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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()
# Finding schools with best math scores
best_math_results = 0.8*800
best_math_schools = schools[schools["average_math"] >= best_math_results ][["school_name", "average_math"]].sort_values("average_math", ascending=False)
# Identify top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools ["average_writing"]
top_10_schools = schools.sort_values("total_SAT",ascending = False)[["school_name", "total_SAT"]].head(10)
# Locate NYC borough with largest std in SAT performance
boroughs = schools.groupby("borough")["total_SAT"].agg(["count","mean","std"]).round(2)
# # Filter for max std and make borough a column
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
# Rename the columns for clarity
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# Optional: Move borough from index to column
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)