Datasets:

misikoff
/

zillow

Tasks:

Tabular Regression

Time Series Forecasting

Languages:

English

License:

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zillow / processors /home_values_forecasts.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


	# In[2]:


	CONFIG_NAME = "home_values_forecasts"


	# In[3]:


	data_frames = []

	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))

	cols = ["Month Over Month %", "Quarter Over Quarter %", "Year Over Year %"]
	if filename.endswith("sm_sa_month.csv"):
	# print('Smoothed')
	cur_df.columns = list(cur_df.columns[:-3]) + [
	x + " (Smoothed) (Seasonally Adjusted)" for x in cols
	]
	else:
	# print('Raw')
	cur_df.columns = list(cur_df.columns[:-3]) + cols

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

	data_frames.append(cur_df)


	combined_df = get_combined_df(
	data_frames,
	[
	"RegionID",
	"RegionType",
	"SizeRank",
	"StateName",
	"BaseDate",
	],
	)

	combined_df


	# In[4]:


	# Adjust columns
	final_df = combined_df
	final_df = combined_df.drop("StateName", axis=1)
	final_df = final_df.rename(
	columns={
	"CountyName": "County",
	"BaseDate": "Date",
	"RegionName": "Region",
	"RegionType": "Region Type",
	"RegionID": "Region ID",
	"SizeRank": "Size Rank",
	}
	)

	# iterate over rows of final_df and populate State and City columns if the regionType is msa
	for index, row in final_df.iterrows():
	if row["Region Type"] == "msa":
	regionName = row["Region"]
	# final_df.at[index, 'Metro'] = regionName

	city = regionName.split(", ")[0]
	final_df.at[index, "City"] = city

	state = regionName.split(", ")[1]
	final_df.at[index, "State"] = state

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

	final_df


	# In[5]:


	save_final_df_as_jsonl(CONFIG_NAME, final_df)