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housing_dataset / data_cleaning.py

HathawayLiu

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	'''
	git clone https://github.com/geopandas/geopandas.git
	cd geopandas
	pip install .
	'''
	import requests
	import pandas as pd
	import numpy as np
	import requests
	import geopandas as gpd
	from shapely.geometry import Point

	# load neighborhood GeoJson file and housing dataset
	neighborhood = gpd.read_file("https://raw.githubusercontent.com/HathawayLiu/Housing_dataset/main/Neighborhood_Map_Atlas_Districts.geojson")
	url = "https://github.com/HathawayLiu/Housing_dataset/raw/main/Building_Permits_20240213.csv"
	df = pd.read_csv(url)

	# Pre-processing of data
	df['OriginalZip'] = pd.to_numeric(df['OriginalZip'], errors='coerce').fillna('NA').astype(str)
	df['OriginalZip'] = df['OriginalZip'].replace(0, 'NA')
	df['OriginalCity'] = df['OriginalCity'].fillna('SEATTLE')
	df['OriginalState'] = df['OriginalState'].fillna('WA')
	df['EstProjectCost'] = pd.to_numeric(df['EstProjectCost'], errors='coerce').astype(float)
	df['IssuedDate'] = pd.to_datetime(df['IssuedDate'], errors='coerce')
	df['HousingUnits'] = pd.to_numeric(df['HousingUnits'], errors='coerce').fillna(0).astype(int)
	df['HousingUnitsRemoved'] = pd.to_numeric(df['HousingUnitsRemoved'], errors='coerce').fillna(0).astype(int)
	df['HousingUnitsAdded'] = pd.to_numeric(df['HousingUnitsAdded'], errors='coerce').fillna(0).astype(int)
	df['Longitude'] = pd.to_numeric(df['Longitude'], errors='coerce')
	df['Latitude'] = pd.to_numeric(df['Latitude'], errors='coerce')

	# Function to get the zip code from coordinates
	def get_zip_code_from_coordinates(latitude, longitude, api_key):
	if pd.isna(latitude) or pd.isna(longitude):
	return 'NA' # Return 'NA' if latitude or longitude is NaN

	api_url = f"https://maps.googleapis.com/maps/api/geocode/json?latlng={latitude},{longitude}&key={api_key}"
	response = requests.get(api_url)

	if response.status_code == 200:
	data = response.json()
	if data['results']:
	for component in data['results'][0]['address_components']:
	if 'postal_code' in component['types']:
	return component['long_name']
	return 'NA' # Return 'NA' if no zip code found
	else:
	return 'NA' # Return 'NA' for non-200 responses

	# Apply the function only to rows where 'OriginalZip' is 'NA'
	api_key = 'Your Own API Key'
	for index, row in df.iterrows():
	if row['OriginalZip'] == 'NA':
	zip_code = get_zip_code_from_coordinates(row['Latitude'], row['Longitude'], api_key)
	df.at[index, 'OriginalZip'] = zip_code
	print(f"Updated row {index} with Zip Code: {zip_code}")

	# Function to get corresponding neighborhood district from coordinates
	gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude), crs='EPSG:4326')
	def get_neighborhood_name(point, neighborhoods):
	for _, row in neighborhoods.iterrows():
	if point.within(row['geometry']):
	print(row['L_HOOD'])
	return row['L_HOOD']
	return 'NA'
	# Apply the function to each row
	gdf['NeighborDistrict'] = gdf['geometry'].apply(lambda x: get_neighborhood_name(x, neighborhood) if pd.notna(x) else 'NA')
	# Merge the new column back to the original DataFrame
	df['NeighborDistrict'] = gdf['NeighborDistrict']
	# filtered df to start from year 2000
	df_filtered = df[df['IssuedDate'].dt.year >= 2000]
	df_filtered['IssuedDate'] = df['IssuedDate'].astype(str)
	df_filtered.fillna('NA', inplace=True)

	'''
	Following code is for spliting datasets in train and test dataset
	'''
	# Read the dataset
	housing_df = pd.read_csv('https://github.com/HathawayLiu/Housing_dataset/raw/main/Building_Permits_Cleaned.csv')
	# Shuffle the dataset
	housing_df = housing_df.sample(frac=1).reset_index(drop=True)

	# Splitting the dataset into training and test sets
	split_ratio = 0.8 # 80% for training, 20% for testing
	split_index = int(len(housing_df) * split_ratio)

	train_df = housing_df[:split_index]
	test_df = housing_df[split_index:]

	# Export to CSV
	train_df.to_csv('/Users/hathawayliu/Desktop/train_dataset.csv', index=False)
	test_df.to_csv('/Users/hathawayliu/Desktop/test_dataset.csv', index=False)