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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
1962d49
·
1 Parent(s): 7f2f9cb

feat: process home value forecasts into a single csv

Browse files
Files changed (4) hide show
  1. .gitignore +0 -0
  2. .vscode/settings.json +3 -2
  3. process_home_value_forecasts.ipynb +800 -0
  4. processed/home_value_forecasts/final.csv +0 -0
.gitignore ADDED
File without changes
.vscode/settings.json CHANGED
@@ -2,6 +2,7 @@
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  "[python]": {
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  "editor.defaultFormatter": "ms-python.black-formatter",
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  "editor.formatOnSave": true
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- }
 
 
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  }
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-
 
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  "[python]": {
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  "editor.defaultFormatter": "ms-python.black-formatter",
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  "editor.formatOnSave": true
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+ },
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+ "python.analysis.autoImportCompletions": true,
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+ "python.analysis.typeCheckingMode": "basic"
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  }
 
process_home_value_forecasts.ipynb ADDED
@@ -0,0 +1,800 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "cells": [
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+ {
4
+ "cell_type": "code",
5
+ "execution_count": 22,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import pandas as pd\n",
10
+ "import os"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 23,
16
+ "metadata": {},
17
+ "outputs": [],
18
+ "source": [
19
+ "DATA_DIR = 'data/'\n",
20
+ "PROCESSED_DIR = 'processed/'\n",
21
+ "FACET_DIR = 'home_value_forecasts/'\n",
22
+ "FULL_DATA_DIR_PATH = DATA_DIR + FACET_DIR\n",
23
+ "FULL_PROCESSED_DIR_PATH = PROCESSED_DIR + FACET_DIR"
24
+ ]
25
+ },
26
+ {
27
+ "cell_type": "code",
28
+ "execution_count": 24,
29
+ "metadata": {},
30
+ "outputs": [
31
+ {
32
+ "name": "stdout",
33
+ "output_type": "stream",
34
+ "text": [
35
+ "processing Zip_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
36
+ "processing Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
37
+ "processing Zip_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_month.csv\n",
38
+ "processing Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_month.csv\n"
39
+ ]
40
+ },
41
+ {
42
+ "data": {
43
+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
51
+ " vertical-align: top;\n",
52
+ " }\n",
53
+ "\n",
54
+ " .dataframe thead th {\n",
55
+ " text-align: right;\n",
56
+ " }\n",
57
+ "</style>\n",
58
+ "<table border=\"1\" class=\"dataframe\">\n",
59
+ " <thead>\n",
60
+ " <tr style=\"text-align: right;\">\n",
61
+ " <th></th>\n",
62
+ " <th>RegionID</th>\n",
