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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()
#maximum possible score of 800
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)schools["total_SAT"] = total_SAT = schools[["average_math", "average_reading", "average_writing"]].sum(axis= 1)
top_10_schools = schools.sort_values(by = "total_SAT", ascending = False)[["school_name" , "total_SAT"]].head(10)
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# Find the borough with the largest standard deviation of "total_SAT"
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
largest_std_dev = largest_std_dev.rename(columns={
"count": "num_schools",
"mean": "average_SAT",
"std": "std_SAT"
}).reset_index()
# Print the result
print(largest_std_dev)