From df9b39a232a5e32fc7ba6ec3309a45a4c08e49fc Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Thu, 2 Mar 2023 15:54:58 +0000
Subject: [PATCH 01/26] a whole bunch of 1d constructors

---
 pandas/core/groupby/generic.py | 20 ++++++++++++--------
 pandas/core/groupby/groupby.py |  6 ++++--
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 60c43b6cf0ecd..dedeb2e24fdac 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -271,7 +271,9 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
                 result = self._aggregate_named(func, *args, **kwargs)
 
                 # result is a dict whose keys are the elements of result_index
-                result = Series(result, index=self.grouper.result_index)
+                result = self._obj_1d_constructor(
+                    result, index=self.grouper.result_index
+                )
                 result = self._wrap_aggregated_output(result)
                 return result
 
@@ -674,7 +676,7 @@ def value_counts(
             # in a backward compatible way
             # GH38672 relates to categorical dtype
             ser = self.apply(
-                Series.value_counts,
+                self._obj_1d_constructor.value_counts,
                 normalize=normalize,
                 sort=sort,
                 ascending=ascending,
@@ -693,7 +695,7 @@ def value_counts(
             llab = lambda lab, inc: lab[inc]
         else:
             # lab is a Categorical with categories an IntervalIndex
-            cat_ser = cut(Series(val), bins, include_lowest=True)
+            cat_ser = cut(self.obj._constructor(val), bins, include_lowest=True)
             cat_obj = cast("Categorical", cat_ser._values)
             lev = cat_obj.categories
             lab = lev.take(
@@ -1276,9 +1278,9 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         elif relabeling:
             # this should be the only (non-raising) case with relabeling
             # used reordered index of columns
-            result = cast(DataFrame, result)
+            result = cast(self._obj_1d_constructor, result)
             result = result.iloc[:, order]
-            result = cast(DataFrame, result)
+            result = cast(self._obj_1d_constructor, result)
             # error: Incompatible types in assignment (expression has type
             # "Optional[List[str]]", variable has type
             # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]],
@@ -1321,7 +1323,7 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
                 else:
                     # GH#32040, GH#35246
                     # e.g. test_groupby_as_index_select_column_sum_empty_df
-                    result = cast(DataFrame, result)
+                    result = cast(self._obj_1d_constructor, result)
                     result.columns = self._obj_with_exclusions.columns.copy()
 
         if not self.as_index:
@@ -1449,7 +1451,7 @@ def _wrap_applied_output_series(
         is_transform: bool,
     ) -> DataFrame | Series:
         kwargs = first_not_none._construct_axes_dict()
-        backup = Series(**kwargs)
+        backup = self._obj_1d_constructor(**kwargs)
         values = [x if (x is not None) else backup for x in values]
 
         all_indexed_same = all_indexes_same(x.index for x in values)
@@ -1840,7 +1842,9 @@ def _apply_to_column_groupbys(self, func) -> DataFrame:
 
         if not len(results):
             # concat would raise
-            res_df = DataFrame([], columns=columns, index=self.grouper.result_index)
+            res_df = self.obj._constructor(
+                [], columns=columns, index=self.grouper.result_index
+            )
         else:
             res_df = concat(results, keys=columns, axis=1)
 
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 55e14bc11246b..91c6290c4c2db 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1629,7 +1629,7 @@ def _cumcount_array(self, ascending: bool = True) -> np.ndarray:
 
     @final
     @property
-    def _obj_1d_constructor(self) -> Callable:
+    def _obj_1d_constructor(self):
         # GH28330 preserve subclassed Series/DataFrames
         if isinstance(self.obj, DataFrame):
             return self.obj._constructor_sliced
@@ -1844,7 +1844,9 @@ def mean(
         else:
             result = self._cython_agg_general(
                 "mean",
-                alt=lambda x: Series(x).mean(numeric_only=numeric_only),
+                alt=lambda x: self._obj_1d_constructor(x).mean(
+                    numeric_only=numeric_only
+                ),
                 numeric_only=numeric_only,
             )
             return result.__finalize__(self.obj, method="groupby")

From de8c2314c0665a8bf6930527468716b3f0c1449c Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Thu, 2 Mar 2023 20:11:48 +0000
Subject: [PATCH 02/26] got the first three of Lukas' assertions working

---
 pandas/core/groupby/groupby.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 91c6290c4c2db..e05559de000a8 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1837,6 +1837,16 @@ def mean(
         Name: B, dtype: float64
         """
 
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                print("df")
+                print(df)
+                return self.obj._constructor(df).mean()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_mean
 

From d057cd08eda3c5c4f097c73728a74a5ce5d31b46 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Thu, 2 Mar 2023 20:17:23 +0000
Subject: [PATCH 03/26] remove print statements

---
 pandas/core/groupby/groupby.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index e05559de000a8..f804f50d49b8f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1840,8 +1840,6 @@ def mean(
         if not (type(self.obj) == Series or type(self.obj) == DataFrame):
 
             def f(df, *args, **kwargs):
-                print("df")
-                print(df)
                 return self.obj._constructor(df).mean()
 
             result = self.agg(f)

From 5338d3f0bb09689842276b91fa845ee4b424b545 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 10:17:52 +0000
Subject: [PATCH 04/26] create test for overridden methods

