Analyzing and visualizing ridesharing data with Python Matplotlib, Pandas
The purpose of this project is to perform exploratory analysis of Pyber ride sharing data and visualize the rideshare data for PyBer to help improve access to ride-sharing services and determine affordability for underserved neighborhoods
Data analysis and aggregation of PyBers ride sharing data, I have created a statistical summary of ridesharing and line chart of Total fares by city type from Jan to Apr as you can see in the screenshots below:
By reviewing the summary we can see that there are several key findings including:
1. Urban cities have the highest ridership demand while rural cities have the least.
2. Urban cities have 4x+ more drivers than suburban cities.
3. Suburban cities have 6x + drivers than rural with almost 4.5x the revenue.
4. Rural cities have the highest average fare per ride and driver.
Based on the analysis: Increasing the amount of drivers in Rural areas to ensure there are enough drivers to meet ride demand. Data for rural cities shows that the average fare per ride and average fare per driver is much higher than Suburban and urban cities.