63
+ " <th>SizeRank</th>\n",
64
+ " <th>RegionName</th>\n",
65
+ " <th>RegionType</th>\n",
66
+ " <th>StateName</th>\n",
67
+ " <th>BaseDate</th>\n",
68
+ " <th>Month Over Month % (Smoothed)</th>\n",
69
+ " <th>Quarter Over Quarter % (Smoothed)</th>\n",
70
+ " <th>Year Over Year % (Smoothed)</th>\n",
71
+ " <th>Month Over Month % (Raw)</th>\n",
72
+ " <th>Quarter Over Quarter % (Raw)</th>\n",
73
+ " <th>Year Over Year % (Raw)</th>\n",
74
+ " <th>State</th>\n",
75
+ " <th>City</th>\n",
76
+ " <th>Metro</th>\n",
77
+ " <th>CountyName</th>\n",
78
+ " </tr>\n",
79
+ " </thead>\n",
80
+ " <tbody>\n",
81
+ " <tr>\n",
82
+ " <th>0</th>\n",
83
+ " <td>102001</td>\n",
84
+ " <td>0</td>\n",
85
+ " <td>United States</td>\n",
86
+ " <td>country</td>\n",
87
+ " <td>NaN</td>\n",
88
+ " <td>2023-12-31</td>\n",
89
+ " <td>0.1</td>\n",
90
+ " <td>0.4</td>\n",
91
+ " <td>3.5</td>\n",
92
+ " <td>-0.5</td>\n",
93
+ " <td>0.4</td>\n",
94
+ " <td>3.7</td>\n",
95
+ " <td>NaN</td>\n",
96
+ " <td>NaN</td>\n",
97
+ " <td>NaN</td>\n",
98
+ " <td>NaN</td>\n",
99
+ " </tr>\n",
100
+ " <tr>\n",
101
+ " <th>1</th>\n",
102
+ " <td>394913</td>\n",
103
+ " <td>1</td>\n",
104
+ " <td>New York, NY</td>\n",
105
+ " <td>msa</td>\n",
106
+ " <td>NY</td>\n",
107
+ " <td>2023-12-31</td>\n",
108
+ " <td>0.2</td>\n",
109
+ " <td>0.2</td>\n",
110
+ " <td>1.0</td>\n",
111
+ " <td>-0.7</td>\n",
112
+ " <td>-0.9</td>\n",
113
+ " <td>0.6</td>\n",
114
+ " <td>NaN</td>\n",
115
+ " <td>NaN</td>\n",
116
+ " <td>NaN</td>\n",
117
+ " <td>NaN</td>\n",
118
+ " </tr>\n",
119
+ " <tr>\n",
120
+ " <th>2</th>\n",
121
+ " <td>753899</td>\n",
122
+ " <td>2</td>\n",
123
+ " <td>Los Angeles, CA</td>\n",
124
+ " <td>msa</td>\n",
125
+ " <td>CA</td>\n",
126
+ " <td>2023-12-31</td>\n",
127
+ " <td>-0.1</td>\n",
128
+ " <td>-1.8</td>\n",
129
+ " <td>0.7</td>\n",
130
+ " <td>-0.6</td>\n",
131
+ " <td>0.8</td>\n",
132
+ " <td>1.4</td>\n",
133
+ " <td>NaN</td>\n",
134
+ " <td>NaN</td>\n",
135
+ " <td>NaN</td>\n",
136
+ " <td>NaN</td>\n",
137
+ " </tr>\n",
138
+ " <tr>\n",
139
+ " <th>3</th>\n",
140
+ " <td>394463</td>\n",
141
+ " <td>3</td>\n",
142
+ " <td>Chicago, IL</td>\n",
143
+ " <td>msa</td>\n",
144
+ " <td>IL</td>\n",
145
+ " <td>2023-12-31</td>\n",
146
+ " <td>0.1</td>\n",
147
+ " <td>0.4</td>\n",
148
+ " <td>1.6</td>\n",
149
+ " <td>-0.8</td>\n",
150
+ " <td>-0.2</td>\n",
151
+ " <td>1.4</td>\n",
152
+ " <td>NaN</td>\n",
153
+ " <td>NaN</td>\n",
154
+ " <td>NaN</td>\n",
155
+ " <td>NaN</td>\n",
156
+ " </tr>\n",
157
+ " <tr>\n",
158
+ " <th>4</th>\n",
159
+ " <td>394514</td>\n",
160
+ " <td>4</td>\n",
161
+ " <td>Dallas, TX</td>\n",
162
+ " <td>msa</td>\n",
163
+ " <td>TX</td>\n",
164
+ " <td>2023-12-31</td>\n",
165
+ " <td>-0.1</td>\n",
166
+ " <td>0.0</td>\n",
167
+ " <td>3.2</td>\n",
168
+ " <td>-0.6</td>\n",
169
+ " <td>0.9</td>\n",
170
+ " <td>3.6</td>\n",
171
+ " <td>NaN</td>\n",
172
+ " <td>NaN</td>\n",
173
+ " <td>NaN</td>\n",
174
+ " <td>NaN</td>\n",
175
+ " </tr>\n",
176
+ " <tr>\n",
177
+ " <th>...</th>\n",
178
+ " <td>...</td>\n",
179
+ " <td>...</td>\n",
