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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")

schools.sort_values('school_name', ascending = False)

schools.head()

# Start coding here...
# Add as many cells as you like...
schools.sort_values('average_math', ascending = False)

print(schools.head(10))
best_math_schools = schools.sort_values('average_math',ascending = False)

best_math_schools = best_math_schools[['school_name','average_math']]

best_math_schools = best_math_schools[best_math_schools['average_math'] >= 640]

print(best_math_schools)

combined_scores = schools

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

combined_scores = combined_scores.sort_values('total_SAT', ascending = False)

top_10_schools = combined_scores[['school_name','total_SAT']]

top_10_schools = top_10_schools.head(10)

print(top_10_schools)
largest_std_dev = schools

largest_std_dev = pd.merge(combined_scores, largest_std_dev, on = 'school_name', how = 'left', suffixes = ("", "_top"))

print(largest_std_dev.head(10))

print(pd.DataFrame(largest_std_dev.columns, columns = ['Column Names']))
largest_std_dev = largest_std_dev.groupby('borough').agg(
    num_schools = ('school_name', 'count'),
    average_SAT = ('total_SAT', 'mean'),
    std_SAT = ('total_SAT', 'std')
)

for column in largest_std_dev.columns:
    if pd.api.types.is_numeric_dtype(largest_std_dev[column]):
        largest_std_dev[column] = largest_std_dev[column].round(2)

largest_std_dev = largest_std_dev.sort_values('std_SAT', ascending = False)

largest_std_dev = largest_std_dev.head(1)

print(largest_std_dev)