PyBer_Analysis

Project Overview

A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:

• A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
• A box and wiskers chart that shows the ride count data for 2019
• A box and wiskers chart that shows the ride fare data for 2019
• A box and wiskers chart that shows the driver count data for 2019
• A pie chart that shows the % of total fares by the city type
• A pie chart that shows the % of total rides by the city type
• A pie chart that shows the % of total drivers by the city type

Resources

Data Sources:

• city_data.csv
• ride_data.csv

Software:

• Python 3.7.4
• Panda
• Matplotlib

Summary

Results

• For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
• Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
• The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.

Challenge Overview

Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.

Challenge Summary

The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.