diff --git a/doc/source/whatsnew/v1.5.1.rst b/doc/source/whatsnew/v1.5.1.rst
index a3c82111c630a..6a8fae61e23ba 100644
--- a/doc/source/whatsnew/v1.5.1.rst
+++ b/doc/source/whatsnew/v1.5.1.rst
@@ -76,6 +76,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.plot` ignoring invalid ``colormap`` for ``kind="scatter"`` (:issue:`48726`)
 - Fixed performance regression in :func:`factorize` when ``na_sentinel`` is not ``None`` and ``sort=False`` (:issue:`48620`)
 - Fixed Regression in :meth:`DataFrameGroupBy.apply` when user defined function is called on an empty dataframe (:issue:`47985`)
+- Fixed :meth:`.DataFrameGroupBy.size` not returning a Series when ``axis=1`` (:issue:`48738`)
 -
 
 .. ---------------------------------------------------------------------------
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index d5f22b816f908..46ab1f6a86329 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1869,11 +1869,13 @@ def _wrap_transform_fast_result(self, result: NDFrameT) -> NDFrameT:
             out = algorithms.take_nd(result._values, ids)
             output = obj._constructor(out, index=obj.index, name=obj.name)
         else:
+            # `.size()` gives Series output on DataFrame input, need axis 0
+            axis = 0 if result.ndim == 1 else self.axis
             # GH#46209
             # Don't convert indices: negative indices need to give rise
             # to null values in the result
-            output = result._take(ids, axis=self.axis, convert_indices=False)
-            output = output.set_axis(obj._get_axis(self.axis), axis=self.axis)
+            output = result._take(ids, axis=axis, convert_indices=False)
+            output = output.set_axis(obj._get_axis(self.axis), axis=axis)
         return output
 
     # -----------------------------------------------------------------
@@ -2397,13 +2399,6 @@ def size(self) -> DataFrame | Series:
         """
         result = self.grouper.size()
 
-        if self.axis == 1:
-            return DataFrame(
-                data=np.tile(result.values, (self.obj.shape[0], 1)),
-                columns=result.index,
-                index=self.obj.index,
-            )
-
         # GH28330 preserve subclassed Series/DataFrames through calls
         if isinstance(self.obj, Series):
             result = self._obj_1d_constructor(result, name=self.obj.name)
diff --git a/pandas/tests/groupby/test_size.py b/pandas/tests/groupby/test_size.py
index a614cf7abd684..92012436f6b47 100644
--- a/pandas/tests/groupby/test_size.py
+++ b/pandas/tests/groupby/test_size.py
@@ -33,12 +33,12 @@ def test_size_axis_1(df, axis_1, by, sort, dropna):
     counts = {key: sum(value == key for value in by) for key in dict.fromkeys(by)}
     if dropna:
         counts = {key: value for key, value in counts.items() if key is not None}
-    expected = DataFrame(counts, index=df.index)
+    expected = Series(counts)
     if sort:
-        expected = expected.sort_index(axis=1)
+        expected = expected.sort_index()
     grouped = df.groupby(by=by, axis=axis_1, sort=sort, dropna=dropna)
     result = grouped.size()
-    tm.assert_frame_equal(result, expected)
+    tm.assert_series_equal(result, expected)
 
 
 @pytest.mark.parametrize("by", ["A", "B", ["A", "B"]])
diff --git a/pandas/tests/groupby/transform/test_transform.py b/pandas/tests/groupby/transform/test_transform.py
index 113bd4a0c4c65..8a2bd64a3deb0 100644
--- a/pandas/tests/groupby/transform/test_transform.py
+++ b/pandas/tests/groupby/transform/test_transform.py
@@ -213,13 +213,9 @@ def test_transform_axis_1_reducer(request, reduction_func):
     df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}, index=["x", "y"])
     with tm.assert_produces_warning(warn, match=msg):
         result = df.groupby([0, 0, 1], axis=1).transform(reduction_func)
-    if reduction_func == "size":
-        # size doesn't behave in the same manner; hardcode expected result
-        expected = DataFrame(2 * [[2, 2, 1]], index=df.index, columns=df.columns)
-    else:
-        warn = FutureWarning if reduction_func == "mad" else None
-        with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
-            expected = df.T.groupby([0, 0, 1]).transform(reduction_func).T
+    warn = FutureWarning if reduction_func == "mad" else None
+    with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
+        expected = df.T.groupby([0, 0, 1]).transform(reduction_func).T
     tm.assert_equal(result, expected)