fix: simplify processors more
Browse files- processed/days_on_market/final.jsonl +2 -2
- processed/home_values_forecasts/final.jsonl +2 -2
- processors/days_on_market.ipynb +41 -110
- processors/for_sale_listings.ipynb +10 -30
- processors/helpers.py +27 -2
- processors/home_value_forecasts.ipynb +455 -456
- processors/home_values.ipynb +67 -644
- processors/new_construction.ipynb +16 -38
- processors/rentals.ipynb +99 -81
- processors/sales.ipynb +13 -39
processed/days_on_market/final.jsonl
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:52768ecdeb897b82c7fb783c3dde12d04b0fa68eea0adf6f60ad60a630d3cb85
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+
size 232657387
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processed/home_values_forecasts/final.jsonl
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:ae38d8b222b98347a9841ea8ca153cbb834af36a9dae7df6ad3326e64ad3723a
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size 13922709
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processors/days_on_market.ipynb
CHANGED
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@@ -2,19 +2,24 @@
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"cells": [
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{
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"cell_type": "code",
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-
"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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| 9 |
"import pandas as pd\n",
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| 10 |
"import os\n",
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"\n",
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| 12 |
-
"from helpers import
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]
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},
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{
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"cell_type": "code",
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-
"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -27,16 +32,9 @@
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},
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{
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"cell_type": "code",
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-
"execution_count":
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"metadata": {},
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"outputs": [
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-
{
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| 34 |
-
"name": "stdout",
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| 35 |
-
"output_type": "stream",
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| 36 |
-
"text": [
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| 37 |
-
"dict_values(['Mean Listings Price Cut Amount', 'Median Days on Pending', 'Median Days to Close', 'Percent Listings Price Cut'])\n"
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| 38 |
-
]
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| 39 |
-
},
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{
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"data": {
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"text/html": [
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@@ -131,7 +129,7 @@
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" <td>NaN</td>\n",
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" <td>SFR</td>\n",
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" <td>2018-01-27</td>\n",
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| 134 |
-
" <td>
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| 135 |
" <td>0.047930</td>\n",
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| 136 |
" <td>13998.585612</td>\n",
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| 137 |
" <td>NaN</td>\n",
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@@ -147,10 +145,10 @@
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|
| 147 |
" <td>NaN</td>\n",
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" <td>SFR</td>\n",
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" <td>2018-02-03</td>\n",
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| 150 |
-
" <td>
|
| 151 |
" <td>0.047622</td>\n",
|
| 152 |
" <td>14120.035549</td>\n",
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| 153 |
-
" <td>
|
| 154 |
" <td>NaN</td>\n",
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| 155 |
" <td>NaN</td>\n",
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" </tr>\n",
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@@ -256,75 +254,23 @@
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"</div>"
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| 257 |
],
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| 258 |
"text/plain": [
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| 259 |
-
" RegionID
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| 260 |
-
"0 102001
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| 261 |
-
"1 102001
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| 262 |
-
"2 102001
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| 263 |
-
"3 102001
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| 264 |
-
"4 102001
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| 265 |
-
"... ...
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| 266 |
-
"586709 845172
|
| 267 |
-
"586710 845172
|
| 268 |
-
"586711 845172
|
| 269 |
-
"586712 845172
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| 270 |
-
"586713 845172
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| 271 |
-
"\n",
|
| 272 |
-
" Home Type Date \\\n",
|
| 273 |
-
"0 SFR 2018-01-06 \n",
|
| 274 |
-
"1 SFR 2018-01-13 \n",
|
| 275 |
-
"2 SFR 2018-01-20 \n",
|
| 276 |
-
"3 SFR 2018-01-27 \n",
|
| 277 |
-
"4 SFR 2018-02-03 \n",
|
| 278 |
-
"... ... ... \n",
|
| 279 |
-
"586709 all homes (SFR + Condo) 2024-01-06 \n",
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| 280 |
-
"586710 all homes (SFR + Condo) 2024-01-13 \n",
|
| 281 |
-
"586711 all homes (SFR + Condo) 2024-01-20 \n",
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| 282 |
-
"586712 all homes (SFR + Condo) 2024-01-27 \n",
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| 283 |
-
"586713 all homes (SFR + Condo) 2024-02-03 \n",
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| 284 |
-
"\n",
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| 285 |
-
" Mean Listings Price Cut Amount (Smoothed) Percent Listings Price Cut \\\n",
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| 286 |
-
"0 NaN NaN \n",
|
| 287 |
-
"1 NaN 0.049042 \n",
|
| 288 |
-
"2 NaN 0.044740 \n",
|
| 289 |
-
"3 NaN 0.047930 \n",
|
| 290 |
-
"4 NaN 0.047622 \n",
|
| 291 |
-
"... ... ... \n",
|
| 292 |
-
"586709 NaN 0.094017 \n",
|
| 293 |
-
"586710 NaN 0.070175 \n",
|
| 294 |
-
"586711 NaN 0.043478 \n",
|
| 295 |
-
"586712 NaN 0.036697 \n",
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| 296 |
-
"586713 NaN 0.077670 \n",
|
| 297 |
-
"\n",
|
| 298 |
-
" Mean Listings Price Cut Amount Percent Listings Price Cut (Smoothed) \\\n",
|
| 299 |
-
"0 13508.368375 NaN \n",
|
| 300 |
-
"1 14114.788383 NaN \n",
|
| 301 |
-
"2 14326.128956 NaN \n",
|
| 302 |
-
"3 13998.585612 NaN \n",
|
| 303 |
-
"4 14120.035549 NaN \n",
|
| 304 |
-
"... ... ... \n",
|
| 305 |
-
"586709 NaN 0.037378 \n",
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"586710 NaN 0.043203 \n",
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"586711 NaN 0.054073 \n",
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-
"586712 NaN 0.061092 \n",
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| 309 |
-
"586713 NaN 0.057005 \n",
|
| 310 |
-
"\n",
|
| 311 |
-
" Median Days on Pending (Smoothed) Median Days on Pending \n",
|
| 312 |
-
"0 NaN NaN \n",
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| 313 |
-
"1 NaN NaN \n",
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| 314 |
-
"2 NaN NaN \n",
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"3 NaN NaN \n",
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"4 NaN NaN \n",
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"... ... ... \n",
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"586709 NaN NaN \n",
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"586710 NaN NaN \n",
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"586711 NaN NaN \n",
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"586712 NaN NaN \n",
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"586713 NaN NaN \n",
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"\n",
|
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"[586714 rows x 13 columns]"
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]
|
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},
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-
"execution_count":
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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@@ -351,13 +297,13 @@
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"\n",
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| 352 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 353 |
" if filename.endswith(\".csv\"):\n",
|
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-
"
|
| 355 |
-
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
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| 356 |
-
"\n",
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| 357 |
" # skip month files for now since they are redundant\n",
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| 358 |
" if \"month\" in filename:\n",
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| 359 |
" continue\n",
|
| 360 |
"\n",
|
|
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| 361 |
" if \"_uc_sfrcondo_\" in filename:\n",
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| 362 |
" cur_df[\"Home Type\"] = \"all homes (SFR + Condo)\"\n",
|
| 363 |
" # change column type to string\n",
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@@ -365,22 +311,9 @@
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| 365 |
" elif \"_uc_sfr_\" in filename:\n",
|
| 366 |
" cur_df[\"Home Type\"] = \"SFR\"\n",
|
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"\n",
|
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-
"
|
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-
"
|
| 370 |
-
"\n",
|
| 371 |
-
" # iterate over slug column mappings and get df\n",
|
| 372 |
-
" for slug, col_name in slug_column_mappings.items():\n",
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| 373 |
-
" if slug in filename:\n",
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| 374 |
-
" cur_df = get_df(\n",
|
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-
" cur_df,\n",
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-
" exclude_columns,\n",
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-
" columns_to_pivot,\n",
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" col_name,\n",
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" filename,\n",
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-
" )\n",
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| 381 |
-
"\n",
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| 382 |
-
" data_frames.append(cur_df)\n",
|
| 383 |
-
" break\n",
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| 384 |
"\n",
|
| 385 |
"\n",
|
| 386 |
"combined_df = get_combined_df(\n",
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@@ -396,16 +329,14 @@
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| 396 |
" ],\n",
|
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")\n",
|
| 398 |
"\n",
|
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-
"
|
| 400 |
-
"\n",
|
| 401 |
-
"combined_df = coalesce_columns(combined_df, columns_to_coalesce)\n",
|
| 402 |
"\n",
|
| 403 |
"combined_df"
|
| 404 |
]
|
| 405 |
},
|
| 406 |
{
|
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"cell_type": "code",
|
| 408 |
-
"execution_count":
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"metadata": {},
|
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"outputs": [
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{
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@@ -502,7 +433,7 @@
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" <td>NaN</td>\n",
|
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" <td>SFR</td>\n",
|
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" <td>2018-01-27</td>\n",
|
| 505 |
-
" <td>
|
| 506 |
" <td>0.047930</td>\n",
|
| 507 |
" <td>13998.585612</td>\n",
|
| 508 |
" <td>NaN</td>\n",
|
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@@ -518,10 +449,10 @@
|
|
| 518 |
" <td>NaN</td>\n",
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" <td>SFR</td>\n",
|
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" <td>2018-02-03</td>\n",
|
| 521 |
-
" <td>
|
| 522 |
" <td>0.047622</td>\n",
|
| 523 |
" <td>14120.035549</td>\n",
|
| 524 |
-
" <td>
|
| 525 |
" <td>NaN</td>\n",
|
| 526 |
" <td>NaN</td>\n",
|
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" </tr>\n",
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@@ -657,8 +588,8 @@
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| 657 |
"0 NaN NaN \n",
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"1 NaN 0.049042 \n",
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"2 NaN 0.044740 \n",
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-
"3
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"4
|
| 662 |
"... ... ... \n",
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"586709 NaN 0.094017 \n",
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"586710 NaN 0.070175 \n",
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@@ -671,7 +602,7 @@
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| 671 |
"1 14114.788383 NaN \n",
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"2 14326.128956 NaN \n",
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"3 13998.585612 NaN \n",
|
| 674 |
-
"4 14120.035549
|
| 675 |
"... ... ... \n",
|
| 676 |
"586709 NaN 0.037378 \n",
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| 677 |
"586710 NaN 0.043203 \n",
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@@ -695,7 +626,7 @@
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"[586714 rows x 13 columns]"
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]
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},
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-
"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -717,7 +648,7 @@
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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"cells": [
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{
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"cell_type": "code",
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+
"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
|
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"import os\n",
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"\n",
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| 12 |
+
"from helpers import (\n",
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| 13 |
+
" get_combined_df,\n",
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+
" coalesce_columns,\n",
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+
" save_final_df_as_jsonl,\n",
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+
" handle_slug_column_mappings,\n",
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+
")"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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" <td>NaN</td>\n",
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| 130 |
" <td>SFR</td>\n",
|
| 131 |
" <td>2018-01-27</td>\n",
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+
" <td>13998.585612</td>\n",
|
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" <td>0.047930</td>\n",
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" <td>13998.585612</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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| 146 |
" <td>SFR</td>\n",
|
| 147 |
" <td>2018-02-03</td>\n",
|
| 148 |
+
" <td>14120.035549</td>\n",
|
| 149 |
" <td>0.047622</td>\n",
|
| 150 |
" <td>14120.035549</td>\n",
|
| 151 |
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" <td>0.047622</td>\n",
|
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" <td>NaN</td>\n",
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| 153 |
" <td>NaN</td>\n",
|
| 154 |
" </tr>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
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+
" RegionID ... Median Days on Pending\n",
|
| 258 |
+
"0 102001 ... NaN\n",
|
| 259 |
+
"1 102001 ... NaN\n",
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| 260 |
+
"2 102001 ... NaN\n",
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| 261 |
+
"3 102001 ... NaN\n",
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| 262 |
+
"4 102001 ... NaN\n",
|
| 263 |
+
"... ... ... ...\n",
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+
"586709 845172 ... NaN\n",
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"586710 845172 ... NaN\n",
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"586711 845172 ... NaN\n",
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"586712 845172 ... NaN\n",
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"586713 845172 ... NaN\n",
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"\n",
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"[586714 rows x 13 columns]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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"\n",
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"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
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" if filename.endswith(\".csv\"):\n",
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| 300 |
+
" print(\"processing \" + filename)\n",
|
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" # skip month files for now since they are redundant\n",
|
| 302 |
" if \"month\" in filename:\n",
|
| 303 |
" continue\n",
|
| 304 |
"\n",
|
| 305 |
+
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
|
| 306 |
+
"\n",
|
| 307 |
" if \"_uc_sfrcondo_\" in filename:\n",
|
| 308 |
" cur_df[\"Home Type\"] = \"all homes (SFR + Condo)\"\n",
|
| 309 |
" # change column type to string\n",
|
|
|
|
| 311 |
" elif \"_uc_sfr_\" in filename:\n",
|
| 312 |
" cur_df[\"Home Type\"] = \"SFR\"\n",
|
| 313 |
"\n",
|
| 314 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 315 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
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+
" )\n",
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"\n",
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"\n",
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"combined_df = get_combined_df(\n",
|
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" ],\n",
|
| 330 |
")\n",
|
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"\n",
|
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+
"combined_df = coalesce_columns(combined_df)\n",
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"\n",
|
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"combined_df"
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]
|
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},
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{
|
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"cell_type": "code",
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+
"execution_count": 9,
|
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"metadata": {},
|
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"outputs": [
|
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{
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|
|
| 433 |
" <td>NaN</td>\n",
|
| 434 |
" <td>SFR</td>\n",
|
| 435 |
" <td>2018-01-27</td>\n",
|
| 436 |
+
" <td>13998.585612</td>\n",
|
| 437 |
" <td>0.047930</td>\n",
|
| 438 |
" <td>13998.585612</td>\n",
|
| 439 |
" <td>NaN</td>\n",
|
|
|
|
| 449 |
" <td>NaN</td>\n",
|
| 450 |
" <td>SFR</td>\n",
|
| 451 |
" <td>2018-02-03</td>\n",
|
| 452 |
+
" <td>14120.035549</td>\n",
|
| 453 |
" <td>0.047622</td>\n",
|
| 454 |
" <td>14120.035549</td>\n",
|
| 455 |
+
" <td>0.047622</td>\n",
|
| 456 |
" <td>NaN</td>\n",
|
| 457 |
" <td>NaN</td>\n",
|
| 458 |
" </tr>\n",
|
|
|
|
| 588 |
"0 NaN NaN \n",
|
| 589 |
"1 NaN 0.049042 \n",
|
| 590 |
"2 NaN 0.044740 \n",
|
| 591 |
+
"3 13998.585612 0.047930 \n",
|
| 592 |
+
"4 14120.035549 0.047622 \n",
|
| 593 |
"... ... ... \n",
|
| 594 |
"586709 NaN 0.094017 \n",
|
| 595 |
"586710 NaN 0.070175 \n",
|
|
|
|
| 602 |
"1 14114.788383 NaN \n",
|
| 603 |
"2 14326.128956 NaN \n",
|
| 604 |
"3 13998.585612 NaN \n",
|
| 605 |
+
"4 14120.035549 0.047622 \n",
|
| 606 |
"... ... ... \n",
|
| 607 |
"586709 NaN 0.037378 \n",
|
| 608 |
"586710 NaN 0.043203 \n",
|
|
|
|
| 626 |
"[586714 rows x 13 columns]"
|
| 627 |
]
|
| 628 |
},
|
| 629 |
+
"execution_count": 9,
|
| 630 |
"metadata": {},
|
| 631 |
"output_type": "execute_result"
|
| 632 |
}
|
|
|
|
| 648 |
},
|
| 649 |
{
|
| 650 |
"cell_type": "code",
|
| 651 |
+
"execution_count": 5,
|
| 652 |
"metadata": {},
|
| 653 |
"outputs": [],
|
| 654 |
"source": [
|
processors/for_sale_listings.ipynb
CHANGED
|
@@ -9,7 +9,12 @@
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
|
@@ -340,8 +345,6 @@
|
|
| 340 |
}
|
| 341 |
],
|
| 342 |
"source": [
|
| 343 |
-
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 344 |
-
"\n",
|
| 345 |
"exclude_columns = [\n",
|
| 346 |
" \"RegionID\",\n",
|
| 347 |
" \"SizeRank\",\n",
|
|
@@ -376,21 +379,9 @@
|
|
| 376 |
" elif \"condo\" in filename:\n",
|
| 377 |
" cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
|
| 378 |
"\n",
|
| 379 |
-
"
|
| 380 |
-
"
|
| 381 |
-
"\n",
|
| 382 |
-
" for slug, col_name in slug_column_mappings.items():\n",
|
| 383 |
-
" if slug in filename:\n",
|
| 384 |
-
" cur_df = get_df(\n",
|
| 385 |
-
" cur_df,\n",
|
| 386 |
-
" exclude_columns,\n",
|
| 387 |
-
" columns_to_pivot,\n",
|
| 388 |
-
" col_name,\n",
|
| 389 |
-
" filename,\n",
|
| 390 |
-
" )\n",
|
| 391 |
-
"\n",
|
| 392 |
-
" data_frames.append(cur_df)\n",
|
| 393 |
-
" break\n",
|
| 394 |
"\n",
|
| 395 |
"\n",
|
| 396 |
"combined_df = get_combined_df(\n",
|
|
@@ -406,18 +397,7 @@
|
|
| 406 |
" ],\n",
|
| 407 |
")\n",
|
| 408 |
"\n",
|
| 409 |
-
"\n",
|
| 410 |
-
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 411 |
-
"columns_to_coalesce = [\n",
|
| 412 |
-
" \"Median Listing Price\",\n",
|
| 413 |
-
" \"Median Listing Price (Smoothed)\",\n",
|
| 414 |
-
" \"New Listings\",\n",
|
| 415 |
-
" \"New Listings (Smoothed)\",\n",
|
| 416 |
-
" \"New Pending (Smoothed)\",\n",
|
| 417 |
-
" \"New Pending\",\n",
|
| 418 |
-
"]\n",
|
| 419 |
-
"\n",
|
| 420 |
-
"combined_df = coalesce_columns(combined_df, columns_to_coalesce)\n",
|
| 421 |
"\n",
|
| 422 |
"combined_df"
|
| 423 |
]
|
|
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import (\n",
|
| 13 |
+
" get_combined_df,\n",
|
| 14 |
+
" coalesce_columns,\n",
|
| 15 |
+
" save_final_df_as_jsonl,\n",
|
| 16 |
+
" handle_slug_column_mappings,\n",
|
| 17 |
+
")"
|
| 18 |
]
|
| 19 |
},
|
| 20 |
{
|
|
|
|
| 345 |
}
|
| 346 |
],
|
| 347 |
"source": [
|
|
|
|
|
|
|
| 348 |
"exclude_columns = [\n",
|
| 349 |
" \"RegionID\",\n",
|
| 350 |
" \"SizeRank\",\n",
|
|
|
|
| 379 |
" elif \"condo\" in filename:\n",
|
| 380 |
" cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
|
| 381 |
"\n",
|
| 382 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 383 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
| 384 |
+
" )\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 385 |
"\n",
|
| 386 |
"\n",
|
| 387 |
"combined_df = get_combined_df(\n",
|
|
|
|
| 397 |
" ],\n",
|
| 398 |
")\n",
|
| 399 |
"\n",
|
| 400 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 401 |
"\n",
|
| 402 |
"combined_df"
|
| 403 |
]
|
processors/helpers.py
CHANGED
|
@@ -22,7 +22,10 @@ def get_combined_df(data_frames, on):
|
|
| 22 |
return combined_df
|
| 23 |
|
| 24 |
|
| 25 |
-
def coalesce_columns(
|
|
|
|
|
|
|
|
|
|
| 26 |
for index, row in df.iterrows():
|
| 27 |
for col in df.columns:
|
| 28 |
for column_to_coalesce in columns_to_coalesce:
|
|
@@ -31,7 +34,7 @@ def coalesce_columns(df, columns_to_coalesce):
|
|
| 31 |
df.at[index, column_to_coalesce] = row[col]
|
| 32 |
|
| 33 |
# remove columns with underscores
|
| 34 |
-
combined_df = df[
|
| 35 |
return combined_df
|
| 36 |
|
| 37 |
|
|
@@ -67,3 +70,25 @@ def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
|
|
| 67 |
final_df.to_json(
|
| 68 |
FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
|
| 69 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 22 |
return combined_df
|
| 23 |
|
| 24 |
|
| 25 |
+
def coalesce_columns(
|
| 26 |
+
df,
|
| 27 |
+
):
|
| 28 |
+
columns_to_coalesce = [col for col in df.columns if "_" not in col]
|
| 29 |
for index, row in df.iterrows():
|
| 30 |
for col in df.columns:
|
| 31 |
for column_to_coalesce in columns_to_coalesce:
|
|
|
|
| 34 |
df.at[index, column_to_coalesce] = row[col]
|
| 35 |
|
| 36 |
# remove columns with underscores
|
| 37 |
+
combined_df = df[columns_to_coalesce]
|
| 38 |
return combined_df
|
| 39 |
|
| 40 |
|
|
|
|
| 70 |
final_df.to_json(
|
| 71 |
FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
|
| 72 |
)
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
def handle_slug_column_mappings(
|
| 76 |
+
data_frames, slug_column_mappings, exclude_columns, filename, cur_df
|
| 77 |
+
):
|
| 78 |
+
# Identify columns to pivot
|
| 79 |
+
columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]
|
| 80 |
+
|
| 81 |
+
for slug, col_name in slug_column_mappings.items():
|
| 82 |
+
if slug in filename:
|
| 83 |
+
cur_df = get_df(
|
| 84 |
+
cur_df,
|
| 85 |
+
exclude_columns,
|
| 86 |
+
columns_to_pivot,
|
| 87 |
+
col_name,
|
| 88 |
+
filename,
|
| 89 |
+
)
|
| 90 |
+
|
| 91 |
+
data_frames.append(cur_df)
|
| 92 |
+
break
|
| 93 |
+
|
| 94 |
+
return data_frames
|
processors/home_value_forecasts.ipynb
CHANGED
|
@@ -2,19 +2,19 @@
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
-
"execution_count":
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import save_final_df_as_jsonl"
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"cell_type": "code",
|
| 17 |
-
"execution_count":
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
|
@@ -27,7 +27,7 @@
|
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"cell_type": "code",
|
| 30 |
-
"execution_count":
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
@@ -66,114 +66,114 @@
|
|
| 66 |
" <th>RegionName</th>\n",
|
| 67 |
" <th>RegionType</th>\n",
|
| 68 |
" <th>StateName</th>\n",
|
| 69 |
-
" <th>BaseDate</th>\n",
|
| 70 |
-
" <th>Month Over Month % (Smoothed)</th>\n",
|
| 71 |
-
" <th>Quarter Over Quarter % (Smoothed)</th>\n",
|
| 72 |
-
" <th>Year Over Year % (Smoothed)</th>\n",
|
| 73 |
-
" <th>Month Over Month % (Raw)</th>\n",
|
| 74 |
-
" <th>Quarter Over Quarter % (Raw)</th>\n",
|
| 75 |
-
" <th>Year Over Year % (Raw)</th>\n",
|
| 76 |
" <th>State</th>\n",
|
| 77 |
" <th>City</th>\n",
|
| 78 |
" <th>Metro</th>\n",
|
| 79 |
" <th>CountyName</th>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 80 |
" </tr>\n",
|
| 81 |
" </thead>\n",
|
| 82 |
" <tbody>\n",
|
| 83 |
" <tr>\n",
|
| 84 |
" <th>0</th>\n",
|
| 85 |
-
" <td>
|
| 86 |
-
" <td>
|
| 87 |
-
" <td>
|
| 88 |
-
" <td>
|
| 89 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 90 |
" <td>2023-12-31</td>\n",
|
| 91 |
-
" <td>0.1</td>\n",
|
| 92 |
-
" <td>0.4</td>\n",
|
| 93 |
-
" <td>3.5</td>\n",
|
| 94 |
-
" <td>-0.5</td>\n",
|
| 95 |
-
" <td>0.4</td>\n",
|
| 96 |
-
" <td>3.7</td>\n",
|
| 97 |
-
" <td>NaN</td>\n",
|
| 98 |
" <td>NaN</td>\n",
|
| 99 |
" <td>NaN</td>\n",
|
| 100 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
| 101 |
" </tr>\n",
|
| 102 |
" <tr>\n",
|
| 103 |
" <th>1</th>\n",
|
| 104 |
-
" <td>
|
| 105 |
-
" <td>
|
| 106 |
-
" <td>
|
| 107 |
-
" <td>
|
|
|
|
| 108 |
" <td>NY</td>\n",
|
|
|
|
|
|
|
|
|
|
| 109 |
" <td>2023-12-31</td>\n",
|
| 110 |
-
" <td>0.2</td>\n",
|
| 111 |
-
" <td>0.2</td>\n",
|
| 112 |
-
" <td>1.0</td>\n",
|
| 113 |
-
" <td>-0.7</td>\n",
|
| 114 |
-
" <td>-0.9</td>\n",
|
| 115 |
-
" <td>0.6</td>\n",
|
| 116 |
-
" <td>NaN</td>\n",
|
| 117 |
" <td>NaN</td>\n",
|
| 118 |
" <td>NaN</td>\n",
|
| 119 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
| 120 |
" </tr>\n",
|
| 121 |
" <tr>\n",
|
| 122 |
" <th>2</th>\n",
|
| 123 |
-
" <td>
|
| 124 |
-
" <td>
|
| 125 |
-
" <td>
|
| 126 |
-
" <td>
|
| 127 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 128 |
" <td>2023-12-31</td>\n",
|
| 129 |
-
" <td
|
| 130 |
-
" <td
|
| 131 |
-
" <td>
|
| 132 |
" <td>-0.6</td>\n",
|
| 133 |
-
" <td>0.
