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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(10)#Which NYC Schools have the best math results?
best_math_schools = pd.DataFrame(schools[schools["average_math"]>= 640].sort_values("average_math", ascending = False))
best_math_schools = pd.DataFrame(best_math_schools.drop(columns=["percent_tested","average_reading","average_writing", "building_code", "borough"]))print(best_math_schools)#What are the top 10 performing schools based on the combined SAT scores?
schools["total_SAT"] = schools["average_reading"]+schools["average_writing"]+schools["average_math"]schools.sort_values(by= "total_SAT", ascending=False).head(10)top_10_schools = schools.groupby("school_name")["total_SAT"].sum().sort_values(ascending=False).head(10)
top_10_schools = pd.DataFrame(top_10_schools).reset_index()#which borough has the largest standard deviation in the combined SAT score?
#creating a new column to calculate the std by borough
schools.groupby(["borough"])["total_SAT"].std()largest_std_dev = pd.DataFrame(schools.groupby(["borough"])["total_SAT"].std())largest_std_dev["num_schools"] = schools.groupby(["school_name"])["borough"].sum().value_counts()largest_std_dev["average_SAT"] = schools.groupby(["borough"])["total_SAT"].mean()largest_std_dev = largest_std_dev.rename(columns={"total_SAT": "std_SAT"})largest_std_devlargest_std_dev = largest_std_dev.round(2)largest_std_dev.drop(["Bronx", "Brooklyn", "Queens", "Staten Island"], inplace = True)