2013/7/15 Frédéric Bastien <[email protected]> > Just a question, should == behave like a ufunc or like python == for tuple? >
That's what I was also wondering. I see the advantage of consistency for newcomers. I'm not experienced enough to see if this is a problem for numerical practitionners Maybe they wouldn't even imagine that "==" applied to arrays could do anything else than element-wise comparison ? "Explicit is better than implicit" : to me, np.equal(x, y) is more explicit than "x == y". But "Beautiful is better than ugly". Is np.equal(x, y) ugly ? Bruno. > I think that all ndarray comparision (==, !=, <=, ...) should behave the > same. If they don't (like it was said), making them consistent is good. > What is the minimal change to have them behave the same? From my > understanding, it is your proposal to change == and != to behave like real > ufunc. But I'm not sure if the minimal change is the best, for new user, > what they will expect more? The ufunc of the python behavior? > > Anyway, I see the advantage to simplify the interface to something more > consistent. > > Anyway, if we make all comparison behave like ufunc, there is array_equal > as said to have the python behavior of ==, is it useful to have equivalent > function the other comparison? Do they already exist. > > thanks > > Fred > > > On Mon, Jul 15, 2013 at 10:20 AM, Nathaniel Smith <[email protected]> wrote: > >> On Mon, Jul 15, 2013 at 2:09 PM, bruno Piguet <[email protected]> >> wrote: >> > Python itself doesn't raise an exception in such cases : >> > >> >>>> (3,4) != (2, 3, 4) >> > True >> >>>> (3,4) == (2, 3, 4) >> > False >> > >> > Should numpy behave differently ? >> >> The numpy equivalent to Python's scalar "==" is called array_equal, >> and that does indeed behave the same: >> >> In [5]: np.array_equal([3, 4], [2, 3, 4]) >> Out[5]: False >> >> But in numpy, the name "==" is shorthand for the ufunc np.equal, which >> raises an error: >> >> In [8]: np.equal([3, 4], [2, 3, 4]) >> ValueError: operands could not be broadcast together with shapes (2) (3) >> >> -n >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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