|
| 134 |
-
" <td>
|
| 135 |
-
" <td>NaN</td>\n",
|
| 136 |
-
" <td>NaN</td>\n",
|
| 137 |
-
" <td>NaN</td>\n",
|
| 138 |
-
" <td>NaN</td>\n",
|
| 139 |
" </tr>\n",
|
| 140 |
" <tr>\n",
|
| 141 |
" <th>3</th>\n",
|
| 142 |
-
" <td>
|
| 143 |
-
" <td>
|
| 144 |
-
" <td>
|
| 145 |
-
" <td>
|
| 146 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 147 |
" <td>2023-12-31</td>\n",
|
| 148 |
-
" <td>0.
|
| 149 |
-
" <td>0.
|
| 150 |
-
" <td>
|
| 151 |
-
" <td>-0.
|
| 152 |
-
" <td
|
| 153 |
-
" <td>
|
| 154 |
-
" <td>NaN</td>\n",
|
| 155 |
-
" <td>NaN</td>\n",
|
| 156 |
-
" <td>NaN</td>\n",
|
| 157 |
-
" <td>NaN</td>\n",
|
| 158 |
" </tr>\n",
|
| 159 |
" <tr>\n",
|
| 160 |
" <th>4</th>\n",
|
| 161 |
-
" <td>
|
| 162 |
-
" <td>
|
| 163 |
-
" <td>
|
| 164 |
-
" <td>
|
| 165 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 166 |
" <td>2023-12-31</td>\n",
|
| 167 |
-
" <td>-0.1</td>\n",
|
| 168 |
-
" <td>0.0</td>\n",
|
| 169 |
-
" <td>3.2</td>\n",
|
| 170 |
-
" <td>-0.6</td>\n",
|
| 171 |
-
" <td>0.9</td>\n",
|
| 172 |
-
" <td>3.6</td>\n",
|
| 173 |
-
" <td>NaN</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",
|
|
@@ -195,182 +195,195 @@
|
|
| 195 |
" <td>...</td>\n",
|
| 196 |
" </tr>\n",
|
| 197 |
" <tr>\n",
|
| 198 |
-
" <th>
|
| 199 |
-
" <td>
|
| 200 |
-
" <td>
|
| 201 |
-
" <td>
|
| 202 |
" <td>zip</td>\n",
|
| 203 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 204 |
" <td>2023-12-31</td>\n",
|
| 205 |
-
" <td>
|
| 206 |
-
" <td>
|
| 207 |
-
" <td>
|
| 208 |
-
" <td>-0.
|
| 209 |
-
" <td
|
| 210 |
-
" <td>2.
|
| 211 |
-
" <td>MN</td>\n",
|
| 212 |
-
" <td>Warsaw</td>\n",
|
| 213 |
-
" <td>Faribault-Northfield, MN</td>\n",
|
| 214 |
-
" <td>Rice County</td>\n",
|
| 215 |
" </tr>\n",
|
| 216 |
" <tr>\n",
|
| 217 |
-
" <th>
|
| 218 |
-
" <td>
|
| 219 |
-
" <td>
|
| 220 |
-
" <td>
|
| 221 |
" <td>zip</td>\n",
|
| 222 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 223 |
" <td>2023-12-31</td>\n",
|
| 224 |
-
" <td>0.9</td>\n",
|
| 225 |
-
" <td>0.4</td>\n",
|
| 226 |
-
" <td>3.7</td>\n",
|
| 227 |
-
" <td>-0.7</td>\n",
|
| 228 |
-
" <td>0.4</td>\n",
|
| 229 |
-
" <td>2.3</td>\n",
|
| 230 |
-
" <td>IL</td>\n",
|
| 231 |
" <td>NaN</td>\n",
|
| 232 |
-
" <td>
|
| 233 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
| 234 |
" </tr>\n",
|
| 235 |
" <tr>\n",
|
| 236 |
-
" <th>
|
| 237 |
-
" <td>
|
| 238 |
-
" <td>
|
| 239 |
-
" <td>
|
| 240 |
" <td>zip</td>\n",
|
| 241 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 242 |
" <td>2023-12-31</td>\n",
|
| 243 |
-
" <td>-0.5</td>\n",
|
| 244 |
-
" <td>0.3</td>\n",
|
| 245 |
-
" <td>-0.6</td>\n",
|
| 246 |
-
" <td>-0.4</td>\n",
|
| 247 |
-
" <td>0.0</td>\n",
|
| 248 |
-
" <td>1.2</td>\n",
|
| 249 |
-
" <td>TX</td>\n",
|
| 250 |
" <td>NaN</td>\n",
|
| 251 |
-
" <td>
|
| 252 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
| 253 |
" </tr>\n",
|
| 254 |
" <tr>\n",
|
| 255 |
-
" <th>
|
| 256 |
-
" <td>
|
| 257 |
" <td>39992</td>\n",
|
| 258 |
-
" <td>
|
| 259 |
" <td>zip</td>\n",
|
| 260 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 261 |
" <td>2023-12-31</td>\n",
|
| 262 |
-
" <td
|
| 263 |
-
" <td
|
| 264 |
-
" <td
|
| 265 |
-
" <td>-
|
| 266 |
-
" <td
|
| 267 |
-
" <td
|
| 268 |
-
" <td>TX</td>\n",
|
| 269 |
-
" <td>NaN</td>\n",
|
| 270 |
-
" <td>Borger, TX</td>\n",
|
| 271 |
-
" <td>Hutchinson County</td>\n",
|
| 272 |
" </tr>\n",
|
| 273 |
" <tr>\n",
|
| 274 |
-
" <th>
|
| 275 |
-
" <td>
|
| 276 |
-
" <td>
|
| 277 |
-
" <td>
|
| 278 |
" <td>zip</td>\n",
|
| 279 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 280 |
" <td>2023-12-31</td>\n",
|
| 281 |
-
" <td
|
| 282 |
-
" <td
|
| 283 |
-
" <td>
|
| 284 |
-
" <td>-0.
|
| 285 |
-
" <td
|
| 286 |
-
" <td
|
| 287 |
-
" <td>CA</td>\n",
|
| 288 |
-
" <td>Camp Meeker</td>\n",
|
| 289 |
-
" <td>Santa Rosa-Petaluma, CA</td>\n",
|
| 290 |
-
" <td>Sonoma County</td>\n",
|
| 291 |
" </tr>\n",
|
| 292 |
" </tbody>\n",
|
| 293 |
"</table>\n",
|
| 294 |
-
"<p>
|
| 295 |
"</div>"
|
| 296 |
],
|
| 297 |
"text/plain": [
|
| 298 |
-
" RegionID SizeRank
|
| 299 |
-
"0
|
| 300 |
-
"1
|
| 301 |
-
"2
|
| 302 |
-
"3
|
| 303 |
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"4
|
| 304 |
-
"... ... ...
|
| 305 |
-
"
|
| 306 |
-
"
|
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-
"
|
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-
"
|
| 309 |
-
"
|
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|
| 310 |
"\n",
|
| 311 |
-
"
|
| 312 |
-
"0
|
| 313 |
-
"1
|
| 314 |
-
"2
|
| 315 |
-
"3
|
| 316 |
-
"4
|
| 317 |
-
"...
|
| 318 |
-
"
|
| 319 |
-
"
|
| 320 |
-
"
|
| 321 |
-
"
|
| 322 |
-
"
|
| 323 |
"\n",
|
| 324 |
-
"
|
| 325 |
-
"0
|
| 326 |
-
"1
|
| 327 |
-
"2
|
| 328 |
-
"3
|
| 329 |
-
"4
|
| 330 |
-
"...
|
| 331 |
-
"
|
| 332 |
-
"
|
| 333 |
-
"
|
| 334 |
-
"
|
| 335 |
-
"
|
| 336 |
"\n",
|
| 337 |
-
"
|
| 338 |
-
"0
|
| 339 |
-
"1
|
| 340 |
-
"2
|
| 341 |
-
"3
|
| 342 |
-
"4
|
| 343 |
-
"...
|
| 344 |
-
"
|
| 345 |
-
"
|
| 346 |
-
"
|
| 347 |
-
"
|
| 348 |
-
"
|
| 349 |
"\n",
|
| 350 |
-
"
|
| 351 |
-
"0
|
| 352 |
-
"1
|
| 353 |
-
"2
|
| 354 |
-
"3
|
| 355 |
-
"4
|
| 356 |
-
"...
|
| 357 |
-
"
|
| 358 |
-
"
|
| 359 |
-
"
|
| 360 |
-
"
|
| 361 |
-
"
|
| 362 |
"\n",
|
| 363 |
-
"[
|
| 364 |
]
|
| 365 |
},
|
| 366 |
-
"execution_count":
|
| 367 |
"metadata": {},
|
| 368 |
"output_type": "execute_result"
|
| 369 |
}
|
| 370 |
],
|
| 371 |
"source": [
|
| 372 |
-
"
|
| 373 |
-
"
|
| 374 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 375 |
" if filename.endswith(\".csv\"):\n",
|
| 376 |
" print(\"processing \" + filename)\n",
|
|
@@ -380,47 +393,35 @@
|
|
| 380 |
" if filename.endswith(\"sm_sa_month.csv\"):\n",
|
| 381 |
" # print('Smoothed')\n",
|
| 382 |
" cur_df.columns = list(cur_df.columns[:-3]) + [\n",
|
| 383 |
-
" x + \" (Smoothed)\" for x in cols\n",
|
| 384 |
" ]\n",
|
| 385 |
" else:\n",
|
| 386 |
" # print('Raw')\n",
|
| 387 |
-
" cur_df.columns = list(cur_df.columns[:-3]) +
|
|
|
|
| 388 |
"\n",
|
| 389 |
-
"
|
| 390 |
-
" # print('Metro')\n",
|
| 391 |
-
" metro_data_frames.append(cur_df)\n",
|
| 392 |
"\n",
|
| 393 |
-
" elif filename.startswith(\"Zip\"):\n",
|
| 394 |
-
" # print('Zip')\n",
|
| 395 |
-
" zip_data_frames.append(cur_df)\n",
|
| 396 |
-
"\n",
|
| 397 |
-
"\n",
|
| 398 |
-
"def get_combined_df(data_frames):\n",
|
| 399 |
-
" combined_df = None\n",
|
| 400 |
-
" if len(data_frames) > 1:\n",
|
| 401 |
-
" # iterate over dataframes and merge them\n",
|
| 402 |
-
" final_df = data_frames[0]\n",
|
| 403 |
-
" for i in range(1, len(data_frames)):\n",
|
| 404 |
-
" cur_df = data_frames[i]\n",
|
| 405 |
-
" cols = list(cur_df.columns[-3:])\n",
|
| 406 |
-
" cols.append(\"RegionID\")\n",
|
| 407 |
-
" combined_df = pd.merge(final_df, cur_df[cols], on=\"RegionID\")\n",
|
| 408 |
-
" elif len(data_frames) == 1:\n",
|
| 409 |
-
" combined_df = data_frames[0]\n",
|
| 410 |
-
"\n",
|
| 411 |
-
" return combined_df\n",
|
| 412 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 413 |
"\n",
|
| 414 |
-
"
|
| 415 |
-
"combined_zip_dfs = get_combined_df(zip_data_frames)\n",
|
| 416 |
"\n",
|
| 417 |
-
"combined_df = pd.concat([combined_metro_dfs, combined_zip_dfs])\n",
|
| 418 |
"combined_df"
|
| 419 |
]
|
| 420 |
},
|
| 421 |
{
|
| 422 |
"cell_type": "code",
|
| 423 |
-
"execution_count":
|
| 424 |
"metadata": {},
|
| 425 |
"outputs": [
|
| 426 |
{
|
|
@@ -445,112 +446,112 @@
|
|
| 445 |
" <tr style=\"text-align: right;\">\n",
|
| 446 |
" <th></th>\n",
|
| 447 |
" <th>Region ID</th>\n",
|
|
|
|
| 448 |
" <th>Region</th>\n",
|
| 449 |
" <th>RegionType</th>\n",
|
| 450 |
-
" <th>Size Rank</th>\n",
|
| 451 |
" <th>State</th>\n",
|
| 452 |
" <th>City</th>\n",
|
| 453 |
" <th>Metro</th>\n",
|
| 454 |
" <th>County</th>\n",
|
| 455 |
" <th>Date</th>\n",
|
| 456 |
-
" <th>Month Over Month % (Smoothed)</th>\n",
|
| 457 |
-
" <th>Quarter Over Quarter % (Smoothed)</th>\n",
|
| 458 |
-
" <th>Year Over Year % (Smoothed)</th>\n",
|
| 459 |
-
" <th>Month Over Month
|
| 460 |
-
" <th>Quarter Over Quarter
|
| 461 |
-
" <th>Year Over Year
|
| 462 |
" </tr>\n",
|
| 463 |
" </thead>\n",
|
| 464 |
" <tbody>\n",
|
| 465 |
" <tr>\n",
|
| 466 |
" <th>0</th>\n",
|
| 467 |
-
" <td>
|
| 468 |
-
" <td>
|
| 469 |
-
" <td>
|
| 470 |
-
" <td>
|
| 471 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 472 |
" <td>NaN</td>\n",
|
| 473 |
" <td>NaN</td>\n",
|
| 474 |
" <td>NaN</td>\n",
|
| 475 |
-
" <td
|
| 476 |
-
" <td
|
| 477 |
-
" <td>0.
|
| 478 |
-
" <td>3.5</td>\n",
|
| 479 |
-
" <td>-0.5</td>\n",
|
| 480 |
-
" <td>0.4</td>\n",
|
| 481 |
-
" <td>3.7</td>\n",
|
| 482 |
" </tr>\n",
|
| 483 |
" <tr>\n",
|
| 484 |
" <th>1</th>\n",
|
| 485 |
-
" <td>
|
| 486 |
-
" <td>
|
| 487 |
-
" <td>
|
| 488 |
-
" <td>
|
| 489 |
" <td>NY</td>\n",
|
| 490 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 491 |
" <td>NaN</td>\n",
|
| 492 |
" <td>NaN</td>\n",
|
| 493 |
-
" <td>2023-12-31</td>\n",
|
| 494 |
-
" <td>0.2</td>\n",
|
| 495 |
-
" <td>0.2</td>\n",
|
| 496 |
-
" <td>1.0</td>\n",
|
| 497 |
" <td>-0.7</td>\n",
|
| 498 |
" <td>-0.9</td>\n",
|
| 499 |
" <td>0.6</td>\n",
|
| 500 |
" </tr>\n",
|
| 501 |
" <tr>\n",
|
| 502 |
" <th>2</th>\n",
|
| 503 |
-
" <td>
|
| 504 |
-
" <td>
|
| 505 |
-
" <td>
|
| 506 |
-
" <td>
|
| 507 |
-
" <td>
|
| 508 |
-
" <td>
|
| 509 |
-
" <td>
|
| 510 |
-
" <td>
|
| 511 |
" <td>2023-12-31</td>\n",
|
| 512 |
-
" <td
|
| 513 |
-
" <td
|
| 514 |
-
" <td>
|
| 515 |
" <td>-0.6</td>\n",
|
| 516 |
-
" <td>0.
|
| 517 |
-
" <td>
|
| 518 |
" </tr>\n",
|
| 519 |
" <tr>\n",
|
| 520 |
" <th>3</th>\n",
|
| 521 |
-
" <td>
|
| 522 |
-
" <td>
|
| 523 |
-
" <td>
|
| 524 |
-
" <td>
|
| 525 |
-
" <td>
|
| 526 |
-
" <td>
|
| 527 |
-
" <td>
|
| 528 |
-
" <td>
|
| 529 |
" <td>2023-12-31</td>\n",
|
| 530 |
-
" <td>0.
|
| 531 |
-
" <td>0.
|
| 532 |
-
" <td>
|
| 533 |
-
" <td>-0.
|
| 534 |
-
" <td
|
| 535 |
-
" <td>
|
| 536 |
" </tr>\n",
|
| 537 |
" <tr>\n",
|
| 538 |
" <th>4</th>\n",
|
| 539 |
-
" <td>
|
| 540 |
-
" <td>
|
| 541 |
-
" <td>
|
| 542 |
-
" <td>
|
| 543 |
-
" <td>
|
| 544 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
| 545 |
" <td>NaN</td>\n",
|
| 546 |
" <td>NaN</td>\n",
|
| 547 |
-
" <td>
|
| 548 |
-
" <td>-0.
|
| 549 |
" <td>0.0</td>\n",
|
| 550 |
-
" <td>3.
|
| 551 |
-
" <td>-0.6</td>\n",
|
| 552 |
-
" <td>0.9</td>\n",
|
| 553 |
-
" <td>3.6</td>\n",
|
| 554 |
" </tr>\n",
|
| 555 |
" <tr>\n",
|
| 556 |
" <th>...</th>\n",
|
|
@@ -571,193 +572,191 @@
|
|
| 571 |
" <td>...</td>\n",
|
| 572 |
" </tr>\n",
|
| 573 |
" <tr>\n",
|
| 574 |
-
" <th>
|
| 575 |
-
" <td>
|
| 576 |
-
" <td>
|
|
|
|
| 577 |
" <td>zip</td>\n",
|
| 578 |
-
" <td>
|
| 579 |
-
" <td>
|
| 580 |
-
" <td>
|
| 581 |
-
" <td>
|
| 582 |
-
" <td>Rice County</td>\n",
|
| 583 |
" <td>2023-12-31</td>\n",
|
| 584 |
-
" <td>
|
| 585 |
-
" <td>
|
| 586 |
-
" <td>
|
| 587 |
-
" <td>-0.
|
| 588 |
-
" <td
|
| 589 |
-
" <td>2.
|
| 590 |
" </tr>\n",
|
| 591 |
" <tr>\n",
|
| 592 |
-
" <th>
|
| 593 |
-
" <td>
|
| 594 |
-
" <td>
|
|
|
|
| 595 |
" <td>zip</td>\n",
|
| 596 |
-
" <td>
|
| 597 |
-
" <td>
|
| 598 |
-
" <td>
|
| 599 |
-
" <td>
|
| 600 |
-
" <td>Macoupin County</td>\n",
|
| 601 |
" <td>2023-12-31</td>\n",
|
| 602 |
-
" <td>
|
| 603 |
-
" <td>
|
| 604 |
-
" <td>
|
| 605 |
" <td>-0.7</td>\n",
|
| 606 |
-
" <td
|
| 607 |
-
" <td>
|
| 608 |
" </tr>\n",
|
| 609 |
" <tr>\n",
|
| 610 |
-
" <th>
|
| 611 |
-
" <td>
|
| 612 |
-
" <td>
|
|
|
|
| 613 |
" <td>zip</td>\n",
|
| 614 |
-
" <td>
|
| 615 |
-
" <td>
|
| 616 |
-
" <td>
|
| 617 |
-
" <td>
|
| 618 |
-
" <td>Chambers County</td>\n",
|
| 619 |
" <td>2023-12-31</td>\n",
|
| 620 |
-
" <td
|
| 621 |
-
" <td>
|
| 622 |
-
" <td
|
| 623 |
-
" <td>-0
|
| 624 |
" <td>0.0</td>\n",
|
| 625 |
-
" <td>
|
| 626 |
" </tr>\n",
|
| 627 |
" <tr>\n",
|
| 628 |
-
" <th>
|
| 629 |
-
" <td>
|
| 630 |
-
" <td>79078</td>\n",
|
| 631 |
-
" <td>zip</td>\n",
|
| 632 |
" <td>39992</td>\n",
|
| 633 |
-
" <td>
|
| 634 |
-
" <td>
|
| 635 |
-
" <td>
|
| 636 |
-
" <td>
|
|
|
|
|
|
|
| 637 |
" <td>2023-12-31</td>\n",
|
| 638 |
-
" <td
|
| 639 |
-
" <td
|
| 640 |
-
" <td
|
| 641 |
-
" <td>-
|
| 642 |
-
" <td
|
| 643 |
-
" <td
|
| 644 |
" </tr>\n",
|
| 645 |
" <tr>\n",
|
| 646 |
-
" <th>
|
| 647 |
-
" <td>
|
| 648 |
-
" <td>
|
|
|
|
| 649 |
" <td>zip</td>\n",
|
| 650 |
-
" <td>
|
| 651 |
-
" <td>
|
| 652 |
-
" <td>
|
| 653 |
-
" <td>
|
| 654 |
-
" <td>Sonoma County</td>\n",
|
| 655 |
" <td>2023-12-31</td>\n",
|
| 656 |
-
" <td
|
| 657 |
-
" <td
|
| 658 |
-
" <td>
|
| 659 |
-
" <td>-0.
