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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...
import pandas as pd

# Read in the data
schools = pd.read_csv("schools.csv")

# Filter for schools with average_math >= 640
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]]

# Sort by average_math in descending order
best_math_schools = best_math_schools.sort_values(by="average_math", ascending=False)

# Print the result
print(best_math_schools)

import pandas as pd

# Read the data
schools = pd.read_csv("schools.csv")

# Calculate total SAT score
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

# Select school name and total_SAT, sort descending, and take top 10
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10)

# Print result
print(top_10_schools)

import pandas as pd

# Read the data
schools = pd.read_csv("schools.csv")

# Create total_SAT column
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

# Group by borough and calculate required stats
borough_stats = schools.groupby("borough").agg(
    num_schools=("school_name", "count"),
    average_SAT=("total_SAT", "mean"),
    std_SAT=("total_SAT", "std")
).reset_index()

# Round numeric columns to two decimals
borough_stats = borough_stats.round({"average_SAT": 2, "std_SAT": 2})

# Find the borough with the largest std_SAT
largest_std_dev = borough_stats.sort_values(by="std_SAT", ascending=False).head(1)

# Print result
print(largest_std_dev)