Python and knowledge of Pandas is used to create a summary DataFrame of the ride-sharing data by city type. Then, using Pandas and Matplotlib, a multiple-line graph is drawn that shows the total weekly fares for each city type.
- The above ride-sharing data includes the total rides, total drivers, total fares, average fare per ride and driver by city type. From the summary we can see Urban city type has the most rides and drivers resulting in the highest fares coming in. Rural city type accruing the least fares but has the highest average fare per ride and driver. Suburban city type is in between for all aspects.
- For the four months period, all three city types are moving steadily with Urban city type at the top in terms of making fares followed by Suburban and Rural city types respectively.
The following points are recommended:
- Conduct an analysis with more data for the rest of the year in order to get more insights when making decisions.
- The number of drivers in Rural city type seems much lower than Urban and Suburban. An effort to increase should bring in more revenue, although a study must be completed to check if the project will be sustainable.
- Make an effort to increase the average fare per driver for Urban city type drivers as there seems to be significant difference from the other city types.