In this project, I used Python to explore data related to the bike share systems of three major cities — Chicago, New York City, and Washington, DC. I wrote code to import the data and compute descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal.
Randomly selected data for the first six months of 2017 are provided for all three cities. The data files contain the same core six columns:
- Start Time (e.g., 2017-01-01 00:07:57)
- End Time (e.g., 2017-01-01 00:20:53)
- Trip Duration (in seconds - e.g., 776)
- Start Station (e.g., Broadway & Barry Ave)
- End Station (e.g., Sedgwick St & North Ave)
- User Type (Subscriber or Customer)
The Chicago and New York City files also provide the following two columns:
- Gender
- Birth Year
I used data provided by Motivate, a national bike share system provider. In order to run the program, the following files are needed.
- chicago.csv
- new_york_city.csv
- washington.csv
The program was written using:
- Python 3
- NumPy and Pandas were installed using Anaconda