Table Of Contents
Table Of Contents

City Bike Challenge

Links: notebook, city_bike_challenge2html.html, PDF, python, city_bike_challenge.slides.html, city_bike_challenge.slides2p.html, GitHub

Based on the data available at Divvy Data, how to guess where people usually live and where the usually work?

from jyquickhelper import add_notebook_menu
add_notebook_menu()

The city

I don’t know Chicago. Assuming I’m looking for a restaurant or a bar, where should I go? Let’s try to find where I should go to walk in lively places and find a bar…

from pyquickhelper.helpgen import NbImage
NbImage("images/chicago.png")
../_images/city_bike_challenge_3_0.png

The data

Divvy Data publishes a sample of the data.

from pyensae.datasource import download_data
file = download_data("Divvy_Trips_2016_Q3Q4.zip", url="https://s3.amazonaws.com/divvy-data/tripdata/")

We know the stations.

import pandas
stations = df = pandas.read_csv("Divvy_Stations_2016_Q3.csv")
df.head()
id name latitude longitude dpcapacity online_date
0 456 2112 W Peterson Ave 41.991178 -87.683593 15 5/12/2015
1 101 63rd St Beach 41.781016 -87.576120 23 4/20/2015
2 109 900 W Harrison St 41.874675 -87.650019 19 8/6/2013
3 21 Aberdeen St & Jackson Blvd 41.877726 -87.654787 15 6/21/2013
4 80 Aberdeen St & Monroe St 41.880420 -87.655599 19 6/26/2013

And we know the trips.

bikes = df = pandas.read_csv("Divvy_Trips_2016_Q3.csv")
df.head()
trip_id starttime stoptime bikeid tripduration from_station_id from_station_name to_station_id to_station_name usertype gender birthyear
0 12150160 9/30/2016 23:59:58 10/1/2016 00:04:03 4959 245 69 Damen Ave & Pierce Ave 17 Wood St & Division St Subscriber Male 1988.0
1 12150159 9/30/2016 23:59:58 10/1/2016 00:04:09 2589 251 383 Ashland Ave & Harrison St 320 Loomis St & Lexington St Subscriber Female 1990.0
2 12150158 9/30/2016 23:59:51 10/1/2016 00:24:51 3656 1500 302 Sheffield Ave & Wrightwood Ave 334 Lake Shore Dr & Belmont Ave Customer NaN NaN
3 12150157 9/30/2016 23:59:51 10/1/2016 00:03:56 3570 245 475 Washtenaw Ave & Lawrence Ave 471 Francisco Ave & Foster Ave Subscriber Female 1988.0
4 12150156 9/30/2016 23:59:32 10/1/2016 00:26:50 3158 1638 302 Sheffield Ave & Wrightwood Ave 492 Leavitt St & Addison St Customer NaN NaN

The challenge

We know how people use bicycles. People, people… it is us. What do I know about myself I could use to explore the data and determines living and working areas of Chicago?

A few graph

Display the city with two colors. The following shows the stations with more than 20 slots.

from ensae_projects.datainc.data_bikes import folium_html_stations_map
xy = []
for els in stations.apply(lambda row: (row["latitude"], row["longitude"], row["dpcapacity"] >= 20), axis=1):
    xy.append( ( (els[0], els[1]), "red" if els[2] else "blue"))
folium_html_stations_map(xy, width="80%")