This project was created to facilitate learning of common data-science related python packages and practices, such as analyzing .csv data, using pandas for data cleaning, conducting exploratory data analysis and creating visualizations with matplotlib and seaborn, as well as performing geospatial analysis with geopandas. GTFS data was also analyzed and used in this project.
Throughout this project, I learned about the workflow and integration of Git, GitHub, and structuring project files properly to allow for readibility, project cleanliness, and transparency.
Please see "07_insights_and_recommendations" for a more detailed recounting of the processes used throughout this project.
If you'd like to view the raw and processed data files, they are too large to store on GitHub, so I've saved them onto a google drive link where you can download them:
Langley Commuting Analysis Data Files
Please download the files and place them in the '../data/' directory to run the analysis.