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...
best_math_schools = schools[schools['average_math'] >= 0.8 * 800][['school_name', 'average_math']].sort_values(by='average_math', ascending=False)
#print(best_math_schools)
schools['total_SAT'] = schools.apply(
lambda row: row['average_math'] + row['average_reading'] + row['average_writing'],
axis=1
)
top_10_schools = schools.sort_values(by='total_SAT', ascending=False)[['school_name', 'total_SAT']].head(10)
largest_std_dev = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
largest_std_dev = largest_std_dev[largest_std_dev['std'] == largest_std_dev['std'].max()]
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std':'std_SAT'})