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    Introduction To Data Visualization With Seaborn Chapter3

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    Fgpeqw
    This document discusses different types of categorical plots that can be created using the seaborn library in Python. It covers count plots, bar plots, box plots, and point plots. It provides examples of how to generate each type of plot using seaborn functions like catplot(), countplot(), and pointplot(). It also demonstrates how to customize aspects of the plots like changing the order of categories, turning off confidence intervals, and adjusting the whiskers on box plots.

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    © All Rights Reserved
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    Introduction To Data Visualization With Seaborn Chapter3

    Uploaded by

    Fgpeqw
    48 views32 pages
    This document discusses different types of categorical plots that can be created using the seaborn library in Python. It covers count plots, bar plots, box plots, and point plots. It provides examples of how to generate each type of plot using seaborn functions like catplot(), countplot(), and pointplot(). It also demonstrates how to customize aspects of the plots like changing the order of categories, turning off confidence intervals, and adjusting the whiskers on box plots.

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    Introduction to Data Visualization with Seaborn chapter3

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    This document discusses different types of categorical plots that can be created using the seaborn library in Python. It covers count plots, bar plots, box plots, and point plots. It provides examples of how to generate each type of plot using seaborn functions like catplot(), countplot(), and pointplot(). It also demonstrates how to customize aspects of the plots like changing the order of categories, turning off confidence intervals, and adjusting the whiskers on box plots.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    48 views32 pages

    Introduction To Data Visualization With Seaborn Chapter3

    Uploaded by

    Fgpeqw
    This document discusses different types of categorical plots that can be created using the seaborn library in Python. It covers count plots, bar plots, box plots, and point plots. It provides examples of how to generate each type of plot using seaborn functions like catplot(), countplot(), and pointplot(). It also demonstrates how to customize aspects of the plots like changing the order of categories, turning off confidence intervals, and adjusting the whiskers on box plots.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 32

    Count plots and bar

    plots
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N

    Erin Case
    Data Scientist
    Categorical plots
    Examples: count plots, bar plots

    Involve a categorical variable

    Comparisons between groups

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    catplot()
    Used to create categorical plots

    Same advantages of relplot()

    Easily create subplots with col= and row=

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    countplot() vs. catplot()
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.countplot(x="how_masculine",
    data=masculinity_data)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    countplot() vs. catplot()
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="how_masculine",
    data=masculinity_data,
    kind="count")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Changing the order
    import matplotlib.pyplot as plt
    import seaborn as sns

    category_order = ["No answer",


    "Not at all",
    "Not very",
    "Somewhat",
    "Very"]

    sns.catplot(x="how_masculine",
    data=masculinity_data,
    kind="count",
    order=category_order)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Bar plots
    Displays mean of quantitative variable per
    category

    import matplotlib.pyplot as plt


    import seaborn as sns

    sns.catplot(x="day",
    y="total_bill",
    data=tips,
    kind="bar")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Con dence intervals
    Lines show 95% con dence intervals for the
    mean

    Shows uncertainty about our estimate

    Assumes our data is a random sample

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Turning off con dence intervals
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="day",
    y="total_bill",
    data=tips,
    kind="bar",
    ci=None)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Changing the orientation
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="total_bill",
    y="day",
    data=tips,
    kind="bar")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Let's practice!
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N
    Creating a box plot
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N

    Erin Case
    Data Scientist
    What is a box plot?
    Shows the distribution of quantitative data

    See median, spread, skewness, and outliers

    Facilitates comparisons between groups

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    How to create a box plot
    import matplotlib.pyplot as plt
    import seaborn as sns

    g = sns.catplot(x="time",
    y="total_bill",
    data=tips,
    kind="box")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Change the order of categories
    import matplotlib.pyplot as plt
    import seaborn as sns

    g = sns.catplot(x="time",
    y="total_bill",
    data=tips,
    kind="box",
    order=["Dinner",
    "Lunch"])

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Omitting the outliers using `sym`
    import matplotlib.pyplot as plt
    import seaborn as sns

    g = sns.catplot(x="time",
    y="total_bill",
    data=tips,
    kind="box",
    sym="")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Changing the whiskers using `whis`
    By default, the whiskers extend to 1.5 * the interquartile range

    Make them extend to 2.0 * IQR: whis=2.0

    Show the 5th and 95th percentiles: whis=[5, 95]

    Show min and max values: whis=[0, 100]

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Changing the whiskers using `whis`
    import matplotlib.pyplot as plt
    import seaborn as sns

    g = sns.catplot(x="time",
    y="total_bill",
    data=tips,
    kind="box",
    whis=[0, 100])

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Let's practice!
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N
    Point plots
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N

    Erin Case
    Data Scientist
    What are point plots?
    Points show mean of quantitative variable

    Vertical lines show 95% con dence intervals

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Line plot: average level of nitrogen dioxide over Point plot: average restaurant bill, smokers vs.
    time non-smokers

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Point plots vs. line plots
    Both show:

    Mean of quantitative variable

    95% con dence intervals for the mean

    Differences:

    Line plot has quantitative variable (usually time) on x-axis

    Point plot has categorical variable on x-axis

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Point plots vs. bar plots
    Both show:

    Mean of quantitative variable

    95% con dence intervals for the mean

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Point plots vs. bar plots

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Creating a point plot
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="age",
    y="masculinity_important",
    data=masculinity_data,
    hue="feel_masculine",
    kind="point")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Disconnecting the points
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="age",
    y="masculinity_important",
    data=masculinity_data,
    hue="feel_masculine",
    kind="point",
    join=False)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Displaying the median
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="smoker",
    y="total_bill",
    data=tips,
    kind="point")

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Displaying the median
    import matplotlib.pyplot as plt
    import seaborn as sns
    from numpy import median

    sns.catplot(x="smoker",
    y="total_bill",
    data=tips,
    kind="point",
    estimator=median)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Customizing the con dence intervals
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="smoker",
    y="total_bill",
    data=tips,
    kind="point",
    capsize=0.2)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Turning off con dence intervals
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.catplot(x="smoker",
    y="total_bill",
    data=tips,
    kind="point",
    ci=None)

    plt.show()

    INTRODUCTION TO DATA VISUALIZATION WITH SEABORN


    Let's practice!
    I N T R O D U C T I O N TO D ATA V I S U A L I Z AT I O N W I T H S E A B O R N

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