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BUG: nunique not ignoring both None and np.nan #37607

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Merged
merged 11 commits into from
Nov 10, 2020
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BUG: nunique not ignoring both None and np.nan #37607

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BUG: nunique not ignoring both None and np.nan
GYHHAHA Nov 3, 2020
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Update test_unique.py
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Update v1.2.0.rst
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.2.0.rst
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Expand Up @@ -562,6 +562,7 @@ Other
- Bug in :meth:`Index.union` behaving differently depending on whether operand is a :class:`Index` or other list-like (:issue:`36384`)
- Passing an array with 2 or more dimensions to the :class:`Series` constructor now raises the more specific ``ValueError``, from a bare ``Exception`` previously (:issue:`35744`)
- Bug in ``accessor.DirNamesMixin``, where ``dir(obj)`` wouldn't show attributes defined on the instance (:issue:`37173`).
- Bug in :meth:`Series.nunique` with ``dropna=True`` was returning incorrect results when both ``NA`` and ``None`` missing values were present (:issue:`37566`)

.. ---------------------------------------------------------------------------

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9 changes: 3 additions & 6 deletions pandas/core/base.py
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Expand Up @@ -33,7 +33,7 @@
is_scalar,
)
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.missing import isna, remove_na_arraylike

from pandas.core import algorithms
from pandas.core.accessor import DirNamesMixin
Expand Down Expand Up @@ -1032,11 +1032,8 @@ def nunique(self, dropna: bool = True) -> int:
>>> s.nunique()
4
"""
uniqs = self.unique()
n = len(uniqs)
if dropna and isna(uniqs).any():
n -= 1
return n
obj = remove_na_arraylike(self) if dropna else self
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Late comment, but would it be more performant if we removed the NAs after calculating the uniques? (assume the uniques is typically a much smaller array, and not this does an extra scan and creates a copy of the full array)

return len(obj.unique())

@property
def is_unique(self) -> bool:
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8 changes: 8 additions & 0 deletions pandas/tests/base/test_unique.py
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Expand Up @@ -121,3 +121,11 @@ def test_unique_bad_unicode(idx_or_series_w_bad_unicode):
else:
expected = np.array(["\ud83d"], dtype=object)
tm.assert_numpy_array_equal(result, expected)


@pytest.mark.parametrize("dropna", [True, False])
def test_nunique_dropna(dropna):
# GH37566
s = pd.Series(["yes", "yes", pd.NA, np.nan, None, pd.NaT])
res = s.nunique(dropna)
assert res == 1 if dropna else 5