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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()

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.
# Sort the highest math scores
best_math_schools = schools.sort_values("average_math", ascending=False)
best_math_schools = best_math_schools[best_math_schools["average_math"] >= 640]
best_math_schools = best_math_schools[["school_name", "average_math"]]
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).
# Add total_SAT to schools DataFrame
schools["total_SAT"] = schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)

# Filter SAT scores from descending order
SAT_srt = schools.sort_values("total_SAT", ascending=False)

# Create a DataFrame with top 10 schools
top_10_schools = SAT_srt[["school_name", "total_SAT"]].head(10)

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.
# Group by borough and calculate statistics
borough_stats = schools.groupby("borough").agg(
    num_schools=("total_SAT", "size"),
    average_SAT=("total_SAT", "mean"),
    std_SAT=("total_SAT", "std")
)

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

# Find the row with the largest standard deviation
largest_std_dev_row = borough_stats.loc[largest_std_dev_borough]

# Create a DataFrame and reset index to include borough as a column
largest_std_dev = pd.DataFrame([largest_std_dev_row]).round(2)
largest_std_dev["borough"] = largest_std_dev_borough

# Reset index and reorder columns
largest_std_dev = largest_std_dev.reset_index(drop=True)
largest_std_dev = largest_std_dev[["borough", "num_schools", "average_SAT", "std_SAT"]]

# Display the result
print(largest_std_dev)