BUG: Maintain the order of the bins in group_quantile. Updated tests p…

…andas-dev#33200

doc/source/whatsnew/v1.1.0.rst

@@ -603,6 +603,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.resample` where an ``AmbiguousTimeError`` would be raised when the resulting timezone aware :class:`DatetimeIndex` had a DST transition at midnight (:issue:`25758`)
 - Bug in :meth:`DataFrame.groupby` where a ``ValueError`` would be raised when grouping by a categorical column with read-only categories and ``sort=False`` (:issue:`33410`)
 - Bug in :meth:`GroupBy.first` and :meth:`GroupBy.last` where None is not preserved in object dtype (:issue:`32800`)
+- Bug in :meth:`SeriesGroupBy.quantile` causes the quantiles to be shifted when the ``by`` axis contains ``NaN`` (:issue:`33200`)
 
 Reshaping
 ^^^^^^^^^

pandas/_libs/groupby.pyx

@@ -779,9 +779,10 @@ def group_quantile(ndarray[float64_t] out,
                 non_na_counts[lab] += 1
 
     # Get an index of values sorted by labels and then values
-    order = (values, labels)
-    sort_arr = np.lexsort(order).astype(np.int64, copy=False)
-
+    sort_arr = np.arange(len(labels), dtype=np.int64)
+    mask = labels != -1
+    order = (np.asarray(values)[mask], labels[mask])
+    sort_arr[mask] = np.lexsort(order).astype(np.int64, copy=False)
     with nogil:
         for i in range(ngroups):
             # Figure out how many group elements there are
@@ -819,7 +820,9 @@ def group_quantile(ndarray[float64_t] out,
 
             # Increment the index reference in sorted_arr for the next group
             grp_start += grp_sz
-
+    print(out)
+    # out = np.roll(out, -(len(out) - np.sum(counts)))
+    print(out)
 
 # ----------------------------------------------------------------------
 # group_nth, group_last, group_rank

pandas/tests/groupby/test_function.py

@@ -1507,14 +1507,22 @@ def test_quantile_missing_group_values_no_segfaults():
         grp.quantile()
 
 
-def test_quantile_missing_group_values_correct_results():
-    # GH 28662
-    data = np.array([1.0, np.nan, 3.0, np.nan])
-    df = pd.DataFrame(dict(key=data, val=range(4)))
+@pytest.mark.parametrize(
+    "key, val, expected_key, expected_val",
+    [
+        ([1.0, np.nan, 3.0, np.nan], range(4), [1.0, 3.0], [0.0, 1.0]),
+        (["a", "b", "b", np.nan], range(4), ["a", "b"], [0, 1.5]),
+    ],
+)
+def test_quantile_missing_group_values_correct_results(
+    key, val, expected_key, expected_val
+):
+    # GH 28662, GH 33200
+    df = pd.DataFrame({"key": key, "val": val})
 
     result = df.groupby("key").quantile()
     expected = pd.DataFrame(
-        [1.0, 3.0], index=pd.Index([1.0, 3.0], name="key"), columns=["val"]
+        expected_val, index=pd.Index(expected_key, name="key"), columns=["val"]
     )
     tm.assert_frame_equal(result, expected)