ds.to_dict with data as arrays, not lists

doc/whats-new.rst

@@ -102,6 +102,10 @@ 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>`_.
+- Keyword argument `data='array'` to both :py:meth:`xarray.Dataset.to_dict` and
+  :py:meth:`xarray.DataArray.to_dict` will now return data as the underlying array type. Python lists are returned for `data='list'` or `data=True`. Supplying `data=False` only returns the schema without data. ``encoding=True`` returns the encoding dictionary for the underlying variable also.
+  (:issue:`1599`, :pull:`7739`) .
+  By `James McCreight <https://github.com/jmccreight>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

xarray/core/dataarray.py

@@ -4174,7 +4174,9 @@ def to_zarr(
             zarr_version=zarr_version,
         )
 
-    def to_dict(self, data: bool = True, encoding: bool = False) -> dict[str, Any]:
+    def to_dict(
+        self, data: bool | Literal["list", "array"] = "list", encoding: bool = False
+    ) -> dict[str, Any]:
         """
         Convert this xarray.DataArray into a dictionary following xarray
         naming conventions.
@@ -4185,9 +4187,14 @@ def to_dict(self, data: bool = True, encoding: bool = False) -> dict[str, Any]:
 
         Parameters
         ----------
-        data : bool, default: True
+        data : bool or {"list", "array"}, default: "list"
             Whether to include the actual data in the dictionary. When set to
-            False, returns just the schema.
+            False, returns just the schema. If set to "array", returns data as
+            underlying array type. If set to "list" (or True for backwards
+            compatibility), returns data in lists of Python data types. Note
+            that for obtaining the "list" output efficiently, use
+            `da.compute().to_dict(data="list")`.
+
         encoding : bool, default: False
             Whether to include the Dataset's encoding in the dictionary.
 

xarray/core/dataset.py

@@ -6441,7 +6441,9 @@ def to_dask_dataframe(
 
         return df
 
-    def to_dict(self, data: bool = True, encoding: bool = False) -> dict[str, Any]:
+    def to_dict(
+        self, data: bool | Literal["list", "array"] = "list", encoding: bool = False
+    ) -> dict[str, Any]:
         """
         Convert this dataset to a dictionary following xarray naming
         conventions.
@@ -6452,9 +6454,14 @@ def to_dict(self, data: bool = True, encoding: bool = False) -> dict[str, Any]:
 
         Parameters
         ----------
-        data : bool, default: True
+        data : bool or {"list", "array"}, default: "list"
             Whether to include the actual data in the dictionary. When set to
-            False, returns just the schema.
+            False, returns just the schema. If set to "array", returns data as
+            underlying array type. If set to "list" (or True for backwards
+            compatibility), returns data in lists of Python data types. Note
+            that for obtaining the "list" output efficiently, use
+            `ds.compute().to_dict(data="list")`.
+
         encoding : bool, default: False
             Whether to include the Dataset's encoding in the dictionary.
 
@@ -6560,7 +6567,8 @@ def from_dict(cls: type[T_Dataset], d: Mapping[Any, Any]) -> T_Dataset:
             )
         try:
             variable_dict = {
-                k: (v["dims"], v["data"], v.get("attrs")) for k, v in variables
+                k: (v["dims"], v["data"], v.get("attrs"), v.get("encoding"))
+                for k, v in variables
             }
         except KeyError as e:
             raise ValueError(

xarray/core/variable.py

@@ -633,11 +633,23 @@ def to_index(self) -> pd.Index:
         """Convert this variable to a pandas.Index"""
         return self.to_index_variable().to_index()
 
-    def to_dict(self, data: bool = True, encoding: bool = False) -> dict:
+    def to_dict(
+        self, data: bool | str = "list", encoding: bool = False
+    ) -> dict[str, Any]:
         """Dictionary representation of variable."""
-        item = {"dims": self.dims, "attrs": decode_numpy_dict_values(self.attrs)}
-        if data:
-            item["data"] = ensure_us_time_resolution(self.values).tolist()
+        item: dict[str, Any] = {
+            "dims": self.dims,
+            "attrs": decode_numpy_dict_values(self.attrs),
+        }
+        if data is not False:
+            if data in [True, "list"]:
+                item["data"] = ensure_us_time_resolution(self.to_numpy()).tolist()
+            elif data == "array":
+                item["data"] = ensure_us_time_resolution(self.data)
+            else:
+                msg = 'data argument must be bool, "list", or "array"'
+                raise ValueError(msg)
+
         else:
             item.update({"dtype": str(self.dtype), "shape": self.shape})
 

