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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]
best_math_schools = best_math_schools[['school_name','average_math']]
best_math_schools.sort_values(['average_math'], ascending = False)
best_math_schools = best_math_schools.sort_values(['average_math'], ascending = False)
print(best_math_schools)

#creating a new colum called TOTAL SAT 

schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

#creating the top 10 performing schools based on the SAT score

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)
print(top_10_schools)


# Agrupar por borough e calcular estatísticas
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)

# Encontrar o borough com maior desvio padrão
max_std_borough = borough_stats['std'].idxmax()

# Criar o DataFrame resultado
largest_std_dev = pd.DataFrame({
    'borough': [max_std_borough],
    'num_schools': [int(borough_stats.loc[max_std_borough, 'count'])],
    'average_SAT': [borough_stats.loc[max_std_borough, 'mean']],
    'std_SAT': [borough_stats.loc[max_std_borough, 'std']]
})