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Analyzing Exam Performance
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  • Analyzing exam scores

    Now let's now move on to the competition and challenge.

    📖 Background

    Your best friend is an administrator at a large school. The school makes every student take year-end math, reading, and writing exams.

    Since you have recently learned data manipulation and visualization, you suggest helping your friend analyze the score results. The school's principal wants to know if test preparation courses are helpful. She also wants to explore the effect of parental education level on test scores.

    💾 The data

    The file has the following fields (source):
    • "gender" - male / female
    • "race/ethnicity" - one of 5 combinations of race/ethnicity
    • "parent_education_level" - highest education level of either parent
    • "lunch" - whether the student receives free/reduced or standard lunch
    • "test_prep_course" - whether the student took the test preparation course
    • "math" - exam score in math
    • "reading" - exam score in reading
    • "writing" - exam score in writing

    💪 Challenge

    Create a report to answer the principal's questions. Include:

    1. What are the average reading scores for students with/without the test preparation course?
    2. What are the average scores for the different parental education levels?
    3. Create plots to visualize findings for questions 1 and 2.
    4. [Optional] Look at the effects within subgroups. Compare the average scores for students with/without the test preparation course for different parental education levels (e.g., faceted plots).
    5. [Optional 2] The principal wants to know if kids who perform well on one subject also score well on the others. Look at the correlations between scores.
    6. Summarize your findings.
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    sns.set_style('whitegrid')
    
    pd.set_option('display.float_format', '{:,.2f}'.format)
    df = pd.read_csv("data/exams.csv")
    df.columns = ['gender', 'race', 'parent_education_level', 'lunch', 'test_prep_course', 'math', 'reading', 'writing']
    df.head()
    df['total'] = df['math'] + df['reading'] + df['writing']

    Q1. What are the average reading scores for students with/without the test preparation course?

    average_scores_test_prep = df.groupby("test_prep_course")['reading'].mean().reset_index()
    average_scores_test_prep.columns = ['test_prep', 'avg_reading_score']
    average_scores_test_prep

    Q2. What are the average scores for the different parental education levels?

    average_score_parents = df.groupby("parent_education_level")[['math','reading','writing']].mean().reset_index()
    average_score_parents.columns = ['parent_background', 'avg_math', 'avg_reading', 'avg_writing']
    average_score_parents_melt = pd.melt(average_score_parents, id_vars = 'parent_background', value_vars = ['avg_math', 'avg_reading', 'avg_writing'])
    average_score_parents_melt.columns = ['parent_background', 'subject', 'score']
    average_score_parents
    average_score_parents['avg'] = average_score_parents.mean(axis = 1)
    average_score_parents = average_score_parents.sort_values(by = 'avg', ascending = True)
    sorted_categories = [
        'some high school',
        'high school',
        'some college',
        "associate's degree",
        "bachelor's degree",
        "master's degree"
    ]

    Q3. Create plots to visualize findings for questions 1 and 2.

    fig, axs = plt.subplots(1, 2, figsize = (20, 6))
    
    sns.barplot(data = average_scores_test_prep, x = 'test_prep', y = 'avg_reading_score', ax = axs[0], order = ['none', 'completed'])
    sns.pointplot(data = average_score_parents_melt, x = 'parent_background', y = 'score', ax = axs[1], hue = 'subject', errorbar=('ci', 95), order = sorted_categories)
    
    axs[0].set_title("Average test score based on test prep")
    axs[1].set_title("Average score based on parent's educational attainment")
    
    plt.legend(loc = 'upper left')
    plt.tight_layout()

    Q4. Look at the effects within subgroups. Compare the average scores for students with/without the test preparation course for different parental education levels (e.g., faceted plots).