Fix groupby().count() for datetimelike columns (#18167)

Author: watercrossing

doc/source/whatsnew/v0.21.1.txt

@@ -60,6 +60,7 @@ Bug Fixes
 - Bug in :class:`TimedeltaIndex` subtraction could incorrectly overflow when ``NaT`` is present (:issue:`17791`)
 - Bug in :class:`DatetimeIndex` subtracting datetimelike from DatetimeIndex could fail to overflow (:issue:`18020`)
 - Bug in ``pd.Series.rolling.skew()`` and ``rolling.kurt()`` with all equal values has floating issue (:issue:`18044`)
+- Bug in ``pd.DataFrameGroupBy.count()`` when counting over a datetimelike column (:issue:`13393`)
 
 Conversion
 ^^^^^^^^^^

pandas/core/groupby.py

@@ -4365,7 +4365,8 @@ def count(self):
         ids, _, ngroups = self.grouper.group_info
         mask = ids != -1
 
-        val = ((mask & ~isna(blk.get_values())) for blk in data.blocks)
+        val = ((mask & ~isna(np.atleast_2d(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

@@ -2,9 +2,11 @@
 from __future__ import print_function
 
 import numpy as np
+import pytest
 
-from pandas import (DataFrame, Series, MultiIndex)
-from pandas.util.testing import assert_series_equal
+from pandas import (DataFrame, Series, MultiIndex, Timestamp, Timedelta,
+                    Period)
+from pandas.util.testing import (assert_series_equal, assert_frame_equal)
 from pandas.compat import (range, product as cart_product)
 
 
@@ -195,3 +197,18 @@ def test_ngroup_respects_groupby_order(self):
                                 g.ngroup())
             assert_series_equal(Series(df['group_index'].values),
                                 g.cumcount())
+
+    @pytest.mark.parametrize('datetimelike', [
+        [Timestamp('2016-05-%02d 20:09:25+00:00' % i) for i in range(1, 4)],
+        [Timestamp('2016-05-%02d 20:09:25' % i) for i in range(1, 4)],
+        [Timedelta(x, unit="h") for x in range(1, 4)],
+        [Period(freq="2W", year=2017, month=x) for x in range(1, 4)]])
+    def test_count_with_datetimelike(self, datetimelike):
+        # test for #13393, where DataframeGroupBy.count() fails
+        # when counting a datetimelike column.
+
+        df = DataFrame({'x': ['a', 'a', 'b'], 'y': datetimelike})
+        res = df.groupby('x').count()
+        expected = DataFrame({'y': [2, 1]}, index=['a', 'b'])
+        expected.index.name = "x"
+        assert_frame_equal(expected, res)