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fix(preprocessor): handle pandas categorical missing values - #74

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Boulea7:ln-cx/pretab-pandas-missing-values
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Boulea7 wants to merge 1 commit into
OpenTabular:mainfrom
Boulea7:ln-cx/pretab-pandas-missing-values

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@Boulea7 Boulea7 commented Oct 2, 2026

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Fixes #64.

Treat None and pd.NA consistently with NaN during default categorical imputation and automatic one-hot encoding. Ordinal encoding maps missing values to zero, while raw passthrough preserves them without crashing the output report. Explicit imputer, category and drop settings retain their existing semantics.

Validation: 1,479 core tests passed with locked dependencies and with minimum NumPy/pandas/scikit-learn/SciPy versions; locked branch coverage is 92.66%. Ruff and Pyright pass.

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None / pd.NA categorical missing values are not treated as missing (nullable dtypes crash, None becomes a category)

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