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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()
# The best math results
best_math = schools[schools["average_math"] >= 640]
best_math_schools = pd.DataFrame(best_math)[["school_name" , "average_math"]].sort_values("average_math" , ascending = False)
# print(best_math_schools)
# top_10_pperformance
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools[["school_name" , "total_SAT"]].sort_values("total_SAT" , ascending = False).head(10)
# print(top_10_schools)
# The largest standard div in combined SAT
std_borough = schools.groupby("borough")["total_SAT"].std()
largest_std_borough = std_borough.idxmax()
std_SAT = schools[schools['borough'] == largest_std_borough]["total_SAT"].std()
average_SAT = schools[schools['borough'] == largest_std_borough]["total_SAT"].mean()
num_schools = schools[schools['borough'] == largest_std_borough].shape[0]
dic = {
"borough" : largest_std_borough,
"num_schools" : [num_schools],
"average_SAT" : [round(average_SAT, 2)],
"std_SAT" : [round(std_SAT,2)]
}
largest_std_dev = pd.DataFrame(dic)
print(largest_std_dev)