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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...
# Add as many cells as you like...

Which NYC schools have the best math results?

import pandas as pd

# Create a DataFrame
schools = pd.read_csv("schools.csv")

# Calculate the threshold for the best math results
threshold = 0.8 * 800

# Filter schools with average math scores above the threshold
best_math_schools = schools[schools['average_math'] >= threshold]

# Select 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 result
best_math_schools

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

# Create a DataFrame
schools = pd.read_csv("schools.csv")

# Calculate the total SAT score
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Select 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 result
top_10_schools

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

import pandas as pd
import numpy as np 

# Create a DataFrame
boroughs = pd.read_csv("schools.csv")

# Check the columns in the DataFrame
print(boroughs.columns)

# Which NYC borough has the highest standard deviation for total_SAT?
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)

# Filter for max std and make borough a column
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]

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

# Optional: Move borough from index to column
largest_std_dev.reset_index(inplace=True)