def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]: """ Returns the indices that would sort the index and its underlying data. Returns ------- np.ndarray[np.intp] See Also -------- numpy.ndarray.argsort """ ascending = kwargs.pop("ascending", True) # EA compat kwargs.pop("kind", None) # e.g. "mergesort" is irrelevant nv.validate_argsort(args, kwargs) if self._range.step > 0: result = np.arange(len(self), dtype=np.intp) else: result = np.arange(len(self) - 1, -1, -1, dtype=np.intp) if not ascending: result = result[::-1] return result
def argsort(self, *args, **kwargs): """ Returns the indices that would sort the index and its underlying data. Returns ------- argsorted : numpy array See also -------- numpy.ndarray.argsort """ nv.validate_argsort(args, kwargs) if self._step > 0: return np.arange(len(self)) else: return np.arange(len(self) - 1, -1, -1)
def argsort(self, *args, **kwargs) -> np.ndarray: """ Returns the indices that would sort the index and its underlying data. Returns ------- argsorted : numpy array See Also -------- numpy.ndarray.argsort """ ascending = kwargs.pop("ascending", True) # EA compat nv.validate_argsort(args, kwargs) if self._range.step > 0: result = np.arange(len(self)) else: result = np.arange(len(self) - 1, -1, -1) if not ascending: result = result[::-1] return result