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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()

Which NYC schools have the best math results?

print(schools.columns)
# Filter schools with math scores at least 80% of the maximum possible score of 800
best_math_schools = schools[schools['average_math'] >= 0.8*800]
# Display the schools with the best math results
best_math_schools
# Select the relevant columns and sort by "average_math" in descending order
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values(by='average_math', ascending=False)

# Display the sorted DataFrame
best_math_schools

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

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

# Select the relevant columns and sort 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 top 10 performing schools based on the combined SAT scores
top_10_schools
# Group the data by "borough" and calculate the count of schools, mean, and standard deviation of "total_SAT"
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)

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

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

# Display the result
largest_std_dev
# Round all numeric values in the DataFrames to two decimal places

# Round numeric values in top_10_schools
top_10_schools = top_10_schools.round(2)
top_10_schools 

# Round numeric values in borough_stats
borough_stats = borough_stats.round(2)
borough_stats

# Round numeric values in largest_std_dev
largest_std_dev = largest_std_dev.round(2)
largest_std_dev

# Round numeric values in best_math_schools
best_math_schools = best_math_schools.round(2)

# Round numeric values in schools
schools = schools.round(2)
schools