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name: special-data-types description: "Use Lux semantic data types, temporal and geographic handling, ID suppression, and index/grouped dataframe recommendations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Lux Special Data Types

Use this sub-skill when a Lux workflow depends on the semantic meaning of columns rather than only Pandas dtypes: inferred or overridden data types, temporal columns, geographic columns, identifier suppression, named indexes, and grouped or crosstabbed dataframes.

Route here for

• Inspecting df.data_type and correcting Lux semantic types with df.set_data_type(...).
• Explaining quantitative, nominal, geographical, temporal, and id columns.
• Fixing date-like columns so Lux uses temporal line charts and time-aware recommendations.
• Preparing state or country columns for geographic recommendations.
• Understanding why ID-like fields are not visualized.
• Working with named row or column indexes, groupby aggregations, pivots, and crosstabs that produce Row Groups or Column Groups recommendations.
• Diagnosing empty, very small, or hierarchical-index dataframe messages.

Route elsewhere

• For general Pandas import order, df.intent, recommendation keys, and default/intent action behavior, use pandas-intent-recommendations.
• For Clause, Vis, VisList, chart marks, Altair/Matplotlib/Vega-Lite export, or exported code, use visualization-export.
• For global Lux config, widget setup, plotting backend/style, or custom recommendation actions, use configuration-actions.
• For PostgreSQL-backed LuxSQLTable workflows, use sql-backend.

Operating references

• Read references/data-types.md for the self-contained data-type, temporal, geographic, ID, and index/group workflow guide.
• Read references/troubleshooting.md when Lux gives a warning/message or when expected recommendations are missing.
• Run scripts/data_type_smoke.py --help or scripts/data_type_smoke.py in an environment with lux-api installed to verify the core offline data-type behaviors on tiny fixtures.

Minimal workflow reminder

1. Import Lux before creating Pandas dataframes: import lux; import pandas as pd.
2. Inspect df.data_type after constructing or mutating a dataframe.
3. Convert real temporal columns with pd.to_datetime(...); if a pre-existing Vis was built from the old source, refresh it with vis.refresh_source(df).
4. Override semantic mistakes with df.set_data_type({"column": "nominal"}), using only Lux's accepted override values.
5. Flatten unsupported hierarchical indexes with df.reset_index() before asking Lux for recommendations.