BUG: Fix Series.reindex losing values when reindexing to MultiIndex (#61969)

Co-authored-by: Roline Stapny Saldanha <[email protected]>

Author: Roline-Stapny

doc/source/whatsnew/v3.0.0.rst

@@ -793,6 +793,7 @@ MultiIndex
 - Bug in :class:`DataFrame` arithmetic operations in case of unaligned MultiIndex columns (:issue:`60498`)
 - Bug in :class:`DataFrame` arithmetic operations with :class:`Series` in case of unaligned MultiIndex (:issue:`61009`)
 - Bug in :meth:`MultiIndex.from_tuples` causing wrong output with input of type tuples having NaN values (:issue:`60695`, :issue:`60988`)
+- Bug in :meth:`DataFrame.reindex` and :meth:`Series.reindex` where reindexing :class:`Index` to a :class:`MultiIndex` would incorrectly set all values to ``NaN``.(:issue:`60923`)
 
 I/O
 ^^^

pandas/core/generic.py

@@ -5373,6 +5373,18 @@ def reindex(
         """
         # TODO: Decide if we care about having different examples for different
         # kinds
+
+        # Automatically detect matching level when reindexing from Index to MultiIndex.
+        # This prevents values from being incorrectly set to NaN when the source index
+        # name matches a index name in the target MultiIndex
+        if (
+            level is None
+            and index is not None
+            and isinstance(index, MultiIndex)
+            and not isinstance(self.index, MultiIndex)
+            and self.index.name in index.names
+        ):
+            level = self.index.name
         self._check_copy_deprecation(copy)
 
         if index is not None and columns is not None and labels is not None:

pandas/tests/frame/methods/test_reindex.py

@@ -1258,3 +1258,31 @@ def test_invalid_method(self):
         msg = "Invalid fill method"
         with pytest.raises(ValueError, match=msg):
             df.reindex([1, 0, 2], method="asfreq")
+
+    def test_reindex_index_name_matches_multiindex_level(self):
+        df = DataFrame(
+            {"value": [1, 2], "other": ["A", "B"]},
+            index=Index([10, 20], name="a"),
+        )
+        target = MultiIndex.from_product(
+            [[10, 20], ["x", "y"]],
+            names=["a", "b"],
+        )
+
+        result = df.reindex(index=target)
+        expected = DataFrame(
+            data={"value": [1, 1, 2, 2], "other": ["A", "A", "B", "B"]},
+            index=MultiIndex.from_product([[10, 20], ["x", "y"]], names=["a", "b"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_index_name_no_match_multiindex_level(self):
+        df = DataFrame({"value": [1, 2]}, index=Index([10, 20], name="different_name"))
+        target = MultiIndex.from_product([[10, 20], ["x", "y"]], names=["a", "b"])
+
+        result = df.reindex(index=target)
+        expected = DataFrame(
+            data={"value": [np.nan] * 4},
+            index=MultiIndex.from_product([[10, 20], ["x", "y"]], names=["a", "b"]),
+        )
+        tm.assert_frame_equal(result, expected)

pandas/tests/series/methods/test_reindex.py

@@ -434,3 +434,41 @@ def test_reindex_expand_nonnano_nat(dtype):
         np.array([1, getattr(np, dtype)("nat", "s")], dtype=f"{dtype}[s]")
     )
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "name, expected_match_level_a",
+    [
+        ("a", True),
+        (None, False),
+        ("x", False),
+    ],
+)
+def test_reindex_multiindex_automatic_level(name, expected_match_level_a):
+    series = Series([26.73, 24.255], index=Index([81, 82], name=name))
+    target = MultiIndex.from_product(
+        [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]], names=["a", "b", "c"]
+    )
+
+    result = series.reindex(target)
+
+    if expected_match_level_a:
+        expected = Series(
+            data=[26.73, 26.73, 24.255, 24.255],
+            index=MultiIndex.from_product(
+                [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]],
+                names=["a", "b", "c"],
+            ),
+            dtype=series.dtype,
+        )
+    else:
+        expected = Series(
+            data=[np.nan] * 4,
+            index=MultiIndex.from_product(
+                [[81, 82], [np.nan], ["2018-06-01", "2018-07-01"]],
+                names=["a", "b", "c"],
+            ),
+            dtype=series.dtype,
+        )
+
+    tm.assert_series_equal(result, expected)