TYP: groupby, sorting (pandas-dev#46133)

pandas/core/groupby/grouper.py

@@ -459,7 +459,7 @@ class Grouping:
       * groups : dict of {group -> label_list}
     """
 
-    _codes: np.ndarray | None = None
+    _codes: npt.NDArray[np.signedinteger] | None = None
     _group_index: Index | None = None
     _passed_categorical: bool
     _all_grouper: Categorical | None
@@ -614,7 +614,7 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         return values._reverse_indexer()
 
     @property
-    def codes(self) -> np.ndarray:
+    def codes(self) -> npt.NDArray[np.signedinteger]:
         if self._codes is not None:
             # _codes is set in __init__ for MultiIndex cases
             return self._codes
@@ -657,7 +657,7 @@ def group_index(self) -> Index:
         return Index._with_infer(uniques, name=self.name)
 
     @cache_readonly
-    def _codes_and_uniques(self) -> tuple[np.ndarray, ArrayLike]:
+    def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
         if self._passed_categorical:
             # we make a CategoricalIndex out of the cat grouper
             # preserving the categories / ordered attributes

pandas/core/groupby/ops.py

@@ -797,7 +797,7 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
 
     @final
     @property
-    def codes(self) -> list[np.ndarray]:
+    def codes(self) -> list[npt.NDArray[np.signedinteger]]:
         return [ping.codes for ping in self.groupings]
 
     @property
@@ -860,11 +860,14 @@ def codes_info(self) -> npt.NDArray[np.intp]:
         return ids
 
     @final
-    def _get_compressed_codes(self) -> tuple[np.ndarray, npt.NDArray[np.intp]]:
+    def _get_compressed_codes(
+        self,
+    ) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
         # The first returned ndarray may have any signed integer dtype
         if len(self.groupings) > 1:
             group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
             return compress_group_index(group_index, sort=self._sort)
+            # FIXME: compress_group_index's second return value is int64, not intp
 
         ping = self.groupings[0]
         return ping.codes, np.arange(len(ping.group_index), dtype=np.intp)
@@ -875,7 +878,7 @@ def ngroups(self) -> int:
         return len(self.result_index)
 
     @property
-    def reconstructed_codes(self) -> list[np.ndarray]:
+    def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]:
         codes = self.codes
         ids, obs_ids, _ = self.group_info
         return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)

pandas/core/indexes/base.py

@@ -2187,7 +2187,9 @@ def _drop_level_numbers(self, levnums: list[int]):
                 verify_integrity=False,
             )
 
-    def _get_grouper_for_level(self, mapper, *, level=None):
+    def _get_grouper_for_level(
+        self, mapper, *, level=None
+    ) -> tuple[Index, npt.NDArray[np.signedinteger] | None, Index | None]:
         """
         Get index grouper corresponding to an index level
 

pandas/core/indexes/multi.py

@@ -1481,7 +1481,9 @@ def _set_names(self, names, *, level=None, validate: bool = True):
     # --------------------------------------------------------------------
 
     @doc(Index._get_grouper_for_level)
-    def _get_grouper_for_level(self, mapper, *, level):
+    def _get_grouper_for_level(
+        self, mapper, *, level=None
+    ) -> tuple[Index, npt.NDArray[np.signedinteger] | None, Index | None]:
         indexer = self.codes[level]
         level_index = self.levels[level]
 

pandas/core/reshape/reshape.py

@@ -185,7 +185,6 @@ def _make_selectors(self):
 
         self.group_index = comp_index
         self.mask = mask
-        self.unique_groups = obs_ids
         self.compressor = comp_index.searchsorted(np.arange(ngroups))
 
     @cache_readonly

pandas/core/sorting.py

@@ -224,7 +224,9 @@ def is_int64_overflow_possible(shape: Shape) -> bool:
     return the_prod >= lib.i8max
 
 
-def decons_group_index(comp_labels, shape: Shape):
+def _decons_group_index(
+    comp_labels: npt.NDArray[np.intp], shape: Shape
+) -> list[npt.NDArray[np.intp]]:
     # reconstruct labels
     if is_int64_overflow_possible(shape):
         # at some point group indices are factorized,
@@ -233,7 +235,7 @@ def decons_group_index(comp_labels, shape: Shape):
 
     label_list = []
     factor = 1
-    y = 0
+    y = np.array(0)
     x = comp_labels
     for i in reversed(range(len(shape))):
         labels = (x - y) % (factor * shape[i]) // factor
@@ -245,24 +247,32 @@ def decons_group_index(comp_labels, shape: Shape):
 
 
 def decons_obs_group_ids(
-    comp_ids: npt.NDArray[np.intp], obs_ids, shape: Shape, labels, xnull: bool
-):
+    comp_ids: npt.NDArray[np.intp],
+    obs_ids: npt.NDArray[np.intp],
+    shape: Shape,
+    labels: Sequence[npt.NDArray[np.signedinteger]],
+    xnull: bool,
+) -> list[npt.NDArray[np.intp]]:
     """
     Reconstruct labels from observed group ids.
 
     Parameters
     ----------
     comp_ids : np.ndarray[np.intp]
+    obs_ids: np.ndarray[np.intp]
+    shape : tuple[int]
+    labels : Sequence[np.ndarray[np.signedinteger]]
     xnull : bool
         If nulls are excluded; i.e. -1 labels are passed through.
     """
     if not xnull:
-        lift = np.fromiter(((a == -1).any() for a in labels), dtype="i8")
-        shape = np.asarray(shape, dtype="i8") + lift
+        lift = np.fromiter(((a == -1).any() for a in labels), dtype=np.intp)
+        arr_shape = np.asarray(shape, dtype=np.intp) + lift
+        shape = tuple(arr_shape)
 
     if not is_int64_overflow_possible(shape):
         # obs ids are deconstructable! take the fast route!
-        out = decons_group_index(obs_ids, shape)
+        out = _decons_group_index(obs_ids, shape)
         return out if xnull or not lift.any() else [x - y for x, y in zip(out, lift)]
 
     indexer = unique_label_indices(comp_ids)

pandas/tests/test_sorting.py

@@ -22,7 +22,7 @@
 from pandas.core.algorithms import safe_sort
 import pandas.core.common as com
 from pandas.core.sorting import (
-    decons_group_index,
+    _decons_group_index,
     get_group_index,
     is_int64_overflow_possible,
     lexsort_indexer,
@@ -389,7 +389,7 @@ def align(df):
 )
 def test_decons(codes_list, shape):
     group_index = get_group_index(codes_list, shape, sort=True, xnull=True)
-    codes_list2 = decons_group_index(group_index, shape)
+    codes_list2 = _decons_group_index(group_index, shape)
 
     for a, b in zip(codes_list, codes_list2):
         tm.assert_numpy_array_equal(a, b)