BUG: concat should keep series names unless ignore_index=True

doc/source/whatsnew/v2.2.0.rst

@@ -636,6 +636,7 @@ Groupby/resample/rolling
 Reshaping
 ^^^^^^^^^
 - Bug in :func:`concat` ignoring ``sort`` parameter when passed :class:`DatetimeIndex` indexes (:issue:`54769`)
+- Bug in :func:`concat` renaming :class:`Series` when ``ignore_index=False`` (:issue:`15047`)
 - Bug in :func:`merge_asof` raising ``TypeError`` when ``by`` dtype is not ``object``, ``int64``, or ``uint64`` (:issue:`22794`)
 - Bug in :func:`merge` returning columns in incorrect order when left and/or right is empty (:issue:`51929`)
 - Bug in :meth:`DataFrame.melt` where an exception was raised if ``var_name`` was not a string (:issue:`55948`)

pandas/core/reshape/concat.py

@@ -464,7 +464,7 @@ def __init__(
         # if we have mixed ndims, then convert to highest ndim
         # creating column numbers as needed
         if len(ndims) > 1:
-            objs, sample = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
+            objs = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
 
         self.objs = objs
 
@@ -580,7 +580,7 @@ def _sanitize_mixed_ndim(
         sample: Series | DataFrame,
         ignore_index: bool,
         axis: AxisInt,
-    ) -> tuple[list[Series | DataFrame], Series | DataFrame]:
+    ) -> list[Series | DataFrame]:
         # if we have mixed ndims, then convert to highest ndim
         # creating column numbers as needed
 
@@ -601,19 +601,21 @@ def _sanitize_mixed_ndim(
             else:
                 name = getattr(obj, "name", None)
                 if ignore_index or name is None:
-                    name = current_column
-                    current_column += 1
-
-                # doing a row-wise concatenation so need everything
-                # to line up
-                if self._is_frame and axis == 1:
-                    name = 0
+                    if axis == 1:
+                        # doing a row-wise concatenation so need everything
+                        # to line up
+                        name = 0
+                    else:
+                        # doing a column-wise concatenation so need series
+                        # to have unique names
+                        name = current_column
+                        current_column += 1
 
                 obj = sample._constructor({name: obj}, copy=False)
 
             new_objs.append(obj)
 
-        return new_objs, sample
+        return new_objs
 
     def get_result(self):
         cons: Callable[..., DataFrame | Series]

pandas/tests/reshape/concat/test_concat.py

@@ -267,11 +267,10 @@ def test_with_mixed_tuples(self, sort):
         # it works
         concat([df1, df2], sort=sort)
 
-    def test_concat_mixed_objs(self):
-        # concat mixed series/frames
+    def test_concat_mixed_objs_columns(self):
+        # Test column-wise concat for mixed series/frames (axis=1)
         # G2385
 
-        # axis 1
         index = date_range("01-Jan-2013", periods=10, freq="h")
         arr = np.arange(10, dtype="int64")
         s1 = Series(arr, index=index)
@@ -324,13 +323,41 @@ def test_concat_mixed_objs(self):
         result = concat([s1, df, s2], axis=1, ignore_index=True)
         tm.assert_frame_equal(result, expected)
 
-        # axis 0
+    def test_concat_mixed_objs_index(self):
+        # Test row-wise concat for mixed series/frames with a common name
+        # GH2385, GH15047
+
+        index = date_range("01-Jan-2013", periods=10, freq="h")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index)
+        s2 = Series(arr, index=index)
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
         expected = DataFrame(
             np.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
         )
         result = concat([s1, df, s2])
         tm.assert_frame_equal(result, expected)
 
+    def test_concat_mixed_objs_index_names(self):
+        # Test row-wise concat for mixed series/frames with distinct names
+        # GH2385, GH15047
+
+        index = date_range("01-Jan-2013", periods=10, freq="h")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index, name="foo")
+        s2 = Series(arr, index=index, name="bar")
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
+        expected = DataFrame(
+            np.kron(np.where(np.identity(3) == 1, 1, np.nan), arr).T,
+            index=index.tolist() * 3,
+            columns=["foo", 0, "bar"],
+        )
+        result = concat([s1, df, s2])
+        tm.assert_frame_equal(result, expected)
+
+        # Rename all series to 0 when ignore_index=True
         expected = DataFrame(np.tile(arr, 3).reshape(-1, 1), columns=[0])
         result = concat([s1, df, s2], ignore_index=True)
         tm.assert_frame_equal(result, expected)