fix: simplify structure of processors through shared functions
Browse files- .gitignore +3 -1
- README.md +1 -1
- processed/days_on_market/final.jsonl +3 -0
- processed/for_sale_listings/final.jsonl +3 -0
- processed/home_values/final.jsonl +3 -0
- processed/new_construction/final.jsonl +3 -0
- processed/rentals/final.jsonl +3 -0
- processed/sales/final.jsonl +3 -0
- processors/days_on_market.ipynb +27 -76
- processors/for_sale_listings.ipynb +199 -253
- processors/helpers.py +69 -0
- processors/home_value_forecasts.ipynb +11 -12
- processors/home_values.ipynb +382 -166
- processors/new_construction.ipynb +24 -50
- processors/rentals.ipynb +33 -95
- processors/sales.ipynb +47 -144
.gitignore
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*.DS_STORE
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*.DS_STORE
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*__pycache__*
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README.md
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This dataset contains several configs produced based on files available at https://www.zillow.com/research/data/.
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-
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<!-- list each with a short description (1 sentence) -->
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- [`home_values`](#home-values): Zillow Home Value Index (ZHVI) for all homes, mid-tier, bottom-tier, and top-tier homes.
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- [`home_values_forecasts`](#home-values-forecasts): Zillow Home Value Forecast (ZHVF) for all homes, mid-tier, bottom-tier, and top-tier homes.
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This dataset contains several configs produced based on files available at https://www.zillow.com/research/data/.
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Supported configs:
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<!-- list each with a short description (1 sentence) -->
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- [`home_values`](#home-values): Zillow Home Value Index (ZHVI) for all homes, mid-tier, bottom-tier, and top-tier homes.
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- [`home_values_forecasts`](#home-values-forecasts): Zillow Home Value Forecast (ZHVF) for all homes, mid-tier, bottom-tier, and top-tier homes.
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processed/days_on_market/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:1cf82e9ce68b4ebf991214a7de3fbc8f25de319da470741761d44d11d5cc89f3
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size 230154547
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processed/for_sale_listings/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:38e3f7794b23cfdb27f446d888b6c930078e5fb511311c7d216a248f27c74757
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size 179627939
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processed/home_values/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:2e50e8888742d20a9cf36f4dc41aeabaf37933a8c90de9825f160d2e5e37a011
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size 88318760
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processed/new_construction/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:276b20bd2011faa1fb59f58892ef16b8bbfbb8111a10c7e8d4f433a9226bf3c5
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size 10903095
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processed/rentals/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:f8881aca35bd30388f8ce14417b5f6edc4db01dca1be18e8a7e467fcb4258dac
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size 413052557
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processed/sales/final.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:99077b13eeb65343b0676dcbc58b673265ab88468abc1fc4a7fc161c40f490d7
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size 279576767
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processors/days_on_market.ipynb
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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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"import pandas as pd\n",
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"import os"
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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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},
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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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"[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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@@ -347,52 +349,6 @@
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"}\n",
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"\n",
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"\n",
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-
"def get_df(\n",
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" df, exclude_columns, columns_to_pivot, col_name, smoothed, seasonally_adjusted\n",
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-
"):\n",
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" if smoothed:\n",
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" col_name += \" (Smoothed)\"\n",
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" if seasonally_adjusted:\n",
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" col_name += \" (Seasonally Adjusted)\"\n",
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"\n",
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" df = pd.melt(\n",
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" df,\n",
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" id_vars=exclude_columns,\n",
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" value_vars=columns_to_pivot,\n",
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" var_name=\"Date\",\n",
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" value_name=col_name,\n",
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" )\n",
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" return df\n",
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"\n",
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"\n",
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"def get_combined_df(data_frames):\n",
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" combined_df = None\n",
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" if len(data_frames) > 1:\n",
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" # iterate over dataframes and merge or concat\n",
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" combined_df = data_frames[0]\n",
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" for i in range(1, len(data_frames)):\n",
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" cur_df = data_frames[i]\n",
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" combined_df = pd.merge(\n",
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" combined_df,\n",
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" cur_df,\n",
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" on=[\n",
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" \"RegionID\",\n",
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" \"SizeRank\",\n",
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" \"RegionName\",\n",
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" \"RegionType\",\n",
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" \"StateName\",\n",
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" \"Home Type\",\n",
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" \"Date\",\n",
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" ],\n",
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" how=\"outer\",\n",
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" suffixes=(\"\", \"_\" + str(i)),\n",
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" )\n",
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" elif len(data_frames) == 1:\n",
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" combined_df = data_frames[0]\n",
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"\n",
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" return combined_df\n",
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"\n",
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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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" # print(\"processing \" + filename)\n",
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@@ -412,9 +368,6 @@
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" # Identify columns to pivot\n",
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" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
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"\n",
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-
" smoothed = \"_sm_\" in filename\n",
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" seasonally_adjusted = \"_sa_\" in filename\n",
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"\n",
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" # iterate over slug column mappings and get df\n",
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" for slug, col_name in slug_column_mappings.items():\n",
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" if slug in filename:\n",
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@@ -423,35 +376,36 @@
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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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"
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" seasonally_adjusted,\n",
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" )\n",
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"\n",
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" data_frames.append(cur_df)\n",
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" break\n",
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"\n",
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"\n",
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-
"combined_df = get_combined_df(
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"\n",
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"columns_to_coalesce = slug_column_mappings.values()\n",
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-
"print(columns_to_coalesce)\n",
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-
"\n",
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-
"for index, row in combined_df.iterrows():\n",
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-
" for col in combined_df.columns:\n",
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-
" for column_to_coalesce in columns_to_coalesce:\n",
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-
" if column_to_coalesce in col and \"_\" in col:\n",
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-
" if not pd.isna(row[col]):\n",
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-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
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"\n",
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-
"
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-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\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":
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"metadata": {},
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"outputs": [
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{
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@@ -741,14 +695,14 @@
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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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],
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"source": [
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-
"
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-
"final_df =
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" columns={\n",
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" \"RegionID\": \"Region ID\",\n",
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" \"SizeRank\": \"Size Rank\",\n",
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@@ -763,14 +717,11 @@
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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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-
"
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-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
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"\n",
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-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
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]
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}
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],
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"cells": [
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{
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"cell_type": "code",
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+
"execution_count": 5,
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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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"from helpers import get_combined_df, coalesce_columns, get_df, save_final_df_as_jsonl"
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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": 6,
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"[586714 rows x 13 columns]"
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]
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},
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+
"execution_count": 7,
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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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"\n",
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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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| 354 |
" # print(\"processing \" + filename)\n",
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| 368 |
" # Identify columns to pivot\n",
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" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
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"\n",
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| 371 |
" # iterate over slug column mappings and get df\n",
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" for slug, col_name in slug_column_mappings.items():\n",
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" if slug in filename:\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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"\n",
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" data_frames.append(cur_df)\n",
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" break\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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+
" data_frames,\n",
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" [\n",
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+
" \"RegionID\",\n",
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+
" \"SizeRank\",\n",
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+
" \"RegionName\",\n",
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+
" \"RegionType\",\n",
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+
" \"StateName\",\n",
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+
" \"Home Type\",\n",
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+
" \"Date\",\n",
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+
" ],\n",
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+
")\n",
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"\n",
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"columns_to_coalesce = slug_column_mappings.values()\n",
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"\n",
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+
"combined_df = coalesce_columns(combined_df, columns_to_coalesce)\n",
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"\n",
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| 403 |
"combined_df"
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]
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},
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{
|
| 407 |
"cell_type": "code",
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| 408 |
+
"execution_count": 8,
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| 409 |
"metadata": {},
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| 410 |
"outputs": [
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| 411 |
{
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"[586714 rows x 13 columns]"
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| 696 |
]
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| 697 |
},
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| 698 |
+
"execution_count": 8,
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| 699 |
"metadata": {},
|
| 700 |
"output_type": "execute_result"
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| 701 |
}
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| 702 |
],
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| 703 |
"source": [
|
| 704 |
+
"# Adjust column names\n",
|
| 705 |
+
"final_df = combined_df.rename(\n",
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| 706 |
" columns={\n",
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| 707 |
" \"RegionID\": \"Region ID\",\n",
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| 708 |
" \"SizeRank\": \"Size Rank\",\n",
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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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| 723 |
"source": [
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+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
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]
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}
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],
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processors/for_sale_listings.ipynb
CHANGED
|
@@ -2,17 +2,19 @@
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| 2 |
"cells": [
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{
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| 4 |
"cell_type": "code",
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| 5 |
-
"execution_count":
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
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| 8 |
"source": [
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| 9 |
"import pandas as pd\n",
|
| 10 |
-
"import os"
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]
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| 12 |
},
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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": [
|
|
@@ -25,7 +27,7 @@
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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": {},
|
| 30 |
"outputs": [
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| 31 |
{
|
|
@@ -86,12 +88,12 @@
|
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| 86 |
" <th>StateName</th>\n",
|
| 87 |
" <th>Home Type</th>\n",
|
| 88 |
" <th>Date</th>\n",
|
| 89 |
-
" <th>New Pending (Smoothed)</th>\n",
|
| 90 |
" <th>Median Listing Price</th>\n",
|
| 91 |
" <th>Median Listing Price (Smoothed)</th>\n",
|
| 92 |
-
" <th>New Pending</th>\n",
|
| 93 |
" <th>New Listings</th>\n",
|
| 94 |
" <th>New Listings (Smoothed)</th>\n",
|
|
|
|
| 95 |
" </tr>\n",
|
| 96 |
" </thead>\n",
|
| 97 |
" <tbody>\n",
|
|
@@ -104,12 +106,12 @@
|
|
| 104 |
" <td>NaN</td>\n",
|
| 105 |
" <td>SFR</td>\n",
|
| 106 |
" <td>2018-01-13</td>\n",
|
| 107 |
-
" <td>NaN</td>\n",
|
| 108 |
" <td>259000.0</td>\n",
|
| 109 |
" <td>NaN</td>\n",
|
| 110 |
" <td>NaN</td>\n",
|
| 111 |
" <td>NaN</td>\n",
|
| 112 |
" <td>NaN</td>\n",
|
|
|
|
| 113 |
" </tr>\n",
|
| 114 |
" <tr>\n",
|
| 115 |
" <th>1</th>\n",
|
|
@@ -120,12 +122,12 @@
|
|
| 120 |
" <td>NaN</td>\n",
|
| 121 |
" <td>SFR</td>\n",
|
| 122 |
" <td>2018-01-20</td>\n",
|
| 123 |
-
" <td>NaN</td>\n",
|
| 124 |
" <td>259900.0</td>\n",
|
| 125 |
" <td>NaN</td>\n",
|
| 126 |
" <td>NaN</td>\n",
|
| 127 |
" <td>NaN</td>\n",
|
| 128 |
" <td>NaN</td>\n",
|
|
|
|
| 129 |
" </tr>\n",
|
| 130 |
" <tr>\n",
|
| 131 |
" <th>2</th>\n",
|
|
@@ -136,12 +138,12 @@
|
|
| 136 |
" <td>NaN</td>\n",
|
| 137 |
" <td>SFR</td>\n",
|
| 138 |
" <td>2018-01-27</td>\n",
|
| 139 |
-
" <td>NaN</td>\n",
|
| 140 |
" <td>259900.0</td>\n",
|
| 141 |
" <td>NaN</td>\n",
|
| 142 |
" <td>NaN</td>\n",
|
| 143 |
" <td>NaN</td>\n",
|
| 144 |
" <td>NaN</td>\n",
|
|
|
|
| 145 |
" </tr>\n",
|
| 146 |
" <tr>\n",
|
| 147 |
" <th>3</th>\n",
|
|
@@ -151,9 +153,9 @@
|
|
| 151 |
" <td>country</td>\n",
|
| 152 |
" <td>NaN</td>\n",
|
| 153 |
" <td>SFR</td>\n",
|
| 154 |
-
" <td>2018-
|
| 155 |
-
" <td>
|
| 156 |
-
" <td>
|
| 157 |
" <td>NaN</td>\n",
|
| 158 |
" <td>NaN</td>\n",
|
| 159 |
" <td>NaN</td>\n",
|
|
@@ -167,10 +169,10 @@
|
|
| 167 |
" <td>country</td>\n",
|
| 168 |
" <td>NaN</td>\n",
|
| 169 |
" <td>SFR</td>\n",
|
| 170 |
-
" <td>2018-02-
|
|
|
|
|
|
|
| 171 |
" <td>NaN</td>\n",
|
| 172 |
-
" <td>260000.0</td>\n",
|
| 173 |
-
" <td>259700.0</td>\n",
|
| 174 |
" <td>NaN</td>\n",
|
| 175 |
" <td>NaN</td>\n",
|
| 176 |
" <td>NaN</td>\n",
|
|
@@ -192,71 +194,71 @@
|
|
| 192 |
" <td>...</td>\n",
|
| 193 |
" </tr>\n",
|
| 194 |
" <tr>\n",
|
| 195 |
-
" <th>
|
| 196 |
" <td>845172</td>\n",
|
| 197 |
" <td>769</td>\n",
|
| 198 |
" <td>Winfield, KS</td>\n",
|
| 199 |
" <td>msa</td>\n",
|
| 200 |
" <td>KS</td>\n",
|
| 201 |
" <td>all homes</td>\n",
|
| 202 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 203 |
" <td>NaN</td>\n",
|
| 204 |
-
" <td>133938.0</td>\n",
|
| 205 |
-
" <td>133938.0</td>\n",
|
| 206 |
" <td>NaN</td>\n",
|
| 207 |
" <td>NaN</td>\n",
|
| 208 |
" <td>NaN</td>\n",
|
| 209 |
" </tr>\n",
|
| 210 |
" <tr>\n",
|
| 211 |
-
" <th>
|
| 212 |
" <td>845172</td>\n",
|
| 213 |
" <td>769</td>\n",
|
| 214 |
" <td>Winfield, KS</td>\n",
|
| 215 |
" <td>msa</td>\n",
|
| 216 |
" <td>KS</td>\n",
|
| 217 |
" <td>all homes</td>\n",
|
| 218 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 219 |
" <td>NaN</td>\n",
|
| 220 |
-
" <td>126463.0</td>\n",
|
| 221 |
-
" <td>126463.0</td>\n",
|
| 222 |
" <td>NaN</td>\n",
|
| 223 |
" <td>NaN</td>\n",
|
| 224 |
" <td>NaN</td>\n",
|
| 225 |
" </tr>\n",
|
| 226 |
" <tr>\n",
|
| 227 |
-
" <th>
|
| 228 |
" <td>845172</td>\n",
|
| 229 |
" <td>769</td>\n",
|
| 230 |
" <td>Winfield, KS</td>\n",
|
| 231 |
" <td>msa</td>\n",
|
| 232 |
" <td>KS</td>\n",
|
| 233 |
" <td>all homes</td>\n",
|
| 234 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 235 |
" <td>NaN</td>\n",
|
| 236 |
-
" <td>123225.0</td>\n",
|
| 237 |
-
" <td>123225.0</td>\n",
|
| 238 |
" <td>NaN</td>\n",
|
| 239 |
" <td>NaN</td>\n",
|
| 240 |
" <td>NaN</td>\n",
|
| 241 |
" </tr>\n",
|
| 242 |
" <tr>\n",
|
| 243 |
-
" <th>
|
| 244 |
" <td>845172</td>\n",
|
| 245 |
" <td>769</td>\n",
|
| 246 |
" <td>Winfield, KS</td>\n",
|
| 247 |
" <td>msa</td>\n",
|
| 248 |
" <td>KS</td>\n",
|
| 249 |
" <td>all homes</td>\n",
|
| 250 |
-
" <td>2023-12-
|
| 251 |
-
" <td>
|
| 252 |
-
" <td>
|
| 253 |
-
" <td>
|
| 254 |
-
" <td>
|
| 255 |
-
" <td>
|
| 256 |
-
" <td>
|
| 257 |
" </tr>\n",
|
| 258 |
" <tr>\n",
|
| 259 |
-
" <th>
|
| 260 |
" <td>845172</td>\n",
|
| 261 |
" <td>769</td>\n",
|
| 262 |
" <td>Winfield, KS</td>\n",
|
|
@@ -264,16 +266,16 @@
|
|
| 264 |
" <td>KS</td>\n",
|
| 265 |
" <td>all homes</td>\n",
|
| 266 |
" <td>2024-01-06</td>\n",
|
| 267 |
-
" <td>
|
| 268 |
-
" <td>121488.0</td>\n",
|
| 269 |
" <td>121488.0</td>\n",
|
| 270 |
" <td>NaN</td>\n",
|
| 271 |
" <td>NaN</td>\n",
|
| 272 |
" <td>NaN</td>\n",
|
|
|
|
| 273 |
" </tr>\n",
|
| 274 |
" </tbody>\n",
|
| 275 |
"</table>\n",
|
| 276 |
-
"<p>
|
| 277 |
"</div>"
|
| 278 |
],
|
| 279 |
"text/plain": [
|
|
@@ -284,55 +286,55 @@
|
|
| 284 |
"3 102001 0 United States country NaN SFR \n",
|
| 285 |
"4 102001 0 United States country NaN SFR \n",
|
| 286 |
"... ... ... ... ... ... ... \n",
|
| 287 |
-
"
|
| 288 |
-
"
|
| 289 |
-
"
|
| 290 |
-
"
|
| 291 |
-
"
|
| 292 |
"\n",
|
| 293 |
-
" Date
|
| 294 |
-
"0 2018-01-13
|
| 295 |
-
"1 2018-01-20
|
| 296 |
-
"2 2018-01-27
|
| 297 |
-
"3 2018-
|
| 298 |
-
"4 2018-02-
|
| 299 |
-
"... ...
