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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.

    # Import required modules
    import pandas as pd
    
    # Read in the data
    schools = pd.read_csv("schools.csv")
    
    # Preview the data
    schools.head()
    
    #Shape of the data
    schools.shape
    
    #Finding 80%
    best_math_schools = schools[schools['average_math'] >= 0.8 * 800]
    
    #Sorting values for average math
    best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math', ascending=False)
    print("Best math schools:")
    print(best_math_schools)
    
    #Creating SAT column
    schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
    
    #Finding best 10 school
    sorted_totalsat = schools.sort_values("total_SAT", ascending = False)
    
    #Subsetting columns
    cols_to_sub = ["school_name", "total_SAT"]
    top_schools = sorted_totalsat[cols_to_sub]
    top_10_schools = top_schools[:10]
    pd.DataFrame(top_10_schools)
    #The top 10 performing schools 
    print("\nThe top 10 performing schools:")
    print(top_10_schools)
    
    
    
    #Top sd
    boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
    largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
    largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
    print("\nLargest standard deviation:")
    print(largest_std_dev)