Understanding KDE Plots and Strip Plots

Before aligning these plots is undertaken, let’s briefly understand what they are and how they are used.

• KDE Plots: The underlying distribution of a dataset is visualized by a KDE plot, also known as a kernel density estimate plot, which is a statistical method. The data is smoothed, and patterns and trends that might be obscured by a histogram are identified.
• Strip Plots: The distribution of a single variable across different categories is displayed by a strip plot, which is a type of categorical plot. The relationship between a categorical variable and a continuous variable is visualized.

A high-level interface for creating attractive and informative statistical graphics is offered by a powerful python library Seaborn. One of the most common tasks in data visualization is aligning different types of plots in one graph to gain insights into the data.

In this article, we will understand and implement how to align a KDE (Kernel Density Estimate) plot with a strip plot in seaborn.