PyBer Analysis using Python, Matplotlib, Pandas and Jupyter Notebook.
This analysis summarizes how the data differs by city type and how those differences can be used by decision-makers at PyBer. The data was grouped by city types of Urban, Suburban and Rural to analyze the differences that the city type had on the data.
Key details from the analysis by city type is shown below:
o Urban cities have the most rides at 1,625, the most drivers at 2,405 and the highest total fares of $39,854.38. Urban cities have the lowest average fare per ride of $24.53 and average fare per driver of $16.57.
o Suburban cities have 625 rides, 490 drivers and total fares of $19,356.33. The average fare per ride is $30.97 and the average fare per driver is $39.50.
o Rural cities have the lowest riders at 125, the fewest drivers at 78, and the lowest total fares at $4,327.93. Rural cities have the highest average fare per ride of $34.62 and the highest average fare per driver of $55.49.
Below is a line plot that shows the total weekly fares for each type of city.
Based on the results I recommend:
oAdditional analysis to review the profitability of the rural markets.
oAdditional deep dive on if there is an opportunity to combine drivers and use them in other city types if demand exists.
oPerform a case study to estimate the demand of rides for the city types. Is the allocation of drivers appropriate, if not is there an incentive to right size the drivers with the riders to increase profit.