ENH: retain masked EA dtypes in groupby with as_index=False

doc/source/whatsnew/v1.4.0.rst

@@ -29,6 +29,7 @@ enhancement2
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
+- :class:`DataFrameGroupBy` operations with ``as_index=False`` now correctly retain ``ExtensionDtype`` dtypes for columns being grouped on (:issue:`41373`)
 - :meth:`Series.sample`, :meth:`DataFrame.sample`, and :meth:`.GroupBy.sample` now accept a ``np.random.Generator`` as input to ``random_state``. A generator will be more performant, especially with ``replace=False`` (:issue:`38100`)
 - :meth:`Series.ewm`, :meth:`DataFrame.ewm`, now support a ``method`` argument with a ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`. See :ref:`Window Overview <window.overview>` for performance and functional benefits (:issue:`42273`)
 -

pandas/core/groupby/generic.py

@@ -1033,7 +1033,7 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
             self._insert_inaxis_grouper_inplace(result)
             result.index = Index(range(len(result)))
 
-        return result._convert(datetime=True)
+        return result
 
     agg = aggregate
 
@@ -1691,6 +1691,8 @@ def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
         if self.axis == 1:
             result = result.T
 
+        # Note: we only need to pass datetime=True in order to get numeric
+        #  values converted
         return self._reindex_output(result)._convert(datetime=True)
 
     def _iterate_column_groupbys(self, obj: FrameOrSeries):

pandas/core/groupby/grouper.py

@@ -617,11 +617,20 @@ def group_arraylike(self) -> ArrayLike:
         Analogous to result_index, but holding an ArrayLike to ensure
         we can can retain ExtensionDtypes.
         """
+        if self._group_index is not None:
+            # _group_index is set in __init__ for MultiIndex cases
+            return self._group_index._values
+
+        elif self._all_grouper is not None:
+            # retain dtype for categories, including unobserved ones
+            return self.result_index._values
+
         return self._codes_and_uniques[1]
 
     @cache_readonly
     def result_index(self) -> Index:
-        # TODO: what's the difference between result_index vs group_index?
+        # result_index retains dtype for categories, including unobserved ones,
+        #  which group_index does not
         if self._all_grouper is not None:
             group_idx = self.group_index
             assert isinstance(group_idx, CategoricalIndex)
@@ -633,7 +642,8 @@ def group_index(self) -> Index:
         if self._group_index is not None:
             # _group_index is set in __init__ for MultiIndex cases
             return self._group_index
-        uniques = self.group_arraylike
+
+        uniques = self._codes_and_uniques[1]
         return Index(uniques, name=self.name)
 
     @cache_readonly

pandas/core/groupby/ops.py

@@ -924,6 +924,7 @@ def result_arraylike(self) -> ArrayLike:
         if len(self.groupings) == 1:
             return self.groupings[0].group_arraylike
 
+        # result_index is MultiIndex
         return self.result_index._values
 
     @cache_readonly
@@ -942,12 +943,12 @@ def get_group_levels(self) -> list[ArrayLike]:
         # Note: only called from _insert_inaxis_grouper_inplace, which
         #  is only called for BaseGrouper, never for BinGrouper
         if len(self.groupings) == 1:
-            return [self.groupings[0].result_index]
+            return [self.groupings[0].group_arraylike]
 
         name_list = []
         for ping, codes in zip(self.groupings, self.reconstructed_codes):
             codes = ensure_platform_int(codes)
-            levels = ping.result_index.take(codes)
+            levels = ping.group_arraylike.take(codes)
 
             name_list.append(levels)
 
@@ -1027,7 +1028,10 @@ def agg_series(
         else:
             result = self._aggregate_series_fast(obj, func)
 
-        npvalues = lib.maybe_convert_objects(result, try_float=False)
+        convert_datetime = obj.dtype.kind == "M"
+        npvalues = lib.maybe_convert_objects(
+            result, try_float=False, convert_datetime=convert_datetime
+        )
         if preserve_dtype:
             out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
         else:

pandas/tests/extension/base/groupby.py

@@ -22,14 +22,14 @@ def test_grouping_grouper(self, data_for_grouping):
     def test_groupby_extension_agg(self, as_index, data_for_grouping):
         df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
         result = df.groupby("B", as_index=as_index).A.mean()
-        _, index = pd.factorize(data_for_grouping, sort=True)
+        _, uniques = pd.factorize(data_for_grouping, sort=True)
 
-        index = pd.Index(index, name="B")
-        expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
         if as_index:
+            index = pd.Index(uniques, name="B")
+            expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
             self.assert_series_equal(result, expected)
         else:
-            expected = expected.reset_index()
+            expected = pd.DataFrame({"B": uniques, "A": [3.0, 1.0, 4.0]})
             self.assert_frame_equal(result, expected)
 
     def test_groupby_agg_extension(self, data_for_grouping):

pandas/tests/extension/json/test_json.py

@@ -312,10 +312,6 @@ def test_groupby_extension_apply(self):
         we'll be able to dispatch unique.
         """
 
-    @pytest.mark.parametrize("as_index", [True, False])
-    def test_groupby_extension_agg(self, as_index, data_for_grouping):
-        super().test_groupby_extension_agg(as_index, data_for_grouping)
-
     @pytest.mark.xfail(reason="GH#39098: Converts agg result to object")
     def test_groupby_agg_extension(self, data_for_grouping):
         super().test_groupby_agg_extension(data_for_grouping)

pandas/tests/extension/test_boolean.py

@@ -269,14 +269,14 @@ def test_grouping_grouper(self, data_for_grouping):
     def test_groupby_extension_agg(self, as_index, data_for_grouping):
         df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1], "B": data_for_grouping})
         result = df.groupby("B", as_index=as_index).A.mean()
-        _, index = pd.factorize(data_for_grouping, sort=True)
+        _, uniques = pd.factorize(data_for_grouping, sort=True)
 
-        index = pd.Index(index, name="B")
-        expected = pd.Series([3.0, 1.0], index=index, name="A")
         if as_index:
+            index = pd.Index(uniques, name="B")
+            expected = pd.Series([3.0, 1.0], index=index, name="A")
             self.assert_series_equal(result, expected)
         else:
-            expected = expected.reset_index()
+            expected = pd.DataFrame({"B": uniques, "A": [3.0, 1.0]})
             self.assert_frame_equal(result, expected)
 
     def test_groupby_agg_extension(self, data_for_grouping):

pandas/tests/groupby/test_groupby.py

@@ -717,6 +717,10 @@ def test_ops_not_as_index(reduction_func):
         expected = expected.rename("size")
     expected = expected.reset_index()
 
+    if reduction_func != "size":
+        # 32 bit compat -> groupby preserves dtype whereas reset_index casts to int64
+        expected["a"] = expected["a"].astype(df["a"].dtype)
+
     g = df.groupby("a", as_index=False)
 
     result = getattr(g, reduction_func)()