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Every year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.
Analyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.
You have been provided with a dataset called schools.csv, which is previewed below.
You have been tasked with answering three key questions about New York City (NYC) public school SAT performance.
# Re-run this cell 
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
# Start coding here...
# Add as many cells as you like...best_math_schools = schools[(schools['average_math'] / 800) > 0.8]
best_math_schools = best_math_schools.sort_values('average_math', ascending=False)
best_math_schools = best_math_schools[['school_name', 'average_math']]
best_math_schoolstotal_SAT = schools
total_SAT['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
total_SAT = total_SAT.sort_values('total_SAT', ascending=False).head(10)
top_10_schools = total_SAT[['school_name', 'total_SAT']]
top_10_schoolstotal_SAT = schools
total_SAT['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
num_schools = total_SAT.groupby('borough')['total_SAT'].count()
average_SAT = total_SAT.groupby('borough')['total_SAT'].mean().round(2)
std_SAT = total_SAT.groupby('borough')['total_SAT'].std().round(2)
largest_std_dev = pd.DataFrame({
    'num_schools': num_schools,
    'average_SAT': average_SAT,
    'std_SAT': std_SAT
})
largest_std_dev = largest_std_dev.sort_values('std_SAT', ascending=False).head(1)
largest_std_dev