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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...# NaN check
print(schools.isna().sum())
# Which NYC schools have the best math results?
best_math_schools = schools[schools["average_math"] >= 80 * 800 / 100][["school_name", "average_math"]].sort_values("average_math", ascending=False)
# Preview the data
print(best_math_schools.head())# total_SAT column
schools["total_SAT"] = schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)
# Preview total_SAT column
print(schools["total_SAT"].head())
# What are the top 10 performing schools based on the combined SAT scores?
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)
# Preview the data
print(top_10_schools)# how many boroughs?
nb_borough = schools[["borough"]].drop_duplicates(subset="borough").count()
print(nb_borough)
# average_SAT column
schools["average_SAT"] = schools.groupby("borough")["total_SAT"].transform('mean').round(2)
# std_SAT column
schools["std_SAT"] = schools.groupby("borough")["total_SAT"].transform('std').round(2)
# num_schools column
schools["num_schools"] = schools.groupby("borough")["school_name"].transform('count')
# # Preview the data
print(schools[["borough", "average_SAT", "std_SAT"]].drop_duplicates(subset="borough").head(5))
# Which single borough has the largest standard deviation in the combined SAT score?
largest_std_dev = schools.sort_values("std_SAT", ascending=False)[["borough", "num_schools", "average_SAT", "std_SAT"]].head(1)
print(largest_std_dev)# another solution
# boroughs summary statistics
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
print(boroughs)
# largest_std_dev
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
print(largest_std_dev)
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print(largest_std_dev)
largest_std_dev.reset_index(inplace=False)