xarray/tests/test_dataarray.py

@@ -6,7 +6,7 @@
 from collections.abc import Hashable
 from copy import deepcopy
 from textwrap import dedent
-from typing import Any, Final, cast
+from typing import Any, Final, Literal, cast
 
 import numpy as np
 import pandas as pd
@@ -3345,46 +3345,70 @@ def test_series_categorical_index(self) -> None:
         arr = DataArray(s)
         assert "'a'" in repr(arr)  # should not error
 
+    @pytest.mark.parametrize("use_dask", [True, False])
+    @pytest.mark.parametrize("data", ["list", "array", True])
     @pytest.mark.parametrize("encoding", [True, False])
-    def test_to_and_from_dict(self, encoding) -> None:
+    def test_to_and_from_dict(
+        self, encoding: bool, data: bool | Literal["list", "array"], use_dask: bool
+    ) -> None:
+        if use_dask and not has_dask:
+            pytest.skip("requires dask")
+        encoding_data = {"bar": "spam"}
         array = DataArray(
             np.random.randn(2, 3), {"x": ["a", "b"]}, ["x", "y"], name="foo"
         )
-        array.encoding = {"bar": "spam"}
-        expected = {
+        array.encoding = encoding_data
+
+        return_data = array.to_numpy()
+        coords_data = np.array(["a", "b"])
+        if data == "list" or data is True:
+            return_data = return_data.tolist()
+            coords_data = coords_data.tolist()
+
+        expected: dict[str, Any] = {
             "name": "foo",
             "dims": ("x", "y"),
-            "data": array.values.tolist(),
+            "data": return_data,
             "attrs": {},
-            "coords": {"x": {"dims": ("x",), "data": ["a", "b"], "attrs": {}}},
+            "coords": {"x": {"dims": ("x",), "data": coords_data, "attrs": {}}},
         }
         if encoding:
-            expected["encoding"] = {"bar": "spam"}
-        actual = array.to_dict(encoding=encoding)
+            expected["encoding"] = encoding_data
+
+        if has_dask:
+            da = array.chunk()
+        else:
+            da = array
+
+        if data == "array" or data is False:
+            with raise_if_dask_computes():
+                actual = da.to_dict(encoding=encoding, data=data)
+        else:
+            actual = da.to_dict(encoding=encoding, data=data)
 
         # check that they are identical
-        assert expected == actual
+        np.testing.assert_equal(expected, actual)
 
         # check roundtrip
-        assert_identical(array, DataArray.from_dict(actual))
+        assert_identical(da, DataArray.from_dict(actual))
 
         # a more bare bones representation still roundtrips
         d = {
             "name": "foo",
             "dims": ("x", "y"),
-            "data": array.values.tolist(),
+            "data": da.values.tolist(),
             "coords": {"x": {"dims": "x", "data": ["a", "b"]}},
         }
-        assert_identical(array, DataArray.from_dict(d))
+        assert_identical(da, DataArray.from_dict(d))
 
         # and the most bare bones representation still roundtrips
-        d = {"name": "foo", "dims": ("x", "y"), "data": array.values}
-        assert_identical(array.drop_vars("x"), DataArray.from_dict(d))
+        d = {"name": "foo", "dims": ("x", "y"), "data": da.values}
+        assert_identical(da.drop_vars("x"), DataArray.from_dict(d))
 
         # missing a dims in the coords
         d = {
             "dims": ("x", "y"),
-            "data": array.values,
+            "data": da.values,
             "coords": {"x": {"data": ["a", "b"]}},
         }
         with pytest.raises(
@@ -3407,7 +3431,7 @@ def test_to_and_from_dict(self, encoding) -> None:
         endiantype = "<U1" if sys.byteorder == "little" else ">U1"
         expected_no_data["coords"]["x"].update({"dtype": endiantype, "shape": (2,)})
         expected_no_data.update({"dtype": "float64", "shape": (2, 3)})
-        actual_no_data = array.to_dict(data=False, encoding=encoding)
+        actual_no_data = da.to_dict(data=False, encoding=encoding)
         assert expected_no_data == actual_no_data
 
     def test_to_and_from_dict_with_time_dim(self) -> None:

xarray/tests/test_dataset.py

@@ -8,7 +8,7 @@
 from copy import copy, deepcopy
 from io import StringIO
 from textwrap import dedent
-from typing import Any
+from typing import Any, Literal
 
 import numpy as np
 import pandas as pd
@@ -4596,7 +4596,11 @@ def test_convert_dataframe_with_many_types_and_multiindex(self) -> None:
         expected = df.apply(np.asarray)
         assert roundtripped.equals(expected)
 
