Incorporate feedback

Updated code as per code review suggestions.

Signed-off-by: Michael Tiemann <[email protected]>

Author: MichaelTiemannOSC

pandas/core/arrays/_mixins.py

@@ -262,12 +262,10 @@ def __setitem__(self, key, value) -> None:
         try:
             self._ndarray[key] = value
         except TypeError as exc:
-            # Don't let Python's handling of `complex` make extra complexity for Pandas
-            if self._ndarray.dtype.kind == "c":
-                raise ValueError(
-                    *(x.replace("real", "complex") for x in exc.args)
-                ) from exc
-            raise exc
+            # Note: when `self._ndarray.dtype.kind == "c"`, Numpy incorrectly complains
+            # that `must be real number, not ...` when in reality
+            # a complex argument is more likely what's expected
+            raise ValueError(exc.args) from exc
 
     def _validate_setitem_value(self, value):
         return value

pandas/tests/arithmetic/test_numeric.py

@@ -1122,67 +1122,67 @@ def test_ufunc_coercions(self, index_or_series, dtype):
         box = index_or_series
 
         result = np.sqrt(idx)
+        assert isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index(np.sqrt(np.array([1, 2, 3, 4, 5], dtype=exp_dtype)), name="x")
         exp = tm.box_expected(exp, box)
         tm.assert_equal(result, exp)
 
         result = np.divide(idx, 2.0)
+        assert isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=exp_dtype, name="x")
         exp = tm.box_expected(exp, box)
         tm.assert_equal(result, exp)
 
         # _evaluate_numeric_binop
         result = idx + 2.0
+        isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index([3.0, 4.0, 5.0, 6.0, 7.0], dtype=exp_dtype, name="x")
         exp = tm.box_expected(exp, box)
         tm.assert_equal(result, exp)
 
         result = idx - 2.0
+        isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index([-1.0, 0.0, 1.0, 2.0, 3.0], dtype=exp_dtype, name="x")
         exp = tm.box_expected(exp, box)
         tm.assert_equal(result, exp)
 
         result = idx * 1.0
+        isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index([1.0, 2.0, 3.0, 4.0, 5.0], dtype=exp_dtype, name="x")
         exp = tm.box_expected(exp, box)
         tm.assert_equal(result, exp)
 
         result = idx / 2.0
+        isinstance(result, box)
         if result.dtype.kind == "c":
-            assert result.dtype == dtype and isinstance(result, box)
             exp_dtype = dtype
         else:
-            assert result.dtype == "f8" and isinstance(result, box)
+            # assert result.dtype == "f8"
             exp_dtype = np.float64
         exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=exp_dtype, name="x")
         exp = tm.box_expected(exp, box)