diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index a467798adf2e4..bf6887ed27005 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -27,6 +27,7 @@
     ArrayLike,
     DtypeObj,
     Scalar,
+    npt,
 )
 from pandas.util._decorators import doc
 
@@ -528,7 +529,7 @@ def factorize_array(
     size_hint: int | None = None,
     na_value=None,
     mask: np.ndarray | None = None,
-) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[npt.NDArray[np.intp], np.ndarray]:
     """
     Factorize an array-like to codes and uniques.
 
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 3fdb52a73dc3e..4a3ccc34f9723 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -37,6 +37,7 @@
     Ordered,
     Scalar,
     Shape,
+    npt,
     type_t,
 )
 from pandas.compat.numpy import function as nv
@@ -2048,7 +2049,7 @@ def _validate_setitem_value(self, value):
         codes = self.categories.get_indexer(rvalue)
         return codes.astype(self._ndarray.dtype, copy=False)
 
-    def _reverse_indexer(self) -> dict[Hashable, np.ndarray]:
+    def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         """
         Compute the inverse of a categorical, returning
         a dict of categories -> indexers.
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 874d7395b1950..51d2a7700be56 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -32,6 +32,7 @@
     FrameOrSeries,
     Shape,
     final,
+    npt,
 )
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
@@ -677,7 +678,7 @@ def __init__(
         sort: bool = True,
         group_keys: bool = True,
         mutated: bool = False,
-        indexer: np.ndarray | None = None,
+        indexer: npt.NDArray[np.intp] | None = None,
         dropna: bool = True,
     ):
         assert isinstance(axis, Index), axis
@@ -1268,7 +1269,13 @@ def _is_indexed_like(obj, axes, axis: int) -> bool:
 
 
 class DataSplitter(Generic[FrameOrSeries]):
-    def __init__(self, data: FrameOrSeries, labels, ngroups: int, axis: int = 0):
+    def __init__(
+        self,
+        data: FrameOrSeries,
+        labels: npt.NDArray[np.intp],
+        ngroups: int,
+        axis: int = 0,
+    ):
         self.data = data
         self.labels = ensure_platform_int(labels)  # _should_ already be np.intp
         self.ngroups = ngroups
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 2f1d85f1340a4..d369624d30cdf 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -45,6 +45,7 @@
     Shape,
     T,
     final,
+    npt,
 )
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
@@ -306,8 +307,7 @@ class Index(IndexOpsMixin, PandasObject):
     #  given the dtypes of the passed arguments
 
     @final
-    def _left_indexer_unique(self: _IndexT, other: _IndexT) -> np.ndarray:
-        # -> np.ndarray[np.intp]
+    def _left_indexer_unique(self: _IndexT, other: _IndexT) -> npt.NDArray[np.intp]:
         # Caller is responsible for ensuring other.dtype == self.dtype
         sv = self._get_join_target()
         ov = other._get_join_target()
@@ -316,7 +316,7 @@ def _left_indexer_unique(self: _IndexT, other: _IndexT) -> np.ndarray:
     @final
     def _left_indexer(
         self: _IndexT, other: _IndexT
-    ) -> tuple[ArrayLike, np.ndarray, np.ndarray]:
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         # Caller is responsible for ensuring other.dtype == self.dtype
         sv = self._get_join_target()
         ov = other._get_join_target()
@@ -327,7 +327,7 @@ def _left_indexer(
     @final
     def _inner_indexer(
         self: _IndexT, other: _IndexT
-    ) -> tuple[ArrayLike, np.ndarray, np.ndarray]:
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         # Caller is responsible for ensuring other.dtype == self.dtype
         sv = self._get_join_target()
         ov = other._get_join_target()
@@ -338,7 +338,7 @@ def _inner_indexer(
     @final
     def _outer_indexer(
         self: _IndexT, other: _IndexT
-    ) -> tuple[ArrayLike, np.ndarray, np.ndarray]:
+    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         # Caller is responsible for ensuring other.dtype == self.dtype
         sv = self._get_join_target()
         ov = other._get_join_target()
@@ -3460,8 +3460,7 @@ def get_indexer(
         method: str_t | None = None,
         limit: int | None = None,
         tolerance=None,
-    ) -> np.ndarray:
-        # returned ndarray is np.intp
+    ) -> npt.NDArray[np.intp]:
         method = missing.clean_reindex_fill_method(method)
         target = self._maybe_cast_listlike_indexer(target)
 
