The python program Bikeharing_project.py is created November 16th 2019. The README.md file is initially created December 3rd 2019.
Explore US bikeshare data project, issued by Dirk Kadijk.
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, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.
What value does this project offer?
In summary: Calculate statistics and build an interactive environment where a user chooses the data and filter for a dataset to analyze. The bikeshare.py file is set up as a script that takes in raw input to create an interactive experience in the terminal that answers questions about the dataset. The experience is interactive because depending on a user's input, the answers to the questions on the previous page will change! There are four questions that will change the answers:
- Would you like to see data for Chicago, New York, or Washington?
- Would you like to filter the data by month, day, or not at all?
- (If they chose month) Which month - January, February, March, April, May, or June?
- (If they chose day) Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?
In this project the following three city dataset files are used:
- chicago.csv
- new_york_city.csv
- washington.csv
The original files are much larger and messier, and you don't need to download them, but they can be accessed here if you'd like to see them (Chicago, New York City, Washington). Some data wrangling has been performed to condense these files to the above core six columns to make your analysis and the evaluation of your Python skills more straightforward.
Credits are owed to Udacity who created both this US bikesharing data project as the github project assignments: