " <tr>\n",
211
- " <th>2398145</th>\n",
212
  " <td>845172</td>\n",
213
  " <td>769</td>\n",
214
  " <td>Winfield, KS</td>\n",
215
  " <td>msa</td>\n",
216
  " <td>KS</td>\n",
217
- " <td>SFR</td>\n",
218
- " <td>2024-01-06</td>\n",
219
- " <td>NaN</td>\n",
220
- " <td>131088.0</td>\n",
221
  " <td>NaN</td>\n",
 
 
222
  " <td>NaN</td>\n",
223
  " <td>NaN</td>\n",
224
  " <td>NaN</td>\n",
225
  " </tr>\n",
226
  " <tr>\n",
227
- " <th>2398146</th>\n",
228
  " <td>845172</td>\n",
229
  " <td>769</td>\n",
230
  " <td>Winfield, KS</td>\n",
231
  " <td>msa</td>\n",
232
  " <td>KS</td>\n",
233
- " <td>SFR</td>\n",
234
- " <td>2024-01-06</td>\n",
235
- " <td>135450.0</td>\n",
236
- " <td>NaN</td>\n",
237
  " <td>NaN</td>\n",
 
 
238
  " <td>NaN</td>\n",
239
  " <td>NaN</td>\n",
240
  " <td>NaN</td>\n",
241
  " </tr>\n",
242
  " <tr>\n",
243
- " <th>2398147</th>\n",
244
  " <td>845172</td>\n",
245
  " <td>769</td>\n",
246
  " <td>Winfield, KS</td>\n",
247
  " <td>msa</td>\n",
248
  " <td>KS</td>\n",
249
  " <td>all homes</td>\n",
250
- " <td>2024-01-06</td>\n",
251
- " <td>128000.0</td>\n",
252
- " <td>NaN</td>\n",
253
- " <td>NaN</td>\n",
254
- " <td>NaN</td>\n",
255
- " <td>NaN</td>\n",
256
- " <td>NaN</td>\n",
257
  " </tr>\n",
258
  " <tr>\n",
259
- " <th>2398148</th>\n",
260
  " <td>845172</td>\n",
261
  " <td>769</td>\n",
262
  " <td>Winfield, KS</td>\n",
@@ -266,73 +266,73 @@
266
  " <td>2024-01-06</td>\n",
267
  " <td>NaN</td>\n",
268
  " <td>121488.0</td>\n",
269
- " <td>NaN</td>\n",
270
  " <td>NaN</td>\n",
271
  " <td>NaN</td>\n",
272
  " <td>NaN</td>\n",
273
  " </tr>\n",
274
  " </tbody>\n",
275
  "</table>\n",
276
- "<p>2398149 rows × 13 columns</p>\n",
277
  "</div>"
278
  ],
279
  "text/plain": [
280
- " RegionID SizeRank RegionName RegionType StateName Home Type \\\n",
281
- "0 102001 0 United States country NaN all homes \n",
282
- "1 102001 0 United States country NaN SFR \n",
283
- "2 102001 0 United States country NaN all homes \n",
284
- "3 102001 0 United States country NaN SFR \n",
285
- "4 102001 0 United States country NaN all homes \n",
286
- "... ... ... ... ... ... ... \n",
287
- "2398144 845172 769 Winfield, KS msa KS all homes \n",
288
- "2398145 845172 769 Winfield, KS msa KS SFR \n",
289
- "2398146 845172 769 Winfield, KS msa KS SFR \n",
290
- "2398147 845172 769 Winfield, KS msa KS all homes \n",
291
- "2398148 845172 769 Winfield, KS msa KS all homes \n",
292
  "\n",
293
- " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
294
- "0 2018-01-06 NaN NaN \n",
295
- "1 2018-01-13 259000.0 NaN \n",
296
- "2 2018-01-13 259900.0 NaN \n",
297
- "3 2018-01-20 259900.0 NaN \n",
298
- "4 2018-01-20 259900.0 NaN \n",
299
- "... ... ... ... \n",
300
- "2398144 2023-12-31 NaN 136233.0 \n",
301
- "2398145 2024-01-06 NaN 131088.0 \n",
302
- "2398146 2024-01-06 135450.0 NaN \n",
303
- "2398147 2024-01-06 128000.0 NaN \n",
304
- "2398148 2024-01-06 NaN 121488.0 \n",
305
  "\n",
306
- " New Listings New Listings (Smoothed) New Pending (Smoothed) \\\n",
307
- "0 NaN NaN NaN \n",
308
- "1 NaN NaN NaN \n",
309
- "2 71177.0 NaN NaN \n",
310
- "3 NaN NaN NaN \n",
311
- "4 72625.0 NaN NaN \n",
312
- "... ... ... ... \n",
313
- "2398144 NaN 28.0 NaN \n",
314
- "2398145 NaN NaN NaN \n",
315
- "2398146 NaN NaN NaN \n",
316
- "2398147 NaN NaN NaN \n",
317
- "2398148 NaN NaN NaN \n",
318
  "\n",
319
- " New Pending \n",
320
- "0 24766.0 \n",
321
- "1 NaN \n",
322
- "2 35229.0 \n",
323
- "3 NaN \n",
324
- "4 38281.0 \n",
325
- "... ... \n",
326
- "2398144 24.0 \n",
327
- "2398145 NaN \n",
328
- "2398146 NaN \n",
329
- "2398147 NaN \n",
330
- "2398148 NaN \n",
331
  "\n",
332
- "[2398149 rows x 13 columns]"
333
  ]
334
  },
335
- "execution_count": 10,
336
  "metadata": {},
337
  "output_type": "execute_result"
338
  }
@@ -349,7 +349,7 @@
349
  " \"Home Type\",\n",
350
  "]\n",
351
  "\n",
352
- "batches = {\"mlp\": [], \"new_listings\": [], \"new_pending\": []}\n",
353
  "\n",
354
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
355
  " if filename.endswith(\".csv\"):\n",
@@ -384,7 +384,7 @@
384
  " else \"Median Listing Price (Smoothed)\"\n",
385
  " ),\n",
386
  " )\n",
387
- " batches[\"mlp\"].append(cur_df)\n",
388
  "\n",
389
  " elif \"_new_listings_\" in filename:\n",
390
  " cur_df = pd.melt(\n",
@@ -396,7 +396,7 @@
396
  " \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
397
  " ),\n",
398
  " )\n",
399
- " batches[\"new_listings\"].append(cur_df)\n",
400
  "\n",
401
  " elif \"new_pending\" in filename:\n",
402
  " cur_df = pd.melt(\n",
@@ -406,7 +406,7 @@
406
  " var_name=\"Date\",\n",
407
  " value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
408
  " )\n",
409
- " batches[\"new_pending\"].append(cur_df)\n",
410
  "\n",
411
  "matching_cols = [\n",
412
  " \"RegionID\",\n",
@@ -418,25 +418,64 @@
418
  " \"Home Type\",\n",
419
  "]\n",
420
  "\n",
421
- "combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
422
  "\n",
423
- "if len(combined_batches) > 0:\n",
424
- " combined_df = combined_batches[0]\n",
425
- " for batch in combined_batches[1:]:\n",
426
- " combined_df = pd.merge(\n",
427
- " combined_df,\n",
428
- " batch,\n",
429
- " on=matching_cols,\n",
430
- " how=\"outer\",\n",
431
- " )\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
432
  "\n",
433
  "\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
434
  "combined_df"
435
  ]
436
  },
437
  {
438
  "cell_type": "code",
439
- "execution_count": 11,
440
  "metadata": {},
441
  "outputs": [
442
  {
@@ -467,12 +506,12 @@
467
  " <th>State</th>\n",
468
  " <th>Home Type</th>\n",
469
  " <th>Date</th>\n",
 
470
  " <th>Median Listing Price</th>\n",
471
  " <th>Median Listing Price (Smoothed)</th>\n",
 
472
  " <th>New Listings</th>\n",
473
  " <th>New Listings (Smoothed)</th>\n",
474
- " <th>New Pending (Smoothed)</th>\n",
475
- " <th>New Pending</th>\n",
476
  " </tr>\n",
477
  " </thead>\n",
478
  " <tbody>\n",
@@ -483,14 +522,14 @@
483
  " <td>United States</td>\n",
484
  " <td>country</td>\n",
485
  " <td>NaN</td>\n",
486
- " <td>all homes</td>\n",
487
- " <td>2018-01-06</td>\n",
488
  " <td>NaN</td>\n",
 
489
  " <td>NaN</td>\n",
490
  " <td>NaN</td>\n",
491
  " <td>NaN</td>\n",
492
  " <td>NaN</td>\n",
493
- " <td>24766.0</td>\n",
494
  " </tr>\n",
495
  " <tr>\n",
496
  " <th>1</th>\n",
@@ -500,9 +539,9 @@
500
  " <td>country</td>\n",
501
  " <td>NaN</td>\n",
502
  " <td>SFR</td>\n",
503
- " <td>2018-01-13</td>\n",
504
- " <td>259000.0</td>\n",
505
  " <td>NaN</td>\n",
 
506
  " <td>NaN</td>\n",
507
  " <td>NaN</td>\n",
508
  " <td>NaN</td>\n",
@@ -515,14 +554,14 @@
515
  " <td>United States</td>\n",
516
  " <td>country</td>\n",
517
  " <td>NaN</td>\n",
518
- " <td>all homes</td>\n",
519
- " <td>2018-01-13</td>\n",
 
520
  " <td>259900.0</td>\n",
521
  " <td>NaN</td>\n",
522
- " <td>71177.0</td>\n",
523
  " <td>NaN</td>\n",
524
  " <td>NaN</td>\n",
525
- " <td>35229.0</td>\n",
526
  " </tr>\n",
527
  " <tr>\n",
528
  " <th>3</th>\n",
@@ -532,9 +571,9 @@
532
  " <td>country</td>\n",
533
  " <td>NaN</td>\n",
534
  " <td>SFR</td>\n",
535
- " <td>2018-01-20</td>\n",
536
- " <td>259900.0</td>\n",
537
  " <td>NaN</td>\n",
 
538
  " <td>NaN</td>\n",
539
  " <td>NaN</td>\n",
540
  " <td>NaN</td>\n",
@@ -547,14 +586,14 @@
547
  " <td>United States</td>\n",
548
  " <td>country</td>\n",
549
  " <td>NaN</td>\n",
550
- " <td>all homes</td>\n",
551
- " <td>2018-01-20</td>\n",
552
- " <td>259900.0</td>\n",
 
 
553
  " <td>NaN</td>\n",
554
- " <td>72625.0</td>\n",
555
  " <td>NaN</td>\n",
556
  " <td>NaN</td>\n",
557
- " <td>38281.0</td>\n",
558
  " </tr>\n",
559
  " <tr>\n",
560
  " <th>...</th>\n",
@@ -573,71 +612,71 @@
573
  " <td>...</td>\n",
574
  " </tr>\n",
575
  " <tr>\n",
576
- " <th>2398144</th>\n",
577
  " <td>845172</td>\n",
578
  " <td>769</td>\n",
579
  " <td>Winfield, KS</td>\n",
580
  " <td>msa</td>\n",
581
  " <td>KS</td>\n",
582
  " <td>all homes</td>\n",
583
- " <td>2023-12-31</td>\n",
 
 
 
