Datasets:

misikoff
/

zillow

Tasks:

Tabular Regression

Time Series Forecasting

Languages:

English

License:

Dataset card Files Files and versions Community

zillow / processors /rentals.py

misikoff

Revert "feat: try removing all non essential python and notebook files"

c83a125 3 months ago

raw history blame contribute delete

No virus

3.39 kB

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

	# In[2]:


	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[1]:


	CONFIG_NAME = "rentals"


	# In[3]:


	data_frames = []

	slug_column_mappings = {"": "Rent"}

	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",
	"Home Type",
	]

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

	cur_df = set_home_type(cur_df, filename)

	if "City" in filename:
	exclude_columns = [
	"RegionID",
	"SizeRank",
	"RegionName",
	"RegionType",
	"StateName",
	"Home Type",
	# City Specific
	"State",
	"Metro",
	"CountyName",
	]
	elif "Zip" in filename:
	exclude_columns = [
	"RegionID",
	"SizeRank",
	"RegionName",
	"RegionType",
	"StateName",
	"Home Type",
	# Zip Specific
	"State",
	"City",
	"Metro",
	"CountyName",
	]
	elif "County" in filename:
	exclude_columns = [
	"RegionID",
	"SizeRank",
	"RegionName",
	"RegionType",
	"StateName",
	"Home Type",
	# County Specific
	"State",
	"Metro",
	"StateCodeFIPS",
	"MunicipalCodeFIPS",
	]

	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",
	"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"]

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

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


	# In[5]:


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

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

	final_df


	# In[7]:


	save_final_df_as_jsonl(CONFIG_NAME, final_df)