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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...
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
schools['borough'].value_counts()
# getting the best math school

max_score = 800
threshold = max_score * 0.8

top_math_schools = schools.loc[schools['average_math'] >= threshold, ['school_name', 'average_math']].sort_values('average_math', ascending = False)
top_math_schools
# getting the top 10 schools

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

top_10 = schools[['school_name', 'Total']]

top_10 = top_10.nlargest(5, 'Total')
top_10
largest_stddev = schools.groupby('borough', as_index = False).agg(num_schools = ('school_name', 'count'), avg_sat = ('Total', 'mean'), std_sat = ('Total', 'std')).round(2)

largest_stddev = largest_stddev.sort_values('std_sat', ascending = False)

largest_stddev