In the Module 5 Analysis, we are asked to use our Python and Pandas knowledge and skills to create a summary of the PyBer ride-sharing data. V. Isualize has asked that we make a create a summary that breaks down the total fares for each type of city that is in PyBer’s radius of operation (urban, suburban, and rural). Pyber will be using the summaries and multi-line graph to help improve accessibility and to establish better operations among communities with less access to their service.
After combining the data, I built a summary of total rides, drivers, and fares, and average fare per ride and driver for each city, which created the following DataFrame:
In the summary that was created, it shows the data among the different types of city. By looking at the summary DataFrame, we can determine that:
• Urban cities have the highest total number of drivers than rural and suburban cities by a wide margin.
• Urban cities have the highest total number of rides of the three city types.
• Rural cities have the lowest number of rides, drivers, and total fares, but the average fare per ride and per driver are significantly higher than the urban city type.
• The total fares of urban and suburban cities are significantly higher than rural cities.
• The summary data shows that the city types with the largest ratio of drivers has higher overall fare revenues.
Based on the data that was compiled into the following multi-line graph,
my recommendations to Pyber are:
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To better serve rural and suburban cities, PyBer needs to hire more drivers in these areas.
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To encourage people in rural and suburban areas to use the ride-share regularly, advertise discount deals for people traveling over a longer distance.
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Collaborate with local business, such as restaurants, music and sporting venues, and retail business to advertise through PyBer. Using perk discounts for rides and/or goods and services, advertisement not only helps promote local business, but also services provided by PyBer.