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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 total SAT score by summing math, reading, and writing scores
schools['total_sat_score'] = (
schools['average_math'] + schools['average_reading'] + schools['average_writing']
)
# Filter for schools with math scores of at least 640
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']]
# Sort the results by 'average_math' in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False).reset_index(drop=True)
# Display the top results
best_math_schools.head()
# Sort by total SAT score and select the top 10 schools
top_10_schools = schools[['school_name', 'total_sat_score']].sort_values(by='total_sat_score', ascending=False).head(10)
# Rename column for clarity
top_10_schools = top_10_schools.rename(columns={'total_sat_score': 'total_SAT'}).reset_index(drop=True)
# Display the top 10 schools
top_10_schools
# Group by borough to calculate statistics
borough_stats = schools.groupby('borough')['total_sat_score'].agg(['mean', 'std', 'count']).reset_index()
# Rename columns for readability
borough_stats = borough_stats.rename(columns={'mean': 'average_SAT', 'std': 'std_SAT', 'count': 'num_schools'})
# Find the borough with the largest standard deviation
largest_std_dev = borough_stats.sort_values(by='std_SAT', ascending=False).head(1)
# Round all numeric values to two decimal places
largest_std_dev = largest_std_dev.round(2).reset_index(drop=True)
# Display the result
largest_std_dev