|
| 300 |
-
"
|
| 301 |
-
"
|
| 302 |
-
"
|
| 303 |
-
"
|
| 304 |
-
"
|
| 305 |
"\n",
|
| 306 |
-
"
|
| 307 |
-
"0
|
| 308 |
-
"1
|
| 309 |
-
"2
|
| 310 |
-
"3
|
| 311 |
-
"4
|
| 312 |
-
"...
|
| 313 |
-
"
|
| 314 |
-
"
|
| 315 |
-
"
|
| 316 |
-
"
|
| 317 |
-
"
|
| 318 |
"\n",
|
| 319 |
-
" New
|
| 320 |
-
"0
|
| 321 |
-
"1
|
| 322 |
-
"2
|
| 323 |
-
"3
|
| 324 |
-
"4
|
| 325 |
-
"...
|
| 326 |
-
"
|
| 327 |
-
"
|
| 328 |
-
"
|
| 329 |
-
"
|
| 330 |
-
"
|
| 331 |
"\n",
|
| 332 |
-
"[
|
| 333 |
]
|
| 334 |
},
|
| 335 |
-
"execution_count":
|
| 336 |
"metadata": {},
|
| 337 |
"output_type": "execute_result"
|
| 338 |
}
|
|
@@ -349,6 +351,13 @@
|
|
| 349 |
" \"Home Type\",\n",
|
| 350 |
"]\n",
|
| 351 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 352 |
"data_frames = []\n",
|
| 353 |
"\n",
|
| 354 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
@@ -357,7 +366,7 @@
|
|
| 357 |
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
|
| 358 |
"\n",
|
| 359 |
" # ignore monthly data for now since it is redundant\n",
|
| 360 |
-
" if \"
|
| 361 |
" continue\n",
|
| 362 |
"\n",
|
| 363 |
" if \"sfrcondo\" in filename:\n",
|
|
@@ -370,84 +379,32 @@
|
|
| 370 |
" # Identify columns to pivot\n",
|
| 371 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 372 |
"\n",
|
| 373 |
-
"
|
| 374 |
-
"
|
| 375 |
-
"
|
| 376 |
-
"
|
| 377 |
-
"
|
| 378 |
-
"
|
| 379 |
-
"
|
| 380 |
-
"
|
| 381 |
-
"
|
| 382 |
-
" \"Median Listing Price\"\n",
|
| 383 |
-
" if not smoothed\n",
|
| 384 |
-
" else \"Median Listing Price (Smoothed)\"\n",
|
| 385 |
-
" ),\n",
|
| 386 |
-
" )\n",
|
| 387 |
-
" data_frames.append(cur_df)\n",
|
| 388 |
"\n",
|
| 389 |
-
"
|
| 390 |
-
"
|
| 391 |
-
" cur_df,\n",
|
| 392 |
-
" id_vars=exclude_columns,\n",
|
| 393 |
-
" value_vars=columns_to_pivot,\n",
|
| 394 |
-
" var_name=\"Date\",\n",
|
| 395 |
-
" value_name=(\n",
|
| 396 |
-
" \"New Listings\" if not smoothed else \"New Listings (Smoothed)\"\n",
|
| 397 |
-
" ),\n",
|
| 398 |
-
" )\n",
|
| 399 |
-
" data_frames.append(cur_df)\n",
|
| 400 |
"\n",
|
| 401 |
-
" elif \"new_pending\" in filename:\n",
|
| 402 |
-
" cur_df = pd.melt(\n",
|
| 403 |
-
" cur_df,\n",
|
| 404 |
-
" id_vars=exclude_columns,\n",
|
| 405 |
-
" value_vars=columns_to_pivot,\n",
|
| 406 |
-
" var_name=\"Date\",\n",
|
| 407 |
-
" value_name=\"New Pending\" if not smoothed else \"New Pending (Smoothed)\",\n",
|
| 408 |
-
" )\n",
|
| 409 |
-
" data_frames.append(cur_df)\n",
|
| 410 |
"\n",
|
| 411 |
-
"
|
| 412 |
-
"
|
| 413 |
-
" \
|
| 414 |
-
"
|
| 415 |
-
"
|
| 416 |
-
"
|
| 417 |
-
"
|
| 418 |
-
"
|
| 419 |
-
"
|
| 420 |
-
"\n",
|
| 421 |
-
"
|
| 422 |
-
"
|
| 423 |
-
" combined_df = None\n",
|
| 424 |
-
" if len(data_frames) > 1:\n",
|
| 425 |
-
" # iterate over dataframes and merge or concat\n",
|
| 426 |
-
" combined_df = data_frames[0]\n",
|
| 427 |
-
" for i in range(1, len(data_frames)):\n",
|
| 428 |
-
" cur_df = data_frames[i]\n",
|
| 429 |
-
" combined_df = pd.merge(\n",
|
| 430 |
-
" combined_df,\n",
|
| 431 |
-
" cur_df,\n",
|
| 432 |
-
" on=[\n",
|
| 433 |
-
" \"RegionID\",\n",
|
| 434 |
-
" \"SizeRank\",\n",
|
| 435 |
-
" \"RegionName\",\n",
|
| 436 |
-
" \"RegionType\",\n",
|
| 437 |
-
" \"StateName\",\n",
|
| 438 |
-
" \"Home Type\",\n",
|
| 439 |
-
" \"Date\",\n",
|
| 440 |
-
" ],\n",
|
| 441 |
-
" suffixes=(\"\", \"_\" + str(i)),\n",
|
| 442 |
-
" how=\"outer\",\n",
|
| 443 |
-
" )\n",
|
| 444 |
-
" elif len(data_frames) == 1:\n",
|
| 445 |
-
" combined_df = data_frames[0]\n",
|
| 446 |
-
"\n",
|
| 447 |
-
" return combined_df\n",
|
| 448 |
-
"\n",
|
| 449 |
-
"\n",
|
| 450 |
-
"combined_df = get_combined_df(data_frames)\n",
|
| 451 |
"\n",
|
| 452 |
"\n",
|
| 453 |
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
|
@@ -460,22 +417,14 @@
|
|
| 460 |
" \"New Pending\",\n",
|
| 461 |
"]\n",
|
| 462 |
"\n",
|
| 463 |
-
"
|
| 464 |
-
" for col in combined_df.columns:\n",
|
| 465 |
-
" for column_to_coalesce in columns_to_coalesce:\n",
|
| 466 |
-
" if column_to_coalesce in col and \"_\" in col:\n",
|
| 467 |
-
" if not pd.isna(row[col]):\n",
|
| 468 |
-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
|
| 469 |
-
"\n",
|
| 470 |
-
"# remove columns with underscores\n",
|
| 471 |
-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
|
| 472 |
"\n",
|
| 473 |
"combined_df"
|
| 474 |
]
|
| 475 |
},
|
| 476 |
{
|
| 477 |
"cell_type": "code",
|
| 478 |
-
"execution_count":
|
| 479 |
"metadata": {},
|
| 480 |
"outputs": [
|
| 481 |
{
|
|
@@ -506,12 +455,12 @@
|
|
| 506 |
" <th>State</th>\n",
|
| 507 |
" <th>Home Type</th>\n",
|
| 508 |
" <th>Date</th>\n",
|
| 509 |
-
" <th>New Pending (Smoothed)</th>\n",
|
| 510 |
" <th>Median Listing Price</th>\n",
|
| 511 |
" <th>Median Listing Price (Smoothed)</th>\n",
|
| 512 |
-
" <th>New Pending</th>\n",
|
| 513 |
" <th>New Listings</th>\n",
|
| 514 |
" <th>New Listings (Smoothed)</th>\n",
|
|
|
|
| 515 |
" </tr>\n",
|
| 516 |
" </thead>\n",
|
| 517 |
" <tbody>\n",
|
|
@@ -524,12 +473,12 @@
|
|
| 524 |
" <td>NaN</td>\n",
|
| 525 |
" <td>SFR</td>\n",
|
| 526 |
" <td>2018-01-13</td>\n",
|
| 527 |
-
" <td>NaN</td>\n",
|
| 528 |
" <td>259000.0</td>\n",
|
| 529 |
" <td>NaN</td>\n",
|
| 530 |
" <td>NaN</td>\n",
|
| 531 |
" <td>NaN</td>\n",
|
| 532 |
" <td>NaN</td>\n",
|
|
|
|
| 533 |
" </tr>\n",
|
| 534 |
" <tr>\n",
|
| 535 |
" <th>1</th>\n",
|
|
@@ -540,12 +489,12 @@
|
|
| 540 |
" <td>NaN</td>\n",
|
| 541 |
" <td>SFR</td>\n",
|
| 542 |
" <td>2018-01-20</td>\n",
|
| 543 |
-
" <td>NaN</td>\n",
|
| 544 |
" <td>259900.0</td>\n",
|
| 545 |
" <td>NaN</td>\n",
|
| 546 |
" <td>NaN</td>\n",
|
| 547 |
" <td>NaN</td>\n",
|
| 548 |
" <td>NaN</td>\n",
|
|
|
|
| 549 |
" </tr>\n",
|
| 550 |
" <tr>\n",
|
| 551 |
" <th>2</th>\n",
|
|
@@ -556,12 +505,12 @@
|
|
| 556 |
" <td>NaN</td>\n",
|
| 557 |
" <td>SFR</td>\n",
|
| 558 |
" <td>2018-01-27</td>\n",
|
| 559 |
-
" <td>NaN</td>\n",
|
| 560 |
" <td>259900.0</td>\n",
|
| 561 |
" <td>NaN</td>\n",
|
| 562 |
" <td>NaN</td>\n",
|
| 563 |
" <td>NaN</td>\n",
|
| 564 |
" <td>NaN</td>\n",
|
|
|
|
| 565 |
" </tr>\n",
|
| 566 |
" <tr>\n",
|
| 567 |
" <th>3</th>\n",
|
|
@@ -571,9 +520,9 @@
|
|
| 571 |
" <td>country</td>\n",
|
| 572 |
" <td>NaN</td>\n",
|
| 573 |
" <td>SFR</td>\n",
|
| 574 |
-
" <td>2018-
|
| 575 |
-
" <td>
|
| 576 |
-
" <td>
|
| 577 |
" <td>NaN</td>\n",
|
| 578 |
" <td>NaN</td>\n",
|
| 579 |
" <td>NaN</td>\n",
|
|
@@ -587,10 +536,10 @@
|
|
| 587 |
" <td>country</td>\n",
|
| 588 |
" <td>NaN</td>\n",
|
| 589 |
" <td>SFR</td>\n",
|
| 590 |
-
" <td>2018-02-
|
|
|
|
|
|
|
| 591 |
" <td>NaN</td>\n",
|
| 592 |
-
" <td>260000.0</td>\n",
|
| 593 |
-
" <td>259700.0</td>\n",
|
| 594 |
" <td>NaN</td>\n",
|
| 595 |
" <td>NaN</td>\n",
|
| 596 |
" <td>NaN</td>\n",
|
|
@@ -612,71 +561,71 @@
|
|
| 612 |
" <td>...</td>\n",
|
| 613 |
" </tr>\n",
|
| 614 |
" <tr>\n",
|
| 615 |
-
" <th>
|
| 616 |
" <td>845172</td>\n",
|
| 617 |
" <td>769</td>\n",
|
| 618 |
" <td>Winfield, KS</td>\n",
|
| 619 |
" <td>msa</td>\n",
|
| 620 |
" <td>KS</td>\n",
|
| 621 |
" <td>all homes</td>\n",
|
| 622 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 623 |
" <td>NaN</td>\n",
|
| 624 |
-
" <td>133938.0</td>\n",
|
| 625 |
-
" <td>133938.0</td>\n",
|
| 626 |
" <td>NaN</td>\n",
|
| 627 |
" <td>NaN</td>\n",
|
| 628 |
" <td>NaN</td>\n",
|
| 629 |
" </tr>\n",
|
| 630 |
" <tr>\n",
|
| 631 |
-
" <th>
|
| 632 |
" <td>845172</td>\n",
|
| 633 |
" <td>769</td>\n",
|
| 634 |
" <td>Winfield, KS</td>\n",
|
| 635 |
" <td>msa</td>\n",
|
| 636 |
" <td>KS</td>\n",
|
| 637 |
" <td>all homes</td>\n",
|
| 638 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 639 |
" <td>NaN</td>\n",
|
| 640 |
-
" <td>126463.0</td>\n",
|
| 641 |
-
" <td>126463.0</td>\n",
|
| 642 |
" <td>NaN</td>\n",
|
| 643 |
" <td>NaN</td>\n",
|
| 644 |
" <td>NaN</td>\n",
|
| 645 |
" </tr>\n",
|
| 646 |
" <tr>\n",
|
| 647 |
-
" <th>
|
| 648 |
" <td>845172</td>\n",
|
| 649 |
" <td>769</td>\n",
|
| 650 |
" <td>Winfield, KS</td>\n",
|
| 651 |
" <td>msa</td>\n",
|
| 652 |
" <td>KS</td>\n",
|
| 653 |
" <td>all homes</td>\n",
|
| 654 |
-
" <td>2023-12-
|
|
|
|
|
|
|
| 655 |
" <td>NaN</td>\n",
|
| 656 |
-
" <td>123225.0</td>\n",
|
| 657 |
-
" <td>123225.0</td>\n",
|
| 658 |
" <td>NaN</td>\n",
|
| 659 |
" <td>NaN</td>\n",
|
| 660 |
" <td>NaN</td>\n",
|
| 661 |
" </tr>\n",
|
| 662 |
" <tr>\n",
|
| 663 |
-
" <th>
|
| 664 |
" <td>845172</td>\n",
|
| 665 |
" <td>769</td>\n",
|
| 666 |
" <td>Winfield, KS</td>\n",
|
| 667 |
" <td>msa</td>\n",
|
| 668 |
" <td>KS</td>\n",
|
| 669 |
" <td>all homes</td>\n",
|
| 670 |
-
" <td>2023-12-
|
| 671 |
-
" <td>
|
| 672 |
-
" <td>
|
| 673 |
-
" <td>
|
| 674 |
-
" <td>
|
| 675 |
-
" <td>
|
| 676 |
-
" <td>
|
| 677 |
" </tr>\n",
|
| 678 |
" <tr>\n",
|
| 679 |
-
" <th>
|
| 680 |
" <td>845172</td>\n",
|
| 681 |
" <td>769</td>\n",
|
| 682 |
" <td>Winfield, KS</td>\n",
|
|
@@ -684,16 +633,16 @@
|
|
| 684 |
" <td>KS</td>\n",
|
| 685 |
" <td>all homes</td>\n",
|
| 686 |
" <td>2024-01-06</td>\n",
|
| 687 |
-
" <td>
|
| 688 |
-
" <td>121488.0</td>\n",
|
| 689 |
" <td>121488.0</td>\n",
|
| 690 |
" <td>NaN</td>\n",
|
| 691 |
" <td>NaN</td>\n",
|
| 692 |
" <td>NaN</td>\n",
|
|
|
|
| 693 |
" </tr>\n",
|
| 694 |
" </tbody>\n",
|
| 695 |
"</table>\n",
|
| 696 |
-
"<p>
|
| 697 |
"</div>"
|
| 698 |
],
|
| 699 |
"text/plain": [
|
|
@@ -704,62 +653,62 @@
|
|
| 704 |
"3 102001 0 United States country NaN SFR \n",
|
| 705 |
"4 102001 0 United States country NaN SFR \n",
|
| 706 |
"... ... ... ... ... ... ... \n",
|
| 707 |
-
"
|
| 708 |
-
"
|
| 709 |
-
"
|
| 710 |
-
"
|
| 711 |
-
"
|
| 712 |
"\n",
|
| 713 |
-
" Date
|
| 714 |
-
"0 2018-01-13
|
| 715 |
-
"1 2018-01-20
|
| 716 |
-
"2 2018-01-27
|
| 717 |
-
"3 2018-
|
| 718 |
-
"4 2018-02-
|
| 719 |
-
"... ...
