def argmin(self, axis=None): """ return a ndarray of the minimum argument indexer See also -------- numpy.ndarray.argmin """ return nanops.nanargmin(self.values)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmin() else: return nanops.nanargmin(delegate, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmin() else: # error: Incompatible return value type (got "Union[int, ndarray]", expected # "int") return nanops.nanargmin( # type: ignore[return-value] delegate, skipna=skipna)
def argmin(self, axis=None, skipna=True): """ Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarray.argmin """ nv.validate_minmax_axis(axis) return nanops.nanargmin(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs): """ Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series. skipna : bool, default True Returns ------- numpy.ndarray See Also -------- numpy.ndarray.argmin : Return indices of the minimum values along the given axis. """ nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs) return nanops.nanargmin(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs) return nanops.nanargmin(self._values, skipna=skipna)