Take a deeper look at some bike share data from three U.S. cities: Chicago, New York City, and Washington, D.C. This project was done as part of the Udacity Data Analyst Nanodegree program.
This project uses bikeshare data provided by Motivate, a bike share system provider for many major cites in the U.S. Below are links to the original datasets:
Please note that, while the data used in the project was derived from these datasets, they had been cleaned up quite a bit. The datasets used in the project had randomly selected data for the first six months of 2017 with only the following 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 have the following two columns:
- Gender
- Birth Year
If you'd like to run the program, you'll have to clean up the origninal data on your own.
This project assumes that pandas version 0.20.0 or above has been installed on your system. To run the program, call the Python 3 interpreter:
python3 bikeshare.py
or, if you're in a UNIX-like environment, you can run the file directly:
./bikeshare.py