Pyber_analysis

Overview of the Analysis

The purpose of this analysis was to compare total ride fares of three different city types: Rural, Suburban, and Urban. We accomplished this by creating a ride-sharhing summary DataFrame by city type and a multiple-line chart of total fares for each city type.

Results

As you can see from Summary DataFrame, the Urban city type has the most rides, drivers, and fares compared to the Suburban and Rural city types. When you look at the average fare per ride and fare per driver, the Urban city type has the lowest of these two categories. In order to visualize the total fares by city type, I created a multiple line chart here: Total Fares by City Type. This line chart emphasizes how much more the Urban toal fares is compared to Suburban and Rural.

Summary

Based on these results, I propose three recommendations on the disparities among the city types:

• Analyze the length of rides and distance covered for each city type. This information would be helpful in giving more indications as to the disparities between these cities.
• Lower the price to ride in Rural and Suburban areas. It can be assumed that due to these areas being less dense, the average ride is probably longer and takes more time. This, combined with high prices, may force customers to turn to alternative methods of transportation.
• Give incentive to drivers to give more rides in Rural and Suburban areas. Based on this data, there were more drivers than rides in Urban cities over this period of time. More available drivers may increase customers willingness to buy rides in Rural and Suburban cities.