This challenge asks the student to import data schools size, type, and budget as well as a second file with reading and math scores for students in each of the schools. The goal is to provide analysis that will help the school board and mayor make strategic decisions regarding future school budgets and priorities.
Data prep steps taken:
-Use pathlib to import csv files
-Use Pandas to store date in dataframes
-Combine the data sets merging on school name
Calculations for district summary
-Total number of schools
-Total number of students
-Total budget
-Average math score
-Average reading score
-Percent of students passing math
-Percent of students passing reading
-Percent of students passing both
-Display calculations in a dataframe
Calculations for school summary
-School type
-Total students per school
-Average spending per student at each school
-Average math and reading test scores per school
-Count of students passing math and passing reading per school
-Count of students passing both math and reading per school
-Percent of students passing math, percent of students passing readin, and percent of students passing both math and reading per school
-Display caluclations in a dataframe
Additional calculations to present to the school board and mayor for the budget planning
-Top performing schools based on percentage of students passing both math and reading
-Bottom performing schools based on percentage of students passing both math and reading
-Math scores by grade and school
-Reading scores by grade and school
-Comparison of scores based on average spending per student and school
-Comparison of scores based on school size
-Comparison of scores based on school type
