def any( self, *, axis: int | None = None, out=None, keepdims: bool = False, skipna: bool = True, ): nv.validate_any((), {"out": out, "keepdims": keepdims}) result = nanops.nanany(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)
def any(self, axis=0, *args, **kwargs): """ Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any """ nv.validate_any(args, kwargs) values = self.sp_values if len(values) != len(self) and np.any(self.fill_value): return True return values.any()
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() # Argument 3 to "putmask" has incompatible type "object"; expected # "Union[_SupportsArray[dtype[Any]], _NestedSequence[ # _SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _Nested # Sequence[Union[bool, int, float, complex, str, bytes]]]" [arg-type] np.putmask(values, self._mask, self._falsey_value) # type: ignore[arg-type] 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 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 any(self, axis=None, out=None, keepdims=False, skipna=True): nv.validate_any((), dict(out=out, keepdims=keepdims)) return nanops.nanany(self._ndarray, axis=axis, skipna=skipna)
def any(self, *, axis=None, out=None, keepdims=False, skipna=True): nv.validate_any((), dict(out=out, keepdims=keepdims)) result = nanops.nanany(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)