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This project analyzes video game purchasing data. Demonstrates use of Python and Pandas library, reading csv and converting to dataframes, merging dataframes, aggregate functions, bins, and cleaning and organizing data.

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purchase-analysis python pandas aggregation bins data-cleansing dataframes

video-game-player-and-purchase-analysis's Introduction

Background

This project analyzes data for a fantasy video game. The game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. I have generated a report that breaks down the game's purchasing data into meaningful insights.

Step by Step

The final report includes the following:

Player Count

  • Total Number of Players

Purchasing Analysis (Total)

  • Number of Unique Items
  • Average Purchase Price
  • Total Number of Purchases
  • Total Revenue

Gender Demographics

  • Percentage and Count of Male Players
  • Percentage and Count of Female Players
  • Percentage and Count of Other / Non-Disclosed

Purchasing Analysis (Gender)

  • The below each broken by gender
    • Purchase Count
    • Average Purchase Price
    • Total Purchase Value
    • Average Purchase Total per Person by Gender

Age Demographics

  • The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.)
    • Purchase Count
    • Average Purchase Price
    • Total Purchase Value
    • Average Purchase Total per Person by Age Group

Top Spenders

  • Identify the the top 5 spenders in the game by total purchase value, then list (in a table):
    • SN
    • Purchase Count
    • Average Purchase Price
    • Total Purchase Value

Most Popular Items

  • Identify the 5 most popular items by purchase count, then list (in a table):
    • Item ID
    • Item Name
    • Purchase Count
    • Item Price
    • Total Purchase Value

Most Profitable Items

  • Identify the 5 most profitable items by total purchase value, then list (in a table):
    • Item ID
    • Item Name
    • Purchase Count
    • Item Price
    • Total Purchase Value

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