Analyzing NYC Public School Test Scores
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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 tasked with answering three key questions about New York City (NYC) public school SAT performance:

    Which schools produce the highest math scores?

    • Specifically, which schools have an average math SAT score of at least 80%?
    • Save the results as a pandas DataFrame called best_math_schools.

    Who are the top 10 schools based on average results across reading, math, and writing?

    • Save the results as a pandas DataFrame called top_10_schools.

    Which NYC borough has the largest standard deviation for SAT results?

    • Save the results as a pandas DataFrame called largest_std_dev.

    Completing the project

    • Create a dictionary called schools_analysis containing the following key-value pairs:
      • "best_math_schools": best_math_schools DataFrame.
      • "top_10_schools": top_10_schools DataFrame.
      • "largest_SAT_std_dev": largest_std_dev DataFrame.
    # Start coding here... 
import pandas as pd

schools= pd.read_csv('schools.csv')
    best_math_schools = schools[schools['average_math']>=640][['school_name', 'average_math']].sort_values('average_math', ascending=False)
    schools['average_SAT']=(schools['average_math']+schools['average_reading']+schools['average_writing'])

top_10_schools=schools[['school_name', 'average_SAT']].sort_values('average_SAT', ascending=False)[:10]
    borough=schools.groupby('borough')['average_SAT'].agg(['count', 'mean', 'std']).sort_values('std', ascending=False)
    largest_std_dev=borough[borough['std']==borough['std'].max()]
    # Share your results in this format
schools_analysis = {"best_math_schools": best_math_schools,
                    "top_10_schools": top_10_schools,
                    "largest_SAT_std_dev": largest_std_dev}

# Call the answer!
print(schools_analysis)
    # Read in the data
schools = pd.read_csv("schools.csv")

# Find the best schools for math
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)

# Calculate average_SAT per school
schools["average_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

# Top 10 performing schools
top_10_schools = schools.sort_values("average_SAT", ascending=False)[["school_name", "average_SAT"]].head(10)

# Which borough has the highest standard deviation for average_SAT?
boroughs = schools.groupby("borough")["average_SAT"].agg(["count", "mean", "std"]).round(0).sort_values("mean", ascending=False)
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]

# Share your results in this format
schools_analysis = {"best_math_schools": best_math_schools,
                    "top_10_schools": top_10_schools,
                    "largest_SAT_std_dev": largest_std_dev}

# Call the answer!
print(schools_analysis)