Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete one of two (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt.
The task is of analyzing the data for the independent game company's 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. The final report includes 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
- Identified 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
- Identified the 5 most popular items by purchase count, then list (in a table):
- Item ID
- Item Name
- Purchase Count
- Item Price
- Total Purchase Value
- Identified 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
Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance.
Your final report should include each of the following:
- Create a high level snapshot (in table form) of the district's key metrics, including:
- Total Schools
- Total Students
- Total Budget
- Average Math Score
- Average Reading Score
- % Passing Math (The percentage of students that passed math.)
- % Passing Reading (The percentage of students that passed reading.)
- % Overall Passing (The percentage of students that passed math and reading.)
- Create an overview table that summarizes key metrics about each school, including:
- School Name
- School Type
- Total Students
- Total School Budget
- Per Student Budget
- Average Math Score
- Average Reading Score
- % Passing Math (The percentage of students that passed math.)
- % Passing Reading (The percentage of students that passed reading.)
- % Overall Passing (The percentage of students that passed math and reading.)
- Create a table that highlights the top 5 performing schools based on % Overall Passing. Include:
- School Name
- School Type
- Total Students
- Total School Budget
- Per Student Budget
- Average Math Score
- Average Reading Score
- % Passing Math (The percentage of students that passed math.)
- % Passing Reading (The percentage of students that passed reading.)
- % Overall Passing (The percentage of students that passed math and reading.)
- Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above.
- Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school.
- Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school.
- Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following:
- Average Math Score
- Average Reading Score
- % Passing Math (The percentage of students that passed math.)
- % Passing Reading (The percentage of students that passed reading.)
- % Overall Passing (The percentage of students that passed math and reading.)
- Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large).
- Repeat the above breakdown, but this time group schools based on school type (Charter vs. District).
Pandas, Jupyter Notebook, GitHub.