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Project: Exploring NYC Public School Test Result 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 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")
    best_math_schools = schools[schools['average_math'] >= 800*0.8]
best_math_schools = best_math_schools.iloc[:, [0, 3]]
best_math_schools = best_math_schools.sort_values(by = ['average_math'], ascending = False)
best_math_schools
    schools['total_SAT'] = schools['average_math']+schools['average_reading']+ schools['average_writing']
    top_10_schools = schools.sort_values(by = ['total_SAT'], ascending = False)
top_10_schools = top_10_schools[["school_name", "total_SAT"]].head(10)
top_10_schools
    largest_std_dev = schools.groupby("borough")['total_SAT'].agg(["count", "mean", "std"]).sort_values(by="std", ascending = False)
    largest_std_dev["count"] = largest_std_dev['count']
largest_std_dev["mean"] = round(largest_std_dev['mean'], 2)
largest_std_dev["std"] = round(largest_std_dev['std'], 2)
largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"}, inplace=True)
    largest_std_dev = largest_std_dev[:1]
largest_std_dev

    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...

    Which schools are best for math?

    best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)

    Calculate total_SAT per school

    schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

    Who are the top 10 performing schools?

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

    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 reset index so borough is 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"})