---
 pandas/tests/groupby/test_groupby_subclass.py | 43 +++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 773c1e60e97af..5c54ece9b290e 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -103,3 +103,46 @@ def test_groupby_resample_preserves_subclass(obj):
     # Confirm groupby.resample() preserves dataframe type
     result = df.groupby("Buyer").resample("5D").sum()
     assert isinstance(result, obj)
+
+
+def test_groupby_overridden_methods():
+    class UnitSeries(Series):
+        @property
+        def _constructor(self):
+            return UnitSeries
+
+        @property
+        def _constructor_expanddim(self):
+            return UnitDataFrame
+
+        def mean(self, *args, **kwargs):
+            return 1
+
+        def beans(self, *args, **kwargs):
+            return "series toast"
+
+    class UnitDataFrame(DataFrame):
+        @property
+        def _constructor(self):
+            return UnitDataFrame
+
+        @property
+        def _constructor_expanddim(self):
+            return UnitSeries
+
+        def mean(self, *args, **kwargs):
+            return 1
+
+        def beans(self, *args, **kwargs):
+            return "df toast"
+
+    params = ["a", "b"]
+    data = np.random.rand(4, 2)
+    udf = UnitDataFrame(data, columns=params)
+    udf["group"] = np.ones(4, dtype=int)
+    udf.loc[2:, "group"] = 2
+
+    assert udf.mean() == 1
+    assert all(udf.groupby("group").mean() == 1)
+    assert udf.beans() == "df toast"
+    # print(udf.groupby('group').beans()) # AttributeError

From 5fe7f821bfe4248c08a350859463dc6a18ba9d54 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 12:37:01 +0000
Subject: [PATCH 05/26] also attack median, std, var, sem, prod, sum, min and
 max

---
 pandas/core/groupby/groupby.py | 69 +++++++++++++++++++++++++++++++++-
 1 file changed, 67 insertions(+), 2 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f804f50d49b8f..bd48671ce25a1 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1880,9 +1880,17 @@ def median(self, numeric_only: bool = False):
         Series or DataFrame
             Median of values within each group.
         """
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).median()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         result = self._cython_agg_general(
             "median",
-            alt=lambda x: Series(x).median(numeric_only=numeric_only),
+            alt=lambda x: self._obj_1d_constructor(x).median(numeric_only=numeric_only),
             numeric_only=numeric_only,
         )
         return result.__finalize__(self.obj, method="groupby")
@@ -1938,6 +1946,14 @@ def std(
         Series or DataFrame
             Standard deviation of values within each group.
         """
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).std()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_var
 
@@ -2021,6 +2037,14 @@ def var(
         Series or DataFrame
             Variance of values within each group.
         """
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).var()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_var
 
@@ -2028,7 +2052,7 @@ def var(
         else:
             return self._cython_agg_general(
                 "var",
-                alt=lambda x: Series(x).var(ddof=ddof),
+                alt=lambda x: self._obj_1d_constructor(x).var(ddof=ddof),
                 numeric_only=numeric_only,
                 ddof=ddof,
             )
@@ -2190,6 +2214,15 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
         Series or DataFrame
             Standard error of the mean of values within each group.
         """
+        # TODO: think sem() needs considering more closely
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).sem()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype):
             raise TypeError(
                 f"{type(self).__name__}.sem called with "
@@ -2248,6 +2281,14 @@ def sum(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).sum()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_sum
 
@@ -2272,6 +2313,14 @@ def sum(
     @final
     @doc(_groupby_agg_method_template, fname="prod", no=False, mc=0)
     def prod(self, numeric_only: bool = False, min_count: int = 0):
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).prod()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )
@@ -2285,6 +2334,14 @@ def min(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).min()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_min_max
 
@@ -2306,6 +2363,14 @@ def max(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
+        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+
+            def f(df, *args, **kwargs):
+                return self.obj._constructor(df).max()
+
+            result = self.agg(f)
+            return result.__finalize__(self.obj, method="groupby")
+
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_min_max
 

From 4aa2b85dbf11aa146f2b65d67eccd0e5b782a161 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 18:01:17 +0000
Subject: [PATCH 06/26] add tests for test of methods

---
 pandas/tests/groupby/test_groupby_subclass.py | 53 +++++++++++++++++--
 1 file changed, 48 insertions(+), 5 deletions(-)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 5c54ece9b290e..13a399c0c663f 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -118,8 +118,26 @@ def _constructor_expanddim(self):
         def mean(self, *args, **kwargs):
             return 1
 
-        def beans(self, *args, **kwargs):
-            return "series toast"
+        def median(self, *args, **kwargs):
+            return 2
+
+        def std(self, *args, **kwargs):
+            return 3
+
+        def var(self, *args, **kwargs):
+            return 4
+
+        def sem(self, *args, **kwargs):
+            return 5
+
+        def prod(self, *args, **kwargs):
+            return 6
+
+        def min(self, *args, **kwargs):
+            return 7
+
+        def max(self, *args, **kwargs):
+            return 8
 
     class UnitDataFrame(DataFrame):
         @property
@@ -131,10 +149,29 @@ def _constructor_expanddim(self):
             return UnitSeries
 
         def mean(self, *args, **kwargs):
+            print("UnitDataFrame mean")
             return 1
 
