Support pandas copy-on-write behaviour (#8846)

* Support pandas copy-on-write behaviour

Closes #8843

* Update xarray/tests/__init__.py

* One more fix

* Fix interp

* Avoid copy

* Try again

xarray/core/variable.py

@@ -209,7 +209,14 @@ def _possibly_convert_objects(values):
     as_series = pd.Series(values.ravel(), copy=False)
     if as_series.dtype.kind in "mM":
         as_series = _as_nanosecond_precision(as_series)
-    return np.asarray(as_series).reshape(values.shape)
+    result = np.asarray(as_series).reshape(values.shape)
+    if not result.flags.writeable:
+        # GH8843, pandas copy-on-write mode creates read-only arrays by default
+        try:
+            result.flags.writeable = True
+        except ValueError:
+            result = result.copy()
+    return result
 
 
 def _possibly_convert_datetime_or_timedelta_index(data):

xarray/tests/__init__.py

@@ -20,6 +20,7 @@
 from xarray.core.duck_array_ops import allclose_or_equiv  # noqa: F401
 from xarray.core.indexing import ExplicitlyIndexed
 from xarray.core.options import set_options
+from xarray.core.variable import IndexVariable
 from xarray.testing import (  # noqa: F401
     assert_chunks_equal,
     assert_duckarray_allclose,
@@ -47,6 +48,15 @@
 )
 
 
+def assert_writeable(ds):
+    readonly = [
+        name
+        for name, var in ds.variables.items()
+        if not isinstance(var, IndexVariable) and not var.data.flags.writeable
+    ]
+    assert not readonly, readonly
+
+
 def _importorskip(
     modname: str, minversion: str | None = None
 ) -> tuple[bool, pytest.MarkDecorator]:
@@ -326,7 +336,7 @@ def create_test_data(
         numbers_values = np.random.randint(0, 3, _dims["dim3"], dtype="int64")
     obj.coords["numbers"] = ("dim3", numbers_values)
     obj.encoding = {"foo": "bar"}
-    assert all(obj.data.flags.writeable for obj in obj.variables.values())
+    assert_writeable(obj)
     return obj
 
 

xarray/tests/test_backends.py

@@ -2605,7 +2605,9 @@ def test_append_with_append_dim_no_overwrite(self) -> None:
             # overwrite a coordinate;
             # for mode='a-', this will not get written to the store
             # because it does not have the append_dim as a dim
-            ds_to_append.lon.data[:] = -999
+            lon = ds_to_append.lon.to_numpy().copy()
+            lon[:] = -999
+            ds_to_append["lon"] = lon
             ds_to_append.to_zarr(
                 store_target, mode="a-", append_dim="time", **self.version_kwargs
             )
@@ -2615,7 +2617,9 @@ def test_append_with_append_dim_no_overwrite(self) -> None:
             # by default, mode="a" will overwrite all coordinates.
             ds_to_append.to_zarr(store_target, append_dim="time", **self.version_kwargs)
             actual = xr.open_dataset(store_target, engine="zarr", **self.version_kwargs)
-            original2.lon.data[:] = -999
+            lon = original2.lon.to_numpy().copy()
+            lon[:] = -999
+            original2["lon"] = lon
             assert_identical(original2, actual)
 
     @requires_dask

xarray/tests/test_dataset.py

@@ -51,6 +51,7 @@
     assert_equal,
     assert_identical,
     assert_no_warnings,
+    assert_writeable,
     create_test_data,
     has_cftime,
     has_dask,
@@ -96,11 +97,11 @@ def create_append_test_data(seed=None) -> tuple[Dataset, Dataset, Dataset]:
     nt2 = 2
     time1 = pd.date_range("2000-01-01", periods=nt1)
     time2 = pd.date_range("2000-02-01", periods=nt2)
-    string_var = np.array(["ae", "bc", "df"], dtype=object)
+    string_var = np.array(["a", "bc", "def"], dtype=object)
     string_var_to_append = np.array(["asdf", "asdfg"], dtype=object)
     string_var_fixed_length = np.array(["aa", "bb", "cc"], dtype="|S2")
     string_var_fixed_length_to_append = np.array(["dd", "ee"], dtype="|S2")
-    unicode_var = ["áó", "áó", "áó"]
+    unicode_var = np.array(["áó", "áó", "áó"])
     datetime_var = np.array(
         ["2019-01-01", "2019-01-02", "2019-01-03"], dtype="datetime64[s]"
     )
@@ -119,17 +120,11 @@ def create_append_test_data(seed=None) -> tuple[Dataset, Dataset, Dataset]:
                     coords=[lat, lon, time1],
                     dims=["lat", "lon", "time"],
                 ),
-                "string_var": xr.DataArray(string_var, coords=[time1], dims=["time"]),
-                "string_var_fixed_length": xr.DataArray(
-                    string_var_fixed_length, coords=[time1], dims=["time"]
-                ),
-                "unicode_var": xr.DataArray(
-                    unicode_var, coords=[time1], dims=["time"]
-                ).astype(np.str_),
-                "datetime_var": xr.DataArray(
-                    datetime_var, coords=[time1], dims=["time"]
-                ),
-                "bool_var": xr.DataArray(bool_var, coords=[time1], dims=["time"]),
+                "string_var": ("time", string_var),
+                "string_var_fixed_length": ("time", string_var_fixed_length),
+                "unicode_var": ("time", unicode_var),
+                "datetime_var": ("time", datetime_var),
+                "bool_var": ("time", bool_var),
             }
         )
 
