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()
# My solution
# Which NYC schools have the best math results?
# "Best" means >= 80% of the max score (800) → 0.8 * 800 = 640. Keep only school_name and average_math columns. Sort in descending order of average_math and reset index to keep row numbers clean
best_math_schools = (
schools.loc[schools["average_math"] >= 0.8 * 800, ["school_name", "average_math"]]
.sort_values(by="average_math", ascending=False)
.reset_index(drop=True)
)
print("Best Math Schools:\n", best_math_schools)
# Top 10 schools based on total SAT
# Create a total_SAT column by summing math, reading, and writing scores
schools["total_SAT"] = (
schools["average_math"] + schools["average_reading"] + schools["average_writing"]
)
# Select only the school_name and total_SAT columns. Sort in descending order of total_SAT. Take the first 10 rows for the top 10
top_10_schools = (
schools.loc[:, ["school_name", "total_SAT"]]
.sort_values(by="total_SAT", ascending=False)
.head(10)
.reset_index(drop=True)
)
print("\nTop 10 Schools:\n", top_10_schools)
# Borough with largest standard deviation in total SAT
# Group the data by borough. Calculate:
# - num_schools = number of schools in the borough
# - average_SAT = mean of total_SAT
# - std_SAT = standard deviation of total_SAT
borough_stats = (
schools.groupby("borough")["total_SAT"]
.agg(num_schools="count", average_SAT="mean", std_SAT="std")
.reset_index()
)
# Find the borough with the maximum std_SAT
# Convert to DataFrame to match output requirement
largest_std_dev = borough_stats.loc[borough_stats["std_SAT"].idxmax()].to_frame().T
# Round average_SAT and std_SAT to 2 decimal places
largest_std_dev[["average_SAT", "std_SAT"]] = (
largest_std_dev[["average_SAT", "std_SAT"]]
.astype(float)
.round(2)
)
print("\nLargest Std Dev Borough:\n", largest_std_dev)