API: consistent sanitize_array-wrapping for lists in arithmetic ops

doc/source/whatsnew/v3.0.0.rst

@@ -743,6 +743,7 @@ Other API changes
 - :class:`Series` "flex" methods like :meth:`Series.add` no longer allow passing a :class:`DataFrame` for ``other``; use the DataFrame reversed method instead (:issue:`46179`)
 - :meth:`CategoricalIndex.append` no longer attempts to cast different-dtype indexes to the caller's dtype (:issue:`41626`)
 - :meth:`ExtensionDtype.construct_array_type` is now a regular method instead of a ``classmethod`` (:issue:`58663`)
+- Arithmetic operations between a :class:`Series`, :class:`Index`, or :class:`ExtensionArray` with a ``list`` now consistently wrap that list with an array equivalent to ``Series(my_list).array``. To do any other kind of type inference or casting, do so explicitly before operating (:issue:`62552`)
 - Comparison operations between :class:`Index` and :class:`Series` now consistently return :class:`Series` regardless of which object is on the left or right (:issue:`36759`)
 - Numpy functions like ``np.isinf`` that return a bool dtype when called on a :class:`Index` object now return a bool-dtype :class:`Index` instead of ``np.ndarray`` (:issue:`52676`)
 

pandas/core/arrays/masked.py

@@ -1017,6 +1017,9 @@ def _maybe_mask_result(
 
             return IntegerArray(result, mask, copy=False)
 
+        elif result.dtype == object:
+            result[mask] = self.dtype.na_value
+            return result
         else:
             result[mask] = np.nan
             return result

pandas/core/indexes/base.py

@@ -7285,19 +7285,7 @@ def _cmp_method(self, other, op):
         else:
             other = np.asarray(other)
 
-        if is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
-            # e.g. PeriodArray, Categorical
-            result = op(self._values, other)
-
-        elif isinstance(self._values, ExtensionArray):
-            result = op(self._values, other)
-
-        elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
-            # don't pass MultiIndex
-            result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
-
-        else:
-            result = ops.comparison_op(self._values, other, op)
+        result = ops.comparison_op(self._values, other, op)
 
         return result
 

pandas/core/ops/array_ops.py

@@ -53,7 +53,10 @@
 
 from pandas.core import roperator
 from pandas.core.computation import expressions
-from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    sanitize_array,
+)
 from pandas.core.ops import missing
 from pandas.core.ops.dispatch import should_extension_dispatch
 from pandas.core.ops.invalid import invalid_comparison
@@ -261,6 +264,10 @@ def arithmetic_op(left: ArrayLike, right: Any, op):
     #  and `maybe_prepare_scalar_for_op` has already been called on `right`
     # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy
     # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390)
+    if isinstance(right, list):
+        # GH#62423
+        right = sanitize_array(right, None)
+    right = ensure_wrapped_if_datetimelike(right)
 
     if (
         should_extension_dispatch(left, right)
@@ -310,7 +317,8 @@ def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
     if isinstance(rvalues, list):
         # We don't catch tuple here bc we may be comparing e.g. MultiIndex
         #  to a tuple that represents a single entry, see test_compare_tuple_strs
-        rvalues = np.asarray(rvalues)
+        rvalues = sanitize_array(rvalues, None)
+    rvalues = ensure_wrapped_if_datetimelike(rvalues)
 
     if isinstance(rvalues, (np.ndarray, ABCExtensionArray)):
         # TODO: make this treatment consistent across ops and classes.

pandas/core/ops/common.py

@@ -11,10 +11,16 @@
 from pandas._libs.missing import is_matching_na
 
 from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
     ABCIndex,
     ABCSeries,
 )
 
+from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
+    sanitize_array,
+)
+
 if TYPE_CHECKING:
     from collections.abc import Callable
 
@@ -56,6 +62,7 @@ def _unpack_zerodim_and_defer(method: F, name: str) -> F:
     -------
     method
     """
+    is_logical = name.strip("_") in ["or", "xor", "and", "ror", "rxor", "rand"]
 
     @wraps(method)
     def new_method(self, other):
@@ -66,6 +73,14 @@ def new_method(self, other):
                 return NotImplemented
 
         other = item_from_zerodim(other)
+        if (
+            isinstance(self, ABCExtensionArray)
+            and isinstance(other, list)
+            and not is_logical
+        ):
+            # See GH#62423
+            other = sanitize_array(other, None)
+            other = ensure_wrapped_if_datetimelike(other)
 
         return method(self, other)
 

pandas/tests/arithmetic/test_numeric.py

@@ -151,6 +151,23 @@ def test_numeric_cmp_string_numexpr_path(self, box_with_array, monkeypatch):
 
