The project aims is to analyze and gather data information about fifteen different schools. Using Pandas, I gather the grades, school sizes, district, and charter; the average percentage of students who passed math and reading, and the average overall percentage, school types. Also, I created a new DataFrame with the averages and percentages, based on the spending bins, to show how school spending affects score averages and passing rates. In Thomas High School's analysis, academic dishonesty was discovered in the reading and math grades of Ninth Grader.
- Distrit Summary DataFrame
- Thomas High School
- Schools Summary
- Spending Summary
- Size Summary
- Type Summary
- Grade Level
- Top Schools
- Bottom Schools
How is the district summary affected;
- The percentage of Math decrease from 75 % to 74.8%
- The overall Passing went from 65 - 64.9%
How is the school summary affected;
- Average Math at Thomas High School decreased from 83.42 to 83.35%
- Average Reading at Thomas High School increased from 83.84 to 83.89%
How does replacing the ninth graders’ math and reading scores affect Thomas High School’s performance relative to the other schools
- Replacing data from the ninth graders’ math and reading scores affect Thomas High Schools has no impacts on the other schools, the budget per student , average math score , avarage reading score, percentages of math and reading all remain the same.
Replacing the ninth-grade scores affect the following;
- The average math Score went from 79 to 78.9 %
- The Average Reading Score remains 81.9 %
- Percentage of Math decrease from 75 - 74.8%
- Percentage of Reading also 86% - 85%
- The overall Passing went from 65 - 64.9%
The Math and Reading scores for Thomas High School was replaced with NaN. The rest of the data should remain the same. After replacing the math and the reading scores. The step was done for the as same from the module. Then, gather information used to illustrate the changes that happen during the analysis.