Python project to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington.
In this project, with the use of Python to exploring data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. The code is written to import the data and answer interesting questions about it by computing descriptive statistics. Also, there is a script that takes in raw input to create an interactive experience in the terminal to present these statistics.
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
In this project, it computes the following statistics:
- 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)
There are four files in this project:
- bikeshare.py
- chicago.csv
- new_york_city.csv
- washington.csv
As the data files are too large to be uploaded here you can find them here.