A python based executive manangement board wants to have a closer insight over the ride sharing app data to properly allocate resources to each city and for better management.
- Pyber Results Objective:
- The total number of rides for each city type
- The total number of drivers for each city type
- The average fare per ride for each city type
- The average fare per driver for each city type
- A PyBer summary DataFrame
- Data Source: city_data.csv , ride_data.csv
- Software: Python 3.9.7. Visual Studio Code
The Pyber data provided some quick insights about the number of rides, drivers, and fare cost by places such as urban, rural, and suburban.
The evaluated dataframe summary clearly shows the lowest cost for the average fare per ride and per driver in the urban cities as compared from the rural and suburban. The data set clearly shows that their is a considerably high demand for rides and drivers in the urban sector as is clearly display in the summary data frame and the line graph summarry.
- There is plenty of data from the urban cities in contrast from the rural and suburban.
- More specific data analytics for the rural and suburban areas are needed to determine the high cost of ride fare in the rural cities.
- There is a need to gather data from the drivers to determine the causes for low driver in the rural and subrural area.