diff --git a/python/cudf/cudf/core/groupby/groupby.py b/python/cudf/cudf/core/groupby/groupby.py
index 2519fda326a..2ed9bed5b49 100644
--- a/python/cudf/cudf/core/groupby/groupby.py
+++ b/python/cudf/cudf/core/groupby/groupby.py
@@ -1204,8 +1204,7 @@ def _jit_groupby_apply(
             {None: chunk_results}, index=group_names
         )
         result.index.names = self.grouping.names
-        result = result.reset_index()
-        result[None] = result.pop(0)
+
         return result
 
     @_cudf_nvtx_annotate
@@ -1364,10 +1363,12 @@ def mult(df):
         ...   lambda group: group['b'].max() - group['b'].min(),
         ...   engine='jit'
         ... )
-           a  None
-        0  1     1
-        1  2     1
-        2  3     1
+        a
+        1    1
+        2    1
+        3    1
+        dtype: int64
+
         """
 
         if self.obj.empty:
diff --git a/python/cudf/cudf/tests/test_groupby.py b/python/cudf/cudf/tests/test_groupby.py
index 7d22cb70803..b01b44da201 100644
--- a/python/cudf/cudf/tests/test_groupby.py
+++ b/python/cudf/cudf/tests/test_groupby.py
@@ -395,7 +395,7 @@ def groupby_jit_data():
 
 
 def run_groupby_apply_jit_test(data, func, keys, *args):
-    expect_groupby_obj = data.to_pandas().groupby(keys, as_index=False)
+    expect_groupby_obj = data.to_pandas().groupby(keys)
     got_groupby_obj = data.groupby(keys)
 
     # compare cuDF jit to pandas
@@ -475,7 +475,19 @@ def func(df):
 
 @pytest.mark.parametrize("dtype", ["float64"])
 @pytest.mark.parametrize("func", ["idxmax", "idxmin"])
-@pytest.mark.parametrize("special_val", [np.nan, np.inf, -np.inf])
+@pytest.mark.parametrize(
+    "special_val",
+    [
+        pytest.param(
+            np.nan,
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/13832"
+            ),
+        ),
+        np.inf,
+        -np.inf,
+    ],
+)
 def test_groupby_apply_jit_idx_reductions_special_vals(
     func, groupby_jit_data, dtype, special_val
 ):
@@ -494,19 +506,10 @@ def func(df):
     groupby_jit_data["val1"] = special_val
     groupby_jit_data["val1"] = groupby_jit_data["val1"].astype(dtype)
 
-    expect = (
-        groupby_jit_data.to_pandas()
-        .groupby("key1", as_index=False)
-        .apply(func)
-    )
-
-    grouped = groupby_jit_data.groupby("key1")
-    sorted = grouped._grouped()[3].to_pandas()
-    expect_vals = sorted["key1"].drop_duplicates().index
-    expect[None] = expect_vals
+    expect = groupby_jit_data.to_pandas().groupby("key1").apply(func)
+    got = groupby_jit_data.groupby("key1").apply(func, engine="jit")
 
-    got = grouped.apply(func, engine="jit")
-    assert_eq(expect, got)
+    assert_eq(expect, got, check_dtype=False)
 
 
 @pytest.mark.parametrize(