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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")
# Filter schools with math results of at least 80% of the maximum score
best_math_schools = schools[schools['average_math'] >= 0.8 * 800][['school_name', 'average_math']]
# Sort by average_math in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
# Get top 10 performing schools based on combined SAT scores
top_10_schools = schools.copy()
top_10_schools['total_SAT'] = top_10_schools['average_math'] + top_10_schools['average_reading'] + top_10_schools['average_writing']
top_10_schools = top_10_schools[['school_name', 'total_SAT']].nlargest(10, 'total_SAT')
# Calculate borough-wise standard deviation of combined SAT scores
# First, ensure the 'total_SAT' column is added to the original 'schools' DataFrame
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
borough_std_dev = schools.groupby('borough')['total_SAT'].std().reset_index()
largest_std_dev = borough_std_dev.nlargest(1, 'total_SAT')
largest_std_dev['num_schools'] = schools['borough'].value_counts()[largest_std_dev['borough']].values
largest_std_dev['average_SAT'] = schools.groupby('borough')['total_SAT'].mean()[largest_std_dev['borough']].values
largest_std_dev.columns = ['borough', 'std_SAT', 'num_schools', 'average_SAT']
largest_std_dev = largest_std_dev.round({'std_SAT': 2, 'average_SAT': 2})
# Print the DataFrames
print("Best Math Schools:")
print(best_math_schools)
print("\nTop 10 Performing Schools based on Combined SAT Scores:")
print(top_10_schools)
print("\nBorough with Largest Standard Deviation in Combined SAT Score:")
print(largest_std_dev)