CLN: remove ndarray cases from maybe_promote

pandas/core/dtypes/cast.py

@@ -520,28 +520,15 @@ def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
     ValueError
         If fill_value is a non-scalar and dtype is not object.
     """
-    if not is_scalar(fill_value) and not is_object_dtype(dtype):
+    if not is_scalar(fill_value):
         # with object dtype there is nothing to promote, and the user can
         #  pass pretty much any weird fill_value they like
-        raise ValueError("fill_value must be a scalar")
-
-    # if we passed an array here, determine the fill value by dtype
-    if isinstance(fill_value, np.ndarray):
-        if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)):
-            fill_value = fill_value.dtype.type("NaT", "ns")
-        else:
-
-            # we need to change to object type as our
-            # fill_value is of object type
-            if fill_value.dtype == np.object_:
-                dtype = np.dtype(np.object_)
-            fill_value = np.nan
-
-        if dtype == np.object_ or dtype.kind in ["U", "S"]:
-            # We treat string-like dtypes as object, and _always_ fill
-            #  with np.nan
-            fill_value = np.nan
-            dtype = np.dtype(np.object_)
+        if not is_object_dtype(dtype):
+            # with object dtype there is nothing to promote, and the user can
+            #  pass pretty much any weird fill_value they like
+            raise ValueError("fill_value must be a scalar")
+        dtype = np.dtype(object)
+        return dtype, fill_value
 
     # returns tuple of (dtype, fill_value)
     if issubclass(dtype.type, np.datetime64):

pandas/tests/dtypes/cast/test_promote.py

@@ -605,31 +605,3 @@ def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype_reduced, nulls_fi
             exp_val_for_scalar = np.nan
 
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
-
-
-@pytest.mark.parametrize("dim", [0, 2, 3])
-def test_maybe_promote_dimensions(any_numpy_dtype_reduced, dim):
-    dtype = np.dtype(any_numpy_dtype_reduced)
-
-    # create 0-dim array of given dtype; casts "1" to correct dtype
-    fill_array = np.array(1, dtype=dtype)
-
-    # expand to desired dimension:
-    for _ in range(dim):
-        fill_array = np.expand_dims(fill_array, 0)
-
-    if dtype != object:
-        # test against 1-dimensional case
-        with pytest.raises(ValueError, match="fill_value must be a scalar"):
-            maybe_promote(dtype, np.array([1], dtype=dtype))
-
-        with pytest.raises(ValueError, match="fill_value must be a scalar"):
-            maybe_promote(dtype, fill_array)
-
-    else:
-        expected_dtype, expected_missing_value = maybe_promote(
-            dtype, np.array([1], dtype=dtype)
-        )
-        result_dtype, result_missing_value = maybe_promote(dtype, fill_array)
-        assert result_dtype == expected_dtype
-        _assert_match(result_missing_value, expected_missing_value)