This project is created on 2018-10-22 and documented on 2018-11-05 by Mehmet Yildirim, who is a student in Programming for Data Science Nanodegree of Udacity.
Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.
Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.
In this project, data related to bike share systems for three major cities in the United States are analyzed:
- Chicago
- New York City
- Washington
- 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)
Python script:
- bikeshare.py
Dataset files:
- chicago.csv
- new_york_city.csv
- washington.csv
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
The dataset was not provided in git repository because of the size of the files. Please contact with Udacity (https://udacity.zendesk.com/hc/en-us/requests/new) if you want to use these dataset.
This project is prepared by Udacity, as a project of Programming for Data Science Nanodegree for the people interested in data science. Data provided by Motivate, a bike share system provider for many major cities in the United States.