584
  " <td>NaN</td>\n",
585
- " <td>136233.0</td>\n",
586
  " <td>NaN</td>\n",
587
- " <td>28.0</td>\n",
588
  " <td>NaN</td>\n",
589
- " <td>24.0</td>\n",
590
  " </tr>\n",
591
  " <tr>\n",
592
- " <th>2398145</th>\n",
593
  " <td>845172</td>\n",
594
  " <td>769</td>\n",
595
  " <td>Winfield, KS</td>\n",
596
  " <td>msa</td>\n",
597
  " <td>KS</td>\n",
598
- " <td>SFR</td>\n",
599
- " <td>2024-01-06</td>\n",
600
- " <td>NaN</td>\n",
601
- " <td>131088.0</td>\n",
602
  " <td>NaN</td>\n",
 
 
603
  " <td>NaN</td>\n",
604
  " <td>NaN</td>\n",
605
  " <td>NaN</td>\n",
606
  " </tr>\n",
607
  " <tr>\n",
608
- " <th>2398146</th>\n",
609
  " <td>845172</td>\n",
610
  " <td>769</td>\n",
611
  " <td>Winfield, KS</td>\n",
612
  " <td>msa</td>\n",
613
  " <td>KS</td>\n",
614
- " <td>SFR</td>\n",
615
- " <td>2024-01-06</td>\n",
616
- " <td>135450.0</td>\n",
617
- " <td>NaN</td>\n",
618
  " <td>NaN</td>\n",
 
 
619
  " <td>NaN</td>\n",
620
  " <td>NaN</td>\n",
621
  " <td>NaN</td>\n",
622
  " </tr>\n",
623
  " <tr>\n",
624
- " <th>2398147</th>\n",
625
  " <td>845172</td>\n",
626
  " <td>769</td>\n",
627
  " <td>Winfield, KS</td>\n",
628
  " <td>msa</td>\n",
629
  " <td>KS</td>\n",
630
  " <td>all homes</td>\n",
631
- " <td>2024-01-06</td>\n",
632
- " <td>128000.0</td>\n",
633
- " <td>NaN</td>\n",
634
- " <td>NaN</td>\n",
635
- " <td>NaN</td>\n",
636
- " <td>NaN</td>\n",
637
- " <td>NaN</td>\n",
638
  " </tr>\n",
639
  " <tr>\n",
640
- " <th>2398148</th>\n",
641
  " <td>845172</td>\n",
642
  " <td>769</td>\n",
643
  " <td>Winfield, KS</td>\n",
@@ -647,73 +686,73 @@
647
  " <td>2024-01-06</td>\n",
648
  " <td>NaN</td>\n",
649
  " <td>121488.0</td>\n",
650
- " <td>NaN</td>\n",
651
  " <td>NaN</td>\n",
652
  " <td>NaN</td>\n",
653
  " <td>NaN</td>\n",
654
  " </tr>\n",
655
  " </tbody>\n",
656
  "</table>\n",
657
- "<p>2398149 rows × 13 columns</p>\n",
658
  "</div>"
659
  ],
660
  "text/plain": [
661
- " Region ID Size Rank Region Region Type State Home Type \\\n",
662
- "0 102001 0 United States country NaN all homes \n",
663
- "1 102001 0 United States country NaN SFR \n",
664
- "2 102001 0 United States country NaN all homes \n",
665
- "3 102001 0 United States country NaN SFR \n",
666
- "4 102001 0 United States country NaN all homes \n",
667
- "... ... ... ... ... ... ... \n",
668
- "2398144 845172 769 Winfield, KS msa KS all homes \n",
669
- "2398145 845172 769 Winfield, KS msa KS SFR \n",
670
- "2398146 845172 769 Winfield, KS msa KS SFR \n",
671
- "2398147 845172 769 Winfield, KS msa KS all homes \n",
672
- "2398148 845172 769 Winfield, KS msa KS all homes \n",
673
  "\n",
674
- " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
675
- "0 2018-01-06 NaN NaN \n",
676
- "1 2018-01-13 259000.0 NaN \n",
677
- "2 2018-01-13 259900.0 NaN \n",
678
- "3 2018-01-20 259900.0 NaN \n",
679
- "4 2018-01-20 259900.0 NaN \n",
680
- "... ... ... ... \n",
681
- "2398144 2023-12-31 NaN 136233.0 \n",
682
- "2398145 2024-01-06 NaN 131088.0 \n",
683
- "2398146 2024-01-06 135450.0 NaN \n",
684
- "2398147 2024-01-06 128000.0 NaN \n",
685
- "2398148 2024-01-06 NaN 121488.0 \n",
686
  "\n",
687
- " New Listings New Listings (Smoothed) New Pending (Smoothed) \\\n",
688
- "0 NaN NaN NaN \n",
689
- "1 NaN NaN NaN \n",
690
- "2 71177.0 NaN NaN \n",
691
- "3 NaN NaN NaN \n",
692
- "4 72625.0 NaN NaN \n",
693
- "... ... ... ... \n",
694
- "2398144 NaN 28.0 NaN \n",
695
- "2398145 NaN NaN NaN \n",
696
- "2398146 NaN NaN NaN \n",
697
- "2398147 NaN NaN NaN \n",
698
- "2398148 NaN NaN NaN \n",
699
  "\n",
700
- " New Pending \n",
701
- "0 24766.0 \n",
702
- "1 NaN \n",
703
- "2 35229.0 \n",
704
- "3 NaN \n",
705
- "4 38281.0 \n",
706
- "... ... \n",
707
- "2398144 24.0 \n",
708
- "2398145 NaN \n",
709
- "2398146 NaN \n",
710
- "2398147 NaN \n",
711
- "2398148 NaN \n",
712
  "\n",
713
- "[2398149 rows x 13 columns]"
714
  ]
715
  },
716
- "execution_count": 11,
717
  "metadata": {},
718
  "output_type": "execute_result"
719
  }
@@ -735,7 +774,7 @@
735
  },
736
  {
737
  "cell_type": "code",
738
- "execution_count": 12,
739
  "metadata": {},
740
  "outputs": [],
741
  "source": [
 
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": 5,
29
  "metadata": {},
30
  "outputs": [
31
  {
 
86
  " <th>StateName</th>\n",
87
  " <th>Home Type</th>\n",
88
  " <th>Date</th>\n",
89
+ " <th>New Pending (Smoothed)</th>\n",
90
  " <th>Median Listing Price</th>\n",
91
  " <th>Median Listing Price (Smoothed)</th>\n",
92
+ " <th>New Pending</th>\n",
93
  " <th>New Listings</th>\n",
94
  " <th>New Listings (Smoothed)</th>\n",
 
 
95
  " </tr>\n",
96
  " </thead>\n",
97
  " <tbody>\n",
 
102
  " <td>United States</td>\n",
103
  " <td>country</td>\n",
104
  " <td>NaN</td>\n",
105
+ " <td>SFR</td>\n",
106
+ " <td>2018-01-13</td>\n",
107
  " <td>NaN</td>\n",
108
+ " <td>259000.0</td>\n",
109
  " <td>NaN</td>\n",
110
  " <td>NaN</td>\n",
111
  " <td>NaN</td>\n",
112
  " <td>NaN</td>\n",
 
113
  " </tr>\n",
114
  " <tr>\n",
115
  " <th>1</th>\n",
 
119
  " <td>country</td>\n",
120
  " <td>NaN</td>\n",
121
  " <td>SFR</td>\n",
122
+ " <td>2018-01-20</td>\n",
 
123
  " <td>NaN</td>\n",
124
+ " <td>259900.0</td>\n",
125
  " <td>NaN</td>\n",
126
  " <td>NaN</td>\n",
127
  " <td>NaN</td>\n",
 
134
  " <td>United States</td>\n",
135
  " <td>country</td>\n",
136
  " <td>NaN</td>\n",
137
+ " <td>SFR</td>\n",
138
+ " <td>2018-01-27</td>\n",
139
+ " <td>NaN</td>\n",
140
  " <td>259900.0</td>\n",
141
  " <td>NaN</td>\n",
 
142
  " <td>NaN</td>\n",
143
  " <td>NaN</td>\n",
144
+ " <td>NaN</td>\n",
145
  " </tr>\n",
146
  " <tr>\n",
147
  " <th>3</th>\n",
 
151
  " <td>country</td>\n",
152
  " <td>NaN</td>\n",
153
  " <td>SFR</td>\n",
154
+ " <td>2018-01-31</td>\n",
 
155
  " <td>NaN</td>\n",
156
+ " <td>254900.0</td>\n",
157
  " <td>NaN</td>\n",
158
  " <td>NaN</td>\n",
159
  " <td>NaN</td>\n",
 
166
  " <td>United States</td>\n",
167
  " <td>country</td>\n",
168
  " <td>NaN</td>\n",
169
+ " <td>SFR</td>\n",
170
+ " <td>2018-02-03</td>\n",
171
+ " <td>NaN</td>\n",
172
+ " <td>260000.0</td>\n",
173
+ " <td>259700.0</td>\n",
174
  " <td>NaN</td>\n",
 
175
  " <td>NaN</td>\n",
176
  " <td>NaN</td>\n",
 
177
  " </tr>\n",
178
  " <tr>\n",
179
  " <th>...</th>\n",
 
192
  " <td>...</td>\n",
193
  " </tr>\n",
194
  " <tr>\n",
195
+ " <th>693656</th>\n",
196
  " <td>845172</td>\n",
197
  " <td>769</td>\n",
198
  " <td>Winfield, KS</td>\n",
199
  " <td>msa</td>\n",
200
  " <td>KS</td>\n",
201
  " <td>all homes</td>\n",
202
+ " <td>2023-12-16</td>\n",
203
+ " <td>NaN</td>\n",
204
+ " <td>133938.0</td>\n",
205
+ " <td>133938.0</td>\n",
206
  " <td>NaN</td>\n",
 
207
  " <td>NaN</td>\n",
 
208
  " <td>NaN</td>\n",
 
209
  " </tr>\n",
210
  " <tr>\n",
211
+ " <th>693657</th>\n",
212
  " <td>845172</td>\n",
213
  " <td>769</td>\n",
214
  " <td>Winfield, KS</td>\n",
215
  " <td>msa</td>\n",
216
  " <td>KS</td>\n",
217
+ " <td>all homes</td>\n",
218
+ " <td>2023-12-23</td>\n",
 
 
219
  " <td>NaN</td>\n",
220
+ " <td>126463.0</td>\n",
221
+ " <td>126463.0</td>\n",
222
  " <td>NaN</td>\n",
223
  " <td>NaN</td>\n",
224
  " <td>NaN</td>\n",
225
  " </tr>\n",
226
  " <tr>\n",
227
+ " <th>693658</th>\n",
228
  " <td>845172</td>\n",
229
  " <td>769</td>\n",
230
  " <td>Winfield, KS</td>\n",
231
  " <td>msa</td>\n",
232
  " <td>KS</td>\n",
233
+ " <td>all homes</td>\n",
234
+ " <td>2023-12-30</td>\n",
 
