In the PyCity Schools project, we utilized Pandas in Jupyter Notebook to analyze district-wide standardized test results. Given data on student's math and reading scores, alongside additional information about the schools they attend, our objective was to aggregate this data to discern observable trends in school performance.
- District Summary: Provide a high-level snapshot of the district's metrics.
- Metrics include total unique schools, student count, total budget, average scores in math & reading, and passing percentages.
- School Summary: Create a detailed overview of the key metrics for each school.
- Performance Analysis: Identify top-performing and bottom-performing schools based on overall passing rates.
- Grade-level Analysis: Calculate the average math & reading scores for students of each grade level (9th through 12th) at individual schools.
- Budget Analysis: Examine the correlation between spending per student and school performance.
- School Size Analysis: Investigate how school size correlates with student outcomes.
- School Type Analysis: Analyze performance trends based on school type (District vs. Charter).
Through this analysis, stakeholders can gain insights into areas of strength and improvement for schools in the district. The project aids in making informed decisions about budget allocation, resource distribution, and setting future priorities.