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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.

Which NYC schools have the best math results?

What are the top 10 performing schools based on the combined SAT scores?

Which single borough has the largest standard deviation in the combined SAT score?

# 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...
print(schools["average_math"].isnull().sum()) #none, can work further

#cut off
best_math_cut = schools[schools["average_math"] >= 800 * 0.8] 

#form top math schools list
best_math_schools = best_math_cut[["school_name", "average_math"]].sort_values("average_math", ascending=False) 

print(best_math_schools)

#Find total
schools["total_SAT"] = schools['average_reading'] + schools['average_math'] + schools['average_writing'] 

#sort schools
sorted_schools = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False)

#Find the top 10
top_10_schools = sorted_schools.head(n=10)
print(top_10_schools)
#duplicate for smooth window-coding
schools["total_SAT"] = schools['average_reading'] + schools['average_math'] + schools['average_writing']

#Standard deviation for the test
schools['std_SAT'] = schools["total_SAT"].std()

#Test scores by district
borough_std = schools.groupby('borough')['total_SAT'].std()

#Find the largest standard deviation, identify the district
largest_sd = borough_std.max()
borough_with_largest_sd = borough_std.idxmax()

#find schools in districts
schools_by_borough = schools['borough'].value_counts()

#mean scores by districts
mean_SAT_borrow = schools.groupby('borough')["total_SAT"].mean()

#form the ultimate DF with all the required columns:
largest_std_dev = pd.DataFrame({
    "borough": [borough_with_largest_sd],
    "num_schools": [schools_by_borough[borough_with_largest_sd]],
    "average_SAT": [mean_SAT_borrow[borough_with_largest_sd]],
    "std_SAT": [borough_std[borough_with_largest_sd]]
}).round(2)

print(largest_std_dev)