---

name: data-utilities description: "Use seaborn data utilities and data-shape contracts for long-form, wide-form, vector, semantic mapping, and example dataset workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Data Utilities

Use this sub-skill when a task is blocked by seaborn data shape, variable mapping, load_dataset, SEABORN_DATA, example data caching, or validating a DataFrame before plotting.

Route Here For

• Long-form/tidy versus wide-form plotting decisions.
• Direct vector/list/array/dict inputs and when named variables require data=.
• Semantic mappings: hue, size, style, row, col, units, weights.
• load_dataset, get_dataset_names, get_data_home, cache directories, network requirements, and example-data misuse.
• Diagnosing missing columns, null-heavy variables, numeric/categorical axis confusion, and heatmap mask shape.
• Creating synthetic no-network data for reproducible examples.

Use Another Sub-skill For

• Plot-function parameters after data is valid: ../function-interface/SKILL.md.
• Grid layout/legend/axes access: ../figure-grids/SKILL.md.
• Palette/theme selection: ../themes-palettes/SKILL.md.

Start With

1. Inspect input object type and columns.
2. Decide long-form, wide-form, or vector mode.
3. Validate every named semantic variable exists in data.
4. Check numeric requirements for x/y or matrix data.
5. Avoid network-backed load_dataset unless examples or bug reports explicitly require seaborn sample data.

References

• Data format guide: references/data-formats.md.
• Dataset utility behavior: references/dataset-utilities.md.
• Failure recovery: references/troubleshooting.md.
• Shared data semantics: ../../references/data-semantics.md.

Quick Preflight

python sub-skills/data-utilities/scripts/validate_plot_data.py --csv data.csv --x x_col --y y_col --hue group_col

Use --demo to validate the script's built-in synthetic data without a CSV.