@@ -3842,7 +3841,7 @@ def _validate_can_reindex(self, indexer: np.ndarray) -> None:
 
     def reindex(
         self, target, method=None, level=None, limit=None, tolerance=None
-    ) -> tuple[Index, np.ndarray | None]:
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
         """
         Create index with target's values.
 
@@ -3918,7 +3917,7 @@ def _maybe_preserve_names(self, target: Index, preserve_names: bool):
     @final
     def _reindex_non_unique(
         self, target: Index
-    ) -> tuple[Index, np.ndarray, np.ndarray | None]:
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp] | None]:
         """
         Create a new index with target's values (move/add/delete values as
         necessary) use with non-unique Index and a possibly non-unique target.
@@ -4206,8 +4205,7 @@ def _join_multi(self, other: Index, how: str_t):
     @final
     def _join_non_unique(
         self, other: Index, how: str_t = "left"
-    ) -> tuple[Index, np.ndarray, np.ndarray]:
-        # returned ndarrays are np.intp
+    ) -> tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         from pandas.core.reshape.merge import get_join_indexers
 
         # We only get here if dtypes match
@@ -4235,8 +4233,7 @@ def _join_non_unique(
     @final
     def _join_level(
         self, other: Index, level, how: str_t = "left", keep_order: bool = True
-    ) -> tuple[MultiIndex, np.ndarray | None, np.ndarray | None]:
-        # Any returned ndarrays are np.intp
+    ) -> tuple[MultiIndex, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
         """
         The join method *only* affects the level of the resulting
         MultiIndex. Otherwise it just exactly aligns the Index data to the
@@ -4248,7 +4245,7 @@ def _join_level(
         """
         from pandas.core.indexes.multi import MultiIndex
 
-        def _get_leaf_sorter(labels: list[np.ndarray]) -> np.ndarray:
+        def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
             """
             Returns sorter for the inner most level while preserving the
             order of higher levels.
@@ -5000,7 +4997,7 @@ def asof(self, label):
 
         return self[loc]
 
-    def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
+    def asof_locs(self, where: Index, mask: np.ndarray) -> npt.NDArray[np.intp]:
         """
         Return the locations (indices) of labels in the index.
 
@@ -5191,7 +5188,7 @@ def shift(self, periods=1, freq=None):
             f"TimedeltaIndex; Got type {type(self).__name__}"
         )
 
-    def argsort(self, *args, **kwargs) -> np.ndarray:
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
         """
         Return the integer indices that would sort the index.
 
@@ -5342,8 +5339,9 @@ def set_value(self, arr, key, value):
         """
 
     @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
-    def get_indexer_non_unique(self, target) -> tuple[np.ndarray, np.ndarray]:
-        # both returned ndarrays are np.intp
+    def get_indexer_non_unique(
+        self, target
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         target = ensure_index(target)
         target = self._maybe_cast_listlike_indexer(target)
 
@@ -5373,7 +5371,7 @@ def get_indexer_non_unique(self, target) -> tuple[np.ndarray, np.ndarray]:
         return ensure_platform_int(indexer), ensure_platform_int(missing)
 
     @final
-    def get_indexer_for(self, target) -> np.ndarray:
+    def get_indexer_for(self, target) -> npt.NDArray[np.intp]:
         """
         Guaranteed return of an indexer even when non-unique.
 
@@ -5393,28 +5391,25 @@ def get_indexer_for(self, target) -> np.ndarray:
     @overload
     def _get_indexer_non_comparable(
         self, target: Index, method, unique: Literal[True] = ...
-    ) -> np.ndarray:
-        # returned ndarray is np.intp
+    ) -> npt.NDArray[np.intp]:
         ...
 
