Pandas, Pandas, Pandas

Purpose:

This project was made to try and push the manipulation of data with pandas in a variety of ways. The data coming in is from two different fields to add to the challenge, but the goals are clearly spelled out. Truth be told, this was my first in depth exploration of the Pandas module, but I had a ton of fun trying to master core concepts!

pandas Recommendation

• Use pandas.concat as opposed to DataFrame.append as there is more control
• Use pandas.merge as opposed to DataFrame.join as there is more control
• refrence

Heroes of Pymoli

A mock company would like you to generate a report that breaks down their game's purchasing data into meaningful insights.

Your final report should include each of the following:

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

PyCitySchools

Analyze the district-wide standardized test results, the responsibility is to aggregate the data to and showcase obvious trends in school performance.

Your final report should include each of the following:

District Summary

• 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.)

School Summary

• 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.)

Top Performing Schools (By % Overall Passing)

• 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.)

Bottom Performing Schools (By % Overall Passing)

• Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above.

Math Scores by Grade**

• Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school.

Reading Scores by Grade

• Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school.

Scores by School Spending

• 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.)

Scores by School Size

• Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large).

Scores by School Type

• Repeat the above breakdown, but this time group schools based on school type (Charter vs. District).