|
| 660 |
-
" <td
|
| 661 |
-
" <td
|
| 662 |
" </tr>\n",
|
| 663 |
" </tbody>\n",
|
| 664 |
"</table>\n",
|
| 665 |
-
"<p>
|
| 666 |
"</div>"
|
| 667 |
],
|
| 668 |
"text/plain": [
|
| 669 |
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" Region ID
|
| 670 |
-
"0
|
| 671 |
-
"1
|
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"2
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"3
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"4
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"... ...
|
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-
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-
"
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|
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"\n",
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"
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"0
|
| 684 |
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"1
|
| 685 |
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|
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|
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"4
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|
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|
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"\n",
|
| 695 |
-
"
|
| 696 |
-
"0
|
| 697 |
-
"1
|
| 698 |
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"2
|
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"3
|
| 700 |
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"4
|
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"...
|
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-
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"
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|
| 706 |
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|
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"\n",
|
| 708 |
-
"
|
| 709 |
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"0
|
| 710 |
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"1
|
| 711 |
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"2
|
| 712 |
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"3
|
| 713 |
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"4
|
| 714 |
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"...
|
| 715 |
-
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|
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"
|
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-
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|
| 718 |
-
"
|
| 719 |
-
"
|
| 720 |
"\n",
|
| 721 |
-
"
|
| 722 |
-
"0
|
| 723 |
-
"1
|
| 724 |
-
"2
|
| 725 |
-
"3
|
| 726 |
-
"4
|
| 727 |
-
"...
|
| 728 |
-
"
|
| 729 |
-
"
|
| 730 |
-
"
|
| 731 |
-
"
|
| 732 |
-
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|
| 733 |
"\n",
|
| 734 |
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"
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|
| 735 |
]
|
| 736 |
},
|
| 737 |
-
"execution_count":
|
| 738 |
"metadata": {},
|
| 739 |
"output_type": "execute_result"
|
| 740 |
}
|
| 741 |
],
|
| 742 |
"source": [
|
| 743 |
-
"
|
| 744 |
-
"
|
| 745 |
-
"\n",
|
| 746 |
-
"all_cols = [\n",
|
| 747 |
-
" \"RegionID\",\n",
|
| 748 |
-
" \"RegionName\",\n",
|
| 749 |
-
" \"RegionType\",\n",
|
| 750 |
-
" \"SizeRank\",\n",
|
| 751 |
-
" \"StateName\",\n",
|
| 752 |
-
" \"State\",\n",
|
| 753 |
-
" \"City\",\n",
|
| 754 |
-
" \"Metro\",\n",
|
| 755 |
-
" \"CountyName\",\n",
|
| 756 |
-
" \"BaseDate\",\n",
|
| 757 |
-
"] + result_cols\n",
|
| 758 |
-
"\n",
|
| 759 |
-
"final_df = combined_df[all_cols]\n",
|
| 760 |
-
"final_df = final_df.drop(\"StateName\", axis=1)\n",
|
| 761 |
"final_df = final_df.rename(\n",
|
| 762 |
" columns={\n",
|
| 763 |
" \"CountyName\": \"County\",\n",
|
|
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import get_combined_df, coalesce_columns, save_final_df_as_jsonl"
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"cell_type": "code",
|
| 17 |
+
"execution_count": 2,
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
|
|
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"cell_type": "code",
|
| 30 |
+
"execution_count": 3,
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
|
|
| 66 |
" <th>RegionName</th>\n",
|
| 67 |
" <th>RegionType</th>\n",
|
| 68 |
" <th>StateName</th>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 69 |
" <th>State</th>\n",
|
| 70 |
" <th>City</th>\n",
|
| 71 |
" <th>Metro</th>\n",
|
| 72 |
" <th>CountyName</th>\n",
|
| 73 |
+
" <th>BaseDate</th>\n",
|
| 74 |
+
" <th>Month Over Month % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 75 |
+
" <th>Quarter Over Quarter % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 76 |
+
" <th>Year Over Year % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 77 |
+
" <th>Month Over Month %</th>\n",
|
| 78 |
+
" <th>Quarter Over Quarter %</th>\n",
|
| 79 |
+
" <th>Year Over Year %</th>\n",
|
| 80 |
" </tr>\n",
|
| 81 |
" </thead>\n",
|
| 82 |
" <tbody>\n",
|
| 83 |
" <tr>\n",
|
| 84 |
" <th>0</th>\n",
|
| 85 |
+
" <td>58001</td>\n",
|
| 86 |
+
" <td>30490</td>\n",
|
| 87 |
+
" <td>501</td>\n",
|
| 88 |
+
" <td>zip</td>\n",
|
| 89 |
+
" <td>NY</td>\n",
|
| 90 |
+
" <td>NY</td>\n",
|
| 91 |
+
" <td>Holtsville</td>\n",
|
| 92 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 93 |
+
" <td>Suffolk County</td>\n",
|
| 94 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 95 |
" <td>NaN</td>\n",
|
| 96 |
" <td>NaN</td>\n",
|
| 97 |
" <td>NaN</td>\n",
|
| 98 |
+
" <td>-0.7</td>\n",
|
| 99 |
+
" <td>-0.9</td>\n",
|
| 100 |
+
" <td>0.6</td>\n",
|
| 101 |
" </tr>\n",
|
| 102 |
" <tr>\n",
|
| 103 |
" <th>1</th>\n",
|
| 104 |
+
" <td>58002</td>\n",
|
| 105 |
+
" <td>30490</td>\n",
|
| 106 |
+
" <td>544</td>\n",
|
| 107 |
+
" <td>zip</td>\n",
|
| 108 |
+
" <td>NY</td>\n",
|
| 109 |
" <td>NY</td>\n",
|
| 110 |
+
" <td>Holtsville</td>\n",
|
| 111 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 112 |
+
" <td>Suffolk County</td>\n",
|
| 113 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 114 |
" <td>NaN</td>\n",
|
| 115 |
" <td>NaN</td>\n",
|
| 116 |
" <td>NaN</td>\n",
|
| 117 |
+
" <td>-0.7</td>\n",
|
| 118 |
+
" <td>-0.9</td>\n",
|
| 119 |
+
" <td>0.6</td>\n",
|
| 120 |
" </tr>\n",
|
| 121 |
" <tr>\n",
|
| 122 |
" <th>2</th>\n",
|
| 123 |
+
" <td>58196</td>\n",
|
| 124 |
+
" <td>7440</td>\n",
|
| 125 |
+
" <td>1001</td>\n",
|
| 126 |
+
" <td>zip</td>\n",
|
| 127 |
+
" <td>MA</td>\n",
|
| 128 |
+
" <td>MA</td>\n",
|
| 129 |
+
" <td>Agawam</td>\n",
|
| 130 |
+
" <td>Springfield, MA</td>\n",
|
| 131 |
+
" <td>Hampden County</td>\n",
|
| 132 |
" <td>2023-12-31</td>\n",
|
| 133 |
+
" <td>0.4</td>\n",
|
| 134 |
+
" <td>0.9</td>\n",
|
| 135 |
+
" <td>3.2</td>\n",
|
| 136 |
" <td>-0.6</td>\n",
|
| 137 |
+
" <td>0.0</td>\n",
|
| 138 |
+
" <td>3.0</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 139 |
" </tr>\n",
|
| 140 |
" <tr>\n",
|
| 141 |
" <th>3</th>\n",
|
| 142 |
+
" <td>58197</td>\n",
|
| 143 |
+
" <td>3911</td>\n",
|
| 144 |
+
" <td>1002</td>\n",
|
| 145 |
+
" <td>zip</td>\n",
|
| 146 |
+
" <td>MA</td>\n",
|
| 147 |
+
" <td>MA</td>\n",
|
| 148 |
+
" <td>Amherst</td>\n",
|
| 149 |
+
" <td>Springfield, MA</td>\n",
|
| 150 |
+
" <td>Hampshire County</td>\n",
|
| 151 |
" <td>2023-12-31</td>\n",
|
| 152 |
+
" <td>0.2</td>\n",
|
| 153 |
+
" <td>0.7</td>\n",
|
| 154 |
+
" <td>2.7</td>\n",
|
| 155 |
+
" <td>-0.6</td>\n",
|
| 156 |
+
" <td>0.0</td>\n",
|
| 157 |
+
" <td>2.9</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 158 |
" </tr>\n",
|
| 159 |
" <tr>\n",
|
| 160 |
" <th>4</th>\n",
|
| 161 |
+
" <td>58198</td>\n",
|
| 162 |
+
" <td>8838</td>\n",
|
| 163 |
+
" <td>1003</td>\n",
|
| 164 |
+
" <td>zip</td>\n",
|
| 165 |
+
" <td>MA</td>\n",
|
| 166 |
+
" <td>MA</td>\n",
|
| 167 |
+
" <td>Amherst</td>\n",
|
| 168 |
+
" <td>Springfield, MA</td>\n",
|
| 169 |
+
" <td>Hampshire County</td>\n",
|
| 170 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 171 |
" <td>NaN</td>\n",
|
| 172 |
" <td>NaN</td>\n",
|
| 173 |
" <td>NaN</td>\n",
|
| 174 |
+
" <td>-0.7</td>\n",
|
| 175 |
+
" <td>0.0</td>\n",
|
| 176 |
+
" <td>3.4</td>\n",
|
| 177 |
" </tr>\n",
|
| 178 |
" <tr>\n",
|
| 179 |
" <th>...</th>\n",
|
|
|
|
| 195 |
" <td>...</td>\n",
|
| 196 |
" </tr>\n",
|
| 197 |
" <tr>\n",
|
| 198 |
+
" <th>31849</th>\n",
|
| 199 |
+
" <td>827279</td>\n",
|
| 200 |
+
" <td>7779</td>\n",
|
| 201 |
+
" <td>72405</td>\n",
|
| 202 |
" <td>zip</td>\n",
|
| 203 |
+
" <td>AR</td>\n",
|
| 204 |
+
" <td>AR</td>\n",
|
| 205 |
+
" <td>Jonesboro</td>\n",
|
| 206 |
+
" <td>Jonesboro, AR</td>\n",
|
| 207 |
+
" <td>Craighead County</td>\n",
|
| 208 |
" <td>2023-12-31</td>\n",
|
| 209 |
+
" <td>NaN</td>\n",
|
| 210 |
+
" <td>NaN</td>\n",
|
| 211 |
+
" <td>NaN</td>\n",
|
| 212 |
+
" <td>-0.7</td>\n",
|
| 213 |
+
" <td>0.0</td>\n",
|
| 214 |
+
" <td>2.5</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 215 |
" </tr>\n",
|
| 216 |
" <tr>\n",
|
| 217 |
+
" <th>31850</th>\n",
|
| 218 |
+
" <td>834213</td>\n",
|
| 219 |
+
" <td>30490</td>\n",
|
| 220 |
+
" <td>11437</td>\n",
|
| 221 |
" <td>zip</td>\n",
|
| 222 |
+
" <td>NY</td>\n",
|
| 223 |
+
" <td>NY</td>\n",
|
| 224 |
+
" <td>New York</td>\n",
|
| 225 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 226 |
+
" <td>Queens County</td>\n",
|
| 227 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 228 |
" <td>NaN</td>\n",
|
| 229 |
+
" <td>NaN</td>\n",
|
| 230 |
+
" <td>NaN</td>\n",
|
| 231 |
+
" <td>-0.7</td>\n",
|
| 232 |
+
" <td>-0.9</td>\n",
|
| 233 |
+
" <td>0.6</td>\n",
|
| 234 |
" </tr>\n",
|
| 235 |
" <tr>\n",
|
| 236 |
+
" <th>31851</th>\n",
|
| 237 |
+
" <td>845914</td>\n",
|
| 238 |
+
" <td>6361</td>\n",
|
| 239 |
+
" <td>85288</td>\n",
|
| 240 |
" <td>zip</td>\n",
|
| 241 |
+
" <td>AZ</td>\n",
|
| 242 |
+
" <td>AZ</td>\n",
|
| 243 |
+
" <td>Tempe</td>\n",
|
| 244 |
+
" <td>Phoenix-Mesa-Chandler, AZ</td>\n",
|
| 245 |
+
" <td>Maricopa County</td>\n",
|
| 246 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 247 |
" <td>NaN</td>\n",
|
| 248 |
+
" <td>NaN</td>\n",
|
| 249 |
+
" <td>NaN</td>\n",
|
| 250 |
+
" <td>-1.0</td>\n",
|
| 251 |
+
" <td>0.0</td>\n",
|
| 252 |
+
" <td>4.5</td>\n",
|
| 253 |
" </tr>\n",
|
| 254 |
" <tr>\n",
|
| 255 |
+
" <th>31852</th>\n",
|
| 256 |
+
" <td>847854</td>\n",
|
| 257 |
" <td>39992</td>\n",
|
| 258 |
+
" <td>20598</td>\n",
|
| 259 |
" <td>zip</td>\n",
|
| 260 |
+
" <td>VA</td>\n",
|
| 261 |
+
" <td>VA</td>\n",
|
| 262 |
+
" <td>Arlington</td>\n",
|
| 263 |
+
" <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
|
| 264 |
+
" <td>Arlington County</td>\n",
|
| 265 |
" <td>2023-12-31</td>\n",
|
| 266 |
+
" <td>NaN</td>\n",
|
| 267 |
+
" <td>NaN</td>\n",
|
| 268 |
+
" <td>NaN</td>\n",
|
| 269 |
+
" <td>-0.4</td>\n",
|
| 270 |
+
" <td>0.9</td>\n",
|
| 271 |
+
" <td>1.2</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 272 |
" </tr>\n",
|
| 273 |
" <tr>\n",
|
| 274 |
+
" <th>31853</th>\n",
|
| 275 |
+
" <td>847855</td>\n",
|
| 276 |
+
" <td>30490</td>\n",
|
| 277 |
+
" <td>34249</td>\n",
|
| 278 |
" <td>zip</td>\n",
|
| 279 |
+
" <td>FL</td>\n",
|
| 280 |
+
" <td>FL</td>\n",
|
| 281 |
+
" <td>Sarasota</td>\n",
|
| 282 |
+
" <td>North Port-Sarasota-Bradenton, FL</td>\n",
|
| 283 |
+
" <td>Sarasota County</td>\n",
|
| 284 |
" <td>2023-12-31</td>\n",
|
| 285 |
+
" <td>NaN</td>\n",
|
| 286 |
+
" <td>NaN</td>\n",
|
| 287 |
+
" <td>NaN</td>\n",
|
| 288 |
+
" <td>-0.9</td>\n",
|
| 289 |
+
" <td>-0.1</td>\n",
|
| 290 |
+
" <td>5.4</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 291 |
" </tr>\n",
|
| 292 |
" </tbody>\n",
|
| 293 |
"</table>\n",
|
| 294 |
+
"<p>31854 rows × 16 columns</p>\n",
|
| 295 |
"</div>"
|
| 296 |
],
|
| 297 |
"text/plain": [
|
| 298 |
+
" RegionID SizeRank RegionName RegionType StateName State City \\\n",
|
| 299 |
+
"0 58001 30490 501 zip NY NY Holtsville \n",
|
| 300 |
+
"1 58002 30490 544 zip NY NY Holtsville \n",
|
| 301 |
+
"2 58196 7440 1001 zip MA MA Agawam \n",
|
| 302 |
+
"3 58197 3911 1002 zip MA MA Amherst \n",
|
| 303 |
+
"4 58198 8838 1003 zip MA MA Amherst \n",
|
| 304 |
+
"... ... ... ... ... ... ... ... \n",
|
| 305 |
+
"31849 827279 7779 72405 zip AR AR Jonesboro \n",
|
| 306 |
+
"31850 834213 30490 11437 zip NY NY New York \n",
|
| 307 |
+
"31851 845914 6361 85288 zip AZ AZ Tempe \n",
|
| 308 |
+
"31852 847854 39992 20598 zip VA VA Arlington \n",
|
| 309 |
+
"31853 847855 30490 34249 zip FL FL Sarasota \n",
|
| 310 |
+
"\n",
|
| 311 |
+
" Metro CountyName \\\n",
|
| 312 |
+
"0 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
|
| 313 |
+
"1 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
|
| 314 |
+
"2 Springfield, MA Hampden County \n",
|
| 315 |
+
"3 Springfield, MA Hampshire County \n",
|
| 316 |
+
"4 Springfield, MA Hampshire County \n",
|
| 317 |
+
"... ... ... \n",
|
| 318 |
+
"31849 Jonesboro, AR Craighead County \n",
|
| 319 |
+
"31850 New York-Newark-Jersey City, NY-NJ-PA Queens County \n",
|
| 320 |
+
"31851 Phoenix-Mesa-Chandler, AZ Maricopa County \n",
|
| 321 |
+
"31852 Washington-Arlington-Alexandria, DC-VA-MD-WV Arlington County \n",
|
| 322 |
+
"31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
|
| 323 |
"\n",
|
| 324 |
+
" BaseDate Month Over Month % (Smoothed) (Seaonally Adjusted) \\\n",
|
| 325 |
+
"0 2023-12-31 NaN \n",
|
| 326 |
+
"1 2023-12-31 NaN \n",
|
| 327 |
+
"2 2023-12-31 0.4 \n",
|
| 328 |
+
"3 2023-12-31 0.2 \n",
|
| 329 |
+
"4 2023-12-31 NaN \n",
|
| 330 |
+
"... ... ... \n",
|
| 331 |
+
"31849 2023-12-31 NaN \n",
|
| 332 |
+
"31850 2023-12-31 NaN \n",
|
| 333 |
+
"31851 2023-12-31 NaN \n",
|
| 334 |
+
"31852 2023-12-31 NaN \n",
|
| 335 |
+
"31853 2023-12-31 NaN \n",
|
| 336 |
"\n",
|
| 337 |
+
" Quarter Over Quarter % (Smoothed) (Seaonally Adjusted) \\\n",
|
| 338 |
+
"0 NaN \n",
|
| 339 |
+
"1 NaN \n",
|
| 340 |
+
"2 0.9 \n",
|
| 341 |
+
"3 0.7 \n",
|
| 342 |
+
"4 NaN \n",
|
| 343 |
+
"... ... \n",
|
| 344 |
+
"31849 NaN \n",
|
| 345 |
+
"31850 NaN \n",
|
| 346 |
+
"31851 NaN \n",
|
| 347 |
+
"31852 NaN \n",
|
| 348 |
+
"31853 NaN \n",
|
| 349 |
"\n",
|
| 350 |
+
" Year Over Year % (Smoothed) (Seaonally Adjusted) Month Over Month % \\\n",
|
| 351 |
+
"0 NaN -0.7 \n",
|
| 352 |
+
"1 NaN -0.7 \n",
|
| 353 |
+
"2 3.2 -0.6 \n",
|
| 354 |
+
"3 2.7 -0.6 \n",
|
| 355 |
+
"4 NaN -0.7 \n",
|
| 356 |
+
"... ... ... \n",
|
| 357 |
+
"31849 NaN -0.7 \n",
|
| 358 |
+
"31850 NaN -0.7 \n",
|
| 359 |
+
"31851 NaN -1.0 \n",
|
| 360 |
+
"31852 NaN -0.4 \n",
|
| 361 |
+
"31853 NaN -0.9 \n",
|
| 362 |
"\n",
|
| 363 |
+
" Quarter Over Quarter % Year Over Year % \n",
|
| 364 |
+
"0 -0.9 0.6 \n",
|
| 365 |
+
"1 -0.9 0.6 \n",
|
| 366 |
+
"2 0.0 3.0 \n",
|
| 367 |
+
"3 0.0 2.9 \n",
|
| 368 |
+
"4 0.0 3.4 \n",
|
| 369 |
+
"... ... ... \n",
|
| 370 |
+
"31849 0.0 2.5 \n",
|
| 371 |
+
"31850 -0.9 0.6 \n",
|
| 372 |
+
"31851 0.0 4.5 \n",
|
| 373 |
+
"31852 0.9 1.2 \n",
|
| 374 |
+
"31853 -0.1 5.4 \n",
|
| 375 |
"\n",
|
| 376 |
+
"[31854 rows x 16 columns]"
|
| 377 |
]
|
| 378 |
},
|
| 379 |
+
"execution_count": 3,
|
| 380 |
"metadata": {},
|
| 381 |
"output_type": "execute_result"
|
| 382 |
}
|
| 383 |
],
|
| 384 |
"source": [
|
| 385 |
+
"data_frames = []\n",
|
| 386 |
+
"\n",
|
| 387 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 388 |
" if filename.endswith(\".csv\"):\n",
|
| 389 |
" print(\"processing \" + filename)\n",
|
|
|
|
| 393 |
" if filename.endswith(\"sm_sa_month.csv\"):\n",
|
| 394 |
" # print('Smoothed')\n",
|
