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Introduction to Data Visualization with Seaborn
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  • Introduction to Data Visualization with Seaborn

    Run the hidden code cell below to import the data used in this course.

    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!
    # Import Matplotlib, pandas, and Seaborn
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    
    
    # Create a DataFrame from csv file
    df = pd.read_csv(csv_filepath)
    
    
    # Create a count plot with "Spiders" on the x-axis
    sns.countplot( data = df, x = "Spiders")
    
    # Display the plot
    plt.show()
    # Import Matplotlib and Seaborn
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    # Change the legend order in the scatter plot
    sns.scatterplot(x="absences", y="G3", 
                    data=student_data, 
                    hue="location", hue_order = ["Rural", "Urban"])
    
    # Show plot
    plt.show()
    # Import Matplotlib and Seaborn
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    # Create a dictionary mapping subgroup values to colors
    palette_colors = {"Rural": "green", "Urban": "blue"}
    
    # Create a count plot of school with location subgroups
    
    sns.countplot(data = student_data, x = "school" , hue = "location", palette = palette_colors) 
    
    
    # Display plot
    plt.show()