Support first, last with dask arrays

doc/whats-new.rst

@@ -25,6 +25,8 @@ New Features
 
 - Fix :py:meth:`xr.cov` and :py:meth:`xr.corr` now support complex valued arrays  (:issue:`7340`, :pull:`7392`).
   By `Michael Niklas <https://github.com/headtr1ck>`_.
+- Support dask arrays in ``first`` and ``last`` reductions.
+  By `Deepak Cherian <https://github.com/dcherian>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

xarray/core/dask_array_ops.py

@@ -1,5 +1,9 @@
 from __future__ import annotations
 
+from functools import partial
+
+from numpy.core.multiarray import normalize_axis_index  # type: ignore[attr-defined]
+
 from xarray.core import dtypes, nputils
 
 
@@ -92,3 +96,36 @@ def _fill_with_last_one(a, b):
         axis=axis,
         dtype=array.dtype,
     )
+
+
+def _first_last_wrapper(array, *, axis, op, keepdims):
+    return op(array, axis, keepdims=keepdims)
+
+
+def _first_or_last(darray, axis, op):
+    import dask.array
+
+    # This will raise the same error message seen for numpy
+    axis = normalize_axis_index(axis, darray.ndim)
+
+    wrapped_op = partial(_first_last_wrapper, op=op)
+    return dask.array.reduction(
+        darray,
+        chunk=wrapped_op,
+        aggregate=wrapped_op,
+        axis=axis,
+        dtype=darray.dtype,
+        keepdims=False,  # match numpy version
+    )
+
+
+def nanfirst(darray, axis):
+    from xarray.core.duck_array_ops import nanfirst
+
+    return _first_or_last(darray, axis, op=nanfirst)
+
+
+def nanlast(darray, axis):
+    from xarray.core.duck_array_ops import nanlast
+
+    return _first_or_last(darray, axis, op=nanlast)

xarray/core/duck_array_ops.py

@@ -9,7 +9,6 @@
 import datetime
 import inspect
 import warnings
-from functools import partial
 from importlib import import_module
 
 import numpy as np
@@ -637,27 +636,25 @@ def cumsum(array, axis=None, **kwargs):
     return _nd_cum_func(cumsum_1d, array, axis, **kwargs)
 
 
-_fail_on_dask_array_input_skipna = partial(
-    fail_on_dask_array_input,
-    msg="%r with skipna=True is not yet implemented on dask arrays",
-)
-
-
 def first(values, axis, skipna=None):
     """Return the first non-NA elements in this array along the given axis"""
     if (skipna or skipna is None) and values.dtype.kind not in "iSU":
         # only bother for dtypes that can hold NaN
-        _fail_on_dask_array_input_skipna(values)
-        return nanfirst(values, axis)
+        if is_duck_dask_array(values):
+            return dask_array_ops.nanfirst(values, axis)
+        else:
+            return nanfirst(values, axis)
     return take(values, 0, axis=axis)
 
 
 def last(values, axis, skipna=None):
     """Return the last non-NA elements in this array along the given axis"""
     if (skipna or skipna is None) and values.dtype.kind not in "iSU":
         # only bother for dtypes that can hold NaN
-        _fail_on_dask_array_input_skipna(values)
-        return nanlast(values, axis)
+        if is_duck_dask_array(values):
+            return dask_array_ops.nanlast(values, axis)
+        else:
+            return nanlast(values, axis)
     return take(values, -1, axis=axis)
 
 

xarray/core/nputils.py

@@ -24,17 +24,29 @@ def _select_along_axis(values, idx, axis):
     return values[sl]
 
 
-def nanfirst(values, axis):
+def nanfirst(values, axis, keepdims=False):
+    if isinstance(axis, tuple):
+        (axis,) = axis
     axis = normalize_axis_index(axis, values.ndim)
     idx_first = np.argmax(~pd.isnull(values), axis=axis)
-    return _select_along_axis(values, idx_first, axis)
+    result = _select_along_axis(values, idx_first, axis)
+    if keepdims:
+        return np.expand_dims(result, axis=axis)
+    else:
+        return result
 
 
-def nanlast(values, axis):
+def nanlast(values, axis, keepdims=False):
+    if isinstance(axis, tuple):
+        (axis,) = axis
     axis = normalize_axis_index(axis, values.ndim)
     rev = (slice(None),) * axis + (slice(None, None, -1),)
     idx_last = -1 - np.argmax(~pd.isnull(values)[rev], axis=axis)
-    return _select_along_axis(values, idx_last, axis)
+    result = _select_along_axis(values, idx_last, axis)
+    if keepdims:
+        return np.expand_dims(result, axis=axis)
+    else:
+        return result
 
 
 def inverse_permutation(indices):

xarray/tests/test_dask.py

@@ -549,17 +549,22 @@ def test_rolling(self):
             actual = v.rolling(x=2).mean()
         self.assertLazyAndAllClose(expected, actual)
 
-    def test_groupby_first(self):
+    @pytest.mark.parametrize("func", ["first", "last"])
+    def test_groupby_first_last(self, func):
+        method = operator.methodcaller(func)
         u = self.eager_array
         v = self.lazy_array
 
         for coords in [u.coords, v.coords]:
             coords["ab"] = ("x", ["a", "a", "b", "b"])
-        with pytest.raises(NotImplementedError, match=r"dask"):
-            v.groupby("ab").first()
-        expected = u.groupby("ab").first()
+        expected = method(u.groupby("ab"))
+
+        with raise_if_dask_computes():
+            actual = method(v.groupby("ab"))
+        self.assertLazyAndAllClose(expected, actual)
+
         with raise_if_dask_computes():
-            actual = v.groupby("ab").first(skipna=False)
+            actual = method(v.groupby("ab"))
         self.assertLazyAndAllClose(expected, actual)
 
     def test_reindex(self):

xarray/tests/test_duck_array_ops.py

@@ -48,7 +48,11 @@ class TestOps:
     def setUp(self):
         self.x = array(
             [
-                [[nan, nan, 2.0, nan], [nan, 5.0, 6.0, nan], [8.0, 9.0, 10.0, nan]],
+                [
+                    [nan, nan, 2.0, nan],
+                    [nan, 5.0, 6.0, nan],
+                    [8.0, 9.0, 10.0, nan],
+                ],
                 [
                     [nan, 13.0, 14.0, 15.0],
                     [nan, 17.0, 18.0, nan],
@@ -128,6 +132,29 @@ def test_all_nan_arrays(self):
         assert np.isnan(mean([np.nan, np.nan]))
 
 
+@requires_dask
+class TestDaskOps(TestOps):
+    @pytest.fixture(autouse=True)
+    def setUp(self):
+        import dask.array
+
+        self.x = dask.array.from_array(
+            [
+                [
+                    [nan, nan, 2.0, nan],
+                    [nan, 5.0, 6.0, nan],
+                    [8.0, 9.0, 10.0, nan],
+                ],
+                [
+                    [nan, 13.0, 14.0, 15.0],
+                    [nan, 17.0, 18.0, nan],
+                    [nan, 21.0, nan, nan],
+                ],
+            ],
+            chunks=(2, 1, 2),
+        )
+
+
 def test_cumsum_1d():
     inputs = np.array([0, 1, 2, 3])
     expected = np.array([0, 1, 3, 6])