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BUG: Error upon Series.Groupby.nunique with empty Series (#12553) #14770

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BUG: Error upon Series.Groupby.nunique with empty Series (#12553) #14770

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.19.2.txt
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Expand Up @@ -57,7 +57,7 @@ Bug Fixes
- Bug in resampling a ``DatetimeIndex`` in local TZ, covering a DST change, which would raise ``AmbiguousTimeError`` (:issue:`14682`)



- Bug in ``Series.groupby.nunique()`` raising an ``IndexError`` for an empty ``Series`` (:issue:`12553`)



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6 changes: 5 additions & 1 deletion pandas/core/groupby.py
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Expand Up @@ -2898,6 +2898,7 @@ def true_and_notnull(x, *args, **kwargs):
def nunique(self, dropna=True):
""" Returns number of unique elements in the group """
ids, _, _ = self.grouper.group_info

val = self.obj.get_values()

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actualy I'd rather you fix like this:

diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index afddb86..f720a62 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2928,7 +2928,10 @@ class SeriesGroupBy(GroupBy):
             inc[idx] = 1
 
         out = np.add.reduceat(inc, idx).astype('int64', copy=False)
-        res = out if ids[0] != -1 else out[1:]
+        if len(ids):
+            res = out if ids[0] != -1 else out[1:]
+        else:
+            res = out[1:]
         ri = self.grouper.result_index
 
         # we might have duplications among the bins

try:
Expand Down Expand Up @@ -2928,7 +2929,10 @@ def nunique(self, dropna=True):
inc[idx] = 1

out = np.add.reduceat(inc, idx).astype('int64', copy=False)
res = out if ids[0] != -1 else out[1:]
if len(ids):
res = out if ids[0] != -1 else out[1:]
else:
res = out[1:]
ri = self.grouper.result_index

# we might have duplications among the bins
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7 changes: 7 additions & 0 deletions pandas/tests/test_groupby.py
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Expand Up @@ -6754,6 +6754,13 @@ def test_nunique_with_object(self):
expected = pd.Series([1] * 5, name='name', index=index)
tm.assert_series_equal(result, expected)

def test_nunique_with_empty_series(self):
# GH 12553
data = pd.Series(name='name')
result = data.groupby(level=0).nunique()
expected = pd.Series(name='name', dtype='int64')
tm.assert_series_equal(result, expected)

def test_transform_with_non_scalar_group(self):
# GH 10165
cols = pd.MultiIndex.from_tuples([
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