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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']>= 0.8 * 800][['school_name','average_math']]
best_math_schools = best_math_schools.sort_values('average_math', ascending= False)

best_math_schools.head()
schools['total_SAT'] = schools['average_math']+ schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']]
top_10_schools = top_10_schools.sort_values('total_SAT',ascending= False)
top_10_schools = top_10_schools.head(10)
top_10_schools
tot_SAT_Sch = schools[['borough']]
tot_SAT_Sch['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

tot_SAT_Sch.head()
schools['total_SAT'] = tot_SAT_Sch['total_SAT']
borough_stats = schools.groupby('borough')['total_SAT'].agg(
    num_schools='count',
    average_SAT='mean',
    std_SAT='std'
).reset_index()
largest_std_dev = borough_stats.sort_values('std_SAT', ascending=False).head(1).reset_index(drop=True)
largest_std_dev = largest_std_dev.round(2)
largest_std_dev