Update docs on view / copies (#8744)

* Clarify #8728 in docs

- Add reference to numpy docs on view / copies in the corresponding section of the xarray docs, to help clarify #8728 .
- Add note that `da.values()` returns a view in the header for `da.values()`.

* tweaks to the  header

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

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

* flip order of new .to_values() doc header paragraphs

---------

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

Author: ks905383

doc/user-guide/indexing.rst

@@ -748,7 +748,7 @@ Whether array indexing returns a view or a copy of the underlying
 data depends on the nature of the labels.
 
 For positional (integer)
-indexing, xarray follows the same rules as NumPy:
+indexing, xarray follows the same `rules`_ as NumPy:
 
 * Positional indexing with only integers and slices returns a view.
 * Positional indexing with arrays or lists returns a copy.
@@ -765,8 +765,10 @@ Whether data is a copy or a view is more predictable in xarray than in pandas, s
 unlike pandas, xarray does not produce `SettingWithCopy warnings`_. However, you
 should still avoid assignment with chained indexing.
 
-.. _SettingWithCopy warnings: https://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy
+Note that other operations (such as :py:meth:`~xarray.DataArray.values`) may also return views rather than copies.
 
+.. _SettingWithCopy warnings: https://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy
+.. _rules: https://numpy.org/doc/stable/user/basics.copies.html
 
 .. _multi-level indexing:
 

xarray/core/dataarray.py

@@ -761,11 +761,15 @@ def data(self, value: Any) -> None:
     @property
     def values(self) -> np.ndarray:
         """
-        The array's data as a numpy.ndarray.
+        The array's data converted to numpy.ndarray.
 
-        If the array's data is not a numpy.ndarray this will attempt to convert
-        it naively using np.array(), which will raise an error if the array
-        type does not support coercion like this (e.g. cupy).
+        This will attempt to convert the array naively using np.array(),
+        which will raise an error if the array type does not support
+        coercion like this (e.g. cupy).
+
+        Note that this array is not copied; operations on it follow
+        numpy's rules of what generates a view vs. a copy, and changes
+        to this array may be reflected in the DataArray as well.
         """
         return self.variable.values