 
235
  " <td>NaN</td>\n",
236
+ " <td>123225.0</td>\n",
237
+ " <td>123225.0</td>\n",
238
  " <td>NaN</td>\n",
239
  " <td>NaN</td>\n",
240
  " <td>NaN</td>\n",
241
  " </tr>\n",
242
  " <tr>\n",
243
+ " <th>693659</th>\n",
244
  " <td>845172</td>\n",
245
  " <td>769</td>\n",
246
  " <td>Winfield, KS</td>\n",
247
  " <td>msa</td>\n",
248
  " <td>KS</td>\n",
249
  " <td>all homes</td>\n",
250
+ " <td>2023-12-31</td>\n",
251
+ " <td>24.0</td>\n",
252
+ " <td>136233.0</td>\n",
253
+ " <td>136233.0</td>\n",
254
+ " <td>24.0</td>\n",
255
+ " <td>28.0</td>\n",
256
+ " <td>28.0</td>\n",
257
  " </tr>\n",
258
  " <tr>\n",
259
+ " <th>693660</th>\n",
260
  " <td>845172</td>\n",
261
  " <td>769</td>\n",
262
  " <td>Winfield, KS</td>\n",
 
266
  " <td>2024-01-06</td>\n",
267
  " <td>NaN</td>\n",
268
  " <td>121488.0</td>\n",
269
+ " <td>121488.0</td>\n",
270
  " <td>NaN</td>\n",
271
  " <td>NaN</td>\n",
272
  " <td>NaN</td>\n",
273
  " </tr>\n",
274
  " </tbody>\n",
275
  "</table>\n",
276
+ "<p>693661 rows × 13 columns</p>\n",
277
  "</div>"
278
  ],
279
  "text/plain": [
280
+ " RegionID SizeRank RegionName RegionType StateName Home Type \\\n",
281
+ "0 102001 0 United States country NaN SFR \n",
282
+ "1 102001 0 United States country NaN SFR \n",
283
+ "2 102001 0 United States country NaN SFR \n",
284
+ "3 102001 0 United States country NaN SFR \n",
285
+ "4 102001 0 United States country NaN SFR \n",
286
+ "... ... ... ... ... ... ... \n",
287
+ "693656 845172 769 Winfield, KS msa KS all homes \n",
288
+ "693657 845172 769 Winfield, KS msa KS all homes \n",
289
+ "693658 845172 769 Winfield, KS msa KS all homes \n",
290
+ "693659 845172 769 Winfield, KS msa KS all homes \n",
291
+ "693660 845172 769 Winfield, KS msa KS all homes \n",
292
  "\n",
293
+ " Date New Pending (Smoothed) Median Listing Price \\\n",
294
+ "0 2018-01-13 NaN 259000.0 \n",
295
+ "1 2018-01-20 NaN 259900.0 \n",
296
+ "2 2018-01-27 NaN 259900.0 \n",
297
+ "3 2018-01-31 NaN 254900.0 \n",
298
+ "4 2018-02-03 NaN 260000.0 \n",
299
+ "... ... ... ... \n",
300
+ "693656 2023-12-16 NaN 133938.0 \n",
301
+ "693657 2023-12-23 NaN 126463.0 \n",
302
+ "693658 2023-12-30 NaN 123225.0 \n",
303
+ "693659 2023-12-31 24.0 136233.0 \n",
304
+ "693660 2024-01-06 NaN 121488.0 \n",
305
  "\n",
306
+ " Median Listing Price (Smoothed) New Pending New Listings \\\n",
307
+ "0 NaN NaN NaN \n",
308
+ "1 NaN NaN NaN \n",
309
+ "2 NaN NaN NaN \n",
310
+ "3 NaN NaN NaN \n",
311
+ "4 259700.0 NaN NaN \n",
312
+ "... ... ... ... \n",
313
+ "693656 133938.0 NaN NaN \n",
314
+ "693657 126463.0 NaN NaN \n",
315
+ "693658 123225.0 NaN NaN \n",
316
+ "693659 136233.0 24.0 28.0 \n",
317
+ "693660 121488.0 NaN NaN \n",
318
  "\n",
319
+ " New Listings (Smoothed) \n",
320
+ "0 NaN \n",
321
+ "1 NaN \n",
322
+ "2 NaN \n",
323
+ "3 NaN \n",
324
+ "4 NaN \n",
325
+ "... ... \n",
326
+ "693656 NaN \n",
327
+ "693657 NaN \n",
328
+ "693658 NaN \n",
329
+ "693659 28.0 \n",
330
+ "693660 NaN \n",
331
  "\n",
332
+ "[693661 rows x 13 columns]"
333
  ]
334
  },
335
+ "execution_count": 5,
336
  "metadata": {},
337
  "output_type": "execute_result"
338
  }
 
349
  " \"Home Type\",\n",
350
  "]\n",
351
  "\n",
352
+ "data_frames = []\n",
353
  "\n",
354
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
355
  " if filename.endswith(\".csv\"):\n",
 
384
  " else \"Median Listing Price (Smoothed)\"\n",
385
  " ),\n",
386
  " )\n",
387
+ " data_frames.append(cur_df)\n",
388
  "\n",
389
  " elif \"_new_listings_\" in filename:\n",
390
  " cur_df = pd.melt(\n",
 
396
  " \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
397
  " ),\n",
398
  " )\n",
399
+ " data_frames.append(cur_df)\n",
400
  "\n",
401
  " elif \"new_pending\" in filename:\n",
402
  " cur_df = pd.melt(\n",
 
406
  " var_name=\"Date\",\n",
407
  " value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
408
  " )\n",
409
+ " data_frames.append(cur_df)\n",
410
  "\n",
411
  "matching_cols = [\n",
412
  " \"RegionID\",\n",
 
418
  " \"Home Type\",\n",
419
  "]\n",
420
  "\n",
 
421
  "\n",
422
+ "def get_combined_df(data_frames):\n",
423
+ " combined_df = None\n",
424
+ " if len(data_frames) > 1:\n",
425
+ " # iterate over dataframes and merge or concat\n",
426
+ " combined_df = data_frames[0]\n",
427
+ " for i in range(1, len(data_frames)):\n",
428
+ " cur_df = data_frames[i]\n",
429
+ " combined_df = pd.merge(\n",
430
+ " combined_df,\n",
431
+ " cur_df,\n",
432
+ " on=[\n",
433
+ " \"RegionID\",\n",
434
+ " \"SizeRank\",\n",
435
+ " \"RegionName\",\n",
436
+ " \"RegionType\",\n",
437
+ " \"StateName\",\n",
438
+ " \"Home Type\",\n",
439
+ " \"Date\",\n",
440
+ " ],\n",
441
+ " suffixes=(\"\", \"_\" + str(i)),\n",
442
+ " how=\"outer\",\n",
443
+ " )\n",
444
+ " elif len(data_frames) == 1:\n",
445
+ " combined_df = data_frames[0]\n",
446
+ "\n",
447
+ " return combined_df\n",
448
+ "\n",
449
+ "\n",
450
+ "combined_df = get_combined_df(data_frames)\n",
451
  "\n",
452
  "\n",
453
+ "# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
454
+ "columns_to_coalesce = [\n",
455
+ " \"Median Listing Price\",\n",
456
+ " \"Median Listing Price (Smoothed)\",\n",
457
+ " \"New Listings\",\n",
458
+ " \"New Listings (Smoothed)\",\n",
459
+ " \"New Pending (Smoothed)\",\n",
460
+ " \"New Pending\",\n",
461
+ "]\n",
462
+ "\n",
463
+ "for index, row in combined_df.iterrows():\n",
464
+ " for col in combined_df.columns:\n",
465
+ " for column_to_coalesce in columns_to_coalesce:\n",
466
+ " if column_to_coalesce in col and \"_\" in col:\n",
467
+ " if not pd.isna(row[col]):\n",
468
+ " combined_df.at[index, column_to_coalesce] = row[col]\n",
469
+ "\n",
470
+ "# remove columns with underscores\n",
471
+ "combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
472
+ "\n",
473
  "combined_df"
474
  ]
475
  },
476
  {
477
  "cell_type": "code",
478
+ "execution_count": 6,
479
  "metadata": {},
480
  "outputs": [
481
  {
 
506
  " <th>State</th>\n",
507
  " <th>Home Type</th>\n",
508
  " <th>Date</th>\n",
509
+ " <th>New Pending (Smoothed)</th>\n",
510
  " <th>Median Listing Price</th>\n",
511
  " <th>Median Listing Price (Smoothed)</th>\n",
512
+ " <th>New Pending</th>\n",
513
  " <th>New Listings</th>\n",
514
  " <th>New Listings (Smoothed)</th>\n",
 
 
515
  " </tr>\n",
516
  " </thead>\n",
517
  " <tbody>\n",
 
522
  " <td>United States</td>\n",
523
  " <td>country</td>\n",
524
  " <td>NaN</td>\n",
525
+ " <td>SFR</td>\n",
526
+ " <td>2018-01-13</td>\n",
527
  " <td>NaN</td>\n",
528
+ " <td>259000.0</td>\n",
529
  " <td>NaN</td>\n",
530
  " <td>NaN</td>\n",
531
  " <td>NaN</td>\n",
532
  " <td>NaN</td>\n",
 
533
  " </tr>\n",
534
  " <tr>\n",
535
  " <th>1</th>\n",
 
539
  " <td>country</td>\n",
540
  " <td>NaN</td>\n",
541
  " <td>SFR</td>\n",
542
+ " <td>2018-01-20</td>\n",
 
543
  " <td>NaN</td>\n",
544
+ " <td>259900.0</td>\n",
545
  " <td>NaN</td>\n",
546
  " <td>NaN</td>\n",
547
  " <td>NaN</td>\n",
 
554
  " <td>United States</td>\n",
555
  " <td>country</td>\n",
556
  " <td>NaN</td>\n",
557
+ " <td>SFR</td>\n",
558
+ " <td>2018-01-27</td>\n",
559
+ " <td>NaN</td>\n",
560
  " <td>259900.0</td>\n",
561
  " <td>NaN</td>\n",
 
562
  " <td>NaN</td>\n",
563
  " <td>NaN</td>\n",
564
+ " <td>NaN</td>\n",
565
  " </tr>\n",
566
  " <tr>\n",
567
  " <th>3</th>\n",
 
571
  " <td>country</td>\n",
572
  " <td>NaN</td>\n",
573
  " <td>SFR</td>\n",
574
+ " <td>2018-01-31</td>\n",
 
575
  " <td>NaN</td>\n",
576
+ " <td>254900.0</td>\n",
577
  " <td>NaN</td>\n",
578
  " <td>NaN</td>\n",
579
  " <td>NaN</td>\n",
 
586
  " <td>United States</td>\n",
587
  " <td>country</td>\n",
588
  " <td>NaN</td>\n",
589
+ " <td>SFR</td>\n",
590
+ " <td>2018-02-03</td>\n",
591
+ " <td>NaN</td>\n",
592
+ " <td>260000.0</td>\n",
593
+ " <td>259700.0</td>\n",
594
  " <td>NaN</td>\n",
 
595
  " <td>NaN</td>\n",
596
  " <td>NaN</td>\n",
 
597
  " </tr>\n",
598
  " <tr>\n",
599
  " <th>...</th>\n",
 
612
  " <td>...</td>\n",
613
  " </tr>\n",
614
  " <tr>\n",
615
+ " <th>693656</th>\n",
616
  " <td>845172</td>\n",
617
  " <td>769</td>\n",
618
  " <td>Winfield, KS</td>\n",
619
  " <td>msa</td>\n",
620
  " <td>KS</td>\n",
621
  " <td>all homes</td>\n",
622
+ " <td>2023-12-16</td>\n",
623
+ " <td>NaN</td>\n",
624
+ " <td>133938.0</td>\n",
625
+ " <td>133938.0</td>\n",
626
  " <td>NaN</td>\n",
 
