This Python 3 script is written to explore data related to bike share systems for Chicago, New York City, and Washington. It imports data from csv files and computes descriptive statistics from the data. It is run from the terminal (or from Jupyter notebooks).
The datasets used for this script contain bike share data for the first six months of 2017. You can access the original data files here Chicago, New York City , Washington. Some data wrangling, to reduce columns and reformat, has been performed to condense these files to the core six columns used in this project. This makes the analysis and the evaluation in this project more straightforward.
The data is provided by Motivate, which is a bike share system provider for many cities in the United States. The data files for all three cities contain the same six columns:
- Start Time
- End Time
- Trip Duration (in seconds)
- Start Station
- End Station
- User Type (Subscriber or Customer)
The Chicago and New York City files also contain the following two columns:
- Gender
- Birth Year
The script answers the following questions about the bike share data:
Popular times of travel
- What is the most popular month for start time?
- What is the most popular day of week (Monday, Tuesday, etc.)?
- What is the most popular hour of day for start time?
Popular stations and trip
- What is the most popular start station and most popular end station?
- What is the most popular trip?
Trip duration
- What is the total trip duration and average trip duration?
User information
- What are the counts of each user type?
- What are the counts of gender? (only available for NYC and Chicago)
- What are the earliest (i.e. oldest person), most recent (i.e. youngest person), and most popular birth years?