| 395 |
" cur_df.columns = list(cur_df.columns[:-3]) + [\n",
|
| 396 |
+
" x + \" (Smoothed) (Seaonally Adjusted)\" for x in cols\n",
|
| 397 |
" ]\n",
|
| 398 |
" else:\n",
|
| 399 |
" # print('Raw')\n",
|
| 400 |
+
" cur_df.columns = list(cur_df.columns[:-3]) + cols\n",
|
| 401 |
+
" cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
|
| 402 |
"\n",
|
| 403 |
+
" data_frames.append(cur_df)\n",
|
|
|
|
|
|
|
| 404 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 405 |
"\n",
|
| 406 |
+
"combined_df = get_combined_df(\n",
|
| 407 |
+
" data_frames,\n",
|
| 408 |
+
" [\n",
|
| 409 |
+
" \"RegionID\",\n",
|
| 410 |
+
" \"RegionType\",\n",
|
| 411 |
+
" \"SizeRank\",\n",
|
| 412 |
+
" \"StateName\",\n",
|
| 413 |
+
" \"BaseDate\",\n",
|
| 414 |
+
" ],\n",
|
| 415 |
+
")\n",
|
| 416 |
"\n",
|
| 417 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
|
|
|
| 418 |
"\n",
|
|
|
|
| 419 |
"combined_df"
|
| 420 |
]
|
| 421 |
},
|
| 422 |
{
|
| 423 |
"cell_type": "code",
|
| 424 |
+
"execution_count": 4,
|
| 425 |
"metadata": {},
|
| 426 |
"outputs": [
|
| 427 |
{
|
|
|
|
| 446 |
" <tr style=\"text-align: right;\">\n",
|
| 447 |
" <th></th>\n",
|
| 448 |
" <th>Region ID</th>\n",
|
| 449 |
+
" <th>Size Rank</th>\n",
|
| 450 |
" <th>Region</th>\n",
|
| 451 |
" <th>RegionType</th>\n",
|
|
|
|
| 452 |
" <th>State</th>\n",
|
| 453 |
" <th>City</th>\n",
|
| 454 |
" <th>Metro</th>\n",
|
| 455 |
" <th>County</th>\n",
|
| 456 |
" <th>Date</th>\n",
|
| 457 |
+
" <th>Month Over Month % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 458 |
+
" <th>Quarter Over Quarter % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 459 |
+
" <th>Year Over Year % (Smoothed) (Seaonally Adjusted)</th>\n",
|
| 460 |
+
" <th>Month Over Month %</th>\n",
|
| 461 |
+
" <th>Quarter Over Quarter %</th>\n",
|
| 462 |
+
" <th>Year Over Year %</th>\n",
|
| 463 |
" </tr>\n",
|
| 464 |
" </thead>\n",
|
| 465 |
" <tbody>\n",
|
| 466 |
" <tr>\n",
|
| 467 |
" <th>0</th>\n",
|
| 468 |
+
" <td>58001</td>\n",
|
| 469 |
+
" <td>30490</td>\n",
|
| 470 |
+
" <td>501</td>\n",
|
| 471 |
+
" <td>zip</td>\n",
|
| 472 |
+
" <td>NY</td>\n",
|
| 473 |
+
" <td>Holtsville</td>\n",
|
| 474 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 475 |
+
" <td>Suffolk County</td>\n",
|
| 476 |
+
" <td>2023-12-31</td>\n",
|
| 477 |
" <td>NaN</td>\n",
|
| 478 |
" <td>NaN</td>\n",
|
| 479 |
" <td>NaN</td>\n",
|
| 480 |
+
" <td>-0.7</td>\n",
|
| 481 |
+
" <td>-0.9</td>\n",
|
| 482 |
+
" <td>0.6</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 483 |
" </tr>\n",
|
| 484 |
" <tr>\n",
|
| 485 |
" <th>1</th>\n",
|
| 486 |
+
" <td>58002</td>\n",
|
| 487 |
+
" <td>30490</td>\n",
|
| 488 |
+
" <td>544</td>\n",
|
| 489 |
+
" <td>zip</td>\n",
|
| 490 |
" <td>NY</td>\n",
|
| 491 |
+
" <td>Holtsville</td>\n",
|
| 492 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 493 |
+
" <td>Suffolk County</td>\n",
|
| 494 |
+
" <td>2023-12-31</td>\n",
|
| 495 |
+
" <td>NaN</td>\n",
|
| 496 |
" <td>NaN</td>\n",
|
| 497 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 498 |
" <td>-0.7</td>\n",
|
| 499 |
" <td>-0.9</td>\n",
|
| 500 |
" <td>0.6</td>\n",
|
| 501 |
" </tr>\n",
|
| 502 |
" <tr>\n",
|
| 503 |
" <th>2</th>\n",
|
| 504 |
+
" <td>58196</td>\n",
|
| 505 |
+
" <td>7440</td>\n",
|
| 506 |
+
" <td>1001</td>\n",
|
| 507 |
+
" <td>zip</td>\n",
|
| 508 |
+
" <td>MA</td>\n",
|
| 509 |
+
" <td>Agawam</td>\n",
|
| 510 |
+
" <td>Springfield, MA</td>\n",
|
| 511 |
+
" <td>Hampden County</td>\n",
|
| 512 |
" <td>2023-12-31</td>\n",
|
| 513 |
+
" <td>0.4</td>\n",
|
| 514 |
+
" <td>0.9</td>\n",
|
| 515 |
+
" <td>3.2</td>\n",
|
| 516 |
" <td>-0.6</td>\n",
|
| 517 |
+
" <td>0.0</td>\n",
|
| 518 |
+
" <td>3.0</td>\n",
|
| 519 |
" </tr>\n",
|
| 520 |
" <tr>\n",
|
| 521 |
" <th>3</th>\n",
|
| 522 |
+
" <td>58197</td>\n",
|
| 523 |
+
" <td>3911</td>\n",
|
| 524 |
+
" <td>1002</td>\n",
|
| 525 |
+
" <td>zip</td>\n",
|
| 526 |
+
" <td>MA</td>\n",
|
| 527 |
+
" <td>Amherst</td>\n",
|
| 528 |
+
" <td>Springfield, MA</td>\n",
|
| 529 |
+
" <td>Hampshire County</td>\n",
|
| 530 |
" <td>2023-12-31</td>\n",
|
| 531 |
+
" <td>0.2</td>\n",
|
| 532 |
+
" <td>0.7</td>\n",
|
| 533 |
+
" <td>2.7</td>\n",
|
| 534 |
+
" <td>-0.6</td>\n",
|
| 535 |
+
" <td>0.0</td>\n",
|
| 536 |
+
" <td>2.9</td>\n",
|
| 537 |
" </tr>\n",
|
| 538 |
" <tr>\n",
|
| 539 |
" <th>4</th>\n",
|
| 540 |
+
" <td>58198</td>\n",
|
| 541 |
+
" <td>8838</td>\n",
|
| 542 |
+
" <td>1003</td>\n",
|
| 543 |
+
" <td>zip</td>\n",
|
| 544 |
+
" <td>MA</td>\n",
|
| 545 |
+
" <td>Amherst</td>\n",
|
| 546 |
+
" <td>Springfield, MA</td>\n",
|
| 547 |
+
" <td>Hampshire County</td>\n",
|
| 548 |
+
" <td>2023-12-31</td>\n",
|
| 549 |
" <td>NaN</td>\n",
|
| 550 |
" <td>NaN</td>\n",
|
| 551 |
+
" <td>NaN</td>\n",
|
| 552 |
+
" <td>-0.7</td>\n",
|
| 553 |
" <td>0.0</td>\n",
|
| 554 |
+
" <td>3.4</td>\n",
|
|
|
|
|
|
|
|
|
|
| 555 |
" </tr>\n",
|
| 556 |
" <tr>\n",
|
| 557 |
" <th>...</th>\n",
|
|
|
|
| 572 |
" <td>...</td>\n",
|
| 573 |
" </tr>\n",
|
| 574 |
" <tr>\n",
|
| 575 |
+
" <th>31849</th>\n",
|
| 576 |
+
" <td>827279</td>\n",
|
| 577 |
+
" <td>7779</td>\n",
|
| 578 |
+
" <td>72405</td>\n",
|
| 579 |
" <td>zip</td>\n",
|
| 580 |
+
" <td>AR</td>\n",
|
| 581 |
+
" <td>Jonesboro</td>\n",
|
| 582 |
+
" <td>Jonesboro, AR</td>\n",
|
| 583 |
+
" <td>Craighead County</td>\n",
|
|
|
|
| 584 |
" <td>2023-12-31</td>\n",
|
| 585 |
+
" <td>NaN</td>\n",
|
| 586 |
+
" <td>NaN</td>\n",
|
| 587 |
+
" <td>NaN</td>\n",
|
| 588 |
+
" <td>-0.7</td>\n",
|
| 589 |
+
" <td>0.0</td>\n",
|
| 590 |
+
" <td>2.5</td>\n",
|
| 591 |
" </tr>\n",
|
| 592 |
" <tr>\n",
|
| 593 |
+
" <th>31850</th>\n",
|
| 594 |
+
" <td>834213</td>\n",
|
| 595 |
+
" <td>30490</td>\n",
|
| 596 |
+
" <td>11437</td>\n",
|
| 597 |
" <td>zip</td>\n",
|
| 598 |
+
" <td>NY</td>\n",
|
| 599 |
+
" <td>New York</td>\n",
|
| 600 |
+
" <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
|
| 601 |
+
" <td>Queens County</td>\n",
|
|
|
|
| 602 |
" <td>2023-12-31</td>\n",
|
| 603 |
+
" <td>NaN</td>\n",
|
| 604 |
+
" <td>NaN</td>\n",
|
| 605 |
+
" <td>NaN</td>\n",
|
| 606 |
" <td>-0.7</td>\n",
|
| 607 |
+
" <td>-0.9</td>\n",
|
| 608 |
+
" <td>0.6</td>\n",
|
| 609 |
" </tr>\n",
|
| 610 |
" <tr>\n",
|
| 611 |
+
" <th>31851</th>\n",
|
| 612 |
+
" <td>845914</td>\n",
|
| 613 |
+
" <td>6361</td>\n",
|
| 614 |
+
" <td>85288</td>\n",
|
| 615 |
" <td>zip</td>\n",
|
| 616 |
+
" <td>AZ</td>\n",
|
| 617 |
+
" <td>Tempe</td>\n",
|
| 618 |
+
" <td>Phoenix-Mesa-Chandler, AZ</td>\n",
|
| 619 |
+
" <td>Maricopa County</td>\n",
|
|
|
|
| 620 |
" <td>2023-12-31</td>\n",
|
| 621 |
+
" <td>NaN</td>\n",
|
| 622 |
+
" <td>NaN</td>\n",
|
| 623 |
+
" <td>NaN</td>\n",
|
| 624 |
+
" <td>-1.0</td>\n",
|
| 625 |
" <td>0.0</td>\n",
|
| 626 |
+
" <td>4.5</td>\n",
|
| 627 |
" </tr>\n",
|
| 628 |
" <tr>\n",
|
| 629 |
+
" <th>31852</th>\n",
|
| 630 |
+
" <td>847854</td>\n",
|
|
|
|
|
|
|
| 631 |
" <td>39992</td>\n",
|
| 632 |
+
" <td>20598</td>\n",
|
| 633 |
+
" <td>zip</td>\n",
|
| 634 |
+
" <td>VA</td>\n",
|
| 635 |
+
" <td>Arlington</td>\n",
|
| 636 |
+
" <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
|
| 637 |
+
" <td>Arlington County</td>\n",
|
| 638 |
" <td>2023-12-31</td>\n",
|
| 639 |
+
" <td>NaN</td>\n",
|
| 640 |
+
" <td>NaN</td>\n",
|
| 641 |
+
" <td>NaN</td>\n",
|
| 642 |
+
" <td>-0.4</td>\n",
|
| 643 |
+
" <td>0.9</td>\n",
|
| 644 |
+
" <td>1.2</td>\n",
|
| 645 |
" </tr>\n",
|
| 646 |
" <tr>\n",
|
| 647 |
+
" <th>31853</th>\n",
|
| 648 |
+
" <td>847855</td>\n",
|
| 649 |
+
" <td>30490</td>\n",
|
| 650 |
+
" <td>34249</td>\n",
|
| 651 |
" <td>zip</td>\n",
|
| 652 |
+
" <td>FL</td>\n",
|
| 653 |
+
" <td>Sarasota</td>\n",
|
| 654 |
+
" <td>North Port-Sarasota-Bradenton, FL</td>\n",
|
| 655 |
+
" <td>Sarasota County</td>\n",
|
|
|
|
| 656 |
" <td>2023-12-31</td>\n",
|
| 657 |
+
" <td>NaN</td>\n",
|
| 658 |
+
" <td>NaN</td>\n",
|
| 659 |
+
" <td>NaN</td>\n",
|
| 660 |
+
" <td>-0.9</td>\n",
|
| 661 |
+
" <td>-0.1</td>\n",
|
| 662 |
+
" <td>5.4</td>\n",
|
| 663 |
" </tr>\n",
|
| 664 |
" </tbody>\n",
|
| 665 |
"</table>\n",
|
| 666 |
+
"<p>31854 rows × 15 columns</p>\n",
|
| 667 |
"</div>"
|
| 668 |
],
|
| 669 |
"text/plain": [
|
| 670 |
+
" Region ID Size Rank Region RegionType State City \\\n",
|
| 671 |
+
"0 58001 30490 501 zip NY Holtsville \n",
|
| 672 |
+
"1 58002 30490 544 zip NY Holtsville \n",
|
| 673 |
+
"2 58196 7440 1001 zip MA Agawam \n",
|
| 674 |
+
"3 58197 3911 1002 zip MA Amherst \n",
|
| 675 |
+
"4 58198 8838 1003 zip MA Amherst \n",
|
| 676 |
+
"... ... ... ... ... ... ... \n",
|
| 677 |
+
"31849 827279 7779 72405 zip AR Jonesboro \n",
|
| 678 |
+
"31850 834213 30490 11437 zip NY New York \n",
|
| 679 |
+
"31851 845914 6361 85288 zip AZ Tempe \n",
|
| 680 |
+
"31852 847854 39992 20598 zip VA Arlington \n",
|
| 681 |
+
"31853 847855 30490 34249 zip FL Sarasota \n",
|
| 682 |
"\n",
|
| 683 |
+
" Metro County \\\n",
|
| 684 |
+
"0 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
|
| 685 |
+
"1 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
|
| 686 |
+
"2 Springfield, MA Hampden County \n",
|
| 687 |
+
"3 Springfield, MA Hampshire County \n",
|
| 688 |
+
"4 Springfield, MA Hampshire County \n",
|
| 689 |
+
"... ... ... \n",
|
| 690 |
+
"31849 Jonesboro, AR Craighead County \n",
|
| 691 |
+
"31850 New York-Newark-Jersey City, NY-NJ-PA Queens County \n",
|
| 692 |
+
"31851 Phoenix-Mesa-Chandler, AZ Maricopa County \n",
|
| 693 |
+
"31852 Washington-Arlington-Alexandria, DC-VA-MD-WV Arlington County \n",
|
| 694 |
+
"31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
|
| 695 |
"\n",
|
| 696 |
+
" Date Month Over Month % (Smoothed) (Seaonally Adjusted) \\\n",
|
| 697 |
+
"0 2023-12-31 NaN \n",
|
| 698 |
+
"1 2023-12-31 NaN \n",
|
| 699 |
+
"2 2023-12-31 0.4 \n",
|
| 700 |
+
"3 2023-12-31 0.2 \n",
|
| 701 |
+
"4 2023-12-31 NaN \n",
|
| 702 |
+
"... ... ... \n",
|
| 703 |
+
"31849 2023-12-31 NaN \n",
|
| 704 |
+
"31850 2023-12-31 NaN \n",
|
| 705 |
+
"31851 2023-12-31 NaN \n",
|
| 706 |
+
"31852 2023-12-31 NaN \n",
|
| 707 |
+
"31853 2023-12-31 NaN \n",
|
| 708 |
"\n",
|
| 709 |
+
" Quarter Over Quarter % (Smoothed) (Seaonally Adjusted) \\\n",
|
| 710 |
+
"0 NaN \n",
|
| 711 |
+
"1 NaN \n",
|
| 712 |
+
"2 0.9 \n",
|
| 713 |
+
"3 0.7 \n",
|
| 714 |
+
"4 NaN \n",
|
| 715 |
+
"... ... \n",
|
| 716 |
+
"31849 NaN \n",
|
| 717 |
+
"31850 NaN \n",
|
| 718 |
+
"31851 NaN \n",
|
| 719 |
+
"31852 NaN \n",
|
| 720 |
+
"31853 NaN \n",
|
| 721 |
"\n",
|
| 722 |
+
" Year Over Year % (Smoothed) (Seaonally Adjusted) Month Over Month % \\\n",
|
| 723 |
+
"0 NaN -0.7 \n",
|
| 724 |
+
"1 NaN -0.7 \n",
|
| 725 |
+
"2 3.2 -0.6 \n",
|
| 726 |
+
"3 2.7 -0.6 \n",
|
| 727 |
+
"4 NaN -0.7 \n",
|
| 728 |
+
"... ... ... \n",
|
| 729 |
+
"31849 NaN -0.7 \n",
|
| 730 |
+
"31850 NaN -0.7 \n",
|
| 731 |
+
"31851 NaN -1.0 \n",
|
| 732 |
+
"31852 NaN -0.4 \n",
|
| 733 |
+
"31853 NaN -0.9 \n",
|
| 734 |
"\n",
|
| 735 |
+
" Quarter Over Quarter % Year Over Year % \n",
|
| 736 |
+
"0 -0.9 0.6 \n",
|
| 737 |
+
"1 -0.9 0.6 \n",
|
| 738 |
+
"2 0.0 3.0 \n",
|
| 739 |
+
"3 0.0 2.9 \n",
|
| 740 |
+
"4 0.0 3.4 \n",
|
| 741 |
+
"... ... ... \n",
|
| 742 |
+
"31849 0.0 2.5 \n",
|
| 743 |
+
"31850 -0.9 0.6 \n",
|
| 744 |
+
"31851 0.0 4.5 \n",
|
| 745 |
+
"31852 0.9 1.2 \n",
|
| 746 |
+
"31853 -0.1 5.4 \n",
|
| 747 |
+
"\n",
|
| 748 |
+
"[31854 rows x 15 columns]"
|
| 749 |
]
|
| 750 |
},
|
| 751 |
+
"execution_count": 4,
|
| 752 |
"metadata": {},
|
| 753 |
"output_type": "execute_result"
|
| 754 |
}
|
| 755 |
],
|
| 756 |
"source": [
|
| 757 |
+
"# Adjust columns\n",
|
| 758 |
+
"final_df = combined_df\n",
|
| 759 |
+
"final_df = combined_df.drop(\"StateName\", axis=1)\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 760 |
"final_df = final_df.rename(\n",
|
| 761 |
" columns={\n",
|
| 762 |
" \"CountyName\": \"County\",\n",
|
processors/home_values.ipynb
CHANGED
|
@@ -2,19 +2,24 @@
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
-
"execution_count":
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"cell_type": "code",
|
| 17 |
-
"execution_count":
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
|
@@ -27,7 +32,7 @@
|
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"cell_type": "code",
|
| 30 |
-
"execution_count":
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
@@ -122,15 +127,8 @@
|
|
| 122 |
" <th>Home Type</th>\n",
|
| 123 |
" <th>Date</th>\n",
|
| 124 |
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 125 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1</th>\n",
|
| 126 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2</th>\n",
|
| 127 |
" <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 128 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4</th>\n",
|
| 129 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5</th>\n",
|
| 130 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6</th>\n",
|
| 131 |
" <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 132 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
|
| 133 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
|
| 134 |
" </tr>\n",
|
| 135 |
" </thead>\n",
|
| 136 |
" <tbody>\n",
|
|
@@ -141,19 +139,12 @@
|
|
| 141 |
" <td>Alaska</td>\n",
|
| 142 |
" <td>state</td>\n",
|
| 143 |
" <td>nan</td>\n",
|
| 144 |
-
" <td>1-
|
| 145 |
" <td>all homes (SFR/condo)</td>\n",
|
| 146 |
" <td>2000-01-31</td>\n",
|
|
|
|
| 147 |
" <td>NaN</td>\n",
|
| 148 |
" <td>NaN</td>\n",
|
| 149 |
-
" <td>NaN</td>\n",
|
| 150 |
-
" <td>NaN</td>\n",
|
| 151 |
-
" <td>NaN</td>\n",
|
| 152 |
-
" <td>NaN</td>\n",
|
| 153 |
-
" <td>NaN</td>\n",
|
| 154 |
-
" <td>NaN</td>\n",
|
| 155 |
-
" <td>NaN</td>\n",
|
| 156 |
-
" <td>81310.639504</td>\n",
|
| 157 |
" </tr>\n",
|
| 158 |
" <tr>\n",
|
| 159 |
" <th>1</th>\n",
|
|
@@ -162,19 +153,12 @@
|
|
| 162 |
" <td>Alaska</td>\n",
|
| 163 |
" <td>state</td>\n",
|
| 164 |
" <td>nan</td>\n",
|
| 165 |
-
" <td>1-
|
| 166 |
" <td>all homes (SFR/condo)</td>\n",
|
| 167 |
" <td>2000-02-29</td>\n",
|
|
|
|
| 168 |
" <td>NaN</td>\n",
|
| 169 |
" <td>NaN</td>\n",
|
| 170 |
-
" <td>NaN</td>\n",
|
| 171 |
-
" <td>NaN</td>\n",
|
| 172 |
-
" <td>NaN</td>\n",
|
| 173 |
-
" <td>NaN</td>\n",
|
| 174 |
-
" <td>NaN</td>\n",
|
| 175 |
-
" <td>NaN</td>\n",
|
| 176 |
-
" <td>NaN</td>\n",
|
| 177 |
-
" <td>80419.761984</td>\n",
|
| 178 |
" </tr>\n",
|
| 179 |
" <tr>\n",
|
| 180 |
" <th>2</th>\n",
|
|
@@ -183,19 +167,12 @@
|
|
| 183 |
" <td>Alaska</td>\n",
|
| 184 |
" <td>state</td>\n",
|
| 185 |
" <td>nan</td>\n",
|
| 186 |
-
" <td>1-
|
| 187 |
" <td>all homes (SFR/condo)</td>\n",
|
| 188 |
" <td>2000-03-31</td>\n",
|
|
|
|
| 189 |
" <td>NaN</td>\n",
|
| 190 |
" <td>NaN</td>\n",
|
| 191 |
-
" <td>NaN</td>\n",
|
| 192 |
-
" <td>NaN</td>\n",
|
| 193 |
-
" <td>NaN</td>\n",
|
| 194 |
-
" <td>NaN</td>\n",
|
| 195 |
-
" <td>NaN</td>\n",
|
| 196 |
-
" <td>NaN</td>\n",
|
| 197 |
-
" <td>NaN</td>\n",
|
| 198 |
-
" <td>80480.449461</td>\n",
|
| 199 |
" </tr>\n",
|
| 200 |
" <tr>\n",
|
| 201 |
" <th>3</th>\n",
|
|
@@ -204,19 +181,12 @@
|
|
| 204 |
" <td>Alaska</td>\n",
|
| 205 |
" <td>state</td>\n",
|
| 206 |
" <td>nan</td>\n",
|
| 207 |
-
" <td>1-
|
| 208 |
" <td>all homes (SFR/condo)</td>\n",
|
| 209 |
" <td>2000-04-30</td>\n",
|
|
|
|
| 210 |
" <td>NaN</td>\n",
|
| 211 |
" <td>NaN</td>\n",
|
| 212 |
-
" <td>NaN</td>\n",
|
| 213 |
-
" <td>NaN</td>\n",
|
| 214 |
-
" <td>NaN</td>\n",
|
| 215 |
-
" <td>NaN</td>\n",
|
| 216 |