180
+ " <td>...</td>\n",
181
+ " <td>...</td>\n",
182
+ " <td>...</td>\n",
183
+ " <td>...</td>\n",
184
+ " <td>...</td>\n",
185
+ " <td>...</td>\n",
186
+ " <td>...</td>\n",
187
+ " <td>...</td>\n",
188
+ " <td>...</td>\n",
189
+ " <td>...</td>\n",
190
+ " <td>...</td>\n",
191
+ " <td>...</td>\n",
192
+ " <td>...</td>\n",
193
+ " <td>...</td>\n",
194
+ " </tr>\n",
195
+ " <tr>\n",
196
+ " <th>20162</th>\n",
197
+ " <td>82097</td>\n",
198
+ " <td>39992</td>\n",
199
+ " <td>55087</td>\n",
200
+ " <td>zip</td>\n",
201
+ " <td>MN</td>\n",
202
+ " <td>2023-12-31</td>\n",
203
+ " <td>0.1</td>\n",
204
+ " <td>0.7</td>\n",
205
+ " <td>1.8</td>\n",
206
+ " <td>-0.9</td>\n",
207
+ " <td>-0.2</td>\n",
208
+ " <td>2.6</td>\n",
209
+ " <td>MN</td>\n",
210
+ " <td>Warsaw</td>\n",
211
+ " <td>Faribault-Northfield, MN</td>\n",
212
+ " <td>Rice County</td>\n",
213
+ " </tr>\n",
214
+ " <tr>\n",
215
+ " <th>20163</th>\n",
216
+ " <td>85325</td>\n",
217
+ " <td>39992</td>\n",
218
+ " <td>62093</td>\n",
219
+ " <td>zip</td>\n",
220
+ " <td>IL</td>\n",
221
+ " <td>2023-12-31</td>\n",
222
+ " <td>0.9</td>\n",
223
+ " <td>0.4</td>\n",
224
+ " <td>3.7</td>\n",
225
+ " <td>-0.7</td>\n",
226
+ " <td>0.4</td>\n",
227
+ " <td>2.3</td>\n",
228
+ " <td>IL</td>\n",
229
+ " <td>NaN</td>\n",
230
+ " <td>St. Louis, MO-IL</td>\n",
231
+ " <td>Macoupin County</td>\n",
232
+ " </tr>\n",
233
+ " <tr>\n",
234
+ " <th>20164</th>\n",
235
+ " <td>92085</td>\n",
236
+ " <td>39992</td>\n",
237
+ " <td>77661</td>\n",
238
+ " <td>zip</td>\n",
239
+ " <td>TX</td>\n",
240
+ " <td>2023-12-31</td>\n",
241
+ " <td>-0.5</td>\n",
242
+ " <td>0.3</td>\n",
243
+ " <td>-0.6</td>\n",
244
+ " <td>-0.4</td>\n",
245
+ " <td>0.0</td>\n",
246
+ " <td>1.2</td>\n",
247
+ " <td>TX</td>\n",
248
+ " <td>NaN</td>\n",
249
+ " <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
250
+ " <td>Chambers County</td>\n",
251
+ " </tr>\n",
252
+ " <tr>\n",
253
+ " <th>20165</th>\n",
254
+ " <td>92811</td>\n",
255
+ " <td>39992</td>\n",
256
+ " <td>79078</td>\n",
257
+ " <td>zip</td>\n",
258
+ " <td>TX</td>\n",
259
+ " <td>2023-12-31</td>\n",
260
+ " <td>-1.2</td>\n",
261
+ " <td>-1.1</td>\n",
262
+ " <td>-3.1</td>\n",
263
+ " <td>-1.7</td>\n",
264
+ " <td>-2.6</td>\n",
265
+ " <td>-1.9</td>\n",
266
+ " <td>TX</td>\n",
267
+ " <td>NaN</td>\n",
268
+ " <td>Borger, TX</td>\n",
269
+ " <td>Hutchinson County</td>\n",
270
+ " </tr>\n",
271
+ " <tr>\n",
272
+ " <th>20166</th>\n",
273
+ " <td>98183</td>\n",
274
+ " <td>39992</td>\n",
275
+ " <td>95419</td>\n",
276
+ " <td>zip</td>\n",
277
+ " <td>CA</td>\n",
278
+ " <td>2023-12-31</td>\n",
279
+ " <td>-0.5</td>\n",
280
+ " <td>-0.2</td>\n",
281
+ " <td>0.0</td>\n",
282
+ " <td>-0.5</td>\n",
283
+ " <td>0.6</td>\n",
284
+ " <td>-0.4</td>\n",
285
+ " <td>CA</td>\n",
286
+ " <td>Camp Meeker</td>\n",
287
+ " <td>Santa Rosa-Petaluma, CA</td>\n",
288
+ " <td>Sonoma County</td>\n",
289
+ " </tr>\n",
290
+ " </tbody>\n",
291
+ "</table>\n",
292
+ "<p>21062 rows × 16 columns</p>\n",
293
+ "</div>"
294
+ ],
295
+ "text/plain": [
296
