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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...
best_math_schools = schools[schools["average_math"] >= 640][["school_name","average_math"]].sort_values(by="average_math",ascending=False)
best_math_schools.head()
schools["total_SAT"] = schools["average_math"] + schools["average_writing"] + schools["average_reading"]
top_10_schools = schools[["school_name","total_SAT"]].sort_values(by="total_SAT",ascending=False).head(10)
top_10_schools
std = schools.groupby("borough")["total_SAT"].std()
highest_std = std.idxmax()
num_schools = schools[schools["borough"] == highest_std]["school_name"].count().round(2)
average_SAT = schools[schools["borough"] == highest_std]["total_SAT"].mean().round(2)
std_SAT = schools[schools["borough"] == highest_std]["total_SAT"].std().round(2)

dictionary = {"borough": [highest_std],
              "num_schools": [num_schools],
              "average_SAT": [average_SAT],
              "std_SAT": [std_SAT]}

largest_std_dev = pd.DataFrame(dictionary,index=[0])
largest_std_dev