-        def beans(self, *args, **kwargs):
-            return "df toast"
+        def median(self, *args, **kwargs):
+            return 2
+
+        def std(self, *args, **kwargs):
+            return 3
+
+        def var(self, *args, **kwargs):
+            return 4
+
+        def sem(self, *args, **kwargs):
+            return 5
+
+        def prod(self, *args, **kwargs):
+            return 6
+
+        def min(self, *args, **kwargs):
+            return 7
+
+        def max(self, *args, **kwargs):
+            return 8
 
     params = ["a", "b"]
     data = np.random.rand(4, 2)
@@ -144,5 +181,11 @@ def beans(self, *args, **kwargs):
 
     assert udf.mean() == 1
     assert all(udf.groupby("group").mean() == 1)
-    assert udf.beans() == "df toast"
+    assert all(udf.groupby("group").median() == 1)
+    assert all(udf.groupby("group").std() == 1)
+    assert all(udf.groupby("group").var() == 1)
+    assert all(udf.groupby("group").sem() == 1)
+    assert all(udf.groupby("group").prod() == 1)
+    assert all(udf.groupby("group").min() == 1)
+    assert all(udf.groupby("group").max() == 1)
     # print(udf.groupby('group').beans()) # AttributeError

From 8d7346dadd2edbab978c652e4a88c6992af8da5f Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 18:09:00 +0000
Subject: [PATCH 07/26] change 1d constructors to constructors

---
 pandas/core/groupby/generic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 8e3f7b165b745..db6f9d7b87025 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1291,9 +1291,9 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         elif relabeling:
             # this should be the only (non-raising) case with relabeling
             # used reordered index of columns
-            result = cast(self._obj_1d_constructor, result)
+            result = cast(self.obj._constructor, result)
             result = result.iloc[:, order]
-            result = cast(self._obj_1d_constructor, result)
+            result = cast(self.obj._constructor, result)
             # error: Incompatible types in assignment (expression has type
             # "Optional[List[str]]", variable has type
             # "Union[Union[Union[ExtensionArray, ndarray[Any, Any]],

From 37ae233a780d5ed2ef80a53ef15796a1062c206f Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 18:15:53 +0000
Subject: [PATCH 08/26] tidy up

---
 pandas/tests/groupby/test_groupby_subclass.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 13a399c0c663f..90ca26ffecf6a 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -149,7 +149,6 @@ def _constructor_expanddim(self):
             return UnitSeries
 
         def mean(self, *args, **kwargs):
-            print("UnitDataFrame mean")
             return 1
 
         def median(self, *args, **kwargs):
@@ -179,13 +178,11 @@ def max(self, *args, **kwargs):
     udf["group"] = np.ones(4, dtype=int)
     udf.loc[2:, "group"] = 2
 
-    assert udf.mean() == 1
     assert all(udf.groupby("group").mean() == 1)
-    assert all(udf.groupby("group").median() == 1)
-    assert all(udf.groupby("group").std() == 1)
-    assert all(udf.groupby("group").var() == 1)
-    assert all(udf.groupby("group").sem() == 1)
-    assert all(udf.groupby("group").prod() == 1)
-    assert all(udf.groupby("group").min() == 1)
-    assert all(udf.groupby("group").max() == 1)
-    # print(udf.groupby('group').beans()) # AttributeError
+    assert all(udf.groupby("group").median() == 2)
+    assert all(udf.groupby("group").std() == 3)
+    assert all(udf.groupby("group").var() == 4)
+    assert all(udf.groupby("group").sem() == 5)
+    assert all(udf.groupby("group").prod() == 6)
+    assert all(udf.groupby("group").min() == 7)
+    assert all(udf.groupby("group").max() == 8)

From aa57cc2257570cd71dd4f5e947b45220ec383deb Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 18:22:45 +0000
Subject: [PATCH 09/26] change to np.all

---
 pandas/tests/groupby/test_groupby_subclass.py | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 90ca26ffecf6a..b20ed0e3a21c1 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -116,7 +116,7 @@ def _constructor_expanddim(self):
             return UnitDataFrame
 
         def mean(self, *args, **kwargs):
-            return 1
+            return 2
 
         def median(self, *args, **kwargs):
             return 2
@@ -149,7 +149,7 @@ def _constructor_expanddim(self):
             return UnitSeries
 
         def mean(self, *args, **kwargs):
-            return 1
+            return 2
 
         def median(self, *args, **kwargs):
             return 2
@@ -178,11 +178,11 @@ def max(self, *args, **kwargs):
     udf["group"] = np.ones(4, dtype=int)
     udf.loc[2:, "group"] = 2
 
-    assert all(udf.groupby("group").mean() == 1)
-    assert all(udf.groupby("group").median() == 2)
-    assert all(udf.groupby("group").std() == 3)
-    assert all(udf.groupby("group").var() == 4)
-    assert all(udf.groupby("group").sem() == 5)
-    assert all(udf.groupby("group").prod() == 6)
-    assert all(udf.groupby("group").min() == 7)
-    assert all(udf.groupby("group").max() == 8)
+    assert np.all(udf.groupby("group").mean() == 1)
+    assert np.all(udf.groupby("group").median() == 2)
+    assert np.all(udf.groupby("group").std() == 3)
+    assert np.all(udf.groupby("group").var() == 4)
+    assert np.all(udf.groupby("group").sem() == 5)
+    assert np.all(udf.groupby("group").prod() == 6)
+    assert np.all(udf.groupby("group").min() == 7)
+    assert np.all(udf.groupby("group").max() == 8)

From b3df075a4691ada5e0ccb7e81aeab13e0a68c3c3 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 19:17:00 +0000
Subject: [PATCH 10/26] remove deliberate test failure

