Overview
Using Python, create a summary DataFrame of ride-sharing data from a fictional dataset. Then, create a multiple-line graph that shows the total weekly fares for each city type. Finally, summarize how the data differs by city type and how decision-makes can use the information.
Analysis
The summary DataFrame includes rural, suburban, and urban city types and information about total rides, total drivers, total fares, average fare per ride, and average fair per driver. This encompasses the months January through April. As could be expected, the total rides, drivers, and fares rise as you go from rural to urban since the population increases. However, the average fare per ride and driver sees the inverse effect. This is likely due to there being more people and less distance to travel between stops as you go towards the urban city type.
The multiple line chart shows this relationship well. You can see where each city type falls within each month also. It is interesting to note that the urban city type spikes up around the time of St. Patrick's Day in March. This may be due to more people needing to ride-share because they are or will be unable to drive.
Recommendations
There are a few recommendations I can make based on these results. One is for an increase in advertising in rural and suburban areas to attract more drivers and/or customers. This would help increase profits in these areas and ensure coverage. People in rural areas generally need to travel further to get places. Therefore, I would suggest that a fare become discounted if the ride distance goes over a certain threshold. This may draw in more customers from these areas as they tend to have less disposable income. Finally, an incentive program may be beneficial. Perhaps a discounted or free ride after so many rides at full price.
Please contact me with any questions or concerns. My GitHub has all the code used for this analysis: https://github.com/two-suns/PyBer_Analysis . Thank you for reading, and if you liked this please connect on LinkedIn.
Comments