This readme file and project created in 8/24/2018 .

Explore US Bikeshare Data and work on github

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.

Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.

In this project, I will perform an exploratory analysis on data provided by Motivate, a bike-share system provider for many major cities in the United States. I will compare the system usage between three large cities: New York City, Chicago, and Washington, DC. I will also see if there are any differences within each system for those users that are registered, regular users and those users that are short-term, casual users.

This project uses Python to explore data related to bike share systems for three major cities in the United States--Chicago, New York City, and Washington. The program reads in the specified CSV data file and answers interesting questions about it by computing descriptive statistics. This program also supports direct interaction by allowing the user to choose the city file and optionally specify a time period filter.

• Data files with extension CSV washignton.csv , new_york.csv and chicago.csv
• Download the data files
• bikeshare.py - main program, read in user specified data file, allow specification of time period filter, tabulate statistics and show results

I made a lot of work research in this project also googling with many sites to help me for python coding using panda and visualization

Here it is some of the links i used during my work :

• https://stackoverflow.com/
• https://www.quora.com/
• https://pandas.pydata.org/pandas-docs/stable/
• https://anaconda.org
• http://www.republicbike.com
• https://www.motivateco.com/
• https://en.wikipedia.org/wiki/Data_wrangling
• https://github.com/udacity/pdsnd_github