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Photo by Jannis Lucas on Unsplash.

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()

1. Which NYC schools have the best math results?

# Find schools with math scores at least 80% of the maximum possible score (800)
best_math_schools = schools[schools['average_math'] >= 0.8 * 800]

# Select relevant columns and sort by average math score in descending order
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values(by='average_math', 
                                                                                   ascending=False)

# Show the first few rows of the data
best_math_schools.head()

2. What are the top 10 performing schools based on the combined SAT scores?

# Calculate total SAT score for each school and find the top 10 performing schools
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', 
                                                                   ascending=False).head(10)

# Show the results
top_10_schools

3. Which single borough has the largest standard deviation in the combined SAT score?

# Group by borough and calculate statistics
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)

# Find the borough with the largest standard deviation in SAT scores
largest_std_dev = borough_stats[borough_stats['std'] == borough_stats['std'].max()]

# Rename columns for clarity
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'})

# Show the results
largest_std_dev