Exploring US Bikeshare Data

Overview

In this project, I made use of Python to exploring 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 answer interesting questions about it by computing descriptive statistics. this code takes in raw input to create an interactive experience to present these statistics.

What Software Do you Need?

the following software requirements apply:

You should have Python 3, NumPy and Pandas installed using Anaconda

The Datasets

are 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

Statistics Computed

computing a variety of descriptive statistics about bike share use in Chicago, New York City, and Washington In this project, I wrote 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) The Files

You will need the three city dataset files if you wanted to do the project yourself:

chicago.csv

new_york_city.csv

washington.csv