/Pandas-Pandas-Pandas

The data dive continues!

Primary LanguageJupyter Notebook

Pandas, Pandas, Pandas

Analysis

Player Count

  • Total Number of Players

Purchasing Analysis

  • 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

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
    • Purchase Count
    • Average Purchase Price
    • Total Purchase Value
    • Average Purchase Total per Person by Age Group

Top Spenders

  • Identifying the the top 5 spenders in the game by total purchase value and list in a table:
    • SN
    • Purchase Count
    • Average Purchase Price
    • Total Purchase Value

Most Popular Items

  • Identifying the 5 most popular items by purchase count and list in a table:
    • Item ID
    • Item Name
    • Purchase Count
    • Item Price
    • Total Purchase Value

Most Profitable Items

  • Identifying the 5 most profitable items by total purchase value and list in a table:
    • Item ID
    • Item Name
    • Purchase Count
    • Item Price
    • Total Purchase Value