How to visualize KDE Plot using Seaborn?

We learn the usage of some parameters through some specific examples:

Importing Libraries

First import the corresponding library

Draw a simple one-dimensional kde image:

Let’s see the Kde of our variable x-axis and y-axis, so let pass the x variable into the kdeplot() methods.

Output:

Then after check for y-axis.

Output:

Use Shade to fill the area covered by curve:

We can highlight the plot using shade to the area covered by the curve. If True, shadow processing is performed in the area below the kde curve, and color controls the color of the curve and shadow.

Output:

You can change the Shade color with color attributes:

Output:

Use Vertical to draw indicates whether to draw on the X axis or on the Y axis

Output:

Bivariate Kdeplot for two variables: 

Simple pass the two variables into the seaborn.kdeplot() methods.

Output:

Shade the area covered by a curve with shade attributes:

Output:

Now you can change the color with cmap attributes:

Output:

Use of Cbar: If True, add a colorbar to annotate the color mapping in a bivariate plot. Note: Does not currently support plots with a hue variable well.

Output:

Kernel Density Estimate (KDE) Plot is a powerful tool for estimating the probability density function of continuous or non-parametric data. KDE plot is implemented through the kdeplot function in Seaborn. This article explores the syntax and usage of kdeplot in Python, focusing on one-dimensional and bivariate scenarios for efficient data visualization.