README.md

Data Analysis using Pandas.

This project is analyzing the data for a fantasy game, Heroes of Pymoli, using Pandas and Jupyter lab.

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. Here, I am generating a report that breaks down the game's purchasing data into meaningful insights.

The report includes each of 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

• The top 5 spenders in the game by total purchase value, listed in a table:
  • SN
  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value

Most Popular Items

• The 5 most popular items by purchase count, listed in a table:
  • Item ID
  • Item Name
  • Purchase Count
  • Item Price
  • Total Purchase Value

Most Profitable Items

• The 5 most profitable items by total purchase value, listed in a table:
  • Item ID
  • Item Name
  • Purchase Count
  • Item Price
  • Total Purchase Value