 
 class TestNumericArraylikeArithmeticWithDatetimeLike:
+    def test_mul_timedelta_list(self, box_with_array):
+        # GH#62524
+        box = box_with_array
+        left = np.array([3, 4])
+        left = tm.box_expected(left, box)
+
+        right = [Timedelta(days=1), Timedelta(days=2)]
+
+        result = left * right
+
+        expected = TimedeltaIndex([Timedelta(days=3), Timedelta(days=8)])
+        expected = tm.box_expected(expected, box)
+        tm.assert_equal(result, expected)
+
+        result2 = right * left
+        tm.assert_equal(result2, expected)
+
     @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
     @pytest.mark.parametrize(
         "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)

pandas/tests/arithmetic/test_string.py

@@ -213,9 +213,22 @@ def test_add_2d(any_string_dtype, request):
         s + b
 
 
-def test_add_sequence(any_string_dtype, request):
+def test_add_sequence(any_string_dtype, request, using_infer_string):
     dtype = any_string_dtype
-    if dtype == np.dtype(object):
+    if (
+        dtype != object
+        and dtype.storage == "python"
+        and dtype.na_value is np.nan
+        and HAS_PYARROW
+        and using_infer_string
+    ):
+        mark = pytest.mark.xfail(
+            reason="As of GH#62522, the list gets wrapped with sanitize_array, "
+            "which casts to a higher-priority StringArray, so we get "
+            "NotImplemented."
+        )
+        request.applymarker(mark)
+    if dtype == np.dtype(object) and using_infer_string:
         mark = pytest.mark.xfail(reason="Cannot broadcast list")
         request.applymarker(mark)
 
@@ -415,30 +428,45 @@ def test_comparison_methods_array_arrow_extension(comparison_op, any_string_dtyp
     tm.assert_extension_array_equal(result, expected)
 
 
-def test_comparison_methods_list(comparison_op, any_string_dtype):
+@pytest.mark.parametrize("box", [pd.array, pd.Index, Series])
+def test_comparison_methods_list(comparison_op, any_string_dtype, box, request):
     dtype = any_string_dtype
+
+    if box is pd.array and dtype != object and dtype.na_value is np.nan:
+        mark = pytest.mark.xfail(
+            reason="After wrapping list, op returns NotImplemented, see GH#62522"
+        )
+        request.applymarker(mark)
+
     op_name = f"__{comparison_op.__name__}__"
 
-    a = pd.array(["a", None, "c"], dtype=dtype)
+    a = box(pd.array(["a", None, "c"], dtype=dtype))
+    item = "c"
     other = [None, None, "c"]
     result = comparison_op(a, other)
 
     # ensure operation is commutative
     result2 = comparison_op(other, a)
     tm.assert_equal(result, result2)
 
-    if dtype == object or dtype.na_value is np.nan:
+    if dtype == np.dtype(object) or dtype.na_value is np.nan:
         if operator.ne == comparison_op:
             expected = np.array([True, True, False])
         else:
             expected = np.array([False, False, False])
-            expected[-1] = getattr(other[-1], op_name)(a[-1])
-        result = extract_array(result, extract_numpy=True)
-        tm.assert_numpy_array_equal(result, expected)
+            expected[-1] = getattr(item, op_name)(item)
+        if box is not pd.Index:
+            # if GH#62766 is addressed this check can be removed
+            expected = box(expected, dtype=expected.dtype)
+        tm.assert_equal(result, expected)
 
     else:
         expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
         expected = np.full(len(a), fill_value=None, dtype="object")
-        expected[-1] = getattr(other[-1], op_name)(a[-1])
+        expected[-1] = getattr(item, op_name)(item)
         expected = pd.array(expected, dtype=expected_dtype)
-        tm.assert_extension_array_equal(result, expected)
+        expected = extract_array(expected, extract_numpy=True)
+        if box is not pd.Index:
+            # if GH#62766 is addressed this check can be removed
+            expected = tm.box_expected(expected, box)
+        tm.assert_equal(result, expected)

pandas/tests/arithmetic/test_timedelta64.py

@@ -274,6 +274,23 @@ class TestTimedelta64ArithmeticUnsorted:
     # Tests moved from type-specific test files but not
     #  yet sorted/parametrized/de-duplicated
 
