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Photo by Jannis Lucas on Unsplash.

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'] >= 640][['school_name', 'average_math']].sort_values(by='average_math', ascending=0)
best_math_schools.head()
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=0).head(10)
std_dev = schools.groupby('borough')['total_SAT'].std()
school_count = schools.groupby('borough')['school_name'].count()
avg_sat = schools.groupby('borough')['total_SAT'].mean()
largest_std_dev = pd.DataFrame({
    'num_schools': school_count,
    'average_SAT': avg_sat,
    'std_SAT': std_dev
})
largest_std_dev = largest_std_dev.sort_values(by='std_SAT', ascending=0).head(1).round(2)
largest_std_dev