BUG: value_counts can handle the case even with empty groups (pandas-dev#28479)

* If applying rep to recons_labels go fail, use ids which has no
  consecutive duplicates instead.

Author: dongho-jung

doc/source/whatsnew/v1.0.0.rst

@@ -243,6 +243,7 @@ Other
 - Using :meth:`DataFrame.replace` with overlapping keys in a nested dictionary will no longer raise, now matching the behavior of a flat dictionary (:issue:`27660`)
 - :meth:`DataFrame.to_csv` and :meth:`Series.to_csv` now support dicts as ``compression`` argument with key ``'method'`` being the compression method and others as additional compression options when the compression method is ``'zip'``. (:issue:`26023`)
 - :meth:`Series.append` will no longer raise a ``TypeError`` when passed a tuple of ``Series`` (:issue:`28410`)
+- :meth:`SeriesGroupBy.value_counts` will be able to handle the case even when the :class:`Grouper` makes empty groups (:issue: 28479)
 
 .. _whatsnew_1000.contributors:
 

pandas/core/groupby/generic.py

@@ -1264,9 +1264,16 @@ def value_counts(
 
         # num. of times each group should be repeated
         rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
-
-        # multi-index components
-        labels = list(map(rep, self.grouper.recons_labels)) + [llab(lab, inc)]
+        
+        #multi-index components
+        try:
+            labels = list(map(rep, self.grouper.recons_labels )) + [llab(lab, inc)]
+        except ValueError:
+            # If applying rep to recons_labels go fail, use ids which has no
+            # consecutive duplicates instead.
+            _ids_idx = np.ones(len(ids), dtype=bool)
+            _ids_idx[1:] = ids[1:] != ids[:-1]
+            labels = list(map(rep, [ids[_ids_idx]])) + [llab(lab, inc)]
         levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
         names = self.grouper.names + [self._selection_name]
 

pandas/tests/groupby/test_value_counts.py

@@ -9,7 +9,7 @@
 import numpy as np
 import pytest
 
-from pandas import DataFrame, MultiIndex, Series, date_range
+from pandas import DataFrame, MultiIndex, Series, date_range, Grouper
 from pandas.util import testing as tm
 
 
@@ -78,3 +78,21 @@ def rebuild_index(df):
         # have to sort on index because of unstable sort on values
         left, right = map(rebuild_index, (left, right))  # xref GH9212
         tm.assert_series_equal(left.sort_index(), right.sort_index())
+
+@pytest.mark.parametrize('freq, size, frac', product(['1D', '2D', '1W', '1Y'], [100, 1000], [0.1, 0.5, 1]))
+def test_series_groupby_value_counts_with_grouper(freq, size, frac):
+    np.random.seed(42)
+
+    df = DataFrame.from_dict({'date': date_range('2019-09-25', periods=size),
+                              'name': np.random.choice(list('abcd'), size)
+                }).sample(frac=frac)
+
+    gr = df.groupby(Grouper(key='date', freq=freq))['name']
+
+    # have to sort on index because of unstable sort on values xref GH9212
+    result = gr.value_counts().sort_index()
+    expected = gr.apply(Series.value_counts).sort_index()
+    expected.index.names = result.index.names  # .apply(Series.value_counts) can't create all names
+
+    tm.assert_series_equal(result, expected)
+