In order to gain understanding of PyBer's user data and fare metrics, I have created a new DataFrame summarizing the ride-sharing data by city type. Doing so will allow Pyber to improve access to its services in underserved communities. To make this data and summary easy to understand, I have created data visualizations to help express the analysis and conclusions made in this project.
We can infer from the data provided that there is significant difference from urban, suburban, and rural cities across the board. Based on the data, rural cities have the least amount of rides, but average the highest fares. Urban cities have the most rides with the lowest fares. What the data shows is that there are fewer people using Pyber in rural areas than in urban areas, but have the highest prices. The images below are the data visualizations generated to help explain the resuts.
DataFrame summary:
Line-Chart:
We have determined that Pyber's ridership data is significantly less in rural areas than urban areas. Rural areas have higher prices and the driver to rider ratio is off. This could be leading to less users and higher rider fares. One recommendation would be to increase drivers in the area to combat the issue of driver to rider ratio. Another suggestion would be to lower the cost per ride in rural areas specifically. This could increase motivation for people in the area to use Pyber's services.
When thinking about the adjustments to make, it is important to consider the demographic of these areas. For example, consider the total population of these areas and age group that largely makes up the area. Also, the distance that riders are traveling. All of these factors contribute to the data resuls. There may be less people living in rural areas which account for fewer riders. Perhaps the majority of the population are elderly indivduals who are not going out as much. Perhaps the reason for higher fares is that there may be a greater distance between destinations in rural areas than in urban areas that account for higher fares.
Ultimately, it is important to consider all of the contributing factors that affect the end results. While we can make recommendations to the PyBer CEO, it would not be the most informed or effective recommendation. We would have to gather more data to make a more precise and targetted change in order to address the issues at hand.