Squashed commit of the following:

commit cfe87e0
Merge: 1c751a6 1f81338
Author: Deepak Cherian <[email protected]>
Date:   Thu Oct 17 01:13:49 2019 +0000

    Merge branch 'master' into fix/groupby-nan

commit 1c751a6
Author: dcherian <[email protected]>
Date:   Wed Oct 16 19:09:19 2019 -0600

    whats-new

commit 71df146
Author: dcherian <[email protected]>
Date:   Wed Oct 16 19:03:22 2019 -0600

    Add NaTs

commit 1f81338
Author: keewis <[email protected]>
Date:   Wed Oct 16 20:54:27 2019 +0200

    Fixes to the resample docs (pydata#3400)

    * add a missing newline to make sphinx detect the code block

    * update the link to the pandas documentation

    * explicitly state that this only works with datetime dimensions

    * also put the datetime dim requirement into the function description

    * add Series.resample and DataFrame.resample as reference

    * add the changes to whats-new.rst

    * move references to the bottom of the docstring

commit 5bf94a8
Author: dcherian <[email protected]>
Date:   Mon Oct 14 13:25:32 2019 -0600

    Drop nans in grouped variable.

Author: dcherian

xarray/core/groupby.py

@@ -350,6 +350,13 @@ def __init__(
                 group_indices = [slice(i, i + 1) for i in group_indices]
             unique_coord = group
         else:
+            if group.isnull().any():
+                # drop any NaN valued groups.
+                # also drop obj values where group was NaN
+                # Use where instead of reindex to account for duplicate coordinate labels.
+                obj = obj.where(group.notnull(), drop=True)
+                group = group.dropna(group_dim)
+
             # look through group to find the unique values
             unique_values, group_indices = unique_value_groups(
                 safe_cast_to_index(group), sort=(bins is None)

xarray/tests/test_groupby.py

@@ -5,7 +5,7 @@
 import xarray as xr
 from xarray.core.groupby import _consolidate_slices
 
-from . import assert_identical, raises_regex
+from . import assert_equal, assert_identical, raises_regex
 
 
 def test_consolidate_slices():
@@ -40,14 +40,14 @@ def test_multi_index_groupby_apply():
         {"foo": (("x", "y"), np.random.randn(3, 4))},
         {"x": ["a", "b", "c"], "y": [1, 2, 3, 4]},
     )
-    doubled = 2 * ds
-    group_doubled = (
+    expected = 2 * ds
+    actual = (
         ds.stack(space=["x", "y"])
         .groupby("space")
         .apply(lambda x: 2 * x)
         .unstack("space")
     )
-    assert doubled.equals(group_doubled)
+    assert_equal(expected, actual)
 
 
 def test_multi_index_groupby_sum():
@@ -58,7 +58,7 @@ def test_multi_index_groupby_sum():
     )
     expected = ds.sum("z")
     actual = ds.stack(space=["x", "y"]).groupby("space").sum("z").unstack("space")
-    assert expected.equals(actual)
+    assert_equal(expected, actual)
 
 
 def test_groupby_da_datetime():
@@ -78,15 +78,15 @@ def test_groupby_da_datetime():
     expected = xr.DataArray(
         [3, 7], coords=dict(reference_date=reference_dates), dims="reference_date"
     )
-    assert actual.equals(expected)
+    assert_equal(expected, actual)
 
 
 def test_groupby_duplicate_coordinate_labels():
     # fix for http://stackoverflow.com/questions/38065129
     array = xr.DataArray([1, 2, 3], [("x", [1, 1, 2])])
     expected = xr.DataArray([3, 3], [("x", [1, 2])])
     actual = array.groupby("x").sum()
-    assert expected.equals(actual)
+    assert_equal(expected, actual)
 
 
 def test_groupby_input_mutation():
@@ -255,6 +255,72 @@ def test_groupby_repr_datetime(obj):
     assert actual == expected
 
 
+def test_groupby_drops_nans():
+    # GH2383
+    # nan in 2D data variable (requires stacking)
+    ds = xr.Dataset(
+        {
+            "variable": (("lat", "lon", "time"), np.arange(60.0).reshape((4, 3, 5))),
+            "id": (("lat", "lon"), np.arange(12.0).reshape((4, 3))),
+        },
+        coords={"lat": np.arange(4), "lon": np.arange(3), "time": np.arange(5)},
+    )
+
+    ds["id"].values[0, 0] = np.nan
+    ds["id"].values[3, 0] = np.nan
+    ds["id"].values[-1, -1] = np.nan
+
+    grouped = ds.groupby(ds.id)
+
+    # non reduction operation
+    expected = ds.copy()
+    expected.variable.values[0, 0, :] = np.nan
+    expected.variable.values[-1, -1, :] = np.nan
+    expected.variable.values[3, 0, :] = np.nan
+    actual = grouped.apply(lambda x: x).transpose(*ds.variable.dims)
+    assert_identical(actual, expected)
+
+    # reduction along grouped dimension
+    actual = grouped.mean()
+    stacked = ds.stack({"xy": ["lat", "lon"]})
+    expected = (
+        stacked.variable.where(stacked.id.notnull()).rename({"xy": "id"}).to_dataset()
+    )
+    expected["id"] = stacked.id.values
+    assert_identical(actual, expected.dropna("id").transpose(*actual.dims))
+
+    # reduction operation along a different dimension
+    actual = grouped.mean("time")
+    expected = ds.mean("time").where(ds.id.notnull())
+    assert_identical(actual, expected)
+
+    # NaN in non-dimensional coordinate
+    array = xr.DataArray([1, 2, 3], [("x", [1, 2, 3])])
+    array["x1"] = ("x", [1, 1, np.nan])
+    expected = xr.DataArray(3, [("x1", [1])])
+    actual = array.groupby("x1").sum()
+    assert_equal(expected, actual)
+
+    # NaT in non-dimensional coordinate
+    array["t"] = (
+        "x",
+        [
+            np.datetime64("2001-01-01"),
+            np.datetime64("2001-01-01"),
+            np.datetime64("NaT"),
+        ],
+    )
+    expected = xr.DataArray(3, [("t", [np.datetime64("2001-01-01")])])
+    actual = array.groupby("t").sum()
+    assert_equal(expected, actual)
+
+    # test for repeated coordinate labels
+    array = xr.DataArray([0, 1, 2, 4, 3, 4], [("x", [np.nan, 1, 1, np.nan, 2, np.nan])])
+    expected = xr.DataArray([3, 3], [("x", [1, 2])])
+    actual = array.groupby("x").sum()
+    assert_equal(expected, actual)
+
+
 def test_groupby_grouping_errors():
     dataset = xr.Dataset({"foo": ("x", [1, 1, 1])}, {"x": [1, 2, 3]})
     with raises_regex(ValueError, "None of the data falls within bins with edges"):