|
| 720 |
-
"
|
| 721 |
-
"
|
| 722 |
-
"
|
| 723 |
-
"
|
| 724 |
-
"
|
| 725 |
"\n",
|
| 726 |
-
"
|
| 727 |
-
"0
|
| 728 |
-
"1
|
| 729 |
-
"2
|
| 730 |
-
"3
|
| 731 |
-
"4
|
| 732 |
-
"...
|
| 733 |
-
"
|
| 734 |
-
"
|
| 735 |
-
"
|
| 736 |
-
"
|
| 737 |
-
"
|
| 738 |
"\n",
|
| 739 |
-
" New
|
| 740 |
-
"0
|
| 741 |
-
"1
|
| 742 |
-
"2
|
| 743 |
-
"3
|
| 744 |
-
"4
|
| 745 |
-
"...
|
| 746 |
-
"
|
| 747 |
-
"
|
| 748 |
-
"
|
| 749 |
-
"
|
| 750 |
-
"
|
| 751 |
"\n",
|
| 752 |
-
"[
|
| 753 |
]
|
| 754 |
},
|
| 755 |
-
"execution_count":
|
| 756 |
"metadata": {},
|
| 757 |
"output_type": "execute_result"
|
| 758 |
}
|
| 759 |
],
|
| 760 |
"source": [
|
| 761 |
-
"
|
| 762 |
-
"final_df =
|
| 763 |
" columns={\n",
|
| 764 |
" \"RegionID\": \"Region ID\",\n",
|
| 765 |
" \"SizeRank\": \"Size Rank\",\n",
|
|
@@ -774,14 +723,11 @@
|
|
| 774 |
},
|
| 775 |
{
|
| 776 |
"cell_type": "code",
|
| 777 |
-
"execution_count":
|
| 778 |
"metadata": {},
|
| 779 |
"outputs": [],
|
| 780 |
"source": [
|
| 781 |
-
"
|
| 782 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 783 |
-
"\n",
|
| 784 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 785 |
]
|
| 786 |
}
|
| 787 |
],
|
|
|
|
| 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, get_df, 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 |
{
|
|
|
|
| 88 |
" <th>StateName</th>\n",
|
| 89 |
" <th>Home Type</th>\n",
|
| 90 |
" <th>Date</th>\n",
|
|
|
|
| 91 |
" <th>Median Listing Price</th>\n",
|
| 92 |
" <th>Median Listing Price (Smoothed)</th>\n",
|
| 93 |
+
" <th>New Pending (Smoothed)</th>\n",
|
| 94 |
" <th>New Listings</th>\n",
|
| 95 |
" <th>New Listings (Smoothed)</th>\n",
|
| 96 |
+
" <th>New Pending</th>\n",
|
| 97 |
" </tr>\n",
|
| 98 |
" </thead>\n",
|
| 99 |
" <tbody>\n",
|
|
|
|
| 106 |
" <td>NaN</td>\n",
|
| 107 |
" <td>SFR</td>\n",
|
| 108 |
" <td>2018-01-13</td>\n",
|
|
|
|
| 109 |
" <td>259000.0</td>\n",
|
| 110 |
" <td>NaN</td>\n",
|
| 111 |
" <td>NaN</td>\n",
|
| 112 |
" <td>NaN</td>\n",
|
| 113 |
" <td>NaN</td>\n",
|
| 114 |
+
" <td>NaN</td>\n",
|
| 115 |
" </tr>\n",
|
| 116 |
" <tr>\n",
|
| 117 |
" <th>1</th>\n",
|
|
|
|
| 122 |
" <td>NaN</td>\n",
|
| 123 |
" <td>SFR</td>\n",
|
| 124 |
" <td>2018-01-20</td>\n",
|
|
|
|
| 125 |
" <td>259900.0</td>\n",
|
| 126 |
" <td>NaN</td>\n",
|
| 127 |
" <td>NaN</td>\n",
|
| 128 |
" <td>NaN</td>\n",
|
| 129 |
" <td>NaN</td>\n",
|
| 130 |
+
" <td>NaN</td>\n",
|
| 131 |
" </tr>\n",
|
| 132 |
" <tr>\n",
|
| 133 |
" <th>2</th>\n",
|
|
|
|
| 138 |
" <td>NaN</td>\n",
|
| 139 |
" <td>SFR</td>\n",
|
| 140 |
" <td>2018-01-27</td>\n",
|
|
|
|
| 141 |
" <td>259900.0</td>\n",
|
| 142 |
" <td>NaN</td>\n",
|
| 143 |
" <td>NaN</td>\n",
|
| 144 |
" <td>NaN</td>\n",
|
| 145 |
" <td>NaN</td>\n",
|
| 146 |
+
" <td>NaN</td>\n",
|
| 147 |
" </tr>\n",
|
| 148 |
" <tr>\n",
|
| 149 |
" <th>3</th>\n",
|
|
|
|
| 153 |
" <td>country</td>\n",
|
| 154 |
" <td>NaN</td>\n",
|
| 155 |
" <td>SFR</td>\n",
|
| 156 |
+
" <td>2018-02-03</td>\n",
|
| 157 |
+
" <td>260000.0</td>\n",
|
| 158 |
+
" <td>259700.0</td>\n",
|
| 159 |
" <td>NaN</td>\n",
|
| 160 |
" <td>NaN</td>\n",
|
| 161 |
" <td>NaN</td>\n",
|
|
|
|
| 169 |
" <td>country</td>\n",
|
| 170 |
" <td>NaN</td>\n",
|
| 171 |
" <td>SFR</td>\n",
|
| 172 |
+
" <td>2018-02-10</td>\n",
|
| 173 |
+
" <td>264900.0</td>\n",
|
| 174 |
+
" <td>261175.0</td>\n",
|
| 175 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 176 |
" <td>NaN</td>\n",
|
| 177 |
" <td>NaN</td>\n",
|
| 178 |
" <td>NaN</td>\n",
|
|
|
|
| 194 |
" <td>...</td>\n",
|
| 195 |
" </tr>\n",
|
| 196 |
" <tr>\n",
|
| 197 |
+
" <th>578648</th>\n",
|
| 198 |
" <td>845172</td>\n",
|
| 199 |
" <td>769</td>\n",
|
| 200 |
" <td>Winfield, KS</td>\n",
|
| 201 |
" <td>msa</td>\n",
|
| 202 |
" <td>KS</td>\n",
|
| 203 |
" <td>all homes</td>\n",
|
| 204 |
+
" <td>2023-12-09</td>\n",
|
| 205 |
+
" <td>134950.0</td>\n",
|
| 206 |
+
" <td>138913.0</td>\n",
|
| 207 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 208 |
" <td>NaN</td>\n",
|
| 209 |
" <td>NaN</td>\n",
|
| 210 |
" <td>NaN</td>\n",
|
| 211 |
" </tr>\n",
|
| 212 |
" <tr>\n",
|
| 213 |
+
" <th>578649</th>\n",
|
| 214 |
" <td>845172</td>\n",
|
| 215 |
" <td>769</td>\n",
|
| 216 |
" <td>Winfield, KS</td>\n",
|
| 217 |
" <td>msa</td>\n",
|
| 218 |
" <td>KS</td>\n",
|
| 219 |
" <td>all homes</td>\n",
|
| 220 |
+
" <td>2023-12-16</td>\n",
|
| 221 |
+
" <td>120000.0</td>\n",
|
| 222 |
+
" <td>133938.0</td>\n",
|
| 223 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 224 |
" <td>NaN</td>\n",
|
| 225 |
" <td>NaN</td>\n",
|
| 226 |
" <td>NaN</td>\n",
|
| 227 |
" </tr>\n",
|
| 228 |
" <tr>\n",
|
| 229 |
+
" <th>578650</th>\n",
|
| 230 |
" <td>845172</td>\n",
|
| 231 |
" <td>769</td>\n",
|
| 232 |
" <td>Winfield, KS</td>\n",
|
| 233 |
" <td>msa</td>\n",
|
| 234 |
" <td>KS</td>\n",
|
| 235 |
" <td>all homes</td>\n",
|
| 236 |
+
" <td>2023-12-23</td>\n",
|
| 237 |
+
" <td>111000.0</td>\n",
|
| 238 |
+
" <td>126463.0</td>\n",
|
| 239 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 240 |
" <td>NaN</td>\n",
|
| 241 |
" <td>NaN</td>\n",
|
| 242 |
" <td>NaN</td>\n",
|
| 243 |
" </tr>\n",
|
| 244 |
" <tr>\n",
|
| 245 |
+
" <th>578651</th>\n",
|
| 246 |
" <td>845172</td>\n",
|
| 247 |
" <td>769</td>\n",
|
| 248 |
" <td>Winfield, KS</td>\n",
|
| 249 |
" <td>msa</td>\n",
|
| 250 |
" <td>KS</td>\n",
|
| 251 |
" <td>all homes</td>\n",
|
| 252 |
+
" <td>2023-12-30</td>\n",
|
| 253 |
+
" <td>126950.0</td>\n",
|
| 254 |
+
" <td>123225.0</td>\n",
|
| 255 |
+
" <td>NaN</td>\n",
|
| 256 |
+
" <td>NaN</td>\n",
|
| 257 |
+
" <td>NaN</td>\n",
|
| 258 |
+
" <td>NaN</td>\n",
|
| 259 |
" </tr>\n",
|
| 260 |
" <tr>\n",
|
| 261 |
+
" <th>578652</th>\n",
|
| 262 |
" <td>845172</td>\n",
|
| 263 |
" <td>769</td>\n",
|
| 264 |
" <td>Winfield, KS</td>\n",
|
|
|
|
| 266 |
" <td>KS</td>\n",
|
| 267 |
" <td>all homes</td>\n",
|
| 268 |
" <td>2024-01-06</td>\n",
|
| 269 |
+
" <td>128000.0</td>\n",
|
|
|
|
| 270 |
" <td>121488.0</td>\n",
|
| 271 |
" <td>NaN</td>\n",
|
| 272 |
" <td>NaN</td>\n",
|
| 273 |
" <td>NaN</td>\n",
|
| 274 |
+
" <td>NaN</td>\n",
|
| 275 |
" </tr>\n",
|
| 276 |
" </tbody>\n",
|
| 277 |
"</table>\n",
|
| 278 |
+
"<p>578653 rows × 13 columns</p>\n",
|
| 279 |
"</div>"
|
| 280 |
],
|
| 281 |
"text/plain": [
|
|
|
|
| 286 |
"3 102001 0 United States country NaN SFR \n",
|
| 287 |
"4 102001 0 United States country NaN SFR \n",
|
| 288 |
"... ... ... ... ... ... ... \n",
|
| 289 |
+
"578648 845172 769 Winfield, KS msa KS all homes \n",
|
| 290 |
+
"578649 845172 769 Winfield, KS msa KS all homes \n",
|
| 291 |
+
"578650 845172 769 Winfield, KS msa KS all homes \n",
|
| 292 |
+
"578651 845172 769 Winfield, KS msa KS all homes \n",
|
| 293 |
+
"578652 845172 769 Winfield, KS msa KS all homes \n",
|
| 294 |
"\n",
|
| 295 |
+
" Date Median Listing Price Median Listing Price (Smoothed) \\\n",
|
| 296 |
+
"0 2018-01-13 259000.0 NaN \n",
|
| 297 |
+
"1 2018-01-20 259900.0 NaN \n",
|
| 298 |
+
"2 2018-01-27 259900.0 NaN \n",
|
| 299 |
+
"3 2018-02-03 260000.0 259700.0 \n",
|
| 300 |
+
"4 2018-02-10 264900.0 261175.0 \n",
|
| 301 |
+
"... ... ... ... \n",
|
| 302 |
+
"578648 2023-12-09 134950.0 138913.0 \n",
|
| 303 |
+
"578649 2023-12-16 120000.0 133938.0 \n",
|
| 304 |
+
"578650 2023-12-23 111000.0 126463.0 \n",
|
| 305 |
+
"578651 2023-12-30 126950.0 123225.0 \n",
|
| 306 |
+
"578652 2024-01-06 128000.0 121488.0 \n",
|
| 307 |
"\n",
|
| 308 |
+
" New Pending (Smoothed) New Listings New Listings (Smoothed) \\\n",
|
| 309 |
+
"0 NaN NaN NaN \n",
|
| 310 |
+
"1 NaN NaN NaN \n",
|
| 311 |
+
"2 NaN NaN NaN \n",
|
| 312 |
+
"3 NaN NaN NaN \n",
|
| 313 |
+
"4 NaN NaN NaN \n",
|
| 314 |
+
"... ... ... ... \n",
|
| 315 |
+
"578648 NaN NaN NaN \n",
|
| 316 |
+
"578649 NaN NaN NaN \n",
|
| 317 |
+
"578650 NaN NaN NaN \n",
|
| 318 |
+
"578651 NaN NaN NaN \n",
|
| 319 |
+
"578652 NaN NaN NaN \n",
|
| 320 |
"\n",
|
| 321 |
+
" New Pending \n",
|
| 322 |
+
"0 NaN \n",
|
| 323 |
+
"1 NaN \n",
|
| 324 |
+
"2 NaN \n",
|
| 325 |
+
"3 NaN \n",
|
| 326 |
+
"4 NaN \n",
|
| 327 |
+
"... ... \n",
|
| 328 |
+
"578648 NaN \n",
|
| 329 |
+
"578649 NaN \n",
|
| 330 |
+
"578650 NaN \n",
|
| 331 |
+
"578651 NaN \n",
|
| 332 |
+
"578652 NaN \n",
|
| 333 |
"\n",
|
| 334 |
+
"[578653 rows x 13 columns]"
|
| 335 |
]
|
| 336 |
},
|
| 337 |
+
"execution_count": 3,
|
| 338 |
"metadata": {},
|
| 339 |
"output_type": "execute_result"
|
| 340 |
}
|
|
|
|
| 351 |
" \"Home Type\",\n",
|
| 352 |
"]\n",
|
| 353 |
"\n",
|
| 354 |
+
"slug_column_mappings = {\n",
|
| 355 |
+
" \"_mlp_\": \"Median Listing Price\",\n",
|
| 356 |
+
" \"_new_listings_\": \"New Listings\",\n",
|
| 357 |
+
" \"new_pending\": \"New Pending\",\n",
|
| 358 |
+
"}\n",
|
| 359 |
+
"\n",
|
| 360 |
+
"\n",
|
| 361 |
"data_frames = []\n",
|
| 362 |
"\n",
|
| 363 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
|
|
| 366 |
" cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
|
| 367 |
"\n",
|
| 368 |
" # ignore monthly data for now since it is redundant\n",
|
| 369 |
+
" if \"month\" in filename:\n",
|
| 370 |
" continue\n",
|
| 371 |
"\n",
|
| 372 |
" if \"sfrcondo\" in filename:\n",
|
|
|
|
| 379 |
" # Identify columns to pivot\n",
|
| 380 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 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",
|
| 397 |
+
" data_frames,\n",
|
| 398 |
+
" [\n",
|
| 399 |
+
" \"RegionID\",\n",
|
| 400 |
+
" \"SizeRank\",\n",
|
| 401 |
+
" \"RegionName\",\n",
|
| 402 |
+
" \"RegionType\",\n",
|
| 403 |
+
" \"StateName\",\n",
|
| 404 |
+
" \"Home Type\",\n",
|
| 405 |
+
" \"Date\",\n",
|
| 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",
|
|
|
|
| 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 |
]
|
| 424 |
},
|
| 425 |
{
|
| 426 |
"cell_type": "code",
|
| 427 |
+
"execution_count": 4,
|
| 428 |
"metadata": {},
|
| 429 |
"outputs": [
|
| 430 |
{
|
|
|
|
| 455 |
" <th>State</th>\n",
|
| 456 |
" <th>Home Type</th>\n",
|
| 457 |
" <th>Date</th>\n",
|
|
|
|
| 458 |
" <th>Median Listing Price</th>\n",
|
| 459 |
" <th>Median Listing Price (Smoothed)</th>\n",
|
| 460 |
+
" <th>New Pending (Smoothed)</th>\n",
|
| 461 |
" <th>New Listings</th>\n",
|
| 462 |
" <th>New Listings (Smoothed)</th>\n",
|
| 463 |
+
" <th>New Pending</th>\n",
|
| 464 |
" </tr>\n",
|
| 465 |
" </thead>\n",
|
| 466 |
" <tbody>\n",
|
|
|
|
| 473 |
" <td>NaN</td>\n",
|
| 474 |
" <td>SFR</td>\n",
|
| 475 |
" <td>2018-01-13</td>\n",
|
|
|
|
| 476 |
" <td>259000.0</td>\n",
|
| 477 |
" <td>NaN</td>\n",
|
| 478 |
" <td>NaN</td>\n",
|
| 479 |
" <td>NaN</td>\n",
|
| 480 |
" <td>NaN</td>\n",
|
| 481 |
+
" <td>NaN</td>\n",
|
| 482 |
" </tr>\n",
|
| 483 |
" <tr>\n",
|
| 484 |
" <th>1</th>\n",
|
|
|
|
| 489 |
" <td>NaN</td>\n",
|
| 490 |
" <td>SFR</td>\n",
|
| 491 |
" <td>2018-01-20</td>\n",
|
|
|
|
| 492 |
" <td>259900.0</td>\n",
|
| 493 |
" <td>NaN</td>\n",
|
| 494 |
" <td>NaN</td>\n",
|
| 495 |
" <td>NaN</td>\n",
|
| 496 |
" <td>NaN</td>\n",
|
| 497 |
+
" <td>NaN</td>\n",
|
| 498 |
" </tr>\n",
|
| 499 |
" <tr>\n",
|
| 500 |
" <th>2</th>\n",
|
|
|
|
| 505 |
" <td>NaN</td>\n",
|
| 506 |
" <td>SFR</td>\n",
|
| 507 |
" <td>2018-01-27</td>\n",
|
|
|
|
| 508 |
" <td>259900.0</td>\n",
|
| 509 |
" <td>NaN</td>\n",
|
| 510 |
" <td>NaN</td>\n",
|
| 511 |
" <td>NaN</td>\n",
|
| 512 |
" <td>NaN</td>\n",
|
| 513 |
+
" <td>NaN</td>\n",
|
| 514 |
" </tr>\n",
|
| 515 |
" <tr>\n",
|
| 516 |
" <th>3</th>\n",
|
|
|
|
| 520 |
" <td>country</td>\n",
|
| 521 |
" <td>NaN</td>\n",
|
| 522 |
" <td>SFR</td>\n",
|
| 523 |
+
" <td>2018-02-03</td>\n",
|
| 524 |
+
" <td>260000.0</td>\n",
|
| 525 |
+
" <td>259700.0</td>\n",
|
| 526 |
" <td>NaN</td>\n",
|
| 527 |
" <td>NaN</td>\n",
|
| 528 |
" <td>NaN</td>\n",
|
|
|
|
| 536 |
" <td>country</td>\n",
|
| 537 |
" <td>NaN</td>\n",
|
| 538 |
" <td>SFR</td>\n",
|
| 539 |
+
" <td>2018-02-10</td>\n",
|
| 540 |
+
" <td>264900.0</td>\n",
|
| 541 |
+
" <td>261175.0</td>\n",
|
| 542 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 543 |
" <td>NaN</td>\n",
|
| 544 |
" <td>NaN</td>\n",
|
| 545 |
" <td>NaN</td>\n",
|
|
|
|
| 561 |
" <td>...</td>\n",
|
| 562 |
" </tr>\n",
|
| 563 |
" <tr>\n",
|
| 564 |
+
" <th>578648</th>\n",
|
