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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...

Which NYC schools have the best math results?

The best math results are at least 80% of the maximum possible score of 800 for math. Save your results in a pandas DataFrame called best_math_schools, including "school_name" and "average_math" columns, sorted by "average_math" in descending order.

import pandas as pd

# Load your dataset (update 'your_file.csv' to your actual filename)
schools = pd.read_csv("schools.csv")

# Print the column names to check exact naming
print(schools.columns)

best_math_schools = schools[schools["average_math"] >= 640]
best_math_schools = best_math_schools[["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values(by="average_math", ascending=False)
print(best_math_schools)

What are the top 10 performing schools based on the combined SAT scores?

Save your results as a pandas DataFrame called top_10_schools containing the "school_name" and a new column named "total_SAT", with results ordered by "total_SAT" in descending order ("total_SAT" being the sum of math, reading, and writing scores).

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

# Sort by total_SAT in descending order and select top 10
top_10_schools = schools.sort_values(by="total_SAT", ascending=False).head(10)

# Keep only relevant columns
top_10_schools = top_10_schools[["school_name", "total_SAT"]]

# Display the result
print(top_10_schools)

Which single borough has the largest standard deviation in the combined SAT score?

Save your results as a pandas DataFrame called largest_std_dev. The DataFrame should contain one row, with: "borough" - the name of the NYC borough with the largest standard deviation of "total_SAT". "num_schools" - the number of schools in the borough. "average_SAT" - the mean of "total_SAT". "std_SAT" - the standard deviation of "total_SAT". Round all numeric values to two decimal places.


# Step 1: Create the total_SAT column
schools["total_SAT"] = (
    schools["average_math"] +
    schools["average_reading"] +
    schools["average_writing"]
)

# Step 2: Group by borough and compute 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()

# Step 3: Find the borough with the largest std_SAT
max_std_row = borough_stats.loc[borough_stats["std_SAT"].idxmax()]

# Step 4: Create a DataFrame with one row and round numeric values
largest_std_dev = pd.DataFrame([{
    "borough": max_std_row["borough"],
    "num_schools": int(max_std_row["num_schools"]),
    "average_SAT": round(max_std_row["average_SAT"], 2),
    "std_SAT": round(max_std_row["std_SAT"], 2)
}])

# Display the result
print(largest_std_dev)