cmdenys / pandas-analysis

A break down of user purchasing data for a fictional video game into useful observations including age demographics, purchasing trends, and most popular and profitable items.

Below are individual break downs of user demographics and purchasing data into meaningful insights.

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Before any analysis could be completed the dependencies needed to manipulate the data were imported. Next, using Pandas the CSV containing the data was read and the first data frame was created.

Player Count

• Total Player Count

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 listed in a table:
• SN
• Purchase Count
• Average Purchase Price
• Total Purchase Value

Most Popular Items

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

Output for Most Popular Items

Most Profitable Items

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