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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()
# Start coding here...
# Add as many cells as you like...best_math_schools = schools[schools["average_math"] >= 640][['school_name', 'average_math']]
best_math_schools = best_math_schools.sort_values(by = "average_math", ascending = False)
schools["total_SAT"] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
schools.head()top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by = 'total_SAT', ascending = False).head(10)
top_10_schools
borough = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)borough.head()largest_std_dev = borough[borough["std"] == borough["std"].max()]largest_std_dev.head()largest_std_dev = largest_std_dev.rename(columns = {"count": "num_schools", "mean" : "average_SAT", "std" : "std_SAT"})largest_std_dev.head()