REF: simplify maybe_upcast (#38428)

Author: jbrockmendel

pandas/core/dtypes/cast.py

@@ -924,45 +924,31 @@ def maybe_infer_dtype_type(element):
 
 
 def maybe_upcast(
- values: ArrayLike,
+ values: np.ndarray,
  fill_value: Scalar = np.nan,
- dtype: Dtype = None,
  copy: bool = False,
-) -> Tuple[ArrayLike, Scalar]:
+) -> Tuple[np.ndarray, Scalar]:
  """
  Provide explicit type promotion and coercion.
 
  Parameters
  ----------
- values : ndarray or ExtensionArray
- The array that we want to maybe upcast.
+ values : np.ndarray
+ The array that we may want to upcast.
  fill_value : what we want to fill with
- dtype : if None, then use the dtype of the values, else coerce to this type
  copy : bool, default True
  If True always make a copy even if no upcast is required.
 
  Returns
  -------
- values: ndarray or ExtensionArray
+ values: np.ndarray
  the original array, possibly upcast
  fill_value:
  the fill value, possibly upcast
  """
- if not is_scalar(fill_value) and not is_object_dtype(values.dtype):
- # We allow arbitrary fill values for object dtype
- raise ValueError("fill_value must be a scalar")
-
- if is_extension_array_dtype(values):
- if copy:
- values = values.copy()
- else:
- if dtype is None:
- dtype = values.dtype
- new_dtype, fill_value = maybe_promote(dtype, fill_value)
- if new_dtype != values.dtype:
- values = values.astype(new_dtype)
- elif copy:
- values = values.copy()
+ new_dtype, fill_value = maybe_promote(values.dtype, fill_value)
+ # We get a copy in all cases _except_ (values.dtype == new_dtype and not copy)
+ values = values.astype(new_dtype, copy=copy)
 
  return values, fill_value