+ " RegionID SizeRank RegionName RegionType StateName BaseDate \\\n",
297
+ "0 102001 0 United States country NaN 2023-12-31 \n",
298
+ "1 394913 1 New York, NY msa NY 2023-12-31 \n",
299
+ "2 753899 2 Los Angeles, CA msa CA 2023-12-31 \n",
300
+ "3 394463 3 Chicago, IL msa IL 2023-12-31 \n",
301
+ "4 394514 4 Dallas, TX msa TX 2023-12-31 \n",
302
+ "... ... ... ... ... ... ... \n",
303
+ "20162 82097 39992 55087 zip MN 2023-12-31 \n",
304
+ "20163 85325 39992 62093 zip IL 2023-12-31 \n",
305
+ "20164 92085 39992 77661 zip TX 2023-12-31 \n",
306
+ "20165 92811 39992 79078 zip TX 2023-12-31 \n",
307
+ "20166 98183 39992 95419 zip CA 2023-12-31 \n",
308
+ "\n",
309
+ " Month Over Month % (Smoothed) Quarter Over Quarter % (Smoothed) \\\n",
310
+ "0 0.1 0.4 \n",
311
+ "1 0.2 0.2 \n",
312
+ "2 -0.1 -1.8 \n",
313
+ "3 0.1 0.4 \n",
314
+ "4 -0.1 0.0 \n",
315
+ "... ... ... \n",
316
+ "20162 0.1 0.7 \n",
317
+ "20163 0.9 0.4 \n",
318
+ "20164 -0.5 0.3 \n",
319
+ "20165 -1.2 -1.1 \n",
320
+ "20166 -0.5 -0.2 \n",
321
+ "\n",
322
+ " Year Over Year % (Smoothed) Month Over Month % (Raw) \\\n",
323
+ "0 3.5 -0.5 \n",
324
+ "1 1.0 -0.7 \n",
325
+ "2 0.7 -0.6 \n",
326
+ "3 1.6 -0.8 \n",
327
+ "4 3.2 -0.6 \n",
328
+ "... ... ... \n",
329
+ "20162 1.8 -0.9 \n",
330
+ "20163 3.7 -0.7 \n",
331
+ "20164 -0.6 -0.4 \n",
332
+ "20165 -3.1 -1.7 \n",
333
+ "20166 0.0 -0.5 \n",
334
+ "\n",
335
+ " Quarter Over Quarter % (Raw) Year Over Year % (Raw) State \\\n",
336
+ "0 0.4 3.7 NaN \n",
337
+ "1 -0.9 0.6 NaN \n",
338
+ "2 0.8 1.4 NaN \n",
339
+ "3 -0.2 1.4 NaN \n",
340
+ "4 0.9 3.6 NaN \n",
341
+ "... ... ... ... \n",
342
+ "20162 -0.2 2.6 MN \n",
343
+ "20163 0.4 2.3 IL \n",
344
+ "20164 0.0 1.2 TX \n",
345
+ "20165 -2.6 -1.9 TX \n",
346
+ "20166 0.6 -0.4 CA \n",
347
+ "\n",
348
+ " City Metro CountyName \n",
349
+ "0 NaN NaN NaN \n",
350
+ "1 NaN NaN NaN \n",
351
+ "2 NaN NaN NaN \n",
352
+ "3 NaN NaN NaN \n",
353
+ "4 NaN NaN NaN \n",
354
+ "... ... ... ... \n",
355
+ "20162 Warsaw Faribault-Northfield, MN Rice County \n",
356
+ "20163 NaN St. Louis, MO-IL Macoupin County \n",
357
+ "20164 NaN Houston-The Woodlands-Sugar Land, TX Chambers County \n",
358
+ "20165 NaN Borger, TX Hutchinson County \n",
359
+ "20166 Camp Meeker Santa Rosa-Petaluma, CA Sonoma County \n",
360
+ "\n",
361
+ "[21062 rows x 16 columns]"
362
+ ]
363
+ },
364
+ "execution_count": 24,
365
+ "metadata": {},
366
+ "output_type": "execute_result"
367
+ }
368
+ ],
369
+ "source": [
370
+ "metro_data_frames = []\n",
371
+ "zip_data_frames = []\n",
372
+ "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
373
+ " if filename.endswith('.csv'):\n",
374
+ " print('processing ' + filename)\n",
375
+ " cur_df = pd.read_csv(FULL_DATA_DIR_PATH + filename)\n",
376
+ " \n",
377
+ " cols = ['Month Over Month %', 'Quarter Over Quarter %', 'Year Over Year %']\n",
378
+ " if (filename.endswith('sm_sa_month.csv')):\n",
379
+ " # print('Smoothed')\n",
380
+ " cur_df.columns = list(cur_df.columns[:-3]) + [x + ' (Smoothed)' for x in cols]\n",
381
+ " else:\n",
382
+ " # print('Raw')\n",
383
