Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Explore a gallery of examples showcasing various features and functionalities of the seaborn library for data visualization.

User guide and tutorial # An introduction to seaborn A high-level API for statistical graphics Multivariate views on complex datasets Opinionated defaults and flexible customization

An introduction to seaborn # Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures. Seaborn helps you explore and …

API reference # Objects interface # Plot object # Mark objects # Dot marks

The seaborn codebase is pure Python, and the library should generally install without issue. Occasionally, difficulties will arise because the dependencies include compiled code and link to …

Most of your interactions with seaborn will happen through a set of plotting functions. Later chapters in the tutorial will explore the specific features offered by each function.

seaborn.lineplot(data=None, *, x=None, y=None, hue=None, size=None, style=None, units=None, weights=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, …

There are several ways to draw a scatter plot in seaborn. The most basic, which should be used when both variables are numeric, is the scatterplot() function. In the categorical visualization tutorial, we …

Distribution visualization in other settings # Several other figure-level plotting functions in seaborn make use of the histplot() and kdeplot() functions. Plotting joint and marginal distributions # The first is …