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][["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values('average_math', ascending=False)
best_math_schools.head(10)schools["total_SAT"] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[["school_name", "total_SAT"]]
top_10_schools = top_10_schools.sort_values('total_SAT', ascending=False).head(10)schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2).loc[lambda df: df["std"] == df["std"].max()]
largest_std_dev = (
schools.groupby("borough")["total_SAT"]
.agg(num_schools="count", average_SAT="mean", std_SAT="std")
.round(2)
.loc[lambda df: df["std_SAT"] == df["std_SAT"].max()]
)