-
" <td>NaN</td>\n",
|
| 217 |
-
" <td>NaN</td>\n",
|
| 218 |
-
" <td>NaN</td>\n",
|
| 219 |
-
" <td>79799.206525</td>\n",
|
| 220 |
" </tr>\n",
|
| 221 |
" <tr>\n",
|
| 222 |
" <th>4</th>\n",
|
|
@@ -225,19 +195,12 @@
|
|
| 225 |
" <td>Alaska</td>\n",
|
| 226 |
" <td>state</td>\n",
|
| 227 |
" <td>nan</td>\n",
|
| 228 |
-
" <td>1-
|
| 229 |
" <td>all homes (SFR/condo)</td>\n",
|
| 230 |
" <td>2000-05-31</td>\n",
|
|
|
|
| 231 |
" <td>NaN</td>\n",
|
| 232 |
" <td>NaN</td>\n",
|
| 233 |
-
" <td>NaN</td>\n",
|
| 234 |
-
" <td>NaN</td>\n",
|
| 235 |
-
" <td>NaN</td>\n",
|
| 236 |
-
" <td>NaN</td>\n",
|
| 237 |
-
" <td>NaN</td>\n",
|
| 238 |
-
" <td>NaN</td>\n",
|
| 239 |
-
" <td>NaN</td>\n",
|
| 240 |
-
" <td>79666.469861</td>\n",
|
| 241 |
" </tr>\n",
|
| 242 |
" <tr>\n",
|
| 243 |
" <th>...</th>\n",
|
|
@@ -252,13 +215,6 @@
|
|
| 252 |
" <td>...</td>\n",
|
| 253 |
" <td>...</td>\n",
|
| 254 |
" <td>...</td>\n",
|
| 255 |
-
" <td>...</td>\n",
|
| 256 |
-
" <td>...</td>\n",
|
| 257 |
-
" <td>...</td>\n",
|
| 258 |
-
" <td>...</td>\n",
|
| 259 |
-
" <td>...</td>\n",
|
| 260 |
-
" <td>...</td>\n",
|
| 261 |
-
" <td>...</td>\n",
|
| 262 |
" </tr>\n",
|
| 263 |
" <tr>\n",
|
| 264 |
" <th>117907</th>\n",
|
|
@@ -270,16 +226,9 @@
|
|
| 270 |
" <td>All Bedrooms</td>\n",
|
| 271 |
" <td>condo</td>\n",
|
| 272 |
" <td>2023-09-30</td>\n",
|
| 273 |
-
" <td>NaN</td>\n",
|
| 274 |
-
" <td>NaN</td>\n",
|
| 275 |
-
" <td>NaN</td>\n",
|
| 276 |
-
" <td>NaN</td>\n",
|
| 277 |
-
" <td>NaN</td>\n",
|
| 278 |
" <td>486974.735908</td>\n",
|
| 279 |
" <td>NaN</td>\n",
|
| 280 |
" <td>NaN</td>\n",
|
| 281 |
-
" <td>NaN</td>\n",
|
| 282 |
-
" <td>NaN</td>\n",
|
| 283 |
" </tr>\n",
|
| 284 |
" <tr>\n",
|
| 285 |
" <th>117908</th>\n",
|
|
@@ -291,16 +240,9 @@
|
|
| 291 |
" <td>All Bedrooms</td>\n",
|
| 292 |
" <td>condo</td>\n",
|
| 293 |
" <td>2023-10-31</td>\n",
|
| 294 |
-
" <td>NaN</td>\n",
|
| 295 |
-
" <td>NaN</td>\n",
|
| 296 |
-
" <td>NaN</td>\n",
|
| 297 |
-
" <td>NaN</td>\n",
|
| 298 |
-
" <td>NaN</td>\n",
|
| 299 |
" <td>485847.539614</td>\n",
|
| 300 |
" <td>NaN</td>\n",
|
| 301 |
" <td>NaN</td>\n",
|
| 302 |
-
" <td>NaN</td>\n",
|
| 303 |
-
" <td>NaN</td>\n",
|
| 304 |
" </tr>\n",
|
| 305 |
" <tr>\n",
|
| 306 |
" <th>117909</th>\n",
|
|
@@ -312,16 +254,9 @@
|
|
| 312 |
" <td>All Bedrooms</td>\n",
|
| 313 |
" <td>condo</td>\n",
|
| 314 |
" <td>2023-11-30</td>\n",
|
| 315 |
-
" <td>NaN</td>\n",
|
| 316 |
-
" <td>NaN</td>\n",
|
| 317 |
-
" <td>NaN</td>\n",
|
| 318 |
-
" <td>NaN</td>\n",
|
| 319 |
-
" <td>NaN</td>\n",
|
| 320 |
" <td>484223.885775</td>\n",
|
| 321 |
" <td>NaN</td>\n",
|
| 322 |
" <td>NaN</td>\n",
|
| 323 |
-
" <td>NaN</td>\n",
|
| 324 |
-
" <td>NaN</td>\n",
|
| 325 |
" </tr>\n",
|
| 326 |
" <tr>\n",
|
| 327 |
" <th>117910</th>\n",
|
|
@@ -333,16 +268,9 @@
|
|
| 333 |
" <td>All Bedrooms</td>\n",
|
| 334 |
" <td>condo</td>\n",
|
| 335 |
" <td>2023-12-31</td>\n",
|
| 336 |
-
" <td>NaN</td>\n",
|
| 337 |
-
" <td>NaN</td>\n",
|
| 338 |
-
" <td>NaN</td>\n",
|
| 339 |
-
" <td>NaN</td>\n",
|
| 340 |
-
" <td>NaN</td>\n",
|
| 341 |
" <td>481522.403338</td>\n",
|
| 342 |
" <td>NaN</td>\n",
|
| 343 |
" <td>NaN</td>\n",
|
| 344 |
-
" <td>NaN</td>\n",
|
| 345 |
-
" <td>NaN</td>\n",
|
| 346 |
" </tr>\n",
|
| 347 |
" <tr>\n",
|
| 348 |
" <th>117911</th>\n",
|
|
@@ -354,29 +282,22 @@
|
|
| 354 |
" <td>All Bedrooms</td>\n",
|
| 355 |
" <td>condo</td>\n",
|
| 356 |
" <td>2024-01-31</td>\n",
|
| 357 |
-
" <td>NaN</td>\n",
|
| 358 |
-
" <td>NaN</td>\n",
|
| 359 |
-
" <td>NaN</td>\n",
|
| 360 |
-
" <td>NaN</td>\n",
|
| 361 |
-
" <td>NaN</td>\n",
|
| 362 |
" <td>481181.718200</td>\n",
|
| 363 |
" <td>NaN</td>\n",
|
| 364 |
" <td>NaN</td>\n",
|
| 365 |
-
" <td>NaN</td>\n",
|
| 366 |
-
" <td>NaN</td>\n",
|
| 367 |
" </tr>\n",
|
| 368 |
" </tbody>\n",
|
| 369 |
"</table>\n",
|
| 370 |
-
"<p>117912 rows ×
|
| 371 |
"</div>"
|
| 372 |
],
|
| 373 |
"text/plain": [
|
| 374 |
" RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
|
| 375 |
-
"0 3 48 Alaska state nan
|
| 376 |
-
"1 3 48 Alaska state nan
|
| 377 |
-
"2 3 48 Alaska state nan
|
| 378 |
-
"3 3 48 Alaska state nan
|
| 379 |
-
"4 3 48 Alaska state nan
|
| 380 |
"... ... ... ... ... ... ... \n",
|
| 381 |
"117907 62 51 Wyoming state nan All Bedrooms \n",
|
| 382 |
"117908 62 51 Wyoming state nan All Bedrooms \n",
|
|
@@ -398,43 +319,17 @@
|
|
| 398 |
"117911 condo 2024-01-31 \n",
|
| 399 |
"\n",
|
| 400 |
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 401 |
-
"0
|
| 402 |
-
"1
|
| 403 |
-
"2
|
| 404 |
-
"3
|
| 405 |
-
"4
|
| 406 |
"... ... \n",
|
| 407 |
-
"117907
|
| 408 |
-
"117908
|
| 409 |
-
"117909
|
| 410 |
-
"117910
|
| 411 |
-
"117911
|
| 412 |
-
"\n",
|
| 413 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
|
| 414 |
-
"0 NaN \n",
|
| 415 |
-
"1 NaN \n",
|
| 416 |
-
"2 NaN \n",
|
| 417 |
-
"3 NaN \n",
|
| 418 |
-
"4 NaN \n",
|
| 419 |
-
"... ... \n",
|
| 420 |
-
"117907 NaN \n",
|
| 421 |
-
"117908 NaN \n",
|
| 422 |
-
"117909 NaN \n",
|
| 423 |
-
"117910 NaN \n",
|
| 424 |
-
"117911 NaN \n",
|
| 425 |
-
"\n",
|
| 426 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
|
| 427 |
-
"0 NaN \n",
|
| 428 |
-
"1 NaN \n",
|
| 429 |
-
"2 NaN \n",
|
| 430 |
-
"3 NaN \n",
|
| 431 |
-
"4 NaN \n",
|
| 432 |
-
"... ... \n",
|
| 433 |
-
"117907 NaN \n",
|
| 434 |
-
"117908 NaN \n",
|
| 435 |
-
"117909 NaN \n",
|
| 436 |
-
"117910 NaN \n",
|
| 437 |
-
"117911 NaN \n",
|
| 438 |
"\n",
|
| 439 |
" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 440 |
"0 NaN \n",
|
|
@@ -449,97 +344,37 @@
|
|
| 449 |
"117910 NaN \n",
|
| 450 |
"117911 NaN \n",
|
| 451 |
"\n",
|
| 452 |
-
"
|
| 453 |
-
"0
|
| 454 |
-
"1
|
| 455 |
-
"2
|
| 456 |
-
"3
|
| 457 |
-
"4
|
| 458 |
-
"...
|
| 459 |
-
"117907
|
| 460 |
-
"117908
|
| 461 |
-
"117909
|
| 462 |
-
"117910
|
| 463 |
-
"117911
|
| 464 |
-
"\n",
|
| 465 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
|
| 466 |
-
"0 NaN \n",
|
| 467 |
-
"1 NaN \n",
|
| 468 |
-
"2 NaN \n",
|
| 469 |
-
"3 NaN \n",
|
| 470 |
-
"4 NaN \n",
|
| 471 |
-
"... ... \n",
|
| 472 |
-
"117907 486974.735908 \n",
|
| 473 |
-
"117908 485847.539614 \n",
|
| 474 |
-
"117909 484223.885775 \n",
|
| 475 |
-
"117910 481522.403338 \n",
|
| 476 |
-
"117911 481181.718200 \n",
|
| 477 |
-
"\n",
|
| 478 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6 \\\n",
|
| 479 |
-
"0 NaN \n",
|
| 480 |
-
"1 NaN \n",
|
| 481 |
-
"2 NaN \n",
|
| 482 |
-
"3 NaN \n",
|
| 483 |
-
"4 NaN \n",
|
| 484 |
-
"... ... \n",
|
| 485 |
-
"117907 NaN \n",
|
| 486 |
-
"117908 NaN \n",
|
| 487 |
-
"117909 NaN \n",
|
| 488 |
-
"117910 NaN \n",
|
| 489 |
-
"117911 NaN \n",
|
| 490 |
-
"\n",
|
| 491 |
-
" Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 492 |
-
"0 NaN \n",
|
| 493 |
-
"1 NaN \n",
|
| 494 |
-
"2 NaN \n",
|
| 495 |
-
"3 NaN \n",
|
| 496 |
-
"4 NaN \n",
|
| 497 |
-
"... ... \n",
|
| 498 |
-
"117907 NaN \n",
|
| 499 |
-
"117908 NaN \n",
|
| 500 |
-
"117909 NaN \n",
|
| 501 |
-
"117910 NaN \n",
|
| 502 |
-
"117911 NaN \n",
|
| 503 |
"\n",
|
| 504 |
-
"
|
| 505 |
-
"0 NaN \n",
|
| 506 |
-
"1 NaN \n",
|
| 507 |
-
"2 NaN \n",
|
| 508 |
-
"3 NaN \n",
|
| 509 |
-
"4 NaN \n",
|
| 510 |
-
"... ... \n",
|
| 511 |
-
"117907 NaN \n",
|
| 512 |
-
"117908 NaN \n",
|
| 513 |
-
"117909 NaN \n",
|
| 514 |
-
"117910 NaN \n",
|
| 515 |
-
"117911 NaN \n",
|
| 516 |
-
"\n",
|
| 517 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
|
| 518 |
-
"0 81310.639504 \n",
|
| 519 |
-
"1 80419.761984 \n",
|
| 520 |
-
"2 80480.449461 \n",
|
| 521 |
-
"3 79799.206525 \n",
|
| 522 |
-
"4 79666.469861 \n",
|
| 523 |
-
"... ... \n",
|
| 524 |
-
"117907 NaN \n",
|
| 525 |
-
"117908 NaN \n",
|
| 526 |
-
"117909 NaN \n",
|
| 527 |
-
"117910 NaN \n",
|
| 528 |
-
"117911 NaN \n",
|
| 529 |
-
"\n",
|
| 530 |
-
"[117912 rows x 18 columns]"
|
| 531 |
]
|
| 532 |
},
|
| 533 |
-
"execution_count":
|
| 534 |
"metadata": {},
|
| 535 |
"output_type": "execute_result"
|
| 536 |
}
|
| 537 |
],
|
| 538 |
"source": [
|
| 539 |
-
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 540 |
-
"\n",
|
| 541 |
"data_frames = []\n",
|
| 542 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 543 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 544 |
" if filename.endswith(\".csv\"):\n",
|
| 545 |
" print(\"processing \" + filename)\n",
|
|
@@ -566,59 +401,23 @@
|
|
| 566 |
" continue\n",
|
| 567 |
"\n",
|
| 568 |
" if \"City\" in filename:\n",
|
| 569 |
-
" exclude_columns = [\n",
|
| 570 |
-
" \"RegionID\",\n",
|
| 571 |
-
" \"SizeRank\",\n",
|
| 572 |
-
" \"RegionName\",\n",
|
| 573 |
-
" \"RegionType\",\n",
|
| 574 |
-
" \"StateName\",\n",
|
| 575 |
-
" \"Bedroom Count\",\n",
|
| 576 |
-
" \"Home Type\",\n",
|
| 577 |
-
" # City Specific\n",
|
| 578 |
-
" \"State\",\n",
|
| 579 |
-
" \"Metro\",\n",
|
| 580 |
-
" \"CountyName\",\n",
|
| 581 |
-
" ]\n",
|
| 582 |
" elif \"Zip\" in filename:\n",
|
| 583 |
-
" exclude_columns = [\n",
|
| 584 |
-
" \"RegionID\",\n",
|
| 585 |
-
" \"SizeRank\",\n",
|
| 586 |
-
" \"RegionName\",\n",
|
| 587 |
-
" \"RegionType\",\n",
|
| 588 |
-
" \"StateName\",\n",
|
| 589 |
-
" \"Bedroom Count\",\n",
|
| 590 |
-
" \"Home Type\",\n",
|
| 591 |
-
" # Zip Specific\n",
|
| 592 |
" \"State\",\n",
|
| 593 |
" \"City\",\n",
|
| 594 |
" \"Metro\",\n",
|
| 595 |
" \"CountyName\",\n",
|
| 596 |
" ]\n",
|
| 597 |
" elif \"County\" in filename:\n",
|
| 598 |
-
" exclude_columns = [\n",
|
| 599 |
-
" \"RegionID\",\n",
|
| 600 |
-
" \"SizeRank\",\n",
|
| 601 |
-
" \"RegionName\",\n",
|
| 602 |
-
" \"RegionType\",\n",
|
| 603 |
-
" \"StateName\",\n",
|
| 604 |
-
" \"Bedroom Count\",\n",
|
| 605 |
-
" \"Home Type\",\n",
|
| 606 |
-
" # County Specific\n",
|
| 607 |
" \"State\",\n",
|
| 608 |
" \"Metro\",\n",
|
| 609 |
" \"StateCodeFIPS\",\n",
|
| 610 |
" \"MunicipalCodeFIPS\",\n",
|
| 611 |
" ]\n",
|
| 612 |
" elif \"Neighborhood\" in filename:\n",
|
| 613 |
-
" exclude_columns = [\n",
|
| 614 |
-
" \"RegionID\",\n",
|
| 615 |
-
" \"SizeRank\",\n",
|
| 616 |
-
" \"RegionName\",\n",
|
| 617 |
-
" \"RegionType\",\n",
|
| 618 |
-
" \"StateName\",\n",
|
| 619 |
-
" \"Bedroom Count\",\n",
|
| 620 |
-
" \"Home Type\",\n",
|
| 621 |
-
" # Neighborhood Specific\n",
|
| 622 |
" \"State\",\n",
|
| 623 |
" \"City\",\n",
|
| 624 |
" \"Metro\",\n",
|
|
@@ -626,15 +425,15 @@
|
|
| 626 |
" ]\n",
|
| 627 |
"\n",
|
| 628 |
" if \"_bdrmcnt_1_\" in filename:\n",
|
| 629 |
-
" cur_df[\"Bedroom Count\"] = \"1-
|
| 630 |
" elif \"_bdrmcnt_2_\" in filename:\n",
|
| 631 |
" cur_df[\"Bedroom Count\"] = \"2-Bedrooms\"\n",
|
| 632 |
" elif \"_bdrmcnt_3_\" in filename:\n",
|
| 633 |
" cur_df[\"Bedroom Count\"] = \"3-Bedrooms\"\n",
|
| 634 |
" elif \"_bdrmcnt_4_\" in filename:\n",
|
| 635 |
-
" cur_df[\"Bedroom Count\"] = \"4
|
| 636 |
" elif \"_bdrmcnt_5_\" in filename:\n",
|
| 637 |
-
" cur_df[\"Bedroom Count\"] = \"5
|
| 638 |
" else:\n",
|
| 639 |
" cur_df[\"Bedroom Count\"] = \"All Bedrooms\"\n",
|
| 640 |
"\n",
|
|
@@ -645,73 +444,12 @@
|
|
| 645 |
" elif \"_uc_condo_\" in filename:\n",
|
| 646 |
" cur_df[\"Home Type\"] = \"condo\"\n",
|
| 647 |
"\n",
|
| 648 |
-
" # Identify columns to pivot\n",
|
| 649 |
-
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 650 |
-
"\n",
|
| 651 |
-
" smoothed = \"_sm_\" in filename\n",
|
| 652 |
-
" seasonally_adjusted = \"_sa_\" in filename\n",
|
| 653 |
-
"\n",
|
| 654 |
-
" if \"_tier_0.33_0.67_\" in filename:\n",
|
| 655 |
-
" col_name = \"Mid Tier ZHVI\"\n",
|
| 656 |
-
" if smoothed:\n",
|
| 657 |
-
" col_name += \" (Smoothed)\"\n",
|
| 658 |
-
" if seasonally_adjusted:\n",
|
| 659 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 660 |
-
"\n",
|
| 661 |
-
" cur_df = pd.melt(\n",
|
| 662 |
-
" cur_df,\n",
|
| 663 |
-
" id_vars=exclude_columns,\n",
|
| 664 |
-
" value_vars=columns_to_pivot,\n",
|
| 665 |
-
" var_name=\"Date\",\n",
|
| 666 |
-
" value_name=col_name,\n",
|
| 667 |
-
" )\n",
|
| 668 |
-
" elif \"_tier_0.0_0.33_\" in filename:\n",
|
| 669 |
-
" col_name = \"Bottom Tier ZHVI\"\n",
|
| 670 |
-
" if smoothed:\n",
|
| 671 |
-
" col_name += \" (Smoothed)\"\n",
|
| 672 |
-
" if seasonally_adjusted:\n",
|
| 673 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 674 |
-
"\n",
|
| 675 |
-
" cur_df = pd.melt(\n",
|
| 676 |
-
" cur_df,\n",
|
| 677 |
-
" id_vars=exclude_columns,\n",
|
| 678 |
-
" value_vars=columns_to_pivot,\n",
|
| 679 |
-
" var_name=\"Date\",\n",
|
| 680 |
-
" value_name=col_name,\n",
|
| 681 |
-
" )\n",
|
| 682 |
-
" elif \"_tier_0.67_1.0_\" in filename:\n",
|
| 683 |
-
" col_name = \"Top Tier ZHVI\"\n",
|
| 684 |
-
" if smoothed:\n",
|
| 685 |
-
" col_name += \" (Smoothed)\"\n",
|
| 686 |
-
" if seasonally_adjusted:\n",
|
| 687 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 688 |
-
"\n",
|
| 689 |
-
" cur_df = pd.melt(\n",
|
| 690 |
-
" cur_df,\n",
|
| 691 |
-
" id_vars=exclude_columns,\n",
|
| 692 |
-
" value_vars=columns_to_pivot,\n",
|
| 693 |
-
" var_name=\"Date\",\n",
|
| 694 |
-
" value_name=col_name,\n",
|
| 695 |
-
" )\n",
|
| 696 |
-
" else:\n",
|
| 697 |
-
" col_name = \"ZHVI\"\n",
|
| 698 |
-
" if smoothed:\n",
|
| 699 |
-
" col_name += \" (Smoothed)\"\n",
|
| 700 |
-
" if seasonally_adjusted:\n",
|
| 701 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 702 |
-
"\n",
|
| 703 |
-
" cur_df = pd.melt(\n",
|
| 704 |
-
" cur_df,\n",
|
| 705 |
-
" id_vars=exclude_columns,\n",
|
| 706 |
-
" value_vars=columns_to_pivot,\n",
|
| 707 |
-
" var_name=\"Date\",\n",
|
| 708 |
-
" value_name=col_name,\n",
|
| 709 |
-
" )\n",
|
| 710 |
-
"\n",
|
| 711 |
" cur_df[\"StateName\"] = cur_df[\"StateName\"].astype(str)\n",
|
| 712 |
" cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
|
| 713 |
"\n",
|
| 714 |
-
" data_frames
|
|
|
|
|
|
|
| 715 |
"\n",
|
| 716 |
"\n",
|
| 717 |
"combined_df = get_combined_df(\n",
|
|
@@ -727,323 +465,8 @@
|
|
| 727 |
" \"Date\",\n",
|
| 728 |
" ],\n",
|
| 729 |
")\n",
|
| 730 |
-
"combined_df"
|
| 731 |
-
]
|
| 732 |
-
},
|
| 733 |
-
{
|
| 734 |
-
"cell_type": "code",
|
| 735 |
-
"execution_count": 4,
|
| 736 |
-
"metadata": {},
|
| 737 |
-
"outputs": [
|
| 738 |
-
{
|
| 739 |
-
"data": {
|
| 740 |
-
"text/html": [
|
| 741 |
-
"<div>\n",
|
| 742 |
-
"<style scoped>\n",
|
| 743 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 744 |
-
" vertical-align: middle;\n",
|
| 745 |
-
" }\n",
|
| 746 |
-
"\n",
|
| 747 |
-
" .dataframe tbody tr th {\n",
|
| 748 |
-
" vertical-align: top;\n",
|
| 749 |
-
" }\n",
|
| 750 |
-
"\n",
|
| 751 |
-
" .dataframe thead th {\n",
|
| 752 |
-
" text-align: right;\n",
|
| 753 |
-
" }\n",
|
| 754 |
-
"</style>\n",
|
| 755 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 756 |
-
" <thead>\n",
|
| 757 |
-
" <tr style=\"text-align: right;\">\n",
|
| 758 |
-
" <th></th>\n",
|
| 759 |
-
" <th>RegionID</th>\n",
|
| 760 |
-
" <th>SizeRank</th>\n",
|
| 761 |
-
" <th>RegionName</th>\n",
|
| 762 |
-
" <th>RegionType</th>\n",
|
| 763 |
-
" <th>StateName</th>\n",
|
| 764 |
-
" <th>Bedroom Count</th>\n",
|
| 765 |
-
" <th>Home Type</th>\n",
|
| 766 |
-
" <th>Date</th>\n",
|
| 767 |
-
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 768 |
-
" <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 769 |
-
" <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 770 |
-
" <th>ZHVI</th>\n",
|
| 771 |
-
" <th>Mid Tier ZHVI</th>\n",
|
| 772 |
-
" </tr>\n",
|
| 773 |
-
" </thead>\n",
|
| 774 |
-
" <tbody>\n",
|
| 775 |
-
" <tr>\n",
|
| 776 |
-
" <th>0</th>\n",
|
| 777 |
-
" <td>3</td>\n",
