Fix groupby().count() for datetime columns

Author: watercrossing

pandas/core/groupby.py

@@ -4365,7 +4365,10 @@ def count(self):
         ids, _, ngroups = self.grouper.group_info
         mask = ids != -1
 
-        val = ((mask & ~isna(blk.get_values())) for blk in data.blocks)
+        makeNDarray = lambda vals: vals[None, :] if vals.ndim == 1 else vals
+
+        val = ((mask & ~isna(makeNDarray(blk.get_values())))
+               for blk in data.blocks)
         loc = (blk.mgr_locs for blk in data.blocks)
 
         counter = partial(count_level_2d, labels=ids, max_bin=ngroups, axis=1)

pandas/tests/groupby/test_counting.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from pandas import (DataFrame, Series, MultiIndex)
-from pandas.util.testing import assert_series_equal
+from pandas import (DataFrame, Series, MultiIndex, Timestamp)
+from pandas.util.testing import (assert_series_equal, assert_frame_equal)
 from pandas.compat import (range, product as cart_product)
 
 
@@ -195,3 +195,13 @@ def test_ngroup_respects_groupby_order(self):
                                 g.ngroup())
             assert_series_equal(Series(df['group_index'].values),
                                 g.cumcount())
+
+    def test_count_with_datetime(self):
+        df = DataFrame({'x': ['a', 'a', 'b'],
+                        'y': [Timestamp('2016-05-07 20:09:25+00:00'),
+                              Timestamp('2016-05-07 20:09:29+00:00'),
+                              Timestamp('2016-05-07 20:09:29+00:00')]})
+        expected = DataFrame({'y': [2, 1]}, index=['a', 'b'])
+        expected.index.name = "x"
+        res = df.groupby('x').count()
+        assert_frame_equal(expected, res)