array API fixes for astype

xarray/core/accessor_str.py

@@ -51,6 +51,7 @@
 
 import numpy as np
 
+from xarray.core import duck_array_ops
 from xarray.core.computation import apply_ufunc
 from xarray.core.types import T_DataArray
 
@@ -2085,13 +2086,16 @@ def _get_res_multi(val, pat):
         else:
             # dtype MUST be object or strings can be truncated
             # See: https://github.com/numpy/numpy/issues/8352
-            return self._apply(
-                func=_get_res_multi,
-                func_args=(pat,),
-                dtype=np.object_,
-                output_core_dims=[[dim]],
-                output_sizes={dim: maxgroups},
-            ).astype(self._obj.dtype.kind)
+            return duck_array_ops.astype(
+                self._apply(
+                    func=_get_res_multi,
+                    func_args=(pat,),
+                    dtype=np.object_,
+                    output_core_dims=[[dim]],
+                    output_sizes={dim: maxgroups},
+                ),
+                self._obj.dtype.kind,
+            )
 
     def extractall(
         self,
@@ -2258,15 +2262,18 @@ def _get_res(val, ipat, imaxcount=maxcount, dtype=self._obj.dtype):
 
             return res
 
-        return self._apply(
-            # dtype MUST be object or strings can be truncated
-            # See: https://github.com/numpy/numpy/issues/8352
-            func=_get_res,
-            func_args=(pat,),
-            dtype=np.object_,
-            output_core_dims=[[group_dim, match_dim]],
-            output_sizes={group_dim: maxgroups, match_dim: maxcount},
-        ).astype(self._obj.dtype.kind)
+        return duck_array_ops.astype(
+            self._apply(
+                # dtype MUST be object or strings can be truncated
+                # See: https://github.com/numpy/numpy/issues/8352
+                func=_get_res,
+                func_args=(pat,),
+                dtype=np.object_,
+                output_core_dims=[[group_dim, match_dim]],
+                output_sizes={group_dim: maxgroups, match_dim: maxcount},
+            ),
+            self._obj.dtype.kind,
+        )
 
     def findall(
         self,
@@ -2385,13 +2392,16 @@ def _partitioner(
 
         # dtype MUST be object or strings can be truncated
         # See: https://github.com/numpy/numpy/issues/8352
-        return self._apply(
-            func=arrfunc,
-            func_args=(sep,),
-            dtype=np.object_,
-            output_core_dims=[[dim]],
-            output_sizes={dim: 3},
-        ).astype(self._obj.dtype.kind)
+        return duck_array_ops.astype(
+            self._apply(
+                func=arrfunc,
+                func_args=(sep,),
+                dtype=np.object_,
+                output_core_dims=[[dim]],
+                output_sizes={dim: 3},
+            ),
+            self._obj.dtype.kind,
+        )
 
     def partition(
         self,
@@ -2510,13 +2520,16 @@ def _dosplit(mystr, sep, maxsplit=maxsplit, dtype=self._obj.dtype):
 
         # dtype MUST be object or strings can be truncated
         # See: https://github.com/numpy/numpy/issues/8352
-        return self._apply(
-            func=_dosplit,
-            func_args=(sep,),
-            dtype=np.object_,
-            output_core_dims=[[dim]],
-            output_sizes={dim: maxsplit},
-        ).astype(self._obj.dtype.kind)
+        return duck_array_ops.astype(
+            self._apply(
+                func=_dosplit,
+                func_args=(sep,),
+                dtype=np.object_,
+                output_core_dims=[[dim]],
+                output_sizes={dim: maxsplit},
+            ),
+            self._obj.dtype.kind,
+        )
 
     def split(
         self,

xarray/core/variable.py

@@ -1420,7 +1420,7 @@ def _shift_one_dim(self, dim, count, fill_value=dtypes.NA):
         pads = [(0, 0) if d != dim else dim_pad for d in self.dims]
 
         data = np.pad(
-            trimmed_data.astype(dtype),
+            duck_array_ops.astype(trimmed_data, dtype),
             pads,
             mode="constant",
             constant_values=fill_value,
@@ -1569,7 +1569,7 @@ def pad(
             pad_option_kwargs["reflect_type"] = reflect_type
 
         array = np.pad(
-            self.data.astype(dtype, copy=False),
+            duck_array_ops.astype(self.data, dtype, copy=False),
             pad_width_by_index,
             mode=mode,
             **pad_option_kwargs,
@@ -2437,7 +2437,7 @@ def rolling_window(
         """
         if fill_value is dtypes.NA:  # np.nan is passed
             dtype, fill_value = dtypes.maybe_promote(self.dtype)
-            var = self.astype(dtype, copy=False)
+            var = duck_array_ops.astype(self, dtype, copy=False)
         else:
             dtype = self.dtype
             var = self