Seaborn Themeing - Introduction



The main aim while producing a data visualization is to convey the data's insights. While styling affects how your audience understands what you're trying to say, visualizing conveys crucial information.

The final step of data visualization is styling, which comes after formatting and visualizing your data. Customizing the overall appearance of your visualization or figure is the process of styling. Making deliberate choices will enhance the impact of the visualization and distinguish your work. Using backdrop colors, grids, spines, and ticks, we'll look at how to change the overall appearance of your figure in the following articles.

There are many theming methods available in Seaborn. They are named below.

S.No Method and Description
1 set_theme()

Sets the visual theme components for all seaborn and matplotlib plots.

2 axes_styles()

Obtain the settings that govern the plots' overall look.

3 set_style()

Sets the parameters that control the general style of the plots.

4 plotting_context()

Gets the parameters that control the scaling of plot elements.

5 set_context()

Sets the parameters that control the scaling of plot elements.

6 set_color_codes()

Changes how matplotlib color shorthands are interpreted.

7 reset_default()

Restores all RC params to default settings.

8 reset_orig()

Restores all RC params to original settings

9 set()

It is another name for set_theme() which is the preferred interface.

seaborn_function_reference.htm
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