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Pyber_Matplotlib_ Plots_Data Analysis and Visualization

Observable Trends from the Pyber ride sharing data 2016

The data illustrates that the average fare, number of rides and number of drivers varies for each of the city types: rural, suburban, and urban.

The relationship of city type with average fare, along with the driver count per city, is reflected in the size of the circles within the bubble chart illustration.

The urban market area dominates the percentage of total fares, percentage of total rides and percentage of total drivers when analyzing the pie charts. This would be due to the higher density of population in the urban cities, with the higher number of drivers and the higher demand for rides.

What is noticeable, are the lower fares as compared to the fare values that the surburban and rural areas are able to charge for rides. This signifies that in the urban cities, there is the threat of saturation of drivers, which leads to more competition, and the fare values to remain lower. A possibility for the drivers to look at, could be for more focus to be drawn to specific areas within the urban cities that create the highest demand so that drivers could maintain positive profit margins and remain competitive.

While the rural market area yields the lowest percentages of all three of the pie charts, rural and suburban market areas are able to charge higher fares as compared to the urban cities. This would be in part due to the longer distances to travel to destinations, less demand and less competition as a concern.