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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.
schools.dtypes# 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...
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']].sort_values(by='average_math', ascending=False)schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False).head(10)largest_std_dev = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"])
largest_std_dev.columns = ["num_schools", "average_SAT", "std_SAT"]
largest_std_dev = largest_std_dev.round(2)
largest_std_dev = largest_std_dev[largest_std_dev["std_SAT"] == largest_std_dev["std_SAT"].max()]