627
  " <td>NaN</td>\n",
 
628
  " <td>NaN</td>\n",
 
629
  " </tr>\n",
630
  " <tr>\n",
631
+ " <th>693657</th>\n",
632
  " <td>845172</td>\n",
633
  " <td>769</td>\n",
634
  " <td>Winfield, KS</td>\n",
635
  " <td>msa</td>\n",
636
  " <td>KS</td>\n",
637
+ " <td>all homes</td>\n",
638
+ " <td>2023-12-23</td>\n",
 
 
639
  " <td>NaN</td>\n",
640
+ " <td>126463.0</td>\n",
641
+ " <td>126463.0</td>\n",
642
  " <td>NaN</td>\n",
643
  " <td>NaN</td>\n",
644
  " <td>NaN</td>\n",
645
  " </tr>\n",
646
  " <tr>\n",
647
+ " <th>693658</th>\n",
648
  " <td>845172</td>\n",
649
  " <td>769</td>\n",
650
  " <td>Winfield, KS</td>\n",
651
  " <td>msa</td>\n",
652
  " <td>KS</td>\n",
653
+ " <td>all homes</td>\n",
654
+ " <td>2023-12-30</td>\n",
 
 
655
  " <td>NaN</td>\n",
656
+ " <td>123225.0</td>\n",
657
+ " <td>123225.0</td>\n",
658
  " <td>NaN</td>\n",
659
  " <td>NaN</td>\n",
660
  " <td>NaN</td>\n",
661
  " </tr>\n",
662
  " <tr>\n",
663
+ " <th>693659</th>\n",
664
  " <td>845172</td>\n",
665
  " <td>769</td>\n",
666
  " <td>Winfield, KS</td>\n",
667
  " <td>msa</td>\n",
668
  " <td>KS</td>\n",
669
  " <td>all homes</td>\n",
670
+ " <td>2023-12-31</td>\n",
671
+ " <td>24.0</td>\n",
672
+ " <td>136233.0</td>\n",
673
+ " <td>136233.0</td>\n",
674
+ " <td>24.0</td>\n",
675
+ " <td>28.0</td>\n",
676
+ " <td>28.0</td>\n",
677
  " </tr>\n",
678
  " <tr>\n",
679
+ " <th>693660</th>\n",
680
  " <td>845172</td>\n",
681
  " <td>769</td>\n",
682
  " <td>Winfield, KS</td>\n",
 
686
  " <td>2024-01-06</td>\n",
687
  " <td>NaN</td>\n",
688
  " <td>121488.0</td>\n",
689
+ " <td>121488.0</td>\n",
690
  " <td>NaN</td>\n",
691
  " <td>NaN</td>\n",
692
  " <td>NaN</td>\n",
693
  " </tr>\n",
694
  " </tbody>\n",
695
  "</table>\n",
696
+ "<p>693661 rows × 13 columns</p>\n",
697
  "</div>"
698
  ],
699
  "text/plain": [
700
+ " Region ID Size Rank Region Region Type State Home Type \\\n",
701
+ "0 102001 0 United States country NaN SFR \n",
702
+ "1 102001 0 United States country NaN SFR \n",
703
+ "2 102001 0 United States country NaN SFR \n",
704
+ "3 102001 0 United States country NaN SFR \n",
705
+ "4 102001 0 United States country NaN SFR \n",
706
+ "... ... ... ... ... ... ... \n",
707
+ "693656 845172 769 Winfield, KS msa KS all homes \n",
708
+ "693657 845172 769 Winfield, KS msa KS all homes \n",
709
+ "693658 845172 769 Winfield, KS msa KS all homes \n",
710
+ "693659 845172 769 Winfield, KS msa KS all homes \n",
711
+ "693660 845172 769 Winfield, KS msa KS all homes \n",
712
  "\n",
713
+ " Date New Pending (Smoothed) Median Listing Price \\\n",
714
+ "0 2018-01-13 NaN 259000.0 \n",
715
+ "1 2018-01-20 NaN 259900.0 \n",
716
+ "2 2018-01-27 NaN 259900.0 \n",
717
+ "3 2018-01-31 NaN 254900.0 \n",
718
+ "4 2018-02-03 NaN 260000.0 \n",
719
+ "... ... ... ... \n",
720
+ "693656 2023-12-16 NaN 133938.0 \n",
721
+ "693657 2023-12-23 NaN 126463.0 \n",
722
+ "693658 2023-12-30 NaN 123225.0 \n",
723
+ "693659 2023-12-31 24.0 136233.0 \n",
724
+ "693660 2024-01-06 NaN 121488.0 \n",
725
  "\n",
726
+ " Median Listing Price (Smoothed) New Pending New Listings \\\n",
727
+ "0 NaN NaN NaN \n",
728
+ "1 NaN NaN NaN \n",
729
+ "2 NaN NaN NaN \n",
730
+ "3 NaN NaN NaN \n",
731
+ "4 259700.0 NaN NaN \n",
732
+ "... ... ... ... \n",
733
+ "693656 133938.0 NaN NaN \n",
734
+ "693657 126463.0 NaN NaN \n",
735
+ "693658 123225.0 NaN NaN \n",
736
+ "693659 136233.0 24.0 28.0 \n",
737
+ "693660 121488.0 NaN NaN \n",
738
  "\n",
739
+ " New Listings (Smoothed) \n",
740
+ "0 NaN \n",
741
+ "1 NaN \n",
742
+ "2 NaN \n",
743
+ "3 NaN \n",
744
+ "4 NaN \n",
745
+ "... ... \n",
746
+ "693656 NaN \n",
747
+ "693657 NaN \n",
748
+ "693658 NaN \n",
749
+ "693659 28.0 \n",
750
+ "693660 NaN \n",
751
  "\n",
752
+ "[693661 rows x 13 columns]"
753
  ]
754
  },
755
+ "execution_count": 6,
756
  "metadata": {},
757
  "output_type": "execute_result"
758
  }
 
774
  },
775
  {
776
  "cell_type": "code",
777
+ "execution_count": 7,
778
  "metadata": {},
779
  "outputs": [],
780
  "source": [
processors/new_constructions.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 64,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -12,7 +12,7 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 65,
16
  "metadata": {},
17
  "outputs": [],
18
  "source": [
@@ -25,7 +25,7 @@
25
  },
26
  {
27
  "cell_type": "code",
28
- "execution_count": 66,
29
  "metadata": {},
30
  "outputs": [
31
  {
@@ -71,9 +71,9 @@
71
  " <th>StateName</th>\n",
72
  " <th>Home Type</th>\n",
73
  " <th>Date</th>\n",
 
74
  " <th>Median Sale Price per Sqft</th>\n",
75
  " <th>Median Sale Price</th>\n",
76
- " <th>Sales Count</th>\n",
77
  " </tr>\n",
78
  " </thead>\n",
79
  " <tbody>\n",
@@ -86,9 +86,9 @@
86
  " <td>NaN</td>\n",
87
  " <td>SFR</td>\n",
88
  " <td>2018-01-31</td>\n",
 
89
  " <td>137.412316</td>\n",
90
  " <td>309000.0</td>\n",
91
- " <td>33940.0</td>\n",
92
  " </tr>\n",
93
  " <tr>\n",
94
  " <th>1</th>\n",
@@ -97,11 +97,11 @@
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>140.504620</td>\n",
103
- " <td>314596.0</td>\n",
104
- " <td>37135.0</td>\n",
105
  " </tr>\n",
106
  " <tr>\n",
107
  " <th>2</th>\n",
@@ -110,11 +110,11 @@
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>238.300000</td>\n",
116
- " <td>388250.0</td>\n",
117
- " <td>3195.0</td>\n",
118
  " </tr>\n",
119
  " <tr>\n",
120
  " <th>3</th>\n",
@@ -124,10 +124,10 @@
124
  " <td>country</td>\n",
125
  " <td>NaN</td>\n",
126
  " <td>SFR</td>\n",
127
- " <td>2018-02-28</td>\n",
128
- " <td>137.199170</td>\n",
129
- " <td>309072.5</td>\n",
130
- " <td>33304.0</td>\n",
131
  " </tr>\n",
132
  " <tr>\n",
133
  " <th>4</th>\n",
@@ -136,11 +136,11 @@
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>140.304966</td>\n",
142
- " <td>314608.0</td>\n",
143
- " <td>36493.0</td>\n",
144
  " </tr>\n",
145
  " <tr>\n",
146
  " <th>...</th>\n",
@@ -163,10 +163,10 @@
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",
@@ -175,11 +175,11 @@
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",
@@ -189,10 +189,10 @@
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",
@@ -201,11 +201,11 @@
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",
@@ -216,9 +216,9 @@
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",
@@ -226,49 +226,36 @@
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 Median Sale Price per Sqft \\\n",
243
- "0 SFR 2018-01-31 137.412316 \n",
244
- "1 all homes 2018-01-31 140.504620 \n",
245
- "2 condo/co-op only 2018-01-31 238.300000 \n",
246
- "3 SFR 2018-02-28 137.199170 \n",
247
- "4 all homes 2018-02-28 140.304966 \n",
248
- "... ... ... ... \n",
249
- "49482 all homes 2023-09-30 NaN \n",
250
- "49483 SFR 2023-10-31 NaN \n",
251
- "49484 all homes 2023-10-31 NaN \n",
252
- "49485 SFR 2023-11-30 NaN \n",
253
- "49486 all homes 2023-11-30 NaN \n",
254
  "\n",
255
- " Median Sale Price Sales Count \n",
256
- "0 309000.0 33940.0 \n",
257
- "1 314596.0 37135.0 \n",
258
- "2 388250.0 3195.0 \n",
259
- "3 309072.5 33304.0 \n",
260
- "4 314608.0 36493.0 \n",
261
- "... ... ... \n",
262
- "49482 NaN 26.0 \n",
263
- "49483 NaN 24.0 \n",
264
- "49484 NaN 24.0 \n",
265
- "49485 NaN 16.0 \n",
266
- "49486 NaN 16.0 \n",
267
  "\n",
268
  "[49487 rows x 10 columns]"
269
  ]
270
  },
271
- "execution_count": 66,
272
  "metadata": {},
273
  "output_type": "execute_result"
274
  }
@@ -285,7 +272,7 @@
285
  " \"Home Type\",\n",
286
  "]\n",
287
  "\n",
288
- "batches = {\"median_sale_price_per_sqft\": [], \"median_sale_price\": [], \"sales_count\": []}\n",
289
  "\n",
290
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
291
  " if filename.endswith(\".csv\"):\n",
@@ -310,7 +297,7 @@
310
  " var_name=\"Date\",\n",
311
  " value_name=\"Median Sale Price per Sqft\",\n",
312
  " )\n",
313
- " batches[\"median_sale_price_per_sqft\"].append(cur_df)\n",
314
  "\n",
315
  " elif \"median_sale_price\" in filename:\n",
316
  " cur_df = pd.melt(\n",
@@ -320,7 +307,7 @@
320
  " var_name=\"Date\",\n",
321
  " value_name=\"Median Sale Price\",\n",
322
  " )\n",
323
- " batches[\"median_sale_price\"].append(cur_df)\n",
324
  "\n",
325
  " elif \"sales_count\" in filename:\n",
326
  " cur_df = pd.melt(\n",
@@ -330,37 +317,57 @@
330
  " var_name=\"Date\",\n",
331
  " value_name=\"Sales Count\",\n",
332
  " )\n",
333
- " batches[\"sales_count\"].append(cur_df)\n",
334
  "\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
- " \"Home Type\",\n",
344
- "]\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
345
  "\n",
346
- "combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
 
