NaN values impact binary or operations asymmetrically

[behzadnouri]

I am not sure if this is on purpose, but as far as I know, or operation should be symmetric ( I mean a | b == b | a ); not in pandas:

>>> a = pd.Series( [True] )
>>> b = pd.Series( [np.nan] )
>>> a | b
0    True
dtype: bool
>>> b | a
0    False
dtype: bool

is there any reason pandas is treating NaN values asymmetrically in here?

also, this is a weird outcome as well:

>>> ( b == True ) | ( a == True )
0    True
dtype: bool

and this:

 >>> b.astype( bool )
 0    True
 dtype: bool

this even has internal inconsistency; so before in b | a, NaN was treated as False, but now here it is True;

[jreback]

this could be a bug, but you boolean comparison of True == np.nan is very odd in the first place
should the result be np.nan because its missing, False because True is equivalent of np.nan?

See the long and compliacted tests here:
https://github.com/pydata/pandas/blob/master/pandas/tests/test_series.py#L3142

if you see a bug, pls submit a PR

will mark it as a bug, but come back to us with your thoughts

[behzadnouri]

@jreback there are 3 issues above:

1. a | b is not equal to b | a;
2. b.astype( bool ) converts NaN values to True; this is worst of 3 possible values, namely NaN, False, True; and also not consistent with [3] below
3. b == True is not consistent with [2] in above;

this is really bad; because outcome of | should not depend on order; especially when NaN values are implicitly generated:

>>> ts = pd.Series( [True, False] )
>>> ts | ts.shift( 1 )
0    True
1    True
dtype: bool
>>> ts.shift( 1 ) | ts
0    False
1     True
dtype: bool

[jreback]

@behzadnouri ok.....pls make a PR to fix! Keep in mind their may be some reasons buried for this; e.g. put in tests for the 'correct' behavior and see what breaks.

1. symmetry, I don't think it tested
2. This is filling behavior, I am not really sure what to do here; the astype actually should fail IMHO. it is really unclear what to do with np.nan, you normally b.fillna(False).astype(bool).
3. test as well

[jreback]

@hayd pls chime in here when you can....IIRC you and I discussed these types of things at length

[hayd]

some discussion was here: #4953.

A lot of this is as we're fighting "strange" truthiness for numpy's and python's (!) nan, so things like astype we can't/shouldn't fix... kinda a "gotcha".

IMO asymmetry is a bug but only because we're forcing a boolean return value (the reason this happens is cos in numpy and in general a|b is not symmetric). Now I think about it, I do remember this coming up...

[seth-p]

My 2c: I would separate this out to two issues:

Issue 1: Any boolean operations should behave the same as if .astype(bool) were first invoked on the structure(s). So, for example, a | b should return the same as a.astype(bool) | b.astype(bool), and b == True should return the same as b.astype(bool) == True. If this is the case, this should address the commutativity problem (as I'm assuming the boolean operations on boolean structures are commutative).

Issue 2: Should .astype(bool) convert a NaN to True or False? Currently it converts it to True, which is consistent with the fact that bool(NaN) is True, as defined by numpy. Personally, I would have defined bool(NaN) as False, and this may be a situation where we want the Pandas behavior (of .astype(bool)) to differ from numpy's. But this should be considered an API change, not a bug fix. I also just raised this issue on https://groups.google.com/forum/#!topic/pydata/U7gQyI_gLMc.

At any rate, I view these as two separate (but obviously related) issues, which I think are being slightly conflated in this issue (and #8151). I would suggest first addressing Issue 1, which seems to me clearly to be a bug -- keeping the existing behavior of treating NaN as True (so in the first example above, a | b, b | a, and b == True would all return Series([True])); and then separately, after further discussion and reflection, consider changing the behavior of .astype(bool) (and related boolean operations) to convert NaN to False.

[behzadnouri]

@seth-p

Any boolean operations should behave the same as if .astype(bool) were first invoked on the structure(s)

as long as the logical operations are bound to return dtype='bool'. that is exactly what i am testing here

and b == True should return the same as b.astype(bool) == True

that would not be achievable. for example, 2 != True and 2 != False, so you cannot type cast a series unless it is only zeros and ones.

this may be a situation where we want the Pandas behavior (of .astype(bool)) to differ from numpy's

strongly discourage that, because:

-1- it is not only numpy, but as you also mentioned, and much more importantly that is how python works:

>>> np.where([np.nan], True, False)
array([ True], dtype=bool)
>>> True if np.nan else False
True

so, it can easily break things as simple as ts.map(bool) == ts.astype(bool).

-2- the gain of breaking that consistency, if any, is less than minimal. because, if a user wants to deviate from python's convention that is more than easy: ts.fillna(False), or ts.fillna(0).

[seth-p]

D'oh! @behzadnouri, of course you're right about == and !=. I guess I should have said that any logical operations that expect booleans should produce the same results as for .astype(bool).

Re: .astype(bool) converting NaN to True, fair enough. And you're right, of course, that what I'm suggesting is simply the same as .fillna(False).astype(bool).