chaitanyacherukuri / explore-us-bikeshare-data Goto Github PK

PROJECT OVERVIEW:

In this project, I made 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 wrote code to import the data and answered interesting questions about it by computing descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

PROJECT DETAILS: Bike share Data

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

THE QUESTIONS:

I wrote code to answer the following questions about the bike share data:

1. What month occurs most often in the start time?
2. What day of the week (Monday, Tuesday, etc.) occurs most often in the start time?
3. What hour of the day occurs most often in the start time?
4. What is the total trip duration and average trip duration?
5. What is the most frequently used start station and most frequently used end station?
6. What is the most common trip (i.e., the combination of start station and end station that occurs the most often)?
7. What are the counts of each user type?
8. What are the counts of gender?
9. What is the earliest birth year (when the oldest person was born), most recent birth year, and most common birth year?