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

# Preview the data
schools.head()

# Start coding here...
# Add as many cells as you like...
# Finding schools with the best math scores
best_math_results = 0.8 * 800

best_math_schools = schools[schools["average_math"] >= best_math_results][["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values("average_math", ascending=False)

print(best_math_schools)
# Finding top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

top_10_schools = schools[["school_name", "total_SAT"]]
top_10_schools = top_10_schools.sort_values("total_SAT", ascending=False)
top_10_schools = top_10_schools[0:10]
print(top_10_schools)
# Finding which single borough has the largest standard deviation in the combined SAT score
import numpy as np

boroughs = schools.groupby("borough")["total_SAT"].agg([np.mean, np.std]).round(2)

large_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]

largest_std_dev = pd.DataFrame({
    "borough": [large_std_dev.index[0]],
    "average_SAT": [large_std_dev["mean"].values[0]],
    "std_SAT": [large_std_dev["std"].values[0]],
    "num_schools": [len(schools[schools["borough"] == large_std_dev.index[0]])]
})

largest_std_dev