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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
import numpy as np

# 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...
schools.tail()

Task 1 : Which NYC schools have the best math results?

  • The best math results are at least 80% of the maximum possible score of 800 for math.
  • Save your results in a pandas DataFrame called best_math_schools, including "school_name" and "average_math" columns, sorted by "average_math" in descending order.

Best math results criteria

  • Maximum Possible Marks = 800
  • Best math results criteria = 80% of maximum possible marks, therefore (800 * 80)/100 = 640 marks
# Step 1: Subset school_name and average math scores
best_math_schools = schools[["school_name","average_math"]]
best_math_score_criteria = best_math_schools["average_math"] >= 640
best_math_schools = best_math_schools[best_math_score_criteria]

# Step 2: Sort the score in descending order
best_math_schools = best_math_schools.sort_values("average_math", ascending = False)
display(best_math_schools)

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

Save your results as a pandas DataFrame called top_10_schools containing the "school_name" and a new column named "total_SAT", with results ordered by "total_SAT" in descending order.

# Step 1: Create new column "total_SAT" & Add all the individual subject scores to get the combined SAT scores
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

# Step 2 : Sort the combined SAT scores to view the top 10 performing schools
top_performing_schools = schools.sort_values('total_SAT', ascending=False)

# Step 3: Extract the top 10 performing schools
top_10_schools = top_performing_schools[["school_name","total_SAT"]].head(10)
display(top_10_schools)

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

schools.head()
# Step 1: Group the data by "borough"
summary_statistics_by_borough = schools.groupby("borough")["total_SAT"].agg(['count', np.mean, np.std]).round(2)
summary_statistics_by_borough

# Step 2: Filter for the largest standard deviation

largest_std_dev = summary_statistics_by_borough[summary_statistics_by_borough["std"] == summary_statistics_by_borough["std"].max()]
largest_std_dev

# Step 3: Rename columns
largest_std_dev = largest_std_dev.rename(columns = {"count":"num_schools",
                                 "mean":"average_SAT",
                                 "std" : "std_SAT"})
display(largest_std_dev)

Manhattan "bourough" has the largest standard deviation in the combined SAT score