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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()
# Computing for percentage of math score
schools["math_score_per"] = schools["average_math"]/800 * 100
# Schools with the best math scores
high_score = schools[schools["math_score_per"] >= 80].sort_values("average_math", ascending=False)
# Schools and average math score
best_math = high_score[["school_name", "average_math"]]
best_math_schools = pd.DataFrame(best_math)
# Top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10 = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False).head(10)
top_10_schools = pd.DataFrame(top_10)
# Single borough with largest standard deviation
school_group = schools.groupby("borough")["total_SAT"].agg(["mean", "std", "count"]).reset_index()
school_group.head(5)
l_std = school_group.loc[school_group["std"].idxmax()]
largest_std_dev = pd.DataFrame({
"borough": [l_std["borough"]],
"num_schools": [l_std["count"]],
"average_SAT": [round(l_std["mean"],2)],
"std_SAT": [round(l_std["std"],2)]
})
print(top_10_schools)