BUG: groupby.apply incorrectly dropping nan (pandas-dev#43236)

Author: debnathshoham

doc/source/whatsnew/v1.3.3.rst

@@ -17,6 +17,7 @@ Fixed regressions
 - Fixed regression in :class:`DataFrame` constructor failing to broadcast for defined :class:`Index` and len one list of :class:`Timestamp` (:issue:`42810`)
 - Performance regression in :meth:`core.window.ewm.ExponentialMovingWindow.mean` (:issue:`42333`)
 - Fixed regression in :meth:`.GroupBy.agg` incorrectly raising in some cases (:issue:`42390`)
+- Fixed regression in :meth:`.GroupBy.apply` where ``nan`` values were dropped even with ``dropna=False`` (:issue:`43205`)
 - Fixed regression in :meth:`.GroupBy.quantile` which was failing with ``pandas.NA`` (:issue:`42849`)
 - Fixed regression in :meth:`merge` where ``on`` columns with ``ExtensionDtype`` or ``bool`` data types were cast to ``object`` in ``right`` and ``outer`` merge (:issue:`40073`)
 - Fixed regression in :meth:`RangeIndex.where` and :meth:`RangeIndex.putmask` raising ``AssertionError`` when result did not represent a :class:`RangeIndex` (:issue:`43240`)

pandas/core/groupby/groupby.py

@@ -1011,7 +1011,11 @@ def reset_identity(values):
 
         if not not_indexed_same:
             result = concat(values, axis=self.axis)
-            ax = self.filter(lambda x: True).axes[self.axis]
+            ax = (
+                self.filter(lambda x: True).axes[self.axis]
+                if self.dropna
+                else self._selected_obj._get_axis(self.axis)
+            )
 
             # this is a very unfortunate situation
             # we can't use reindex to restore the original order

pandas/tests/groupby/test_apply.py

@@ -1062,25 +1062,19 @@ def test_apply_by_cols_equals_apply_by_rows_transposed():
     tm.assert_frame_equal(by_cols, df)
 
 
-def test_apply_dropna_with_indexed_same():
+@pytest.mark.parametrize("dropna", [True, False])
+def test_apply_dropna_with_indexed_same(dropna):
     # GH 38227
-
+    # GH#43205
     df = DataFrame(
         {
             "col": [1, 2, 3, 4, 5],
             "group": ["a", np.nan, np.nan, "b", "b"],
         },
         index=list("xxyxz"),
     )
-    result = df.groupby("group").apply(lambda x: x)
-    expected = DataFrame(
-        {
-            "col": [1, 4, 5],
-            "group": ["a", "b", "b"],
-        },
-        index=list("xxz"),
-    )
-
+    result = df.groupby("group", dropna=dropna).apply(lambda x: x)
+    expected = df.dropna() if dropna else df.iloc[[0, 3, 1, 2, 4]]
     tm.assert_frame_equal(result, expected)