|
| 778 |
-
" <td>48</td>\n",
|
| 779 |
-
" <td>Alaska</td>\n",
|
| 780 |
-
" <td>state</td>\n",
|
| 781 |
-
" <td>nan</td>\n",
|
| 782 |
-
" <td>1-Bedrooms</td>\n",
|
| 783 |
-
" <td>all homes (SFR/condo)</td>\n",
|
| 784 |
-
" <td>2000-01-31</td>\n",
|
| 785 |
-
" <td>NaN</td>\n",
|
| 786 |
-
" <td>NaN</td>\n",
|
| 787 |
-
" <td>NaN</td>\n",
|
| 788 |
-
" <td>81310.639504</td>\n",
|
| 789 |
-
" <td>81310.639504</td>\n",
|
| 790 |
-
" </tr>\n",
|
| 791 |
-
" <tr>\n",
|
| 792 |
-
" <th>1</th>\n",
|
| 793 |
-
" <td>3</td>\n",
|
| 794 |
-
" <td>48</td>\n",
|
| 795 |
-
" <td>Alaska</td>\n",
|
| 796 |
-
" <td>state</td>\n",
|
| 797 |
-
" <td>nan</td>\n",
|
| 798 |
-
" <td>1-Bedrooms</td>\n",
|
| 799 |
-
" <td>all homes (SFR/condo)</td>\n",
|
| 800 |
-
" <td>2000-02-29</td>\n",
|
| 801 |
-
" <td>NaN</td>\n",
|
| 802 |
-
" <td>NaN</td>\n",
|
| 803 |
-
" <td>NaN</td>\n",
|
| 804 |
-
" <td>80419.761984</td>\n",
|
| 805 |
-
" <td>80419.761984</td>\n",
|
| 806 |
-
" </tr>\n",
|
| 807 |
-
" <tr>\n",
|
| 808 |
-
" <th>2</th>\n",
|
| 809 |
-
" <td>3</td>\n",
|
| 810 |
-
" <td>48</td>\n",
|
| 811 |
-
" <td>Alaska</td>\n",
|
| 812 |
-
" <td>state</td>\n",
|
| 813 |
-
" <td>nan</td>\n",
|
| 814 |
-
" <td>1-Bedrooms</td>\n",
|
| 815 |
-
" <td>all homes (SFR/condo)</td>\n",
|
| 816 |
-
" <td>2000-03-31</td>\n",
|
| 817 |
-
" <td>NaN</td>\n",
|
| 818 |
-
" <td>NaN</td>\n",
|
| 819 |
-
" <td>NaN</td>\n",
|
| 820 |
-
" <td>80480.449461</td>\n",
|
| 821 |
-
" <td>80480.449461</td>\n",
|
| 822 |
-
" </tr>\n",
|
| 823 |
-
" <tr>\n",
|
| 824 |
-
" <th>3</th>\n",
|
| 825 |
-
" <td>3</td>\n",
|
| 826 |
-
" <td>48</td>\n",
|
| 827 |
-
" <td>Alaska</td>\n",
|
| 828 |
-
" <td>state</td>\n",
|
| 829 |
-
" <td>nan</td>\n",
|
| 830 |
-
" <td>1-Bedrooms</td>\n",
|
| 831 |
-
" <td>all homes (SFR/condo)</td>\n",
|
| 832 |
-
" <td>2000-04-30</td>\n",
|
| 833 |
-
" <td>NaN</td>\n",
|
| 834 |
-
" <td>NaN</td>\n",
|
| 835 |
-
" <td>NaN</td>\n",
|
| 836 |
-
" <td>79799.206525</td>\n",
|
| 837 |
-
" <td>79799.206525</td>\n",
|
| 838 |
-
" </tr>\n",
|
| 839 |
-
" <tr>\n",
|
| 840 |
-
" <th>4</th>\n",
|
| 841 |
-
" <td>3</td>\n",
|
| 842 |
-
" <td>48</td>\n",
|
| 843 |
-
" <td>Alaska</td>\n",
|
| 844 |
-
" <td>state</td>\n",
|
| 845 |
-
" <td>nan</td>\n",
|
| 846 |
-
" <td>1-Bedrooms</td>\n",
|
| 847 |
-
" <td>all homes (SFR/condo)</td>\n",
|
| 848 |
-
" <td>2000-05-31</td>\n",
|
| 849 |
-
" <td>NaN</td>\n",
|
| 850 |
-
" <td>NaN</td>\n",
|
| 851 |
-
" <td>NaN</td>\n",
|
| 852 |
-
" <td>79666.469861</td>\n",
|
| 853 |
-
" <td>79666.469861</td>\n",
|
| 854 |
-
" </tr>\n",
|
| 855 |
-
" <tr>\n",
|
| 856 |
-
" <th>...</th>\n",
|
| 857 |
-
" <td>...</td>\n",
|
| 858 |
-
" <td>...</td>\n",
|
| 859 |
-
" <td>...</td>\n",
|
| 860 |
-
" <td>...</td>\n",
|
| 861 |
-
" <td>...</td>\n",
|
| 862 |
-
" <td>...</td>\n",
|
| 863 |
-
" <td>...</td>\n",
|
| 864 |
-
" <td>...</td>\n",
|
| 865 |
-
" <td>...</td>\n",
|
| 866 |
-
" <td>...</td>\n",
|
| 867 |
-
" <td>...</td>\n",
|
| 868 |
-
" <td>...</td>\n",
|
| 869 |
-
" <td>...</td>\n",
|
| 870 |
-
" </tr>\n",
|
| 871 |
-
" <tr>\n",
|
| 872 |
-
" <th>117907</th>\n",
|
| 873 |
-
" <td>62</td>\n",
|
| 874 |
-
" <td>51</td>\n",
|
| 875 |
-
" <td>Wyoming</td>\n",
|
| 876 |
-
" <td>state</td>\n",
|
| 877 |
-
" <td>nan</td>\n",
|
| 878 |
-
" <td>All Bedrooms</td>\n",
|
| 879 |
-
" <td>condo</td>\n",
|
| 880 |
-
" <td>2023-09-30</td>\n",
|
| 881 |
-
" <td>NaN</td>\n",
|
| 882 |
-
" <td>NaN</td>\n",
|
| 883 |
-
" <td>NaN</td>\n",
|
| 884 |
-
" <td>486974.735908</td>\n",
|
| 885 |
-
" <td>486974.735908</td>\n",
|
| 886 |
-
" </tr>\n",
|
| 887 |
-
" <tr>\n",
|
| 888 |
-
" <th>117908</th>\n",
|
| 889 |
-
" <td>62</td>\n",
|
| 890 |
-
" <td>51</td>\n",
|
| 891 |
-
" <td>Wyoming</td>\n",
|
| 892 |
-
" <td>state</td>\n",
|
| 893 |
-
" <td>nan</td>\n",
|
| 894 |
-
" <td>All Bedrooms</td>\n",
|
| 895 |
-
" <td>condo</td>\n",
|
| 896 |
-
" <td>2023-10-31</td>\n",
|
| 897 |
-
" <td>NaN</td>\n",
|
| 898 |
-
" <td>NaN</td>\n",
|
| 899 |
-
" <td>NaN</td>\n",
|
| 900 |
-
" <td>485847.539614</td>\n",
|
| 901 |
-
" <td>485847.539614</td>\n",
|
| 902 |
-
" </tr>\n",
|
| 903 |
-
" <tr>\n",
|
| 904 |
-
" <th>117909</th>\n",
|
| 905 |
-
" <td>62</td>\n",
|
| 906 |
-
" <td>51</td>\n",
|
| 907 |
-
" <td>Wyoming</td>\n",
|
| 908 |
-
" <td>state</td>\n",
|
| 909 |
-
" <td>nan</td>\n",
|
| 910 |
-
" <td>All Bedrooms</td>\n",
|
| 911 |
-
" <td>condo</td>\n",
|
| 912 |
-
" <td>2023-11-30</td>\n",
|
| 913 |
-
" <td>NaN</td>\n",
|
| 914 |
-
" <td>NaN</td>\n",
|
| 915 |
-
" <td>NaN</td>\n",
|
| 916 |
-
" <td>484223.885775</td>\n",
|
| 917 |
-
" <td>484223.885775</td>\n",
|
| 918 |
-
" </tr>\n",
|
| 919 |
-
" <tr>\n",
|
| 920 |
-
" <th>117910</th>\n",
|
| 921 |
-
" <td>62</td>\n",
|
| 922 |
-
" <td>51</td>\n",
|
| 923 |
-
" <td>Wyoming</td>\n",
|
| 924 |
-
" <td>state</td>\n",
|
| 925 |
-
" <td>nan</td>\n",
|
| 926 |
-
" <td>All Bedrooms</td>\n",
|
| 927 |
-
" <td>condo</td>\n",
|
| 928 |
-
" <td>2023-12-31</td>\n",
|
| 929 |
-
" <td>NaN</td>\n",
|
| 930 |
-
" <td>NaN</td>\n",
|
| 931 |
-
" <td>NaN</td>\n",
|
| 932 |
-
" <td>481522.403338</td>\n",
|
| 933 |
-
" <td>481522.403338</td>\n",
|
| 934 |
-
" </tr>\n",
|
| 935 |
-
" <tr>\n",
|
| 936 |
-
" <th>117911</th>\n",
|
| 937 |
-
" <td>62</td>\n",
|
| 938 |
-
" <td>51</td>\n",
|
| 939 |
-
" <td>Wyoming</td>\n",
|
| 940 |
-
" <td>state</td>\n",
|
| 941 |
-
" <td>nan</td>\n",
|
| 942 |
-
" <td>All Bedrooms</td>\n",
|
| 943 |
-
" <td>condo</td>\n",
|
| 944 |
-
" <td>2024-01-31</td>\n",
|
| 945 |
-
" <td>NaN</td>\n",
|
| 946 |
-
" <td>NaN</td>\n",
|
| 947 |
-
" <td>NaN</td>\n",
|
| 948 |
-
" <td>481181.718200</td>\n",
|
| 949 |
-
" <td>481181.718200</td>\n",
|
| 950 |
-
" </tr>\n",
|
| 951 |
-
" </tbody>\n",
|
| 952 |
-
"</table>\n",
|
| 953 |
-
"<p>117912 rows × 13 columns</p>\n",
|
| 954 |
-
"</div>"
|
| 955 |
-
],
|
| 956 |
-
"text/plain": [
|
| 957 |
-
" RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
|
| 958 |
-
"0 3 48 Alaska state nan 1-Bedrooms \n",
|
| 959 |
-
"1 3 48 Alaska state nan 1-Bedrooms \n",
|
| 960 |
-
"2 3 48 Alaska state nan 1-Bedrooms \n",
|
| 961 |
-
"3 3 48 Alaska state nan 1-Bedrooms \n",
|
| 962 |
-
"4 3 48 Alaska state nan 1-Bedrooms \n",
|
| 963 |
-
"... ... ... ... ... ... ... \n",
|
| 964 |
-
"117907 62 51 Wyoming state nan All Bedrooms \n",
|
| 965 |
-
"117908 62 51 Wyoming state nan All Bedrooms \n",
|
| 966 |
-
"117909 62 51 Wyoming state nan All Bedrooms \n",
|
| 967 |
-
"117910 62 51 Wyoming state nan All Bedrooms \n",
|
| 968 |
-
"117911 62 51 Wyoming state nan All Bedrooms \n",
|
| 969 |
-
"\n",
|
| 970 |
-
" Home Type Date \\\n",
|
| 971 |
-
"0 all homes (SFR/condo) 2000-01-31 \n",
|
| 972 |
-
"1 all homes (SFR/condo) 2000-02-29 \n",
|
| 973 |
-
"2 all homes (SFR/condo) 2000-03-31 \n",
|
| 974 |
-
"3 all homes (SFR/condo) 2000-04-30 \n",
|
| 975 |
-
"4 all homes (SFR/condo) 2000-05-31 \n",
|
| 976 |
-
"... ... ... \n",
|
| 977 |
-
"117907 condo 2023-09-30 \n",
|
| 978 |
-
"117908 condo 2023-10-31 \n",
|
| 979 |
-
"117909 condo 2023-11-30 \n",
|
| 980 |
-
"117910 condo 2023-12-31 \n",
|
| 981 |
-
"117911 condo 2024-01-31 \n",
|
| 982 |
-
"\n",
|
| 983 |
-
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 984 |
-
"0 NaN \n",
|
| 985 |
-
"1 NaN \n",
|
| 986 |
-
"2 NaN \n",
|
| 987 |
-
"3 NaN \n",
|
| 988 |
-
"4 NaN \n",
|
| 989 |
-
"... ... \n",
|
| 990 |
-
"117907 NaN \n",
|
| 991 |
-
"117908 NaN \n",
|
| 992 |
-
"117909 NaN \n",
|
| 993 |
-
"117910 NaN \n",
|
| 994 |
-
"117911 NaN \n",
|
| 995 |
-
"\n",
|
| 996 |
-
" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 997 |
-
"0 NaN \n",
|
| 998 |
-
"1 NaN \n",
|
| 999 |
-
"2 NaN \n",
|
| 1000 |
-
"3 NaN \n",
|
| 1001 |
-
"4 NaN \n",
|
| 1002 |
-
"... ... \n",
|
| 1003 |
-
"117907 NaN \n",
|
| 1004 |
-
"117908 NaN \n",
|
| 1005 |
-
"117909 NaN \n",
|
| 1006 |
-
"117910 NaN \n",
|
| 1007 |
-
"117911 NaN \n",
|
| 1008 |
-
"\n",
|
| 1009 |
-
" Top Tier ZHVI (Smoothed) (Seasonally Adjusted) ZHVI \\\n",
|
| 1010 |
-
"0 NaN 81310.639504 \n",
|
| 1011 |
-
"1 NaN 80419.761984 \n",
|
| 1012 |
-
"2 NaN 80480.449461 \n",
|
| 1013 |
-
"3 NaN 79799.206525 \n",
|
| 1014 |
-
"4 NaN 79666.469861 \n",
|
| 1015 |
-
"... ... ... \n",
|
| 1016 |
-
"117907 NaN 486974.735908 \n",
|
| 1017 |
-
"117908 NaN 485847.539614 \n",
|
| 1018 |
-
"117909 NaN 484223.885775 \n",
|
| 1019 |
-
"117910 NaN 481522.403338 \n",
|
| 1020 |
-
"117911 NaN 481181.718200 \n",
|
| 1021 |
-
"\n",
|
| 1022 |
-
" Mid Tier ZHVI \n",
|
| 1023 |
-
"0 81310.639504 \n",
|
| 1024 |
-
"1 80419.761984 \n",
|
| 1025 |
-
"2 80480.449461 \n",
|
| 1026 |
-
"3 79799.206525 \n",
|
| 1027 |
-
"4 79666.469861 \n",
|
| 1028 |
-
"... ... \n",
|
| 1029 |
-
"117907 486974.735908 \n",
|
| 1030 |
-
"117908 485847.539614 \n",
|
| 1031 |
-
"117909 484223.885775 \n",
|
| 1032 |
-
"117910 481522.403338 \n",
|
| 1033 |
-
"117911 481181.718200 \n",
|
| 1034 |
-
"\n",
|
| 1035 |
-
"[117912 rows x 13 columns]"
|
| 1036 |
-
]
|
| 1037 |
-
},
|
| 1038 |
-
"execution_count": 4,
|
| 1039 |
-
"metadata": {},
|
| 1040 |
-
"output_type": "execute_result"
|
| 1041 |
-
}
|
| 1042 |
-
],
|
| 1043 |
-
"source": [
|
| 1044 |
-
"columns_to_coalesce = [\"ZHVI\", \"Mid Tier ZHVI\", \"Bottom Tier ZHVI\", \"Top Tier ZHVI\"]\n",
|
| 1045 |
"\n",
|
| 1046 |
-
"combined_df = coalesce_columns(combined_df
|
| 1047 |
"\n",
|
| 1048 |
"combined_df"
|
| 1049 |
]
|
|
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import (\n",
|
| 13 |
+
" get_combined_df,\n",
|
| 14 |
+
" coalesce_columns,\n",
|
| 15 |
+
" save_final_df_as_jsonl,\n",
|
| 16 |
+
" handle_slug_column_mappings,\n",
|
| 17 |
+
")"
|
| 18 |
]
|
| 19 |
},
|
| 20 |
{
|
| 21 |
"cell_type": "code",
|
| 22 |
+
"execution_count": 2,
|
| 23 |
"metadata": {},
|
| 24 |
"outputs": [],
|
| 25 |
"source": [
|
|
|
|
| 32 |
},
|
| 33 |
{
|
| 34 |
"cell_type": "code",
|
| 35 |
+
"execution_count": 5,
|
| 36 |
"metadata": {},
|
| 37 |
"outputs": [
|
| 38 |
{
|
|
|
|
| 127 |
" <th>Home Type</th>\n",
|
| 128 |
" <th>Date</th>\n",
|
| 129 |
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
|
|
|
|
|
|
| 130 |
" <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
|
|
|
|
|
|
|
|
|
| 131 |
" <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
|
|
|
|
|
|
| 132 |
" </tr>\n",
|
| 133 |
" </thead>\n",
|
| 134 |
" <tbody>\n",
|
|
|
|
| 139 |
" <td>Alaska</td>\n",
|
| 140 |
" <td>state</td>\n",
|
| 141 |
" <td>nan</td>\n",
|
| 142 |
+
" <td>1-Bedroom</td>\n",
|
| 143 |
" <td>all homes (SFR/condo)</td>\n",
|
| 144 |
" <td>2000-01-31</td>\n",
|
| 145 |
+
" <td>81310.639504</td>\n",
|
| 146 |
" <td>NaN</td>\n",
|
| 147 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 148 |
" </tr>\n",
|
| 149 |
" <tr>\n",
|
| 150 |
" <th>1</th>\n",
|
|
|
|
| 153 |
" <td>Alaska</td>\n",
|
| 154 |
" <td>state</td>\n",
|
| 155 |
" <td>nan</td>\n",
|
| 156 |
+
" <td>1-Bedroom</td>\n",
|
| 157 |
" <td>all homes (SFR/condo)</td>\n",
|
| 158 |
" <td>2000-02-29</td>\n",
|
| 159 |
+
" <td>80419.761984</td>\n",
|
| 160 |
" <td>NaN</td>\n",
|
| 161 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 162 |
" </tr>\n",
|
| 163 |
" <tr>\n",
|
| 164 |
" <th>2</th>\n",
|
|
|
|
| 167 |
" <td>Alaska</td>\n",
|
| 168 |
" <td>state</td>\n",
|
| 169 |
" <td>nan</td>\n",
|
| 170 |
+
" <td>1-Bedroom</td>\n",
|
| 171 |
" <td>all homes (SFR/condo)</td>\n",
|
| 172 |
" <td>2000-03-31</td>\n",
|
| 173 |
+
" <td>80480.449461</td>\n",
|
| 174 |
" <td>NaN</td>\n",
|
| 175 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 176 |
" </tr>\n",
|
| 177 |
" <tr>\n",
|
| 178 |
" <th>3</th>\n",
|
|
|
|
| 181 |
" <td>Alaska</td>\n",
|
| 182 |
" <td>state</td>\n",
|
| 183 |
" <td>nan</td>\n",
|
| 184 |
+
" <td>1-Bedroom</td>\n",
|
| 185 |
" <td>all homes (SFR/condo)</td>\n",
|
| 186 |
" <td>2000-04-30</td>\n",
|
| 187 |
+
" <td>79799.206525</td>\n",
|
| 188 |
" <td>NaN</td>\n",
|
| 189 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 190 |
" </tr>\n",
|
| 191 |
" <tr>\n",
|
| 192 |
" <th>4</th>\n",
|
|
|
|
| 195 |
" <td>Alaska</td>\n",
|
| 196 |
" <td>state</td>\n",
|
| 197 |
" <td>nan</td>\n",
|
| 198 |
+
" <td>1-Bedroom</td>\n",
|
| 199 |
" <td>all homes (SFR/condo)</td>\n",
|
| 200 |
" <td>2000-05-31</td>\n",
|
| 201 |
+
" <td>79666.469861</td>\n",
|
| 202 |
" <td>NaN</td>\n",
|
| 203 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 204 |
" </tr>\n",
|
| 205 |
" <tr>\n",
|
| 206 |
" <th>...</th>\n",
|
|
|
|
| 215 |
" <td>...</td>\n",
|
| 216 |
" <td>...</td>\n",
|
| 217 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 218 |
" </tr>\n",
|
| 219 |
" <tr>\n",
|
| 220 |
" <th>117907</th>\n",
|
|
|
|
| 226 |
" <td>All Bedrooms</td>\n",
|
| 227 |
" <td>condo</td>\n",
|
| 228 |
" <td>2023-09-30</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 229 |
" <td>486974.735908</td>\n",
|
| 230 |
" <td>NaN</td>\n",
|
| 231 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 232 |
" </tr>\n",
|
| 233 |
" <tr>\n",
|
| 234 |
" <th>117908</th>\n",
|
|
|
|
| 240 |
" <td>All Bedrooms</td>\n",
|
| 241 |
" <td>condo</td>\n",
|
| 242 |
" <td>2023-10-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 243 |
" <td>485847.539614</td>\n",
|
| 244 |
" <td>NaN</td>\n",
|
| 245 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 246 |
" </tr>\n",
|
| 247 |
" <tr>\n",
|
| 248 |
" <th>117909</th>\n",
|
|
|
|
| 254 |
" <td>All Bedrooms</td>\n",
|
| 255 |
" <td>condo</td>\n",
|
| 256 |
" <td>2023-11-30</td>\n",
|
|
|
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|
|
|
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|
|
|
|
|
| 257 |
" <td>484223.885775</td>\n",
|
| 258 |
" <td>NaN</td>\n",
|
| 259 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 260 |
" </tr>\n",
|
| 261 |
" <tr>\n",
|
| 262 |
" <th>117910</th>\n",
|
|
|
|
| 268 |
" <td>All Bedrooms</td>\n",
|
| 269 |
" <td>condo</td>\n",
|
| 270 |
" <td>2023-12-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 271 |
" <td>481522.403338</td>\n",
|
| 272 |
" <td>NaN</td>\n",
|
| 273 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 274 |
" </tr>\n",
|
| 275 |
" <tr>\n",
|
| 276 |
" <th>117911</th>\n",
|
|
|
|
| 282 |
" <td>All Bedrooms</td>\n",
|
| 283 |
" <td>condo</td>\n",
|
| 284 |
" <td>2024-01-31</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 285 |
" <td>481181.718200</td>\n",
|
| 286 |
" <td>NaN</td>\n",
|
| 287 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 288 |
" </tr>\n",
|
| 289 |
" </tbody>\n",
|
| 290 |
"</table>\n",
|
| 291 |
+
"<p>117912 rows × 11 columns</p>\n",
|
| 292 |
"</div>"
|
| 293 |
],
|
| 294 |
"text/plain": [
|
| 295 |
" RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
|
| 296 |
+
"0 3 48 Alaska state nan 1-Bedroom \n",
|
| 297 |
+
"1 3 48 Alaska state nan 1-Bedroom \n",
|
| 298 |
+
"2 3 48 Alaska state nan 1-Bedroom \n",
|
| 299 |
+
"3 3 48 Alaska state nan 1-Bedroom \n",
|
| 300 |
+
"4 3 48 Alaska state nan 1-Bedroom \n",
|
| 301 |
"... ... ... ... ... ... ... \n",
|
| 302 |
"117907 62 51 Wyoming state nan All Bedrooms \n",
|
| 303 |
"117908 62 51 Wyoming state nan All Bedrooms \n",
|
|
|
|
| 319 |
"117911 condo 2024-01-31 \n",
|
| 320 |
"\n",
|
| 321 |
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 322 |
+
"0 81310.639504 \n",
|
| 323 |
+
"1 80419.761984 \n",
|
| 324 |
+
"2 80480.449461 \n",
|
| 325 |
+
"3 79799.206525 \n",
|
| 326 |
+
"4 79666.469861 \n",
|
| 327 |
"... ... \n",
|
| 328 |
+
"117907 486974.735908 \n",
|
| 329 |
+
"117908 485847.539614 \n",
|
