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...
# Finding schools with the best math scores
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)
print(best_math_schools)
# Identifying the top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)
print(top_10_schools.head(10))
# Locating the NYC borough with the largest standard deviation in SAT performance
# Grouping the data by borough
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
print(boroughs)
# Filtering for the largest standard deviation
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()].round(2)
print(largest_std_dev)
# Renaming columns
largest_std_dev.rename(columns = {"count":"num_schools", "mean":"average_SAT", "std":"std_SAT"}, inplace=True)
print(largest_std_dev)