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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'] >= 0.8 * 800].sort_values('average_math', ascending=False)[['school_name','average_math']]
best_math_schools
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False).head(10)
top_10_schools
# Find the borough with the largest standard deviation of "total_SAT"
max_std_borough = borough_stats.loc[borough_stats['std'].idxmax()]

# Extract the required information
borough_name = max_std_borough['borough']
num_schools = int(max_std_borough['count'])
average_SAT = round(max_std_borough['mean'], 2)
std_SAT = round(max_std_borough['std'], 2)

# Create the DataFrame with the required information
largest_std_dev = pd.DataFrame({
    'borough': [borough_name],
    'num_schools': [num_schools],
    'average_SAT': [average_SAT],
    'std_SAT': [std_SAT]
})

largest_std_dev