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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()print(f'Number of rows: {schools.shape[0]}\nNumber of columns: {schools.shape[1]}')
schools.describe()print(f'Unique value from borough columns: {schools["borough"].unique()}')# Which NYC schools have the best math results?
# The best math results are at least 80% of the *maximum possible score of 800* for math. [['school_name','average_math']]
best_math_schools = schools[schools['average_math'] >= 800*0.8].sort_values(by='average_math',ascending=False)
best_math_schools = best_math_schools[['school_name','average_math']]
best_math_schools# What are the top 10 performing schools based on the combined SAT scores?
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
schools.sort_values(by='total_SAT', ascending=False, inplace=True)
top_10_schools = schools[['school_name','total_SAT']].head(10)
top_10_schoolsschools.total_SAT.describe()# Which single borough has the largest standard deviation in the combined SAT score?
largest_std_dev = schools.groupby('borough').agg({'total_SAT':'std'}).sort_values(by='total_SAT',ascending=False)
largest_std_dev = largest_std_dev[:1]
largest_std_dev.rename(columns={'total_SAT':'std_SAT'},inplace=True)
largest_std_dev.reset_index(drop=False,inplace=True)
largest_std_dev = schools.merge(largest_std_dev, on='borough', how='inner')
largest_std_dev = largest_std_dev.groupby('borough').agg({'school_name':'count','total_SAT':'mean','std_SAT':'max'})
largest_std_dev.reset_index(drop=False,inplace=True)
largest_std_dev.rename(columns={'school_name':'num_schools','total_SAT':'average_SAT'},inplace=True)
largest_std_dev['average_SAT'] = largest_std_dev['average_SAT'].apply(lambda x: round(x,2))
largest_std_dev['std_SAT'] = largest_std_dev['std_SAT'].apply(lambda x: round(x,2))
largest_std_dev