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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...


best_math_schools = schools[schools["average_math"]>= (800*.8)][["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values(by="average_math", ascending=False)
print(best_math_schools)
# Calculate the total SAT score for each school
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

# Select the top 10 schools based on total SAT score
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10)

# Display the top 10 schools
top_10_schools
# Calculate the standard deviation of total_SAT for each borough
borough_stats = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).reset_index()

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

# Create the DataFrame with the required information
largest_std_dev = pd.DataFrame({
    "borough": [largest_std_dev_borough["borough"]],
    "num_schools": [largest_std_dev_borough["count"]],
    "average_SAT": [round(largest_std_dev_borough["mean"], 2)],
    "std_SAT": [round(largest_std_dev_borough["std"], 2)]
})

# Display the result
largest_std_dev