Explore bike-sharing system data for major US cities (Chicago, New York, and Washington) using Python. The project involves importing datasets, computing descriptive statistics, and creating an interactive terminal experience.
Analyze bike share data from Motivate for Chicago, NYC, and Washington (Jan-Jun 2017). Datasets include core columns: Start Time, End Time, Trip Duration, Start Station, End Station, User Type. (Additional columns for Chicago and NYC: Gender, Birth Year)
- 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
- Total travel time
- Average travel time
- Counts of each user type
- Counts of each gender (NYC and Chicago only)
- Earliest, most recent, and most common year of birth (NYC and Chicago only)
- Explore the Jupyter Notebook for detailed Python code implementation and analysis. Enhance your Python skills through practical data exploration.