Given the task of analyzing the data for an independent gaming company of their most recent fantasy game Heroes of Pymoli. Need to generate a report that breaks down the game's purchasing data into meaningful insights.
The final report will include the following below:
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 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
Observable Trends The three observable trends based on the data look into the gender and age demographics, and the most profitable item purchases.
- Of the players who purchased and that declared their gender, 45 percent where men and 15 percent were women
- The top three age ranges that purchased game items were: 20 to 24 years old, 15 to 19 years old, and 25 to 29 years old.
- The most purchased game item was the “Oathbreaker, Last Hope of the Breaking Storm” that is currently priced at $4.23 and had a total of 12 purchasing instances with the “Nirvana” as the second most purchased.
Congratulations! 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:
- Total Number of Players
- Number of Unique Items
- Average Purchase Price
- Total Number of Purchases
- Total Revenue
- Percentage and Count of Male Players
- Percentage and Count of Female Players
- Percentage and Count of Other / Non-Disclosed
- The below each broken by gender
- Purchase Count
- Average Purchase Price
- Total Purchase Value
- Average Purchase Total per Person by Gender
- 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
- 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
- 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
- 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.
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