17 Dec. 2022
Explore US Bikeshare Data project
In this project, I created Python code to import US bike share data and used descriptive statistics to answer interesting questions about it. In addition, this script takes raw input to create an interactive terminal experience that displays this information.
Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) 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
- Popular times of travel (i.e., occurs most often in the start time)
- most common month
- most common day of week
- most common hour of day
- Popular stations and trip
- most common start station
- most common end station
- most common trip from start to end (i.e., most frequent combination of start station and end station)
- Trip duration
- total travel time
- average travel time
- User info
- counts of each user type
- counts of each gender (only available for NYC and Chicago)
- earliest, most recent, most common year of birth (only available for NYC and Chicago)
- bikeshare.py
- chicago.csv
- new_york_city.csv
- washington.csv