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misikoff
/

zillow

Tasks:

Tabular Regression

Time Series Forecasting

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zillow / processors /home_values.py

misikoff

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	#!/usr/bin/env python
	# coding: utf-8

	# In[1]:


	import pandas as pd
	import os

	from helpers import (
	get_data_path_for_config,
	get_combined_df,
	save_final_df_as_jsonl,
	handle_slug_column_mappings,
	set_home_type,
	)


	# In[2]:


	CONFIG_NAME = "home_values"


	# In[3]:


	data_frames = []

	slug_column_mappings = {
	"_tier_0.0_0.33_": "Bottom Tier ZHVI",
	"_tier_0.33_0.67_": "Mid Tier ZHVI",
	"_tier_0.67_1.0_": "Top Tier ZHVI",
	"": "ZHVI",
	}

	data_dir_path = get_data_path_for_config(CONFIG_NAME)

	for filename in os.listdir(data_dir_path):
	if filename.endswith(".csv"):
	print("processing " + filename)
	cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
	exclude_columns = [
	"RegionID",
	"SizeRank",
	"RegionName",
	"RegionType",
	"StateName",
	"Bedroom Count",
	"Home Type",
	]

	if "Zip" in filename:
	continue
	if "Neighborhood" in filename:
	continue
	if "City" in filename:
	continue
	if "Metro" in filename:
	continue
	if "County" in filename:
	continue

	if "City" in filename:
	exclude_columns = exclude_columns + ["State", "Metro", "CountyName"]
	elif "Zip" in filename:
	exclude_columns = exclude_columns + [
	"State",
	"City",
	"Metro",
	"CountyName",
	]
	elif "County" in filename:
	exclude_columns = exclude_columns + [
	"State",
	"Metro",
	"StateCodeFIPS",
	"MunicipalCodeFIPS",
	]
	elif "Neighborhood" in filename:
	exclude_columns = exclude_columns + [
	"State",
	"City",
	"Metro",
	"CountyName",
	]

	if "_bdrmcnt_1_" in filename:
	cur_df["Bedroom Count"] = "1-Bedroom"
	elif "_bdrmcnt_2_" in filename:
	cur_df["Bedroom Count"] = "2-Bedrooms"
	elif "_bdrmcnt_3_" in filename:
	cur_df["Bedroom Count"] = "3-Bedrooms"
	elif "_bdrmcnt_4_" in filename:
	cur_df["Bedroom Count"] = "4-Bedrooms"
	elif "_bdrmcnt_5_" in filename:
	cur_df["Bedroom Count"] = "5+-Bedrooms"
	else:
	cur_df["Bedroom Count"] = "All Bedrooms"

	cur_df = set_home_type(cur_df, filename)

	cur_df["StateName"] = cur_df["StateName"].astype(str)
	cur_df["RegionName"] = cur_df["RegionName"].astype(str)

	data_frames = handle_slug_column_mappings(
	data_frames, slug_column_mappings, exclude_columns, filename, cur_df
	)


	combined_df = get_combined_df(
	data_frames,
	[
	"RegionID",
	"SizeRank",
	"RegionName",
	"RegionType",
	"StateName",
	"Bedroom Count",
	"Home Type",
	"Date",
	],
	)

	combined_df


	# In[4]:


	final_df = combined_df

	for index, row in final_df.iterrows():
	if row["RegionType"] == "city":
	final_df.at[index, "City"] = row["RegionName"]
	elif row["RegionType"] == "county":
	final_df.at[index, "County"] = row["RegionName"]
	if row["RegionType"] == "state":
	final_df.at[index, "StateName"] = row["RegionName"]

	# coalesce State and StateName columns
	# final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
	# final_df["County"] = final_df["County"].combine_first(final_df["CountyName"])

	# final_df = final_df.drop(
	# columns=[
	# "StateName",
	# # "CountyName"
	# ]
	# )
	final_df


	# In[5]:


	final_df = final_df.rename(
	columns={
	"RegionID": "Region ID",
	"SizeRank": "Size Rank",
	"RegionName": "Region",
	"RegionType": "Region Type",
	"StateCodeFIPS": "State Code FIPS",
	"StateName": "State",
	"MunicipalCodeFIPS": "Municipal Code FIPS",
	}
	)

	final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")

	final_df


	# In[6]:


	save_final_df_as_jsonl(CONFIG_NAME, final_df)