+ " cur_df.columns = list(cur_df.columns[:-3]) + [x + ' (Raw)' for x in cols]\n",
384
+ " \n",
385
+ " if (filename.startswith('Metro')):\n",
386
+ " # print('Metro')\n",
387
+ " metro_data_frames.append(cur_df)\n",
388
+ "\n",
389
+ " elif (filename.startswith('Zip')):\n",
390
+ " # print('Zip')\n",
391
+ " zip_data_frames.append(cur_df)\n",
392
+ "\n",
393
+ "def get_combined_df(data_frames):\n",
394
+ " combined_df = None\n",
395
+ " if len(data_frames) > 1:\n",
396
+ " # iterate over dataframes and merge them\n",
397
+ " final_df = data_frames[0]\n",
398
+ " for i in range(1, len(data_frames)):\n",
399
+ " cur_df = data_frames[i]\n",
400
+ " cols = list(cur_df.columns[-3:])\n",
401
+ " cols.append('RegionID')\n",
402
+ " combined_df = pd.merge(final_df, cur_df[cols], on='RegionID')\n",
403
+ " elif len(data_frames) == 1:\n",
404
+ " combined_df = data_frames[0]\n",
405
+ " \n",
406
+ " \n",
407
+ " return(combined_df)\n",
408
+ "\n",
409
+ "combined_metro_dfs = get_combined_df(metro_data_frames)\n",
410
+ "combined_zip_dfs = get_combined_df(zip_data_frames)\n",
411
+ "\n",
412
+ "combined_df = pd.concat([combined_metro_dfs, combined_zip_dfs])\n",
413
+ "combined_df"
414
+ ]
415
+ },
416
+ {
417
+ "cell_type": "code",
418
+ "execution_count": 34,
419
+ "metadata": {},
420
+ "outputs": [
421
+ {
422
+ "data": {
423
+ "text/html": [
424
+ "<div>\n",
425
+ "<style scoped>\n",
426
+ " .dataframe tbody tr th:only-of-type {\n",
427
+ " vertical-align: middle;\n",
428
+ " }\n",
429
+ "\n",
430
+ " .dataframe tbody tr th {\n",
431
+ " vertical-align: top;\n",
432
+ " }\n",
433
+ "\n",
434
+ " .dataframe thead th {\n",
435
+ " text-align: right;\n",
436
+ " }\n",
437
+ "</style>\n",
438
+ "<table border=\"1\" class=\"dataframe\">\n",
439
+ " <thead>\n",
440
+ " <tr style=\"text-align: right;\">\n",
441
+ " <th></th>\n",
442
+ " <th>RegionID</th>\n",
443
+ " <th>RegionName</th>\n",
444
+ " <th>RegionType</th>\n",
445
+ " <th>SizeRank</th>\n",
446
+ " <th>State</th>\n",
447
+ " <th>City</th>\n",
448
+ " <th>Metro</th>\n",
449
+ " <th>CountyName</th>\n",
450
+ " <th>BaseDate</th>\n",
451
+ " <th>Month Over Month % (Smoothed)</th>\n",
452
+ " <th>Quarter Over Quarter % (Smoothed)</th>\n",
453
+ " <th>Year Over Year % (Smoothed)</th>\n",
454
+ " <th>Month Over Month % (Raw)</th>\n",
455
+ " <th>Quarter Over Quarter % (Raw)</th>\n",
456
+ " <th>Year Over Year % (Raw)</th>\n",
457
+ " </tr>\n",
458
+ " </thead>\n",
459
+ " <tbody>\n",
460
+ " <tr>\n",
461
+ " <th>0</th>\n",
462
+ " <td>102001</td>\n",
463
+ " <td>United States</td>\n",
464
+ " <td>country</td>\n",
465
+ " <td>0</td>\n",
466
+ " <td>NaN</td>\n",
467
+ " <td>NaN</td>\n",
468
+ " <td>NaN</td>\n",
469
+ " <td>NaN</td>\n",
470
+ " <td>2023-12-31</td>\n",
471
+ " <td>0.1</td>\n",
472
+ " <td>0.4</td>\n",
473
+ " <td>3.5</td>\n",
474
+ " <td>-0.5</td>\n",
475
+ " <td>0.4</td>\n",
476
+ " <td>3.7</td>\n",
477
+ " </tr>\n",
478
+ " <tr>\n",
479
+ " <th>1</th>\n",
480
+ " <td>394913</td>\n",
481
+ " <td>New York, NY</td>\n",
482
+ " <td>msa</td>\n",
483
+ " <td>1</td>\n",
484
