12/07/2019

Bikeshare Data Project

This Project gives insight into bikeshare data in the cities of Chicago, New York City, and Washington by computing a variety of descriptive statistics:

#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)

bikeshare.py

Credit to Udacity team and stackoverflow.com