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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...# Which NYC schools have the best math results?
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending = False)
best_math_schools.head()
# Identifying the top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
subset1 = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending = False)
top_10_schools = subset1.head(10)
top_10_schools# Which single borough has the largest standard deviation in the combined SAT score?
subset = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_dev = subset[subset["std"] == subset["std"].max()]
largest_std_dev.columns = ["num_schools", "average_SAT", "std_SAT"]
# Reset index to bring borough back as a column
largest_std_dev = largest_std_dev.reset_index()
largest_std_dev