changes according to comments

Author: tp

doc/source/whatsnew/v0.23.4.txt

@@ -32,7 +32,6 @@ Bug Fixes
 
 - Bug where calling :func:`DataFrameGroupBy.agg` with a list of functions including ``ohlc`` as the non-initial element would raise a ``ValueError`` (:issue:`21716`)
 - Bug in ``roll_quantile`` caused a memory leak when calling ``.rolling(...).quantile(q)`` with ``q`` in (0,1) (:issue:`21965`)
-- Bug in :func:`pandas.core.groupby.SeriesGroupBy.count` when using numpy < 1.13 and ngroups=0 (:issue:`21956`).
 -
 
 **Conversion**

doc/source/whatsnew/v0.24.0.txt

@@ -536,11 +536,11 @@ Groupby/Resample/Rolling
 
 - Bug in :func:`pandas.core.groupby.GroupBy.first` and :func:`pandas.core.groupby.GroupBy.last` with ``as_index=False`` leading to the loss of timezone information (:issue:`15884`)
 - Bug in :meth:`DatetimeIndex.resample` when downsampling across a DST boundary (:issue:`8531`)
--
--
-
+- Bug where ``ValueError`` is wrongly raised when calling :func:`~pandas.core.groupby.SeriesGroupBy.count` method of a
+  ``SeriesGroupBy`` when the grouping variable only contains NaNs and numpy version < 1.13 (:issue:`21956`).
 - Multiple bugs in :func:`pandas.core.Rolling.min` with ``closed='left'` and a
   datetime-like index leading to incorrect results and also segfault. (:issue:`21704`)
+-
 
 Sparse
 ^^^^^^

pandas/core/groupby/generic.py

@@ -46,7 +46,6 @@
 from pandas.core.index import Index, MultiIndex, CategoricalIndex
 from pandas.core.arrays.categorical import Categorical
 from pandas.core.internals import BlockManager, make_block
-from pandas.compat.numpy import _np_version_under1p13
 
 from pandas.plotting._core import boxplot_frame_groupby
 
@@ -1208,10 +1207,7 @@ def count(self):
 
         mask = (ids != -1) & ~isna(val)
         ids = ensure_platform_int(ids)
-        minlength = ngroups or 0
-        if _np_version_under1p13 and minlength == 0:
-            minlength = None
-        out = np.bincount(ids[mask], minlength=minlength)
+        out = np.bincount(ids[mask], minlength=ngroups or None)
 
         return Series(out,
                       index=self.grouper.result_index,