We need to use our Python skills and knowledge of Pandas to create a summary DataFrame of the ride-sharing data by city type for a big company PyBer then create a multiple-line graph that shows the total weekly fares for each city type and tell a compelling story about it.
As we can see in the summary bellow the cities with the most drivers are the Urban cities (2,405), and the cities with the fewer drivers are the rural ones (78), that could explain why the most expensive ride fares are the rural (Average fare per ride= $34.62)
In the next graph we can realize that despite being the average fare for the rural rides the most expensive, the sum of the total fares is below $500 per week.
Based omn the analysis made, we have the following recommendations:
- 1 Implement more drivers in rural zones.
- 2 Implement ways to ear money in between rides, because sometimes the distances are long.
- 3 Try to offer better prices on january and december so there could be more rides.