PANDAS-CHALLENGE

This Pandas Project helps a local school board and mayor make strategic decisions regarding future school budgets and priorities. The project analyzes the district-wide standardized test results using student's math and reading scores data, as well as various information about the schools they attend. The overarching task is to aggregate the data and showcase obvious trends in school performance as well as to draw conclusions on what factors might be propagatng these trends based on the given data.

District Summary

In this sub-catergory I perform the necessary calculations and then create a high-level snapshot of the district's key metrics in a DataFrame to include the following:

1. Total number of unique schools
2. Total students
3. Total budget
4. Average math score
5. Average reading score
6. % passing math (the percentage of students who passed math)
7. % passing reading (the percentage of students who passed reading)
8. % overall passing (the percentage of students who passed math AND reading)

School Summary

In this sub-catergory I perform the necessary calculations and then create a DataFrame that summarizes key metrics about each school to include the following:

1. School name
2. School type
3. Total students
4. Total school budget
5. Per student budget
6. Average math score
7. Average reading score
8. % passing math (the percentage of students who passed math)
9. % passing reading (the percentage of students who passed reading) 10.% overall passing (the percentage of students who passed math AND reading)

Highest-Performing Schools (by % Overall Passing)

In this sub-catergory I sort the schools by % Overall Passing in descending order and display the top 5 rows and save the results in a DataFrame called "top_schools".

Lowest-Performing Schools (by % Overall Passing)

In this sub-catergory I sort the schools by % Overall Passing in ascending order and display the top 5 rows and save the results in a DataFrame called "bottom_schools".

Math Scores by Grade

In this sub-catergory I perform the necessary calculations to create a DataFrame that lists the average math score for students of each grade level (9th, 10th, 11th, 12th) at each school.

Reading Scores by Grade

In this sub-catergory I create a DataFrame that lists the average reading score for students of each grade level (9th, 10th, 11th, 12th) at each school.

Scores by School Spending

In this sub-catergory I create a table that breaks down school performance based on average spending ranges (per student). I then use the scores above to create a DataFrame called "spending_summary" which includes the following metrics in the table:

1. Average math score
2. Average reading score
3. % passing math (the percentage of students who passed math)
4. % passing reading (the percentage of students who passed reading)
5. % overall passing (the percentage of students who passed math AND reading)

Scores by School Size

In this sub-catergory I create a DataFrame called "size_summary" that breaks down school performance based on school size (small, medium, or large).

Scores by School Type

In this sub-catergory I create a DataFrame that shows school performance based on the "School Type"