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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()# Calculate the maximum possible score for math
max_math_score = 800
# Filter schools with average math score at least 80% of the maximum
best_math_schools = schools[schools['average_math'] >= 0.8 * max_math_score]
# Select only the necessary columns
best_math_schools = best_math_schools[['school_name', 'average_math']]
# Sort by average_math score in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
# Reset index
best_math_schools.reset_index(drop=True, inplace=True)
# Display the resulting DataFrame
print(best_math_schools)# Calculate the total SAT score for each school
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Select the necessary columns
top_10_schools = schools[['school_name', 'total_SAT']]
# Sort by total SAT score in descending order
top_10_schools = top_10_schools.sort_values(by='total_SAT', ascending=False)
# Take the top 10 performing schools
top_10_schools = top_10_schools.head(10)
# Reset index
top_10_schools.reset_index(drop=True, inplace=True)
# Display the resulting DataFrame
print(top_10_schools)# Calculate total SAT score for each school
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Group by borough and calculate mean and standard deviation of total SAT score
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std'])
# Rename columns
borough_stats.columns = ['num_schools', 'average_SAT', 'std_SAT']
# Find the borough with the largest standard deviation
largest_std_dev = borough_stats[borough_stats['std_SAT'] == borough_stats['std_SAT'].max()]
# Round numeric values to two decimal places
largest_std_dev = largest_std_dev.round(2)
# Reset index to make 'borough' a column
largest_std_dev.reset_index(inplace=True)
# Set 'borough' as the index
largest_std_dev.set_index('borough', inplace=True)
# Display the resulting DataFrame
print(largest_std_dev)