---
 pandas/tests/groupby/test_groupby_subclass.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index b20ed0e3a21c1..4b444d6759fed 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -116,7 +116,7 @@ def _constructor_expanddim(self):
             return UnitDataFrame
 
         def mean(self, *args, **kwargs):
-            return 2
+            return 1
 
         def median(self, *args, **kwargs):
             return 2
@@ -149,7 +149,7 @@ def _constructor_expanddim(self):
             return UnitSeries
 
         def mean(self, *args, **kwargs):
-            return 2
+            return 1
 
         def median(self, *args, **kwargs):
             return 2

From adc132abfff72ffbe39f95955e3404e2f5c9cd25 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 19:20:31 +0000
Subject: [PATCH 11/26] check that self._obj_1d_constructor is Series

---
 pandas/core/groupby/generic.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index db6f9d7b87025..24d1308a1e274 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -682,6 +682,12 @@ def value_counts(
 
         index_names = self.grouper.names + [self.obj.name]
 
+        constructor_1d = (
+            self._obj_1d_constructor
+            if isinstance(self._obj_1d_constructor, Series)
+            else Series
+        )
+
         if is_categorical_dtype(val.dtype) or (
             bins is not None and not np.iterable(bins)
         ):
@@ -689,7 +695,7 @@ def value_counts(
             # in a backward compatible way
             # GH38672 relates to categorical dtype
             ser = self.apply(
-                self._obj_1d_constructor.value_counts,
+                constructor_1d.value_counts,
                 normalize=normalize,
                 sort=sort,
                 ascending=ascending,

From 31868ffd75aba9cb8efb6ce96f8b9f541965ef17 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Fri, 3 Mar 2023 19:29:25 +0000
Subject: [PATCH 12/26] add entry to docs

---
 doc/source/whatsnew/v2.1.0.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index c0ca5b2320338..696207611eaa8 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -185,6 +185,10 @@ Plotting
 -
 -
 
+Groupby
+- Bug in :meth:`GroupBy.mean`, :meth:`GroupBy.median`, :meth:`GroupBy.std`, :meth:`GroupBy.var`, :meth:`GroupBy.sem`, :meth:`GroupBy.prod`, :meth:`GroupBy.min`, :meth:`GroupBy.max` don't use corresponding methods of subclasses of :class:`Series` or :class:`DataFrame` (:issue:`51757`)
+-
+
 Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 - Bug in :meth:`DataFrameGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmax` return wrong dtype when used on empty DataFrameGroupBy or SeriesGroupBy (:issue:`51423`)

From 2efa052dead0ce6bd66e2b5499792619d5f56b83 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 13:46:23 +0000
Subject: [PATCH 13/26] check for equality of mean methods

---
 pandas/core/groupby/groupby.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 63df9181a8bc6..956efecedc488 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1837,7 +1837,7 @@ def mean(
         Name: B, dtype: float64
         """
 
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.mean is Series.mean or self.obj.mean is DataFrame.mean):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).mean()

From 1505a1c98d10e9a384f6af81405c14a93be9f49d Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 14:06:14 +0000
Subject: [PATCH 14/26] repeat for other methods

---
 pandas/core/groupby/groupby.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 956efecedc488..e805ed1e40aa0 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1880,7 +1880,9 @@ def median(self, numeric_only: bool = False):
         Series or DataFrame
             Median of values within each group.
         """
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (
+            self.obj.median is Series.median or self.obj.median is DataFrame.median
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).median()
@@ -1946,7 +1948,7 @@ def std(
         Series or DataFrame
             Standard deviation of values within each group.
         """
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.std is Series.std or self.obj.std is DataFrame.std):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).std()
@@ -2037,7 +2039,7 @@ def var(
         Series or DataFrame
             Variance of values within each group.
         """
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.var is Series.var or self.obj.var is DataFrame.var):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).var()
@@ -2215,7 +2217,7 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
             Standard error of the mean of values within each group.
         """
         # TODO: think sem() needs considering more closely
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.sem is Series.sem or self.obj.sem is DataFrame.sem):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).sem()
@@ -2281,7 +2283,7 @@ def sum(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.sum is Series.sum or self.obj.sum is DataFrame.sum):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).sum()
@@ -2313,7 +2315,7 @@ def f(df, *args, **kwargs):
     @final
     @doc(_groupby_agg_method_template, fname="prod", no=False, mc=0)
     def prod(self, numeric_only: bool = False, min_count: int = 0):
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.prod is Series.prod or self.obj.prod is DataFrame.prod):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).prod()
@@ -2334,7 +2336,7 @@ def min(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.min is Series.min or self.obj.min is DataFrame.min):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).min()
@@ -2363,7 +2365,7 @@ def max(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (type(self.obj) == Series or type(self.obj) == DataFrame):
+        if not (self.obj.max is Series.max or self.obj.max is DataFrame.max):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).max()

From af9ac261d1bde1c55bdf0e74c2c82b1b43b3be15 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 14:35:01 +0000
Subject: [PATCH 15/26] also test Series

