diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index a30d68319cafe..504d03419b5dc 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -1126,6 +1126,7 @@ Groupby/resample/rolling
 - Bug in :class:`.DataFrameGroupBy` would raise when used with an empty DataFrame, categorical grouper, and ``dropna=False`` (:issue:`50634`)
 - Bug in :meth:`.SeriesGroupBy.value_counts` did not respect ``sort=False`` (:issue:`50482`)
 - Bug in :meth:`.DataFrameGroupBy.resample` raises ``KeyError`` when getting the result from a key list when resampling on time index (:issue:`50840`)
+- Bug in :meth:`.DataFrameGroupBy.transform` and :meth:`.SeriesGroupBy.transform` would raise incorrectly when grouper had ``axis=1`` for ``"ngroup"`` argument (:issue:`45986`)
 -
 
 Reshaping
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index c15948ce877a8..a2fa58e795da5 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3394,7 +3394,7 @@ def ngroup(self, ascending: bool = True):
         dtype: int64
         """
         with self._group_selection_context():
-            index = self._selected_obj.index
+            index = self._selected_obj._get_axis(self.axis)
             comp_ids = self.grouper.group_info[0]
 
             dtype: type
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index ab4f2bd5e529b..81dca42b1d74f 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -164,10 +164,6 @@ def test_transform_broadcast(tsframe, ts):
 def test_transform_axis_1(request, transformation_func):
     # GH 36308
 
-    if transformation_func == "ngroup":
-        msg = "ngroup fails with axis=1: #45986"
-        request.node.add_marker(pytest.mark.xfail(reason=msg))
-
     df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}, index=["x", "y"])
     args = get_groupby_method_args(transformation_func, df)
     result = df.groupby([0, 0, 1], axis=1).transform(transformation_func, *args)