Tasks

1. Identify top math-performing schools

   • Select schools where the average math score is at least 80% of the maximum possible (≥ 640 out of 800).

   • Save results in a DataFrame named best_math_schools with the following columns:

     • school_name
     • average_math

   • Sort the results by average_math in descending order.

2. Find the top 10 schools by combined SAT scores

   • Create a new column total_SAT as the sum of math, reading, and writing scores.

   • Save results in a DataFrame named top_10_schools with the following columns:

     • school_name
     • total_SAT

   • Order results by total_SAT in descending order and select the top 10 schools.

3. Determine borough with largest score variability

   • Group schools by borough and compute statistics on total_SAT.

   • Save results in a DataFrame named largest_std_dev containing only one row with:

     • borough – the name of the borough with the largest standard deviation in total_SAT.
     • num_schools – number of schools in that borough.
     • average_SAT – mean of total_SAT.
     • std_SAT – standard deviation of total_SAT.

   • Round all numeric values to two decimal places.