Avoid reindexing values assigned to a multi-index when cols_droplevel contains empty values (#62404)

Author: matiaslindgren

doc/source/whatsnew/v3.0.0.rst

@@ -999,6 +999,7 @@ 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.__setitem__` where column alignment logic would reindex the assigned value with an empty index, incorrectly setting all values to ``NaN``.(:issue:`61841`)
 - 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/frame.py

@@ -4452,6 +4452,11 @@ def _set_item_frame_value(self, key, value: DataFrame) -> None:
                 loc, (slice, Series, np.ndarray, Index)
             ):
                 cols_droplevel = maybe_droplevels(cols, key)
+                if (
+                    not isinstance(cols_droplevel, MultiIndex)
+                    and not cols_droplevel.any()
+                ):
+                    return
                 if len(cols_droplevel) and not cols_droplevel.equals(value.columns):
                     value = value.reindex(cols_droplevel, axis=1)
 

pandas/tests/indexing/multiindex/test_multiindex.py

@@ -249,3 +249,25 @@ def test_groupyby_rename_categories_operation_with_multiindex(self, operation):
         expected = getattr(a, operation)(b.sort_index(ascending=False))
 
         tm.assert_series_equal(result, expected)
+
+    def test_multiindex_assign_aligns_as_implicit_tuple(self):
+        # GH 61841
+        cols = MultiIndex.from_tuples([("A", "B")])
+        df1 = DataFrame([[i] for i in range(3)], columns=cols)
+        df2 = df1.copy()
+        df3 = df1.copy()
+        s1 = df1["A"].rolling(2).mean()
+        s2 = s1.copy()
+        s3 = s1.copy()
+
+        df2["C"] = s2
+        df3[("C", "")] = s3
+        tm.assert_frame_equal(df2, df3)
+
+        df1["C"] = s1
+        tm.assert_frame_equal(df1, df2)
+        tm.assert_frame_equal(df1, df3)
+
+        df1["C"] = s1
+        tm.assert_frame_equal(df1, df2)
+        tm.assert_frame_equal(df1, df3)