CLN: more descriptive names, annotations in groupby

pandas/core/groupby/generic.py

@@ -524,7 +524,8 @@ def _aggregate_named(self, func, *args, **kwargs):
         for name, group in self:
             # Each step of this loop corresponds to
             #  libreduction._BaseGrouper._apply_to_group
-            group.name = name  # NB: libreduction does not pin name
+            # NB: libreduction does not pin name
+            object.__setattr__(group, "name", name)
 
             output = func(group, *args, **kwargs)
             output = libreduction.extract_result(output)
@@ -567,9 +568,9 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
         # Temporarily set observed for dealing with categoricals.
         with com.temp_setattr(self, "observed", True):
             result = getattr(self, func)(*args, **kwargs)
-        return self._transform_fast(result)
+        return self._wrap_transform_fast_result(result)
 
-    def _transform_general(self, func, *args, **kwargs):
+    def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
         """
         Transform with a callable func`.
         """
@@ -599,7 +600,7 @@ def _transform_general(self, func, *args, **kwargs):
         result.name = self._selected_obj.name
         return result
 
-    def _transform_fast(self, result) -> Series:
+    def _wrap_transform_fast_result(self, result: Series) -> Series:
         """
         fast version of transform, only applicable to
         builtin/cythonizable functions
@@ -1436,11 +1437,11 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
             if isinstance(result, DataFrame) and result.columns.equals(
                 self._obj_with_exclusions.columns
             ):
-                return self._transform_fast(result)
+                return self._wrap_transform_fast_result(result)
 
         return self._transform_general(func, *args, **kwargs)
 
-    def _transform_fast(self, result: DataFrame) -> DataFrame:
+    def _wrap_transform_fast_result(self, result: DataFrame) -> DataFrame:
         """
         Fast transform path for aggregations
         """
@@ -1449,14 +1450,9 @@ def _transform_fast(self, result: DataFrame) -> DataFrame:
         # for each col, reshape to size of original frame by take operation
         ids, _, _ = self.grouper.group_info
         result = result.reindex(self.grouper.result_index, copy=False)
-        output = [
-            algorithms.take_nd(result.iloc[:, i].values, ids)
-            for i, _ in enumerate(result.columns)
-        ]
-
-        return self.obj._constructor._from_arrays(
-            output, columns=result.columns, index=obj.index
-        )
+        output = result.take(ids, axis=0)
+        output.index = obj.index
+        return output
 
     def _define_paths(self, func, *args, **kwargs):
         if isinstance(func, str):
@@ -1653,7 +1649,7 @@ def _gotitem(self, key, ndim: int, subset=None):
 
         raise AssertionError("invalid ndim for _gotitem")
 
-    def _wrap_frame_output(self, result, obj: DataFrame) -> DataFrame:
+    def _wrap_frame_output(self, result: dict, obj: DataFrame) -> DataFrame:
         result_index = self.grouper.levels[0]
 
         if self.axis == 0:

pandas/core/groupby/groupby.py

@@ -18,6 +18,7 @@ class providing the base-class of operations.
 from textwrap import dedent
 import types
 from typing import (
+    TYPE_CHECKING,
     Callable,
     Generic,
     Hashable,
@@ -104,6 +105,9 @@ class providing the base-class of operations.
 from pandas.core.sorting import get_group_index_sorter
 from pandas.core.util.numba_ import NUMBA_FUNC_CACHE
 
+if TYPE_CHECKING:
+    from typing import Literal
+
 _common_see_also = """
         See Also
         --------
@@ -1989,7 +1993,7 @@ def ewm(self, *args, **kwargs):
         )
 
     @final
-    def _fill(self, direction, limit=None):
+    def _fill(self, direction: Literal["ffill", "bfill"], limit=None):
         """
         Shared function for `pad` and `backfill` to call Cython method.
 
