Repository: pandas-dev/pandas (python)

Issue:
BUG: DataFrameGroupBy.apply ignores group_keys setting when empty
### Pandas version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1, 2, 3], 'C': [4, 6, 5]})
g1 = df.groupby('A', group_keys=False)

df = pd.DataFrame({'A': [], 'B': [], 'C': []})
g2 = df.groupby('A', group_keys=False)
g3 = df.groupby('A', group_keys=True)

r1 = g1.apply(lambda x: x / x.sum())
r2 = g2.apply(lambda x: x / x.sum())
r3 = g3.apply(lambda x: x / x.sum())

print(r1.index) # Index([0, 1, 2], dtype='int64')
print(r2.index) # Index([], dtype='float64', name='A')
print(r3.index) # Index([], dtype='float64', name='A')
```


### Issue Description

The group_keys parameter has no effect when the source dataframe is empty

### Expected Behavior

group_keys=False should not include the group keys into the index regardless of whether the source dataframe is empty
I would expect results such as:
```python
print(r2.index) # Index([], dtype='float64')
print(r2.index) # RangeIndex(start=0, stop=0, step=1)
```

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.10.11
python-bits           : 64
OS                    : Windows
OS-release            : 10
Version               : 10.0.22631
machine               : AMD64
processor             : Intel64 Family 6 Model 141 Stepping 1, GenuineIntel
byteorder             : little
LC_ALL                : None
LANG                  : None
LOCALE                : es_ES.cp1252

pandas                : 2.2.3
numpy                 : 1.26.4
pytz                  : 2024.2
dateutil              : 2.9.0.post0
pip                   : 23.0.1
Cython                : None
sphinx                : None
IPython               : 8.30.0
adbc-driver-postgresql: None
...
zstandard             : None
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None

</details>


Pull request:
BUG: Fix bug in GroupBy that ignores group_keys arg for empty datafra…
…mes/series

- [x] closes #60471(Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/v3.0.0.rst` file if fixing a bug or adding a new feature.

When using `groupby.apply` on an empty dataframe or series with `group_keys=False`, the output index is incorrectly set to the grouping keys, while it is expected to be a `RangeIndex(0, 0, 1)`.
From the original issue:
```
>>> df = pd.DataFrame({'A': [], 'B': [], 'C': []})
>>> g2 = df.groupby('A', group_keys=False)
>>> r2 = g2.apply(lambda x: x / x.sum())
>>> print(r2.index)
Index([], dtype='float64', name='A')
```
Expected:
```
>>> print(r2.index)
RangeIndex(start=0, stop=0, step=1)
```

Patch:
diff --git a/doc/source/user_guide/dsintro.rst b/doc/source/user_guide/dsintro.rst
index b9c285ca30..89981786d6 100644
--- a/doc/source/user_guide/dsintro.rst
+++ b/doc/source/user_guide/dsintro.rst
@@ -326,7 +326,7 @@ This case is handled identically to a dict of arrays.
 
 .. ipython:: python
 
-   data = np.zeros((2,), dtype=[("A", "i4"), ("B", "f4"), ("C", "a10")])
+   data = np.zeros((2,), dtype=[("A", "i4"), ("B", "f4"), ("C", "S10")])
    data[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
 
    pd.DataFrame(data)
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index bb9f48d17b..ab5746eca1 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -733,6 +733,7 @@ Groupby/resample/rolling
 - Bug in :meth:`.Resampler.interpolate` on a :class:`DataFrame` with non-uniform sampling and/or indices not aligning with the resulting resampled index would result in wrong interpolation (:issue:`21351`)
 - Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`)
 - Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
+- Bug in :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` for empty data frame with ``group_keys=False`` still creating output index using group keys. (:issue:`60471`)
 - Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
 - Bug in :meth:`DataFrameGroupBy.apply` with ``as_index=False`` that was returning :class:`MultiIndex` instead of returning :class:`Index`. (:issue:`58291`)
 - Bug in :meth:`DataFrameGroupBy.cumsum` and :meth:`DataFrameGroupBy.cumprod` where ``numeric_only`` parameter was passed indirectly through kwargs instead of passing directly. (:issue:`58811`)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 3fa34007a7..f4e3f3e8b1 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -583,6 +583,8 @@ class SeriesGroupBy(GroupBy[Series]):
             if is_transform:
                 # GH#47787 see test_group_on_empty_multiindex
                 res_index = data.index
+            elif not self.group_keys:
+                res_index = None
             else:
                 res_index = self._grouper.result_index
 
@@ -1967,6 +1969,8 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
             if is_transform:
                 # GH#47787 see test_group_on_empty_multiindex
                 res_index = data.index
+            elif not self.group_keys:
+                res_index = None
             else:
                 res_index = self._grouper.result_index
 


Test patch:
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index 64220f1d3d..b7e6e55739 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -159,6 +159,7 @@ def test_agg_apply_corner(ts, tsframe):
     tm.assert_frame_equal(grouped.agg("sum"), exp_df)
 
     res = grouped.apply(np.sum, axis=0)
+    exp_df = exp_df.reset_index(drop=True)
     tm.assert_frame_equal(res, exp_df)
 
 
diff --git a/pandas/tests/groupby/test_all_methods.py b/pandas/tests/groupby/test_all_methods.py
index 945c3e421a..4625c5c27a 100644
--- a/pandas/tests/groupby/test_all_methods.py
+++ b/pandas/tests/groupby/test_all_methods.py
@@ -22,7 +22,7 @@ from pandas.tests.groupby import get_groupby_method_args
 def test_multiindex_group_all_columns_when_empty(groupby_func):
     # GH 32464
     df = DataFrame({"a": [], "b": [], "c": []}).set_index(["a", "b", "c"])
-    gb = df.groupby(["a", "b", "c"], group_keys=False)
+    gb = df.groupby(["a", "b", "c"], group_keys=True)
     method = getattr(gb, groupby_func)
     args = get_groupby_method_args(groupby_func, df)
     if groupby_func == "corrwith":
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index 366eb59ee2..4e7c0acb12 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -777,10 +777,21 @@ class TestGrouping:
         # (not testing other agg fns, because they return
         # different index objects.
         df = DataFrame({1: [], 2: []})
-        g = df.groupby(1, group_keys=False)
+        g = df.groupby(1, group_keys=True)
         result = getattr(g[2], func)(lambda x: x)
         tm.assert_series_equal(result, expected)
 
+    def test_groupby_apply_empty_with_group_keys_false(self):
+        # 60471
+        # test apply'ing empty groups with group_keys False
+        # (not testing other agg fns, because they return
+        # different index objects.
+        df = DataFrame({"A": [], "B": [], "C": []})
+        g = df.groupby("A", group_keys=False)
+        result = g.apply(lambda x: x / x.sum(), include_groups=False)
+        expected = DataFrame({"B": [], "C": []}, index=None)
+        tm.assert_frame_equal(result, expected)
+
     def test_groupby_empty(self):
         # https://github.com/pandas-dev/pandas/issues/27190
         s = Series([], name="name", dtype="float64")