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doc/data_interoperability.rst
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Christian Lorentzen
DOC data interoperability and pandas/polars output for transformers (#33788)
06 май 2026, 18:27
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06 май 2026, 18:27
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===================== Data Interoperability ===================== .. currentmodule:: sklearn Scikit-learn handles four kinds of data for :term:`X` as used in `fit(X, y)`, `fit(X)`, `fit_transform(X)` and `transform(X)` as well as :term:`Xt` as returned by `transform(X)` and `fit_transform(X)`: - :term:`array-like` objects In `fit(X)` and `transform(X)`, array-like `X` is converted to a numpy ndarray by calling `numpy.asarray` upon them. The returned `Xt` of `transform` and `fit_transform` is also a numpy ndarray or it is a sparse matrix or sparse array, see next bullet. - :term:`sparse matrices <sparse matrix>` and sparse arrays Many estimators can deal with sparse `X`, some cannot and will raise an error. For instance, :class:`linear_model.LogisticRegression` can be fit on sparse `X`, :class:`isotonic.IsotonicRegression` can not. Some transformers return sparse `Xt` from `transform` and `fit_transform`. Most often, it can be controlled by a `sparse_output` parameter as in :class:`preprocessing.SplineTransformer`. To control whether it returns a sparse matrix or a sparse array, use `sparse_interface` in :func:`config_context` or :func:`set_config`. This also controls whether sparse attributes are sparse matrices or sparse arrays. - tabular data: pandas and polars dataframes See :ref:`df_output_transform`. - Array API compliant arrays Very importantly, this includes arrays on the GPU, see :ref:`array_api`. .. toctree:: :maxdepth: 2 modules/df_output_transform modules/array_api