spatial_join.py

import pandas as pd
from geopandas import GeoDataFrame, points_from_xy
import geopandas as gpd
import time

# Load the Uber point data

df = pd.read_csv('uber_all.csv')

# Create the GeoDataFrame and assign the proper projection

uber_pickups = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(data.Lon, data.Lat))
uber_pickups.crs = "EPSG:4326"

# Load the Census Block Group data

block_groups = gpd.read_file('./blockgroups.geojson')

# Perform the spatial join

start = time.time()
joined_data = gpd.sjoin(uber_pickups, block_groups, op='within')
end = time.time()
print(end - start)

# Group the data by Block Group ID and join to the original Block Groups File

grouped = joined_data.groupby('blockgroup').size()
final_data = grouped.to_frame().reset_index()
final_data.columns = ['blockgroup', 'count']
merged_areas = block_groups.merge(df, on='blockgroup', how='outer')