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.
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
#print(schools.head())
# Preview the data
print(schools.info())
# Start coding here...
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']].sort_values('average_math', ascending=False)
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False).head(10)
# Which NYC borough has the highest standard deviation for total_SAT?
# Fix: Use correct aggregation function names and order of operations
boroughs = schools.groupby('borough')['total_SAT'].agg(
num_schools='count',
std_SAT='std',
average_SAT='mean'
).sort_values("std_SAT", ascending=False).round(2)
largest_std_dev = boroughs[boroughs['std_SAT'] == boroughs['std_SAT'].max()]
print(largest_std_dev)