feat: add DaskExpr.quantile (#835)

narwhals/_dask/expr.py

@@ -4,6 +4,7 @@
 from typing import TYPE_CHECKING
 from typing import Any
 from typing import Callable
+from typing import Literal
 from typing import NoReturn
 
 from narwhals._dask.utils import add_row_index
@@ -515,6 +516,22 @@ def len(self: Self) -> Self:
             returns_scalar=True,
         )
 
+    def quantile(
+        self: Self,
+        quantile: float,
+        interpolation: Literal["nearest", "higher", "lower", "midpoint", "linear"],
+    ) -> Self:
+        if interpolation == "linear":
+            return self._from_call(
+                lambda _input, quantile: _input.quantile(q=quantile, method="dask"),
+                "quantile",
+                quantile,
+                returns_scalar=True,
+            )
+        else:
+            msg = "`higher`, `lower`, `midpoint`, `nearest` - interpolation methods are not supported by Dask. Please use `linear` instead."
+            raise NotImplementedError(msg)
+
     def is_first_distinct(self: Self) -> Self:
         def func(_input: Any) -> Any:
             _name = _input.name

narwhals/expr.py

@@ -1537,7 +1537,9 @@ def quantile(
         r"""Get quantile value.
 
         Note:
-            pandas and Polars may have implementation differences for a given interpolation method.
+            * pandas and Polars may have implementation differences for a given interpolation method.
+            * [dask](https://docs.dask.org/en/stable/generated/dask.dataframe.Series.quantile.html) has its own method to approximate quantile and it doesn't implement 'nearest', 'higher', 'lower', 'midpoint'
+            as interpolation method - use 'linear' which is closest to the native 'dask' - method.
 
         Arguments:
             quantile : float

tests/expr_and_series/quantile_test.py

@@ -26,7 +26,7 @@ def test_quantile_expr(
     expected: dict[str, list[float]],
     request: Any,
 ) -> None:
-    if "dask" in str(constructor):
+    if "dask" in str(constructor) and interpolation != "linear":
         request.applymarker(pytest.mark.xfail)
     q = 0.3
     data = {"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]}