| 565 |
" <td>845172</td>\n",
|
| 566 |
" <td>769</td>\n",
|
| 567 |
" <td>Winfield, KS</td>\n",
|
| 568 |
" <td>msa</td>\n",
|
| 569 |
" <td>KS</td>\n",
|
| 570 |
" <td>all homes</td>\n",
|
| 571 |
+
" <td>2023-12-09</td>\n",
|
| 572 |
+
" <td>134950.0</td>\n",
|
| 573 |
+
" <td>138913.0</td>\n",
|
| 574 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 575 |
" <td>NaN</td>\n",
|
| 576 |
" <td>NaN</td>\n",
|
| 577 |
" <td>NaN</td>\n",
|
| 578 |
" </tr>\n",
|
| 579 |
" <tr>\n",
|
| 580 |
+
" <th>578649</th>\n",
|
| 581 |
" <td>845172</td>\n",
|
| 582 |
" <td>769</td>\n",
|
| 583 |
" <td>Winfield, KS</td>\n",
|
| 584 |
" <td>msa</td>\n",
|
| 585 |
" <td>KS</td>\n",
|
| 586 |
" <td>all homes</td>\n",
|
| 587 |
+
" <td>2023-12-16</td>\n",
|
| 588 |
+
" <td>120000.0</td>\n",
|
| 589 |
+
" <td>133938.0</td>\n",
|
| 590 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 591 |
" <td>NaN</td>\n",
|
| 592 |
" <td>NaN</td>\n",
|
| 593 |
" <td>NaN</td>\n",
|
| 594 |
" </tr>\n",
|
| 595 |
" <tr>\n",
|
| 596 |
+
" <th>578650</th>\n",
|
| 597 |
" <td>845172</td>\n",
|
| 598 |
" <td>769</td>\n",
|
| 599 |
" <td>Winfield, KS</td>\n",
|
| 600 |
" <td>msa</td>\n",
|
| 601 |
" <td>KS</td>\n",
|
| 602 |
" <td>all homes</td>\n",
|
| 603 |
+
" <td>2023-12-23</td>\n",
|
| 604 |
+
" <td>111000.0</td>\n",
|
| 605 |
+
" <td>126463.0</td>\n",
|
| 606 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 607 |
" <td>NaN</td>\n",
|
| 608 |
" <td>NaN</td>\n",
|
| 609 |
" <td>NaN</td>\n",
|
| 610 |
" </tr>\n",
|
| 611 |
" <tr>\n",
|
| 612 |
+
" <th>578651</th>\n",
|
| 613 |
" <td>845172</td>\n",
|
| 614 |
" <td>769</td>\n",
|
| 615 |
" <td>Winfield, KS</td>\n",
|
| 616 |
" <td>msa</td>\n",
|
| 617 |
" <td>KS</td>\n",
|
| 618 |
" <td>all homes</td>\n",
|
| 619 |
+
" <td>2023-12-30</td>\n",
|
| 620 |
+
" <td>126950.0</td>\n",
|
| 621 |
+
" <td>123225.0</td>\n",
|
| 622 |
+
" <td>NaN</td>\n",
|
| 623 |
+
" <td>NaN</td>\n",
|
| 624 |
+
" <td>NaN</td>\n",
|
| 625 |
+
" <td>NaN</td>\n",
|
| 626 |
" </tr>\n",
|
| 627 |
" <tr>\n",
|
| 628 |
+
" <th>578652</th>\n",
|
| 629 |
" <td>845172</td>\n",
|
| 630 |
" <td>769</td>\n",
|
| 631 |
" <td>Winfield, KS</td>\n",
|
|
|
|
| 633 |
" <td>KS</td>\n",
|
| 634 |
" <td>all homes</td>\n",
|
| 635 |
" <td>2024-01-06</td>\n",
|
| 636 |
+
" <td>128000.0</td>\n",
|
|
|
|
| 637 |
" <td>121488.0</td>\n",
|
| 638 |
" <td>NaN</td>\n",
|
| 639 |
" <td>NaN</td>\n",
|
| 640 |
" <td>NaN</td>\n",
|
| 641 |
+
" <td>NaN</td>\n",
|
| 642 |
" </tr>\n",
|
| 643 |
" </tbody>\n",
|
| 644 |
"</table>\n",
|
| 645 |
+
"<p>578653 rows × 13 columns</p>\n",
|
| 646 |
"</div>"
|
| 647 |
],
|
| 648 |
"text/plain": [
|
|
|
|
| 653 |
"3 102001 0 United States country NaN SFR \n",
|
| 654 |
"4 102001 0 United States country NaN SFR \n",
|
| 655 |
"... ... ... ... ... ... ... \n",
|
| 656 |
+
"578648 845172 769 Winfield, KS msa KS all homes \n",
|
| 657 |
+
"578649 845172 769 Winfield, KS msa KS all homes \n",
|
| 658 |
+
"578650 845172 769 Winfield, KS msa KS all homes \n",
|
| 659 |
+
"578651 845172 769 Winfield, KS msa KS all homes \n",
|
| 660 |
+
"578652 845172 769 Winfield, KS msa KS all homes \n",
|
| 661 |
"\n",
|
| 662 |
+
" Date Median Listing Price Median Listing Price (Smoothed) \\\n",
|
| 663 |
+
"0 2018-01-13 259000.0 NaN \n",
|
| 664 |
+
"1 2018-01-20 259900.0 NaN \n",
|
| 665 |
+
"2 2018-01-27 259900.0 NaN \n",
|
| 666 |
+
"3 2018-02-03 260000.0 259700.0 \n",
|
| 667 |
+
"4 2018-02-10 264900.0 261175.0 \n",
|
| 668 |
+
"... ... ... ... \n",
|
| 669 |
+
"578648 2023-12-09 134950.0 138913.0 \n",
|
| 670 |
+
"578649 2023-12-16 120000.0 133938.0 \n",
|
| 671 |
+
"578650 2023-12-23 111000.0 126463.0 \n",
|
| 672 |
+
"578651 2023-12-30 126950.0 123225.0 \n",
|
| 673 |
+
"578652 2024-01-06 128000.0 121488.0 \n",
|
| 674 |
"\n",
|
| 675 |
+
" New Pending (Smoothed) New Listings New Listings (Smoothed) \\\n",
|
| 676 |
+
"0 NaN NaN NaN \n",
|
| 677 |
+
"1 NaN NaN NaN \n",
|
| 678 |
+
"2 NaN NaN NaN \n",
|
| 679 |
+
"3 NaN NaN NaN \n",
|
| 680 |
+
"4 NaN NaN NaN \n",
|
| 681 |
+
"... ... ... ... \n",
|
| 682 |
+
"578648 NaN NaN NaN \n",
|
| 683 |
+
"578649 NaN NaN NaN \n",
|
| 684 |
+
"578650 NaN NaN NaN \n",
|
| 685 |
+
"578651 NaN NaN NaN \n",
|
| 686 |
+
"578652 NaN NaN NaN \n",
|
| 687 |
"\n",
|
| 688 |
+
" New Pending \n",
|
| 689 |
+
"0 NaN \n",
|
| 690 |
+
"1 NaN \n",
|
| 691 |
+
"2 NaN \n",
|
| 692 |
+
"3 NaN \n",
|
| 693 |
+
"4 NaN \n",
|
| 694 |
+
"... ... \n",
|
| 695 |
+
"578648 NaN \n",
|
| 696 |
+
"578649 NaN \n",
|
| 697 |
+
"578650 NaN \n",
|
| 698 |
+
"578651 NaN \n",
|
| 699 |
+
"578652 NaN \n",
|
| 700 |
"\n",
|
| 701 |
+
"[578653 rows x 13 columns]"
|
| 702 |
]
|
| 703 |
},
|
| 704 |
+
"execution_count": 4,
|
| 705 |
"metadata": {},
|
| 706 |
"output_type": "execute_result"
|
| 707 |
}
|
| 708 |
],
|
| 709 |
"source": [
|
| 710 |
+
"# Adjust column names\n",
|
| 711 |
+
"final_df = combined_df.rename(\n",
|
| 712 |
" columns={\n",
|
| 713 |
" \"RegionID\": \"Region ID\",\n",
|
| 714 |
" \"SizeRank\": \"Size Rank\",\n",
|
|
|
|
| 723 |
},
|
| 724 |
{
|
| 725 |
"cell_type": "code",
|
| 726 |
+
"execution_count": 5,
|
| 727 |
"metadata": {},
|
| 728 |
"outputs": [],
|
| 729 |
"source": [
|
| 730 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 731 |
]
|
| 732 |
}
|
| 733 |
],
|
processors/helpers.py
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import pandas as pd
|
| 2 |
+
import os
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
def get_combined_df(data_frames, on):
|
| 6 |
+
combined_df = None
|
| 7 |
+
if len(data_frames) > 1:
|
| 8 |
+
# iterate over dataframes and merge or concat
|
| 9 |
+
combined_df = data_frames[0]
|
| 10 |
+
for i in range(1, len(data_frames)):
|
| 11 |
+
cur_df = data_frames[i]
|
| 12 |
+
combined_df = pd.merge(
|
| 13 |
+
combined_df,
|
| 14 |
+
cur_df,
|
| 15 |
+
on=on,
|
| 16 |
+
how="outer",
|
| 17 |
+
suffixes=("", "_" + str(i)),
|
| 18 |
+
)
|
| 19 |
+
elif len(data_frames) == 1:
|
| 20 |
+
combined_df = data_frames[0]
|
| 21 |
+
|
| 22 |
+
return combined_df
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def coalesce_columns(df, columns_to_coalesce):
|
| 26 |
+
for index, row in df.iterrows():
|
| 27 |
+
for col in df.columns:
|
| 28 |
+
for column_to_coalesce in columns_to_coalesce:
|
| 29 |
+
if column_to_coalesce in col and "_" in col:
|
| 30 |
+
if not pd.isna(row[col]):
|
| 31 |
+
df.at[index, column_to_coalesce] = row[col]
|
| 32 |
+
|
| 33 |
+
# remove columns with underscores
|
| 34 |
+
combined_df = df[[col for col in df.columns if "_" not in col]]
|
| 35 |
+
return combined_df
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def get_df(
|
| 39 |
+
df,
|
| 40 |
+
exclude_columns,
|
| 41 |
+
columns_to_pivot,
|
| 42 |
+
col_name,
|
| 43 |
+
filename,
|
| 44 |
+
):
|
| 45 |
+
smoothed = "_sm_" in filename
|
| 46 |
+
seasonally_adjusted = "_sa_" in filename
|
| 47 |
+
|
| 48 |
+
if smoothed:
|
| 49 |
+
col_name += " (Smoothed)"
|
| 50 |
+
if seasonally_adjusted:
|
| 51 |
+
col_name += " (Seasonally Adjusted)"
|
| 52 |
+
|
| 53 |
+
df = pd.melt(
|
| 54 |
+
df,
|
| 55 |
+
id_vars=exclude_columns,
|
| 56 |
+
value_vars=columns_to_pivot,
|
| 57 |
+
var_name="Date",
|
| 58 |
+
value_name=col_name,
|
| 59 |
+
)
|
| 60 |
+
return df
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
|
| 64 |
+
if not os.path.exists(FULL_PROCESSED_DIR_PATH):
|
| 65 |
+
os.makedirs(FULL_PROCESSED_DIR_PATH)
|
| 66 |
+
|
| 67 |
+
final_df.to_json(
|
| 68 |
+
FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
|
| 69 |
+
)
|
processors/home_value_forecasts.ipynb
CHANGED
|
@@ -2,17 +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"
|
|
|
|
|
|
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
| 14 |
"cell_type": "code",
|
| 15 |
-
"execution_count":
|
| 16 |
"metadata": {},
|
| 17 |
"outputs": [],
|
| 18 |
"source": [
|
|
@@ -25,7 +27,7 @@
|
|
| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -361,7 +363,7 @@
|
|
| 361 |
"[21062 rows x 16 columns]"
|
| 362 |
]
|
| 363 |
},
|
| 364 |
-
"execution_count":
|
| 365 |
"metadata": {},
|
| 366 |
"output_type": "execute_result"
|
| 367 |
}
|
|
@@ -418,7 +420,7 @@
|
|
| 418 |
},
|
| 419 |
{
|
| 420 |
"cell_type": "code",
|
| 421 |
-
"execution_count":
|
| 422 |
"metadata": {},
|
| 423 |
"outputs": [
|
| 424 |
{
|
|
@@ -732,7 +734,7 @@
|
|
| 732 |
"[21062 rows x 15 columns]"
|
| 733 |
]
|
| 734 |
},
|
| 735 |
-
"execution_count":
|
| 736 |
"metadata": {},
|
| 737 |
"output_type": "execute_result"
|
| 738 |
}
|
|
@@ -783,14 +785,11 @@
|
|
| 783 |
},
|
| 784 |
{
|
| 785 |
"cell_type": "code",
|
| 786 |
-
"execution_count":
|
| 787 |
"metadata": {},
|
| 788 |
"outputs": [],
|
| 789 |
"source": [
|
| 790 |
-
"
|
| 791 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 792 |
-
"\n",
|
| 793 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 794 |
]
|
| 795 |
}
|
| 796 |
],
|
|
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 4,
|
| 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": 5,
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
|
|
|
| 27 |
},
|
| 28 |
{
|
| 29 |
"cell_type": "code",
|
| 30 |
+
"execution_count": 6,
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
|
|
| 363 |
"[21062 rows x 16 columns]"
|
| 364 |
]
|
| 365 |
},
|
| 366 |
+
"execution_count": 6,
|
| 367 |
"metadata": {},
|
| 368 |
"output_type": "execute_result"
|
| 369 |
}
|
|
|
|
| 420 |
},
|
| 421 |
{
|
| 422 |
"cell_type": "code",
|
| 423 |
+
"execution_count": 8,
|
| 424 |
"metadata": {},
|
| 425 |
"outputs": [
|
| 426 |
{
|
|
|
|
| 734 |
"[21062 rows x 15 columns]"
|
| 735 |
]
|
| 736 |
},
|
| 737 |
+
"execution_count": 8,
|
| 738 |
"metadata": {},
|
| 739 |
"output_type": "execute_result"
|
| 740 |
}
|
|
|
|
| 785 |
},
|
| 786 |
{
|
| 787 |
"cell_type": "code",
|
| 788 |
+
"execution_count": 9,
|
| 789 |
"metadata": {},
|
| 790 |
"outputs": [],
|
| 791 |
"source": [
|
| 792 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 793 |
]
|
| 794 |
}
|
| 795 |
],
|
processors/home_values.ipynb
CHANGED
|
@@ -2,17 +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"
|
|
|
|
|
|
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
| 14 |
"cell_type": "code",
|
| 15 |
-
"execution_count":
|
| 16 |
"metadata": {},
|
| 17 |
"outputs": [],
|
| 18 |
"source": [
|
|
@@ -25,7 +27,7 @@
|
|
| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -65,7 +67,6 @@
|
|
| 65 |
"processing Zip_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 66 |
"processing Neighborhood_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 67 |
"processing City_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 68 |
-
"processing County_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_month (1).csv\n",
|
| 69 |
"processing County_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 70 |
"processing Neighborhood_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 71 |
"processing Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_month.csv\n",
|
|
@@ -88,25 +89,7 @@
|
|
| 88 |
"processing Neighborhood_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 89 |
"processing County_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 90 |
"processing County_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 91 |
-
"processing Metro_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n"
|
| 92 |
-
"1\n",
|
| 93 |
-
"10\n",
|
| 94 |
-
"2\n",
|
| 95 |
-
"10\n",
|
| 96 |
-
"3\n",
|
| 97 |
-
"10\n",
|
| 98 |
-
"4\n",
|
| 99 |
-
"10\n",
|
| 100 |
-
"5\n",
|
| 101 |
-
"10\n",
|
| 102 |
-
"6\n",
|
| 103 |
-
"10\n",
|
| 104 |
-
"7\n",
|
| 105 |
-
"10\n",
|
| 106 |
-
"8\n",
|
| 107 |
-
"10\n",
|
| 108 |
-
"9\n",
|
| 109 |
-
"10\n"
|
| 110 |
]
|
| 111 |
},
|
| 112 |
{
|
|
@@ -547,7 +530,7 @@
|
|
| 547 |
"[117912 rows x 18 columns]"
|
| 548 |
]
|
| 549 |
},
|
| 550 |
-
"execution_count":
|
| 551 |
"metadata": {},
|
| 552 |
"output_type": "execute_result"
|
| 553 |
}
|
|
@@ -731,56 +714,27 @@
|
|
| 731 |
" data_frames.append(cur_df)\n",
|
| 732 |
"\n",
|
| 733 |
"\n",
|
| 734 |
-
"
|
| 735 |
-
"
|
| 736 |
-
"
|
| 737 |
-
"
|
| 738 |
-
"
|
| 739 |
-
"
|
| 740 |
-
"
|
| 741 |
-
"
|
| 742 |
-
"
|
| 743 |
-
"
|
| 744 |