| 330 |
+
"117909 484223.885775 \n",
|
| 331 |
+
"117910 481522.403338 \n",
|
| 332 |
+
"117911 481181.718200 \n",
|
|
|
|
|
|
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|
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|
| 333 |
"\n",
|
| 334 |
" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 335 |
"0 NaN \n",
|
|
|
|
| 344 |
"117910 NaN \n",
|
| 345 |
"117911 NaN \n",
|
| 346 |
"\n",
|
| 347 |
+
" Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \n",
|
| 348 |
+
"0 NaN \n",
|
| 349 |
+
"1 NaN \n",
|
| 350 |
+
"2 NaN \n",
|
| 351 |
+
"3 NaN \n",
|
| 352 |
+
"4 NaN \n",
|
| 353 |
+
"... ... \n",
|
| 354 |
+
"117907 NaN \n",
|
| 355 |
+
"117908 NaN \n",
|
| 356 |
+
"117909 NaN \n",
|
| 357 |
+
"117910 NaN \n",
|
| 358 |
+
"117911 NaN \n",
|
|
|
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|
| 359 |
"\n",
|
| 360 |
+
"[117912 rows x 11 columns]"
|
|
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|
| 361 |
]
|
| 362 |
},
|
| 363 |
+
"execution_count": 5,
|
| 364 |
"metadata": {},
|
| 365 |
"output_type": "execute_result"
|
| 366 |
}
|
| 367 |
],
|
| 368 |
"source": [
|
|
|
|
|
|
|
| 369 |
"data_frames = []\n",
|
| 370 |
"\n",
|
| 371 |
+
"slug_column_mappings = {\n",
|
| 372 |
+
" \"_tier_0.0_0.33_\": \"Bottom Tier ZHVI\",\n",
|
| 373 |
+
" \"_tier_0.33_0.67_\": \"Mid Tier ZHVI\",\n",
|
| 374 |
+
" \"_tier_0.67_1.0_\": \"Top Tier ZHVI\",\n",
|
| 375 |
+
" \"\": \"ZHVI\",\n",
|
| 376 |
+
"}\n",
|
| 377 |
+
"\n",
|
| 378 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 379 |
" if filename.endswith(\".csv\"):\n",
|
| 380 |
" print(\"processing \" + filename)\n",
|
|
|
|
| 401 |
" continue\n",
|
| 402 |
"\n",
|
| 403 |
" if \"City\" in filename:\n",
|
| 404 |
+
" exclude_columns = exclude_columns + [\"State\", \"Metro\", \"CountyName\"]\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 405 |
" elif \"Zip\" in filename:\n",
|
| 406 |
+
" exclude_columns = exclude_columns + [\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
| 407 |
" \"State\",\n",
|
| 408 |
" \"City\",\n",
|
| 409 |
" \"Metro\",\n",
|
| 410 |
" \"CountyName\",\n",
|
| 411 |
" ]\n",
|
| 412 |
" elif \"County\" in filename:\n",
|
| 413 |
+
" exclude_columns = exclude_columns + [\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
| 414 |
" \"State\",\n",
|
| 415 |
" \"Metro\",\n",
|
| 416 |
" \"StateCodeFIPS\",\n",
|
| 417 |
" \"MunicipalCodeFIPS\",\n",
|
| 418 |
" ]\n",
|
| 419 |
" elif \"Neighborhood\" in filename:\n",
|
| 420 |
+
" exclude_columns = exclude_columns + [\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 421 |
" \"State\",\n",
|
| 422 |
" \"City\",\n",
|
| 423 |
" \"Metro\",\n",
|
|
|
|
| 425 |
" ]\n",
|
| 426 |
"\n",
|
| 427 |
" if \"_bdrmcnt_1_\" in filename:\n",
|
| 428 |
+
" cur_df[\"Bedroom Count\"] = \"1-Bedroom\"\n",
|
| 429 |
" elif \"_bdrmcnt_2_\" in filename:\n",
|
| 430 |
" cur_df[\"Bedroom Count\"] = \"2-Bedrooms\"\n",
|
| 431 |
" elif \"_bdrmcnt_3_\" in filename:\n",
|
| 432 |
" cur_df[\"Bedroom Count\"] = \"3-Bedrooms\"\n",
|
| 433 |
" elif \"_bdrmcnt_4_\" in filename:\n",
|
| 434 |
+
" cur_df[\"Bedroom Count\"] = \"4-Bedrooms\"\n",
|
| 435 |
" elif \"_bdrmcnt_5_\" in filename:\n",
|
| 436 |
+
" cur_df[\"Bedroom Count\"] = \"5+-Bedrooms\"\n",
|
| 437 |
" else:\n",
|
| 438 |
" cur_df[\"Bedroom Count\"] = \"All Bedrooms\"\n",
|
| 439 |
"\n",
|
|
|
|
| 444 |
" elif \"_uc_condo_\" in filename:\n",
|
| 445 |
" cur_df[\"Home Type\"] = \"condo\"\n",
|
| 446 |
"\n",
|
|
|
|
|
|
|
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|
|
| 447 |
" cur_df[\"StateName\"] = cur_df[\"StateName\"].astype(str)\n",
|
| 448 |
" cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
|
| 449 |
"\n",
|
| 450 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 451 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
| 452 |
+
" )\n",
|
| 453 |
"\n",
|
| 454 |
"\n",
|
| 455 |
"combined_df = get_combined_df(\n",
|
|
|
|
| 465 |
" \"Date\",\n",
|
| 466 |
" ],\n",
|
| 467 |
")\n",
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 468 |
"\n",
|
| 469 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
| 470 |
"\n",
|
| 471 |
"combined_df"
|
| 472 |
]
|
processors/new_construction.ipynb
CHANGED
|
@@ -9,7 +9,12 @@
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
|
@@ -263,8 +268,6 @@
|
|
| 263 |
}
|
| 264 |
],
|
| 265 |
"source": [
|
| 266 |
-
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 267 |
-
"\n",
|
| 268 |
"exclude_columns = [\n",
|
| 269 |
" \"RegionID\",\n",
|
| 270 |
" \"SizeRank\",\n",
|
|
@@ -274,6 +277,12 @@
|
|
| 274 |
" \"Home Type\",\n",
|
| 275 |
"]\n",
|
| 276 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 277 |
"data_frames = []\n",
|
| 278 |
"\n",
|
| 279 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
@@ -288,38 +297,9 @@
|
|
| 288 |
" elif \"condo\" in filename:\n",
|
| 289 |
" cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
|
| 290 |
"\n",
|
| 291 |
-
"
|
| 292 |
-
"
|
| 293 |
-
"\n",
|
| 294 |
-
" if \"median_sale_price_per_sqft\" in filename:\n",
|
| 295 |
-
" cur_df = pd.melt(\n",
|
| 296 |
-
" cur_df,\n",
|
| 297 |
-
" id_vars=exclude_columns,\n",
|
| 298 |
-
" value_vars=columns_to_pivot,\n",
|
| 299 |
-
" var_name=\"Date\",\n",
|
| 300 |
-
" value_name=\"Median Sale Price per Sqft\",\n",
|
| 301 |
-
" )\n",
|
| 302 |
-
" data_frames.append(cur_df)\n",
|
| 303 |
-
"\n",
|
| 304 |
-
" elif \"median_sale_price\" in filename:\n",
|
| 305 |
-
" cur_df = pd.melt(\n",
|
| 306 |
-
" cur_df,\n",
|
| 307 |
-
" id_vars=exclude_columns,\n",
|
| 308 |
-
" value_vars=columns_to_pivot,\n",
|
| 309 |
-
" var_name=\"Date\",\n",
|
| 310 |
-
" value_name=\"Median Sale Price\",\n",
|
| 311 |
-
" )\n",
|
| 312 |
-
" data_frames.append(cur_df)\n",
|
| 313 |
-
"\n",
|
| 314 |
-
" elif \"sales_count\" in filename:\n",
|
| 315 |
-
" cur_df = pd.melt(\n",
|
| 316 |
-
" cur_df,\n",
|
| 317 |
-
" id_vars=exclude_columns,\n",
|
| 318 |
-
" value_vars=columns_to_pivot,\n",
|
| 319 |
-
" var_name=\"Date\",\n",
|
| 320 |
-
" value_name=\"Sales Count\",\n",
|
| 321 |
-
" )\n",
|
| 322 |
-
" data_frames.append(cur_df)\n",
|
| 323 |
"\n",
|
| 324 |
"\n",
|
| 325 |
"combined_df = get_combined_df(\n",
|
|
@@ -334,10 +314,8 @@
|
|
| 334 |
" \"Date\",\n",
|
| 335 |
" ],\n",
|
| 336 |
")\n",
|
| 337 |
-
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 338 |
-
"columns_to_coalesce = [\"Sales Count\", \"Median Sale Price\", \"Median Sale Price per Sqft\"]\n",
|
| 339 |
"\n",
|
| 340 |
-
"combined_df = coalesce_columns(combined_df
|
| 341 |
"\n",
|
| 342 |
"combined_df"
|
| 343 |
]
|
|
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import (\n",
|
| 13 |
+
" get_combined_df,\n",
|
| 14 |
+
" coalesce_columns,\n",
|
| 15 |
+
" save_final_df_as_jsonl,\n",
|
| 16 |
+
" handle_slug_column_mappings,\n",
|
| 17 |
+
")"
|
| 18 |
]
|
| 19 |
},
|
| 20 |
{
|
|
|
|
| 268 |
}
|
| 269 |
],
|
| 270 |
"source": [
|
|
|
|
|
|
|
| 271 |
"exclude_columns = [\n",
|
| 272 |
" \"RegionID\",\n",
|
| 273 |
" \"SizeRank\",\n",
|
|
|
|
| 277 |
" \"Home Type\",\n",
|
| 278 |
"]\n",
|
| 279 |
"\n",
|
| 280 |
+
"slug_column_mappings = {\n",
|
| 281 |
+
" \"_median_sale_price_per_sqft\": \"Median Sale Price per Sqft\",\n",
|
| 282 |
+
" \"_median_sale_price\": \"Median Sale Price\",\n",
|
| 283 |
+
" \"sales_count\": \"Sales Count\",\n",
|
| 284 |
+
"}\n",
|
| 285 |
+
"\n",
|
| 286 |
"data_frames = []\n",
|
| 287 |
"\n",
|
| 288 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
|
|
| 297 |
" elif \"condo\" in filename:\n",
|
| 298 |
" cur_df[\"Home Type\"] = \"condo/co-op only\"\n",
|
| 299 |
"\n",
|
| 300 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 301 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
| 302 |
+
" )\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 303 |
"\n",
|
| 304 |
"\n",
|
| 305 |
"combined_df = get_combined_df(\n",
|
|
|
|
| 314 |
" \"Date\",\n",
|
| 315 |
" ],\n",
|
| 316 |
")\n",
|
|
|
|
|
|
|
| 317 |
"\n",
|
| 318 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
| 319 |
"\n",
|
| 320 |
"combined_df"
|
| 321 |
]
|
processors/rentals.ipynb
CHANGED
|
@@ -2,19 +2,24 @@
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
-
"execution_count":
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"cell_type": "code",
|
| 17 |
-
"execution_count":
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
|
@@ -27,7 +32,7 @@
|
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"cell_type": "code",
|
| 30 |
-
"execution_count":
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
@@ -77,7 +82,7 @@
|
|
| 77 |
" <td>county</td>\n",
|
| 78 |
" <td>ID</td>\n",
|
| 79 |
" <td>all homes plus multifamily</td>\n",
|
| 80 |
-
" <td>
|
| 81 |
" <td>Boise City, ID</td>\n",
|
| 82 |
" <td>16.0</td>\n",
|
| 83 |
" <td>1.0</td>\n",
|
|
@@ -95,7 +100,7 @@
|
|
| 95 |
" <td>county</td>\n",
|
| 96 |
" <td>ID</td>\n",
|
| 97 |
" <td>all homes plus multifamily</td>\n",
|
| 98 |
-
" <td>
|
| 99 |
" <td>Boise City, ID</td>\n",
|
| 100 |
" <td>16.0</td>\n",
|
| 101 |
" <td>1.0</td>\n",
|
|
@@ -113,7 +118,7 @@
|
|
| 113 |
" <td>county</td>\n",
|
| 114 |
" <td>ID</td>\n",
|
| 115 |
" <td>all homes plus multifamily</td>\n",
|
| 116 |
-
" <td>
|
| 117 |
" <td>Boise City, ID</td>\n",
|
| 118 |
" <td>16.0</td>\n",
|
| 119 |
" <td>1.0</td>\n",
|
|
@@ -131,7 +136,7 @@
|
|
| 131 |
" <td>county</td>\n",
|
| 132 |
" <td>ID</td>\n",
|
| 133 |
" <td>all homes plus multifamily</td>\n",
|
| 134 |
-
" <td>
|
| 135 |
" <td>Boise City, ID</td>\n",
|
| 136 |
" <td>16.0</td>\n",
|
| 137 |
" <td>1.0</td>\n",
|
|
@@ -149,7 +154,7 @@
|
|
| 149 |
" <td>county</td>\n",
|
| 150 |
" <td>ID</td>\n",
|
| 151 |
" <td>all homes plus multifamily</td>\n",
|
| 152 |
-
" <td>
|
| 153 |
" <td>Boise City, ID</td>\n",
|
| 154 |
" <td>16.0</td>\n",
|
| 155 |
" <td>1.0</td>\n",
|
|
@@ -185,8 +190,8 @@
|
|
| 185 |
" <td>city</td>\n",
|
| 186 |
" <td>NJ</td>\n",
|
| 187 |
" <td>all homes plus multifamily</td>\n",
|
| 188 |
-
" <td>
|
| 189 |
-
" <td>
|
| 190 |
" <td>NaN</td>\n",
|
| 191 |
" <td>NaN</td>\n",
|
| 192 |
" <td>2023-08-31</td>\n",
|
|
@@ -203,8 +208,8 @@
|
|
| 203 |
" <td>city</td>\n",
|
| 204 |
" <td>NJ</td>\n",
|
| 205 |
" <td>all homes plus multifamily</td>\n",
|
| 206 |
-
" <td>
|
| 207 |
-
" <td>
|
| 208 |
" <td>NaN</td>\n",
|
| 209 |
" <td>NaN</td>\n",
|
| 210 |
" <td>2023-09-30</td>\n",
|
|
@@ -221,8 +226,8 @@
|
|
| 221 |
" <td>city</td>\n",
|
| 222 |
" <td>NJ</td>\n",
|
| 223 |
" <td>all homes plus multifamily</td>\n",
|
| 224 |
-
" <td>
|
| 225 |
-
" <td>
|
| 226 |
" <td>NaN</td>\n",
|
| 227 |
" <td>NaN</td>\n",
|
| 228 |
" <td>2023-10-31</td>\n",
|
|
@@ -239,8 +244,8 @@
|
|
| 239 |
" <td>city</td>\n",
|
| 240 |
" <td>NJ</td>\n",
|
| 241 |
" <td>all homes plus multifamily</td>\n",
|
| 242 |
-
" <td>
|
| 243 |
-
" <td>
|
| 244 |
" <td>NaN</td>\n",
|
| 245 |
" <td>NaN</td>\n",
|
| 246 |
" <td>2023-11-30</td>\n",
|
|
@@ -257,8 +262,8 @@
|
|
| 257 |
" <td>city</td>\n",
|
| 258 |
" <td>NJ</td>\n",
|
| 259 |
" <td>all homes plus multifamily</td>\n",
|
| 260 |
-
" <td>
|
| 261 |
-
" <td>
|
| 262 |
" <td>NaN</td>\n",
|
| 263 |
" <td>NaN</td>\n",
|
| 264 |
" <td>2023-12-31</td>\n",
|
|
@@ -286,18 +291,31 @@
|
|
| 286 |
"1258738 857850 713 Cherry Hill city NJ \n",
|
| 287 |
"1258739 857850 713 Cherry Hill city NJ \n",
|
| 288 |
"\n",
|
| 289 |
-
" Home Type State
|
| 290 |
-
"0 all homes plus multifamily
|
| 291 |
-
"1 all homes plus multifamily
|
| 292 |
-
"2 all homes plus multifamily
|
| 293 |
-
"3 all homes plus multifamily
|
| 294 |
-
"4 all homes plus multifamily
|
| 295 |
-
"... ... ...
|
| 296 |
-
"1258735 all homes plus multifamily
|
| 297 |
-
"1258736 all homes plus multifamily
|
| 298 |
-
"1258737 all homes plus multifamily
|
| 299 |
-
"1258738 all homes plus multifamily
|
| 300 |
-
"1258739 all homes plus multifamily
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 301 |
"\n",
|
| 302 |
" MunicipalCodeFIPS Date Rent (Smoothed) CountyName \\\n",
|
| 303 |
"0 1.0 2015-01-31 927.493763 NaN \n",
|
|
@@ -328,16 +346,16 @@
|
|
| 328 |
"[1258740 rows x 15 columns]"
|
| 329 |
]
|
| 330 |
},
|
| 331 |
-
"execution_count":
|
| 332 |
"metadata": {},
|
| 333 |
"output_type": "execute_result"
|
| 334 |
}
|
| 335 |
],
|
| 336 |
"source": [
|
| 337 |
-
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 338 |
-
"\n",
|
| 339 |
"data_frames = []\n",
|
| 340 |
"\n",
|
|
|
|
|
|
|
| 341 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 342 |
" if filename.endswith(\".csv\"):\n",
|
| 343 |
" # print(\"processing \" + filename)\n",
|
|
@@ -402,19 +420,10 @@
|
|
| 402 |
" elif \"_mfr_\" in filename:\n",
|
| 403 |
" cur_df[\"Home Type\"] = \"multifamily\"\n",
|
| 404 |
"\n",
|
| 405 |
-
"
|
| 406 |
-
"
|
| 407 |
-
"\n",
|
| 408 |
-
" cur_df = get_df(\n",
|
| 409 |
-
" cur_df,\n",
|
| 410 |
-
" exclude_columns,\n",
|
| 411 |
-
" columns_to_pivot,\n",
|
| 412 |
-
" \"Rent\",\n",
|
| 413 |
-
" filename,\n",
|
| 414 |
" )\n",
|
| 415 |
"\n",
|
| 416 |
-
" data_frames.append(cur_df)\n",
|
| 417 |
-
"\n",
|
| 418 |
"\n",
|
| 419 |
"combined_df = get_combined_df(\n",
|
| 420 |
" data_frames,\n",
|
|
@@ -429,18 +438,14 @@
|
|
| 429 |
" ],\n",
|
| 430 |
")\n",
|
| 431 |
"\n",
|
| 432 |
-
"\n",
|
| 433 |
-
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 434 |
-
"columns_to_coalesce = [\"Rent (Smoothed)\", \"Rent (Smoothed) (Seasonally Adjusted)\"]\n",
|
| 435 |
-
"\n",
|
| 436 |
-
"combined_df = coalesce_columns(combined_df, columns_to_coalesce)\n",
|
| 437 |
"\n",
|
| 438 |
"combined_df"
|
| 439 |
]
|
| 440 |
},
|
| 441 |
{
|
| 442 |
"cell_type": "code",
|
| 443 |
-
"execution_count":
|
| 444 |
"metadata": {},
|
| 445 |
"outputs": [
|
| 446 |
{
|
|
@@ -591,7 +596,7 @@
|
|
| 591 |
" <td>city</td>\n",
|
| 592 |
" <td>all homes plus multifamily</td>\n",
|
| 593 |
" <td>Camden County</td>\n",
|
| 594 |
-
" <td>
|
| 595 |
" <td>NaN</td>\n",
|
| 596 |
" <td>NaN</td>\n",
|
| 597 |
" <td>2023-08-31</td>\n",
|
|
@@ -608,7 +613,7 @@
|
|
| 608 |
" <td>city</td>\n",
|
| 609 |
" <td>all homes plus multifamily</td>\n",
|
| 610 |
" <td>Camden County</td>\n",
|
| 611 |
-
" <td>
|
| 612 |
" <td>NaN</td>\n",
|
| 613 |
" <td>NaN</td>\n",
|
| 614 |
" <td>2023-09-30</td>\n",
|
|
@@ -625,7 +630,7 @@
|
|
| 625 |
" <td>city</td>\n",
|
| 626 |
" <td>all homes plus multifamily</td>\n",
|
| 627 |
" <td>Camden County</td>\n",
|
| 628 |
-
" <td>
|
| 629 |
" <td>NaN</td>\n",
|
| 630 |
" <td>NaN</td>\n",
|
| 631 |
" <td>2023-10-31</td>\n",
|
|
@@ -642,7 +647,7 @@
|
|
| 642 |
" <td>city</td>\n",
|
| 643 |
" <td>all homes plus multifamily</td>\n",
|
| 644 |
" <td>Camden County</td>\n",
|
| 645 |
-
" <td>
|
| 646 |
" <td>NaN</td>\n",
|
| 647 |
" <td>NaN</td>\n",
|
| 648 |
" <td>2023-11-30</td>\n",
|
|
@@ -659,7 +664,7 @@
|
|
| 659 |
" <td>city</td>\n",
|
| 660 |
" <td>all homes plus multifamily</td>\n",
|
| 661 |
" <td>Camden County</td>\n",
|
| 662 |
-
" <td>
|
| 663 |
" <td>NaN</td>\n",
|
| 664 |
" <td>NaN</td>\n",
|
| 665 |
" <td>2023-12-31</td>\n",
|
|
@@ -687,31 +692,44 @@
|
|
| 687 |
"1258738 857850 713 Cherry Hill city \n",
|
| 688 |
"1258739 857850 713 Cherry Hill city \n",
|
| 689 |
"\n",
|
| 690 |
-
" Home Type State
|
| 691 |
-
"0 all homes plus multifamily Ada County
|
| 692 |
-
"1 all homes plus multifamily Ada County
|
| 693 |
-
"2 all homes plus multifamily Ada County
|
| 694 |
-
"3 all homes plus multifamily Ada County
|
| 695 |
-
"4 all homes plus multifamily Ada County
|
| 696 |
-
"... ... ...
