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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()
# Start coding here...
# Add as many cells as you like...best_math_schools = schools[schools['average_math'] >= (.8*800)][['school_name','average_math']].sort_values('average_math', ascending=False)
print(best_math_schools)schools['total_SAT'] = (schools['average_math'] +
schools['average_reading'] +
schools['average_writing'])
print(schools.head())top_10_schools = schools.sort_values('total_SAT', ascending=False)[['school_name', 'total_SAT']].head(10)
print(top_10_schools)# Ensure 'borough' is a column in the DataFrame before grouping
schools_sorted = schools.groupby('borough')['total_SAT'].agg(['mean', 'std', 'count']).round(2)
schools_sorted.rename(columns={'mean':'average_SAT',
'std':'std_SAT',
'count':'num_schools'}, inplace=True)
largest_std_dev = schools_sorted[schools_sorted['std_SAT'] == schools_sorted['std_SAT'].max()]
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)