BUG: Fix groupby nth with axis=1

doc/source/whatsnew/v1.4.0.rst

@@ -506,8 +506,10 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.apply` with time-based :class:`Grouper` objects incorrectly raising ``ValueError`` in corner cases where the grouping vector contains a ``NaT`` (:issue:`43500`, :issue:`43515`)
 - Bug in :meth:`GroupBy.mean` failing with ``complex`` dtype (:issue:`43701`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not calculating window bounds correctly for the first row when ``center=True`` and index is decreasing (:issue:`43927`)
+- Bug in :meth:`GroupBy.nth` failing on ``axis=1`` (:issue:`43926`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not respecting right bound on centered datetime-like windows, if the index contain duplicates (:issue:`#3944`)
 
+
 Reshaping
 ^^^^^^^^^
 - Improved error message when creating a :class:`DataFrame` column from a multi-dimensional :class:`numpy.ndarray` (:issue:`42463`)

pandas/core/groupby/groupby.py

@@ -2446,7 +2446,7 @@ def backfill(self, limit=None):
     @Substitution(see_also=_common_see_also)
     def nth(
         self, n: int | list[int], dropna: Literal["any", "all", None] = None
-    ) -> DataFrame:
+    ) -> NDFrameT:
         """
         Take the nth row from each group if n is an int, or a subset of rows
         if n is a list of ints.
@@ -2545,18 +2545,22 @@ def nth(
                 # Drop NA values in grouping
                 mask = mask & (ids != -1)
 
-                out = self._selected_obj[mask]
+                out = self._mask_selected_obj(mask)
+
                 if not self.as_index:
                     return out
 
                 result_index = self.grouper.result_index
-                out.index = result_index[ids[mask]]
+                if self.axis == 0:
+                    out.index = result_index[ids[mask]]
+                    if not self.observed and isinstance(result_index, CategoricalIndex):
+                        out = out.reindex(result_index)
 
-                if not self.observed and isinstance(result_index, CategoricalIndex):
-                    out = out.reindex(result_index)
+                    out = self._reindex_output(out)
+                else:
+                    out.columns = result_index[ids[mask]]
 
-                out = self._reindex_output(out)
-                return out.sort_index() if self.sort else out
+                return out.sort_index(axis=self.axis) if self.sort else out
 
         # dropna is truthy
         if isinstance(n, valid_containers):
@@ -2599,7 +2603,9 @@ def nth(
                 mutated=self.mutated,
             )
 
-        grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
+        grb = dropped.groupby(
+            grouper, as_index=self.as_index, sort=self.sort, axis=self.axis
+        )
         sizes, result = grb.size(), grb.nth(n)
         mask = (sizes < max_len)._values
 
@@ -3317,10 +3323,7 @@ def head(self, n=5):
         """
         self._reset_group_selection()
         mask = self._cumcount_array() < n
-        if self.axis == 0:
-            return self._selected_obj[mask]
-        else:
-            return self._selected_obj.iloc[:, mask]
+        return self._mask_selected_obj(mask)
 
     @final
     @Substitution(name="groupby")
@@ -3355,6 +3358,23 @@ def tail(self, n=5):
         """
         self._reset_group_selection()
         mask = self._cumcount_array(ascending=False) < n
+        return self._mask_selected_obj(mask)
+
+    @final
+    def _mask_selected_obj(self, mask: np.ndarray) -> NDFrameT:
+        """
+        Return _selected_obj with mask applied to the correct axis.
+
+        Parameters
+        ----------
+        mask : np.ndarray
+            Boolean mask to apply.
+
+        Returns
+        -------
+        Series or DataFrame
+            Filtered _selected_obj.
+        """
         if self.axis == 0:
             return self._selected_obj[mask]
         else:

pandas/tests/groupby/test_nth.py

@@ -706,3 +706,26 @@ def test_groupby_last_first_nth_with_none(method, nulls_fixture):
         result = getattr(data, method)()
 
     tm.assert_series_equal(result, expected)
+
+
+def test_groupby_nth_with_column_axis():
+    # GH43926
+    df = DataFrame(
+        [
+            [4, 5, 6],
+            [8, 8, 7],
+        ],
+        index=["z", "y"],
+        columns=["C", "B", "A"],
+    )
+    result = df.groupby(df.iloc[1], axis=1).nth(0)
+    expected = DataFrame(
+        [
+            [6, 4],
+            [7, 8],
+        ],
+        index=["z", "y"],
+        columns=[7, 8],
+    )
+    expected.columns.name = "y"
+    tm.assert_frame_equal(result, expected)