 
 
 
 
347
  "\n",
348
- "if len(combined_batches) > 0:\n",
349
- " combined_df = combined_batches[0]\n",
350
- " for batch in combined_batches[1:]:\n",
351
- " combined_df = pd.merge(\n",
352
- " combined_df,\n",
353
- " batch,\n",
354
- " on=matching_cols,\n",
355
- " how=\"outer\",\n",
356
- " )\n",
357
  "\n",
358
  "combined_df"
359
  ]
360
  },
361
  {
362
  "cell_type": "code",
363
- "execution_count": 67,
364
  "metadata": {},
365
  "outputs": [
366
  {
@@ -391,9 +398,9 @@
391
  " <th>State</th>\n",
392
  " <th>Home Type</th>\n",
393
  " <th>Date</th>\n",
 
394
  " <th>Median Sale Price per Sqft</th>\n",
395
  " <th>Median Sale Price</th>\n",
396
- " <th>Sales Count</th>\n",
397
  " </tr>\n",
398
  " </thead>\n",
399
  " <tbody>\n",
@@ -406,9 +413,9 @@
406
  " <td>NaN</td>\n",
407
  " <td>SFR</td>\n",
408
  " <td>2018-01-31</td>\n",
 
409
  " <td>137.412316</td>\n",
410
  " <td>309000.0</td>\n",
411
- " <td>33940.0</td>\n",
412
  " </tr>\n",
413
  " <tr>\n",
414
  " <th>1</th>\n",
@@ -417,11 +424,11 @@
417
  " <td>United States</td>\n",
418
  " <td>country</td>\n",
419
  " <td>NaN</td>\n",
420
- " <td>all homes</td>\n",
421
- " <td>2018-01-31</td>\n",
422
- " <td>140.504620</td>\n",
423
- " <td>314596.0</td>\n",
424
- " <td>37135.0</td>\n",
425
  " </tr>\n",
426
  " <tr>\n",
427
  " <th>2</th>\n",
@@ -430,11 +437,11 @@
430
  " <td>United States</td>\n",
431
  " <td>country</td>\n",
432
  " <td>NaN</td>\n",
433
- " <td>condo/co-op only</td>\n",
434
- " <td>2018-01-31</td>\n",
435
- " <td>238.300000</td>\n",
436
- " <td>388250.0</td>\n",
437
- " <td>3195.0</td>\n",
438
  " </tr>\n",
439
  " <tr>\n",
440
  " <th>3</th>\n",
@@ -444,10 +451,10 @@
444
  " <td>country</td>\n",
445
  " <td>NaN</td>\n",
446
  " <td>SFR</td>\n",
447
- " <td>2018-02-28</td>\n",
448
- " <td>137.199170</td>\n",
449
- " <td>309072.5</td>\n",
450
- " <td>33304.0</td>\n",
451
  " </tr>\n",
452
  " <tr>\n",
453
  " <th>4</th>\n",
@@ -456,11 +463,11 @@
456
  " <td>United States</td>\n",
457
  " <td>country</td>\n",
458
  " <td>NaN</td>\n",
459
- " <td>all homes</td>\n",
460
- " <td>2018-02-28</td>\n",
461
- " <td>140.304966</td>\n",
462
- " <td>314608.0</td>\n",
463
- " <td>36493.0</td>\n",
464
  " </tr>\n",
465
  " <tr>\n",
466
  " <th>...</th>\n",
@@ -483,10 +490,10 @@
483
  " <td>msa</td>\n",
484
  " <td>TX</td>\n",
485
  " <td>all homes</td>\n",
486
- " <td>2023-09-30</td>\n",
 
487
  " <td>NaN</td>\n",
488
  " <td>NaN</td>\n",
489
- " <td>26.0</td>\n",
490
  " </tr>\n",
491
  " <tr>\n",
492
  " <th>49483</th>\n",
@@ -495,11 +502,11 @@
495
  " <td>Granbury, TX</td>\n",
496
  " <td>msa</td>\n",
497
  " <td>TX</td>\n",
498
- " <td>SFR</td>\n",
499
- " <td>2023-10-31</td>\n",
 
500
  " <td>NaN</td>\n",
501
  " <td>NaN</td>\n",
502
- " <td>24.0</td>\n",
503
  " </tr>\n",
504
  " <tr>\n",
505
  " <th>49484</th>\n",
@@ -509,10 +516,10 @@
509
  " <td>msa</td>\n",
510
  " <td>TX</td>\n",
511
  " <td>all homes</td>\n",
512
- " <td>2023-10-31</td>\n",
 
513
  " <td>NaN</td>\n",
514
  " <td>NaN</td>\n",
515
- " <td>24.0</td>\n",
516
  " </tr>\n",
517
  " <tr>\n",
518
  " <th>49485</th>\n",
@@ -521,11 +528,11 @@
521
  " <td>Granbury, TX</td>\n",
522
  " <td>msa</td>\n",
523
  " <td>TX</td>\n",
524
- " <td>SFR</td>\n",
525
- " <td>2023-11-30</td>\n",
 
526
  " <td>NaN</td>\n",
527
  " <td>NaN</td>\n",
528
- " <td>16.0</td>\n",
529
  " </tr>\n",
530
  " <tr>\n",
531
  " <th>49486</th>\n",
@@ -536,9 +543,9 @@
536
  " <td>TX</td>\n",
537
  " <td>all homes</td>\n",
538
  " <td>2023-11-30</td>\n",
 
539
  " <td>NaN</td>\n",
540
  " <td>NaN</td>\n",
541
- " <td>16.0</td>\n",
542
  " </tr>\n",
543
  " </tbody>\n",
544
  "</table>\n",
@@ -546,49 +553,36 @@
546
  "</div>"
547
  ],
548
  "text/plain": [
549
- " Region ID Size Rank Region Region Type State \\\n",
550
- "0 102001 0 United States country NaN \n",
551
- "1 102001 0 United States country NaN \n",
552
- "2 102001 0 United States country NaN \n",
553
- "3 102001 0 United States country NaN \n",
554
- "4 102001 0 United States country NaN \n",
555
- "... ... ... ... ... ... \n",
556
- "49482 845162 535 Granbury, TX msa TX \n",
557
- "49483 845162 535 Granbury, TX msa TX \n",
558
- "49484 845162 535 Granbury, TX msa TX \n",
559
- "49485 845162 535 Granbury, TX msa TX \n",
560
- "49486 845162 535 Granbury, TX msa TX \n",
561
- "\n",
562
- " Home Type Date Median Sale Price per Sqft \\\n",
563
- "0 SFR 2018-01-31 137.412316 \n",
564
- "1 all homes 2018-01-31 140.504620 \n",
565
- "2 condo/co-op only 2018-01-31 238.300000 \n",
566
- "3 SFR 2018-02-28 137.199170 \n",
567
- "4 all homes 2018-02-28 140.304966 \n",
568
- "... ... ... ... \n",
569
- "49482 all homes 2023-09-30 NaN \n",
570
- "49483 SFR 2023-10-31 NaN \n",
571
- "49484 all homes 2023-10-31 NaN \n",
572
- "49485 SFR 2023-11-30 NaN \n",
573
- "49486 all homes 2023-11-30 NaN \n",
574
  "\n",
575
- " Median Sale Price Sales Count \n",
576
- "0 309000.0 33940.0 \n",
577
- "1 314596.0 37135.0 \n",
578
- "2 388250.0 3195.0 \n",
579
- "3 309072.5 33304.0 \n",
580
- "4 314608.0 36493.0 \n",
581
- "... ... ... \n",
582
- "49482 NaN 26.0 \n",
583
- "49483 NaN 24.0 \n",
584
- "49484 NaN 24.0 \n",
585
- "49485 NaN 16.0 \n",
586
- "49486 NaN 16.0 \n",
587
  "\n",
588
  "[49487 rows x 10 columns]"
589
  ]
590
  },
591
- "execution_count": 67,
592
  "metadata": {},
593
  "output_type": "execute_result"
594
  }
@@ -605,12 +599,12 @@
605
  " }\n",
606
  ")\n",
607
  "\n",
608
- "final_df"
609
  ]
610
  },
611
  {
612
  "cell_type": "code",
613
- "execution_count": 68,
614
  "metadata": {},
615
  "outputs": [],
616
  "source": [
 
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": 38,
29
  "metadata": {},
30
  "outputs": [
31
  {
 
71
  " <th>StateName</th>\n",
72
  " <th>Home Type</th>\n",
73
  " <th>Date</th>\n",
74
+ " <th>Sales Count</th>\n",
75
  " <th>Median Sale Price per Sqft</th>\n",
76
  " <th>Median Sale Price</th>\n",
 
77
  " </tr>\n",
78
  " </thead>\n",
79
  " <tbody>\n",
 
86
  " <td>NaN</td>\n",
87
  " <td>SFR</td>\n",
88
  " <td>2018-01-31</td>\n",
89
+ " <td>33940.0</td>\n",
90
  " <td>137.412316</td>\n",
91
  " <td>309000.0</td>\n",
 
92
  " </tr>\n",
93
  " <tr>\n",
94
  " <th>1</th>\n",
 
97
  " <td>United States</td>\n",
98
  " <td>country</td>\n",
99
  " <td>NaN</td>\n",
100
+ " <td>SFR</td>\n",
101
+ " <td>2018-02-28</td>\n",
102
+ " <td>33304.0</td>\n",
103
+ " <td>137.199170</td>\n",
104
+ " <td>309072.5</td>\n",
105
  " </tr>\n",
106
  " <tr>\n",
107
  " <th>2</th>\n",
 
110
  " <td>United States</td>\n",
111
  " <td>country</td>\n",
112
  " <td>NaN</td>\n",
113
+ " <td>SFR</td>\n",
114
+ " <td>2018-03-31</td>\n",
115
+ " <td>42641.0</td>\n",
116
+ " <td>139.520863</td>\n",
117
+ " <td>315488.0</td>\n",
118
  " </tr>\n",
119
  " <tr>\n",
120
  " <th>3</th>\n",
 
124
  " <td>country</td>\n",
125
  " <td>NaN</td>\n",
126
  " <td>SFR</td>\n",
127
+ " <td>2018-04-30</td>\n",
128
+ " <td>37588.0</td>\n",
129
+ " <td>139.778110</td>\n",
130
+ " <td>314990.0</td>\n",
131
  " </tr>\n",
132
  " <tr>\n",
133
  " <th>4</th>\n",
 
136
  " <td>United States</td>\n",
137
  " <td>country</td>\n",
138
  " <td>NaN</td>\n",
139
+ " <td>SFR</td>\n",
140
+ " <td>2018-05-31</td>\n",
141
+ " <td>39933.0</td>\n",
142
+ " <td>143.317968</td>\n",
143
+ " <td>324500.0</td>\n",
144
  " </tr>\n",
145
  " <tr>\n",
146
  " <th>...</th>\n",
 
