seaborn

Topics in the source

• Seaborn Statistical Visualization
• Overview
• Design Philosophy
• Quick Start
• Core Plotting Interfaces
• Function Interface (Traditional)
• Objects Interface (Modern)
• Plotting Functions by Category

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code. Seaborn follows these core principles: 1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates 2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles) 3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals 4. Aesthetic defaults: Publication-ready themes and color palettes out of the box 5. Matplotlib integration: Full compatibility with matplotlib customization when needed The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting). When to use: Quick exploratory analysis Single-purpose visualizations When you need a specific plot type The seaborn.objects interface provides a declarative, composable API similar to ggplot2. Build visualizations by c

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