satchinard / pdsnd_github

GitHub project (Project 3) repository for PDSND

Date created

6th october 2019.

Project Title

Bike Share Data

Description

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, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

The Datasets

Randomly selected data files contain the same core six (6) columns:

Column title	Data example
Start Time	2017-01-01 00:07:57
End Time	2017-01-01 00:20:53
Trip Duration	in seconds - e.g., 776
Start Station	Broadway & Barry Ave
End Station	Sedgwick St & North Ave
User Type	Subscriber or Customer

Some cities files also have the following two columns:

Gender and Birth Year

Statistics Computed

You will learn about bike share use in cities by computing a variety of descriptive statistics. This project will provide the following information:

• 1 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

• 2 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)

• 3 Trip duration

• total travel time
• average travel time

• 4 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)

Files used

bikeshare.py contains the Python code.

You will need the three city dataset files too:

• chicago.csv
• new_york_city.csv
• washington.csv

Credits

Feel free to update the code to fit your objectives.