Seaborn

Seaborn is a Python data visualization library based on Matplotlib, created by Michael Waskom. It provides a high-level interface for drawing attractive and informative statistical graphics, specifically designed for visualizing complex datasets with minimal code. While Matplotlib provides fine-grained control over every aspect of a plot, it requires significant boilerplate code and manual configuration to produce aesthetically pleasing results. Seaborn addresses this by providing sensible default styles, color palettes, and plot types that are specifically tailored for statistical data exploration. The library integrates closely with pandas DataFrames, allowing plots to be created by simply specifying column names rather than manually extracting and formatting data arrays. Key plot types include: relational plots (relplot for scatter and line plots, with automatic hue, size, and style mapping for categorical variables), distribution plots (histplot for histograms, kdeplot for kernel density estimates, ecdfplot for empirical cumulative distribution functions, and rugplot for marginal tick marks), categorical plots (barplot, countplot, boxplot, violinplot, stripplot, swarmplot, pointplot, and boxenplot), regression plots (regplot and lmplot for fitting and visualizing linear regression models with confidence intervals), matrix plots (heatmap and clustermap for visualizing matrix data with hierarchical clustering), multi-plot grids (FacetGrid for creating grids of subplots conditioned on categorical variables, PairGrid for pairwise relationships between multiple variables, and JointGrid for bivariate distributions with marginal univariate distributions), and built-in theme management (five preset themes: darkgrid, whitegrid, dark, white, and ticks, and color palette management with sequential, diverging, and qualitative palettes). As of 2026, Seaborn has over 13,000 stars.