163
  " <td>msa</td>\n",
164
  " <td>TX</td>\n",
165
  " <td>all homes</td>\n",
166
+ " <td>2023-07-31</td>\n",
167
+ " <td>31.0</td>\n",
168
  " <td>NaN</td>\n",
169
  " <td>NaN</td>\n",
 
170
  " </tr>\n",
171
  " <tr>\n",
172
  " <th>49483</th>\n",
 
175
  " <td>Granbury, TX</td>\n",
176
  " <td>msa</td>\n",
177
  " <td>TX</td>\n",
178
+ " <td>all homes</td>\n",
179
+ " <td>2023-08-31</td>\n",
180
+ " <td>33.0</td>\n",
181
  " <td>NaN</td>\n",
182
  " <td>NaN</td>\n",
 
183
  " </tr>\n",
184
  " <tr>\n",
185
  " <th>49484</th>\n",
 
189
  " <td>msa</td>\n",
190
  " <td>TX</td>\n",
191
  " <td>all homes</td>\n",
192
+ " <td>2023-09-30</td>\n",
193
+ " <td>26.0</td>\n",
194
  " <td>NaN</td>\n",
195
  " <td>NaN</td>\n",
 
196
  " </tr>\n",
197
  " <tr>\n",
198
  " <th>49485</th>\n",
 
201
  " <td>Granbury, TX</td>\n",
202
  " <td>msa</td>\n",
203
  " <td>TX</td>\n",
204
+ " <td>all homes</td>\n",
205
+ " <td>2023-10-31</td>\n",
206
+ " <td>24.0</td>\n",
207
  " <td>NaN</td>\n",
208
  " <td>NaN</td>\n",
 
209
  " </tr>\n",
210
  " <tr>\n",
211
  " <th>49486</th>\n",
 
216
  " <td>TX</td>\n",
217
  " <td>all homes</td>\n",
218
  " <td>2023-11-30</td>\n",
219
+ " <td>16.0</td>\n",
220
  " <td>NaN</td>\n",
221
  " <td>NaN</td>\n",
 
222
  " </tr>\n",
223
  " </tbody>\n",
224
  "</table>\n",
 
226
  "</div>"
227
  ],
228
  "text/plain": [
229
+ " RegionID SizeRank RegionName RegionType StateName Home Type \\\n",
230
+ "0 102001 0 United States country NaN SFR \n",
231
+ "1 102001 0 United States country NaN SFR \n",
232
+ "2 102001 0 United States country NaN SFR \n",
233
+ "3 102001 0 United States country NaN SFR \n",
234
+ "4 102001 0 United States country NaN SFR \n",
235
+ "... ... ... ... ... ... ... \n",
236
+ "49482 845162 535 Granbury, TX msa TX all homes \n",
237
+ "49483 845162 535 Granbury, TX msa TX all homes \n",
238
+ "49484 845162 535 Granbury, TX msa TX all homes \n",
239
+ "49485 845162 535 Granbury, TX msa TX all homes \n",
240
+ "49486 845162 535 Granbury, TX msa TX all homes \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
241
  "\n",
242
+ " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
243
+ "0 2018-01-31 33940.0 137.412316 309000.0 \n",
244
+ "1 2018-02-28 33304.0 137.199170 309072.5 \n",
245
+ "2 2018-03-31 42641.0 139.520863 315488.0 \n",
246
+ "3 2018-04-30 37588.0 139.778110 314990.0 \n",
247
+ "4 2018-05-31 39933.0 143.317968 324500.0 \n",
248
+ "... ... ... ... ... \n",
249
+ "49482 2023-07-31 31.0 NaN NaN \n",
250
+ "49483 2023-08-31 33.0 NaN NaN \n",
251
+ "49484 2023-09-30 26.0 NaN NaN \n",
252
+ "49485 2023-10-31 24.0 NaN NaN \n",
253
+ "49486 2023-11-30 16.0 NaN NaN \n",
254
  "\n",
255
  "[49487 rows x 10 columns]"
256
  ]
257
  },
258
+ "execution_count": 38,
259
  "metadata": {},
260
  "output_type": "execute_result"
261
  }
 
272
  " \"Home Type\",\n",
273
  "]\n",
274
  "\n",
275
+ "data_frames = []\n",
276
  "\n",
277
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
278
  " if filename.endswith(\".csv\"):\n",
 
297
  " var_name=\"Date\",\n",
298
  " value_name=\"Median Sale Price per Sqft\",\n",
299
  " )\n",
300
+ " data_frames.append(cur_df)\n",
301
  "\n",
302
  " elif \"median_sale_price\" in filename:\n",
303
  " cur_df = pd.melt(\n",
 
307
  " var_name=\"Date\",\n",
308
  " value_name=\"Median Sale Price\",\n",
309
  " )\n",
310
+ " data_frames.append(cur_df)\n",
311
  "\n",
312
  " elif \"sales_count\" in filename:\n",
313
  " cur_df = pd.melt(\n",
 
317
  " var_name=\"Date\",\n",
318
  " value_name=\"Sales Count\",\n",
319
  " )\n",
320
+ " data_frames.append(cur_df)\n",
321
  "\n",
322
  "\n",
323
+ "def get_combined_df(data_frames):\n",
324
+ " combined_df = None\n",
325
+ " if len(data_frames) > 1:\n",
326
+ " # iterate over dataframes and merge or concat\n",
327
+ " combined_df = data_frames[0]\n",
328
+ " for i in range(1, len(data_frames)):\n",
329
+ " cur_df = data_frames[i]\n",
330
+ " combined_df = pd.merge(\n",
331
+ " combined_df,\n",
332
+ " cur_df,\n",
333
+ " on=[\n",
334
+ " \"RegionID\",\n",
335
+ " \"SizeRank\",\n",
336
+ " \"RegionName\",\n",
337
+ " \"RegionType\",\n",
338
+ " \"StateName\",\n",
339
+ " \"Home Type\",\n",
340
+ " \"Date\",\n",
341
+ " ],\n",
342
+ " how=\"outer\",\n",
343
+ " suffixes=(\"\", \"_\" + str(i)),\n",
344
+ " )\n",
345
+ " elif len(data_frames) == 1:\n",
346
+ " combined_df = data_frames[0]\n",
347
+ "\n",
348
+ " return combined_df\n",
349
+ "\n",
350
+ "\n",
351
+ "combined_df = get_combined_df(data_frames)\n",
352
+ "# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
353
+ "columns_to_coalesce = [\"Sales Count\", \"Median Sale Price\", \"Median Sale Price per Sqft\"]\n",
354
  "\n",
355
+ "for index, row in combined_df.iterrows():\n",
356
+ " for col in combined_df.columns:\n",
357
+ " for column_to_coalesce in columns_to_coalesce:\n",
358
+ " if column_to_coalesce in col and \"_\" in col:\n",
359
+ " if not pd.isna(row[col]):\n",
360
+ " combined_df.at[index, column_to_coalesce] = row[col]\n",
361
  "\n",
362
+ "# remove columns with underscores\n",
363
+ "combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
 
 
 
 
 
 
 
364
  "\n",
365
  "combined_df"
366
  ]
367
  },
368
  {
369
  "cell_type": "code",
370
+ "execution_count": 39,
371
  "metadata": {},
372
  "outputs": [
373
  {
 
398
  " <th>State</th>\n",
399
  " <th>Home Type</th>\n",
400
  " <th>Date</th>\n",
401
+ " <th>Sales Count</th>\n",
402
  " <th>Median Sale Price per Sqft</th>\n",
403
  " <th>Median Sale Price</th>\n",
 
404
  " </tr>\n",
405
  " </thead>\n",
406
  " <tbody>\n",
 
413
  " <td>NaN</td>\n",
414
  " <td>SFR</td>\n",
415
  " <td>2018-01-31</td>\n",
416
+ " <td>33940.0</td>\n",
417
  " <td>137.412316</td>\n",
418
  " <td>309000.0</td>\n",
 
419
  " </tr>\n",
420
  " <tr>\n",
421
  " <th>1</th>\n",
 
424
  " <td>United States</td>\n",
425
  " <td>country</td>\n",
426
  " <td>NaN</td>\n",
427
+ " <td>SFR</td>\n",
428
+ " <td>2018-02-28</td>\n",
429
+ " <td>33304.0</td>\n",
430
+ " <td>137.199170</td>\n",
431
+ " <td>309072.5</td>\n",
432
  " </tr>\n",
433
  " <tr>\n",
434
  " <th>2</th>\n",
 
437
  " <td>United States</td>\n",
438
  " <td>country</td>\n",
439
  " <td>NaN</td>\n",
440
+ " <td>SFR</td>\n",
441
+ " <td>2018-03-31</td>\n",
442
+ " <td>42641.0</td>\n",
443
+ " <td>139.520863</td>\n",
444
+ " <td>315488.0</td>\n",
445
  " </tr>\n",
446
  " <tr>\n",
447
  " <th>3</th>\n",
 
451
  " <td>country</td>\n",
452
  " <td>NaN</td>\n",
453
  " <td>SFR</td>\n",
454
+ " <td>2018-04-30</td>\n",
455
+ " <td>37588.0</td>\n",
456
+ " <td>139.778110</td>\n",
457
+ " <td>314990.0</td>\n",
458
  " </tr>\n",
459
  " <tr>\n",
460
  " <th>4</th>\n",
 
463
  " <td>United States</td>\n",
464
  " <td>country</td>\n",
465
  " <td>NaN</td>\n",
466
+ " <td>SFR</td>\n",
467
+ " <td>2018-05-31</td>\n",
468
+ " <td>39933.0</td>\n",
469
+ " <td>143.317968</td>\n",
470
+ " <td>324500.0</td>\n",
471
  " </tr>\n",
472
  " <tr>\n",
473
  " <th>...</th>\n",
 
490
  " <td>msa</td>\n",
491
  " <td>TX</td>\n",
492
  " <td>all homes</td>\n",
493
+ " <td>2023-07-31</td>\n",
494
+ " <td>31.0</td>\n",
495
  " <td>NaN</td>\n",
496
  " <td>NaN</td>\n",
 
497
  " </tr>\n",
498
  " <tr>\n",
499
  " <th>49483</th>\n",
 
502
  " <td>Granbury, TX</td>\n",
503
  " <td>msa</td>\n",
504
  " <td>TX</td>\n",
505
+ " <td>all homes</td>\n",
506
+ " <td>2023-08-31</td>\n",
507
+ " <td>33.0</td>\n",
508
  " <td>NaN</td>\n",
509
  " <td>NaN</td>\n",
 
510
  " </tr>\n",
511
  " <tr>\n",
512
  " <th>49484</th>\n",
 
516
  " <td>msa</td>\n",
517
  " <td>TX</td>\n",
518
  " <td>all homes</td>\n",
519
+ " <td>2023-09-30</td>\n",
520
+ " <td>26.0</td>\n",
521
  " <td>NaN</td>\n",
522
  " <td>NaN</td>\n",
 
523
  " </tr>\n",
524
  " <tr>\n",
525
  " <th>49485</th>\n",
 
528
  " <td>Granbury, TX</td>\n",
529
  " <td>msa</td>\n",
530
  " <td>TX</td>\n",
531
+ " <td>all homes</td>\n",
532
+ " <td>2023-10-31</td>\n",
533
+ " <td>24.0</td>\n",
534
  " <td>NaN</td>\n",
535
  " <td>NaN</td>\n",
 
