Use Python to explore data related to bikeshare systems for three major systems in the US. The data will need to be munged into shape before being analyzed using descriptive statistics before being visualised.
The purpose of this taask to to continue to become familliar with the data analysis process.
- This process will be done using a Jupyter Notebook.
- The code should run w/o errors.
- Appropriate use of
- data structures/types
- loops/conditional statements
- Packages
- functions
- coding practices (i.e. Docstrings, comments, variable names & general readability)
- Analysis
- Pose two questions about the data
- Inspect the structure of the original data (very important)
- Clean the data
- Answer questions about the data using descriptive statistics
- Visualize the data (instead of using plt use seaborn or ggplot)
- Perform additional exploratory analysis
- Consider where data analysis can be applied to other fields
- Download relevant information (Done)
- Familiarize myself with the data (Done)
- Pose some questions that can be answered by simple analysis (Done)
- Consider what information I will need to do said analysis (Done)
- Carry out analysis (Done)
- Convert ipynb to PDF - nbconvert package
iPython notebook will be shared as a PDF document. Make sure to cite all appropriate sources. (Contained within this README)
- ./data/ contains the various datasets
- ./examples/ contains an example of the data cleaning process
- ./2016_US_Bikeshare_Analysis.ipynb is a Jupyter notebook containing the work I have done
- ./2016_US_Bikeshare_Analysis.pdf is an exported version of the above file using LaTeX
- ./Bike_Share_Analysis.ipynb is a Jupyter notebook containing the "skeleton" code for this project
- Data provided by:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent