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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")
    
    # Preview the data
    schools.head(10)
    
    # Start coding here...
    # Add as many cells as you like...
    
    # (1) Creating a DataFrame of schools where average math scores are at least 80% of maximum
    
    avg80 = schools[schools['average_math'] >= (800*0.8)]
    best_math_schools = avg80[['school_name', 'average_math']].sort_values('average_math', ascending = False)
    print(best_math_schools.head())
    
    # (2) Creating a DataFrame identifying the top 10 performing schools based on scores across the three SAT sections
    
    schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
    top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending = False).iloc[0:10]
    print(top_10_schools)
    # (3) Creating a DataFrame identifying the NYC borough with the largest standard deviation for "total_SAT"
    
    grouped_borough = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
    print(grouped_borough.head())
    
    largest_std_dev = grouped_borough[grouped_borough['std'] == grouped_borough['std'].max()].round(2)
    largest_std_dev = largest_std_dev.rename(columns = {'count':'num_schools','mean':'average_SAT','std':'std_SAT'})
    print(largest_std_dev)