remove xpass

doc/source/user_guide/groupby.rst

@@ -1017,16 +1017,14 @@ for both ``aggregate`` and ``transform`` in many standard use cases. However,
 
 The dimension of the returned result can also change:
 
-.. ipython::
-
-    In [8]: grouped = df.groupby('A', group_keys=False)['C']
+.. ipython:: python
 
-    In [10]: def f(group):
-       ....:     return pd.DataFrame({'original': group,
-       ....:                          'demeaned': group - group.mean()})
-       ....:
+    grouped = df.groupby('A', group_keys=False)['C']
 
-    In [11]: grouped.apply(f)
+    def f(group):
+        return pd.DataFrame({'original': group,
+                             'demeaned': group - group.mean()})
+    grouped.apply(f)
 
 ``apply`` on a Series can operate on a returned value from the applied function,
 that is itself a series, and possibly upcast the result to a DataFrame:

doc/source/whatsnew/v1.2.0.rst

@@ -32,7 +32,7 @@ Deprecations
 :meth:`~DataFrame.groupby` no longer ignores ``group_keys`` for transform-like ``apply``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-:meth:`~DataFrame.groupby` will no longer ignore the ``group_keys`` argument for functions passed to ``apply`` that return like-indexed outputs (:issue:`34809`).
+:meth:`~DataFrame.groupby` will no longer ignore the ``group_keys`` argument for functions passed to ``apply`` that return like-indexed outputs (:issue:`34809`, :issue:`31612`, :issue:`14927`, :issue:`13056`).
 Previous versions of pandas would add the group keys only when the result from the applied function had a different index to the input.
 
 *Previous behavior*:

pandas/core/generic.py

@@ -7400,7 +7400,7 @@ def clip(
             By default group keys are not included when the result's index
             (and column) labels match the inputs, and are included otherwise.
 
-            .. versionchanged:: 1.1.0
+            .. versionchanged:: 1.2.0
 
                Warns that `group_keys` will no longer be ignored when the
                result from ``apply`` is a like-indexed Series or DataFrame.
@@ -7783,7 +7783,7 @@ def resample(
             Whether to include the group keys in the result index when performing
             a ``.groupby().apply()`` to the resampled object.
 
-            .. versionadded:: 1.1.0
+            .. versionadded:: 1.2.0
 
         Returns
         -------

pandas/core/groupby/groupby.py

@@ -910,6 +910,8 @@ def _python_apply_general(
         if (not not_indexed_same and self.group_keys is lib.no_default) and not (
             is_transform or is_empty_agg
         ):
+            # We've detected value-dependent behavior: the result's index depends on
+            # whether the user's function `f` returned the same index or not.
             if self.ndim == 1:
                 stacklevel = 4
             elif self._selection is None:

pandas/tests/groupby/test_apply.py

@@ -215,7 +215,6 @@ def test_group_apply_once_per_group2(capsys):
     assert result == expected
 
 
-@pytest.mark.xfail(reason="GH-34998")
 def test_apply_fast_slow_identical():
     # GH 31613
 
@@ -239,11 +238,9 @@ def fast(group):
     "func",
     [
         lambda x: x,
-        pytest.param(lambda x: x[:], marks=pytest.mark.xfail(reason="GH-34998")),
+        lambda x: x[:],
         lambda x: x.copy(deep=False),
-        pytest.param(
-            lambda x: x.copy(deep=True), marks=pytest.mark.xfail(reason="GH-34998")
-        ),
+        lambda x: x.copy(deep=True),
     ],
 )
 def test_groupby_apply_identity_maybecopy_index_identical(func):