Created on 9/10/2018

Udacity-GitHub Bike Share Project

In this project, I wrote Python code to import US bike share data and answer interesting questions about it by computing descriptive statistics. Contains a script that takes in raw input to create an interactive experience in the terminal to present statistics.

Files used include bikeshare.py, chicago.csv, new_york_city.csv, and washington.csv files.

The GitHub Project 3 repository for PDSND was used in this project.

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)

Gender
Birth Year

Urls for the original datasets are listed below if you'd like to see them.

https://www.divvybikes.com/system-data https://www.citibikenyc.com/system-data https://www.capitalbikeshare.com/system-data

You will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, you'll write code to provide the following information:

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)