Support keyword API for Dataset.drop (#3128)

* Support for keyword argument-based dropping.

* Cherry picked changes.

* Moved noqa to correct location; added check for when labels are coordinates.

* Added okwarning to drop docs.

* Made unit tests more explicit. Added test for dropping along multiple dimensions.

* Used black.

* Fixed typo.

Co-Authored-By: Maximilian Roos <[email protected]>

* Docs for amended drop API.

doc/indexing.rst

@@ -236,6 +236,7 @@ The :py:meth:`~xarray.Dataset.drop` method returns a new object with the listed
 index labels along a dimension dropped:
 
 .. ipython:: python
+    :okwarning:
 
     ds.drop(['IN', 'IL'], dim='space')
 

doc/whats-new.rst

@@ -57,12 +57,17 @@ Enhancements
 
 - Added ``join='override'``. This only checks that index sizes are equal among objects and skips
   checking indexes for equality. By `Deepak Cherian <https://github.com/dcherian>`_.
+
 - :py:func:`~xarray.concat` and :py:func:`~xarray.open_mfdataset` now support the ``join`` kwarg.
   It is passed down to :py:func:`~xarray.align`. By `Deepak Cherian <https://github.com/dcherian>`_.
+
 - In :py:meth:`~xarray.Dataset.to_zarr`, passing ``mode`` is not mandatory if
   ``append_dim`` is set, as it will automatically be set to ``'a'`` internally.
   By `David Brochart <https://github.com/davidbrochart>`_.
 
