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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.
import pandas as pd
schools = pd.read_csv("schools.csv")
schools.columns
schools.head()
import pandas as pd
schools = pd.read_csv("schools.csv")
best_math_result = 80/100 * 800
updated_school = schools[schools["average_math"] >= best_math_result]
best_math_schools = updated_school[["average_math","school_name"]]
best_math_schools = best_math_schools.sort_values("average_math" , ascending = False)
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
schools = schools.sort_values("total_SAT" , ascending = False)
top_10_schools = schools[["school_name" , "total_SAT"]].head(10)
boroughs = schools.groupby("borough").agg({"school_name":"count","total_SAT" :( "mean", "std")}).round(2)
largest_std_dev = boroughs[boroughs[("total_SAT", "std")] == boroughs[("total_SAT", "std")].max()]
largest_std_dev.columns = ["num_schools", "average_SAT","std_SAT"]
largest_std_dev