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Python Data Analysis on purchasing data of "Pymoli" a fantasy video game to draw meaningful insights using Pandas.

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Pandas Homework - Pandas, Pandas, Pandas

Heroes of Pymoli

Fantasy

The final report includes each of the following:

Player Count

  • Total Number of Players

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Purchasing Analysis (Total)

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

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Gender Demographics

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

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Purchasing Analysis (Gender)

The below each broken by gender:

  • Purchase Count
  • Average Purchase Price
  • Total Purchase Value
  • Average Purchase Total per Person by Gender

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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

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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

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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

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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

Fantasy

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Python Data Analysis on purchasing data of "Pymoli" a fantasy video game to draw meaningful insights using Pandas.

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