@@ -2731,7 +2735,7 @@ def _get_cythonized_result(
             name = obj.name
             values = obj._values
 
-            if numeric_only and not is_numeric_dtype(values):
+            if numeric_only and not is_numeric_dtype(values.dtype):
                 continue
 
             if aggregate:

pandas/core/groupby/grouper.py

@@ -249,6 +249,10 @@ class Grouper:
     Freq: 17T, dtype: int64
     """
 
+    axis: int
+    sort: bool
+    dropna: bool
+
     _attributes: tuple[str, ...] = ("key", "level", "freq", "axis", "sort")
 
     def __new__(cls, *args, **kwargs):
@@ -260,7 +264,13 @@ def __new__(cls, *args, **kwargs):
         return super().__new__(cls)
 
     def __init__(
-        self, key=None, level=None, freq=None, axis=0, sort=False, dropna=True
+        self,
+        key=None,
+        level=None,
+        freq=None,
+        axis: int = 0,
+        sort: bool = False,
+        dropna: bool = True,
     ):
         self.key = key
         self.level = level
@@ -281,11 +291,11 @@ def __init__(
     def ax(self):
         return self.grouper
 
-    def _get_grouper(self, obj, validate: bool = True):
+    def _get_grouper(self, obj: FrameOrSeries, validate: bool = True):
         """
         Parameters
         ----------
-        obj : the subject object
+        obj : Series or DataFrame
         validate : bool, default True
             if True, validate the grouper
 
@@ -296,7 +306,9 @@ def _get_grouper(self, obj, validate: bool = True):
         self._set_grouper(obj)
         # error: Value of type variable "FrameOrSeries" of "get_grouper" cannot be
         # "Optional[Any]"
-        self.grouper, _, self.obj = get_grouper(  # type: ignore[type-var]
+        # error: Incompatible types in assignment (expression has type "BaseGrouper",
+        # variable has type "None")
+        self.grouper, _, self.obj = get_grouper(  # type: ignore[type-var,assignment]
             self.obj,
             [self.key],
             axis=self.axis,
@@ -375,15 +387,19 @@ def _set_grouper(self, obj: FrameOrSeries, sort: bool = False):
             ax = ax.take(indexer)
             obj = obj.take(indexer, axis=self.axis)
 
-        self.obj = obj
-        self.grouper = ax
+        # error: Incompatible types in assignment (expression has type
+        # "FrameOrSeries", variable has type "None")
+        self.obj = obj  # type: ignore[assignment]
+        # error: Incompatible types in assignment (expression has type "Index",
+        # variable has type "None")
+        self.grouper = ax  # type: ignore[assignment]
         return self.grouper
 
     @final
     @property
     def groups(self):
-        # error: Item "None" of "Optional[Any]" has no attribute "groups"
-        return self.grouper.groups  # type: ignore[union-attr]
+        # error: "None" has no attribute "groups"
+        return self.grouper.groups  # type: ignore[attr-defined]
 
     @final
     def __repr__(self) -> str:
@@ -428,7 +444,7 @@ def __init__(
         index: Index,
         grouper=None,
         obj: FrameOrSeries | None = None,
-        name=None,
+        name: Hashable = None,
         level=None,
         sort: bool = True,
         observed: bool = False,
@@ -478,7 +494,12 @@ def __init__(
             # what key/level refer to exactly, don't need to
             # check again as we have by this point converted these
             # to an actual value (rather than a pd.Grouper)
-            _, grouper, _ = self.grouper._get_grouper(self.obj, validate=False)
+            _, grouper, _ = self.grouper._get_grouper(
+                # error: Value of type variable "FrameOrSeries" of "_get_grouper"
+                # of "Grouper" cannot be "Optional[FrameOrSeries]"
+                self.obj,  # type: ignore[type-var]
+                validate=False,
+            )
             if self.name is None:
                 self.name = grouper.result_index.name
             self.obj = self.grouper.obj

pandas/core/groupby/ops.py

@@ -662,6 +662,8 @@ class BaseGrouper:
 
     """
 
+    axis: Index
+
     def __init__(
         self,
         axis: Index,