[Python-Dev] PyObject_RichCompareBool identity shortcut (original) (raw)

Robert Kern robert.kern at gmail.com
Thu Apr 28 05:42:03 CEST 2011

On 2011-04-27 22:16 , Guido van Rossum wrote:

On Wed, Apr 27, 2011 at 11:48 AM, Robert Kern<robert.kern at gmail.com> wrote:

On 4/27/11 12:44 PM, Terry Reedy wrote:

On 4/27/2011 10:53 AM, Guido van Rossum wrote: Maybe we should just call off the odd NaN comparison behavior? Eiffel seems to have survived, though I do not know if it used for numerical work. I wonder how much code would break and what the scipy folks would think. I suspect most of us would oppose changing it on general backwards-compatibility grounds rather than actually liking the current behavior. If the behavior changed with Python floats, we'd have to mull over whether we try to match that behavior with our scalar types (one of which subclasses from float) and our arrays. We would be either incompatible with Python or C, and we'd probably end up choosing Python to diverge from. It would make a mess, honestly. We already have to explain why equality is funky for arrays (arr1 == arr2 is a rich comparison that gives an array, not a bool, so we can't do containment tests for lists of arrays), so NaN is pretty easy to explain afterward. So does NumPy also follow Python's behavior about ignoring the NaN special-casing when doing array ops?

By "ignoring the NaN special-casing", do you mean that identity is checked first? When we use dtype=object arrays (arrays that contain Python objects as their data), yes:

[~] |1> nan = float('nan')

[~] |2> import numpy as np

[~] |3> a = np.array([1, 2, nan], dtype=object)

[~] |4> nan in a True

[~] |5> float('nan') in a False

Just like lists:

[~] |6> nan in [1, 2, nan] True

[~] |7> float('nan') in [1, 2, nan] False

Actually, we go a little further by using PyObject_RichCompareBool() rather than PyObject_RichCompare() to implement the array-wise comparisons in addition to containment:

[~] |8> a == nan array([False, False, True], dtype=bool)

[~] |9> [x == nan for x in [1, 2, nan]] [False, False, False]

But for dtype=float arrays (which contain C doubles, not Python objects) we use C semantics. Literally, we use whatever C's == operator gives us for the two double values. Since there is no concept of identity for this case, there is no cognate behavior of Python to match.

[~] |10> b = np.array([1.0, 2.0, nan], dtype=float)

[~] |11> b == nan array([False, False, False], dtype=bool)

[~] |12> nan in b False

-- Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco

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