---
 pandas/tests/groupby/test_groupby_subclass.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 4b444d6759fed..544fc424001e2 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -186,3 +186,12 @@ def max(self, *args, **kwargs):
     assert np.all(udf.groupby("group").prod() == 6)
     assert np.all(udf.groupby("group").min() == 7)
     assert np.all(udf.groupby("group").max() == 8)
+    for useries in udf:
+        assert np.all(useries.groupby("group").mean() == 1)
+        assert np.all(useries.groupby("group").median() == 2)
+        assert np.all(useries.groupby("group").std() == 3)
+        assert np.all(useries.groupby("group").var() == 4)
+        assert np.all(useries.groupby("group").sem() == 5)
+        assert np.all(useries.groupby("group").prod() == 6)
+        assert np.all(useries.groupby("group").min() == 7)
+        assert np.all(useries.groupby("group").max() == 8)

From f4bc5483249935c340c1ab4e827ed11c20a43b77 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 16:48:56 +0000
Subject: [PATCH 16/26] pass through numeric_only

---
 pandas/core/groupby/groupby.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index e805ed1e40aa0..66f19656f0bec 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1840,7 +1840,7 @@ def mean(
         if not (self.obj.mean is Series.mean or self.obj.mean is DataFrame.mean):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).mean()
+                return self.obj._constructor(df).mean(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -1885,7 +1885,7 @@ def median(self, numeric_only: bool = False):
         ):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).median()
+                return self.obj._constructor(df).median(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -1951,7 +1951,7 @@ def std(
         if not (self.obj.std is Series.std or self.obj.std is DataFrame.std):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).std()
+                return self.obj._constructor(df).std(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2042,7 +2042,7 @@ def var(
         if not (self.obj.var is Series.var or self.obj.var is DataFrame.var):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).var()
+                return self.obj._constructor(df).var(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2220,7 +2220,7 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
         if not (self.obj.sem is Series.sem or self.obj.sem is DataFrame.sem):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).sem()
+                return self.obj._constructor(df).sem(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2286,7 +2286,7 @@ def sum(
         if not (self.obj.sum is Series.sum or self.obj.sum is DataFrame.sum):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).sum()
+                return self.obj._constructor(df).sum(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2318,7 +2318,7 @@ def prod(self, numeric_only: bool = False, min_count: int = 0):
         if not (self.obj.prod is Series.prod or self.obj.prod is DataFrame.prod):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).prod()
+                return self.obj._constructor(df).prod(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2339,7 +2339,7 @@ def min(
         if not (self.obj.min is Series.min or self.obj.min is DataFrame.min):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).min()
+                return self.obj._constructor(df).min(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")
@@ -2368,7 +2368,7 @@ def max(
         if not (self.obj.max is Series.max or self.obj.max is DataFrame.max):
 
             def f(df, *args, **kwargs):
-                return self.obj._constructor(df).max()
+                return self.obj._constructor(df).max(numeric_only=numeric_only)
 
             result = self.agg(f)
             return result.__finalize__(self.obj, method="groupby")

From 12a9fa81a2b47f57317e375808712e0703a853e5 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 17:52:09 +0000
Subject: [PATCH 17/26] reinstate type hinting

---
 pandas/core/groupby/groupby.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 66f19656f0bec..8cf280dc97b23 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1629,7 +1629,7 @@ def _cumcount_array(self, ascending: bool = True) -> np.ndarray:
 
     @final
     @property
-    def _obj_1d_constructor(self):
+    def _obj_1d_constructor(self) -> Callable:
         # GH28330 preserve subclassed Series/DataFrames
         if isinstance(self.obj, DataFrame):
             return self.obj._constructor_sliced

From f46eea95aba4f4647e9b54a02f367a2b87ff0fde Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 6 Mar 2023 17:57:09 +0000
Subject: [PATCH 18/26] add type() to method comparison

---
 pandas/core/groupby/groupby.py | 35 +++++++++++++++++++++++++---------
 1 file changed, 26 insertions(+), 9 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 8cf280dc97b23..b6734eec87374 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1837,7 +1837,9 @@ def mean(
         Name: B, dtype: float64
         """
 
-        if not (self.obj.mean is Series.mean or self.obj.mean is DataFrame.mean):
+        if not (
+            type(self.obj).mean is Series.mean or type(self.obj).mean is DataFrame.mean
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).mean(numeric_only=numeric_only)
@@ -1881,7 +1883,8 @@ def median(self, numeric_only: bool = False):
             Median of values within each group.
         """
         if not (
-            self.obj.median is Series.median or self.obj.median is DataFrame.median
+            type(self.obj).median is Series.median
+            or type(self.obj).median is DataFrame.median
         ):
 