+    def test_td64_op_with_list(self, box_with_array):
+        # GH#62353
+        box = box_with_array
+
+        left = TimedeltaIndex(["2D", "4D"])
+        left = tm.box_expected(left, box)
+
+        right = [Timestamp("2016-01-01"), Timestamp("2016-02-01")]
+
+        result = left + right
+        expected = DatetimeIndex(["2016-01-03", "2016-02-05"], dtype="M8[ns]")
+        expected = tm.box_expected(expected, box)
+        tm.assert_equal(result, expected)
+
+        result2 = right + left
+        tm.assert_equal(result2, expected)
+
     def test_ufunc_coercions(self):
         # normal ops are also tested in tseries/test_timedeltas.py
         idx = TimedeltaIndex(["2h", "4h", "6h", "8h", "10h"], freq="2h", name="x")

pandas/tests/arrays/integer/test_arithmetic.py

@@ -201,10 +201,6 @@ def test_error_invalid_values(data, all_arithmetic_operators):
     ]:  # (data[~data.isna()] >= 0).all():
         res = ops(str_ser)
         expected = pd.Series(["foo" * x for x in data], index=s.index)
-        expected = expected.fillna(np.nan)
-        # TODO: doing this fillna to keep tests passing as we make
-        #  assert_almost_equal stricter, but the expected with pd.NA seems
-        #  more-correct than np.nan here.
         tm.assert_series_equal(res, expected)
     else:
         with tm.external_error_raised(TypeError):

pandas/tests/indexes/categorical/test_category.py

@@ -326,9 +326,10 @@ def test_disallow_addsub_ops(self, func, op_name):
         cat_or_list = "'(Categorical|list)' and '(Categorical|list)'"
         msg = "|".join(
             [
-                f"cannot perform {op_name} with this index type: CategoricalIndex",
-                "can only concatenate list",
                 rf"unsupported operand type\(s\) for [\+-]: {cat_or_list}",
+                "Object with dtype category cannot perform the numpy op (add|subtract)",
+                "operation 'r?(add|sub)' not supported for dtype 'str' "
+                "with dtype 'category'",
             ]
         )
         with pytest.raises(TypeError, match=msg):

pandas/tests/series/test_arithmetic.py

@@ -14,7 +14,6 @@
 import pytest
 
 from pandas._libs import lib
-from pandas.compat._optional import import_optional_dependency
 
 import pandas as pd
 from pandas import (
@@ -30,7 +29,6 @@
 import pandas._testing as tm
 from pandas.core import ops
 from pandas.core.computation import expressions as expr
-from pandas.util.version import Version
 
 
 @pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
@@ -380,36 +378,25 @@ def test_mask_div_propagate_na_for_non_na_dtype(self):
         result = ser2 / ser1
         tm.assert_series_equal(result, expected)
 
-    @pytest.mark.parametrize("val, dtype", [(3, "Int64"), (3.5, "Float64")])
-    def test_add_list_to_masked_array(self, val, dtype):
-        # GH#22962
+    @pytest.mark.parametrize("val", [3, 3.5])
+    def test_add_list_to_masked_array(self, val):
+        # GH#22962, behavior changed by GH#62552
         ser = Series([1, None, 3], dtype="Int64")
         result = ser + [1, None, val]
-        expected = Series([2, None, 3 + val], dtype=dtype)
+        expected = Series([2, pd.NA, 3 + val], dtype="Float64")
         tm.assert_series_equal(result, expected)
 
         result = [1, None, val] + ser
         tm.assert_series_equal(result, expected)
 
-    def test_add_list_to_masked_array_boolean(self, request):
+    def test_add_list_to_masked_array_boolean(self):
         # GH#22962
-        ne = import_optional_dependency("numexpr", errors="ignore")
-        warning = (
-            UserWarning
-            if request.node.callspec.id == "numexpr"
-            and ne
-            and Version(ne.__version__) < Version("2.13.1")
-            else None
-        )
         ser = Series([True, None, False], dtype="boolean")
-        msg = "operator is not supported by numexpr for the bool dtype"
-        with tm.assert_produces_warning(warning, match=msg):
-            result = ser + [True, None, True]
-        expected = Series([True, None, True], dtype="boolean")
+        result = ser + [True, None, True]
+        expected = Series([2, pd.NA, 1], dtype=object)
         tm.assert_series_equal(result, expected)
 
-        with tm.assert_produces_warning(warning, match=msg):
-            result = [True, None, True] + ser
+        result = [True, None, True] + ser
         tm.assert_series_equal(result, expected)
 
 

pandas/tests/series/test_constructors.py

@@ -2216,7 +2216,16 @@ def test_np_string_array_object_cast(self, data):
         arr = np.array(data, dtype=StringDType())
         res = Series(arr)
         assert res.dtype == np.object_
-        assert (res == data).all()
+
+        if data[-1] is np.nan:
+            # as of GH#62522 the comparison op for `res==data` casts data
+            #  using sanitize_array, which casts to 'str' dtype, which does not
+            #  consider string 'nan' to be equal to np.nan,
+            #  (which apparently numpy does?  weird.)
+            assert (res.iloc[:-1] == data[:-1]).all()
+            assert res.iloc[-1] == "nan"
+        else:
+            assert (res == data).all()
 
 
 class TestSeriesConstructorInternals: