PyBer Analysis

Project Overview

This is a report that summarizes how the data differs by city type and how those differences can be used by decision-makers at PyBer.

Resources:

• Data Source: city_data.csv
• Data Source: ride_data.csv
• Software: Jupyter Notebook 6.4.5
• Library: Pandas
• Library: Matplotlib
• Language: Python 3.9.7

Results:

Deliverable 1:

Describes the differences in ride-sharing data among the different city types.

• Urban cities have the highest number of rides and drivers.
• Rural cities have the highest average fair per ride and per driver.

Deliverable 2:

Describes fare rates among different city types between 01/01/2019 & 04/28/2019.

Summary:

My advice for CEOs would be:

• To increase the number of drivers in Rural cities to better meet the demand.
• To increase the number of drivers in Suburbar citties to better meet the demand.
• To decrease the number of drivers in Urban cities which would help increase the average driver's rate and promote a more sustanable balance.