seaborn

What problem does it solve?

Seaborn removes repetitive plotting boilerplate and provides statistical, dataset-oriented defaults so users can quickly produce informative, publication-ready visualizations from pandas DataFrames without manual matplotlib styling.

Core Features & Use Cases

• Dataset-first API: map DataFrame columns to visual properties (hue, size, style) for immediate semantic encodings.
• Comprehensive plot types: relational (scatter, line), distributional (hist, kde, pairplot), categorical (box, violin, swarm), regression tools (regplot, lmplot), and matrix visualizations (heatmap, clustermap).
• Figure-level faceting and axes-level control: build faceted small-multiples with relplot/catplot or compose custom multi-panel figures with matplotlib integration.
• Modern objects interface: declarative, composable plotting for layered and programmatic visualizations.
• Use case: exploratory data analysis, model diagnostics, feature correlation exploration, and generating publication figures for academic or business reports.

Quick Start

Create a scatterplot of total_bill vs tip colored by day with seaborn and save the figure as figure.png.