     @overload
     def _get_indexer_non_comparable(
         self, target: Index, method, unique: Literal[False]
-    ) -> tuple[np.ndarray, np.ndarray]:
-        # both returned ndarrays are np.intp
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         ...
 
     @overload
     def _get_indexer_non_comparable(
         self, target: Index, method, unique: bool = True
-    ) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
-        # any returned ndarrays are np.intp
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         ...
 
     @final
     def _get_indexer_non_comparable(
         self, target: Index, method, unique: bool = True
-    ) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
+    ) -> npt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         """
         Called from get_indexer or get_indexer_non_unique when the target
         is of a non-comparable dtype.
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index 6500a9c8eef61..5e4aecfe83a0a 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -14,6 +14,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.util._decorators import doc
 
@@ -368,7 +369,7 @@ def fillna(self, value, downcast=None):
 
     def reindex(
         self, target, method=None, level=None, limit=None, tolerance=None
-    ) -> tuple[Index, np.ndarray | None]:
+    ) -> tuple[Index, npt.NDArray[np.intp] | None]:
         """
         Create index with target's values (move/add/delete values as necessary)
 
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 90ed6a5e0ccfe..9712a5d95a234 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -33,6 +33,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.util._decorators import (
     cache_readonly,
@@ -807,7 +808,7 @@ def inferred_type(self) -> str:
         # sure we can't have ambiguous indexing
         return "datetime64"
 
-    def indexer_at_time(self, time, asof: bool = False) -> np.ndarray:
+    def indexer_at_time(self, time, asof: bool = False) -> npt.NDArray[np.intp]:
         """
         Return index locations of values at particular time of day
         (e.g. 9:30AM).
@@ -848,7 +849,7 @@ def indexer_at_time(self, time, asof: bool = False) -> np.ndarray:
 
     def indexer_between_time(
         self, start_time, end_time, include_start: bool = True, include_end: bool = True
-    ) -> np.ndarray:
+    ) -> npt.NDArray[np.intp]:
         """
         Return index locations of values between particular times of day
         (e.g., 9:00-9:30AM).
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index 3d83063d94bac..c401ad0c1e0d5 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -28,6 +28,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.errors import InvalidIndexError
 from pandas.util._decorators import (
@@ -644,8 +645,7 @@ def _get_indexer(
         method: str | None = None,
         limit: int | None = None,
         tolerance: Any | None = None,
-    ) -> np.ndarray:
-        # returned ndarray is np.intp
+    ) -> npt.NDArray[np.intp]:
 
         if isinstance(target, IntervalIndex):
             # non-overlapping -> at most one match per interval in target
@@ -668,8 +668,9 @@ def _get_indexer(
         return ensure_platform_int(indexer)
 
     @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs)
-    def get_indexer_non_unique(self, target: Index) -> tuple[np.ndarray, np.ndarray]:
-        # both returned ndarrays are np.intp
+    def get_indexer_non_unique(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         target = ensure_index(target)
 
         if not self._should_compare(target) and not self._should_partial_index(target):
@@ -689,8 +690,9 @@ def get_indexer_non_unique(self, target: Index) -> tuple[np.ndarray, np.ndarray]
 
         return ensure_platform_int(indexer), ensure_platform_int(missing)
 
-    def _get_indexer_pointwise(self, target: Index) -> tuple[np.ndarray, np.ndarray]:
-        # both returned ndarrays are np.intp
+    def _get_indexer_pointwise(
+        self, target: Index
+    ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
         """
         pointwise implementation for get_indexer and get_indexer_non_unique.
         """
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 30275ac60ee76..0ce99df44a5f9 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -17,7 +17,10 @@
 
 from pandas._libs import index as libindex
 from pandas._libs.lib import no_default
-from pandas._typing import Dtype
+from pandas._typing import (
+    Dtype,
+    npt,
+)
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     cache_readonly,
@@ -395,8 +398,7 @@ def _get_indexer(
         method: str | None = None,
         limit: int | None = None,
         tolerance=None,
-    ) -> np.ndarray:
-        # -> np.ndarray[np.intp]
+    ) -> npt.NDArray[np.intp]:
         if com.any_not_none(method, tolerance, limit):
             return super()._get_indexer(
                 target, method=method, tolerance=tolerance, limit=limit
@@ -502,7 +504,7 @@ def max(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
         nv.validate_max(args, kwargs)
         return self._minmax("max")
 
