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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()# Schools information
schools.info()# Which NYC schools have the best math results?
best_math_schools = schools[['school_name', 'average_math']][schools.average_math > 80/100 * 800].sort_values(by='average_math', ascending=False)
best_math_schools# What are the top 10 performing schools based on the combined SAT scores?
# Calculate the total SAT
schools['total_SAT'] = schools.average_math + schools.average_reading + schools.average_writing
# Sort and subset the top ten schools
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
top_10_schools# Which single borough has the largest standard deviation in the combined SAT score?
# Grouping by borough
by_borough = schools.groupby('borough')['total_SAT'].agg(['size', 'mean', 'std']).round(2)
# Subsetting with max()
largest_std_dev = by_borough[by_borough['std'] == by_borough['std'].max()]
# Renaming the columns
largest_std_dev = largest_std_dev.rename(columns={'std': 'std_SAT', 'size':'num_schools', 'mean':'average_SAT'})
largest_std_dev