REF: use base class for BooleanArray any/all (#44549)

Author: mzeitlin11

pandas/core/arrays/boolean.py

@@ -20,7 +20,6 @@
     npt,
     type_t,
 )
-from pandas.compat.numpy import function as nv
 
 from pandas.core.dtypes.common import (
     is_bool_dtype,
@@ -445,144 +444,6 @@ def _values_for_argsort(self) -> np.ndarray:
         data[self._mask] = -1
         return data
 
-    def any(self, *, skipna: bool = True, axis: int | None = 0, **kwargs):
-        """
-        Return whether any element is True.
-
-        Returns False unless there is at least one element that is True.
-        By default, NAs are skipped. If ``skipna=False`` is specified and
-        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
-        is used as for logical operations.
-
-        Parameters
-        ----------
-        skipna : bool, default True
-            Exclude NA values. If the entire array is NA and `skipna` is
-            True, then the result will be False, as for an empty array.
-            If `skipna` is False, the result will still be True if there is
-            at least one element that is True, otherwise NA will be returned
-            if there are NA's present.
-        axis : int or None, default 0
-        **kwargs : any, default None
-            Additional keywords have no effect but might be accepted for
-            compatibility with NumPy.
-
-        Returns
-        -------
-        bool or :attr:`pandas.NA`
-
-        See Also
-        --------
-        numpy.any : Numpy version of this method.
-        BooleanArray.all : Return whether all elements are True.
-
-        Examples
-        --------
-        The result indicates whether any element is True (and by default
-        skips NAs):
-
-        >>> pd.array([True, False, True]).any()
-        True
-        >>> pd.array([True, False, pd.NA]).any()
-        True
-        >>> pd.array([False, False, pd.NA]).any()
-        False
-        >>> pd.array([], dtype="boolean").any()
-        False
-        >>> pd.array([pd.NA], dtype="boolean").any()
-        False
-
-        With ``skipna=False``, the result can be NA if this is logically
-        required (whether ``pd.NA`` is True or False influences the result):
-
-        >>> pd.array([True, False, pd.NA]).any(skipna=False)
-        True
-        >>> pd.array([False, False, pd.NA]).any(skipna=False)
-        <NA>
-        """
-        kwargs.pop("axis", None)
-        nv.validate_any((), kwargs)
-
-        values = self._data.copy()
-        np.putmask(values, self._mask, False)
-        result = values.any(axis=axis)
-
-        if skipna:
-            return result
-        else:
-            if result or self.size == 0 or not self._mask.any():
-                return result
-            else:
-                return self.dtype.na_value
-
-    def all(self, *, skipna: bool = True, axis: int | None = 0, **kwargs):
-        """
-        Return whether all elements are True.
-
-        Returns True unless there is at least one element that is False.
-        By default, NAs are skipped. If ``skipna=False`` is specified and
-        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
-        is used as for logical operations.
-
-        Parameters
-        ----------
-        skipna : bool, default True
-            Exclude NA values. If the entire array is NA and `skipna` is
-            True, then the result will be True, as for an empty array.
-            If `skipna` is False, the result will still be False if there is
-            at least one element that is False, otherwise NA will be returned
-            if there are NA's present.
-        axis : int or None, default 0
-        **kwargs : any, default None
-            Additional keywords have no effect but might be accepted for
-            compatibility with NumPy.
-
-        Returns
-        -------
-        bool or :attr:`pandas.NA`
-
-        See Also
-        --------
-        numpy.all : Numpy version of this method.
-        BooleanArray.any : Return whether any element is True.
-
-        Examples
-        --------
-        The result indicates whether any element is True (and by default
-        skips NAs):
-
-        >>> pd.array([True, True, pd.NA]).all()
-        True
-        >>> pd.array([True, False, pd.NA]).all()
-        False
-        >>> pd.array([], dtype="boolean").all()
-        True
-        >>> pd.array([pd.NA], dtype="boolean").all()
-        True
-
-        With ``skipna=False``, the result can be NA if this is logically
-        required (whether ``pd.NA`` is True or False influences the result):
-
-        >>> pd.array([True, True, pd.NA]).all(skipna=False)
-        <NA>
-        >>> pd.array([True, False, pd.NA]).all(skipna=False)
-        False
-        """
-        kwargs.pop("axis", None)
-        nv.validate_all((), kwargs)
-
-        values = self._data.copy()
-        np.putmask(values, self._mask, True)
-        result = values.all(axis=axis)
-
-        if skipna:
-            return result
-        else:
-            if not result or self.size == 0 or not self._mask.any():
-                return result
-            else:
-                return self.dtype.na_value
-
     def _logical_method(self, other, op):
 
         assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}