shargr2 / Fantasy-Game-Analysis

This project analyzes the data for a fantasy game Heroes of Pymoli. Demonstrates use of Python and Pandas library, reading csv and converting to dataframes, merging dataframes, aggregate functions, bins, and cleaning and organizing data.

Background

In this project I analyzed the data for a fantasy game Heroes of Pymoli.

Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. I generated a report that breaks down the game's purchasing data into meaningful insights.

The final report includes:

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

• Identified 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

• Identified 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

• Identified 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