Method transform

sklearn/preprocessing/_encoders.py:1584–1614

Transform X to ordinal codes. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to encode. Returns ------- X_out : ndarray of shape (n_samples, n_features) Transformed input.

(self, X)

        return self

    def transform(self, X):
        """
        Transform X to ordinal codes.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data to encode.

        Returns
        -------
        X_out : ndarray of shape (n_samples, n_features)
            Transformed input.
        """
        check_is_fitted(self, "categories_")
        X_int, X_mask = self._transform(
            X,
            handle_unknown=self.handle_unknown,
            ensure_all_finite="allow-nan",
            ignore_category_indices=self._missing_indices,
        )
        X_trans = X_int.astype(self.dtype, copy=False)

        for cat_idx, missing_idx in self._missing_indices.items():
            X_missing_mask = X_int[:, cat_idx] == missing_idx
            X_trans[X_missing_mask, cat_idx] = self.encoded_missing_value

        # create separate category for unknown values
        if self.handle_unknown == "use_encoded_value":
            X_trans[~X_mask] = self.unknown_value
        return X_trans

    def inverse_transform(self, X):
        """