+ " <td>NY</td>\n",
485
+ " <td>New York</td>\n",
486
+ " <td>New York, NY</td>\n",
487
+ " <td>NaN</td>\n",
488
+ " <td>2023-12-31</td>\n",
489
+ " <td>0.2</td>\n",
490
+ " <td>0.2</td>\n",
491
+ " <td>1.0</td>\n",
492
+ " <td>-0.7</td>\n",
493
+ " <td>-0.9</td>\n",
494
+ " <td>0.6</td>\n",
495
+ " </tr>\n",
496
+ " <tr>\n",
497
+ " <th>2</th>\n",
498
+ " <td>753899</td>\n",
499
+ " <td>Los Angeles, CA</td>\n",
500
+ " <td>msa</td>\n",
501
+ " <td>2</td>\n",
502
+ " <td>CA</td>\n",
503
+ " <td>Los Angeles</td>\n",
504
+ " <td>Los Angeles, CA</td>\n",
505
+ " <td>NaN</td>\n",
506
+ " <td>2023-12-31</td>\n",
507
+ " <td>-0.1</td>\n",
508
+ " <td>-1.8</td>\n",
509
+ " <td>0.7</td>\n",
510
+ " <td>-0.6</td>\n",
511
+ " <td>0.8</td>\n",
512
+ " <td>1.4</td>\n",
513
+ " </tr>\n",
514
+ " <tr>\n",
515
+ " <th>3</th>\n",
516
+ " <td>394463</td>\n",
517
+ " <td>Chicago, IL</td>\n",
518
+ " <td>msa</td>\n",
519
+ " <td>3</td>\n",
520
+ " <td>IL</td>\n",
521
+ " <td>Chicago</td>\n",
522
+ " <td>Chicago, IL</td>\n",
523
+ " <td>NaN</td>\n",
524
+ " <td>2023-12-31</td>\n",
525
+ " <td>0.1</td>\n",
526
+ " <td>0.4</td>\n",
527
+ " <td>1.6</td>\n",
528
+ " <td>-0.8</td>\n",
529
+ " <td>-0.2</td>\n",
530
+ " <td>1.4</td>\n",
531
+ " </tr>\n",
532
+ " <tr>\n",
533
+ " <th>4</th>\n",
534
+ " <td>394514</td>\n",
535
+ " <td>Dallas, TX</td>\n",
536
+ " <td>msa</td>\n",
537
+ " <td>4</td>\n",
538
+ " <td>TX</td>\n",
539
+ " <td>Dallas</td>\n",
540
+ " <td>Dallas, TX</td>\n",
541
+ " <td>NaN</td>\n",
542
+ " <td>2023-12-31</td>\n",
543
+ " <td>-0.1</td>\n",
544
+ " <td>0.0</td>\n",
545
+ " <td>3.2</td>\n",
546
+ " <td>-0.6</td>\n",
547
+ " <td>0.9</td>\n",
548
+ " <td>3.6</td>\n",
549
+ " </tr>\n",
550
+ " <tr>\n",
551
+ " <th>...</th>\n",
552
+ " <td>...</td>\n",
553
+ " <td>...</td>\n",
554
+ " <td>...</td>\n",
555
+ " <td>...</td>\n",
556
+ " <td>...</td>\n",
557
+ " <td>...</td>\n",
558
+ " <td>...</td>\n",
559
+ " <td>...</td>\n",
560
+ " <td>...</td>\n",
561
+ " <td>...</td>\n",
562
+ " <td>...</td>\n",
563
+ " <td>...</td>\n",
564
+ " <td>...</td>\n",
565
+ " <td>...</td>\n",
566
+ " <td>...</td>\n",
567
+ " </tr>\n",
568
+ " <tr>\n",
569
+ " <th>20162</th>\n",
570
+ " <td>82097</td>\n",
571
+ " <td>55087</td>\n",
572
+ " <td>zip</td>\n",
573
+ " <td>39992</td>\n",
574
+ " <td>MN</td>\n",
575
+ " <td>Warsaw</td>\n",
576
+ " <td>Faribault-Northfield, MN</td>\n",
577
+ " <td>Rice County</td>\n",
578
+ " <td>2023-12-31</td>\n",
579
+ " <td>0.1</td>\n",
580
+ " <td>0.7</td>\n",
581
+ " <td>1.8</td>\n",
582
+ " <td>-0.9</td>\n",
583
+ " <td>-0.2</td>\n",
584
+ " <td>2.6</td>\n",
585
+ " </tr>\n",
586
+ " <tr>\n",
587
+ " <th>20163</th>\n",
588
+ " <td>85325</td>\n",
589
+ " <td>62093</td>\n",
590
+ " <td>zip</td>\n",
591
+ " <td>39992</td>\n",
592
+ " <td>IL</td>\n",
593
+ " <td>NaN</td>\n",
594
+ " <td>St. Louis, MO-IL</td>\n",
595
+ " <td>Macoupin County</td>\n",
596
+ " <td>2023-12-31</td>\n",
597
+ " <td>0.9</td>\n",
598
+ " <td>0.4</td>\n",
599
+ " <td>3.7</td>\n",
600
+ " <td>-0.7</td>\n",
601
+ " <td>0.4</td>\n",
602
