I was given the task to create a dataframe that visualized the provided dataset by city type. The purpose of this requested analysis was to be produce a visual aid showing Ride sharing fare by city type ( Rural , Suburban and Urban) on a monthly basis for the year 2019. To achieve this task I had to utilized various functions such as the grouby, pivot, and resample functions to set the data to be transformed into a line chart. Another task of this process was cleaning the data , so only pertinent remained during the process.

When review the data that was derived from the provided dataset, there was no surprise that the urban city type possessed the most drivers, and accumulated the most fares. However the rural city type was the almost the opposite of their urban counterpart. The rural city had the least drive, rides and total fares but lead with the lartgest avaerage fare per driver which was 55.49 which was 3.4 times more than than the average for Urban drivers. Lastly, The Suburban were situated between both the rural and urban statistic and did not lead or tail in any catergory. Below is a full breakdown:

Summary and Recommendations:

1. After reviewing all the data one disparity I noted was the drastically low average fare per driver for the urban drivers. Urban driver would need to complete 3-4 rides compare to one ride of a rural driver to obtain comparitive fares. PyBer should looking into driver incentives and promotions to retain their urban drivers and looking into other factor like mileage to increase the fare for the urban drivers .
2. In regards to the lack of drivers in the rural areas, there should more advertising done in the rural area to recruit more drivers (including incentives like sign-on bonuses). If there are more drivers available that may possiblity lower the cost of the fare for the PyBer riding and increase accessbility and total rides.
3. I would also make the same recommendation for recruiting more driver in the suburban areas also only. But I would review the population counts compared to the urban first to ensure the driver to ride ratio make sense.