Data analysis of Divvy bikes, a bike sharing system in Chicago
Divvy bikes is a bike sharing system across Chicago. It offers users to rent a bike and provides various bike types with options to become…
Data analysis of Divvy bikes, a bike sharing system in Chicago
Divvy bikes is a bike sharing system across Chicago. It offers users to rent a bike and provides various bike types with options to become a member. The goal of this analysis is to understand which kind of bikes are popular, in which areas of the city and during what time or day of the week. The result of the analysis is intended to help the company’s marketing team in increasing the rider memberships and to work with the city municipality to create bike friendly roads and footpaths around popular bike stations.
Divvy bikes website provides historical trips data and is publicly available. Historical trips data from a 12 month timeframe, latitude and longitude locations of bike stations, and Chicago city boundaries in geojson format was used for the analysis. The data was cleaned and had to convert format for few columns when needed.
The below python code, plots and figures show the following:
- Classic and Electric bike usage pattern among casual riders and members on various days of the week across the full year.
- Top five busy bike stations on weekends, these happen to be near the most popular tourist destinations in Chicago.
- Top five busy bike stations among casual riders and members. Members were mostly renting around office and commercial locations of the city whereas casual riders were mostly using them around tourist destinations on weekends.
- Top five station pairs that have the largest traffic of bike rentals.
The analysis provides a methodical approach to understand rental bike usage pattern that can be used for other similar data analytics applications.
Importing the needed libraries
Loading 12-month historical trip data together with bike station locations and Chicago city boundaries
Converting column values to the correct format as needed
Bike usage for each month
Bike usage pattern within a week
Ride-time density plot
Classic bike versus electric bike usage differences across various months
Classic bike versus electric bike usage differences among members and casual riders
Removing stations that do not have a valid name
Top five stations used during weekends
Top five stations used during weekends plotted on the map
Top five stations used by casual riders
Top five stations used by casual riders plotted on the map
Top five stations used by members
Top five stations used by members plotted on the map
Top five station pairs that have the largest traffic
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- 80aedb0c6cab
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- https://medium.com/@shravandataanalytics/data-analysis-of-divvy-bikes-a-ride-sharing-system-in-chicago-80aedb0c6cab
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- https://medium.com/@shravandataanalytics/data-analysis-of-divvy-bikes-a-ride-sharing-system-in-chicago-80aedb0c6cab
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- ok
- fetched_at
- 2026-06-27 18:20:27
