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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 package
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()# The best math schools
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)
print(best_math_schools.head(10))# top 10 best performing school
schools['total_SAT']= schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools= schools[['school_name','total_SAT']].sort_values(by = 'total_SAT', ascending = False).head(10)
print(top_10_schools.head)# locating borough with the large STD
boroughs= schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
largest_std_dev= boroughs[boroughs['std'] == boroughs['std'].max()]
print(largest_std_dev)# renaming the columns
largest_std_dev = largest_std_dev.rename(columns= {'count' : 'num_schools', 'mean':'average_SAT', 'std': 'std_SAT'})
print(largest_std_dev)