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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.

# Import pandas
import pandas as pd

# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data
print(schools.head())

# Define the minimum threshold for the best math results (80% of 800)
math_threshold = 0.8 * 800

# Filter schools with average math scores greater than or equal to the threshold
best_math_schools = schools[schools['average_math'] >= math_threshold][['school_name', 'average_math']]

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

# Display the filtered and sorted results
print("Best Math Schools:")
print(best_math_schools)

# Create a new column 'total_SAT' as the sum of math, reading, and writing scores
schools['total_SAT'] = (
    schools['average_math'] + 
    schools['average_reading'] + 
    schools['average_writing']
)

# Select relevant columns: 'school_name' and 'total_SAT'
top_10_schools = schools[['school_name', 'total_SAT']]

# Sort by 'total_SAT' in descending order
top_10_schools = top_10_schools.sort_values(by='total_SAT', ascending=False).head(10)

# Display the top 10 performing schools
print("Top 10 Schools by Total SAT:")
print(top_10_schools)

# Group by borough to calculate statistics for each borough
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_stats.loc[borough_stats['std_SAT'].idxmax()]

# Convert the result to a DataFrame with a single row
largest_std_dev = pd.DataFrame([largest_std_dev])

# Round numeric values to two decimal places
largest_std_dev[['average_SAT', 'std_SAT']] = largest_std_dev[['average_SAT', 'std_SAT']].round(2)

# Display the result
print("Borough with the Largest Standard Deviation in Total SAT:")
print(largest_std_dev)


# Add as many cells as you like...