In this project, I used Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. I wrote the code to import the data and answer interesting questions about it by computing descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics.
Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.
Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.
In this project, I used the data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You can compare the system usage between three large cities: Chicago, New York City, and Washington, DC.