BUG: Series.__rfloordiv__ with list/tuple

doc/source/whatsnew/v1.4.0.rst

@@ -621,6 +621,7 @@ Timezones
 
 Numeric
 ^^^^^^^
+- Bug in floor-dividing a list or tuple of integers by a :class:`Series` incorrectly raising (:issue:`44674`)
 - Bug in :meth:`DataFrame.rank` raising ``ValueError`` with ``object`` columns and ``method="first"`` (:issue:`41931`)
 - Bug in :meth:`DataFrame.rank` treating missing values and extreme values as equal (for example ``np.nan`` and ``np.inf``), causing incorrect results when ``na_option="bottom"`` or ``na_option="top`` used (:issue:`41931`)
 - Bug in ``numexpr`` engine still being used when the option ``compute.use_numexpr`` is set to ``False`` (:issue:`32556`)

pandas/core/ops/array_ops.py

@@ -136,7 +136,7 @@ def _masked_arith_op(x: np.ndarray, y, op):
     return result
 
 
-def _na_arithmetic_op(left, right, op, is_cmp: bool = False):
+def _na_arithmetic_op(left: np.ndarray, right, op, is_cmp: bool = False):
     """
     Return the result of evaluating op on the passed in values.
 
@@ -146,6 +146,7 @@ def _na_arithmetic_op(left, right, op, is_cmp: bool = False):
     ----------
     left : np.ndarray
     right : np.ndarray or scalar
+        Excludes DataFrame, Series, Index, ExtensionArray.
     is_cmp : bool, default False
         If this a comparison operation.
 
@@ -166,7 +167,7 @@ def _na_arithmetic_op(left, right, op, is_cmp: bool = False):
     try:
         result = func(left, right)
     except TypeError:
-        if is_object_dtype(left) or is_object_dtype(right) and not is_cmp:
+        if not is_cmp and (is_object_dtype(left.dtype) or is_object_dtype(right)):
             # For object dtype, fallback to a masked operation (only operating
             #  on the non-missing values)
             # Don't do this for comparisons, as that will handle complex numbers
@@ -178,6 +179,7 @@ def _na_arithmetic_op(left, right, op, is_cmp: bool = False):
     if is_cmp and (is_scalar(result) or result is NotImplemented):
         # numpy returned a scalar instead of operating element-wise
         # e.g. numeric array vs str
+        # TODO: can remove this after dropping some future numpy version?
         return invalid_comparison(left, right, op)
 
     return missing.dispatch_fill_zeros(op, left, right, result)
@@ -251,7 +253,8 @@ def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
 
     rvalues = lib.item_from_zerodim(rvalues)
     if isinstance(rvalues, list):
-        # TODO: same for tuples?
+        # 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)
 
     if isinstance(rvalues, (np.ndarray, ABCExtensionArray)):
@@ -270,7 +273,7 @@ def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
         # Call the method on lvalues
         res_values = op(lvalues, rvalues)
 
-    elif is_scalar(rvalues) and isna(rvalues):
+    elif is_scalar(rvalues) and isna(rvalues):  # TODO: but not pd.NA?
         # numpy does not like comparisons vs None
         if op is operator.ne:
             res_values = np.ones(lvalues.shape, dtype=bool)
@@ -412,12 +415,13 @@ def get_array_op(op):
     """
     if isinstance(op, partial):
         # We get here via dispatch_to_series in DataFrame case
-        # TODO: avoid getting here
+        # e.g. test_rolling_consistency_var_debiasing_factors
         return op
 
     op_name = op.__name__.strip("_").lstrip("r")
     if op_name == "arith_op":
-        # Reached via DataFrame._combine_frame
+        # Reached via DataFrame._combine_frame i.e. flex methods
+        # e.g. test_df_add_flex_filled_mixed_dtypes
         return op
 
     if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}:

pandas/core/ops/missing.py

@@ -103,15 +103,15 @@ def mask_zero_div_zero(x, y, result: np.ndarray) -> np.ndarray:
     array([ inf,  nan, -inf])
     """
 
-    if is_scalar(y):
+    if not hasattr(y, "dtype"):
+        # e.g. scalar, tuple
         y = np.array(y)
+    if not hasattr(x, "dtype"):
+        # e.g scalar, tuple
+        x = np.array(x)
 
     zmask = y == 0
 
-    if isinstance(zmask, bool):
-        # FIXME: numpy did not evaluate pointwise, seen in docs build
-        return result
-
     if zmask.any():
 
         # Flip sign if necessary for -0.0

pandas/tests/series/test_arithmetic.py

@@ -729,13 +729,6 @@ def test_series_ops_name_retention(
         # GH#33930 consistent name renteiton
         op = all_binary_operators
 
-        if op is ops.rfloordiv and box in [list, tuple] and not flex:
-            request.node.add_marker(
-                pytest.mark.xfail(
-                    reason="op fails because of inconsistent ndarray-wrapping GH#28759"
-                )
-            )
-
         left = Series(range(10), name=names[0])
         right = Series(range(10), name=names[1])