dropna() for a Series indexed by a CFTimeIndex

doc/whats-new.rst

@@ -50,13 +50,14 @@ Enhancements
 - Add ``tolerance`` option to ``resample()`` methods ``bfill``, ``pad``,
   ``nearest``. (:issue:`2695`)
   By `Hauke Schulz <https://github.com/observingClouds>`_.
-
 - :py:meth:`~xarray.DataArray.integrate` and
   :py:meth:`~xarray.Dataset.integrate` are newly added.
   See :ref:`_compute.using_coordinates` for the detail.
   (:issue:`1332`)
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
-
+- :py:meth:`pandas.Series.dropna` is now supported for a
+  :py:class:`pandas.Series` indexed by a :py:class:`~xarray.CFTimeIndex`
+  (:issue:`2688`). By `Spencer Clark <https://github.com/spencerkclark>`_.
 
 Bug fixes
 ~~~~~~~~~

xarray/coding/cftimeindex.py

@@ -335,11 +335,13 @@ def _maybe_cast_slice_bound(self, label, side, kind):
     # e.g. series[1:5].
     def get_value(self, series, key):
         """Adapted from pandas.tseries.index.DatetimeIndex.get_value"""
-        if not isinstance(key, slice):
-            return series.iloc[self.get_loc(key)]
-        else:
+        if np.asarray(key).dtype is np.dtype(bool):
+            return series.iloc[key]
+        elif isinstance(key, slice):
             return series.iloc[self.slice_indexer(
                 key.start, key.stop, key.step)]
+        else:
+            return series.iloc[self.get_loc(key)]
 
     def __contains__(self, key):
         """Adapted from

xarray/tests/test_cftimeindex.py

@@ -585,6 +585,14 @@ def test_indexing_in_series_iloc(series, index):
     assert series.iloc[:2].equals(expected)
 
 
+@pytest.mark.skipif(not has_cftime, reason='cftime not installed')
+def test_series_dropna(index):
+    series = pd.Series([0., 1., np.nan, np.nan], index=index)
+    expected = series.iloc[:2]
+    result = series.dropna()
+    assert result.equals(expected)
+
+
 @pytest.mark.skipif(not has_cftime, reason='cftime not installed')
 def test_indexing_in_dataframe_loc(df, index, scalar_args, range_args):
     expected = pd.Series([1], name=index[0])