-
"
|
| 745 |
-
"
|
| 746 |
-
"
|
| 747 |
-
" \"RegionID\",\n",
|
| 748 |
-
" \"SizeRank\",\n",
|
| 749 |
-
" \"RegionName\",\n",
|
| 750 |
-
" \"RegionType\",\n",
|
| 751 |
-
" \"StateName\",\n",
|
| 752 |
-
" \"Bedroom Count\",\n",
|
| 753 |
-
" \"Home Type\",\n",
|
| 754 |
-
" \"Date\",\n",
|
| 755 |
-
" ],\n",
|
| 756 |
-
" how=\"outer\",\n",
|
| 757 |
-
" suffixes=(\"\", \"_\" + str(i)),\n",
|
| 758 |
-
" )\n",
|
| 759 |
-
" elif len(data_frames) == 1:\n",
|
| 760 |
-
" combined_df = data_frames[0]\n",
|
| 761 |
-
"\n",
|
| 762 |
-
" return combined_df\n",
|
| 763 |
-
"\n",
|
| 764 |
-
"\n",
|
| 765 |
-
"combined_df = get_combined_df(data_frames)\n",
|
| 766 |
"combined_df"
|
| 767 |
]
|
| 768 |
},
|
| 769 |
{
|
| 770 |
"cell_type": "code",
|
| 771 |
-
"execution_count":
|
| 772 |
"metadata": {},
|
| 773 |
"outputs": [
|
| 774 |
-
{
|
| 775 |
-
"name": "stdout",
|
| 776 |
-
"output_type": "stream",
|
| 777 |
-
"text": [
|
| 778 |
-
"ZHVI\n",
|
| 779 |
-
"Mid Tier ZHVI\n",
|
| 780 |
-
"Bottom Tier ZHVI\n",
|
| 781 |
-
"Top Tier ZHVI\n"
|
| 782 |
-
]
|
| 783 |
-
},
|
| 784 |
{
|
| 785 |
"data": {
|
| 786 |
"text/html": [
|
|
@@ -1081,33 +1035,22 @@
|
|
| 1081 |
"[117912 rows x 13 columns]"
|
| 1082 |
]
|
| 1083 |
},
|
| 1084 |
-
"execution_count":
|
| 1085 |
"metadata": {},
|
| 1086 |
"output_type": "execute_result"
|
| 1087 |
}
|
| 1088 |
],
|
| 1089 |
"source": [
|
| 1090 |
-
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 1091 |
"columns_to_coalesce = [\"ZHVI\", \"Mid Tier ZHVI\", \"Bottom Tier ZHVI\", \"Top Tier ZHVI\"]\n",
|
| 1092 |
"\n",
|
| 1093 |
-
"
|
| 1094 |
-
" print(column_to_coalesce)\n",
|
| 1095 |
-
" for index, row in combined_df.iterrows():\n",
|
| 1096 |
-
" for col in combined_df.columns:\n",
|
| 1097 |
-
" if column_to_coalesce in col and \"_\" in col:\n",
|
| 1098 |
-
" if not pd.isna(row[col]):\n",
|
| 1099 |
-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
|
| 1100 |
-
"\n",
|
| 1101 |
-
"# remove columns with underscores\n",
|
| 1102 |
-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
|
| 1103 |
-
"\n",
|
| 1104 |
"\n",
|
| 1105 |
"combined_df"
|
| 1106 |
]
|
| 1107 |
},
|
| 1108 |
{
|
| 1109 |
"cell_type": "code",
|
| 1110 |
-
"execution_count":
|
| 1111 |
"metadata": {},
|
| 1112 |
"outputs": [
|
| 1113 |
{
|
|
@@ -1140,10 +1083,15 @@
|
|
| 1140 |
" <th>Home Type</th>\n",
|
| 1141 |
" <th>Date</th>\n",
|
| 1142 |
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
|
|
|
|
|
|
| 1143 |
" <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
|
|
|
|
|
|
|
|
|
| 1144 |
" <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 1145 |
-
" <th>ZHVI</th>\n",
|
| 1146 |
-
" <th>Mid Tier ZHVI</th>\n",
|
| 1147 |
" </tr>\n",
|
| 1148 |
" </thead>\n",
|
| 1149 |
" <tbody>\n",
|
|
@@ -1160,7 +1108,12 @@
|
|
| 1160 |
" <td>NaN</td>\n",
|
| 1161 |
" <td>NaN</td>\n",
|
| 1162 |
" <td>NaN</td>\n",
|
| 1163 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1164 |
" <td>81310.639504</td>\n",
|
| 1165 |
" </tr>\n",
|
| 1166 |
" <tr>\n",
|
|
@@ -1176,7 +1129,12 @@
|
|
| 1176 |
" <td>NaN</td>\n",
|
| 1177 |
" <td>NaN</td>\n",
|
| 1178 |
" <td>NaN</td>\n",
|
| 1179 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1180 |
" <td>80419.761984</td>\n",
|
| 1181 |
" </tr>\n",
|
| 1182 |
" <tr>\n",
|
|
@@ -1192,7 +1150,12 @@
|
|
| 1192 |
" <td>NaN</td>\n",
|
| 1193 |
" <td>NaN</td>\n",
|
| 1194 |
" <td>NaN</td>\n",
|
| 1195 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1196 |
" <td>80480.449461</td>\n",
|
| 1197 |
" </tr>\n",
|
| 1198 |
" <tr>\n",
|
|
@@ -1208,7 +1171,12 @@
|
|
| 1208 |
" <td>NaN</td>\n",
|
| 1209 |
" <td>NaN</td>\n",
|
| 1210 |
" <td>NaN</td>\n",
|
| 1211 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1212 |
" <td>79799.206525</td>\n",
|
| 1213 |
" </tr>\n",
|
| 1214 |
" <tr>\n",
|
|
@@ -1224,7 +1192,12 @@
|
|
| 1224 |
" <td>NaN</td>\n",
|
| 1225 |
" <td>NaN</td>\n",
|
| 1226 |
" <td>NaN</td>\n",
|
| 1227 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1228 |
" <td>79666.469861</td>\n",
|
| 1229 |
" </tr>\n",
|
| 1230 |
" <tr>\n",
|
|
@@ -1242,6 +1215,11 @@
|
|
| 1242 |
" <td>...</td>\n",
|
| 1243 |
" <td>...</td>\n",
|
| 1244 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1245 |
" </tr>\n",
|
| 1246 |
" <tr>\n",
|
| 1247 |
" <th>117907</th>\n",
|
|
@@ -1256,8 +1234,13 @@
|
|
| 1256 |
" <td>NaN</td>\n",
|
| 1257 |
" <td>NaN</td>\n",
|
| 1258 |
" <td>NaN</td>\n",
|
|
|
|
|
|
|
| 1259 |
" <td>486974.735908</td>\n",
|
| 1260 |
-
" <td>
|
|
|
|
|
|
|
|
|
|
| 1261 |
" </tr>\n",
|
| 1262 |
" <tr>\n",
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| 1263 |
" <th>117908</th>\n",
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@@ -1272,8 +1255,13 @@
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| 1272 |
" <td>NaN</td>\n",
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| 1277 |
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|
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| 1288 |
" <td>NaN</td>\n",
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|
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-
" <td>
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| 1293 |
" </tr>\n",
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| 1294 |
" <tr>\n",
|
| 1295 |
" <th>117910</th>\n",
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@@ -1304,8 +1297,13 @@
|
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| 1304 |
" <td>NaN</td>\n",
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| 1306 |
" <td>NaN</td>\n",
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" <td>481522.403338</td>\n",
|
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-
" <td>
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| 1309 |
" </tr>\n",
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" <th>117911</th>\n",
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@@ -1320,12 +1318,17 @@
|
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| 1320 |
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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| 1322 |
" <td>NaN</td>\n",
|
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| 1323 |
" <td>481181.718200</td>\n",
|
| 1324 |
-
" <td>
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| 1325 |
" </tr>\n",
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| 1326 |
" </tbody>\n",
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| 1327 |
"</table>\n",
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-
"<p>117912 rows ×
|
| 1329 |
"</div>"
|
| 1330 |
],
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"text/plain": [
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| 1371 |
" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 1372 |
"0 NaN \n",
|
| 1373 |
"1 NaN \n",
|
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@@ -1381,36 +1410,88 @@
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|
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" Mid Tier ZHVI
|
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|
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"execution_count":
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@@ -1441,7 +1522,7 @@
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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": [
|
| 1447 |
{
|
|
@@ -1474,10 +1555,15 @@
|
|
| 1474 |
" <th>Home Type</th>\n",
|
| 1475 |
" <th>Date</th>\n",
|
| 1476 |
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
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| 1477 |
" <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
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| 1478 |
" <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
|
| 1479 |
-
" <th>ZHVI</th>\n",
|
| 1480 |
-
" <th>Mid Tier ZHVI</th>\n",
|
| 1481 |
" </tr>\n",
|
| 1482 |
" </thead>\n",
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| 1483 |
" <tbody>\n",
|
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@@ -1494,7 +1580,12 @@
|
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| 1494 |
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| 1498 |
" <td>81310.639504</td>\n",
|
| 1499 |
" </tr>\n",
|
| 1500 |
" <tr>\n",
|
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@@ -1510,7 +1601,12 @@
|
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| 1510 |
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| 1514 |
" <td>80419.761984</td>\n",
|
| 1515 |
" </tr>\n",
|
| 1516 |
" <tr>\n",
|
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@@ -1526,7 +1622,12 @@
|
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|
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" </tr>\n",
|
| 1532 |
" <tr>\n",
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@@ -1542,7 +1643,12 @@
|
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" <td>79799.206525</td>\n",
|
| 1547 |
" </tr>\n",
|
| 1548 |
" <tr>\n",
|
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@@ -1558,7 +1664,12 @@
|
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| 1562 |
" <td>79666.469861</td>\n",
|
| 1563 |
" </tr>\n",
|
| 1564 |
" <tr>\n",
|
|
@@ -1576,6 +1687,11 @@
|
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| 1576 |
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" </tr>\n",
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|
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@@ -1590,8 +1706,13 @@
|
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| 1590 |
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| 1592 |
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| 1595 |
" </tr>\n",
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|
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|
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@@ -1606,8 +1727,13 @@
|
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| 1606 |
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" </tr>\n",
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@@ -1622,8 +1748,13 @@
|
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| 1622 |
" <td>NaN</td>\n",
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|
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| 1627 |
" </tr>\n",
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| 1628 |
" <tr>\n",
|
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" <th>117910</th>\n",
|
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@@ -1638,8 +1769,13 @@
|
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| 1638 |
" <td>NaN</td>\n",
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| 1640 |
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|
| 1642 |
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| 1643 |
" </tr>\n",
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|
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" <th>117911</th>\n",
|
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@@ -1654,12 +1790,17 @@
|
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| 1654 |
" <td>NaN</td>\n",
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|
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-
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| 1659 |
" </tr>\n",
|
| 1660 |
" </tbody>\n",
|
| 1661 |
"</table>\n",
|
| 1662 |
-
"<p>117912 rows ×
|
| 1663 |
"</div>"
|
| 1664 |
],
|
| 1665 |
"text/plain": [
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" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 1706 |
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|
| 1707 |
"1 NaN \n",
|
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@@ -1715,36 +1882,88 @@
|
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|
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|
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|
| 1728 |
-
"117910
|
| 1729 |
-
"117911
|
| 1730 |
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|
| 1731 |
-
" Mid Tier ZHVI
|
| 1732 |
-
"0
|
| 1733 |
-
"1
|
| 1734 |
-
"2
|
| 1735 |
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|
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|
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"...
