Allow multiple dims to be passed with min_count (pydata#4356)

* Allow multiple dims to be passed with min_count

* Add whatsnew

doc/whats-new.rst

@@ -30,8 +30,11 @@ New Features
 - Support multiple outputs in :py:func:`xarray.apply_ufunc` when using ``dask='parallelized'``. (:issue:`1815`, :pull:`4060`)
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
 - :py:meth:`~xarray.DataArray.rolling` and :py:meth:`~xarray.Dataset.rolling`
-  now accept more than 1 dimension.(:pull:`4219`)
+  now accept more than 1 dimension. (:pull:`4219`)
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
+- ``min_count`` can be supplied to reductions such as ``.sum`` when specifying
+  multiple dimension to reduce over. (:pull:`4356`) 
+  By `Maximilian Roos <https://github.com/max-sixty>`_.
 - Build ``CFTimeIndex.__repr__`` explicitly as :py:class:`pandas.Index`. Add ``calendar`` as a new
   property for :py:class:`CFTimeIndex` and show ``calendar`` and ``length`` in
   ``CFTimeIndex.__repr__`` (:issue:`2416`, :pull:`4092`)

xarray/core/nanops.py

@@ -26,13 +26,9 @@ def _maybe_null_out(result, axis, mask, min_count=1):
     """
     xarray version of pandas.core.nanops._maybe_null_out
     """
-    if hasattr(axis, "__len__"):  # if tuple or list
-        raise ValueError(
-            "min_count is not available for reduction with more than one dimensions."
-        )
 
     if axis is not None and getattr(result, "ndim", False):
-        null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
+        null_mask = (np.take(mask.shape, axis).prod() - mask.sum(axis) - min_count) < 0
         if null_mask.any():
             dtype, fill_value = dtypes.maybe_promote(result.dtype)
             result = result.astype(dtype)

xarray/tests/test_duck_array_ops.py

@@ -595,6 +595,24 @@ def test_min_count(dim_num, dtype, dask, func, aggdim):
     assert_dask_array(actual, dask)
 
 
+@pytest.mark.parametrize("dtype", [float, int, np.float32, np.bool_])
+@pytest.mark.parametrize("dask", [False, True])
+@pytest.mark.parametrize("func", ["sum", "prod"])
+def test_min_count_nd(dtype, dask, func):
+    if dask and not has_dask:
+        pytest.skip("requires dask")
+
+    min_count = 3
+    dim_num = 3
+    da = construct_dataarray(dim_num, dtype, contains_nan=True, dask=dask)
+    actual = getattr(da, func)(dim=["x", "y", "z"], skipna=True, min_count=min_count)
+    # Supplying all dims is equivalent to supplying `...` or `None`
+    expected = getattr(da, func)(dim=..., skipna=True, min_count=min_count)
+
+    assert_allclose(actual, expected)
+    assert_dask_array(actual, dask)
+
+
 @pytest.mark.parametrize("func", ["sum", "prod"])
 def test_min_count_dataset(func):
     da = construct_dataarray(2, dtype=float, contains_nan=True, dask=False)
@@ -606,14 +624,15 @@ def test_min_count_dataset(func):
 
 @pytest.mark.parametrize("dtype", [float, int, np.float32, np.bool_])
 @pytest.mark.parametrize("dask", [False, True])
+@pytest.mark.parametrize("skipna", [False, True])
 @pytest.mark.parametrize("func", ["sum", "prod"])
-def test_multiple_dims(dtype, dask, func):
+def test_multiple_dims(dtype, dask, skipna, func):
     if dask and not has_dask:
         pytest.skip("requires dask")
     da = construct_dataarray(3, dtype, contains_nan=True, dask=dask)
 
-    actual = getattr(da, func)(("x", "y"))
-    expected = getattr(getattr(da, func)("x"), func)("y")
+    actual = getattr(da, func)(("x", "y"), skipna=skipna)
+    expected = getattr(getattr(da, func)("x", skipna=skipna), func)("y", skipna=skipna)
     assert_allclose(actual, expected)