def transform(self, X, y=None):
        """Transform the columns in ``X`` according to ``self.categories_``.

        Parameters
        ----------
        X : pandas.DataFrame or dask.DataFrame
        y : ignored

        Returns
        -------
        X_trn : pandas.DataFrame or dask.DataFrame
            Same type as the input. The columns in ``self.categories_`` will
            be converted to categorical dtype.
        """
        check_is_fitted(self, "categories_")
        X = self._check_array(X).copy()
        categories = self.categories_

        for k, dtype in categories.items():
            if _HAS_CTD:
                if not isinstance(dtype, pd.api.types.CategoricalDtype):
                    dtype = pd.api.types.CategoricalDtype(*dtype)
                X[k] = X[k].astype(dtype)
            else:
                cat, ordered = dtype
                X[k] = X[k].astype("category").cat.set_categories(cat, ordered)

        return X
def transform ( self , X , y = None ) : check_is_fitted ( self , "categories_" ) X = self . _check_array ( X ) . copy ( ) categories = self . categories_ for k , dtype in categories . items ( ) : if _HAS_CTD : if not isinstance ( dtype , pd . api . types . CategoricalDtype ) : dtype = pd . api . types . CategoricalDtype ( * dtype ) X [ k ] = X [ k ] . astype ( dtype ) else : cat , ordered = dtype X [ k ] = X [ k ] . astype ( "category" ) . cat . set_categories ( cat , ordered ) return X
Transform the columns in ``X`` according to ``self.categories_``.

        Parameters
        ----------
        X : pandas.DataFrame or dask.DataFrame
        y : ignored

        Returns
        -------
        X_trn : pandas.DataFrame or dask.DataFrame
            Same type as the input. The columns in ``self.categories_`` will
            be converted to categorical dtype.