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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.
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
print(schools.head())
# Identifying schools that perform well in math
best_math_schools = schools[schools["average_math"] > 640].sort_values("average_math", ascending=False)[["school_name", "average_math"]]
print(best_math_schools.head())
# Calculating total SAT score and identifying top 10 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())
# Calculating number of schools, average total SAT score and std per borough
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "std", "mean"]).round(2)
print(boroughs)
# Inspecting borough with largest standard deviation
large_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
largest_std_dev = large_std_dev.rename(columns={"count" : "num_schools", "std" : "std_SAT", "mean" : "average_SAT"})
print(largest_std_dev)