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|
| 1740 |
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|
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|
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|
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},
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-
"execution_count":
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| 1748 |
"metadata": {},
|
| 1749 |
"output_type": "execute_result"
|
| 1750 |
}
|
|
@@ -1767,14 +1986,11 @@
|
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| 1767 |
},
|
| 1768 |
{
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| 1769 |
"cell_type": "code",
|
| 1770 |
-
"execution_count":
|
| 1771 |
"metadata": {},
|
| 1772 |
"outputs": [],
|
| 1773 |
"source": [
|
| 1774 |
-
"
|
| 1775 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 1776 |
-
"\n",
|
| 1777 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 1778 |
]
|
| 1779 |
}
|
| 1780 |
],
|
|
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|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 8,
|
| 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, get_df, save_final_df_as_jsonl"
|
| 13 |
]
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"cell_type": "code",
|
| 17 |
+
"execution_count": 9,
|
| 18 |
"metadata": {},
|
| 19 |
"outputs": [],
|
| 20 |
"source": [
|
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},
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| 28 |
{
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| 29 |
"cell_type": "code",
|
| 30 |
+
"execution_count": 10,
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
|
|
| 67 |
"processing Zip_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 68 |
"processing Neighborhood_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 69 |
"processing City_zhvi_bdrmcnt_3_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
|
|
|
| 70 |
"processing County_zhvi_bdrmcnt_1_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 71 |
"processing Neighborhood_zhvi_uc_condo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 72 |
"processing Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_month.csv\n",
|
|
|
|
| 89 |
"processing Neighborhood_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 90 |
"processing County_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 91 |
"processing County_zhvi_bdrmcnt_2_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n",
|
| 92 |
+
"processing Metro_zhvi_bdrmcnt_4_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv\n"
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|
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{
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" <th>Home Type</th>\n",
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" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
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"metadata": {},
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| 1527 |
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| 1528 |
{
|
|
|
|
| 1555 |
" <th>Home Type</th>\n",
|
| 1556 |
" <th>Date</th>\n",
|
| 1557 |
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| 1558 |
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|
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| 1561 |
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|
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| 1564 |
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|
| 1565 |
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" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
|
| 1566 |
+
" <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
|
| 1567 |
" </tr>\n",
|
| 1568 |
" </thead>\n",
|
| 1569 |
" <tbody>\n",
|
|
|
|
| 1580 |
" <td>NaN</td>\n",
|
| 1581 |
" <td>NaN</td>\n",
|
| 1582 |
" <td>NaN</td>\n",
|
| 1583 |
+
" <td>NaN</td>\n",
|
| 1584 |
+
" <td>NaN</td>\n",
|
| 1585 |
+
" <td>NaN</td>\n",
|
| 1586 |
+
" <td>NaN</td>\n",
|
| 1587 |
+
" <td>NaN</td>\n",
|
| 1588 |
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" <td>NaN</td>\n",
|
| 1589 |
" <td>81310.639504</td>\n",
|
| 1590 |
" </tr>\n",
|
| 1591 |
" <tr>\n",
|
|
|
|
| 1601 |
" <td>NaN</td>\n",
|
| 1602 |
" <td>NaN</td>\n",
|
| 1603 |
" <td>NaN</td>\n",
|
| 1604 |
+
" <td>NaN</td>\n",
|
| 1605 |
+
" <td>NaN</td>\n",
|
| 1606 |
+
" <td>NaN</td>\n",
|
| 1607 |
+
" <td>NaN</td>\n",
|
| 1608 |
+
" <td>NaN</td>\n",
|
| 1609 |
+
" <td>NaN</td>\n",
|
| 1610 |
" <td>80419.761984</td>\n",
|
| 1611 |
" </tr>\n",
|
| 1612 |
" <tr>\n",
|
|
|
|
| 1622 |
" <td>NaN</td>\n",
|
| 1623 |
" <td>NaN</td>\n",
|
| 1624 |
" <td>NaN</td>\n",
|
| 1625 |
+
" <td>NaN</td>\n",
|
| 1626 |
+
" <td>NaN</td>\n",
|
| 1627 |
+
" <td>NaN</td>\n",
|
| 1628 |
+
" <td>NaN</td>\n",
|
| 1629 |
+
" <td>NaN</td>\n",
|
| 1630 |
+
" <td>NaN</td>\n",
|
| 1631 |
" <td>80480.449461</td>\n",
|
| 1632 |
" </tr>\n",
|
| 1633 |
" <tr>\n",
|
|
|
|
| 1643 |
" <td>NaN</td>\n",
|
| 1644 |
" <td>NaN</td>\n",
|
| 1645 |
" <td>NaN</td>\n",
|
| 1646 |
+
" <td>NaN</td>\n",
|
| 1647 |
+
" <td>NaN</td>\n",
|
| 1648 |
+
" <td>NaN</td>\n",
|
| 1649 |
+
" <td>NaN</td>\n",
|
| 1650 |
+
" <td>NaN</td>\n",
|
| 1651 |
+
" <td>NaN</td>\n",
|
| 1652 |
" <td>79799.206525</td>\n",
|
| 1653 |
" </tr>\n",
|
| 1654 |
" <tr>\n",
|
|
|
|
| 1664 |
" <td>NaN</td>\n",
|
| 1665 |
" <td>NaN</td>\n",
|
| 1666 |
" <td>NaN</td>\n",
|
| 1667 |
+
" <td>NaN</td>\n",
|
| 1668 |
+
" <td>NaN</td>\n",
|
| 1669 |
+
" <td>NaN</td>\n",
|
| 1670 |
+
" <td>NaN</td>\n",
|
| 1671 |
+
" <td>NaN</td>\n",
|
| 1672 |
+
" <td>NaN</td>\n",
|
| 1673 |
" <td>79666.469861</td>\n",
|
| 1674 |
" </tr>\n",
|
| 1675 |
" <tr>\n",
|
|
|
|
| 1687 |
" <td>...</td>\n",
|
| 1688 |
" <td>...</td>\n",
|
| 1689 |
" <td>...</td>\n",
|
| 1690 |
+
" <td>...</td>\n",
|
| 1691 |
+
" <td>...</td>\n",
|
| 1692 |
+
" <td>...</td>\n",
|
| 1693 |
+
" <td>...</td>\n",
|
| 1694 |
+
" <td>...</td>\n",
|
| 1695 |
" </tr>\n",
|
| 1696 |
" <tr>\n",
|
| 1697 |
" <th>117907</th>\n",
|
|
|
|
| 1706 |
" <td>NaN</td>\n",
|
| 1707 |
" <td>NaN</td>\n",
|
| 1708 |
" <td>NaN</td>\n",
|
| 1709 |
+
" <td>NaN</td>\n",
|
| 1710 |
+
" <td>NaN</td>\n",
|
| 1711 |
" <td>486974.735908</td>\n",
|
| 1712 |
+
" <td>NaN</td>\n",
|
| 1713 |
+
" <td>NaN</td>\n",
|
| 1714 |
+
" <td>NaN</td>\n",
|
| 1715 |
+
" <td>NaN</td>\n",
|
| 1716 |
" </tr>\n",
|
| 1717 |
" <tr>\n",
|
| 1718 |
" <th>117908</th>\n",
|
|
|
|
| 1727 |
" <td>NaN</td>\n",
|
| 1728 |
" <td>NaN</td>\n",
|
| 1729 |
" <td>NaN</td>\n",
|
| 1730 |
+
" <td>NaN</td>\n",
|
| 1731 |
+
" <td>NaN</td>\n",
|
| 1732 |
" <td>485847.539614</td>\n",
|
| 1733 |
+
" <td>NaN</td>\n",
|
| 1734 |
+
" <td>NaN</td>\n",
|
| 1735 |
+
" <td>NaN</td>\n",
|
| 1736 |
+
" <td>NaN</td>\n",
|
| 1737 |
" </tr>\n",
|
| 1738 |
" <tr>\n",
|
| 1739 |
" <th>117909</th>\n",
|
|
|
|
| 1748 |
" <td>NaN</td>\n",
|
| 1749 |
" <td>NaN</td>\n",
|
| 1750 |
" <td>NaN</td>\n",
|
| 1751 |
+
" <td>NaN</td>\n",
|
| 1752 |
+
" <td>NaN</td>\n",
|
| 1753 |
" <td>484223.885775</td>\n",
|
| 1754 |
+
" <td>NaN</td>\n",
|
| 1755 |
+
" <td>NaN</td>\n",
|
| 1756 |
+
" <td>NaN</td>\n",
|
| 1757 |
+
" <td>NaN</td>\n",
|
| 1758 |
" </tr>\n",
|
| 1759 |
" <tr>\n",
|
| 1760 |
" <th>117910</th>\n",
|
|
|
|
| 1769 |
" <td>NaN</td>\n",
|
| 1770 |
" <td>NaN</td>\n",
|
| 1771 |
" <td>NaN</td>\n",
|
| 1772 |
+
" <td>NaN</td>\n",
|
| 1773 |
+
" <td>NaN</td>\n",
|
| 1774 |
" <td>481522.403338</td>\n",
|
| 1775 |
+
" <td>NaN</td>\n",
|
| 1776 |
+
" <td>NaN</td>\n",
|
| 1777 |
+
" <td>NaN</td>\n",
|
| 1778 |
+
" <td>NaN</td>\n",
|
| 1779 |
" </tr>\n",
|
| 1780 |
" <tr>\n",
|
| 1781 |
" <th>117911</th>\n",
|
|
|
|
| 1790 |
" <td>NaN</td>\n",
|
| 1791 |
" <td>NaN</td>\n",
|
| 1792 |
" <td>NaN</td>\n",
|
| 1793 |
+
" <td>NaN</td>\n",
|
| 1794 |
+
" <td>NaN</td>\n",
|
| 1795 |
" <td>481181.718200</td>\n",
|
| 1796 |
+
" <td>NaN</td>\n",
|
| 1797 |
+
" <td>NaN</td>\n",
|
| 1798 |
+
" <td>NaN</td>\n",
|
| 1799 |
+
" <td>NaN</td>\n",
|
| 1800 |
" </tr>\n",
|
| 1801 |
" </tbody>\n",
|
| 1802 |
"</table>\n",
|
| 1803 |
+
"<p>117912 rows × 18 columns</p>\n",
|
| 1804 |
"</div>"
|
| 1805 |
],
|
| 1806 |
"text/plain": [
|
|
|
|
| 1843 |
"117910 NaN \n",
|
| 1844 |
"117911 NaN \n",
|
| 1845 |
"\n",
|
| 1846 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
|
| 1847 |
+
"0 NaN \n",
|
| 1848 |
+
"1 NaN \n",
|
| 1849 |
+
"2 NaN \n",
|
| 1850 |
+
"3 NaN \n",
|
| 1851 |
+
"4 NaN \n",
|
| 1852 |
+
"... ... \n",
|
| 1853 |
+
"117907 NaN \n",
|
| 1854 |
+
"117908 NaN \n",
|
| 1855 |
+
"117909 NaN \n",
|
| 1856 |
+
"117910 NaN \n",
|
| 1857 |
+
"117911 NaN \n",
|
| 1858 |
+
"\n",
|
| 1859 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
|
| 1860 |
+
"0 NaN \n",
|
| 1861 |
+
"1 NaN \n",
|
| 1862 |
+
"2 NaN \n",
|
| 1863 |
+
"3 NaN \n",
|
| 1864 |
+
"4 NaN \n",
|
| 1865 |
+
"... ... \n",
|
| 1866 |
+
"117907 NaN \n",
|
| 1867 |
+
"117908 NaN \n",
|
| 1868 |
+
"117909 NaN \n",
|
| 1869 |
+
"117910 NaN \n",
|
| 1870 |
+
"117911 NaN \n",
|
| 1871 |
+
"\n",
|
| 1872 |
" Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 1873 |
"0 NaN \n",
|
| 1874 |
"1 NaN \n",
|
|
|
|
| 1882 |
"117910 NaN \n",
|
| 1883 |
"117911 NaN \n",
|
| 1884 |
"\n",
|
| 1885 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4 \\\n",
|
| 1886 |
+
"0 NaN \n",
|
| 1887 |
+
"1 NaN \n",
|
| 1888 |
+
"2 NaN \n",
|
| 1889 |
+
"3 NaN \n",
|
| 1890 |
+
"4 NaN \n",
|
| 1891 |
+
"... ... \n",
|
| 1892 |
+
"117907 NaN \n",
|
| 1893 |
+
"117908 NaN \n",
|
| 1894 |
+
"117909 NaN \n",
|
| 1895 |
+
"117910 NaN \n",
|
| 1896 |
+
"117911 NaN \n",
|
| 1897 |
"\n",
|
| 1898 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
|
| 1899 |
+
"0 NaN \n",
|
| 1900 |
+
"1 NaN \n",
|
| 1901 |
+
"2 NaN \n",
|
| 1902 |
+
"3 NaN \n",
|
| 1903 |
+
"4 NaN \n",
|
| 1904 |
+
"... ... \n",
|
| 1905 |
+
"117907 486974.735908 \n",
|
| 1906 |
+
"117908 485847.539614 \n",
|
| 1907 |
+
"117909 484223.885775 \n",
|
| 1908 |
+
"117910 481522.403338 \n",
|
| 1909 |
+
"117911 481181.718200 \n",
|
| 1910 |
"\n",
|
| 1911 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6 \\\n",
|
| 1912 |
+
"0 NaN \n",
|
| 1913 |
+
"1 NaN \n",
|
| 1914 |
+
"2 NaN \n",
|
| 1915 |
+
"3 NaN \n",
|
| 1916 |
+
"4 NaN \n",
|
| 1917 |
+
"... ... \n",
|
| 1918 |
+
"117907 NaN \n",
|
| 1919 |
+
"117908 NaN \n",
|
| 1920 |
+
"117909 NaN \n",
|
| 1921 |
+
"117910 NaN \n",
|
| 1922 |
+
"117911 NaN \n",
|
| 1923 |
+
"\n",
|
| 1924 |
+
" Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
|
| 1925 |
+
"0 NaN \n",
|
| 1926 |
+
"1 NaN \n",
|
| 1927 |
+
"2 NaN \n",
|
| 1928 |
+
"3 NaN \n",
|
| 1929 |
+
"4 NaN \n",
|
| 1930 |
+
"... ... \n",
|
| 1931 |
+
"117907 NaN \n",
|
| 1932 |
+
"117908 NaN \n",
|
| 1933 |
+
"117909 NaN \n",
|
| 1934 |
+
"117910 NaN \n",
|
| 1935 |
+
"117911 NaN \n",
|
| 1936 |
+
"\n",
|
| 1937 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8 \\\n",
|
| 1938 |
+
"0 NaN \n",
|
| 1939 |
+
"1 NaN \n",
|
| 1940 |
+
"2 NaN \n",
|
| 1941 |
+
"3 NaN \n",
|
| 1942 |
+
"4 NaN \n",
|
| 1943 |
+
"... ... \n",
|
| 1944 |
+
"117907 NaN \n",
|
| 1945 |
+
"117908 NaN \n",
|
| 1946 |
+
"117909 NaN \n",
|
| 1947 |
+
"117910 NaN \n",
|
| 1948 |
+
"117911 NaN \n",
|
| 1949 |
+
"\n",
|
| 1950 |
+
" Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
|
| 1951 |
+
"0 81310.639504 \n",
|
| 1952 |
+
"1 80419.761984 \n",
|
| 1953 |
+
"2 80480.449461 \n",
|
| 1954 |
+
"3 79799.206525 \n",
|
| 1955 |
+
"4 79666.469861 \n",
|
| 1956 |
+
"... ... \n",
|
| 1957 |
+
"117907 NaN \n",
|
| 1958 |
+
"117908 NaN \n",
|
| 1959 |
+
"117909 NaN \n",
|
| 1960 |
+
"117910 NaN \n",
|
| 1961 |
