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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()# 1. Find schools with the best math scores
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)# 2. Identify the top 10 performing schools
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools.sort_values('total_SAT', ascending=False)[['school_name','total_SAT']].head(10)
print(top_10_schools)#3. Locate the NYC borough with the largest standard deviation in SAT performance
borough = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
largest_std_dev = borough[borough['std'] == borough['std'].max()]
largest_std_dev = largest_std_dev.rename(columns={'count':'num_schools','mean':'average_SAT','std':'std_SAT'})
largest_std_dev