|
| 697 |
-
"1258735 all homes plus multifamily Camden County
|
| 698 |
-
"1258736 all homes plus multifamily Camden County
|
| 699 |
-
"1258737 all homes plus multifamily Camden County
|
| 700 |
-
"1258738 all homes plus multifamily Camden County
|
| 701 |
-
"1258739 all homes plus multifamily Camden County
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 702 |
"\n",
|
| 703 |
-
"
|
| 704 |
-
"0
|
| 705 |
-
"1
|
| 706 |
-
"2
|
| 707 |
-
"3
|
| 708 |
-
"4
|
| 709 |
-
"...
|
| 710 |
-
"1258735
|
| 711 |
-
"1258736
|
| 712 |
-
"1258737
|
| 713 |
-
"1258738
|
| 714 |
-
"1258739
|
| 715 |
"\n",
|
| 716 |
" Rent (Smoothed) (Seasonally Adjusted) City County \n",
|
| 717 |
"0 927.493763 NaN Ada County \n",
|
|
@@ -729,7 +747,7 @@
|
|
| 729 |
"[1258740 rows x 14 columns]"
|
| 730 |
]
|
| 731 |
},
|
| 732 |
-
"execution_count":
|
| 733 |
"metadata": {},
|
| 734 |
"output_type": "execute_result"
|
| 735 |
}
|
|
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 2,
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import (\n",
|
| 13 |
+
" get_combined_df,\n",
|
| 14 |
+
" coalesce_columns,\n",
|
| 15 |
+
" save_final_df_as_jsonl,\n",
|
| 16 |
+
" handle_slug_column_mappings,\n",
|
| 17 |
+
")"
|
| 18 |
]
|
| 19 |
},
|
| 20 |
{
|
| 21 |
"cell_type": "code",
|
| 22 |
+
"execution_count": 3,
|
| 23 |
"metadata": {},
|
| 24 |
"outputs": [],
|
| 25 |
"source": [
|
|
|
|
| 32 |
},
|
| 33 |
{
|
| 34 |
"cell_type": "code",
|
| 35 |
+
"execution_count": 7,
|
| 36 |
"metadata": {},
|
| 37 |
"outputs": [
|
| 38 |
{
|
|
|
|
| 82 |
" <td>county</td>\n",
|
| 83 |
" <td>ID</td>\n",
|
| 84 |
" <td>all homes plus multifamily</td>\n",
|
| 85 |
+
" <td>16.0</td>\n",
|
| 86 |
" <td>Boise City, ID</td>\n",
|
| 87 |
" <td>16.0</td>\n",
|
| 88 |
" <td>1.0</td>\n",
|
|
|
|
| 100 |
" <td>county</td>\n",
|
| 101 |
" <td>ID</td>\n",
|
| 102 |
" <td>all homes plus multifamily</td>\n",
|
| 103 |
+
" <td>16.0</td>\n",
|
| 104 |
" <td>Boise City, ID</td>\n",
|
| 105 |
" <td>16.0</td>\n",
|
| 106 |
" <td>1.0</td>\n",
|
|
|
|
| 118 |
" <td>county</td>\n",
|
| 119 |
" <td>ID</td>\n",
|
| 120 |
" <td>all homes plus multifamily</td>\n",
|
| 121 |
+
" <td>16.0</td>\n",
|
| 122 |
" <td>Boise City, ID</td>\n",
|
| 123 |
" <td>16.0</td>\n",
|
| 124 |
" <td>1.0</td>\n",
|
|
|
|
| 136 |
" <td>county</td>\n",
|
| 137 |
" <td>ID</td>\n",
|
| 138 |
" <td>all homes plus multifamily</td>\n",
|
| 139 |
+
" <td>16.0</td>\n",
|
| 140 |
" <td>Boise City, ID</td>\n",
|
| 141 |
" <td>16.0</td>\n",
|
| 142 |
" <td>1.0</td>\n",
|
|
|
|
| 154 |
" <td>county</td>\n",
|
| 155 |
" <td>ID</td>\n",
|
| 156 |
" <td>all homes plus multifamily</td>\n",
|
| 157 |
+
" <td>16.0</td>\n",
|
| 158 |
" <td>Boise City, ID</td>\n",
|
| 159 |
" <td>16.0</td>\n",
|
| 160 |
" <td>1.0</td>\n",
|
|
|
|
| 190 |
" <td>city</td>\n",
|
| 191 |
" <td>NJ</td>\n",
|
| 192 |
" <td>all homes plus multifamily</td>\n",
|
| 193 |
+
" <td>NJ</td>\n",
|
| 194 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 195 |
" <td>NaN</td>\n",
|
| 196 |
" <td>NaN</td>\n",
|
| 197 |
" <td>2023-08-31</td>\n",
|
|
|
|
| 208 |
" <td>city</td>\n",
|
| 209 |
" <td>NJ</td>\n",
|
| 210 |
" <td>all homes plus multifamily</td>\n",
|
| 211 |
+
" <td>NJ</td>\n",
|
| 212 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 213 |
" <td>NaN</td>\n",
|
| 214 |
" <td>NaN</td>\n",
|
| 215 |
" <td>2023-09-30</td>\n",
|
|
|
|
| 226 |
" <td>city</td>\n",
|
| 227 |
" <td>NJ</td>\n",
|
| 228 |
" <td>all homes plus multifamily</td>\n",
|
| 229 |
+
" <td>NJ</td>\n",
|
| 230 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 231 |
" <td>NaN</td>\n",
|
| 232 |
" <td>NaN</td>\n",
|
| 233 |
" <td>2023-10-31</td>\n",
|
|
|
|
| 244 |
" <td>city</td>\n",
|
| 245 |
" <td>NJ</td>\n",
|
| 246 |
" <td>all homes plus multifamily</td>\n",
|
| 247 |
+
" <td>NJ</td>\n",
|
| 248 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 249 |
" <td>NaN</td>\n",
|
| 250 |
" <td>NaN</td>\n",
|
| 251 |
" <td>2023-11-30</td>\n",
|
|
|
|
| 262 |
" <td>city</td>\n",
|
| 263 |
" <td>NJ</td>\n",
|
| 264 |
" <td>all homes plus multifamily</td>\n",
|
| 265 |
+
" <td>NJ</td>\n",
|
| 266 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 267 |
" <td>NaN</td>\n",
|
| 268 |
" <td>NaN</td>\n",
|
| 269 |
" <td>2023-12-31</td>\n",
|
|
|
|
| 291 |
"1258738 857850 713 Cherry Hill city NJ \n",
|
| 292 |
"1258739 857850 713 Cherry Hill city NJ \n",
|
| 293 |
"\n",
|
| 294 |
+
" Home Type State \\\n",
|
| 295 |
+
"0 all homes plus multifamily 16.0 \n",
|
| 296 |
+
"1 all homes plus multifamily 16.0 \n",
|
| 297 |
+
"2 all homes plus multifamily 16.0 \n",
|
| 298 |
+
"3 all homes plus multifamily 16.0 \n",
|
| 299 |
+
"4 all homes plus multifamily 16.0 \n",
|
| 300 |
+
"... ... ... \n",
|
| 301 |
+
"1258735 all homes plus multifamily NJ \n",
|
| 302 |
+
"1258736 all homes plus multifamily NJ \n",
|
| 303 |
+
"1258737 all homes plus multifamily NJ \n",
|
| 304 |
+
"1258738 all homes plus multifamily NJ \n",
|
| 305 |
+
"1258739 all homes plus multifamily NJ \n",
|
| 306 |
+
"\n",
|
| 307 |
+
" Metro StateCodeFIPS \\\n",
|
| 308 |
+
"0 Boise City, ID 16.0 \n",
|
| 309 |
+
"1 Boise City, ID 16.0 \n",
|
| 310 |
+
"2 Boise City, ID 16.0 \n",
|
| 311 |
+
"3 Boise City, ID 16.0 \n",
|
| 312 |
+
"4 Boise City, ID 16.0 \n",
|
| 313 |
+
"... ... ... \n",
|
| 314 |
+
"1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 315 |
+
"1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 316 |
+
"1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 317 |
+
"1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 318 |
+
"1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 319 |
"\n",
|
| 320 |
" MunicipalCodeFIPS Date Rent (Smoothed) CountyName \\\n",
|
| 321 |
"0 1.0 2015-01-31 927.493763 NaN \n",
|
|
|
|
| 346 |
"[1258740 rows x 15 columns]"
|
| 347 |
]
|
| 348 |
},
|
| 349 |
+
"execution_count": 7,
|
| 350 |
"metadata": {},
|
| 351 |
"output_type": "execute_result"
|
| 352 |
}
|
| 353 |
],
|
| 354 |
"source": [
|
|
|
|
|
|
|
| 355 |
"data_frames = []\n",
|
| 356 |
"\n",
|
| 357 |
+
"slug_column_mappings = {\"\": \"Rent\"}\n",
|
| 358 |
+
"\n",
|
| 359 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 360 |
" if filename.endswith(\".csv\"):\n",
|
| 361 |
" # print(\"processing \" + filename)\n",
|
|
|
|
| 420 |
" elif \"_mfr_\" in filename:\n",
|
| 421 |
" cur_df[\"Home Type\"] = \"multifamily\"\n",
|
| 422 |
"\n",
|
| 423 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 424 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 425 |
" )\n",
|
| 426 |
"\n",
|
|
|
|
|
|
|
| 427 |
"\n",
|
| 428 |
"combined_df = get_combined_df(\n",
|
| 429 |
" data_frames,\n",
|
|
|
|
| 438 |
" ],\n",
|
| 439 |
")\n",
|
| 440 |
"\n",
|
| 441 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 442 |
"\n",
|
| 443 |
"combined_df"
|
| 444 |
]
|
| 445 |
},
|
| 446 |
{
|
| 447 |
"cell_type": "code",
|
| 448 |
+
"execution_count": 8,
|
| 449 |
"metadata": {},
|
| 450 |
"outputs": [
|
| 451 |
{
|
|
|
|
| 596 |
" <td>city</td>\n",
|
| 597 |
" <td>all homes plus multifamily</td>\n",
|
| 598 |
" <td>Camden County</td>\n",
|
| 599 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 600 |
" <td>NaN</td>\n",
|
| 601 |
" <td>NaN</td>\n",
|
| 602 |
" <td>2023-08-31</td>\n",
|
|
|
|
| 613 |
" <td>city</td>\n",
|
| 614 |
" <td>all homes plus multifamily</td>\n",
|
| 615 |
" <td>Camden County</td>\n",
|
| 616 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 617 |
" <td>NaN</td>\n",
|
| 618 |
" <td>NaN</td>\n",
|
| 619 |
" <td>2023-09-30</td>\n",
|
|
|
|
| 630 |
" <td>city</td>\n",
|
| 631 |
" <td>all homes plus multifamily</td>\n",
|
| 632 |
" <td>Camden County</td>\n",
|
| 633 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 634 |
" <td>NaN</td>\n",
|
| 635 |
" <td>NaN</td>\n",
|
| 636 |
" <td>2023-10-31</td>\n",
|
|
|
|
| 647 |
" <td>city</td>\n",
|
| 648 |
" <td>all homes plus multifamily</td>\n",
|
| 649 |
" <td>Camden County</td>\n",
|
| 650 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 651 |
" <td>NaN</td>\n",
|
| 652 |
" <td>NaN</td>\n",
|
| 653 |
" <td>2023-11-30</td>\n",
|
|
|
|
| 664 |
" <td>city</td>\n",
|
| 665 |
" <td>all homes plus multifamily</td>\n",
|
| 666 |
" <td>Camden County</td>\n",
|
| 667 |
+
" <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
|
| 668 |
" <td>NaN</td>\n",
|
| 669 |
" <td>NaN</td>\n",
|
| 670 |
" <td>2023-12-31</td>\n",
|
|
|
|
| 692 |
"1258738 857850 713 Cherry Hill city \n",
|
| 693 |
"1258739 857850 713 Cherry Hill city \n",
|
| 694 |
"\n",
|
| 695 |
+
" Home Type State \\\n",
|
| 696 |
+
"0 all homes plus multifamily Ada County \n",
|
| 697 |
+
"1 all homes plus multifamily Ada County \n",
|
| 698 |
+
"2 all homes plus multifamily Ada County \n",
|
| 699 |
+
"3 all homes plus multifamily Ada County \n",
|
| 700 |
+
"4 all homes plus multifamily Ada County \n",
|
| 701 |
+
"... ... ... \n",
|
| 702 |
+
"1258735 all homes plus multifamily Camden County \n",
|
| 703 |
+
"1258736 all homes plus multifamily Camden County \n",
|
| 704 |
+
"1258737 all homes plus multifamily Camden County \n",
|
| 705 |
+
"1258738 all homes plus multifamily Camden County \n",
|
| 706 |
+
"1258739 all homes plus multifamily Camden County \n",
|
| 707 |
+
"\n",
|
| 708 |
+
" Metro StateCodeFIPS \\\n",
|
| 709 |
+
"0 Boise City, ID 16.0 \n",
|
| 710 |
+
"1 Boise City, ID 16.0 \n",
|
| 711 |
+
"2 Boise City, ID 16.0 \n",
|
| 712 |
+
"3 Boise City, ID 16.0 \n",
|
| 713 |
+
"4 Boise City, ID 16.0 \n",
|
| 714 |
+
"... ... ... \n",
|
| 715 |
+
"1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 716 |
+
"1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 717 |
+
"1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 718 |
+
"1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 719 |
+
"1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
|
| 720 |
"\n",
|
| 721 |
+
" MunicipalCodeFIPS Date Rent (Smoothed) \\\n",
|
| 722 |
+
"0 1.0 2015-01-31 927.493763 \n",
|
| 723 |
+
"1 1.0 2015-02-28 931.690623 \n",
|
| 724 |
+
"2 1.0 2015-03-31 932.568601 \n",
|
| 725 |
+
"3 1.0 2015-04-30 933.148134 \n",
|
| 726 |
+
"4 1.0 2015-05-31 941.045724 \n",
|
| 727 |
+
"... ... ... ... \n",
|
| 728 |
+
"1258735 NaN 2023-08-31 2291.604800 \n",
|
| 729 |
+
"1258736 NaN 2023-09-30 2296.188906 \n",
|
| 730 |
+
"1258737 NaN 2023-10-31 2292.270938 \n",
|
| 731 |
+
"1258738 NaN 2023-11-30 2253.417140 \n",
|
| 732 |
+
"1258739 NaN 2023-12-31 2280.830303 \n",
|
| 733 |
"\n",
|
| 734 |
" Rent (Smoothed) (Seasonally Adjusted) City County \n",
|
| 735 |
"0 927.493763 NaN Ada County \n",
|
|
|
|
| 747 |
"[1258740 rows x 14 columns]"
|
| 748 |
]
|
| 749 |
},
|
| 750 |
+
"execution_count": 8,
|
| 751 |
"metadata": {},
|
| 752 |
"output_type": "execute_result"
|
| 753 |
}
|
processors/sales.ipynb
CHANGED
|
@@ -9,7 +9,12 @@
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
-
"from helpers import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
|
@@ -468,8 +473,6 @@
|
|
| 468 |
}
|
| 469 |
],
|
| 470 |
"source": [
|
| 471 |
-
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 472 |
-
"\n",
|
| 473 |
"exclude_columns = [\n",
|
| 474 |
" \"RegionID\",\n",
|
| 475 |
" \"SizeRank\",\n",
|
|
@@ -493,33 +496,20 @@
|
|
| 493 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 494 |
" if filename.endswith(\".csv\"):\n",
|
| 495 |
" print(\"processing \" + filename)\n",
|
| 496 |
-
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
|
| 497 |
-
"\n",
|
| 498 |
" # ignore monthly data for now since it is redundant\n",
|
| 499 |
-
" if \"
|
| 500 |
" continue\n",
|
| 501 |
"\n",
|
|
|
|
|
|
|
| 502 |
" if \"_sfrcondo_\" in filename:\n",
|
| 503 |
" cur_df[\"Home Type\"] = \"all homes\"\n",
|
| 504 |
" elif \"_sfr_\" in filename:\n",
|
| 505 |
" cur_df[\"Home Type\"] = \"SFR\"\n",
|
| 506 |
"\n",
|
| 507 |
-
"
|
| 508 |
-
"
|
| 509 |
-
"\n",
|
| 510 |
-
" # iterate over slug column mappings and get df\n",
|
| 511 |
-
" for slug, col_name in slug_column_mappings.items():\n",
|
| 512 |
-
" if slug in filename:\n",
|
| 513 |
-
" cur_df = get_df(\n",
|
| 514 |
-
" cur_df,\n",
|
| 515 |
-
" exclude_columns,\n",
|
| 516 |
-
" columns_to_pivot,\n",
|
| 517 |
-
" col_name,\n",
|
| 518 |
-
" filename,\n",
|
| 519 |
-
" )\n",
|
| 520 |
-
"\n",
|
| 521 |
-
" data_frames.append(cur_df)\n",
|
| 522 |
-
" break\n",
|
| 523 |
"\n",
|
| 524 |
"\n",
|
| 525 |
"combined_df = get_combined_df(\n",
|
|
@@ -535,23 +525,7 @@
|
|
| 535 |
" ],\n",
|
| 536 |
")\n",
|
| 537 |
"\n",
|
| 538 |
-
"
|
| 539 |
-
"columns_to_coalesce = [\n",
|
| 540 |
-
" \"Mean Sale to List Ratio (Smoothed)\"\n",
|
| 541 |
-
" \"Median Sale to List Ratio\"\n",
|
| 542 |
-
" \"Median Sale Price\"\n",
|
| 543 |
-
" \"% Sold Below List (Smoothed)\",\n",
|
| 544 |
-
" \"Median Sale Price (Smoothed) (Seasonally Adjusted)\",\n",
|
| 545 |
-
" \"% Sold Below List\",\n",
|
| 546 |
-
" \"Median Sale Price (Smoothed)\",\n",
|
| 547 |
-
" \"Median Sale to List Ratio (Smoothed)\",\n",
|
| 548 |
-
" \"% Sold Above List\",\n",
|
| 549 |
-
" \"Nowcast\",\n",
|
| 550 |
-
" \"Mean Sale to List Ratio\",\n",
|
| 551 |
-
" \"% Sold Above List (Smoothed)\",\n",
|
| 552 |
-
"]\n",
|
| 553 |
-
"\n",
|
| 554 |
-
"combined_df = coalesce_columns(combined_df, columns_to_coalesce)\n",
|
| 555 |
"\n",
|
| 556 |
"combined_df"
|
| 557 |
]
|
|
|
|
| 9 |
"import pandas as pd\n",
|
| 10 |
"import os\n",
|
| 11 |
"\n",
|
| 12 |
+
"from helpers import (\n",
|
| 13 |
+
" get_combined_df,\n",
|
| 14 |
+
" coalesce_columns,\n",
|
| 15 |
+
" save_final_df_as_jsonl,\n",
|
| 16 |
+
" handle_slug_column_mappings,\n",
|
| 17 |
+
")"
|
| 18 |
]
|
| 19 |
},
|
| 20 |
{
|
|
|
|
| 473 |
}
|
| 474 |
],
|
| 475 |
"source": [
|
|
|
|
|
|
|
| 476 |
"exclude_columns = [\n",
|
| 477 |
" \"RegionID\",\n",
|
| 478 |
" \"SizeRank\",\n",
|
|
|
|
| 496 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
| 497 |
" if filename.endswith(\".csv\"):\n",
|
| 498 |
" print(\"processing \" + filename)\n",
|
|
|
|
|
|
|
| 499 |
" # ignore monthly data for now since it is redundant\n",
|
| 500 |
+
" if \"month\" in filename:\n",
|
| 501 |
" continue\n",
|
| 502 |
"\n",
|
| 503 |
+
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
|
| 504 |
+
"\n",
|
| 505 |
" if \"_sfrcondo_\" in filename:\n",
|
| 506 |
" cur_df[\"Home Type\"] = \"all homes\"\n",
|
| 507 |
" elif \"_sfr_\" in filename:\n",
|
| 508 |
" cur_df[\"Home Type\"] = \"SFR\"\n",
|
| 509 |
"\n",
|
| 510 |
+
" data_frames = handle_slug_column_mappings(\n",
|
| 511 |
+
" data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
|
| 512 |
+
" )\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 513 |
"\n",
|
| 514 |
"\n",
|
| 515 |
"combined_df = get_combined_df(\n",
|
|
|
|
| 525 |
" ],\n",
|
| 526 |
")\n",
|
| 527 |
"\n",
|
| 528 |
+
"combined_df = coalesce_columns(combined_df)\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 529 |
"\n",
|
| 530 |
"combined_df"
|
| 531 |
]
|