             def f(df, *args, **kwargs):
@@ -1948,7 +1951,9 @@ def std(
         Series or DataFrame
             Standard deviation of values within each group.
         """
-        if not (self.obj.std is Series.std or self.obj.std is DataFrame.std):
+        if not (
+            type(self.obj).std is Series.std or type(self.obj).std is DataFrame.std
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).std(numeric_only=numeric_only)
@@ -2039,7 +2044,9 @@ def var(
         Series or DataFrame
             Variance of values within each group.
         """
-        if not (self.obj.var is Series.var or self.obj.var is DataFrame.var):
+        if not (
+            type(self.obj).var is Series.var or type(self.obj).var is DataFrame.var
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).var(numeric_only=numeric_only)
@@ -2217,7 +2224,9 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
             Standard error of the mean of values within each group.
         """
         # TODO: think sem() needs considering more closely
-        if not (self.obj.sem is Series.sem or self.obj.sem is DataFrame.sem):
+        if not (
+            type(self.obj).sem is Series.sem or type(self.obj).sem is DataFrame.sem
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).sem(numeric_only=numeric_only)
@@ -2283,7 +2292,9 @@ def sum(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (self.obj.sum is Series.sum or self.obj.sum is DataFrame.sum):
+        if not (
+            type(self.obj).sum is Series.sum or type(self.obj).sum is DataFrame.sum
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).sum(numeric_only=numeric_only)
@@ -2315,7 +2326,9 @@ def f(df, *args, **kwargs):
     @final
     @doc(_groupby_agg_method_template, fname="prod", no=False, mc=0)
     def prod(self, numeric_only: bool = False, min_count: int = 0):
-        if not (self.obj.prod is Series.prod or self.obj.prod is DataFrame.prod):
+        if not (
+            type(self.obj).prod is Series.prod or type(self.obj).prod is DataFrame.prod
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).prod(numeric_only=numeric_only)
@@ -2336,7 +2349,9 @@ def min(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (self.obj.min is Series.min or self.obj.min is DataFrame.min):
+        if not (
+            type(self.obj).min is Series.min or type(self.obj).min is DataFrame.min
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).min(numeric_only=numeric_only)
@@ -2365,7 +2380,9 @@ def max(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (self.obj.max is Series.max or self.obj.max is DataFrame.max):
+        if not (
+            type(self.obj).max is Series.max or type(self.obj).max is DataFrame.max
+        ):
 
             def f(df, *args, **kwargs):
                 return self.obj._constructor(df).max(numeric_only=numeric_only)

From 185a3c1ed72f9c812764f23f2414d60e304f44e7 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Tue, 7 Mar 2023 10:49:21 +0000
Subject: [PATCH 19/26] test transform

---
 pandas/tests/groupby/test_groupby_subclass.py | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 544fc424001e2..2cff6445f5cd1 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -172,9 +172,9 @@ def min(self, *args, **kwargs):
         def max(self, *args, **kwargs):
             return 8
 
-    params = ["a", "b"]
+    columns = ["a", "b"]
     data = np.random.rand(4, 2)
-    udf = UnitDataFrame(data, columns=params)
+    udf = UnitDataFrame(data, columns=columns)
     udf["group"] = np.ones(4, dtype=int)
     udf.loc[2:, "group"] = 2
 
@@ -186,12 +186,12 @@ def max(self, *args, **kwargs):
     assert np.all(udf.groupby("group").prod() == 6)
     assert np.all(udf.groupby("group").min() == 7)
     assert np.all(udf.groupby("group").max() == 8)
-    for useries in udf:
-        assert np.all(useries.groupby("group").mean() == 1)
-        assert np.all(useries.groupby("group").median() == 2)
-        assert np.all(useries.groupby("group").std() == 3)
-        assert np.all(useries.groupby("group").var() == 4)
-        assert np.all(useries.groupby("group").sem() == 5)
-        assert np.all(useries.groupby("group").prod() == 6)
-        assert np.all(useries.groupby("group").min() == 7)
-        assert np.all(useries.groupby("group").max() == 8)
+
+    assert np.all(udf.groupby("group").transform("mean") == 1)
+    assert np.all(udf.groupby("group").transform("median") == 2)
+    assert np.all(udf.groupby("group").transform("std") == 3)
+    assert np.all(udf.groupby("group").transform("var") == 4)
+    assert np.all(udf.groupby("group").transform("sem") == 5)
+    assert np.all(udf.groupby("group").transform("prod") == 6)
+    assert np.all(udf.groupby("group").transform("min") == 7)
+    assert np.all(udf.groupby("group").transform("max") == 8)

From bf9bde6f69d18335ac027ce2aef1373b052e2b07 Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Tue, 7 Mar 2023 10:53:00 +0000
Subject: [PATCH 20/26] correct _constructor

---
 pandas/core/groupby/generic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 09aae725ffbe4..10b88d8d473ac 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1343,7 +1343,7 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
                 else:
                     # GH#32040, GH#35246
                     # e.g. test_groupby_as_index_select_column_sum_empty_df
-                    result = cast(self._obj_1d_constructor, result)
+                    result = cast(self._constructor, result)
                     result.columns = self._obj_with_exclusions.columns.copy()
 
         if not self.as_index:

From a6be1eaf2f052688d4d4e811cf523f4aa1afc76a Mon Sep 17 00:00:00 2001
From: Ormorod <Adam.Ormondroyd@gmail.com>
Date: Mon, 27 Mar 2023 13:45:22 +0100
Subject: [PATCH 21/26] remove unnecessary(?) if statement

---
 pandas/core/groupby/generic.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index e8d69a4fa2ffb..d4f023f90cf52 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -683,12 +683,6 @@ def value_counts(
 
         index_names = self.grouper.names + [self.obj.name]
 
-        constructor_1d = (
-            self._obj_1d_constructor
-            if isinstance(self._obj_1d_constructor, Series)
-            else Series
-        )
-
         if is_categorical_dtype(val.dtype) or (
             bins is not None and not np.iterable(bins)
         ):
@@ -696,7 +690,7 @@ def value_counts(
             # in a backward compatible way
             # GH38672 relates to categorical dtype
             ser = self.apply(
-                constructor_1d.value_counts,
+                self._obj_1d_constructor.value_counts,
                 normalize=normalize,
                 sort=sort,
                 ascending=ascending,

