Kernel density estimation (KDE) is used to plot univariate or bivariate distributions to observe the distribution of observations in a dataset, analogous to a histogram. KDE helps us to understand the data using a continuous probability density curve.
iris = sns.load_dataset("iris")
sns.kdeplot(data=iris)
To learn data visualization using Python, you can check out this Python Training Course by Intellipaat.
Also, you can watch this video on Python using Seaborn to know more:
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