+ " <td>2.3</td>\n",
603
+ " </tr>\n",
604
+ " <tr>\n",
605
+ " <th>20164</th>\n",
606
+ " <td>92085</td>\n",
607
+ " <td>77661</td>\n",
608
+ " <td>zip</td>\n",
609
+ " <td>39992</td>\n",
610
+ " <td>TX</td>\n",
611
+ " <td>NaN</td>\n",
612
+ " <td>Houston-The Woodlands-Sugar Land, TX</td>\n",
613
+ " <td>Chambers County</td>\n",
614
+ " <td>2023-12-31</td>\n",
615
+ " <td>-0.5</td>\n",
616
+ " <td>0.3</td>\n",
617
+ " <td>-0.6</td>\n",
618
+ " <td>-0.4</td>\n",
619
+ " <td>0.0</td>\n",
620
+ " <td>1.2</td>\n",
621
+ " </tr>\n",
622
+ " <tr>\n",
623
+ " <th>20165</th>\n",
624
+ " <td>92811</td>\n",
625
+ " <td>79078</td>\n",
626
+ " <td>zip</td>\n",
627
+ " <td>39992</td>\n",
628
+ " <td>TX</td>\n",
629
+ " <td>NaN</td>\n",
630
+ " <td>Borger, TX</td>\n",
631
+ " <td>Hutchinson County</td>\n",
632
+ " <td>2023-12-31</td>\n",
633
+ " <td>-1.2</td>\n",
634
+ " <td>-1.1</td>\n",
635
+ " <td>-3.1</td>\n",
636
+ " <td>-1.7</td>\n",
637
+ " <td>-2.6</td>\n",
638
+ " <td>-1.9</td>\n",
639
+ " </tr>\n",
640
+ " <tr>\n",
641
+ " <th>20166</th>\n",
642
+ " <td>98183</td>\n",
643
+ " <td>95419</td>\n",
644
+ " <td>zip</td>\n",
645
+ " <td>39992</td>\n",
646
+ " <td>CA</td>\n",
647
+ " <td>Camp Meeker</td>\n",
648
+ " <td>Santa Rosa-Petaluma, CA</td>\n",
649
+ " <td>Sonoma County</td>\n",
650
+ " <td>2023-12-31</td>\n",
651
+ " <td>-0.5</td>\n",
652
+ " <td>-0.2</td>\n",
653
+ " <td>0.0</td>\n",
654
+ " <td>-0.5</td>\n",
655
+ " <td>0.6</td>\n",
656
+ " <td>-0.4</td>\n",
657
+ " </tr>\n",
658
+ " </tbody>\n",
659
+ "</table>\n",
660
+ "<p>21062 rows × 15 columns</p>\n",
661
+ "</div>"
662
+ ],
663
+ "text/plain": [
664
+ " RegionID RegionName RegionType SizeRank State City \\\n",
665
+ "0 102001 United States country 0 NaN NaN \n",
666
+ "1 394913 New York, NY msa 1 NY New York \n",
667
+ "2 753899 Los Angeles, CA msa 2 CA Los Angeles \n",
668
+ "3 394463 Chicago, IL msa 3 IL Chicago \n",
669
+ "4 394514 Dallas, TX msa 4 TX Dallas \n",
670
+ "... ... ... ... ... ... ... \n",
671
+ "20162 82097 55087 zip 39992 MN Warsaw \n",
672
+ "20163 85325 62093 zip 39992 IL NaN \n",
673
+ "20164 92085 77661 zip 39992 TX NaN \n",
674
+ "20165 92811 79078 zip 39992 TX NaN \n",
675
+ "20166 98183 95419 zip 39992 CA Camp Meeker \n",
676
+ "\n",
677
+ " Metro CountyName BaseDate \\\n",
678
+ "0 NaN NaN 2023-12-31 \n",
679
+ "1 New York, NY NaN 2023-12-31 \n",
680
+ "2 Los Angeles, CA NaN 2023-12-31 \n",
681
+ "3 Chicago, IL NaN 2023-12-31 \n",
682
+ "4 Dallas, TX NaN 2023-12-31 \n",
683
+ "... ... ... ... \n",
684
+ "20162 Faribault-Northfield, MN Rice County 2023-12-31 \n",
685
+ "20163 St. Louis, MO-IL Macoupin County 2023-12-31 \n",
686
+ "20164 Houston-The Woodlands-Sugar Land, TX Chambers County 2023-12-31 \n",
687
+ "20165 Borger, TX Hutchinson County 2023-12-31 \n",
688
+ "20166 Santa Rosa-Petaluma, CA Sonoma County 2023-12-31 \n",
689
+ "\n",
690
+ " Month Over Month % (Smoothed) Quarter Over Quarter % (Smoothed) \\\n",
691
+ "0 0.1 0.4 \n",
692
+ "1 0.2 0.2 \n",
693
+ "2 -0.1 -1.8 \n",
694
+ "3 0.1 0.4 \n",
695
+ "4 -0.1 0.0 \n",
696
