def get_indexer_indexer(
target: Index,
level: Level | list[Level] | None,
ascending: Sequence[bool] | bool,
kind: SortKind,
na_position: NaPosition,
sort_remaining: bool,
key: IndexKeyFunc,
) -> npt.NDArray[np.intp] | None:
"""
Helper method that return the indexer according to input parameters for
the sort_index method of DataFrame and Series.
Parameters
----------
target : Index
level : int or level name or list of ints or list of level names
ascending : bool or list of bools, default True
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
na_position : {'first', 'last'}, default 'last'
sort_remaining : bool, default True
key : callable, optional
Returns
-------
Optional[ndarray[intp]]
The indexer for the new index.
"""
target = ensure_key_mapped(target, key, levels=level)
target = target._sort_levels_monotonic()
if level is not None:
_, indexer = target.sortlevel(
level, ascending=ascending, sort_remaining=sort_remaining
)
elif isinstance(target, ABCMultiIndex):
indexer = lexsort_indexer(
target._get_codes_for_sorting(), orders=ascending, na_position=na_position
)
else:
# Check monotonic-ness before sort an index (GH 11080)
if (ascending and target.is_monotonic_increasing) or (
not ascending and target.is_monotonic_decreasing
):
return None
# ascending can only be a Sequence for MultiIndex
indexer = nargsort(
target,
kind=kind,
ascending=cast(bool, ascending),
na_position=na_position,
)
return indexer
def get_indexer_indexer(
target: Index,
level: Union[str, int, List[str], List[int]],
ascending: Union[Sequence[Union[bool, int]], Union[bool, int]],
kind: str,
na_position: str,
sort_remaining: bool,
key: IndexKeyFunc,
) -> Optional[np.array]: # type: ignore[valid-type]
"""
Helper method that return the indexer according to input parameters for
the sort_index method of DataFrame and Series.
Parameters
----------
target : Index
level : int or level name or list of ints or list of level names
ascending : bool or list of bools, default True
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
na_position : {'first', 'last'}, default 'last'
sort_remaining : bool, default True
key : callable, optional
Returns
-------
Optional[ndarray]
The indexer for the new index.
"""
target = ensure_key_mapped(target, key, levels=level)
target = target._sort_levels_monotonic()
if level is not None:
_, indexer = target.sortlevel(
level, ascending=ascending, sort_remaining=sort_remaining
)
elif isinstance(target, ABCMultiIndex):
indexer = lexsort_indexer(
target._get_codes_for_sorting(), orders=ascending, na_position=na_position
)
else:
# Check monotonic-ness before sort an index (GH 11080)
if (ascending and target.is_monotonic_increasing) or (
not ascending and target.is_monotonic_decreasing
):
return None
indexer = nargsort(
target, kind=kind, ascending=ascending, na_position=na_position
)
return indexer
