Tokyo Photographs#

%matplotlib inline

import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
db = pd.read_csv('data/tokyo.csv')

Randomly subsetting#

# Set the "seed" so every run produces the generates the same random numbers
# Create a sequence of length equal to the number of rows in the table
ri = np.arange(len(db))
# Randomly reorganize (shuffle) the values
# Reindex the table by using only the first 10,000 numbers 
# of the (now randomly arranged) sequence
db = db.iloc[ri[:10000], :]

Reproject XY coordinates in separate columns#

pts = db.apply(lambda r: Point(r.longitude, r.latitude), axis=1)
CPU times: user 431 ms, sys: 4.86 ms, total: 436 ms
Wall time: 436 ms
gdb = gpd.GeoDataFrame(db.assign(geometry=pts), \
                       crs={'init' :'epsg:4326'})
gdb = gdb.to_crs(epsg=3857)
CPU times: user 529 ms, sys: 7.46 ms, total: 536 ms
Wall time: 747 ms
xys = gdb['geometry'].apply(lambda pt: pd.Series({'x': pt.x, 'y': pt.y}))
gdb['x'] = xys['x']
gdb['y'] = xys['y']
CPU times: user 2.13 s, sys: 20.3 ms, total: 2.15 s
Wall time: 2.16 s
gdb.drop('geometry', axis=1).to_csv('tokyo_clean.csv', index=False)