Overview

The purpose of this analysis is to show how the data differs by city type and how those differences can be used by decision-makers at PyBer.

Results

We created a summary DataFrame of the ride-sharing data by city type as well as a multiple-line graph that shows the total weekly fares for each city type. By looking at the results we can tell that there is a big difference in ride-sharing amount in diffrerent city types. The average number of rides in the rural cities is much lower than urban and suburban cities, respectively, as well as the total fare.

Summary

Based on the results we should look into lowering prices for rural city types to potentially increase the number of rides.