536
  " </tr>\n",
537
  " <tr>\n",
538
  " <th>49486</th>\n",
 
543
  " <td>TX</td>\n",
544
  " <td>all homes</td>\n",
545
  " <td>2023-11-30</td>\n",
546
+ " <td>16.0</td>\n",
547
  " <td>NaN</td>\n",
548
  " <td>NaN</td>\n",
 
549
  " </tr>\n",
550
  " </tbody>\n",
551
  "</table>\n",
 
553
  "</div>"
554
  ],
555
  "text/plain": [
556
+ " Region ID Size Rank Region Region Type State Home Type \\\n",
557
+ "0 102001 0 United States country NaN SFR \n",
558
+ "1 102001 0 United States country NaN SFR \n",
559
+ "2 102001 0 United States country NaN SFR \n",
560
+ "3 102001 0 United States country NaN SFR \n",
561
+ "4 102001 0 United States country NaN SFR \n",
562
+ "... ... ... ... ... ... ... \n",
563
+ "49482 845162 535 Granbury, TX msa TX all homes \n",
564
+ "49483 845162 535 Granbury, TX msa TX all homes \n",
565
+ "49484 845162 535 Granbury, TX msa TX all homes \n",
566
+ "49485 845162 535 Granbury, TX msa TX all homes \n",
567
+ "49486 845162 535 Granbury, TX msa TX all homes \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
568
  "\n",
569
+ " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
570
+ "0 2018-01-31 33940.0 137.412316 309000.0 \n",
571
+ "1 2018-02-28 33304.0 137.199170 309072.5 \n",
572
+ "2 2018-03-31 42641.0 139.520863 315488.0 \n",
573
+ "3 2018-04-30 37588.0 139.778110 314990.0 \n",
574
+ "4 2018-05-31 39933.0 143.317968 324500.0 \n",
575
+ "... ... ... ... ... \n",
576
+ "49482 2023-07-31 31.0 NaN NaN \n",
577
+ "49483 2023-08-31 33.0 NaN NaN \n",
578
+ "49484 2023-09-30 26.0 NaN NaN \n",
579
+ "49485 2023-10-31 24.0 NaN NaN \n",
580
+ "49486 2023-11-30 16.0 NaN NaN \n",
581
  "\n",
582
  "[49487 rows x 10 columns]"
583
  ]
584
  },
585
+ "execution_count": 39,
586
  "metadata": {},
587
  "output_type": "execute_result"
588
  }
 
599
  " }\n",
600
  ")\n",
601
  "\n",
602
+ "final_df.sort_values(by=[\"Region ID\", \"Home Type\", \"Date\"])"
603
  ]
604
  },
605
  {
606
  "cell_type": "code",
607
+ "execution_count": 40,
608
  "metadata": {},
609
  "outputs": [],
610
  "source": [
processors/rentals.ipynb CHANGED
@@ -25,28 +25,9 @@
25
  },
26
  {
27
  "cell_type": "code",
28
- "execution_count": 70,
29
  "metadata": {},
30
  "outputs": [
31
- {
32
- "name": "stdout",
33
- "output_type": "stream",
34
- "text": [
35
- "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
36
- " 'Home Type', 'Date', 'Rent (Smoothed)'],\n",
37
- " dtype='object')\n",
38
- "['Rent (Smoothed) (Seasonally Adjusted)', 'RegionID', 'Home Type', 'Date']\n",
39
- "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
40
- " 'Home Type', 'Date', 'Rent (Smoothed)',\n",
41
- " 'Rent (Smoothed) (Seasonally Adjusted)'],\n",
42
- " dtype='object')\n",
43
- "['RegionID', 'Home Type', 'Date']\n",
44
- "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
45
- " 'Home Type', 'Date', 'Rent (Smoothed)',\n",
46
- " 'Rent (Smoothed) (Seasonally Adjusted)'],\n",
47
- " dtype='object')\n"
48
- ]
49
- },
50
  {
51
  "data": {
52
  "text/html": [
@@ -197,8 +178,8 @@
197
  " <td>IL</td>\n",
198
  " <td>multifamily</td>\n",
199
  " <td>2023-11-30</td>\n",
200
- " <td>804.147562</td>\n",
201
- " <td>NaN</td>\n",
202
  " </tr>\n",
203
  " <tr>\n",
204
  " <th>96011</th>\n",
@@ -210,7 +191,7 @@
210
  " <td>multifamily</td>\n",
211
  " <td>2023-12-31</td>\n",
212
  " <td>800.000000</td>\n",
213
- " <td>NaN</td>\n",
214
  " </tr>\n",
215
  " </tbody>\n",
216
  "</table>\n",
@@ -241,13 +222,13 @@
241
  "96007 2023-08-31 NaN NaN \n",
242
  "96008 2023-09-30 NaN NaN \n",
243
  "96009 2023-10-31 NaN NaN \n",
244
- "96010 2023-11-30 804.147562 NaN \n",
245
- "96011 2023-12-31 800.000000 NaN \n",
246
  "\n",
247
  "[96012 rows x 9 columns]"
248
  ]
249
  },
250
- "execution_count": 70,
251
  "metadata": {},
252
  "output_type": "execute_result"
253
  }
@@ -264,7 +245,7 @@
264
  " \"Home Type\",\n",
265
  "]\n",
266
  "\n",
267
- "batches = {\"rent\": []}\n",
268
  "\n",
269
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
270
  " if filename.endswith(\".csv\"):\n",
@@ -299,7 +280,7 @@
299
  " var_name=\"Date\",\n",
300
  " value_name=col_name,\n",
301
  " )\n",
302
- " batches[\"rent\"].append(cur_df)\n",
303
  " # print(filename)\n",
304
  "\n",
305
  "\n",
@@ -310,39 +291,257 @@
310
  " combined_df = data_frames[0]\n",
311
  " for i in range(1, len(data_frames)):\n",
312
  " cur_df = data_frames[i]\n",
313
- " if combined_df.columns.equals(cur_df.columns):\n",
314
- " combined_df = pd.concat([combined_df, cur_df])\n",
315
- " else:\n",
316
- " cols_to_use = list(cur_df.columns.difference(combined_df.columns))\n",
317
- " on = [\"RegionID\", \"Home Type\", \"Date\"]\n",
318
- " for col in on:\n",
319
- " if col not in cols_to_use:\n",
320
- " cols_to_use.append(col)\n",
321
- " print(cols_to_use)\n",
322
- "\n",
323
- " combined_df = pd.merge(\n",
324
- " combined_df,\n",
325
- " cur_df[cols_to_use],\n",
326
- " on=on,\n",
327
- " how=\"outer\",\n",
328
- " )\n",
329
- "\n",
330
- " print(combined_df.columns)\n",
331
  " elif len(data_frames) == 1:\n",
332
  " combined_df = data_frames[0]\n",
333
  "\n",
334
  " return combined_df\n",
335
  "\n",
336
  "\n",
337
- "combined_df = get_combined_df(batches[\"rent\"])\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
338
  "combined_df"
339
  ]
340
  },
341
  {
342
  "cell_type": "code",
343
- "execution_count": 71,
344
  "metadata": {},
345
- "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
346
  "source": [
347
  "final_df = combined_df\n",
348
  "final_df = final_df.rename(\n",
@@ -356,12 +555,12 @@
356
  ")\n",
357
  "\n",
358
  "# sort by region id and date\n",
359
- "# final_df.sort_values(by=[\"Region ID\", \"Date\", \"Home Type\"])"
360
  ]
361
  },
362
  {
363
  "cell_type": "code",
364
- "execution_count": 72,
365
  "metadata": {},
366
  "outputs": [],
367
  "source": [
 
25
  },
26
  {
27
  "cell_type": "code",
28
+ "execution_count": 79,
29
  "metadata": {},
30
  "outputs": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31
  {
32
  "data": {
33
  "text/html": [
 
178
  " <td>IL</td>\n",
179
  " <td>multifamily</td>\n",
180
  " <td>2023-11-30</td>\n",
181
+ " <td>802.086919</td>\n",
182
+ " <td>802.086919</td>\n",
183
  " </tr>\n",
184
  " <tr>\n",
185
  " <th>96011</th>\n",
 
191
  " <td>multifamily</td>\n",
192
  " <td>2023-12-31</td>\n",
193
  " <td>800.000000</td>\n",
194
+ " <td>800.000000</td>\n",
195
  " </tr>\n",
196
  " </tbody>\n",
197
  "</table>\n",
 
222
  "96007 2023-08-31 NaN NaN \n",
223
  "96008 2023-09-30 NaN NaN \n",
224
  "96009 2023-10-31 NaN NaN \n",
225
+ "96010 2023-11-30 802.086919 802.086919 \n",
226
+ "96011 2023-12-31 800.000000 800.000000 \n",
227
  "\n",
228
  "[96012 rows x 9 columns]"
229
  ]
230
  },
231
+ "execution_count": 79,
232
  "metadata": {},
233
  "output_type": "execute_result"
234
  }
 
245
  " \"Home Type\",\n",
246
  "]\n",
247
  "\n",
248
+ "data_frames = []\n",
249
  "\n",
250
  "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
251
  " if filename.endswith(\".csv\"):\n",
 
280
  " var_name=\"Date\",\n",
281
  " value_name=col_name,\n",
282
  " )\n",
283
+ " data_frames.append(cur_df)\n",
284
  " # print(filename)\n",
285
  "\n",
286
  "\n",
 
291
  " combined_df = data_frames[0]\n",
292
  " for i in range(1, len(data_frames)):\n",
293
  " cur_df = data_frames[i]\n",
294
+ " combined_df = pd.merge(\n",
295
+ " combined_df,\n",
296
+ " cur_df,\n",
297
+ " on=[\n",
298
+ " \"RegionID\",\n",
299
+ " \"SizeRank\",\n",
300
+ " \"RegionName\",\n",
301
+ " \"RegionType\",\n",
302
+ " \"StateName\",\n",
303
+ " \"Home Type\",\n",
304
+ " \"Date\",\n",
305
+ " ],\n",
306
+ " how=\"outer\",\n",
307
+ " suffixes=(\"\", \"_\" + str(i)),\n",
308
+ " )\n",
 
 
 
