def all(
self,
*,
axis: int | None = None,
out=None,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_all((), {"out": out, "keepdims": keepdims})
result = nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)
def all(self, axis=0, *args, **kwargs):
"""
Tests whether all elements evaluate True
Returns
-------
all : bool
See Also
--------
numpy.all
"""
nv.validate_all(args, kwargs)
values = self.sp_values
if len(values) != len(self) and not np.all(self.fill_value):
return False
return values.all()
def all(self, axis=0, *args, **kwargs):
"""
Tests whether all elements evaluate True
Returns
-------
all : bool
See Also
--------
numpy.all
"""
nv.validate_all(args, kwargs)
values = self.sp_values
if len(values) != len(self) and not np.all(self.fill_value):
return False
return values.all()
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()
# Argument 3 to "putmask" has incompatible type "object"; expected
# "Union[_SupportsArray[dtype[Any]], _NestedSequence[
# _SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _Neste
# dSequence[Union[bool, int, float, complex, str, bytes]]]" [arg-type]
np.putmask(values, self._mask, self._truthy_value) # type: ignore[arg-type]
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 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 all(self, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_all((), dict(out=out, keepdims=keepdims))
return nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
def all(self, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_all((), dict(out=out, keepdims=keepdims))
return nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
def all(self, *, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_all((), dict(out=out, keepdims=keepdims))
result = nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)