DOC: definition of a scalar in rich comparison methods (#62191)

Author: cmp0xff

pandas/core/ops/docstrings.py

@@ -435,8 +435,10 @@ def make_flex_doc(op_name: str, typ: str) -> str:
 
 Parameters
 ----------
-other : Series or scalar value
-    The second operand in this operation.
+other : object
+    When a Series is provided, will align on indexes. For all other types,
+    will behave the same as ``==`` but with possibly different results due
+    to the other arguments.
 level : int or name
     Broadcast across a level, matching Index values on the
     passed MultiIndex level.

pandas/core/series.py

@@ -6092,8 +6092,10 @@ def eq(
 
         Parameters
         ----------
-        other : Series or scalar value
-            The second operand in this operation.
+        other : object
+            When a Series is provided, will align on indexes. For all other types,
+            will behave the same as ``==`` but with possibly different results due
+            to the other arguments.
         level : int or name
             Broadcast across a level, matching Index values on the
             passed MultiIndex level.
@@ -6161,8 +6163,10 @@ def le(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
 
         Parameters
         ----------
-        other : Series or scalar value
-            The second operand in this operation.
+        other : object
+            When a Series is provided, will align on indexes. For all other types,
+            will behave the same as ``==`` but with possibly different results due
+            to the other arguments.
         level : int or name
             Broadcast across a level, matching Index values on the
             passed MultiIndex level.
@@ -6233,8 +6237,10 @@ def ge(self, other, level=None, fill_value=None, axis: Axis = 0) -> Series:
 
         Parameters
         ----------
-        other : Series or scalar value
-            The second operand in this operation.
+        other : object
+            When a Series is provided, will align on indexes. For all other types,
+            will behave the same as ``==`` but with possibly different results due
+            to the other arguments.
         level : int or name
             Broadcast across a level, matching Index values on the
             passed MultiIndex level.

pandas/tests/series/methods/test_compare.py

@@ -1,3 +1,8 @@
+from enum import (
+    Enum,
+    auto,
+)
+
 import numpy as np
 import pytest
 
@@ -138,3 +143,61 @@ def test_compare_datetime64_and_string():
     tm.assert_series_equal(result_eq1, expected_eq)
     tm.assert_series_equal(result_eq2, expected_eq)
     tm.assert_series_equal(result_neq, expected_neq)
+
+
+def test_eq_objects():
+    # GH#62191 Test eq with Enum and List elements
+
+    class Thing(Enum):
+        FIRST = auto()
+        SECOND = auto()
+
+    left = pd.Series([Thing.FIRST, Thing.SECOND])
+    py_l = [Thing.FIRST, Thing.SECOND]
+
+    result = left.eq(Thing.FIRST)
+    expected = pd.Series([True, False])
+    tm.assert_series_equal(result, expected)
+
+    result = left.eq(py_l)
+    expected = pd.Series([True, True])
+    tm.assert_series_equal(result, expected)
+
+    result = left.eq(np.asarray(py_l))
+    expected = pd.Series([True, True])
+    tm.assert_series_equal(result, expected)
+
+    result = left.eq(pd.Series(py_l))
+    expected = pd.Series([True, True])
+    tm.assert_series_equal(result, expected)
+
+    result = pd.Series([[1, 2], [3, 4]]).eq([1, 2])
+    expected = pd.Series([True, False])
+    with pytest.raises(AssertionError):
+        tm.assert_series_equal(result, expected)
+    expected = pd.Series([False, False])
+    tm.assert_series_equal(result, expected)
+
+
+def test_eq_with_index():
+    # GH#62191 Test eq with non-trivial indices
+    left = pd.Series([1, 2], index=[1, 0])
+    py_l = [1, 2]
+
+    # assuming Python list has the same index as the Series
+    result = left.eq(py_l)
+    expected = pd.Series([True, True], index=[1, 0])
+    tm.assert_series_equal(result, expected)
+
+    # assuming np.ndarray has the same index as the Series
+    result = left.eq(np.asarray(py_l))
+    expected = pd.Series([True, True], index=[1, 0])
+    tm.assert_series_equal(result, expected)
+
+    result = left.eq(pd.Series(py_l))
+    expected = pd.Series([False, False])
+    tm.assert_series_equal(result, expected)
+
+    result = left.eq(pd.Series([2, 1]))
+    expected = pd.Series([True, True])
+    tm.assert_series_equal(result, expected)