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