+
"117911 NaN \n",
|
| 1962 |
+
"\n",
|
| 1963 |
+
"[117912 rows x 18 columns]"
|
| 1964 |
]
|
| 1965 |
},
|
| 1966 |
+
"execution_count": 12,
|
| 1967 |
"metadata": {},
|
| 1968 |
"output_type": "execute_result"
|
| 1969 |
}
|
|
|
|
| 1986 |
},
|
| 1987 |
{
|
| 1988 |
"cell_type": "code",
|
| 1989 |
+
"execution_count": 13,
|
| 1990 |
"metadata": {},
|
| 1991 |
"outputs": [],
|
| 1992 |
"source": [
|
| 1993 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 1994 |
]
|
| 1995 |
}
|
| 1996 |
],
|
processors/new_construction.ipynb
CHANGED
|
@@ -2,17 +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"
|
|
|
|
|
|
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
| 14 |
"cell_type": "code",
|
| 15 |
-
"execution_count":
|
| 16 |
"metadata": {},
|
| 17 |
"outputs": [],
|
| 18 |
"source": [
|
|
@@ -25,7 +27,7 @@
|
|
| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -255,7 +257,7 @@
|
|
| 255 |
"[49487 rows x 10 columns]"
|
| 256 |
]
|
| 257 |
},
|
| 258 |
-
"execution_count":
|
| 259 |
"metadata": {},
|
| 260 |
"output_type": "execute_result"
|
| 261 |
}
|
|
@@ -320,54 +322,29 @@
|
|
| 320 |
" data_frames.append(cur_df)\n",
|
| 321 |
"\n",
|
| 322 |
"\n",
|
| 323 |
-
"
|
| 324 |
-
"
|
| 325 |
-
"
|
| 326 |
-
"
|
| 327 |
-
"
|
| 328 |
-
"
|
| 329 |
-
"
|
| 330 |
-
"
|
| 331 |
-
"
|
| 332 |
-
"
|
| 333 |
-
"
|
| 334 |
-
"
|
| 335 |
-
" \"SizeRank\",\n",
|
| 336 |
-
" \"RegionName\",\n",
|
| 337 |
-
" \"RegionType\",\n",
|
| 338 |
-
" \"StateName\",\n",
|
| 339 |
-
" \"Home Type\",\n",
|
| 340 |
-
" \"Date\",\n",
|
| 341 |
-
" ],\n",
|
| 342 |
-
" how=\"outer\",\n",
|
| 343 |
-
" suffixes=(\"\", \"_\" + str(i)),\n",
|
| 344 |
-
" )\n",
|
| 345 |
-
" elif len(data_frames) == 1:\n",
|
| 346 |
-
" combined_df = data_frames[0]\n",
|
| 347 |
-
"\n",
|
| 348 |
-
" return combined_df\n",
|
| 349 |
-
"\n",
|
| 350 |
-
"\n",
|
| 351 |
-
"combined_df = get_combined_df(data_frames)\n",
|
| 352 |
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 353 |
"columns_to_coalesce = [\"Sales Count\", \"Median Sale Price\", \"Median Sale Price per Sqft\"]\n",
|
| 354 |
"\n",
|
| 355 |
-
"
|
| 356 |
-
" for col in combined_df.columns:\n",
|
| 357 |
-
" for column_to_coalesce in columns_to_coalesce:\n",
|
| 358 |
-
" if column_to_coalesce in col and \"_\" in col:\n",
|
| 359 |
-
" if not pd.isna(row[col]):\n",
|
| 360 |
-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
|
| 361 |
-
"\n",
|
| 362 |
-
"# remove columns with underscores\n",
|
| 363 |
-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
|
| 364 |
"\n",
|
| 365 |
"combined_df"
|
| 366 |
]
|
| 367 |
},
|
| 368 |
{
|
| 369 |
"cell_type": "code",
|
| 370 |
-
"execution_count":
|
| 371 |
"metadata": {},
|
| 372 |
"outputs": [
|
| 373 |
{
|
|
@@ -582,7 +559,7 @@
|
|
| 582 |
"[49487 rows x 10 columns]"
|
| 583 |
]
|
| 584 |
},
|
| 585 |
-
"execution_count":
|
| 586 |
"metadata": {},
|
| 587 |
"output_type": "execute_result"
|
| 588 |
}
|
|
@@ -604,14 +581,11 @@
|
|
| 604 |
},
|
| 605 |
{
|
| 606 |
"cell_type": "code",
|
| 607 |
-
"execution_count":
|
| 608 |
"metadata": {},
|
| 609 |
"outputs": [],
|
| 610 |
"source": [
|
| 611 |
-
"
|
| 612 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 613 |
-
"\n",
|
| 614 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 615 |
]
|
| 616 |
}
|
| 617 |
],
|
|
|
|
| 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, get_df, 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 |
{
|
|
|
|
| 257 |
"[49487 rows x 10 columns]"
|
| 258 |
]
|
| 259 |
},
|
| 260 |
+
"execution_count": 3,
|
| 261 |
"metadata": {},
|
| 262 |
"output_type": "execute_result"
|
| 263 |
}
|
|
|
|
| 322 |
" data_frames.append(cur_df)\n",
|
| 323 |
"\n",
|
| 324 |
"\n",
|
| 325 |
+
"combined_df = get_combined_df(\n",
|
| 326 |
+
" data_frames,\n",
|
| 327 |
+
" [\n",
|
| 328 |
+
" \"RegionID\",\n",
|
| 329 |
+
" \"SizeRank\",\n",
|
| 330 |
+
" \"RegionName\",\n",
|
| 331 |
+
" \"RegionType\",\n",
|
| 332 |
+
" \"StateName\",\n",
|
| 333 |
+
" \"Home Type\",\n",
|
| 334 |
+
" \"Date\",\n",
|
| 335 |
+
" ],\n",
|
| 336 |
+
")\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
| 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, columns_to_coalesce)\n",
|
|
|
|
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|
|
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|
|
|
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|
| 341 |
"\n",
|
| 342 |
"combined_df"
|
| 343 |
]
|
| 344 |
},
|
| 345 |
{
|
| 346 |
"cell_type": "code",
|
| 347 |
+
"execution_count": 4,
|
| 348 |
"metadata": {},
|
| 349 |
"outputs": [
|
| 350 |
{
|
|
|
|
| 559 |
"[49487 rows x 10 columns]"
|
| 560 |
]
|
| 561 |
},
|
| 562 |
+
"execution_count": 4,
|
| 563 |
"metadata": {},
|
| 564 |
"output_type": "execute_result"
|
| 565 |
}
|
|
|
|
| 581 |
},
|
| 582 |
{
|
| 583 |
"cell_type": "code",
|
| 584 |
+
"execution_count": 5,
|
| 585 |
"metadata": {},
|
| 586 |
"outputs": [],
|
| 587 |
"source": [
|
| 588 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 589 |
]
|
| 590 |
}
|
| 591 |
],
|
processors/rentals.ipynb
CHANGED
|
@@ -2,17 +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"
|
|
|
|
|
|
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
| 14 |
"cell_type": "code",
|
| 15 |
-
"execution_count":
|
| 16 |
"metadata": {},
|
| 17 |
"outputs": [],
|
| 18 |
"source": [
|
|
@@ -25,7 +27,7 @@
|
|
| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -326,7 +328,7 @@
|
|
| 326 |
"[1258740 rows x 15 columns]"
|
| 327 |
]
|
| 328 |
},
|
| 329 |
-
"execution_count":
|
| 330 |
"metadata": {},
|
| 331 |
"output_type": "execute_result"
|
| 332 |
}
|
|
@@ -334,7 +336,6 @@
|
|
| 334 |
"source": [
|
| 335 |
"# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
|
| 336 |
"\n",
|
| 337 |
-
"\n",
|
| 338 |
"data_frames = []\n",
|
| 339 |
"\n",
|
| 340 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
@@ -404,103 +405,42 @@
|
|
| 404 |
" # Identify columns to pivot\n",
|
| 405 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 406 |
"\n",
|
| 407 |
-
"
|
| 408 |
-
" seasonally_adjusted = \"_sa_\" in filename\n",
|
| 409 |
-
"\n",
|
| 410 |
-
" col_name = \"Rent\"\n",
|
| 411 |
-
" if smoothed:\n",
|
| 412 |
-
" col_name += \" (Smoothed)\"\n",
|
| 413 |
-
" if seasonally_adjusted:\n",
|
| 414 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 415 |
-
" cur_df = pd.melt(\n",
|
| 416 |
" cur_df,\n",
|
| 417 |
-
"
|
| 418 |
-
"
|
| 419 |
-
"
|
| 420 |
-
"
|
| 421 |
" )\n",
|
| 422 |
-
" data_frames.append(cur_df)\n",
|
| 423 |
-
" # print(filename)\n",
|
| 424 |
-
"\n",
|
| 425 |
-
"\n",
|
| 426 |
-
"def get_combined_df(data_frames):\n",
|
| 427 |
-
" combined_df = None\n",
|
| 428 |
-
" if len(data_frames) > 1:\n",
|
| 429 |
-
" # iterate over dataframes and merge or concat\n",
|
| 430 |
-
" combined_df = data_frames[0]\n",
|
| 431 |
-
" for i in range(1, len(data_frames)):\n",
|
| 432 |
-
" cur_df = data_frames[i]\n",
|
| 433 |
-
" combined_df = pd.merge(\n",
|
| 434 |
-
" combined_df,\n",
|
| 435 |
-
" cur_df,\n",
|
| 436 |
-
" on=[\n",
|
| 437 |
-
" \"RegionID\",\n",
|
| 438 |
-
" \"SizeRank\",\n",
|
| 439 |
-
" \"RegionName\",\n",
|
| 440 |
-
" \"RegionType\",\n",
|
| 441 |
-
" \"StateName\",\n",
|
| 442 |
-
" \"Home Type\",\n",
|
| 443 |
-
" \"Date\",\n",
|
| 444 |
-
" ],\n",
|
| 445 |
-
" how=\"outer\",\n",
|
| 446 |
-
" suffixes=(\"\", \"_\" + str(i)),\n",
|
| 447 |
-
" )\n",
|
| 448 |
-
" elif len(data_frames) == 1:\n",
|
| 449 |
-
" combined_df = data_frames[0]\n",
|
| 450 |
"\n",
|
| 451 |
-
"
|
| 452 |
"\n",
|
| 453 |
"\n",
|
| 454 |
-
"combined_df = get_combined_df(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
| 455 |
"\n",
|
| 456 |
"\n",
|
| 457 |
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 458 |
"columns_to_coalesce = [\"Rent (Smoothed)\", \"Rent (Smoothed) (Seasonally Adjusted)\"]\n",
|
| 459 |
"\n",
|
| 460 |
-
"
|
| 461 |
-
" for col in combined_df.columns:\n",
|
| 462 |
-
" for column_to_coalesce in columns_to_coalesce:\n",
|
| 463 |
-
" if column_to_coalesce in col and \"_\" in col:\n",
|
| 464 |
-
" if not pd.isna(row[col]):\n",
|
| 465 |
-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
|
| 466 |
-
"\n",
|
| 467 |
-
"# remove columns with underscores\n",
|
| 468 |
-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
|
| 469 |
-
"\n",
|
| 470 |
"\n",
|
| 471 |
"combined_df"
|
| 472 |
]
|
| 473 |
},
|
| 474 |
{
|
| 475 |
"cell_type": "code",
|
| 476 |
-
"execution_count":
|
| 477 |
-
"metadata": {},
|
| 478 |
-
"outputs": [
|
| 479 |
-
{
|
| 480 |
-
"data": {
|
| 481 |
-
"text/plain": [
|
| 482 |
-
"Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
|
| 483 |
-
" 'Home Type', 'State', 'Metro', 'StateCodeFIPS', 'MunicipalCodeFIPS',\n",
|
| 484 |
-
" 'Date', 'Rent (Smoothed)', 'CountyName',\n",
|
| 485 |
-
" 'Rent (Smoothed) (Seasonally Adjusted)', 'City'],\n",
|
| 486 |
-
" dtype='object')"
|
| 487 |
-
]
|
| 488 |
-
},
|
| 489 |
-
"execution_count": 27,
|
| 490 |
-
"metadata": {},
|
| 491 |
-
"output_type": "execute_result"
|
| 492 |
-
}
|
| 493 |
-
],
|
| 494 |
-
"source": [
|
| 495 |
-
"combined_df.columns\n",
|
| 496 |
-
"# combined_df[\"RegionType\"].unique()\n",
|
| 497 |
-
"\n",
|
| 498 |
-
"# combined_df"
|
| 499 |
-
]
|
| 500 |
-
},
|
| 501 |
-
{
|
| 502 |
-
"cell_type": "code",
|
| 503 |
-
"execution_count": 32,
|
| 504 |
"metadata": {},
|
| 505 |
"outputs": [
|
| 506 |
{
|
|
@@ -789,7 +729,7 @@
|
|
| 789 |
"[1258740 rows x 14 columns]"
|
| 790 |
]
|
| 791 |
},
|
| 792 |
-
"execution_count":
|
| 793 |
"metadata": {},
|
| 794 |
"output_type": "execute_result"
|
| 795 |
}
|
|
@@ -813,7 +753,7 @@
|
|
| 813 |
},
|
| 814 |
{
|
| 815 |
"cell_type": "code",
|
| 816 |
-
"execution_count":
|
| 817 |
"metadata": {},
|
| 818 |
"outputs": [
|
| 819 |
{
|
|
@@ -1102,12 +1042,13 @@
|
|
| 1102 |
"[1258740 rows x 14 columns]"
|
| 1103 |
]
|
| 1104 |
},
|
| 1105 |
-
"execution_count":
|
| 1106 |
"metadata": {},
|
| 1107 |
"output_type": "execute_result"
|
| 1108 |
}
|
| 1109 |
],
|
| 1110 |
"source": [
|
|
|
|
| 1111 |
"final_df = final_df.rename(\n",
|
| 1112 |
" columns={\n",
|
| 1113 |
" \"RegionID\": \"Region ID\",\n",
|
|
@@ -1124,14 +1065,11 @@
|
|
| 1124 |
},
|
| 1125 |
{
|
| 1126 |
"cell_type": "code",
|
| 1127 |
-
"execution_count":
|
| 1128 |
"metadata": {},
|
| 1129 |
"outputs": [],
|
| 1130 |
"source": [
|
| 1131 |
-
"
|
| 1132 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 1133 |
-
"\n",
|
| 1134 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 1135 |
]
|
| 1136 |
}
|
| 1137 |
],
|
|
|
|
| 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, get_df, 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": 4,
|
| 31 |
"metadata": {},
|
| 32 |
"outputs": [
|
| 33 |
{
|
|
|
|
| 328 |
"[1258740 rows x 15 columns]"
|
| 329 |
]
|
| 330 |
},
|
| 331 |
+
"execution_count": 4,
|
| 332 |
"metadata": {},
|
| 333 |
"output_type": "execute_result"
|
| 334 |
}
|
|
|
|
| 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",
|
|
|
|
| 405 |
" # Identify columns to pivot\n",
|
| 406 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 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",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
| 415 |
"\n",
|
| 416 |
+
" data_frames.append(cur_df)\n",
|
| 417 |
"\n",
|
| 418 |
"\n",
|
| 419 |
+
"combined_df = get_combined_df(\n",
|
| 420 |
+
" data_frames,\n",
|
| 421 |
+
" [\n",
|
| 422 |
+
" \"RegionID\",\n",
|
| 423 |
+
" \"SizeRank\",\n",
|
| 424 |
+
" \"RegionName\",\n",
|
| 425 |
+
" \"RegionType\",\n",
|
| 426 |
+
" \"StateName\",\n",
|
| 427 |
+
" \"Home Type\",\n",
|
| 428 |
+
" \"Date\",\n",
