diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 3f84fa0f0670e..61dadd833be35 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1168,7 +1168,12 @@ def var(self, ddof=1, *args, **kwargs):
         """
         nv.validate_groupby_func('var', args, kwargs)
         if ddof == 1:
-            return self._cython_agg_general('var', **kwargs)
+            try:
+                return self._cython_agg_general('var', **kwargs)
+            except Exception:
+                f = lambda x: x.var(ddof=ddof, **kwargs)
+                with _group_selection_context(self):
+                    return self._python_agg_general(f)
         else:
             f = lambda x: x.var(ddof=ddof, **kwargs)
             with _group_selection_context(self):
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 4a3625b2d7a2d..03ce6852b58d0 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1043,7 +1043,8 @@ def _downsample(self, how, **kwargs):
 
         if is_subperiod(ax.freq, self.freq):
             # Downsampling
-            return self._groupby_and_aggregate(how, grouper=self.grouper)
+            return self._groupby_and_aggregate(how, grouper=self.grouper,
+                                               **kwargs)
         elif is_superperiod(ax.freq, self.freq):
             if how == 'ohlc':
                 # GH #13083
@@ -1051,7 +1052,8 @@ def _downsample(self, how, **kwargs):
                 # for pure aggregating/reducing methods
                 # OHLC reduces along the time dimension, but creates multiple
                 # values for each period -> handle by _groupby_and_aggregate()
-                return self._groupby_and_aggregate(how, grouper=self.grouper)
+                return self._groupby_and_aggregate(how, grouper=self.grouper,
+                                                   **kwargs)
             return self.asfreq()
         elif ax.freq == self.freq:
             return self.asfreq()