---

name: pandas-intent-recommendations description: "Use Lux's Pandas-integrated DataFrame and Series intent workflow to generate and inspect recommendations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pandas Intent Recommendations

Use this sub-skill when the task is about Lux's Pandas integration: creating LuxDataFrame or LuxSeries objects through Pandas, setting or clearing dataframe intent, inspecting recommendation tabs, understanding current_vis and exported, or preserving Pandas behavior while using Lux recommendations.

Route here for

• Import order and Pandas monkeypatch behavior for pd.DataFrame, pd.Series, and pd.read_* loaders.
• LuxDataFrame and LuxSeries basics, including to_pandas() and recommendation access.
• df.intent, df.set_intent(...), df.clear_intent(), and df.set_intent_as_vis(...) workflows.
• Programmatic inspection of df.recommendation, df.current_vis, and the basic meaning of df.exported.
• Default recommendation tabs and intent-specific tabs.
• Local dataframe caveats for head(), tail(), groupby(), stale recommendations, and offline smoke validation.

Route away

• Detailed Clause, Vis, VisList, wildcard enumeration, or chart code export: use visualization-export.
• lux.config, plotting backend/style, custom action registration, sampling, or widget setup: use configuration-actions.
• Semantic data type fixes, temporal/geographic/id behavior, index/group recommendations, or hierarchical-index repair: use special-data-types.
• PostgreSQL, LuxSQLTable, JoinedSQLTable, or SQL executor behavior: use sql-backend.

Operating checklist

1. Import lux before creating or loading the dataframe that should receive Lux behavior.
2. Confirm the object is a LuxDataFrame or LuxSeries before using Lux-only attributes.
3. If no intent is set, inspect default recommendation tabs through df.recommendation or by displaying the dataframe in a supported notebook frontend.
4. Set intent with a list, not a bare string. Use attribute names such as "sales", filters such as "region=West", or a small number of lux.Clause objects for constraints.
5. Treat df.current_vis as the compiled visualization(s) for the intent and df.recommendation as the next-step recommendation dictionary.
6. If recommendations appear stale after dataframe mutation, call df.expire_recs(); if data types or metadata changed, also call df.expire_metadata().
7. For non-notebook validation, run the bundled offline smoke script in scripts/intent_recommendation_smoke.py.

Bundled references

• references/workflows.md — recommended Pandas + Lux workflows and caveats.
• references/api-reference.md — concise API and behavior reference for this sub-skill.
• references/troubleshooting.md — symptom-to-fix guide for import, intent, recommendation, and export-selection issues.