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name: seaborn description: "Use seaborn for statistical data visualization with the function API, objects interface, grids, themes, palettes, dataset utilities, and plotting troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

seaborn

Use this repo skill when a task involves seaborn, import seaborn as sns, seaborn.objects, statistical graphics, tidy/wide-form plotting, figure-level grids, themes, palettes, example datasets, or seaborn-specific errors around matplotlib, pandas, SciPy, or statsmodels.

Seaborn is a pure-Python statistical visualization library built on matplotlib and designed for pandas/NumPy-style data. It provides two user-facing APIs: the classic function interface (sns.scatterplot, sns.catplot, sns.clustermap, etc.) and the declarative seaborn.objects interface.

Start Here

1. Confirm seaborn is installed in the active Python environment with the root check below.
2. Identify whether the user wants a classic function plot, declarative objects composition, multi-plot grid layout, aesthetics/palette work, or data-shape/dataset troubleshooting.
3. Route to the narrow sub-skill and use linked references/scripts there; keep this root file as a router.
4. Use synthetic or user-provided data for reusable examples. Treat sns.load_dataset() as a network-backed convenience, not a requirement for normal plotting.
5. Read references/troubleshooting.md for cross-cutting install/import, display, optional dependency, and data-cache failures.

Install And Import Check

For normal package use:

python -m pip install seaborn
python - <<'PY'
import seaborn as sns
import matplotlib
print('seaborn', sns.__version__)
print('matplotlib backend', matplotlib.get_backend())
PY

Optional statistical features use the documented stats extra:

python -m pip install 'seaborn[stats]'

Run the bundled diagnostic before debugging a local environment:

python scripts/check_seaborn_environment.py

The check imports seaborn, validates mandatory dependencies, reports optional SciPy/statsmodels/ipywidgets/fastcluster availability, and renders tiny noninteractive plots with the Agg backend when requested.

Sub-skill Routes

• function-interface: classic seaborn plotting functions for relational, distribution, categorical, regression, and matrix plots; axes-level versus figure-level function choice; optional SciPy/statsmodels behavior; no-network workflow recipes.
• objects-interface: declarative seaborn.objects workflows with Plot, marks, stats, moves, scales, layering, faceting, theme/label/scale methods, and save/show behavior.
• figure-grids: FacetGrid, PairGrid, JointGrid, pairplot, jointplot, figure-level return objects, legend movement, axes access, layout sizing, and matplotlib customization.
• themes-palettes: set_theme, styles, contexts, palettes, colormaps, color utilities, dark mode, and optional interactive palette widgets.
• data-utilities: long-form/wide-form/vector data contracts, semantic mappings, example dataset cache/network behavior, load_dataset, SEABORN_DATA, and data validation helpers.

Common Workflow Routing

• Exploratory tabular visualization: start with data-utilities for data shape, then function-interface for plot families, then themes-palettes for presentation style.
• Multi-panel statistical figure: use function-interface for figure-level functions (relplot, displot, catplot, lmplot) or figure-grids when direct grid programming/custom layout is needed.
• Layered declarative chart: use objects-interface; cross-route to themes-palettes for reusable theme/palette decisions.
• Publication polish: use figure-grids for figure size/axes/legends and themes-palettes for context/style/palette choices.
• Dataset/cache or data-shape errors: use data-utilities, then return to the plot-owning sub-skill once the input contract is clear.

Shared References And Scripts

• Package-wide API map and return-object distinctions: references/api-summary.md.
• Shared data/semantic conventions: references/data-semantics.md.
• Cross-cutting failure recovery: references/troubleshooting.md.
• Repository snapshot and refresh baseline: references/repo-provenance.md.
• Router import metadata: references/repo-routing-metadata.json.
• Root environment diagnostic: scripts/check_seaborn_environment.py.

Boundaries

• Seaborn creates matplotlib figures and artists; use matplotlib APIs for very deep artist-level customization after seaborn returns an Axes, Grid, or Plotter object.
• Seaborn is not a statistical modeling package. Use SciPy/statsmodels/sklearn/pandas for extracting model results or statistics beyond what seaborn visualizes.
• Do not make runtime guidance depend on original repository examples, docs, notebooks, tests, or source paths. Use the bundled references/scripts in this skill.