We are starting a brand new Data Analytics job at a ride-share company known as Pyber. One of the founders of the company, V. Isualize, has tasked us with presenting findings based upon ride data and city data that Pyber drivers operate. We provided visual representations of the data we found, including information about fares and drivers for three different city types: urban, suburban, and rural. For this challenge, V. Isualize tasked us with creating a summary DataFrame of the ride-sharing data by city type and then using Pandas and Matplotlib to create a multiple-line chart that represents the weekly fares for the three different city types listed above.
A description of the differences in ride-sharing data among the different city types. Ride-sharing data include the total rides, total drivers, total fares, average fare per ride and driver, and total fare by city type.
As shown in the image below, urban cities had the highest totals of rides, drivers, and fare amounts, while rural had the lowest totals. Conversely, when we look at averages (Average Fares per Ride and Average Fare per Driver), rural cities ranked highest and urban cities ranked lowest. Suburban cities fell in the middle of rural and urban cities in every category.
The multiple-line chart below visually shows the discrepancies between each city type based upon total fares in US dollars on a week-to-week basis. As mentioned previously, Urban cities were the highest of all the city types, with suburban cities in the middle and rural cities being the lowest. This was likely expected, as that tends to match the population density expected of each different city type.
Based on the results, provide three business recommendations to the CEO for addressing any disparities among the city types.
Based on these results, I would recommend three potential strategies for addressing the disparities among the city types.
- Decrease the average fare in rural cities. Limiting surge prices in rural cities to ensure costs are not multiplied much higher than standard could be a good way to do this, and it may even encourage people to use Pyber more because the prices would be more affordable during peak hours. If more people use Pyber, then total rides would increase in rural cities which would also help bring the average fare in rural cities down.
- Another strategy for increasing the total number of rides in rural cities is for Pyber to offer loyalty programs or discounts for recommending new riders. If these programs are successful, even with potentially lower fares, we would hope to see an increase in total fares overall because there would be more rides overall.
- Lastly, a potential strategy to bring total fares down in urban cities without decreasing overall revenue would be to introduce a subscription service for Pyber in urban areas where people ride very frequently. People could pay something like $20 a month for 50% off rides, no surge prices, or other perks that would be worthwhile for frequent riders. This strategy could also be used in suburban and rural cities, but is not likely to be as popular in those places as their populations are not currently riding a lot in the first place.