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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...

1) Finding Schools with best Math results

#subsetting the data based on math score of 80% out of 800 maximum marks
#0.8 of 800, is 80% of 800
best_math_schools = schools[schools['average_math'] >= (0.8 * 800)]

#getting only columns 'school_name' and 'average_math' and sorting by 'average_math' in descending order

best_math_schools = best_math_schools[['school_name','average_math']]
best_math_schools=best_math_schools.sort_values(by='average_math',ascending = False)
best_math_schools.head()

2) Finding Top 10 schools based on combined SAT scores

# creating a column to calculate average score across all three SAT sections
schools['total_SAT'] = schools[['average_math','average_reading','average_writing']].sum(axis=1)

#sorting the result on the 'total_SAT' column in descending order
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
top_10_schools

3) Finding the borough that has the largest Standard Deviation in combined SAT

boroughs = schools
boroughs['total_SAT'] = boroughs[['average_math','average_reading','average_writing']].sum(axis=1)

borough_stats = boroughs.groupby('borough').agg(
    num_schools=('school_name', 'count'),
    average_SAT=('total_SAT', 'mean'),
    std_SAT=('total_SAT', 'std')
).round(2).reset_index()

largest_std_dev = borough_stats[borough_stats['std_SAT'] == borough_stats['std_SAT'].max()]
largest_std_dev