diff --git a/pandas/core/groupby/base.py b/pandas/core/groupby/base.py
index 50248d5af8883..7211fc1b71b54 100644
--- a/pandas/core/groupby/base.py
+++ b/pandas/core/groupby/base.py
@@ -7,83 +7,8 @@
 
 import collections
 
-from pandas._typing import final
-
-from pandas.core.dtypes.common import (
-    is_list_like,
-    is_scalar,
-)
-
-from pandas.core.base import PandasObject
-
 OutputKey = collections.namedtuple("OutputKey", ["label", "position"])
 
-
-class ShallowMixin(PandasObject):
-    _attributes: list[str] = []
-
-    @final
-    def _shallow_copy(self, obj, **kwargs):
-        """
-        return a new object with the replacement attributes
-        """
-        if isinstance(obj, self._constructor):
-            obj = obj.obj
-        for attr in self._attributes:
-            if attr not in kwargs:
-                kwargs[attr] = getattr(self, attr)
-        return self._constructor(obj, **kwargs)
-
-
-class GotItemMixin(PandasObject):
-    """
-    Provide the groupby facilities to the mixed object.
-    """
-
-    _attributes: list[str]
-
-    @final
-    def _gotitem(self, key, ndim, subset=None):
-        """
-        Sub-classes to define. Return a sliced object.
-
-        Parameters
-        ----------
-        key : string / list of selections
-        ndim : {1, 2}
-            requested ndim of result
-        subset : object, default None
-            subset to act on
-        """
-        # create a new object to prevent aliasing
-        if subset is None:
-            # error: "GotItemMixin" has no attribute "obj"
-            subset = self.obj  # type: ignore[attr-defined]
-
-        # we need to make a shallow copy of ourselves
-        # with the same groupby
-        kwargs = {attr: getattr(self, attr) for attr in self._attributes}
-
-        # Try to select from a DataFrame, falling back to a Series
-        try:
-            # error: "GotItemMixin" has no attribute "_groupby"
-            groupby = self._groupby[key]  # type: ignore[attr-defined]
-        except IndexError:
-            # error: "GotItemMixin" has no attribute "_groupby"
-            groupby = self._groupby  # type: ignore[attr-defined]
-
-        # error: Too many arguments for "GotItemMixin"
-        # error: Unexpected keyword argument "groupby" for "GotItemMixin"
-        # error: Unexpected keyword argument "parent" for "GotItemMixin"
-        self = type(self)(
-            subset, groupby=groupby, parent=self, **kwargs  # type: ignore[call-arg]
-        )
-        self._reset_cache()
-        if subset.ndim == 2 and (is_scalar(key) and key in subset or is_list_like(key)):
-            self._selection = key
-        return self
-
-
 # special case to prevent duplicate plots when catching exceptions when
 # forwarding methods from NDFrames
 plotting_methods = frozenset(["plot", "hist"])
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index ffe0990a0c8be..58003c10db9e0 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -23,6 +23,7 @@
     T,
     TimedeltaConvertibleTypes,
     TimestampConvertibleTypes,
+    final,
 )
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -39,16 +40,15 @@
 
 import pandas.core.algorithms as algos
 from pandas.core.apply import ResamplerWindowApply
-from pandas.core.base import DataError
+from pandas.core.base import (
+    DataError,
+    PandasObject,
+)
 import pandas.core.common as com
 from pandas.core.generic import (
     NDFrame,
     _shared_docs,
 )
-from pandas.core.groupby.base import (
-    GotItemMixin,
-    ShallowMixin,
-)
 from pandas.core.groupby.generic import SeriesGroupBy
 from pandas.core.groupby.groupby import (
     BaseGroupBy,
@@ -86,7 +86,7 @@
 _shared_docs_kwargs: dict[str, str] = {}
 
 
-class Resampler(BaseGroupBy, ShallowMixin):
+class Resampler(BaseGroupBy, PandasObject):
     """
     Class for resampling datetimelike data, a groupby-like operation.
     See aggregate, transform, and apply functions on this object.
@@ -141,6 +141,18 @@ def __init__(self, obj, groupby=None, axis=0, kind=None, **kwargs):
         if self.groupby is not None:
             self.groupby._set_grouper(self._convert_obj(obj), sort=True)
 
+    @final
+    def _shallow_copy(self, obj, **kwargs):
+        """
+        return a new object with the replacement attributes
+        """
+        if isinstance(obj, self._constructor):
+            obj = obj.obj
+        for attr in self._attributes:
+            if attr not in kwargs:
+                kwargs[attr] = getattr(self, attr)
+        return self._constructor(obj, **kwargs)
+
     def __str__(self) -> str:
         """
         Provide a nice str repr of our rolling object.
@@ -1018,11 +1030,13 @@ def h(self, _method=method):
     setattr(Resampler, method, h)
 
 
-class _GroupByMixin(GotItemMixin):
+class _GroupByMixin(PandasObject):
     """
     Provide the groupby facilities.
     """
 
+    _attributes: list[str]
+
     def __init__(self, obj, *args, **kwargs):
 
         parent = kwargs.pop("parent", None)
@@ -1064,6 +1078,42 @@ def func(x):
     _downsample = _apply
     _groupby_and_aggregate = _apply
 
+    @final
+    def _gotitem(self, key, ndim, subset=None):
+        """
+        Sub-classes to define. Return a sliced object.
+
+        Parameters
+        ----------
+        key : string / list of selections
+        ndim : {1, 2}
+            requested ndim of result
+        subset : object, default None
+            subset to act on
+        """
+        # create a new object to prevent aliasing
+        if subset is None:
+            # error: "GotItemMixin" has no attribute "obj"
+            subset = self.obj  # type: ignore[attr-defined]
+
+        # we need to make a shallow copy of ourselves
+        # with the same groupby
+        kwargs = {attr: getattr(self, attr) for attr in self._attributes}
+
+        # Try to select from a DataFrame, falling back to a Series
+        try:
+            groupby = self._groupby[key]
+        except IndexError:
+            groupby = self._groupby
+
+        self = type(self)(subset, groupby=groupby, parent=self, **kwargs)
+        self._reset_cache()
+        if subset.ndim == 2 and (
+            lib.is_scalar(key) and key in subset or lib.is_list_like(key)
+        ):
+            self._selection = key
+        return self
+
 
 class DatetimeIndexResampler(Resampler):
     @property