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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...
print(schools.describe())
print(schools.shape)
best_math_schools_v1 = schools["average_math"] >= 640
print(best_math_schools_v1.head())
best_math_schools_v1 = schools[schools["average_math"] >= 640]
best_math_schools_v2 = best_math_schools_v1[["school_name","average_math"]]
best_math_schools = best_math_schools_v2.sort_values("average_math",ascending=False)
print(best_math_schools.head(10))
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools_v1 = schools[["school_name","total_SAT"]].sort_values("total_SAT",ascending=False)
top_10_schools = top_10_schools_v1.iloc[:10]
print(top_10_schools)
num_schools_per_borough = schools["borough"].value_counts()
print(num_schools_per_borough)
borough_stats = []

boroughs = schools["borough"].unique()

for borough in boroughs:
   
    borough_data = schools[schools["borough"] == borough]
    
    num_schools = len(borough_data)
    
    average_SAT = borough_data["total_SAT"].mean()
    std_SAT = borough_data["total_SAT"].std()
    
    borough_stats.append({
        "borough": borough,
        "num_schools": num_schools,
        "average_SAT": round(average_SAT, 2),
        "std_SAT": round(std_SAT, 2)
    })

stats_per_borough = pd.DataFrame(borough_stats)

print(stats_per_borough)
largest_std_dev = stats_per_borough.loc[[stats_per_borough["std_SAT"].idxmax()]]
print(largest_std_dev)