## The Datasets
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

#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 - Code file saved under src folder
Data from Bike Share:
* chicago.csv
* new_york_city.csv
* washington.csv

* Python 3
* PyCharm
* Anaconda: Pandas and NumPy
* Git

*[Code Academy] https://www.codecademy.com/learn/paths/analyze-data-with-python
*[Free Code Camp] https://guide.freecodecamp.org/python/how-to-convert-strings-into-integers-in-python/
*[Python Docs] https://docs.python.org/3/tutorial/datastructures.html
*[Tutorials Point] https://www.tutorialspoint.com/python3/python_dictionary.htm