Add type hints to maybe_promote in dtypes.py (#8243)

* Add type hints to maybe_promote

* attempt to type hint fill_value

* Update dtypes.py

* Update dtypes.py

* avoid type redefinition

* I give upp with fill_value, pandas mostly do it as well. Only 1 place had the Scalar typing

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update types.py

* Update dtypes.py

* Update dtypes.py

* Update variables.py

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

xarray/coding/variables.py

@@ -304,6 +304,8 @@ def decode(self, variable: Variable, name: T_Name = None):
                 )
 
             # special case DateTime to properly handle NaT
+            dtype: np.typing.DTypeLike
+            decoded_fill_value: Any
             if _is_time_like(attrs.get("units")) and data.dtype.kind in "iu":
                 dtype, decoded_fill_value = np.int64, np.iinfo(np.int64).min
             else:

xarray/core/dtypes.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import functools
+from typing import Any
 
 import numpy as np
 
@@ -44,7 +45,7 @@ def __eq__(self, other):
 )
 
 
-def maybe_promote(dtype):
+def maybe_promote(dtype: np.dtype) -> tuple[np.dtype, Any]:
     """Simpler equivalent of pandas.core.common._maybe_promote
 
     Parameters
@@ -57,27 +58,33 @@ def maybe_promote(dtype):
     fill_value : Valid missing value for the promoted dtype.
     """
     # N.B. these casting rules should match pandas
+    dtype_: np.typing.DTypeLike
+    fill_value: Any
     if np.issubdtype(dtype, np.floating):
+        dtype_ = dtype
         fill_value = np.nan
     elif np.issubdtype(dtype, np.timedelta64):
         # See https://github.com/numpy/numpy/issues/10685
         # np.timedelta64 is a subclass of np.integer
         # Check np.timedelta64 before np.integer
         fill_value = np.timedelta64("NaT")
+        dtype_ = dtype
     elif np.issubdtype(dtype, np.integer):
-        dtype = np.float32 if dtype.itemsize <= 2 else np.float64
+        dtype_ = np.float32 if dtype.itemsize <= 2 else np.float64
         fill_value = np.nan
     elif np.issubdtype(dtype, np.complexfloating):
+        dtype_ = dtype
         fill_value = np.nan + np.nan * 1j
     elif np.issubdtype(dtype, np.datetime64):
+        dtype_ = dtype
         fill_value = np.datetime64("NaT")
     else:
-        dtype = object
+        dtype_ = object
         fill_value = np.nan
 
-    dtype = np.dtype(dtype)
-    fill_value = dtype.type(fill_value)
-    return dtype, fill_value
+    dtype_out = np.dtype(dtype_)
+    fill_value = dtype_out.type(fill_value)
+    return dtype_out, fill_value
 
 
 NAT_TYPES = {np.datetime64("NaT").dtype, np.timedelta64("NaT").dtype}