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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

# Start coding here...
# Add as many cells as you like...
# Which NYC schools have the best math results?
math_threshold = 800 * 0.8
math_schools = schools[schools["average_math"]>= math_threshold][["school_name", "average_math"]]
best_math_schools = math_schools.sort_values("average_math", ascending=False)
print(best_math_schools)
# What are the top 10 performing schools based on the combined SAT scores?
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools.sort_values("total_SAT", ascending=False).head(10)
top_10_schools = top_10_schools[["school_name", "total_SAT"]]
print(top_10_schools)
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# Group by borough and calculate the required statistics
borough_stats = schools.groupby("borough").agg(
    num_schools = ("school_name", "count"), 
    average_SAT = ("total_SAT", "mean"),
    std_SAT = ("total_SAT", "std")
).reset_index()

# Find the borough with the largest standard deviation
largest_std_dev_row = borough_stats.loc[borough_stats["std_SAT"].idxmax()]

# Format the result as a DataFrame
largest_std_dev = pd.DataFrame([{
    "borough": largest_std_dev_row["borough"],
    "num_schools": largest_std_dev_row["num_schools"],
    "average_SAT": round(largest_std_dev_row["average_SAT"], 2),
    "std_SAT": round(largest_std_dev_row["std_SAT"], 2)
}])

# Print the result
print(largest_std_dev)