From 6631b1ec4b5b181210850ac7132dccaecae98aba Mon Sep 17 00:00:00 2001
From: AdamOrmondroyd <Adam.Ormondroyd@gmail.com>
Date: Tue, 25 Apr 2023 11:33:12 +0100
Subject: [PATCH 22/26] first pass at decorator

---
 pandas/core/groupby/groupby.py | 35 ++++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 8cb04e81c4510..24a84cc5e7243 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1459,6 +1459,30 @@ def _aggregate_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
             res.index = default_index(len(res))
         return res
 
+    def _use_subclass_method(func):
+        """
+        Use the corresponding func method in the case of a
+        subclassed Series or DataFrame.
+        """
+
+        def inner(self, *args, **kwargs):
+            if not (
+                getattr(type(self.obj), func.__name__) is getattr(Series, func.__name__)
+                or getattr(type(self.obj), func.__name__)
+                is getattr(DataFrame, func.__name__)
+            ):
+                print("is a subclass")
+                result = self.agg(
+                    lambda df: getattr(self.obj._constructor(df), func.__name__)(
+                        *args, **kwargs
+                    )
+                )
+                return result.__finalize__(self.obj, method="groupby")
+            print("not a subclass")
+            return func(self, *args, **kwargs)
+
+        return inner
+
     # -----------------------------------------------------------------
     # apply/agg/transform
 
@@ -1898,6 +1922,7 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
         return self._reindex_output(result, fill_value=0)
 
     @final
+    @_use_subclass_method
     @Substitution(name="groupby")
     @Substitution(see_also=_common_see_also)
     def mean(
@@ -1974,16 +1999,6 @@ def mean(
         Name: B, dtype: float64
         """
 
-        if not (
-            type(self.obj).mean is Series.mean or type(self.obj).mean is DataFrame.mean
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).mean(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_mean
 

From 94dc18610353ce2145fc742da0a27ae1397d7113 Mon Sep 17 00:00:00 2001
From: AdamOrmondroyd <Adam.Ormondroyd@gmail.com>
Date: Tue, 25 Apr 2023 11:48:27 +0100
Subject: [PATCH 23/26] add decorator to other methods

---
 pandas/core/groupby/groupby.py | 91 +++-------------------------------
 1 file changed, 7 insertions(+), 84 deletions(-)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 24a84cc5e7243..28335baf8d56c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1471,14 +1471,12 @@ def inner(self, *args, **kwargs):
                 or getattr(type(self.obj), func.__name__)
                 is getattr(DataFrame, func.__name__)
             ):
-                print("is a subclass")
                 result = self.agg(
                     lambda df: getattr(self.obj._constructor(df), func.__name__)(
                         *args, **kwargs
                     )
                 )
                 return result.__finalize__(self.obj, method="groupby")
-            print("not a subclass")
             return func(self, *args, **kwargs)
 
         return inner
@@ -2014,6 +2012,7 @@ def mean(
             return result.__finalize__(self.obj, method="groupby")
 
     @final
+    @_use_subclass_method
     def median(self, numeric_only: bool = False):
         """
         Compute median of groups, excluding missing values.
@@ -2034,17 +2033,6 @@ def median(self, numeric_only: bool = False):
         Series or DataFrame
             Median of values within each group.
         """
-        if not (
-            type(self.obj).median is Series.median
-            or type(self.obj).median is DataFrame.median
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).median(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         result = self._cython_agg_general(
             "median",
             alt=lambda x: self._obj_1d_constructor(x).median(numeric_only=numeric_only),
@@ -2053,6 +2041,7 @@ def f(df, *args, **kwargs):
         return result.__finalize__(self.obj, method="groupby")
 
     @final
+    @_use_subclass_method
     @Substitution(name="groupby")
     @Appender(_common_see_also)
     def std(
@@ -2103,16 +2092,6 @@ def std(
         Series or DataFrame
             Standard deviation of values within each group.
         """
-        if not (
-            type(self.obj).std is Series.std or type(self.obj).std is DataFrame.std
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).std(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_var
 
@@ -2126,6 +2105,7 @@ def f(df, *args, **kwargs):
             )
 
     @final
+    @_use_subclass_method
     @Substitution(name="groupby")
     @Appender(_common_see_also)
     def var(
@@ -2176,16 +2156,6 @@ def var(
         Series or DataFrame
             Variance of values within each group.
         """
-        if not (
-            type(self.obj).var is Series.var or type(self.obj).var is DataFrame.var
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).var(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_var
 
@@ -2332,6 +2302,7 @@ def _value_counts(
         return result.__finalize__(self.obj, method="value_counts")
 
     @final
+    @_use_subclass_method
     def sem(self, ddof: int = 1, numeric_only: bool = False):
         """
         Compute standard error of the mean of groups, excluding missing values.
@@ -2357,17 +2328,6 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
         Series or DataFrame
             Standard error of the mean of values within each group.
         """
-        # TODO: think sem() needs considering more closely
-        if not (
-            type(self.obj).sem is Series.sem or type(self.obj).sem is DataFrame.sem
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).sem(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype):
             raise TypeError(
                 f"{type(self).__name__}.sem called with "
@@ -2412,6 +2372,7 @@ def size(self) -> DataFrame | Series:
         return result
 
     @final
+    @_use_subclass_method
     @doc(_groupby_agg_method_template, fname="sum", no=False, mc=0)
     def sum(
         self,
@@ -2420,16 +2381,6 @@ def sum(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (
-            type(self.obj).sum is Series.sum or type(self.obj).sum is DataFrame.sum
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).sum(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_sum
 
