Merge 0.18.2 hotfix into master (#5349)

* Revert "Use _unstack_once for valid dask and sparse versions (#5315)"

This reverts commit 9165c26.

* 0.18.2 release notes

Author: max-sixty

doc/whats-new.rst

@@ -14,9 +14,9 @@ What's New
 
     np.random.seed(123456)
 
-.. _whats-new.0.18.2:
+.. _whats-new.0.18.3:
 
-v0.18.2 (unreleased)
+v0.18.3 (unreleased)
 ---------------------
 
 New Features
@@ -42,6 +42,12 @@ Documentation
 Internal Changes
 ~~~~~~~~~~~~~~~~
 
+.. _whats-new.0.18.2:
+
+v0.18.2 (19 May 2021)
+---------------------
+
+This release reverts a regression in xarray's unstacking of dask-backed arrays.
 .. _whats-new.0.18.1:
 
 v0.18.1 (18 May 2021)

xarray/core/dataset.py

@@ -78,7 +78,7 @@
 )
 from .missing import get_clean_interp_index
 from .options import OPTIONS, _get_keep_attrs
-from .pycompat import dask_version, is_duck_dask_array
+from .pycompat import is_duck_dask_array, sparse_array_type
 from .utils import (
     Default,
     Frozen,
@@ -4028,24 +4028,36 @@ def unstack(
 
         result = self.copy(deep=False)
         for dim in dims:
-            if not sparse and all(
-                # Dask arrays recently supports assignment by index,
-                # https://github.com/dask/dask/pull/7393
-                dask_version >= "2021.04.0" and is_duck_dask_array(v.data)
-                # Numpy arrays handles the fast path:
-                or isinstance(v.data, np.ndarray)
-                for v in self.variables.values()
+
+            if (
+                # Dask arrays don't support assignment by index, which the fast unstack
+                # function requires.
+                # https://github.com/pydata/xarray/pull/4746#issuecomment-753282125
+                any(is_duck_dask_array(v.data) for v in self.variables.values())
+                # Sparse doesn't currently support (though we could special-case
+                # it)
+                # https://github.com/pydata/sparse/issues/422
+                or any(
+                    isinstance(v.data, sparse_array_type)
+                    for v in self.variables.values()
+                )
+                or sparse
+                # Until https://github.com/pydata/xarray/pull/4751 is resolved,
+                # we check explicitly whether it's a numpy array. Once that is
+                # resolved, explicitly exclude pint arrays.
+                # # pint doesn't implement `np.full_like` in a way that's
+                # # currently compatible.
+                # # https://github.com/pydata/xarray/pull/4746#issuecomment-753425173
+                # # or any(
+                # #     isinstance(v.data, pint_array_type) for v in self.variables.values()
+                # # )
+                or any(
+                    not isinstance(v.data, np.ndarray) for v in self.variables.values()
+                )
             ):
-                # Fast unstacking path:
-                result = result._unstack_once(dim, fill_value)
-            else:
-                # Slower unstacking path, examples of array types that
-                # currently has to use this path:
-                # * sparse doesn't support item assigment,
-                #   https://github.com/pydata/sparse/issues/114
-                # * pint has some circular import issues,
-                #   https://github.com/pydata/xarray/pull/4751
                 result = result._unstack_full_reindex(dim, fill_value, sparse)
+            else:
+                result = result._unstack_once(dim, fill_value)
         return result
 
     def update(self, other: "CoercibleMapping") -> "Dataset":