Understanding .map() and .map_dataframe()

Before delving into the practical examples, it’s essential to understand the core concepts behind .map and .map_dataframe methods in Seaborn.

• .map(): This method allows us to apply custom functions to elements of a Seaborn plot. It operates on each element of the plot, facilitating fine-grained customization based on specific data attributes.
• .map_dataframe(): Similar to .map, this method applies custom functions to elements of a Seaborn plot. However, it operates on the entire DataFrame rather than individual elements, enabling comprehensive data-driven customization.

Seaborn’s Object Interface is a more adaptable and versatile method of generating advanced and personalized visualizations in comparison to its traditional  API. It takes use of the object-oriented programming paradigm, enabling users to construct visualizations by manipulating and altering objects directly. The resulting interface gives you more control over the aspects of a plot, making it simpler to construct complex and elaborate representations.

Seaborn, a powerful data visualization library built on top of Matplotlib, offers a convenient Object Interface for creating stunning visualizations with ease. Using .map() and .map_dataframe() with Seaborn’s object-oriented interface allows for applying custom functions to plot data.

In this article, we will implement these methods and explore how they can be leveraged to enhance data visualization capabilities.