DEPR: deprecate silent alignment on arithmetic operations between Series with non-aligned MultiIndexes

doc/source/whatsnew/v3.0.0.rst

@@ -854,6 +854,7 @@ Other Deprecations
 - Deprecated backward-compatibility behavior for :meth:`DataFrame.select_dtypes` matching ``str`` dtype when ``np.object_`` is specified (:issue:`61916`)
 - Deprecated option ``future.no_silent_downcasting``, as it is no longer used. In a future version accessing this option will raise (:issue:`59502`)
 - Deprecated passing non-Index types to :meth:`Index.join`; explicitly convert to Index first (:issue:`62897`)
+- Deprecated silent alignment on arithmetic operations between :class:`Series` with non-aligned ``MultiIndexes`` (:issue:`25891`)
 - Deprecated silent casting of non-datetime ``other`` to datetime in :meth:`Series.combine_first` (:issue:`62931`)
 - Deprecated silently casting strings to :class:`Timedelta` in binary operations with :class:`Timedelta` (:issue:`59653`)
 - Deprecated slicing on a :class:`Series` or :class:`DataFrame` with a :class:`DatetimeIndex` using a ``datetime.date`` object, explicitly cast to :class:`Timestamp` instead (:issue:`35830`)

pandas/core/series.py

@@ -6662,6 +6662,21 @@ def _logical_method(self, other, op):
         return self._construct_result(res_values, name=res_name, other=other)
 
     def _arith_method(self, other, op):
+        if isinstance(other, Series):
+            if (
+                isinstance(self.index, MultiIndex)
+                and isinstance(other.index, MultiIndex)
+                and self.index.names != other.index.names
+            ):
+                # GH#25891
+                warnings.warn(
+                    "The silent alignment on arithmetic operations between "
+                    "'Series' with non-aligned MultiIndexes will be removed "
+                    "in a future version, please align MultiIndexes.",
+                    Pandas4Warning,
+                    stacklevel=find_stack_level(),
+                )
+
         self, other = self._align_for_op(other)
         return base.IndexOpsMixin._arith_method(self, other, op)
 

pandas/tests/series/test_arithmetic.py

@@ -14,6 +14,7 @@
 import pytest
 
 from pandas._libs import lib
+from pandas.errors import Pandas4Warning
 
 import pandas as pd
 from pandas import (
@@ -1043,14 +1044,19 @@ def test_series_varied_multiindex_alignment():
         [1000 * i for i in range(1, 5)],
         index=pd.MultiIndex.from_product([list("xy"), [1, 2]], names=["xy", "num"]),
     )
-    result = s1.loc[pd.IndexSlice[["a"], :, :]] + s2
+    msg = (
+        "The silent alignment on arithmetic operations between "
+        "'Series' with non-aligned MultiIndexes"
+    )
     expected = Series(
         [1000, 2001, 3002, 4003],
         index=pd.MultiIndex.from_tuples(
             [("a", "x", 1), ("a", "x", 2), ("a", "y", 1), ("a", "y", 2)],
             names=["ab", "xy", "num"],
         ),
     )
+    with tm.assert_produces_warning(Pandas4Warning, match=msg):
+        result = s1.loc[pd.IndexSlice[["a"], :, :]] + s2
     tm.assert_series_equal(result, expected)
 
 
@@ -1060,3 +1066,36 @@ def test_rmod_consistent_large_series():
     expected = Series([1] * 10001)
 
     tm.assert_series_equal(result, expected)
+
+
+def test_alignment_on_arithmetic_with_nonaligned_multiindex_deprecated(
+    all_arithmetic_operators,
+):
+    # GH#25891
+    index1 = pd.MultiIndex.from_product([[], []], names=["T", "N"])
+    s1 = Series(index=index1)
+    s1["T.1A", "N.0"] = 0.5
+    s1["T.1B", "N.0"] = 0.5
+    op = tm.get_op_from_name(all_arithmetic_operators)
+
+    index2 = pd.MultiIndex.from_product([[], []], names=["N", "M"])
+    s2 = Series(index=index2)
+    s2["N.0", "M.0"] = 0.5
+
+    expected = Series(
+        [0.25, 0.25],
+        index=pd.MultiIndex.from_tuples(
+            [("T.1A", "N.0", "M.0"), ("T.1B", "N.0", "M.0")],
+            names=["T", "N", "M"],
+        ),
+    )
+    msg = (
+        "The silent alignment on arithmetic operations between "
+        "'Series' with non-aligned MultiIndexes"
+    )
+    with tm.assert_produces_warning(Pandas4Warning, match=msg):
+        result = s1 * s2
+    tm.assert_series_equal(result, expected)
+
+    with tm.assert_produces_warning(Pandas4Warning, match=msg):
+        op(s1, s2)