-    def argsort(self, *args, **kwargs) -> np.ndarray:
+    def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
         """
         Returns the indices that would sort the index and its
         underlying data.
@@ -529,7 +531,7 @@ def argsort(self, *args, **kwargs) -> np.ndarray:
 
     def factorize(
         self, sort: bool = False, na_sentinel: int | None = -1
-    ) -> tuple[np.ndarray, RangeIndex]:
+    ) -> tuple[npt.NDArray[np.intp], RangeIndex]:
         codes = np.arange(len(self), dtype=np.intp)
         uniques = self
         if sort and self.step < 0:
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 40a208a24699a..cf71bcfffc264 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -28,6 +28,7 @@
     TimedeltaConvertibleTypes,
     TimestampConvertibleTypes,
     final,
+    npt,
 )
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -1768,9 +1769,8 @@ def _get_period_bins(self, ax: PeriodIndex):
 
 
 def _take_new_index(
-    obj: FrameOrSeries, indexer: np.ndarray, new_index: Index, axis: int = 0
+    obj: FrameOrSeries, indexer: npt.NDArray[np.intp], new_index: Index, axis: int = 0
 ) -> FrameOrSeries:
-    # indexer: np.ndarray[np.intp]
 
     if isinstance(obj, ABCSeries):
         new_values = algos.take_nd(obj._values, indexer)
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 143999a4677b3..f82fcfcf172a9 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -29,6 +29,7 @@
     FrameOrSeries,
     IndexLabel,
     Suffixes,
+    npt,
 )
 from pandas.errors import MergeError
 from pandas.util._decorators import (
@@ -1003,7 +1004,7 @@ def _create_join_index(
         self,
         index: Index,
         other_index: Index,
-        indexer: np.ndarray,
+        indexer: npt.NDArray[np.intp],
         how: str = "left",
     ) -> Index:
         """
@@ -1448,7 +1449,7 @@ def _validate(self, validate: str) -> None:
 
 def get_join_indexers(
     left_keys, right_keys, sort: bool = False, how: str = "inner", **kwargs
-) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
     """
 
     Parameters
@@ -1507,9 +1508,9 @@ def restore_dropped_levels_multijoin(
     right: MultiIndex,
     dropped_level_names,
     join_index: Index,
-    lindexer: np.ndarray,
-    rindexer: np.ndarray,
-) -> tuple[list[Index], np.ndarray, list[Hashable]]:
+    lindexer: npt.NDArray[np.intp],
+    rindexer: npt.NDArray[np.intp],
+) -> tuple[list[Index], npt.NDArray[np.intp], list[Hashable]]:
     """
     *this is an internal non-public method*
 
@@ -1539,7 +1540,7 @@ def restore_dropped_levels_multijoin(
     -------
     levels : list of Index
         levels of combined multiindexes
-    labels : intp array
+    labels : np.ndarray[np.intp]
         labels of combined multiindexes
     names : List[Hashable]
         names of combined multiindex levels
@@ -2055,7 +2056,7 @@ def _left_join_on_index(
 
 def _factorize_keys(
     lk: ArrayLike, rk: ArrayLike, sort: bool = True, how: str = "inner"
-) -> tuple[np.ndarray, np.ndarray, int]:
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
     """
     Encode left and right keys as enumerated types.
 
