review comments: mask_missing/infer_dtype_from_array

pandas/core/dtypes/cast.py

@@ -841,8 +841,8 @@ def dict_compat(d: Dict[Scalar, Scalar]) -> Dict[Scalar, Scalar]:
 
 
 def infer_dtype_from_array(
- arr: Union[ArrayLike, PandasScalar], pandas_dtype: bool = False
-) -> Tuple[Dtype, ArrayLike]:
+ arr: Union[ArrayLike, Series, PandasScalar], pandas_dtype: bool = False
+) -> Tuple[DtypeObj, Union[ArrayLike, Series]]:
  """
  Infer the dtype from an array.
 
@@ -890,7 +890,7 @@ def infer_dtype_from_array(
  # don't force numpy coerce with nan's
  inferred = lib.infer_dtype(arr, skipna=False)
  if inferred in ["string", "bytes", "mixed", "mixed-integer"]:
- return (np.dtype(np.object_), arr)
+ return np.dtype(np.object_), arr
 
  arr = np.asarray(arr)
  return arr.dtype, arr

pandas/core/missing.py

@@ -20,10 +20,12 @@
 from pandas.core.dtypes.missing import isna
 
 if TYPE_CHECKING:
- from pandas import Index
+ from pandas import Index, Series
 
 
-def mask_missing(arr: ArrayLike, values_to_mask: ArrayLike) -> np.ndarray:
+def mask_missing(
+ arr: ArrayLike, values_to_mask: "Union[ArrayLike, Scalar, Series]"
+) -> np.ndarray:
  """
  Return a masking array of same size/shape as arr
  with entries equaling any member of values_to_mask set to True