In this work, I used Python to explore data related to bikeshare systems for three major bikeshare systems in the United States. The task of this work is twofold: first, to become famliar with Pyhton and secondly, to discover data analysis process.
Through this process, I performed
- data wrangling to unify the format of data from the three systems and;
- write code to compute descriptive statistics.
- You will also make use of a package that is not part of the standard Python library to help you visualize the data.
By the end of this work I was able to:
- Coose the appropriate data types (e.g. strings, floats) and data structures (e.g. lists, dictionaries) to carry out the required analysis tasks.
- Use correctly loops and conditional statements to process the data.
- Use Functions to reduce repetitive code.
- Use packages to carry out advanced tasks such as reading and writing files and creating visualizations.
- Organize my code and improve its readability by integrating docstrings, comments.
- Ask questions and answer them using data statistics and visulization.
- The data folder contains the 3 bikeshare system datasets of New York, Chicago and Washington cities
- 2016_US_Bikeshare_Analysis.ipynb is a Jupyter notebook containing the work I have done.
- 2016_US_Bikeshare_Analysis.html is a html version of this work