You can find the analysis file here: PyBer_Analysis
The goal of this challenge and analysis is to create a multiple-line graph that shows the total weekly fares for each city type: Rural, Urban and Suburban. In addition to the line chart, we create a summary dataframe of total rides, total drivers, total fares, avergae fare per ride and average fare per driver.
From the summary dataframe (shown below), we can see a trend between the population of city and the total number of rides which has a direct effect on the total number of drivers, total fares and average ride fare. Even though the total number of rides, drivers and fares decrease as we move to further destinations from the urbanized areas, the average fare per driver and per ride seems to increase like in rural cities; Less drivers in rural areas lead to a higher average fare per ride and driver, as prices increase when PyBer accessibility is low.
As you can see below, with multiple-line chart, we can compare total fares by city type over a period of five months. The number of rides has peak at the end of February and fluctuates during the month of March. All the line graphs seem to follow the same trend throughout these months, except for suburban cities, where we see a more intense increase during April.
Because the total fare by city type appears to grow in suburban cities during the month of April, the company should determine what is causing this increase, especially because the total fare for other types of cities appears to decline.
We also know that when there are fewer drivers, the average fare per ride and driver is higher. To reach its profit goal, the corporation might use this information to alter ride-share prices by limiting or increasing the number of drivers during a specific time period.
Finally, from more crowded cities to less populated cities, the average fare per ride indicates a gradual increase. When the same tendency is followed, though, the average fare each drive increases drastically. This indicates that the number of drivers may be lower than expected. The company can allocate more drivers to rural areas to optimize its profits.
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