Seaborn is a powerful data visualization library in Python. Built on top of matplotlib, it offers a high-level, more comfortable interface for creating beautiful, statistical graphics. In particular, Seaborn provides excellent support for complex visualization tasks that involve multiple interrelated plots or producing informative plots with just a few commands.

Here are some key features of Seaborn:

1. Built-in Themes: Seaborn comes with a number of customized themes and a high-level interface for controlling the appearance of matplotlib figures.

2. Dataset-oriented plotting: Seaborn is particularly good at visualizing complex datasets with multiple variables. For example, you can easily create a scatterplot matrix to explore relationships between pairs of variables in your data.

3. Statistical Estimation and Error Bars: Seaborn integrates well with pandas data structures and can also automatically perform statistical estimation to add error bars to your plots.

4. Facet Grids: Seaborn’s FacetGrid allows you to create a matrix of plots that share the same x and y axes, which is extremely useful for exploring interactions between multiple variables.

5. Advanced Plots: Seaborn supports advanced plot types like violin plots, KDE plots, box plots, and pair plots.

Here’s a basic example of how you might use Seaborn:

In this code, we’re first importing seaborn and matplotlib.pyplot. We then load one of Seaborn’s built-in datasets, the Iris dataset, which is a classic dataset in machine learning and statistics. It contains measurements for 150 iris flowers from three different species.

We then create a pairplot, which is a grid of scatterplots, with one scatterplot for each pair of features in the data. The hue parameter is used to color the points by the ‘species’ column. This can give us a quick, visual way to see how different features may distinguish between the species of flower.

Finally, we use matplotlib’s show function to display the plot. Without this line, the plot would be created but not displayed.