@@ -2452,23 +2403,15 @@ def f(df, *args, **kwargs):
             return self._reindex_output(result, fill_value=0)
 
     @final
+    @_use_subclass_method
     @doc(_groupby_agg_method_template, fname="prod", no=False, mc=0)
     def prod(self, numeric_only: bool = False, min_count: int = 0):
-        if not (
-            type(self.obj).prod is Series.prod or type(self.obj).prod is DataFrame.prod
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).prod(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )
 
     @final
+    @_use_subclass_method
     @doc(_groupby_agg_method_template, fname="min", no=False, mc=-1)
     def min(
         self,
@@ -2477,16 +2420,6 @@ def min(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (
-            type(self.obj).min is Series.min or type(self.obj).min is DataFrame.min
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).min(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_min_max
 
@@ -2508,16 +2441,6 @@ def max(
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,
     ):
-        if not (
-            type(self.obj).max is Series.max or type(self.obj).max is DataFrame.max
-        ):
-
-            def f(df, *args, **kwargs):
-                return self.obj._constructor(df).max(numeric_only=numeric_only)
-
-            result = self.agg(f)
-            return result.__finalize__(self.obj, method="groupby")
-
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_min_max
 

From 310c339aa494a355232729413c165a9d99ca84d6 Mon Sep 17 00:00:00 2001
From: AdamOrmondroyd <Adam.Ormondroyd@gmail.com>
Date: Tue, 25 Apr 2023 11:54:18 +0100
Subject: [PATCH 24/26] missed max()

---
 pandas/core/groupby/groupby.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 28335baf8d56c..f0e45c4a53938 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2433,6 +2433,7 @@ def min(
             )
 
     @final
+    @_use_subclass_method
     @doc(_groupby_agg_method_template, fname="max", no=False, mc=-1)
     def max(
         self,

From 27c4ed9a991fdb32d428501a76bfe309705d1699 Mon Sep 17 00:00:00 2001
From: AdamOrmondroyd <Adam.Ormondroyd@gmail.com>
Date: Tue, 25 Apr 2023 16:42:12 +0100
Subject: [PATCH 25/26] add @wraps

---
 pandas/core/groupby/groupby.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f0e45c4a53938..f4140f9b00f00 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1465,6 +1465,7 @@ def _use_subclass_method(func):
         subclassed Series or DataFrame.
         """
 
+        @wraps(func)
         def inner(self, *args, **kwargs):
             if not (
                 getattr(type(self.obj), func.__name__) is getattr(Series, func.__name__)

From 8a9f30f99f56d5abda25c0d58cbf658f9ef1422f Mon Sep 17 00:00:00 2001
From: AdamOrmondroyd <Adam.Ormondroyd@gmail.com>
Date: Wed, 26 Apr 2023 13:24:35 +0100
Subject: [PATCH 26/26] add tests for series example

---
 pandas/tests/groupby/test_groupby_subclass.py | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/pandas/tests/groupby/test_groupby_subclass.py b/pandas/tests/groupby/test_groupby_subclass.py
index 5dc59ca128753..94c7c362380d3 100644
--- a/pandas/tests/groupby/test_groupby_subclass.py
+++ b/pandas/tests/groupby/test_groupby_subclass.py
@@ -182,6 +182,8 @@ def max(self, *args, **kwargs):
     udf["group"] = np.ones(4, dtype=int)
     udf.loc[2:, "group"] = 2
 
+    us = udf[["a", "group"]]
+
     assert np.all(udf.groupby("group").mean() == 1)
     assert np.all(udf.groupby("group").median() == 2)
     assert np.all(udf.groupby("group").std() == 3)
@@ -191,6 +193,15 @@ def max(self, *args, **kwargs):
     assert np.all(udf.groupby("group").min() == 7)
     assert np.all(udf.groupby("group").max() == 8)
 
+    assert np.all(us.groupby("group").mean() == 1)
+    assert np.all(us.groupby("group").median() == 2)
+    assert np.all(us.groupby("group").std() == 3)
+    assert np.all(us.groupby("group").var() == 4)
+    assert np.all(us.groupby("group").sem() == 5)
+    assert np.all(us.groupby("group").prod() == 6)
+    assert np.all(us.groupby("group").min() == 7)
+    assert np.all(us.groupby("group").max() == 8)
+
     assert np.all(udf.groupby("group").transform("mean") == 1)
     assert np.all(udf.groupby("group").transform("median") == 2)
     assert np.all(udf.groupby("group").transform("std") == 3)
@@ -199,3 +210,12 @@ def max(self, *args, **kwargs):
     assert np.all(udf.groupby("group").transform("prod") == 6)
     assert np.all(udf.groupby("group").transform("min") == 7)
     assert np.all(udf.groupby("group").transform("max") == 8)
+
+    assert np.all(us.groupby("group").transform("mean") == 1)
+    assert np.all(us.groupby("group").transform("median") == 2)
+    assert np.all(us.groupby("group").transform("std") == 3)
+    assert np.all(us.groupby("group").transform("var") == 4)
+    assert np.all(us.groupby("group").transform("sem") == 5)
+    assert np.all(us.groupby("group").transform("prod") == 6)
+    assert np.all(us.groupby("group").transform("min") == 7)
+    assert np.all(us.groupby("group").transform("max") == 8)