Seaborn

Seaborn is a Python data visualization library built on top of matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics and is designed to work well with pandas DataFrames. Seaborn emphasizes visualizing statistical relationships and distributions, offering sensible defaults and color schemes that improve readability without requiring extensive customization.

It offers a range of plotting functions, including relplot and catplot, which are figure-level interfaces that generate grid-based plots, as well as specific plots such as scatterplot, lineplot, barplot, boxplot, violinplot, swarmplot, pointplot, histplot, kdeplot, and ecdfplot. It also supports multivariate visuals through pairplot and jointplot and provides grid tools like FacetGrid and PairGrid to facet plots by categorical variables. The API integrates with pandas data structures and handles missing values in a manner consistent with typical data analysis workflows.

Seaborn provides built-in themes and color palettes to improve aesthetic appeal and readability; set_theme configures style and context, and color_palette selects color schemes including deep, muted, bright, and options from ColorBrewer and Matplotlib. The library can load sample datasets via load_dataset for tutorials, examples, and demonstrations. It is commonly used for exploratory data analysis, statistical visualization, and publication-ready graphics in Python data science workflows.

Seaborn is an open-source project that originated in the early 2010s, primarily developed by Michael Waskom and contributors. It is released under the BSD license and continues to be maintained and expanded as part of the scientific Python ecosystem, integrating with NumPy, SciPy, and pandas to support data-driven visualization.