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    The sns.kdeplot function in Seaborn is used to plot Kernel Density Estimation (KDE), which is a method for visualizing the distribution of observations in a dataset. It represents the data using a continuous probability density curve in one or more dimensions.

    Example: Univariate KDE Plot

    import seaborn as sns
    import matplotlib.pyplot as plt

    # Load dataset
    tips = sns.load_dataset("tips")

    # Create KDE plot
    sns.kdeplot(data=tips, x="total_bill")
    plt.show()

    Example: Bivariate KDE Plot

    import seaborn as sns
    import matplotlib.pyplot as plt

    # Load dataset
    geyser = sns.load_dataset("geyser")

    # Create bivariate KDE plot
    sns.kdeplot(data=geyser, x="waiting", y="duration")
    plt.show()

    Important Considerations

    • Smoothing Parameter: The bandwidth, or standard deviation of the smoothing kernel, is crucial. An over-smoothed curve can erase true features, while an under-smoothed curve can create false features.

    • Data Boundaries: The KDE curve can extend to values that do not make sense for a particular dataset. Use the cut and clip parameters to control the extent of the curve.

    Alternative Solutions

    • Histogram: For discrete or "spiky" data, a histogram might be more appropriate.

    • ECDF Plot: For cumulative distribution functions, consider using an ECDF plot.

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    Seaborn Kdeplot – A Comprehensive Guide - GeeksforGeeks
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