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, The data is provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns.
In this project, I need to use the Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. I also need to code to import the data and answer interesting questions about it by computing descriptive statistics. Plus, I make a script that takes in raw input to create an interactive experience in the terminal to present these statistics.
FIND:
- 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)
- bikeshare.py is interpretated in python
- chicago.csv : record of the city
- new_york_city.csv : record of the city
- washington.csv : record of the city
- [Python 3] - jupyter notebook is an open source and used to data analyze with python code
- [ipython] - use it as a terminal
- [numpy] - is a fundamental scientific computing.
- [Pandas] - uses to clean, organize, convert, and merge the data.
MIT