pandas=2.0 support

doc/whats-new.rst

@@ -30,6 +30,8 @@ New Features
 - Added ability to save ``DataArray`` objects directly to Zarr using :py:meth:`~xarray.DataArray.to_zarr`.
   (:issue:`7692`, :pull:`7693`) .
   By `Joe Hamman <https://github.com/jhamman>`_.
+- Support `pandas>=2.0` (:pull:`7724`)
+  By `Justus Magin <https://github.com/keewis>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

xarray/core/accessor_dt.py

@@ -115,7 +115,7 @@ def _get_date_field(values, name, dtype):
             access_method, values, name, dtype=dtype, new_axis=new_axis, chunks=chunks
         )
     else:
-        return access_method(values, name)
+        return access_method(values, name).astype(dtype)
 
 
 def _round_through_series_or_index(values, name, freq):

xarray/core/indexes.py

@@ -15,7 +15,13 @@
     PandasIndexingAdapter,
     PandasMultiIndexingAdapter,
 )
-from xarray.core.utils import Frozen, get_valid_numpy_dtype, is_dict_like, is_scalar
+from xarray.core.utils import (
+    Frozen,
+    emit_user_level_warning,
+    get_valid_numpy_dtype,
+    is_dict_like,
+    is_scalar,
+)
 
 if TYPE_CHECKING:
     from xarray.core.types import ErrorOptions, T_Index
@@ -166,9 +172,21 @@ def safe_cast_to_index(array: Any) -> pd.Index:
     elif isinstance(array, PandasIndexingAdapter):
         index = array.array
     else:
-        kwargs = {}
-        if hasattr(array, "dtype") and array.dtype.kind == "O":
-            kwargs["dtype"] = object
+        kwargs: dict[str, str] = {}
+        if hasattr(array, "dtype"):
+            if array.dtype.kind == "O":
+                kwargs["dtype"] = "object"
+            elif array.dtype == "float16":
+                emit_user_level_warning(
+                    (
+                        "`pandas.Index` does not support the `float16` dtype."
+                        " Casting to `float64` for you, but in the future please"
+                        " manually cast to either `float32` and `float64`."
+                    ),
+                    category=DeprecationWarning,
+                )
+                kwargs["dtype"] = "float64"
+
         index = pd.Index(np.asarray(array), **kwargs)
 
     return _maybe_cast_to_cftimeindex(index)
@@ -259,6 +277,8 @@ def get_indexer_nd(index, labels, method=None, tolerance=None):
     labels
     """
     flat_labels = np.ravel(labels)
+    if flat_labels.dtype == "float16":
+        flat_labels = flat_labels.astype("float64")
     flat_indexer = index.get_indexer(flat_labels, method=method, tolerance=tolerance)
     indexer = flat_indexer.reshape(labels.shape)
     return indexer

xarray/tests/test_accessor_dt.py

@@ -59,6 +59,8 @@ def setup(self):
             "quarter",
             "date",
             "time",
+            "daysinmonth",
+            "days_in_month",
             "is_month_start",
             "is_month_end",
             "is_quarter_start",
@@ -74,7 +76,18 @@ def test_field_access(self, field) -> None:
         else:
             data = getattr(self.times, field)
 
-        expected = xr.DataArray(data, name=field, coords=[self.times], dims=["time"])
+        if data.dtype.kind != "b" and field not in ("date", "time"):
+            # pandas 2.0 returns int32 for integer fields now
+            data = data.astype("int64")
+
+        translations = {
+            "weekday": "dayofweek",
+            "daysinmonth": "days_in_month",
+            "weekofyear": "week",
+        }
+        name = translations.get(field, field)
+
+        expected = xr.DataArray(data, name=name, coords=[self.times], dims=["time"])
 
         if field in ["week", "weekofyear"]:
             with pytest.warns(
@@ -84,7 +97,8 @@ def test_field_access(self, field) -> None:
         else:
             actual = getattr(self.data.time.dt, field)
 
-        assert_equal(expected, actual)
+        assert expected.dtype == actual.dtype
+        assert_identical(expected, actual)
 
