Skip to content

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...
best_math_schools = schools.sort_values("average_math", ascending=False)

best_math_schools = best_math_schools[best_math_schools["average_math"] > 640][["school_name", "average_math"]]


schools["total_SAT"] = schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)

top_10_schools = schools.sort_values("total_SAT", ascending=False)
top_10_schools = top_10_schools[["school_name", "total_SAT"]].head(10)

schools["std_SAT"] = schools[["average_math", "average_reading", "average_writing"]].std(axis=1)
schools = schools.sort_values("std_SAT", ascending=False)
schools["average_SAT"] = schools["std_SAT"].mean()


# Add as many cells as you like...

borough_stats = schools.groupby("borough")["total_SAT"].std().reset_index()

# Finding the borough with the largest standard deviation
largest_std_borough = borough_stats.loc[borough_stats["total_SAT"].idxmax()]

# Filtering the DataFrame to include only schools from the borough with the largest standard deviation
schools_in_largest_std_borough = schools[schools["borough"] == largest_std_borough["borough"]]

# Calculating the number of schools in the borough with the largest standard deviation
num_schools = len(schools_in_largest_std_borough)


average_SAT = schools_in_largest_std_borough["total_SAT"].mean()
std_SAT = schools_in_largest_std_borough["total_SAT"].std()

# Creating a new DataFrame with the required information
largest_std_dev = pd.DataFrame({
    "borough": [largest_std_borough["borough"]],
    "num_schools": [num_schools],
    "average_SAT": [round(average_SAT, 2)],
    "std_SAT": [round(std_SAT, 2)]
})

# Printing the resulting DataFrame
print(largest_std_dev)
top_10_schools.head()