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Introduction to Data Visualization with Seaborn
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  • # Importing the course packages
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    # Importing the course datasets
    country_data = pd.read_csv('datasets/countries-of-the-world.csv', decimal=",")
    mpg = pd.read_csv('datasets/mpg.csv')
    student_data = pd.read_csv('datasets/student-alcohol-consumption.csv', index_col=0)
    survey = pd.read_csv('datasets/young-people-survey-responses.csv', index_col=0)

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here

    Explore Datasets

    Use the DataFrames imported in the first cell to explore the data and practice your skills!

    • From country_data, create a scatter plot to look at the relationship between GDP and Literacy. Use color to segment the data points by region.
    • Use mpg to create a line plot with model_year on the x-axis and weight on the y-axis. Create differentiating lines for each country of origin (origin).
    • Create a box plot from student_data to explore the relationship between the number of failures (failures) and the average final grade (G3).
    • Create a bar plot from survey to compare how Loneliness differs across values for Internet usage. Format it to have two subplots for gender.
    • Make sure to add titles and labels to your plots and adjust their format for readability!

    INTRODUCTION TO SEABORN

    Basic scatter plots with seaborn

    Student data

    student_data
    sns.relplot(x="G1",
                    y="G3",
                    data=student_data,
                    kind="scatter",
                    hue="sex")
    
    plt.show()
    sns.relplot(x="G1",
                y="G3",
                data=student_data,
                kind="scatter",
                col="schoolsup",
                hue="sex", 
                palette={"F": "red", "M": "blue"})
    
    plt.show()
    sns.relplot(data=student_data,
                x="G1",
                y="G3",
                col="schoolsup",
                row="famsup",
                hue="location",
                palette={"Rural": "green", "Urban": "blue"})
    
    plt.show()
    sns.countplot(data=student_data,
                  y="study_time",
                  hue="sex",
                  palette={"F":"red", "M":"blue"})
    
    plt.show()

    RELATIONAL PLOTS

    How to visualize two quantitative variables

    MPG

    mpg
    sns.relplot(x="horsepower", 
                    y="mpg",
                    data=mpg,
                    kind="scatter",
                    size="cylinders",
                    hue="cylinders")
    
    plt.show()
    sns.relplot(x="horsepower",
               y="mpg",
               data=mpg,
               kind="scatter",
               hue="origin",
               style="origin")
    
    plt.show()