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index 0edb150bdc273..12ab08c4e30a1 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -10,7 +10,10 @@
 
 import pandas._libs.reshape as libreshape
 from pandas._libs.sparse import IntIndex
-from pandas._typing import Dtype
+from pandas._typing import (
+    Dtype,
+    npt,
+)
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.cast import maybe_promote
@@ -136,7 +139,7 @@ def __init__(self, index: MultiIndex, level=-1, constructor=None):
     def _indexer_and_to_sort(
         self,
     ) -> tuple[
-        np.ndarray,  # np.ndarray[np.intp]
+        npt.NDArray[np.intp],
         list[np.ndarray],  # each has _some_ signed integer dtype
     ]:
         v = self.level
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 712e9785f47f7..befa67350e182 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -21,6 +21,7 @@
 from pandas._typing import (
     IndexKeyFunc,
     Shape,
+    npt,
 )
 
 from pandas.core.dtypes.common import (
@@ -186,7 +187,9 @@ def maybe_lift(lab, size) -> tuple[np.ndarray, int]:
     return out
 
 
-def get_compressed_ids(labels, sizes: Shape) -> tuple[np.ndarray, np.ndarray]:
+def get_compressed_ids(
+    labels, sizes: Shape
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]:
     """
     Group_index is offsets into cartesian product of all possible labels. This
     space can be huge, so this function compresses it, by computing offsets
@@ -236,7 +239,9 @@ def decons_group_index(comp_labels, shape):
     return label_list[::-1]
 
 
-def decons_obs_group_ids(comp_ids: np.ndarray, obs_ids, shape, labels, xnull: bool):
+def decons_obs_group_ids(
+    comp_ids: npt.NDArray[np.intp], obs_ids, shape, labels, xnull: bool
+):
     """
     Reconstruct labels from observed group ids.
 
@@ -260,8 +265,9 @@ def decons_obs_group_ids(comp_ids: np.ndarray, obs_ids, shape, labels, xnull: bo
     return [lab[indexer].astype(np.intp, subok=False, copy=True) for lab in labels]
 
 
-def indexer_from_factorized(labels, shape: Shape, compress: bool = True) -> np.ndarray:
-    # returned ndarray is np.intp
+def indexer_from_factorized(
+    labels, shape: Shape, compress: bool = True
+) -> npt.NDArray[np.intp]:
     ids = get_group_index(labels, shape, sort=True, xnull=False)
 
     if not compress:
@@ -275,7 +281,7 @@ def indexer_from_factorized(labels, shape: Shape, compress: bool = True) -> np.n
 
 def lexsort_indexer(
     keys, orders=None, na_position: str = "last", key: Callable | None = None
-) -> np.ndarray:
+) -> npt.NDArray[np.intp]:
     """
     Performs lexical sorting on a set of keys
 
@@ -347,7 +353,7 @@ def nargsort(
     na_position: str = "last",
     key: Callable | None = None,
     mask: np.ndarray | None = None,
-):
+) -> npt.NDArray[np.intp]:
     """
     Intended to be a drop-in replacement for np.argsort which handles NaNs.
 
@@ -552,7 +558,7 @@ def ensure_key_mapped(values, key: Callable | None, levels=None):
 
 
 def get_flattened_list(
-    comp_ids: np.ndarray,  # np.ndarray[np.intp]
+    comp_ids: npt.NDArray[np.intp],
     ngroups: int,
     levels: Iterable[Index],
     labels: Iterable[np.ndarray],
@@ -602,8 +608,8 @@ def get_indexer_dict(
 
 
 def get_group_index_sorter(
-    group_index: np.ndarray, ngroups: int | None = None
-) -> np.ndarray:
+    group_index: npt.NDArray[np.intp], ngroups: int | None = None
+) -> npt.NDArray[np.intp]:
     """
     algos.groupsort_indexer implements `counting sort` and it is at least
     O(ngroups), where
@@ -646,7 +652,7 @@ def get_group_index_sorter(
 
 def compress_group_index(
     group_index: np.ndarray, sort: bool = True
-) -> tuple[np.ndarray, np.ndarray]:
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
     """
     Group_index is offsets into cartesian product of all possible labels. This
     space can be huge, so this function compresses it, by computing offsets
@@ -667,8 +673,8 @@ def compress_group_index(
 
 
 def _reorder_by_uniques(
-    uniques: np.ndarray, labels: np.ndarray
-) -> tuple[np.ndarray, np.ndarray]:
+    uniques: npt.NDArray[np.int64], labels: npt.NDArray[np.intp]
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.intp]]:
     """
     Parameters
     ----------