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...
# Filter the schools with average_math > 640 and select relevant columns
best_math_schools = schools[schools['average_math'] > 640][["school_name", "average_math"]]
# Sort the filtered schools by average_math in descending order
best_math_schools = best_math_schools.sort_values('average_math', ascending=False)
# Calculate total SAT score
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Get top 10 schools by total SAT score
top_10_schools = schools[['school_name', 'total_SAT']].sort_values("total_SAT", ascending=False).head(10)
# Calculate borough statistics
borough_stats = schools.groupby("borough").agg(
num_schools=("school_name", "count"),
average_SAT=("total_SAT", "mean"),
std_SAT=("total_SAT", "std")
).reset_index()
# Round numeric values
borough_stats[["average_SAT", "std_SAT"]] = borough_stats[["average_SAT", "std_SAT"]].round(2)
# Get borough with the largest std deviation
largest_std_dev = borough_stats.sort_values(by="std_SAT", ascending=False).head(1)