> ``displot()`` = ``FacetGrid()``(we will study this plot later) + ``histplot()``, ``kdeplot()``, ``ecdfplot()``

The ``displot()`` provides access to several approaches for visualizing the univariate or bivariate distribution of data, including subsets of data defined by semantic mapping and faceting across multiple subplots. 

The kind parameter selects the approach to use: ``histplot()``, ``kdeplot()``, ``ecdfplot()``.

> Some of the **cool** plots allow us to build some plots simultaneously. But it is only for some cool plots! Later we will learn how to create any number of plots simultaneously ( not only for the displot, catplot, etc. ).

In this section, we will perform preparing tasks for the work!

In this section, we will study plots used for data distributions!

It is time to say 'hello' to categorical plot types!

Colorful matrix plots are waiting for you!

These fantastic plots consist of other plots. Let's start this section!

Let's learn plots that are used for regression models!