israakilany15 / us-bikeshare

US-bikeshare

In this project, i make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. I write code to import the data and answer interesting questions about it by computing descriptive statistics. i also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

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, we 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. we will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

The Datasets

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, I'll write code to 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)