@@ -140,21 +135,11 @@ def create_append_test_data(seed=None) -> tuple[Dataset, Dataset, Dataset]:
                     coords=[lat, lon, time2],
                     dims=["lat", "lon", "time"],
                 ),
-                "string_var": xr.DataArray(
-                    string_var_to_append, coords=[time2], dims=["time"]
-                ),
-                "string_var_fixed_length": xr.DataArray(
-                    string_var_fixed_length_to_append, coords=[time2], dims=["time"]
-                ),
-                "unicode_var": xr.DataArray(
-                    unicode_var[:nt2], coords=[time2], dims=["time"]
-                ).astype(np.str_),
-                "datetime_var": xr.DataArray(
-                    datetime_var_to_append, coords=[time2], dims=["time"]
-                ),
-                "bool_var": xr.DataArray(
-                    bool_var_to_append, coords=[time2], dims=["time"]
-                ),
+                "string_var": ("time", string_var_to_append),
+                "string_var_fixed_length": ("time", string_var_fixed_length_to_append),
+                "unicode_var": ("time", unicode_var[:nt2]),
+                "datetime_var": ("time", datetime_var_to_append),
+                "bool_var": ("time", bool_var_to_append),
             }
         )
 
@@ -168,8 +153,9 @@ def create_append_test_data(seed=None) -> tuple[Dataset, Dataset, Dataset]:
             }
         )
 
-    assert all(objp.data.flags.writeable for objp in ds.variables.values())
-    assert all(objp.data.flags.writeable for objp in ds_to_append.variables.values())
+    assert_writeable(ds)
+    assert_writeable(ds_to_append)
+    assert_writeable(ds_with_new_var)
     return ds, ds_to_append, ds_with_new_var
 
 
@@ -182,10 +168,8 @@ def make_datasets(data, data_to_append) -> tuple[Dataset, Dataset]:
         ds_to_append = xr.Dataset(
             {"temperature": (["time"], data_to_append)}, coords={"time": [0, 1, 2]}
         )
-        assert all(objp.data.flags.writeable for objp in ds.variables.values())
-        assert all(
-            objp.data.flags.writeable for objp in ds_to_append.variables.values()
-        )
+        assert_writeable(ds)
+        assert_writeable(ds_to_append)
         return ds, ds_to_append
 
     u2_strings = ["ab", "cd", "ef"]
@@ -2964,10 +2948,11 @@ def test_copy_coords(self, deep, expected_orig) -> None:
             name="value",
         ).to_dataset()
         ds_cp = ds.copy(deep=deep)
-        ds_cp.coords["a"].data[0] = 999
+        new_a = np.array([999, 2])
+        ds_cp.coords["a"] = ds_cp.a.copy(data=new_a)
 
         expected_cp = xr.DataArray(
-            xr.IndexVariable("a", np.array([999, 2])),
+            xr.IndexVariable("a", new_a),
             coords={"a": [999, 2]},
             dims=["a"],
         )

xarray/tests/test_missing.py

@@ -122,10 +122,13 @@ def test_interpolate_pd_compat(method, fill_value) -> None:
                 # for the numpy linear methods.
                 # see https://github.com/pandas-dev/pandas/issues/55144
                 # This aligns the pandas output with the xarray output
-                expected.values[pd.isnull(actual.values)] = np.nan
-                expected.values[actual.values == fill_value] = fill_value
+                fixed = expected.values.copy()
+                fixed[pd.isnull(actual.values)] = np.nan
+                fixed[actual.values == fill_value] = fill_value
+            else:
+                fixed = expected.values
 
-            np.testing.assert_allclose(actual.values, expected.values)
+            np.testing.assert_allclose(actual.values, fixed)
 
 
 @requires_scipy

xarray/tests/test_variable.py

@@ -64,6 +64,21 @@ def var():
     return Variable(dims=list("xyz"), data=np.random.rand(3, 4, 5))
 
 
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.array(["a", "bc", "def"], dtype=object),
+        np.array(["2019-01-01", "2019-01-02", "2019-01-03"], dtype="datetime64[ns]"),
+    ],
+)
+def test_as_compatible_data_writeable(data):
+    pd.set_option("mode.copy_on_write", True)
+    # GH8843, ensure writeable arrays for data_vars even with
+    # pandas copy-on-write mode
+    assert as_compatible_data(data).flags.writeable
+    pd.reset_option("mode.copy_on_write")
+
+
 class VariableSubclassobjects(NamedArraySubclassobjects, ABC):
     @pytest.fixture
     def target(self, data):