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