ENH: nullables use Kleene logic for any/all reductions

doc/source/whatsnew/v1.4.0.rst

@@ -105,6 +105,8 @@ Other enhancements
 - :meth:`read_table` now supports the argument ``storage_options`` (:issue:`39167`)
 - Methods that relied on hashmap based algos such as :meth:`DataFrameGroupBy.value_counts`, :meth:`DataFrameGroupBy.count` and :func:`factorize` ignored imaginary component for complex numbers (:issue:`17927`)
 - Add :meth:`Series.str.removeprefix` and :meth:`Series.str.removesuffix` introduced in Python 3.9 to remove pre-/suffixes from string-type :class:`Series` (:issue:`36944`)
+- :meth:`IntegerArray.all` , :meth:`IntegerArray.any`, :meth:`FloatingArray.any`, and :meth:`FloatingArray.all` use Kleene logic (:issue:`41967`)
+-
 
 .. ---------------------------------------------------------------------------
 

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,
@@ -297,6 +296,9 @@ class BooleanArray(BaseMaskedArray):
 
     # The value used to fill '_data' to avoid upcasting
     _internal_fill_value = False
+    # Fill values used for any/all
+    _truthy_value = True
+    _falsey_value = False
     _TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
     _FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}
 
@@ -477,141 +479,6 @@ def _values_for_argsort(self) -> np.ndarray:
         data[self._mask] = -1
         return data
 
-    def any(self, *, skipna: bool = True, **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.
-        **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()
-        if skipna:
-            return result
-        else:
-            if result or len(self) == 0 or not self._mask.any():
-                return result
-            else:
-                return self.dtype.na_value
-
-    def all(self, *, skipna: bool = True, **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.
-        **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()
-
-        if skipna:
-            return result
-        else:
-            if not result or len(self) == 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"}
@@ -740,13 +607,6 @@ def _arith_method(self, other, op):
 
         return self._maybe_mask_result(result, mask, other, op_name)
 
-    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
-
-        if name in {"any", "all"}:
-            return getattr(self, name)(skipna=skipna, **kwargs)
-
-        return super()._reduce(name, skipna=skipna, **kwargs)
-
     def _maybe_mask_result(self, result, mask, other, op_name: str):
         """
         Parameters

pandas/core/arrays/floating.py

@@ -245,6 +245,9 @@ class FloatingArray(NumericArray):
 
     # The value used to fill '_data' to avoid upcasting
     _internal_fill_value = 0.0
+    # Fill values used for any/all
+    _truthy_value = 1.0
+    _falsey_value = 0.0
 
     @cache_readonly
     def dtype(self) -> FloatingDtype:

pandas/core/arrays/integer.py

@@ -307,6 +307,9 @@ class IntegerArray(NumericArray):
 
     # The value used to fill '_data' to avoid upcasting
     _internal_fill_value = 1
+    # Fill values used for any/all
+    _truthy_value = 1
+    _falsey_value = 0
 
     @cache_readonly
     def dtype(self) -> _IntegerDtype:

pandas/core/arrays/masked.py

@@ -62,7 +62,7 @@
 if TYPE_CHECKING:
     from pandas import Series
     from pandas.core.arrays import BooleanArray
-
+from pandas.compat.numpy import function as nv
 
 BaseMaskedArrayT = TypeVar("BaseMaskedArrayT", bound="BaseMaskedArray")
 
@@ -113,6 +113,9 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
 
     # The value used to fill '_data' to avoid upcasting
     _internal_fill_value: Scalar
+    # Fill values used for any/all
+    _truthy_value = Scalar  # bool(_truthy_value) = True
+    _falsey_value = Scalar  # bool(_falsey_value) = False
 
     def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
         # values is supposed to already be validated in the subclass
@@ -509,6 +512,9 @@ def value_counts(self, dropna: bool = True) -> Series:
         return Series(counts, index=index)
 
     def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
+        if name in {"any", "all"}:
+            return getattr(self, name)(skipna=skipna, **kwargs)
+
         data = self._data
         mask = self._mask
 
@@ -528,3 +534,156 @@ def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
             return libmissing.NA
 
         return result
+
+    def any(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether any element is truthy.
+
+        Returns False unless there is at least one element that is truthy.
+        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.
+
+        .. versionchanged:: 1.4.0
+
+        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 truthy, otherwise NA will be returned
+            if there are NA's present.
+        **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.
+        BaseMaskedArray.all : Return whether all elements are truthy.
+
+        Examples
+        --------
+        The result indicates whether any element is truthy (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
+        >>> pd.array([pd.NA], dtype="Float64").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([1, 0, pd.NA]).any(skipna=False)
+        True
+        >>> pd.array([False, False, pd.NA]).any(skipna=False)
+        <NA>
+        >>> pd.array([0, 0, pd.NA]).any(skipna=False)
+        <NA>
+        """
+        kwargs.pop("axis", None)
+        nv.validate_any((), kwargs)
+
+        values = self._data.copy()
+        np.putmask(values, self._mask, self._falsey_value)
+        result = values.any()
+        if skipna:
+            return result
+        else:
+            if result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value
+
+    def all(self, *, skipna: bool = True, **kwargs):
+        """
+        Return whether all elements are truthy.
+
+        Returns True unless there is at least one element that is falsey.
+        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.
+
+        .. versionchanged:: 1.4.0
+
+        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 falsey, otherwise NA will be returned
+            if there are NA's present.
+        **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 truthy.
+
+        Examples
+        --------
+        The result indicates whether all elements are truthy (and by default
+        skips NAs):
+
+        >>> pd.array([True, True, pd.NA]).all()
+        True
+        >>> pd.array([1, 1, 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
+        >>> pd.array([pd.NA], dtype="Float64").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([1, 1, pd.NA]).all(skipna=False)
+        <NA>
+        >>> pd.array([True, False, pd.NA]).all(skipna=False)
+        False
+        >>> pd.array([1, 0, pd.NA]).all(skipna=False)
+        False
+        """
+        kwargs.pop("axis", None)
+        nv.validate_all((), kwargs)
+
+        values = self._data.copy()
+        np.putmask(values, self._mask, self._truthy_value)
+        result = values.all()
+
+        if skipna:
+            return result
+        else:
+            if not result or len(self) == 0 or not self._mask.any():
+                return result
+            else:
+                return self.dtype.na_value

pandas/tests/extension/test_boolean.py

@@ -381,6 +381,7 @@ def check_reduce(self, s, op_name, skipna):
         tm.assert_almost_equal(result, expected)
 
 
+@pytest.mark.skip(reason="Tested in tests/reductions/test_reductions.py")
 class TestBooleanReduce(base.BaseBooleanReduceTests):
     pass