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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 results are at least 80% of the maximum possible score of 800 for math.
best_math_schools = schools[schools['average_math'] >= 640][["school_name", "average_math"]].sort_values(by="average_math", ascending=False)
#top 10 performing schools
schools ['total_SAT'] = (schools['average_math'] + schools['average_reading'] + schools['average_writing'])
top_10_schools = schools.sort_values(by='total_SAT',ascending = False)[['school_name' , 'total_SAT']][0:10]
top_10_schools# largest_std_dev= largest_std[['borough', 'average_SAT' , 'std_SAT']]
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
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"})
largest_std_dev.reset_index(inplace=True)
largest_std_dev