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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...
# Subsetting data to find schools with average math scores of 80% or higher
best_math_schools = schools[schools["average_math"] >= 0.8 * 800][["school_name", "average_math"]]
# Sorting the schools by average math scores in descending order
best_math_schools = best_math_schools.sort_values(by="average_math", ascending=False)
# Calculating the total SAT score for each school
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# Identifying the top 10 schools based on total SAT scores
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10)
# Calculating the standard deviation of total SAT scores for each borough
borough_stats = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).reset_index()
# Renaming columns for clarity
borough_stats.columns = ["borough", "num_schools", "average_SAT", "std_SAT"]
# Identifying the borough with the largest standard deviation in combined SAT scores
max_std_value = borough_stats["std_SAT"].max()
largest_std_dev = borough_stats[borough_stats["std_SAT"] == max_std_value]
# Rounding off numerical values to two decimal places
largest_std_dev = largest_std_dev.round({"num_schools": 0, "average_SAT": 2, "std_SAT": 2})
# Display the results
print(best_math_schools)
print(top_10_schools)
print(largest_std_dev)