Create a summary DataFrame of the ride-sharing data by city type. Then, create a multiple-line graph that shows the total weekly fares for each city type by using Pandas and Matplotlib. Lastly, I will summarize how the data differs by city type and how those differences can be used by decision-makers at PyBer.
When looking at the Summary dataframe, we see that there are significantly more drivers and rides in urban cities
than both suburban and rural cities. Also, total urban fares outnumbers the fare totals of suburban and rural cities combined.
However, the average fare per ride is lowest in urban cities.
In addition, the average fare per driver is much lower in urban cities compared to rural and suburban.
The total fare by city type line chart supports the information presented in the dataframe.
There is significantly more fare money in urban cities than suburban; and there is significantly more fare money in suburban cities than rural.
Drivers in urban cities would need to be paid more to make up for the diparity in average fare per driver. While there are more customers paying for rides in urban cities, there are so many drivers that each driver makes very little money comparatively. Fare prices could also be lowered in rural areas since the average fare per ride is higher than the othe two city types. This could incentivize customers and help get a higher total number of rides in rural areas. Lastly, PyBer could hire more drivers in rural areas to meet the demand of a spike in customers. This would also help mitigate the average fare per driver, which was already much higher than urban and suburban cities.