+- :py:meth:`~xarray.Dataset.drop` now supports keyword arguments; dropping index labels by specifying both ``dim`` and ``labels`` is deprecated (:issue:`2910`).
+  By `Gregory Gundersen <https://github.com/gwgundersen/>`_.
+
 Bug fixes
 ~~~~~~~~~
 - Fix regression introduced in v0.12.2 where ``copy(deep=True)`` would convert

xarray/core/dataset.py

@@ -54,6 +54,7 @@
 )
 from .coordinates import (
     DatasetCoordinates,
+    DataArrayCoordinates,
     LevelCoordinatesSource,
     assert_coordinate_consistent,
     remap_label_indexers,
@@ -3450,7 +3451,7 @@ def _assert_all_in_dataset(
             )
 
     # Drop variables
-    @overload
+    @overload  # noqa: F811
     def drop(
         self, labels: Union[Hashable, Iterable[Hashable]], *, errors: str = "raise"
     ) -> "Dataset":
@@ -3463,7 +3464,9 @@ def drop(
     ) -> "Dataset":
         ...
 
-    def drop(self, labels, dim=None, *, errors="raise"):  # noqa: F811
+    def drop(  # noqa: F811
+        self, labels=None, dim=None, *, errors="raise", **labels_kwargs
+    ):
         """Drop variables or index labels from this dataset.
 
         Parameters
@@ -3479,34 +3482,75 @@ def drop(self, labels, dim=None, *, errors="raise"):  # noqa: F811
             any of the variable or index labels passed are not
             in the dataset. If 'ignore', any given labels that are in the
             dataset are dropped and no error is raised.
+        **labels_kwargs : {dim: label, ...}, optional
+            The keyword arguments form of ``dim`` and ``labels``.
 
         Returns
         -------
         dropped : Dataset
+
+        Examples
+        --------
+        >>> data = np.random.randn(2, 3)
+        >>> labels = ['a', 'b', 'c']
+        >>> ds = xr.Dataset({'A': (['x', 'y'], data), 'y': labels})
+        >>> ds.drop(y=['a', 'c'])
+        <xarray.Dataset>
+        Dimensions:  (x: 2, y: 1)
+        Coordinates:
+          * y        (y) <U1 'b'
+        Dimensions without coordinates: x
+        Data variables:
+            A        (x, y) float64 -0.3454 0.1734
+        >>> ds.drop(y='b')
+        <xarray.Dataset>
+        Dimensions:  (x: 2, y: 2)
+        Coordinates:
+          * y        (y) <U1 'a' 'c'
+        Dimensions without coordinates: x
+        Data variables:
+            A        (x, y) float64 -0.3944 -1.418 1.423 -1.041
         """
         if errors not in ["raise", "ignore"]:
             raise ValueError('errors must be either "raise" or "ignore"')
 
-        if dim is None:
+        labels_are_coords = isinstance(labels, DataArrayCoordinates)
+        if labels_kwargs or (utils.is_dict_like(labels) and not labels_are_coords):
+            labels_kwargs = utils.either_dict_or_kwargs(labels, labels_kwargs, "drop")
+            if dim is not None:
+                raise ValueError("cannot specify dim and dict-like arguments.")
+            ds = self
+            for dim, labels in labels_kwargs.items():
+                ds = ds._drop_labels(labels, dim, errors=errors)
+            return ds
+        elif dim is None:
             if isinstance(labels, str) or not isinstance(labels, Iterable):
                 labels = {labels}
             else:
                 labels = set(labels)
-
             return self._drop_vars(labels, errors=errors)
         else:
-            # Don't cast to set, as it would harm performance when labels
-            # is a large numpy array
-            if utils.is_scalar(labels):
-                labels = [labels]
-            labels = np.asarray(labels)
-
-            try:
-                index = self.indexes[dim]
-            except KeyError:
-                raise ValueError("dimension %r does not have coordinate labels" % dim)
-            new_index = index.drop(labels, errors=errors)
-            return self.loc[{dim: new_index}]
+            if utils.is_list_like(labels):
+                warnings.warn(
+                    "dropping dimensions using list-like labels is deprecated; "
+                    "use dict-like arguments.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+            return self._drop_labels(labels, dim, errors=errors)
+
+    def _drop_labels(self, labels=None, dim=None, errors="raise"):
+        # Don't cast to set, as it would harm performance when labels
+        # is a large numpy array
+        if utils.is_scalar(labels):
+            labels = [labels]
+        labels = np.asarray(labels)
+        try:
+            index = self.indexes[dim]
+        except KeyError:
+            raise ValueError("dimension %r does not have coordinate labels" % dim)
+        new_index = index.drop(labels, errors=errors)
+        return self.loc[{dim: new_index}]
 
     def _drop_vars(self, names: set, errors: str = "raise") -> "Dataset":
         if errors == "raise":

xarray/core/utils.py

@@ -250,6 +250,10 @@ def is_full_slice(value: Any) -> bool:
     return isinstance(value, slice) and value == slice(None)
 
 
+def is_list_like(value: Any) -> bool:
+    return isinstance(value, list) or isinstance(value, tuple)
+
+
 def either_dict_or_kwargs(
     pos_kwargs: Optional[Mapping[Hashable, T]],
     kw_kwargs: Mapping[str, T],

xarray/tests/test_dataset.py

@@ -2206,6 +2206,37 @@ def test_drop_index_labels(self):
         with raises_regex(ValueError, "does not have coordinate labels"):
             data.drop(1, "y")
 
+    def test_drop_labels_by_keyword(self):
+        # Tests for #2910: Support for a additional `drop()` API.
+        data = Dataset(
+            {"A": (["x", "y"], np.random.randn(2, 6)), "x": ["a", "b"], "y": range(6)}
+        )
+        # Basic functionality.
+        assert len(data.coords["x"]) == 2
+
+        # This API is allowed but deprecated.
+        with pytest.warns(DeprecationWarning):
+            ds1 = data.drop(["a"], dim="x")
+        ds2 = data.drop(x="a")
+        ds3 = data.drop(x=["a"])
+        ds4 = data.drop(x=["a", "b"])
+        ds5 = data.drop(x=["a", "b"], y=range(0, 6, 2))
+
+        assert_array_equal(ds1.coords["x"], ["b"])
+        assert_array_equal(ds2.coords["x"], ["b"])
+        assert_array_equal(ds3.coords["x"], ["b"])
+        assert ds4.coords["x"].size == 0
+        assert ds5.coords["x"].size == 0
+        assert_array_equal(ds5.coords["y"], [1, 3, 5])
+
+        # Error handling if user tries both approaches.
+        with pytest.raises(ValueError):
+            data.drop(labels=["a"], x="a")
+        with pytest.raises(ValueError):
+            data.drop(dim="x", x="a")
+        with pytest.raises(ValueError):
+            data.drop(labels=["a"], dim="x", x="a")
+
     def test_drop_dims(self):
         data = xr.Dataset(
             {