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BUG: Fix ndarray + DataFrame ops #23114

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merged 10 commits into from
Oct 14, 2018
Merged

BUG: Fix ndarray + DataFrame ops #23114

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c158048
Fix ndarray with frame op
jbrockmendel Oct 10, 2018
a28302a
typo fixup
jbrockmendel Oct 12, 2018
8183c20
Merge branch 'master' of https://github.com/pandas-dev/pandas into ar…
jbrockmendel Oct 12, 2018
4e84e53
set __array_priority__
jbrockmendel Oct 12, 2018
d280cf1
update tests
jbrockmendel Oct 13, 2018
d62a6c8
fix test assertion
jbrockmendel Oct 13, 2018
afd53e0
update test
jbrockmendel Oct 13, 2018
bc55de7
un-xfail now-passing test
jbrockmendel Oct 13, 2018
c0100b2
whatsnew
jbrockmendel Oct 14, 2018
ed7174c
Merge branch 'master' of https://github.com/pandas-dev/pandas into ar…
jbrockmendel Oct 14, 2018
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7 changes: 7 additions & 0 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1799,6 +1799,13 @@ def __array__(self, dtype=None):
return com.values_from_object(self)

def __array_wrap__(self, result, context=None):
if (context is not None and context[1] is not None and
len(context[1]) == 2 and context[1][1] is self and
isinstance(context[1][0], np.ndarray)):
# TODO: Can we return NotImplemented earlier? By the time we
# get here we've calculated `result`, which may not be cheap
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@shoyer any idea if there is a more elegant way to do this?

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What are you trying to fix here? Generally I recommend avoiding __array_wrap__ if possible... __array_ufunc__ is a much more complete interface. To be honest, I'm not even sure if returning NotImplemented from __array_wrap__ has well-defined behavior.

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What are you trying to fix here?

#22537. In master ndarray[int] + DataFrame[timedelta64[ns]] treats the ndarray as timedelta64 instead of raising TypeError.

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I'm not familiar with exactly how pandas implements binary ops, but this doesn't look like the right place to fix this.

The problem is likely in the DataFrame.__radd__ or DataFrame.__array_ufunc__ method (which NumPy calls instead of __radd__ if defined, see here for details and recommendations)

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Thanks. Looks like its extra-simple: Series and Index both define __array_priority__ but DataFrame does not.

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Wow, I'm kind of surprised we never defined DataFrame.__array_priority__ :)

return NotImplemented

d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
return self._constructor(result, **d).__finalize__(self)

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2 changes: 1 addition & 1 deletion pandas/tests/arithmetic/test_numeric.py
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Expand Up @@ -156,7 +156,7 @@ def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box):
if box is not pd.Index and broken:
# np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D')
raise pytest.xfail("timedelta64 not converted to nanos; "
"Tick division not imlpemented")
"Tick division not implemented")

expected = TimedeltaIndex(['3 Days', '36 Hours'])

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3 changes: 0 additions & 3 deletions pandas/tests/arithmetic/test_object.py
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Expand Up @@ -140,9 +140,6 @@ def test_objarr_radd_str_invalid(self, dtype, data, box):
operator.sub, ops.rsub])
def test_objarr_add_invalid(self, op, box):
# invalid ops
if box is pd.DataFrame and op is ops.radd:
pytest.xfail(reason="DataFrame op incorrectly casts the np.array"
"case to M8[ns]")

obj_ser = tm.makeObjectSeries()
obj_ser.name = 'objects'
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