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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
school = pd.read_csv("schools.csv")
schools = pd.DataFrame(school)
# Preview the data
schools.head()
#Which NYC schools have the best math results?
threshold = 800 * 0.8
best_math_schools = schools[schools['average_math'] >= threshold]
best_math_schools = best_math_schools[["school_name","average_math"]]
best_math_schools = best_math_schools.sort_values(by ="average_math", ascending = False)
print(best_math_schools)
#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 = top_10_schools[["school_name","total_SAT"]]
top_10_schools = top_10_schools.sort_values(by="total_SAT", ascending = False).head(10)
print(top_10_schools)
#Standard Deviation
std_dev = schools[["borough","school_name","total_SAT"]]
stats = std_dev.groupby("borough").agg(std_SAT=("total_SAT", "std"), average_SAT=("total_SAT","mean"), num_schools=("school_name","count")).reset_index()
largest_row = stats.loc[stats["std_SAT"].idxmax()]
largest_std_dev = pd.DataFrame([largest_row])
largest_std_dev = largest_std_dev.round(2)
print(largest_std_dev)
schools.head()