+ "... ... ... \n",
697
+ "20162 0.1 0.7 \n",
698
+ "20163 0.9 0.4 \n",
699
+ "20164 -0.5 0.3 \n",
700
+ "20165 -1.2 -1.1 \n",
701
+ "20166 -0.5 -0.2 \n",
702
+ "\n",
703
+ " Year Over Year % (Smoothed) Month Over Month % (Raw) \\\n",
704
+ "0 3.5 -0.5 \n",
705
+ "1 1.0 -0.7 \n",
706
+ "2 0.7 -0.6 \n",
707
+ "3 1.6 -0.8 \n",
708
+ "4 3.2 -0.6 \n",
709
+ "... ... ... \n",
710
+ "20162 1.8 -0.9 \n",
711
+ "20163 3.7 -0.7 \n",
712
+ "20164 -0.6 -0.4 \n",
713
+ "20165 -3.1 -1.7 \n",
714
+ "20166 0.0 -0.5 \n",
715
+ "\n",
716
+ " Quarter Over Quarter % (Raw) Year Over Year % (Raw) \n",
717
+ "0 0.4 3.7 \n",
718
+ "1 -0.9 0.6 \n",
719
+ "2 0.8 1.4 \n",
720
+ "3 -0.2 1.4 \n",
721
+ "4 0.9 3.6 \n",
722
+ "... ... ... \n",
723
+ "20162 -0.2 2.6 \n",
724
+ "20163 0.4 2.3 \n",
725
+ "20164 0.0 1.2 \n",
726
+ "20165 -2.6 -1.9 \n",
727
+ "20166 0.6 -0.4 \n",
728
+ "\n",
729
+ "[21062 rows x 15 columns]"
730
+ ]
731
+ },
732
+ "execution_count": 34,
733
+ "metadata": {},
734
+ "output_type": "execute_result"
735
+ }
736
+ ],
737
+ "source": [
738
+ "cols = list(combined_df.columns)\n",
739
+ "result_cols = [x for x in cols if '%' in x]\n",
740
+ "cols\n",
741
+ "# check if string contains string\n",
742
+ "combined_df.columns\n",
743
+ "\n",
744
+ "all_cols = ['RegionID', 'RegionName', 'RegionType', 'SizeRank', 'StateName', 'State', 'City', 'Metro', 'CountyName',\n",
745
+ " 'BaseDate'] + result_cols\n",
746
+ "all_cols\n",
747
+ "\n",
748
+ "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
749
+ " os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
750
+ "\n",
751
+ "final_df = combined_df[all_cols]\n",
752
+ "final_df = final_df.drop('StateName', axis=1)\n",
753
+ "\n",
754
+ "# iterate over rows of final_df and populate State and City columns if the regionType is msa\n",
755
+ "for index, row in final_df.iterrows():\n",
756
+ " if row['RegionType'] == 'msa':\n",
757
+ " regionName = row['RegionName']\n",
758
+ " final_df.at[index, 'Metro'] = regionName\n",
759
+ " \n",
760
+ " city = regionName.split(', ')[0]\n",
761
+ " final_df.at[index, 'City'] = city\n",
762
+ " \n",
763
+ " state = regionName.split(', ')[1]\n",
764
+ " final_df.at[index, 'State'] = state\n",
765
+ "\n",
766
+ "final_df"
767
+ ]
768
+ },
769
+ {
770
+ "cell_type": "code",
771
+ "execution_count": 36,
772
+ "metadata": {},
773
+ "outputs": [],
774
+ "source": [
775
+ "final_df.to_csv(FULL_PROCESSED_DIR_PATH + 'final.csv', index=False)"
776
+ ]
777
+ }
778
+ ],
779
+ "metadata": {
780
+ "kernelspec": {
781
+ "display_name": "Python 3",
782
+ "language": "python",
783
+ "name": "python3"
784
+ },
785
+ "language_info": {
786
+ "codemirror_mode": {
787
+ "name": "ipython",
788
+ "version": 3
789
+ },
790
+ "file_extension": ".py",
791
+ "mimetype": "text/x-python",
792
+ "name": "python",
793
+ "nbconvert_exporter": "python",
794
+ "pygments_lexer": "ipython3",
795
+ "version": "3.12.2"
796
+ }
797
+ },
798
+ "nbformat": 4,
799
+ "nbformat_minor": 2
800
+ }
processed/home_value_forecasts/final.csv ADDED
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