BC_Assignment_4

Pandas Challenge

Content:

1. Details on assignment
2. Additional resources
3. Trend Analysis

1. Details on the assignment:

This assignment required us to use the schools_complete.csv and students_complete.csv to analyse the following:

District Summary

Perform the necessary calculations and then create a high-level snapshot of the district's key metrics in a DataFrame. Include the following:

• Total number of unique schools
• Total students
• Total budget
• Average math score
• Average reading score
• % passing math (the percentage of students who passed math)
• % passing reading (the percentage of students who passed reading)
• % overall passing (the percentage of students who passed math AND reading)

School Summary

Perform the necessary calculations and then create a DataFrame that summarizes key metrics about each school. Include the following:

• School name
• School type
• Total students
• Total school budget
• Per student budget
• Average math score
• Average reading score
• % passing math (the percentage of students who passed math)
• % passing reading (the percentage of students who passed reading)
• % overall passing (the percentage of students who passed math AND reading)

Highest-Performing Schools (by % Overall Passing)

• Sort the schools by % Overall Passing in descending order and display the top 5 rows.
• Save the results in a DataFrame called "top_schools".

Lowest-Performing Schools (by % Overall Passing)

• Sort the schools by % Overall Passing in ascending order and display the top 5 rows.
• Save the results in a DataFrame called "bottom_schools".

Math Scores by Grade

• 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

• 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

• Create a table that breaks down school performance based on average spending ranges (per student).
• Use the code provided below to create four bins with reasonable cutoff values to group school spending.
• Use pd.cut to categorize spending based on the bins.
• Use the given code to then calculate mean scores per spending range.

Include the following metrics in the table:

Average math score Average reading score % passing math (the percentage of students who passed math) % passing reading (the percentage of students who passed reading) % overall passing (the percentage of students who passed math AND reading)

Scores by School Size

• Use the given code to bin the per_school_summary.
• Use pd.cut on the "Total Students" column of the per_school_summary DataFrame.
• Create a DataFrame called size_summary that breaks down school performance based on school size (small, medium, or large).

Scores by School Type

• Use the per_school_summary DataFrame from the previous step to create a new DataFrame called type_summary.
• This new DataFrame should show school performance based on the "School Type".

2. Additional resources:

Along with references from the Jupyter Notebooks discussed in class, I asked AskBCS for help when one pre-given code ran an unexpected Numpy error. I also used Stock Overflow to help me remove the index for two lines of code in order to allign with the results indicated in the starter file.

3. Trend Analysis:

• District Summary: According to the district_summary table, the students are on average better at reading than at maths (81.88% vs 78.99%, rounded to two decimals).
• Performance: Looking at % Overall Passing Scores, Charter schools performed much better than District schools, ie., the top five schools were Charter type while the bottom five schools belonged to District type.
• Scores by Grade: In general, all schools across 9th, 10th, 11th and 12th grade achieved higher scores in reading compared to maths.
• Scores by School Spending: Analysing the spending_summary table, the results show that a higher budget per student did not, in fact, results in higher performance. In fact, the higher the budget, the lower the students performed overall. Where the spending was least, <$585 per student, the schools results show over 90% passing score.
• Scores by School Size: The type_summary table gives a rough overview of preformance per school type. The results show that Charter schools performed better than District schools when comparing average math and reading scores, percent passing marks in both areas and %Overall Passing score.