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