-    def test_to_and_from_dict(self) -> None:
+    @pytest.mark.parametrize("encoding", [True, False])
+    @pytest.mark.parametrize("data", [True, "list", "array"])
+    def test_to_and_from_dict(
+        self, encoding: bool, data: bool | Literal["list", "array"]
+    ) -> None:
         # <xarray.Dataset>
         # Dimensions:  (t: 10)
         # Coordinates:
@@ -4617,14 +4621,25 @@ def test_to_and_from_dict(self) -> None:
                 "b": {"dims": ("t",), "data": y.tolist(), "attrs": {}},
             },
         }
+        if encoding:
+            ds.t.encoding.update({"foo": "bar"})
+            expected["encoding"] = {}
+            expected["coords"]["t"]["encoding"] = ds.t.encoding
+            for vvs in ["a", "b"]:
+                expected["data_vars"][vvs]["encoding"] = {}
 
-        actual = ds.to_dict()
+        actual = ds.to_dict(data=data, encoding=encoding)
 
         # check that they are identical
-        assert expected == actual
+        np.testing.assert_equal(expected, actual)
 
         # check roundtrip
-        assert_identical(ds, Dataset.from_dict(actual))
+        ds_rt = Dataset.from_dict(actual)
+        assert_identical(ds, ds_rt)
+        if encoding:
+            assert set(ds_rt.variables) == set(ds.variables)
+            for vv in ds.variables:
+                np.testing.assert_equal(ds_rt[vv].encoding, ds[vv].encoding)
 
         # check the data=False option
         expected_no_data = expected.copy()
@@ -4635,14 +4650,18 @@ def test_to_and_from_dict(self) -> None:
         expected_no_data["coords"]["t"].update({"dtype": endiantype, "shape": (10,)})
         expected_no_data["data_vars"]["a"].update({"dtype": "float64", "shape": (10,)})
         expected_no_data["data_vars"]["b"].update({"dtype": "float64", "shape": (10,)})
-        actual_no_data = ds.to_dict(data=False)
+        actual_no_data = ds.to_dict(data=False, encoding=encoding)
         assert expected_no_data == actual_no_data
 
         # verify coords are included roundtrip
         expected_ds = ds.set_coords("b")
-        actual2 = Dataset.from_dict(expected_ds.to_dict())
+        actual2 = Dataset.from_dict(expected_ds.to_dict(data=data, encoding=encoding))
 
         assert_identical(expected_ds, actual2)
+        if encoding:
+            assert set(expected_ds.variables) == set(actual2.variables)
+            for vv in ds.variables:
+                np.testing.assert_equal(expected_ds[vv].encoding, actual2[vv].encoding)
 
         # test some incomplete dicts:
         # this one has no attrs field, the dims are strings, and x, y are
@@ -4690,7 +4709,10 @@ def test_to_and_from_dict_with_time_dim(self) -> None:
         roundtripped = Dataset.from_dict(ds.to_dict())
         assert_identical(ds, roundtripped)
 
-    def test_to_and_from_dict_with_nan_nat(self) -> None:
+    @pytest.mark.parametrize("data", [True, "list", "array"])
+    def test_to_and_from_dict_with_nan_nat(
+        self, data: bool | Literal["list", "array"]
+    ) -> None:
         x = np.random.randn(10, 3)
         y = np.random.randn(10, 3)
         y[2] = np.nan
@@ -4706,7 +4728,7 @@ def test_to_and_from_dict_with_nan_nat(self) -> None:
                 "lat": ("lat", lat),
             }
         )
-        roundtripped = Dataset.from_dict(ds.to_dict())
+        roundtripped = Dataset.from_dict(ds.to_dict(data=data))
         assert_identical(ds, roundtripped)
 
     def test_to_dict_with_numpy_attrs(self) -> None: