Groupby agg works with pd.Series.nunique, but groupby nunique fails with axis=1

doc/source/whatsnew/v1.0.0.rst

@@ -833,6 +833,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby` where ``any``, ``all``, ``nunique`` and transform functions would incorrectly handle duplicate column labels (:issue:`21668`)
 - Bug in :meth:`DataFrameGroupBy.agg` with timezone-aware datetime64 column incorrectly casting results to the original dtype (:issue:`29641`)
 - Bug in :meth:`DataFrame.groupby` when using axis=1 and having a single level columns index (:issue:`30208`)
+- Bug in :meth:`DataFrame.groupby` when using nunique on axis=1 (:issue:`30253`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/generic.py

@@ -1813,9 +1813,20 @@ def groupby_series(obj, col=None):
             # Try to consolidate with normal wrapping functions
             from pandas.core.reshape.concat import concat
 
-            results = [groupby_series(content, label) for label, content in obj.items()]
+            axis_number = obj._get_axis_number(self.axis)
+            other_axis = int(not axis_number)
+            if axis_number == 0:
+                iter_func = obj.items
+            else:
+                iter_func = obj.iterrows
+
+            results = [groupby_series(content, label) for label, content in iter_func()]
             results = concat(results, axis=1)
-            results.columns.names = obj.columns.names
+
+            if axis_number == 1:
+                results = results.T
+
+            results._get_axis(other_axis).names = obj._get_axis(other_axis).names
 
         if not self.as_index:
             results.index = ibase.default_index(len(results))

pandas/tests/groupby/test_groupby.py

@@ -1994,3 +1994,20 @@ def test_dup_labels_output_shape(groupby_func, idx):
 
     assert result.shape == (1, 2)
     tm.assert_index_equal(result.columns, idx)
+
+
+def test_groupby_crash_on_nunique(axis):
+    # Fix following 30253
+    df = pd.DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})
+
+    axis_number = df._get_axis_number(axis)
+    if not axis_number:
+        df = df.T
+
+    result = df.groupby(axis=axis_number, level=0).nunique()
+
+    expected = pd.DataFrame({"A": [1, 2], "D": [1, 1]})
+    if not axis_number:
+        expected = expected.T
+
+    tm.assert_frame_equal(result, expected)