Backport PR pandas-dev#31529: BUG: Series multiplication with timedelta scalar numexpr path

Author: jbrockmendel

doc/source/whatsnew/v1.0.1.rst

@@ -44,6 +44,7 @@ Timezones
 Numeric
 ^^^^^^^
 - Bug in dtypes being lost in ``DataFrame.__invert__`` (``~`` operator) with mixed dtypes (:issue:`31183`)
+- Bug in :class:`Series` multiplication when multiplying a numeric :class:`Series` with >10000 elements with a timedelta-like scalar (:issue:`31467`)
 -
 
 Conversion

pandas/core/ops/array_ops.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 
-from pandas._libs import Timestamp, lib, ops as libops
+from pandas._libs import Timedelta, Timestamp, lib, ops as libops
 
 from pandas.core.dtypes.cast import (
     construct_1d_object_array_from_listlike,
@@ -184,11 +184,12 @@ def arithmetic_op(
     rvalues = maybe_upcast_for_op(rvalues, lvalues.shape)
 
     if should_extension_dispatch(left, rvalues) or isinstance(
-        rvalues, (ABCTimedeltaArray, ABCDatetimeArray, Timestamp)
+        rvalues, (ABCTimedeltaArray, ABCDatetimeArray, Timestamp, Timedelta)
     ):
         # TimedeltaArray, DatetimeArray, and Timestamp are included here
         #  because they have `freq` attribute which is handled correctly
         #  by dispatch_to_extension_op.
+        # Timedelta is included because numexpr will fail on it, see GH#31457
         res_values = dispatch_to_extension_op(op, lvalues, rvalues)
 
     else:

pandas/tests/arithmetic/test_numeric.py

@@ -176,6 +176,28 @@ def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box):
         commute = scalar_td * index
         tm.assert_equal(commute, expected)
 
+    @pytest.mark.parametrize(
+        "scalar_td",
+        [
+            Timedelta(days=1),
+            Timedelta(days=1).to_timedelta64(),
+            Timedelta(days=1).to_pytimedelta(),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_numeric_arr_mul_tdscalar_numexpr_path(self, scalar_td, box):
+        arr = np.arange(2 * 10 ** 4).astype(np.int64)
+        obj = tm.box_expected(arr, box, transpose=False)
+
+        expected = arr.view("timedelta64[D]").astype("timedelta64[ns]")
+        expected = tm.box_expected(expected, box, transpose=False)
+
+        result = obj * scalar_td
+        tm.assert_equal(result, expected)
+
+        result = scalar_td * obj
+        tm.assert_equal(result, expected)
+
     def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box):
         index = numeric_idx[1:3]