Over the past decade, bicycle-sharing systems have gained popularity worldwide, providing users with the flexibility to rent bikes for short-term use. These systems generate valuable data, offering insights into bike share usage patterns. This project utilizes data from "Motivate," a bike share system provider for major U.S. cities, to explore and compare bike share usage in Chicago, New York City, and Washington, DC.
Randomly selected data for the first six months of 2017 is provided for all three cities. The datasets include the following core columns:
- Start Time
- End Time
- Trip Duration (in seconds)
- Start Station
- End Station
- User Type
Additional columns, such as Gender and Birth Year, are available for Chicago and New York City. The original, larger datasets have been streamlined for ease of analysis and evaluation of Python skills.
The Python script in this project computes various descriptive statistics to provide insights into bike share usage:
- Most common month
- Most common day of the week
- Most common hour of the day
- Most common start station
- Most common end station
- Most common trip from start to end (frequent combination of start and end station)
- Total travel time
- Average travel time
- Counts of each user type
- Counts of each gender (available for NYC and Chicago)
- Earliest, most recent, and most common year of birth (available for NYC and Chicago)
This project is an important milestone in my self-learning journey, showcasing successful data analysis and Python programming skills.
Explore the bike share data and feel free to contribute or provide feedback. If you have any questions or suggestions, please reach out!