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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()
# Which schools have the best math results?
schools_filtered = schools[schools["average_math"] > 640]
schools_sub = schools_filtered[["school_name", "average_math"]]
print(schools_sub)
best_math_schools = schools_sub.sort_values(["average_math"], ascending=False)
print(best_math_schools.head())
# What are the top 10 performing schools based on the combined SAT scores?
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools_b = schools[["total_SAT", "school_name"]]
top_10_schools = top_10_schools_b.sort_values(["total_SAT", "school_name"], ascending=[False, True]).head(10)
print(top_10_schools)
# Which borough in NYC has the largest standard deviation in SAT performance?
schools_b = schools.groupby("borough")["total_SAT"].agg(['count', 'mean', 'std'])
largest_std_dev = round(schools_b[schools_b["std"] == schools_b["std"].max()],2)
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'})
print(largest_std_dev)