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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

# Start coding here...
# Add as many cells as you like...
Hidden output 
best_math_schools = schools[schools["average_math"] > (.8*800)][["school_name","average_math"]].sort_values(by = "average_math", ascending = False)
best_math_schools
Hidden output 
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools[["school_name","total_SAT"]].sort_values(by = "total_SAT", ascending = False).head(10)
top_10_schools
Hidden output 
best_math_schools = schools[schools["average_math"]>(800*.8)][["school_name","average_math"]].sort_values(by = "average_math", ascending=False)
best_math_schools
Hidden output 
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools[["school_name","total_SAT"]].sort_values(by = "total_SAT", ascending=False).head(10)
top_10_schools
Hidden output 
largest_std_dev = schools.groupby("borough").agg({"total_SAT":["sum","mean","std"]}).round(2)
largest_std_dev.columns = ["num_schools","average_SAT","std_SAT"]
largest_std_dev = largest_std_dev[largest_std_dev["std_SAT"] == largest_std_dev["std_SAT"].max()]
largest_std_dev.reset_index(inplace=True)

boroughs = schools.groupby("borough").agg({"total_SAT":["count","mean","std"]}).round(2)
boroughs.columns = ["num_schools","average_SAT","std_SAT"]
largest_std_dev = boroughs[boroughs["std_SAT"] == boroughs["std_SAT"].max()]
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)
Hidden output 
# Which NYC borough has the highest standard deviation for total_SAT?
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# print(boroughs)
# Filter for max std and make borough a column
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
# print(largest_std_dev)
# Rename the columns for clarity
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# print(largest_std_dev)
# Optional: Move borough from index to column
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)
schools.groupby("borough").agg({"average_math":"mean"})
print(schools.loc[schools['borough'] == 'Manhattan','borough'])