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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 pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
# Start coding here...
# Add as many cells as you like...
#best math school
best_math_schools = schools[['school_name', 'average_math']].sort_values('average_math', ascending=False).head(10)
print(best_math_schools)
#top10 performing schools based on combined SAT scores
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False).head(10)
print(top_10_schools)
#borough with largest standard deviation
largest_std_dev = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2).sort_values('std', ascending=False).head(1)
#renaming columns
largest_std_dev = largest_std_dev.rename(columns={'count':'num_schools', 'mean':'average_SAT', 'std':'std_SAT'})
print(largest_std_dev)