As Chief Data Strategist of the fictional ride sharing company Pyber, I have been given access to the company's complete recordset of rides. This contains information about every active driver and historic ride, including details like city, driver count, individual fares, and city type. I must offer data-backed guidance on new opportunities for market differentiation.
To do this, I have created a Bubble Plot in the included Jupyter Notebook that showcases the relationship between four key variables:
- Average Fare ($) Per City
- Total Number of Rides Per City
- Total Number of Drivers Per City
- City Type (Urban, Suburban, Rural)
In addition, the following three pie charts have also been produced to draw conclusions:
- % of Total Fares by City Type
- % of Total Rides by City Type
- % of Total Drivers by City Type
To demonstrate matplotlib plotting in Python/pandas.