domains / families / data / seaborn

seaborn

combined

Creates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling. Statistical visualization library integrated with pandas; use it when you need fast EDA of distributions, relationships, and categorical comparisons (e.g., box/violin/pair plots and heatmaps) with strong default aesthetics on top of matplotlib.

When to use

• Exploring relationships between variables in a DataFrame (e.g., scatter/line plots with `hue`, `size`, `style`).
• Comparing distributions across categories (e.g., box/violin/swarm plots for groups).
• Inspecting univariate/bivariate distributions (histograms, KDE, ECDF; joint and pairwise views).
• Visualizing correlation matrices or other rectangular data (heatmaps, clustered heatmaps).
• Building faceted "small multiples" quickly (split by `row`/`col` using figure-level APIs).

When not to use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Skill page

Conflict

The K-Dense-AI copy is the most complete and current, containing specific version constraints (Seaborn 0.13.2), installation instructions, and critical API migration notes (e.g., `errorbar` replacing `ci`, `native_scale` for categorical plots) that the other copies lack. The other copies (zLanqing, foryourhealth111, aipoch) provide a more general, version-agnostic reference that is useful for understanding the library's structure but lacks the specific operational details required for a robust agent skill. Combining them ensures the agent has both the high-level conceptual map and the specific, version-aware implementation details.