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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.
import pandas as pd
# Load the dataset
schools = pd.read_csv("schools.csv")
# Create the DataFrame
df = pd.DataFrame(schools)
# Calculate total SAT scores
df["total_SAT"] = df["average_math"] + df["average_reading"] + df["average_writing"]
# Get the top 10 performing schools
top_10_schools = df[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10).reset_index(drop=True)
# Group by borough and calculate statistics for total_SAT
borough_stats = df.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 in total_SAT
largest_std_dev = borough_stats.sort_values(by="std_SAT", ascending=False).head(1)
# Round values to two decimal places
largest_std_dev = largest_std_dev.round(2)
# Save the results
print("Top 10 Schools by Total SAT:")
print(top_10_schools)
print("\nBorough with Largest SAT Standard Deviation:")
print(largest_std_dev)