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BUG: value_count(normalize=True, dropna=True) counting missing in denominator, #12558 #12576

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BUG: value_count(normalize=True, dropna=True) counting missing in denominator, #12558 #12576

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c348314
fixes 12558
Mar 9, 2016
343d609
add time tests and fixed issues
Mar 9, 2016
f290ccc
pep8 tweaks
Mar 10, 2016
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.18.1.txt
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Expand Up @@ -43,3 +43,4 @@ Performance Improvements

Bug Fixes
~~~~~~~~~
- Bug in ``value_counts`` where normalizes over all observations including missing even when ``dropna=True`` (:issue:`12558`)
9 changes: 8 additions & 1 deletion pandas/core/algorithms.py
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Expand Up @@ -342,7 +342,14 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
result = result.sort_values(ascending=ascending)

if normalize:
result = result / float(values.size)
if dropna:
# NaT's dropped above if time, so don't need to do again.
if not com.is_datetime_or_timedelta_dtype(dtype) \
and not is_period and not is_datetimetz:

result = result / float(Series(values).count())
else:
result = result / float(values.size)

return result

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27 changes: 27 additions & 0 deletions pandas/tests/test_algos.py
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Expand Up @@ -517,6 +517,33 @@ def test_dropna(self):
pd.Series([10.3, 5., 5., None]).value_counts(dropna=False),
pd.Series([2, 1, 1], index=[5., 10.3, np.nan]))

def test_normalize(self):
# Issue 12558
tm.assert_series_equal(
pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts(
dropna=True, normalize=True),
pd.Series([0.75, 0.25], index=[10.3, 5.]))

tm.assert_series_equal(
pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts(
dropna=True, normalize=False),
pd.Series([3, 1], index=[10.3, 5.]))

tm.assert_series_equal(
pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts(
dropna=False, normalize=True),
pd.Series([0.6, 0.2, 0.2], index=[10.3, 5., np.nan]))

tm.assert_series_equal(
pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts(
dropna=False, normalize=False),
pd.Series([3, 1, 1], index=[10.3, 5., np.nan]))

s = pd.Series(['2015-01-03T00:00:00.000000000+0000', pd.NaT])
tm.assert_series_equal(
s.value_counts(dropna=True, normalize=True),
pd.Series([1], index=[s.loc[0]]))


class GroupVarTestMixin(object):

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