|
| 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": 5,
|
|
|
|
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|
|
|
|
|
|
|
|
|
| 444 |
"metadata": {},
|
| 445 |
"outputs": [
|
| 446 |
{
|
|
|
|
| 729 |
"[1258740 rows x 14 columns]"
|
| 730 |
]
|
| 731 |
},
|
| 732 |
+
"execution_count": 5,
|
| 733 |
"metadata": {},
|
| 734 |
"output_type": "execute_result"
|
| 735 |
}
|
|
|
|
| 753 |
},
|
| 754 |
{
|
| 755 |
"cell_type": "code",
|
| 756 |
+
"execution_count": 6,
|
| 757 |
"metadata": {},
|
| 758 |
"outputs": [
|
| 759 |
{
|
|
|
|
| 1042 |
"[1258740 rows x 14 columns]"
|
| 1043 |
]
|
| 1044 |
},
|
| 1045 |
+
"execution_count": 6,
|
| 1046 |
"metadata": {},
|
| 1047 |
"output_type": "execute_result"
|
| 1048 |
}
|
| 1049 |
],
|
| 1050 |
"source": [
|
| 1051 |
+
"# Adjust column names\n",
|
| 1052 |
"final_df = final_df.rename(\n",
|
| 1053 |
" columns={\n",
|
| 1054 |
" \"RegionID\": \"Region ID\",\n",
|
|
|
|
| 1065 |
},
|
| 1066 |
{
|
| 1067 |
"cell_type": "code",
|
| 1068 |
+
"execution_count": 7,
|
| 1069 |
"metadata": {},
|
| 1070 |
"outputs": [],
|
| 1071 |
"source": [
|
| 1072 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 1073 |
]
|
| 1074 |
}
|
| 1075 |
],
|
processors/sales.ipynb
CHANGED
|
@@ -2,17 +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"
|
|
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|
|
|
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
| 14 |
"cell_type": "code",
|
| 15 |
-
"execution_count":
|
| 16 |
"metadata": {},
|
| 17 |
"outputs": [],
|
| 18 |
"source": [
|
|
@@ -25,7 +27,7 @@
|
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| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -460,7 +462,7 @@
|
|
| 460 |
"[504608 rows x 19 columns]"
|
| 461 |
]
|
| 462 |
},
|
| 463 |
-
"execution_count":
|
| 464 |
"metadata": {},
|
| 465 |
"output_type": "execute_result"
|
| 466 |
}
|
|
@@ -477,6 +479,15 @@
|
|
| 477 |
" \"Home Type\",\n",
|
| 478 |
"]\n",
|
| 479 |
"\n",
|
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|
| 480 |
"data_frames = []\n",
|
| 481 |
"\n",
|
| 482 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
@@ -496,131 +507,34 @@
|
|
| 496 |
" # Identify columns to pivot\n",
|
| 497 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 498 |
"\n",
|
| 499 |
-
"
|
| 500 |
-
"
|
| 501 |
-
"
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| 502 |
-
"
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| 503 |
-
"
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-
"
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-
"
|
| 506 |
-
"
|
| 507 |
-
"
|
| 508 |
-
"\n",
|
| 509 |
-
" cur_df = pd.melt(\n",
|
| 510 |
-
" cur_df,\n",
|
| 511 |
-
" id_vars=exclude_columns,\n",
|
| 512 |
-
" value_vars=columns_to_pivot,\n",
|
| 513 |
-
" var_name=\"Date\",\n",
|
| 514 |
-
" value_name=col_name,\n",
|
| 515 |
-
" )\n",
|
| 516 |
-
"\n",
|
| 517 |
-
" elif \"_mean_sale_to_list_\" in filename:\n",
|
| 518 |
-
" col_name = \"Mean Sale to List Ratio\"\n",
|
| 519 |
-
" if smoothed:\n",
|
| 520 |
-
" col_name += \" (Smoothed)\"\n",
|
| 521 |
-
" if seasonally_adjusted:\n",
|
| 522 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 523 |
-
"\n",
|
| 524 |
-
" cur_df = pd.melt(\n",
|
| 525 |
-
" cur_df,\n",
|
| 526 |
-
" id_vars=exclude_columns,\n",
|
| 527 |
-
" value_vars=columns_to_pivot,\n",
|
| 528 |
-
" var_name=\"Date\",\n",
|
| 529 |
-
" value_name=col_name,\n",
|
| 530 |
-
" )\n",
|
| 531 |
-
"\n",
|
| 532 |
-
" elif \"_median_sale_price_\" in filename:\n",
|
| 533 |
-
" col_name = \"Median Sale Price\"\n",
|
| 534 |
-
" if smoothed:\n",
|
| 535 |
-
" col_name += \" (Smoothed)\"\n",
|
| 536 |
-
" if seasonally_adjusted:\n",
|
| 537 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 538 |
-
"\n",
|
| 539 |
-
" cur_df = pd.melt(\n",
|
| 540 |
-
" cur_df,\n",
|
| 541 |
-
" id_vars=exclude_columns,\n",
|
| 542 |
-
" value_vars=columns_to_pivot,\n",
|
| 543 |
-
" var_name=\"Date\",\n",
|
| 544 |
-
" value_name=col_name,\n",
|
| 545 |
-
" )\n",
|
| 546 |
"\n",
|
| 547 |
-
"
|
| 548 |
-
"
|
| 549 |
-
" if smoothed:\n",
|
| 550 |
-
" col_name += \" (Smoothed)\"\n",
|
| 551 |
-
" if seasonally_adjusted:\n",
|
| 552 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 553 |
"\n",
|
| 554 |
-
" cur_df = pd.melt(\n",
|
| 555 |
-
" cur_df,\n",
|
| 556 |
-
" id_vars=exclude_columns,\n",
|
| 557 |
-
" value_vars=columns_to_pivot,\n",
|
| 558 |
-
" var_name=\"Date\",\n",
|
| 559 |
-
" value_name=col_name,\n",
|
| 560 |
-
" )\n",
|
| 561 |
-
"\n",
|
| 562 |
-
" elif \"_pct_sold_below_list_\" in filename:\n",
|
| 563 |
-
" col_name = \"% Sold Below List\"\n",
|
| 564 |
-
" if smoothed:\n",
|
| 565 |
-
" col_name += \" (Smoothed)\"\n",
|
| 566 |
-
" if seasonally_adjusted:\n",
|
| 567 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 568 |
-
"\n",
|
| 569 |
-
" cur_df = pd.melt(\n",
|
| 570 |
-
" cur_df,\n",
|
| 571 |
-
" id_vars=exclude_columns,\n",
|
| 572 |
-
" value_vars=columns_to_pivot,\n",
|
| 573 |
-
" var_name=\"Date\",\n",
|
| 574 |
-
" value_name=col_name,\n",
|
| 575 |
-
" )\n",
|
| 576 |
-
"\n",
|
| 577 |
-
" elif \"_sales_count_now_\" in filename:\n",
|
| 578 |
-
" col_name = \"Nowcast\"\n",
|
| 579 |
-
" if smoothed:\n",
|
| 580 |
-
" col_name += \" (Smoothed)\"\n",
|
| 581 |
-
" if seasonally_adjusted:\n",
|
| 582 |
-
" col_name += \" (Seasonally Adjusted)\"\n",
|
| 583 |
-
"\n",
|
| 584 |
-
" cur_df = pd.melt(\n",
|
| 585 |
-
" cur_df,\n",
|
| 586 |
-
" id_vars=exclude_columns,\n",
|
| 587 |
-
" value_vars=columns_to_pivot,\n",
|
| 588 |
-
" var_name=\"Date\",\n",
|
| 589 |
-
" value_name=col_name,\n",
|
| 590 |
-
" )\n",
|
| 591 |
-
"\n",
|
| 592 |
-
" data_frames.append(cur_df)\n",
|
| 593 |
-
"\n",
|
| 594 |
-
"\n",
|
| 595 |
-
"def get_combined_df(data_frames):\n",
|
| 596 |
-
" combined_df = None\n",
|
| 597 |
-
" if len(data_frames) > 1:\n",
|
| 598 |
-
" # iterate over dataframes and merge or concat\n",
|
| 599 |
-
" combined_df = data_frames[0]\n",
|
| 600 |
-
" for i in range(1, len(data_frames)):\n",
|
| 601 |
-
" cur_df = data_frames[i]\n",
|
| 602 |
-
" combined_df = pd.merge(\n",
|
| 603 |
-
" combined_df,\n",
|
| 604 |
-
" cur_df,\n",
|
| 605 |
-
" on=[\n",
|
| 606 |
-
" \"RegionID\",\n",
|
| 607 |
-
" \"SizeRank\",\n",
|
| 608 |
-
" \"RegionName\",\n",
|
| 609 |
-
" \"RegionType\",\n",
|
| 610 |
-
" \"StateName\",\n",
|
| 611 |
-
" \"Home Type\",\n",
|
| 612 |
-
" \"Date\",\n",
|
| 613 |
-
" ],\n",
|
| 614 |
-
" how=\"outer\",\n",
|
| 615 |
-
" suffixes=(\"\", \"_\" + str(i)),\n",
|
| 616 |
-
" )\n",
|
| 617 |
-
" elif len(data_frames) == 1:\n",
|
| 618 |
-
" combined_df = data_frames[0]\n",
|
| 619 |
-
"\n",
|
| 620 |
-
" return combined_df\n",
|
| 621 |
"\n",
|
|
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|
| 622 |
"\n",
|
| 623 |
-
"combined_df = get_combined_df(data_frames)\n",
|
| 624 |
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 625 |
"columns_to_coalesce = [\n",
|
| 626 |
" \"Mean Sale to List Ratio (Smoothed)\"\n",
|
|
@@ -637,22 +551,14 @@
|
|
| 637 |
" \"% Sold Above List (Smoothed)\",\n",
|
| 638 |
"]\n",
|
| 639 |
"\n",
|
| 640 |
-
"
|
| 641 |
-
" for col in combined_df.columns:\n",
|
| 642 |
-
" for column_to_coalesce in columns_to_coalesce:\n",
|
| 643 |
-
" if column_to_coalesce in col and \"_\" in col:\n",
|
| 644 |
-
" if not pd.isna(row[col]):\n",
|
| 645 |
-
" combined_df.at[index, column_to_coalesce] = row[col]\n",
|
| 646 |
-
"\n",
|
| 647 |
-
"# remove columns with underscores\n",
|
| 648 |
-
"combined_df = combined_df[[col for col in combined_df.columns if \"_\" not in col]]\n",
|
| 649 |
"\n",
|
| 650 |
"combined_df"
|
| 651 |
]
|
| 652 |
},
|
| 653 |
{
|
| 654 |
"cell_type": "code",
|
| 655 |
-
"execution_count":
|
| 656 |
"metadata": {},
|
| 657 |
"outputs": [
|
| 658 |
{
|
|
@@ -1053,14 +959,14 @@
|
|
| 1053 |
"[504608 rows x 19 columns]"
|
| 1054 |
]
|
| 1055 |
},
|
| 1056 |
-
"execution_count":
|
| 1057 |
"metadata": {},
|
| 1058 |
"output_type": "execute_result"
|
| 1059 |
}
|
| 1060 |
],
|
| 1061 |
"source": [
|
| 1062 |
-
"
|
| 1063 |
-
"final_df =
|
| 1064 |
" columns={\n",
|
| 1065 |
" \"RegionID\": \"Region ID\",\n",
|
| 1066 |
" \"SizeRank\": \"Size Rank\",\n",
|
|
@@ -1075,14 +981,11 @@
|
|
| 1075 |
},
|
| 1076 |
{
|
| 1077 |
"cell_type": "code",
|
| 1078 |
-
"execution_count":
|
| 1079 |
"metadata": {},
|
| 1080 |
"outputs": [],
|
| 1081 |
"source": [
|
| 1082 |
-
"
|
| 1083 |
-
" os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
|
| 1084 |
-
"\n",
|
| 1085 |
-
"final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
|
| 1086 |
]
|
| 1087 |
}
|
| 1088 |
],
|
|
|
|
| 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, get_df, 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 |
{
|
|
|
|
| 462 |
"[504608 rows x 19 columns]"
|
| 463 |
]
|
| 464 |
},
|
| 465 |
+
"execution_count": 3,
|
| 466 |
"metadata": {},
|
| 467 |
"output_type": "execute_result"
|
| 468 |
}
|
|
|
|
| 479 |
" \"Home Type\",\n",
|
| 480 |
"]\n",
|
| 481 |
"\n",
|
| 482 |
+
"slug_column_mappings = {\n",
|
| 483 |
+
" \"_median_sale_to_list_\": \"Median Sale to List Ratio\",\n",
|
| 484 |
+
" \"_mean_sale_to_list_\": \"Mean Sale to List Ratio\",\n",
|
| 485 |
+
" \"_median_sale_price_\": \"Median Sale Price\",\n",
|
| 486 |
+
" \"_pct_sold_above_list_\": \"% Sold Above List\",\n",
|
| 487 |
+
" \"_pct_sold_below_list_\": \"% Sold Below List\",\n",
|
| 488 |
+
" \"_sales_count_now_\": \"Nowcast\",\n",
|
| 489 |
+
"}\n",
|
| 490 |
+
"\n",
|
| 491 |
"data_frames = []\n",
|
| 492 |
"\n",
|
| 493 |
"for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
|
|
|
|
| 507 |
" # Identify columns to pivot\n",
|
| 508 |
" columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
|
| 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",
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 520 |
"\n",
|
| 521 |
+
" data_frames.append(cur_df)\n",
|
| 522 |
+
" break\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
| 523 |
"\n",
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
| 524 |
"\n",
|
| 525 |
+
"combined_df = get_combined_df(\n",
|
| 526 |
+
" data_frames,\n",
|
| 527 |
+
" [\n",
|
| 528 |
+
" \"RegionID\",\n",
|
| 529 |
+
" \"SizeRank\",\n",
|
| 530 |
+
" \"RegionName\",\n",
|
| 531 |
+
" \"RegionType\",\n",
|
| 532 |
+
" \"StateName\",\n",
|
| 533 |
+
" \"Home Type\",\n",
|
| 534 |
+
" \"Date\",\n",
|
| 535 |
+
" ],\n",
|
| 536 |
+
")\n",
|
| 537 |
"\n",
|
|
|
|
| 538 |
"# iterate over rows of combined df and coalesce column values across columns that start with \"Median Sale Price\"\n",
|
| 539 |
"columns_to_coalesce = [\n",
|
| 540 |
" \"Mean Sale to List Ratio (Smoothed)\"\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 |
]
|
| 558 |
},
|
| 559 |
{
|
| 560 |
"cell_type": "code",
|
| 561 |
+
"execution_count": 4,
|
| 562 |
"metadata": {},
|
| 563 |
"outputs": [
|
| 564 |
{
|
|
|
|
| 959 |
"[504608 rows x 19 columns]"
|
| 960 |
]
|
| 961 |
},
|
| 962 |
+
"execution_count": 4,
|
| 963 |
"metadata": {},
|
| 964 |
"output_type": "execute_result"
|
| 965 |
}
|
| 966 |
],
|
| 967 |
"source": [
|
| 968 |
+
"# Adjust column names\n",
|
| 969 |
+
"final_df = combined_df.rename(\n",
|
| 970 |
" columns={\n",
|
| 971 |
" \"RegionID\": \"Region ID\",\n",
|
| 972 |
" \"SizeRank\": \"Size Rank\",\n",
|
|
|
|
| 981 |
},
|
| 982 |
{
|
| 983 |
"cell_type": "code",
|
| 984 |
+
"execution_count": 5,
|
| 985 |
"metadata": {},
|
| 986 |
"outputs": [],
|
| 987 |
"source": [
|
| 988 |
+
"save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
|
|
|
|
|
|
|
|
|
|
| 989 |
]
|
| 990 |
}
|
| 991 |
],
|