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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].sort_values('average_math', ascending=False)
best_math_schools=best_math_schools[['school_name', 'average_math']]
math=schools['average_math']
read=schools['average_reading']
write=schools['average_writing']
schools['total_SAT']=math+read+write
top_10_schools=schools[['school_name','total_SAT']].sort_values('total_SAT',ascending=False).head(10)
top_10_schools
std_dev_by_borough = schools.groupby('borough')['total_SAT'].agg(['count','mean','std'])


largest = std_dev_by_borough['std'].max()
b=std_dev_by_borough['std'].idxmax()
c=std_dev_by_borough[std_dev_by_borough['std']==largest]



largest_std_dev = pd.DataFrame({
    'borough': [b],
    'num_schools': [round(c['count'].sum(),2)],
    'average_SAT': [round(c['mean'].sum(), 2)],
    'std_SAT': [round(c['std'].sum(), 2)],
})
largest_std_dev