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REGR: Fix assignment bug for unary operators #39971

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Merged
merged 6 commits into from
Feb 23, 2021
Merged

REGR: Fix assignment bug for unary operators #39971

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REGR: Fix assignment bug for unary operators
dsaxton Feb 22, 2021
7fa3768
Nit
dsaxton Feb 22, 2021
70a8bd8
Fix
dsaxton Feb 22, 2021
57b2b27
Include boolean dtype
dsaxton Feb 22, 2021
52bc411
Move to masked
dsaxton Feb 22, 2021
b28c417
Merge branch 'master' into copy-mask-in-unary-ops
jreback Feb 22, 2021
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.2.3.rst
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Expand Up @@ -16,7 +16,7 @@ Fixed regressions
~~~~~~~~~~~~~~~~~

- Fixed regression in :meth:`~DataFrame.to_excel` raising ``KeyError`` when giving duplicate columns with ``columns`` attribute (:issue:`39695`)
-
- Fixed regression in :class:`IntegerArray` unary ops propagating mask on assignment (:issue:`39943`)
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For future reference, I think we should generally say something like "nullable integer dtype" instead of "IntegerArray", as most users should never deal / know about IntegerArray

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Valid point, I can update this

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done. #40019. thanks @dsaxton


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

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4 changes: 2 additions & 2 deletions pandas/core/arrays/integer.py
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Expand Up @@ -316,13 +316,13 @@ def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
super().__init__(values, mask, copy=copy)

def __neg__(self):
return type(self)(-self._data, self._mask)
return type(self)(-self._data, self._mask.copy())

def __pos__(self):
return self
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We should do the same for __pos__?

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probably not for a backport. but indeed this needs some discussion to ensure consistency across the codebase. see TimedeltaArray

    def __pos__(self) -> TimedeltaArray:
        return type(self)(self._data, freq=self.freq)

>>> import pandas as pd
>>> pd.__version__
'1.3.0.dev0+804.g3289f82975'
>>>
>>> arr = pd.timedelta_range(0, periods=10)._values
>>> arr
<TimedeltaArray>
['0 days', '1 days', '2 days', '3 days', '4 days', '5 days', '6 days',
 '7 days', '8 days', '9 days']
Length: 10, dtype: timedelta64[ns]
>>>
>>> arr2 = +arr
>>>
>>> arr2 is arr
False
>>>
>>> arr2._data is arr._data
True
>>>
>>> arr2[5] = None
>>>
>>> arr
<TimedeltaArray>
['0 days', '1 days', '2 days', '3 days', '4 days',      NaT, '6 days',
 '7 days', '8 days', '9 days']
Length: 10, dtype: timedelta64[ns]
>>>
>>>

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OK, I see, so that's indeed something we should fix more generally. Is there already an issue about it?

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In the context here I had been thinking that something like s1 = +s should behave the same way as s1 = s, but actually I think you are right. Likely +s should return something new instead of simply a reference to the thing.

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Is there already an issue about it?

no. will need to create one, since the issue that this PR addresses is slightly different. #39943 is about different arrays sharing a mask, whereas returning self for __pos__, although maybe incorrect, does not create the inconsistencies when assigning null and non-null vales


def __abs__(self):
return type(self)(np.abs(self._data), self._mask)
return type(self)(np.abs(self._data), self._mask.copy())

@classmethod
def _from_sequence(
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2 changes: 1 addition & 1 deletion pandas/core/arrays/masked.py
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Expand Up @@ -172,7 +172,7 @@ def __len__(self) -> int:
return len(self._data)

def __invert__(self: BaseMaskedArrayT) -> BaseMaskedArrayT:
return type(self)(~self._data, self._mask)
return type(self)(~self._data, self._mask.copy())

def to_numpy(
self,
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10 changes: 10 additions & 0 deletions pandas/tests/arrays/integer/test_arithmetic.py
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Expand Up @@ -317,3 +317,13 @@ def test_abs_nullable_int(any_signed_nullable_int_dtype, source, target):
result = abs(s)
expected = pd.array(target, dtype=dtype)
tm.assert_extension_array_equal(result, expected)


@pytest.mark.parametrize("op", ["__neg__", "__abs__", "__invert__"])
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can you test for boolean as well

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Sure, added

def test_unary_op_does_not_propagate_mask(op):
# https://github.com/pandas-dev/pandas/issues/39943
s = pd.Series([1, 2, 3], dtype="Int64")
result = getattr(s, op)()
expected = result.copy(deep=True)
s[0] = None
tm.assert_series_equal(result, expected)