Date created

April 12, 2020

Explore US Bikeshare Data

This project uses Python along with Python libraries NumPy and Pandas to analyze US Bikeshare data provided by Motivate.

Analysis is available for Chicago, New York City and Washington. The user is prompted for which city to analyze and can also filter on month and day.

The following statistics are computed:

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

Credits

Udacity provided a set of csv files to work with, which is a subset of data provided by Motivate. They also provided a template Python script to start with