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Photo by Jannis Lucas on Unsplash.

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
school = pd.read_csv("schools.csv")

# Preview the data
school.head()

# Start coding here...
# Add as many cells as you like...
school.describe()
school.info()
# The best math results are at least 80% of the *maximum possible score of 800* for math.

max_score = 800
threshold = max_score * 0.80

best_math_schools = school[(school["average_math"] >= threshold)][["school_name", "average_math"]].sort_values(by=['average_math'],  ascending=False)
# What are the top 10 performing schools based on the combined SAT scores?

total_SAT = school[["average_math", "average_reading","average_writing"]].sum(axis = 1)
school["total_SAT"] = total_SAT
top_10_schools1 = school.sort_values(by=['total_SAT'], ascending=False).head(10)
top_10_schools = school[["school_name", "total_SAT"]].sort_values(by = ["total_SAT"], ascending = False).head(10)
print(top_10_schools)
# NYC borough that has the highest STD for total_SAT?
borough = school.groupby("borough")["total_SAT"].agg(["count","mean","std"]).round(2)

# Filter the school DataFrame for the borough with the largest standard deviation
largest_std_dev = borough[borough["std"] == borough["std"].max()]

# For clarity
largest_std_dev = largest_std_dev.rename(columns = {"count":"num_schools", 
                                                    "mean":"average_SAT",
                                                    "std":"std_SAT"})
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)