After a lot of hard work in the data munging mines, you’ve landed a job as Lead Analyst for an independent gaming company. You’ve been assigned the task of analyzing the data for their most recent 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. As a first task, the company would like you to generate a report that breaks down the game’s purchasing data into meaningful insights.
Your final report should include each of the following:
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 list (in a table):
SN
Purchase Count
Average Purchase Price
Total Purchase Value
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
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
As final considerations:
You must use the Pandas Library and the Jupyter Notebook.
You must submit a link to your Jupyter Notebook with the viewable Data Frames.
You must include a written description of three observable trends based on the data.
See Example Solution for a reference on expected format(attachment).
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