BUG: Enable Series.equals to compare numpy arrays to scalars (#36161)

doc/source/whatsnew/v1.2.0.rst

@@ -270,6 +270,7 @@ Numeric
 ^^^^^^^
 - Bug in :func:`to_numeric` where float precision was incorrect (:issue:`31364`)
 - Bug in :meth:`DataFrame.any` with ``axis=1`` and ``bool_only=True`` ignoring the ``bool_only`` keyword (:issue:`32432`)
+- Bug in :meth:`Series.equals` where a ``ValueError`` was raised when numpy arrays were compared to scalars (:issue:`35267`)
 -
 
 Conversion

pandas/_libs/lib.pyx

@@ -591,7 +591,16 @@ def array_equivalent_object(left: object[:], right: object[:]) -> bool:
             if "tz-naive and tz-aware" in str(err):
                 return False
             raise
-
+        except ValueError:
+            # Avoid raising ValueError when comparing Numpy arrays to other types
+            if cnp.PyArray_IsAnyScalar(x) != cnp.PyArray_IsAnyScalar(y):
+                # Only compare scalars to scalars and non-scalars to non-scalars
+                return False
+            elif (not (cnp.PyArray_IsPythonScalar(x) or cnp.PyArray_IsPythonScalar(y))
+                  and not (isinstance(x, type(y)) or isinstance(y, type(x)))):
+                # Check if non-scalars have the same type
+                return False
+            raise
     return True
 
 

pandas/tests/dtypes/test_missing.py

@@ -1,3 +1,4 @@
+from contextlib import nullcontext
 from datetime import datetime
 from decimal import Decimal
 
@@ -383,6 +384,20 @@ def test_array_equivalent(dtype_equal):
     assert not array_equivalent(DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan]))
 
 
+@pytest.mark.parametrize(
+    "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None]
+)
+def test_array_equivalent_series(val):
+    arr = np.array([1, 2])
+    cm = (
+        tm.assert_produces_warning(FutureWarning, check_stacklevel=False)
+        if isinstance(val, str)
+        else nullcontext()
+    )
+    with cm:
+        assert not array_equivalent(Series([arr, arr]), Series([arr, val]))
+
+
 def test_array_equivalent_different_dtype_but_equal():
     # Unclear if this is exposed anywhere in the public-facing API
     assert array_equivalent(np.array([1, 2]), np.array([1.0, 2.0]))

pandas/tests/series/methods/test_equals.py

@@ -1,7 +1,10 @@
+from contextlib import nullcontext
+
 import numpy as np
 import pytest
 
 from pandas import MultiIndex, Series
+import pandas._testing as tm
 
 
 @pytest.mark.parametrize(
@@ -24,16 +27,25 @@ def test_equals(arr, idx):
     assert not s1.equals(s2)
 
 
-def test_equals_list_array():
+@pytest.mark.parametrize(
+    "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None]
+)
+def test_equals_list_array(val):
     # GH20676 Verify equals operator for list of Numpy arrays
     arr = np.array([1, 2])
     s1 = Series([arr, arr])
     s2 = s1.copy()
     assert s1.equals(s2)
 
-    # TODO: Series equals should also work between single value and list
-    # s1[1] = 9
-    # assert not s1.equals(s2)
+    s1[1] = val
+
+    cm = (
+        tm.assert_produces_warning(FutureWarning, check_stacklevel=False)
+        if isinstance(val, str)
+        else nullcontext()
+    )
+    with cm:
+        assert not s1.equals(s2)
 
 
 def test_equals_false_negative():