PyBer_Analysis

Analyze and visualize Ride-sharing data using the power of Python with Matplotlib library.

Resources

• Software : Python 3.7, Anaconda, Jupyter Notebook and Matplotlib & basics of the Pandas library
• Resources ; Resources/city_data.csv ,Resources/ride_data.csv ,Resources/PyBer_ride_data.csv

Overview of the analysis :

Purpose:

V.isualize has given me assignment using my python skills and pandas knowledge to create summary dataframe of Ride-sharing data by city type and also by using pandas and matplotlib, I created multiple-line graph that shows the total weekly fares for each city type inorder to gain an understanding of ridership and fare metrics by type of citties in which Pyber operates.

Results :

Through analysis and aggregation of pybers ride sharing data , i have created a statistical overview and summary;

By V.isualizing the summary we can tell that there are a lot key findings including:

• urban cities have the highest rideship demand while rural cities have the least
• urban cities have 4x + more drivers than suburban cities
• suburban cities have 6x + drivers than rural with almost 4.5x the revenue.
• rural cities have the highest average fare per ride and driver.
• the relationship in which fare revenue is higher by city type when there are a larger ratio of drivers to rides.

By Using the object-oriented interface method, i plot the resample DataFrame using the df.plot() function.

Summary:

According to the analysis my business recommendations to the Pyber(CEO) to address disparities among the city types are; 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 driver is much higher than suburban and urban cities,so rural area based riders are taking trips over a longer distance. This could result in majority of drivers bing occupied with current trips and loss in potential revenue when there are peaks in business.