On Mon, Aug 10, 2015 at 4:53 PM, Nathaniel Smith wrote:
> On Aug 10, 2015 3:38 PM, "Charles R Harris"
> wrote:
> >
> > Mingw32 will not compile current numpy due to initialization of a static
> structure slot with a Python C-API function. The function is not considered
> a constant expression by
On Aug 10, 2015 3:38 PM, "Charles R Harris"
wrote:
>
> Mingw32 will not compile current numpy due to initialization of a static
structure slot with a Python C-API function. The function is not considered
a constant expression by the old gcc in mingw32. Compilation does work with
more recent compil
Mingw32 will not compile current numpy due to initialization of a static
structure slot with a Python C-API function. The function is not considered
a constant expression by the old gcc in mingw32. Compilation does work with
more recent compilers; evidently the meaning of "constant expression" is u
On Mon, Aug 10, 2015 at 1:40 PM, Benjamin Root wrote:
> > Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))``
> > returns an object array
>
> Holy crap! To be pedantic, it looks like it turns it into a numpy scalar,
> but still! I wouldn't have expected np.asarray() on a set (or dic
> Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))``
> returns an object array
Holy crap! To be pedantic, it looks like it turns it into a numpy scalar,
but still! I wouldn't have expected np.asarray() on a set (or dictionary,
for that matter) to work because order is not guaranteed
Another case where refusing to implicitly create object arrays would have
avoided a lot of confusion...
On Aug 10, 2015 10:13 AM, "Sebastian Berg"
wrote:
> On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
> > Just came across this one today:
> >
> > >>> np.in1d([1], set([0, 1, 2]), assume_u
On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
> Just came across this one today:
>
> >>> np.in1d([1], set([0, 1, 2]), assume_unique=True)
> array([ False], dtype=bool)
>
> >>> np.in1d([1], [0, 1, 2], assume_unique=True)
>
> array([ True], dtype=bool)
>
>
> I am assuming this has somet
Just came across this one today:
>>> np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
>>> np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not
guaranteed with set() objects? I was
