BUG: GroupBy.quantile error with integer columns and arraylike q (GH30289)

Author: fujiaxiang

doc/source/whatsnew/v1.0.0.rst

@@ -846,6 +846,7 @@ 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
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/groupby.py

@@ -1937,21 +1937,18 @@ 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)
+            order = np.append(np.arange(1, result.index.nlevels), 0)
+            # temporarily saves the index names
+            index_names = np.array(result.index.names)
+            # 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)
-            result = result.reindex(q, level=-1)
+            # restore the index names in order
+            result.index.names = index_names[order]
 
-            # fix order.
-            hi = len(q) * self.ngroups
-            arr = np.arange(0, hi, self.ngroups)
-            arrays = []
-
-            for i in range(self.ngroups):
-                arr2 = arr + i
-                arrays.append(arr2)
-
-            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")