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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...

# Calculate the threshold for the best average math scores
threshold = 0.8 * 800

# Filter the schools dataframe to get the best math schools
best_math_schools = schools[schools['average_math'] >= threshold]

# Select only the school_name and average_math columns
best_math_schools = best_math_schools[['school_name', 'average_math']]

# Sort the dataframe by average_math in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)

# Display the best_math_schools dataframe
best_math_schools

# Add as many cells as you like...
# Calculate the total SAT score for each school
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Sort the schools by total_SAT in descending order
sorted_schools = schools.sort_values(by='total_SAT', ascending=False)

# Select the top 10 schools
top_10_schools = sorted_schools.head(10)

# Select only the school_name and total_SAT columns
top_10_schools = top_10_schools[['school_name', 'total_SAT']]

# Display the top_10_schools dataframe
top_10_schools
# Group by borough and calculate the required statistics
borough_stats = schools.groupby('borough').agg(
    num_schools=('school_name', 'count'),
    average_SAT=('total_SAT', 'mean'),
    std_SAT=('total_SAT', 'std')
).reset_index()

# Find the borough with the largest standard deviation in total_SAT
largest_std_dev_borough = borough_stats.loc[borough_stats['std_SAT'].idxmax()]

# Create the largest_std_dev DataFrame
largest_std_dev = pd.DataFrame({
    'borough': [largest_std_dev_borough['borough']],
    'num_schools': [largest_std_dev_borough['num_schools']],
    'average_SAT': [round(largest_std_dev_borough['average_SAT'], 2)],
    'std_SAT': [round(largest_std_dev_borough['std_SAT'], 2)]
})

# Display the largest_std_dev DataFrame
largest_std_dev