     @pytest.mark.parametrize(
         "field, pandas_field",

xarray/tests/test_dataarray.py

@@ -1023,32 +1023,53 @@ def test_sel_dataarray_datetime_slice(self) -> None:
         result = array.sel(delta=slice(array.delta[0], array.delta[-1]))
         assert_equal(result, array)
 
-    def test_sel_float(self) -> None:
+    @pytest.mark.parametrize(
+        ["coord_values", "indices"],
+        (
+            pytest.param(
+                np.array([0.0, 0.111, 0.222, 0.333], dtype="float64"),
+                slice(1, 3),
+                id="float64",
+            ),
+            pytest.param(
+                np.array([0.0, 0.111, 0.222, 0.333], dtype="float32"),
+                slice(1, 3),
+                id="float32",
+            ),
+            pytest.param(
+                np.array([0.0, 0.111, 0.222, 0.333], dtype="float32"), [2], id="scalar"
+            ),
+        ),
+    )
+    def test_sel_float(self, coord_values, indices) -> None:
         data_values = np.arange(4)
 
-        # case coords are float32 and label is list of floats
-        float_values = [0.0, 0.111, 0.222, 0.333]
-        coord_values = np.asarray(float_values, dtype="float32")
-        array = DataArray(data_values, [("float32_coord", coord_values)])
-        expected = DataArray(data_values[1:3], [("float32_coord", coord_values[1:3])])
-        actual = array.sel(float32_coord=float_values[1:3])
-        # case coords are float16 and label is list of floats
-        coord_values_16 = np.asarray(float_values, dtype="float16")
-        expected_16 = DataArray(
-            data_values[1:3], [("float16_coord", coord_values_16[1:3])]
-        )
-        array_16 = DataArray(data_values, [("float16_coord", coord_values_16)])
-        actual_16 = array_16.sel(float16_coord=float_values[1:3])
+        arr = DataArray(data_values, coords={"x": coord_values}, dims="x")
 
-        # case coord, label are scalars
-        expected_scalar = DataArray(
-            data_values[2], coords={"float32_coord": coord_values[2]}
+        actual = arr.sel(x=coord_values[indices])
+        expected = DataArray(
+            data_values[indices], coords={"x": coord_values[indices]}, dims="x"
         )
-        actual_scalar = array.sel(float32_coord=float_values[2])
 
-        assert_equal(expected, actual)
-        assert_equal(expected_scalar, actual_scalar)
-        assert_equal(expected_16, actual_16)
+        assert_equal(actual, expected)
+
+    def test_sel_float16(self) -> None:
+        data_values = np.arange(4)
+        coord_values = np.array([0.0, 0.111, 0.222, 0.333], dtype="float16")
+        indices = slice(1, 3)
+
+        message = "`pandas.Index` does not support the `float16` dtype.*"
+
+        with pytest.warns(DeprecationWarning, match=message):
+            arr = DataArray(data_values, coords={"x": coord_values}, dims="x")
+        with pytest.warns(DeprecationWarning, match=message):
+            expected = DataArray(
+                data_values[indices], coords={"x": coord_values[indices]}, dims="x"
+            )
+
+        actual = arr.sel(x=coord_values[indices])
+
+        assert_equal(actual, expected)
 
     def test_sel_float_multiindex(self) -> None:
         # regression test https://github.com/pydata/xarray/issues/5691

xarray/tests/test_utils.py

@@ -23,11 +23,13 @@ def new_method():
 
 
 @pytest.mark.parametrize(
-    "a, b, expected", [["a", "b", np.array(["a", "b"])], [1, 2, pd.Index([1, 2])]]
+    ["a", "b", "expected"],
+    [
+        [np.array(["a"]), np.array(["b"]), np.array(["a", "b"])],
+        [np.array([1], dtype="int64"), np.array([2], dtype="int64"), pd.Index([1, 2])],
+    ],
 )
 def test_maybe_coerce_to_str(a, b, expected):
-    a = np.array([a])
-    b = np.array([b])
     index = pd.Index(a).append(pd.Index(b))
 
     actual = utils.maybe_coerce_to_str(index, [a, b])