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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
schools.head()# Best math results in NYC's schools
schools_above_80 = schools[schools["average_math"] > (800 * 0.8)]
best_math_schools = schools_above_80[["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values("average_math", ascending=False)
best_math_schools# Top 10 performing schools based on the combined SAT scores
schools["total_SAT"] = schools["average_math"] + schools["average_writing"] + schools["average_reading"]
top_10_schools = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending = False).head(10)
top_10_schools# The largest standard deviation in the combined SAT score from a single borough
borough = schools.groupby("borough")["total_SAT"].agg(num_schools=("count"), average_SAT=("mean"), std_SAT=("std")).round(2)
print(borough)
largest_std_dev = borough[borough["std_SAT"] == borough["std_SAT"].max()]
print(largest_std_dev)