Bug groupby quantile listlike q and int columns

doc/source/whatsnew/v1.0.0.rst

@@ -848,6 +848,7 @@ Groupby/resample/rolling
 - 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`)
+- Bug in :meth:`GroupBy.quantile` with multiple list-like q value and integer column names (:issue:`30289`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/groupby.py

@@ -1937,21 +1937,22 @@ def post_processor(vals: np.ndarray, inference: Optional[Type]) -> np.ndarray:
             #  >>> result.stack(0).loc[pd.IndexSlice[:, ..., q], :]
             #  but this hits https://github.com/pandas-dev/pandas/issues/10710
             #  which doesn't reorder the list-like `q` on the inner level.
-            order = np.roll(list(range(result.index.nlevels)), -1)
-            result = result.reorder_levels(order)
-            result = result.reindex(q, level=-1)
+            order = list(range(1, result.index.nlevels)) + [0]
+
+            # temporarily saves the index names
+            index_names = np.array(result.index.names)
 
-            # fix order.
-            hi = len(q) * self.ngroups
-            arr = np.arange(0, hi, self.ngroups)
-            arrays = []
+            # set index names to positions to avoid confusion
+            result.index.names = np.arange(len(index_names))
+
+            # place quantiles on the inside
+            result = result.reorder_levels(order)
 
-            for i in range(self.ngroups):
-                arr2 = arr + i
-                arrays.append(arr2)
+            # restore the index names in order
+            result.index.names = index_names[order]
 
-            indices = np.concatenate(arrays)
-            assert len(indices) == len(result)
+            # reorder rows to keep things sorted
+            indices = np.arange(len(result)).reshape([len(q), self.ngroups]).T.flatten()
             return result.take(indices)
 
     @Substitution(name="groupby")

pandas/tests/groupby/test_function.py

@@ -1398,6 +1398,35 @@ def test_quantile_array_multiple_levels():
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize("frame_size", [(2, 3), (100, 10)])
+@pytest.mark.parametrize("groupby", [[0], [0, 1]])
+@pytest.mark.parametrize("q", [[0.5, 0.6]])
+def test_groupby_quantile_with_arraylike_q_and_int_columns(frame_size, groupby, q):
+    # GH30289
+    nrow, ncol = frame_size
+    df = pd.DataFrame(
+        np.array([ncol * [_ % 4] for _ in range(nrow)]), columns=range(ncol)
+    )
+
+    idx_levels = [list(range(min(nrow, 4)))] * len(groupby) + [q]
+    idx_codes = [[x for x in range(min(nrow, 4)) for _ in q]] * len(groupby) + [
+        list(range(len(q))) * min(nrow, 4)
+    ]
+    expected_index = pd.MultiIndex(
+        levels=idx_levels, codes=idx_codes, names=groupby + [None]
+    )
+    expected_values = [
+        [float(x)] * (ncol - len(groupby)) for x in range(min(nrow, 4)) for _ in q
+    ]
+    expected_columns = [x for x in range(ncol) if x not in groupby]
+    expected = pd.DataFrame(
+        expected_values, index=expected_index, columns=expected_columns
+    )
+    result = df.groupby(groupby).quantile(q)
+
+    tm.assert_frame_equal(result, expected)
+
+
 def test_quantile_raises():
     df = pd.DataFrame(
         [["foo", "a"], ["foo", "b"], ["foo", "c"]], columns=["key", "val"]