309
  " elif len(data_frames) == 1:\n",
310
  " combined_df = data_frames[0]\n",
311
  "\n",
312
  " return combined_df\n",
313
  "\n",
314
  "\n",
315
+ "combined_df = get_combined_df(data_frames)\n",
316
+ "\n",
317
+ "\n",
318
+ "# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
319
+ "columns_to_coalesce = [\"Rent (Smoothed)\", \"Rent (Smoothed) (Seasonally Adjusted)\"]\n",
320
+ "\n",
321
+ "for index, row in combined_df.iterrows():\n",
322
+ " for col in combined_df.columns:\n",
323
+ " for column_to_coalesce in columns_to_coalesce:\n",
324
+ " if column_to_coalesce in col and \"_\" in col:\n",
325
+ " if not pd.isna(row[col]):\n",
326
+ " combined_df.at[index, column_to_coalesce] = row[col]\n",
327
+ "\n",
328
+ "# remove columns with underscores\n",
329
+ "combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
330
+ "\n",
331
+ "\n",
332
  "combined_df"
333
  ]
334
  },
335
  {
336
  "cell_type": "code",
337
+ "execution_count": 80,
338
  "metadata": {},
339
+ "outputs": [
340
+ {
341
+ "data": {
342
+ "text/html": [
343
+ "<div>\n",
344
+ "<style scoped>\n",
345
+ " .dataframe tbody tr th:only-of-type {\n",
346
+ " vertical-align: middle;\n",
347
+ " }\n",
348
+ "\n",
349
+ " .dataframe tbody tr th {\n",
350
+ " vertical-align: top;\n",
351
+ " }\n",
352
+ "\n",
353
+ " .dataframe thead th {\n",
354
+ " text-align: right;\n",
355
+ " }\n",
356
+ "</style>\n",
357
+ "<table border=\"1\" class=\"dataframe\">\n",
358
+ " <thead>\n",
359
+ " <tr style=\"text-align: right;\">\n",
360
+ " <th></th>\n",
361
+ " <th>Region ID</th>\n",
362
+ " <th>Size Rank</th>\n",
363
+ " <th>Region</th>\n",
364
+ " <th>Region Type</th>\n",
365
+ " <th>State</th>\n",
366
+ " <th>Home Type</th>\n",
367
+ " <th>Date</th>\n",
368
+ " <th>Rent (Smoothed)</th>\n",
369
+ " <th>Rent (Smoothed) (Seasonally Adjusted)</th>\n",
370
+ " </tr>\n",
371
+ " </thead>\n",
372
+ " <tbody>\n",
373
+ " <tr>\n",
374
+ " <th>0</th>\n",
375
+ " <td>102001</td>\n",
376
+ " <td>0</td>\n",
377
+ " <td>United States</td>\n",
378
+ " <td>country</td>\n",
379
+ " <td>NaN</td>\n",
380
+ " <td>SFR</td>\n",
381
+ " <td>2015-01-31</td>\n",
382
+ " <td>1251.119548</td>\n",
383
+ " <td>1253.380721</td>\n",
384
+ " </tr>\n",
385
+ " <tr>\n",
386
+ " <th>108</th>\n",
387
+ " <td>102001</td>\n",
388
+ " <td>0</td>\n",
389
+ " <td>United States</td>\n",
390
+ " <td>country</td>\n",
391
+ " <td>NaN</td>\n",
392
+ " <td>multifamily</td>\n",
393
+ " <td>2015-01-31</td>\n",
394
+ " <td>1230.637976</td>\n",
395
+ " <td>1230.637976</td>\n",
396
+ " </tr>\n",
397
+ " <tr>\n",
398
+ " <th>1</th>\n",
399
+ " <td>102001</td>\n",
400
+ " <td>0</td>\n",
401
+ " <td>United States</td>\n",
402
+ " <td>country</td>\n",
403
+ " <td>NaN</td>\n",
404
+ " <td>SFR</td>\n",
405
+ " <td>2015-02-28</td>\n",
406
+ " <td>1257.678915</td>\n",
407
+ " <td>1258.745304</td>\n",
408
+ " </tr>\n",
409
+ " <tr>\n",
410
+ " <th>109</th>\n",
411
+ " <td>102001</td>\n",
412
+ " <td>0</td>\n",
413
+ " <td>United States</td>\n",
414
+ " <td>country</td>\n",
415
+ " <td>NaN</td>\n",
416
+ " <td>multifamily</td>\n",
417
+ " <td>2015-02-28</td>\n",
418
+ " <td>1236.170604</td>\n",
419
+ " <td>1236.170604</td>\n",
420
+ " </tr>\n",
421
+ " <tr>\n",
422
+ " <th>2</th>\n",
423
+ " <td>102001</td>\n",
424
+ " <td>0</td>\n",
425
+ " <td>United States</td>\n",
426
+ " <td>country</td>\n",
427
+ " <td>NaN</td>\n",
428
+ " <td>SFR</td>\n",
429
+ " <td>2015-03-31</td>\n",
430
+ " <td>1266.242657</td>\n",
431
+ " <td>1263.914519</td>\n",
432
+ " </tr>\n",
433
+ " <tr>\n",
434
+ " <th>...</th>\n",
435
+ " <td>...</td>\n",
436
+ " <td>...</td>\n",
437
+ " <td>...</td>\n",
438
+ " <td>...</td>\n",
439
+ " <td>...</td>\n",
440
+ " <td>...</td>\n",
441
+ " <td>...</td>\n",
442
+ " <td>...</td>\n",
443
+ " <td>...</td>\n",
444
+ " </tr>\n",
445
+ " <tr>\n",
446
+ " <th>96007</th>\n",
447
+ " <td>845167</td>\n",
448
+ " <td>296</td>\n",
449
+ " <td>Ottawa, IL</td>\n",
450
+ " <td>msa</td>\n",
451
+ " <td>IL</td>\n",
452
+ " <td>multifamily</td>\n",
453
+ " <td>2023-08-31</td>\n",
454
+ " <td>NaN</td>\n",
455
+ " <td>NaN</td>\n",
456
+ " </tr>\n",
457
+ " <tr>\n",
458
+ " <th>96008</th>\n",
459
+ " <td>845167</td>\n",
460
+ " <td>296</td>\n",
461
+ " <td>Ottawa, IL</td>\n",
462
+ " <td>msa</td>\n",
463
+ " <td>IL</td>\n",
464
+ " <td>multifamily</td>\n",
465
+ " <td>2023-09-30</td>\n",
466
+ " <td>NaN</td>\n",
467
+ " <td>NaN</td>\n",
468
+ " </tr>\n",
469
+ " <tr>\n",
470
+ " <th>96009</th>\n",
471
+ " <td>845167</td>\n",
472
+ " <td>296</td>\n",
473
+ " <td>Ottawa, IL</td>\n",
474
+ " <td>msa</td>\n",
475
+ " <td>IL</td>\n",
476
+ " <td>multifamily</td>\n",
477
+ " <td>2023-10-31</td>\n",
478
+ " <td>NaN</td>\n",
479
+ " <td>NaN</td>\n",
480
+ " </tr>\n",
481
+ " <tr>\n",
482
+ " <th>96010</th>\n",
483
+ " <td>845167</td>\n",
484
+ " <td>296</td>\n",
485
+ " <td>Ottawa, IL</td>\n",
486
+ " <td>msa</td>\n",
487
+ " <td>IL</td>\n",
488
+ " <td>multifamily</td>\n",
489
+ " <td>2023-11-30</td>\n",
490
+ " <td>802.086919</td>\n",
491
+ " <td>802.086919</td>\n",
492
+ " </tr>\n",
493
+ " <tr>\n",
494
+ " <th>96011</th>\n",
495
+ " <td>845167</td>\n",
496
+ " <td>296</td>\n",
497
+ " <td>Ottawa, IL</td>\n",
498
+ " <td>msa</td>\n",
499
+ " <td>IL</td>\n",
500
+ " <td>multifamily</td>\n",
501
+ " <td>2023-12-31</td>\n",
502
+ " <td>800.000000</td>\n",
503
+ " <td>800.000000</td>\n",
504
+ " </tr>\n",
505
+ " </tbody>\n",
506
+ "</table>\n",
507
+ "<p>96012 rows × 9 columns</p>\n",
508
+ "</div>"
509
+ ],
510
+ "text/plain": [
511
+ " Region ID Size Rank Region Region Type State Home Type \\\n",
512
+ "0 102001 0 United States country NaN SFR \n",
513
+ "108 102001 0 United States country NaN multifamily \n",
514
+ "1 102001 0 United States country NaN SFR \n",
515
+ "109 102001 0 United States country NaN multifamily \n",
516
+ "2 102001 0 United States country NaN SFR \n",
517
+ "... ... ... ... ... ... ... \n",
518
+ "96007 845167 296 Ottawa, IL msa IL multifamily \n",
519
+ "96008 845167 296 Ottawa, IL msa IL multifamily \n",
520
+ "96009 845167 296 Ottawa, IL msa IL multifamily \n",
521
+ "96010 845167 296 Ottawa, IL msa IL multifamily \n",
522
+ "96011 845167 296 Ottawa, IL msa IL multifamily \n",
523
+ "\n",
524
+ " Date Rent (Smoothed) Rent (Smoothed) (Seasonally Adjusted) \n",
525
+ "0 2015-01-31 1251.119548 1253.380721 \n",
526
+ "108 2015-01-31 1230.637976 1230.637976 \n",
527
+ "1 2015-02-28 1257.678915 1258.745304 \n",
528
+ "109 2015-02-28 1236.170604 1236.170604 \n",
529
+ "2 2015-03-31 1266.242657 1263.914519 \n",
530
+ "... ... ... ... \n",
531
+ "96007 2023-08-31 NaN NaN \n",
532
+ "96008 2023-09-30 NaN NaN \n",
533
+ "96009 2023-10-31 NaN NaN \n",
534
+ "96010 2023-11-30 802.086919 802.086919 \n",
535
+ "96011 2023-12-31 800.000000 800.000000 \n",
536
+ "\n",
537
+ "[96012 rows x 9 columns]"
538
+ ]
539
+ },
540
+ "execution_count": 80,
541
+ "metadata": {},
542
+ "output_type": "execute_result"
543
+ }
544
+ ],
545
  "source": [
546
  "final_df = combined_df\n",
547
  "final_df = final_df.rename(\n",
 
555
  ")\n",
556
  "\n",
557
  "# sort by region id and date\n",
558
+ "final_df.sort_values(by=[\"Region ID\", \"Date\", \"Home Type\"])"
559
  ]
560
  },
561
  {
562
  "cell_type": "code",
563
+ "execution_count": 81,
564
  "metadata": {},
565
  "outputs": [],
566
  "source": [
tester.ipynb CHANGED
@@ -22,18 +22,18 @@
22
  },
23
  {
24
  "cell_type": "code",
25
- "execution_count": 3,
26
  "metadata": {},
27
  "outputs": [
28
  {
29
  "name": "stderr",
30
  "output_type": "stream",
31
  "text": [
32
- "Downloading builder script: 100%|██████████| 16.4k/16.4k [00:00<00:00, 13.8MB/s]\n",
33
- "Downloading data: 100%|██████████| 739M/739M [00:17<00:00, 42.9MB/s] \n",
34
- "Generating train split: 2398149 examples [01:07, 35317.36 examples/s]\n",
35
- "Generating validation split: 2398149 examples [01:08, 35184.06 examples/s]\n",
36
- "Generating test split: 2398149 examples [01:09, 34579.04 examples/s]\n"
37
  ]
38
  }
39
  ],
 
22
  },
23
  {
24
  "cell_type": "code",
25
+ "execution_count": 4,
26
  "metadata": {},
27
  "outputs": [
28
  {
29
  "name": "stderr",
30
  "output_type": "stream",
31
  "text": [
32
+ "Downloading builder script: 100%|██████████| 18.5k/18.5k [00:00<00:00, 11.3MB/s]\n",
33
+ "Downloading data: 100%|██████████| 20.4M/20.4M [00:00<00:00, 33.6MB/s]\n",
34
+ "Generating train split: 96012 examples [00:02, 46188.04 examples/s]\n",
35
+ "Generating validation split: 96012 examples [00:02, 47013.79 examples/s]\n",
36
+ "Generating test split: 96012 examples [00:02, 46947.45 examples/s]\n"
37
  ]
38
  }
39
  ],