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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.
# Importing pandas library
import pandas as pd
# Reading data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
#Best Math schools
best_math_schools = schools[['school_name','average_math']].loc[schools['average_math'] >= 800*0.8].sort_values(by ='average_math', ascending = False )
#Add total_sat
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
#TOP 10
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by ='total_SAT', ascending = False).head(10)
print(top_10_schools)# Largest std deviation by borough
largest_std_dev = schools.groupby('borough').agg(
std_SAT = ('total_SAT','std'),
average_SAT = ('total_SAT','mean'),
num_schools = ('borough', 'count')
).round(2)
#largest_std_dev
largest_std_dev = largest_std_dev[largest_std_dev['std_SAT'] == largest_std_dev['std_SAT'].max()]
largest_std_dev