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
You will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, you'll write code to provide the following information:
- most common month
- most common day of week
- most common hour of day
- 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)
- total travel time
- average travel time
- 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)
You will need to have csv data files called (chicago.csv, new_york_city.csv, washington.csv)
This project is depending on conda, you have to have a conda installed
conda env create --file bikeshare_env.yml
conda activate bikeshare_env
python bikeshare.py