Adds Dataset.query() method, analogous to pandas DataFrame.query()

xarray/core/dataset.py

@@ -6980,5 +6980,36 @@ def argmax(self, dim=None, axis=None, **kwargs):
                 "Dataset.argmin() with a sequence or ... for dim"
             )
 
+    def query(
+        self,
+        queries: Mapping[Hashable, Any] = None,
+        parser: str = "pandas",
+        engine: str = None,
+        missing_dims: str = "raise",
+        **queries_kwargs: Any,
+    ) -> "Dataset":
+        """TODO docstring"""
+
+        # allow queries to be given either as a dict or as kwargs
+        queries = either_dict_or_kwargs(queries, queries_kwargs, "query")
+
+        # check queries
+        for dim, expr in queries.items():
+            if not isinstance(expr, str):
+                msg = f"expr for dim {dim} must be a string to be evaluated, {type(expr)} given"
+                raise ValueError(msg)
+            # TODO check missing dims here, or delegate to isel?
+
+        # evaluate the queries to create the indexers
+        indexers = {
+            dim: pd.eval(expr, resolvers=[self], parser=parser, engine=engine)
+            for dim, expr in queries.items()
+        }
+
+        # TODO any validation of indexers? Or just let isel try to handle it?
+
+        # apply the selection
+        return self.isel(indexers, missing_dims=missing_dims)
+
 
 ops.inject_all_ops_and_reduce_methods(Dataset, array_only=False)

xarray/tests/test_dataset.py

@@ -5807,6 +5807,63 @@ def test_astype_attrs(self):
         assert not data.astype(float, keep_attrs=False).attrs
         assert not data.astype(float, keep_attrs=False).var1.attrs
 
+    def test_query_single_dim(self):
+        """Test querying a single dimension."""
+
+        # setup test data
+        np.random.seed(42)
+        a = np.arange(0, 10, 1)
+        b = np.random.randint(0, 100, size=10)
+        c = np.linspace(0, 1, 20)
+        d = np.arange(0, 200).reshape(10, 20)
+        ds = Dataset(
+            {"a": ("x", a), "b": ("x", b), "c": ("y", c), "d": (("x", "y"), d)}
+        )
+
+        # query single dim, single variable
+        actual = ds.query(x="a > 5")
+        expect = ds.isel(x=(a > 5))
+        assert_identical(expect, actual)
+
+        # query single dim, single variable, via dict
+        actual = ds.query(dict(x="a > 5"))
+        expect = ds.isel(dict(x=(a > 5)))
+        assert_identical(expect, actual)
+
+        # query single dim, single variable
+        actual = ds.query(x="b > 50")
+        expect = ds.isel(x=(b > 50))
+        assert_identical(expect, actual)
+
+        # query single dim, single variable
+        actual = ds.query(y="c < .5")
+        expect = ds.isel(y=(c < 0.5))
+        assert_identical(expect, actual)
+
+        # query single dim, multiple variables
+        actual = ds.query(x="(a > 5) & (b > 50)")
+        expect = ds.isel(x=((a > 5) & (b > 50)))
+        assert_identical(expect, actual)
+
+        # support pandas query parser
+        actual = ds.query(x="(a > 5) and (b > 50)")
+        expect = ds.isel(x=((a > 5) & (b > 50)))
+        assert_identical(expect, actual)
+
+        # query multiple dims via kwargs
+        actual = ds.query(x="a > 5", y="c < .5")
+        expect = ds.isel(x=(a > 5), y=(c < 0.5))
+        assert_identical(expect, actual)
+
+        # query multiple dims via dict
+        actual = ds.query(dict(x="a > 5", y="c < .5"))
+        expect = ds.isel(dict(x=(a > 5), y=(c < 0.5)))
+        assert_identical(expect, actual)
+
+        # TODO test error handling
+
+        # TODO test dask data variables
+
 
 # Py.test tests