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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.

# 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...
# Filter schools with math scores at least 80% of the maximum possible score (800)
math_threshold = 0.8 * 800
best_math_schools = schools[['school_name', 'average_math']].loc[schools['average_math'] >= math_threshold]

# Sort the results by 'average_math' in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)

# Display the DataFrame
print(best_math_schools)

# Add as many cells as you like...
import pandas as pd

# Load the dataset
schools = pd.read_csv('schools.csv')

# Calculate total SAT scores by summing average math, reading, and writing scores
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Select the school_name and total_SAT columns and sort them by total_SAT in descending order
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)

# Display the DataFrame
print(top_10_schools)

# save it to a new CSV file
# top_10_schools.to_csv('top_10_schools.csv', index=False)

import pandas as pd

# Load the dataset
schools = pd.read_csv('schools.csv')

# Clean column names to remove any leading/trailing spaces
schools.columns = schools.columns.str.strip()

# Calculate total SAT scores by summing average math, reading, and writing scores
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Group by borough and calculate statistics: number of schools, mean, and standard deviation of total_SAT
borough_stats = schools.groupby('borough').agg(
    num_schools=('school_name', 'count'),
    average_SAT=('total_SAT', 'mean'),
    std_SAT=('total_SAT', 'std')
)

# Round numeric values to two decimal places
borough_stats = borough_stats.round(2)

# Find the borough with the largest standard deviation of total_SAT
largest_std_dev = borough_stats.loc[[borough_stats['std_SAT'].idxmax()]].reset_index()

# Display the result
print(largest_std_dev)

# Optionally, save the DataFrame to a CSV file
# largest_std_dev.to_csv('largest_std_dev_borough.csv', index=False)