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This project analyzes ride share app data. Demonstrates use of Python, Matplotlib, reading csv files, converting to dataframes, performing aggregate functions, and creating bubble plot and pie chart visualizations of data.

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shargr2/Rideshare-App-Analysis

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Background

For this project I created charts to offer data-backed guidance on new opportunities for market differentiation.

I used a rideshare app's complete recordset of rides. It contains information about every active driver and historic ride including details like city, driver count, individual fares, and city type.

Step by Step

The Bubble Plot showcases the relationship between four key variables:

  • Average Fare ($) Per City
  • Total Number of Rides Per City
  • Total Number of Drivers Per City
  • City Type (Urban, Suburban, Rural)

In addition, there are three pie charts showing the following:

  • % of Total Fares by City Type
  • % of Total Rides by City Type
  • % of Total Drivers by City Type

About

This project analyzes ride share app data. Demonstrates use of Python, Matplotlib, reading csv files, converting to dataframes, performing aggregate functions, and creating bubble plot and pie chart visualizations of data.

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