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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...

Which NYC schools have the best math results?

schools_math_sorted= schools.sort_values(by= 'average_math', ascending= False)
dummy= schools_math_sorted[['school_name', 'average_math']]
best_math_schools= dummy[dummy['average_math'] >= 640]
best_math_schools.head()

What are the top 10 performing schools based on the combined SAT scores?

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= False)[:10]
top_10_schools

Which single borough has the largest standard deviation in the combined SAT score?

# Group by borough and calculate standard deviation
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std'])

# Find the borough with the largest standard deviation
largest_std_borough = borough_stats['std'].idxmax()

# Create the result DataFrame
largest_std_dev = pd.DataFrame({
    'borough': [largest_std_borough],
    'num_schools': [borough_stats.loc[largest_std_borough, 'count']],
    'average_SAT': [borough_stats.loc[largest_std_borough, 'mean']],
    'std_SAT': [borough_stats.loc[largest_std_borough, 'std']]
})

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

largest_std_dev