Hi!
2017-11-26 4:31 GMT+03:00 Juan Nunez-Iglesias :
>
> On 26 Nov 2017, 12:27 PM +1100, Nathaniel Smith , wrote:
>
> It turns out that the PEP 484 type system is *mostly* not useful for
> this. They're really designed for checking consistency across a large
>

On minor thing that instead of 'ret' there should be 'x'.
With kind regards, -gdg
On Dec 12, 2017 22:51, "Joe" wrote:
Hi,
the best example I found was this one:
https://stackoverflow.com/a/29319864/7919597
def func_for_scalars_or_vectors(x):
x = np.asarray(x)

Oh, sorry for noise...
With kind regards, -gdg
On Dec 12, 2017 23:05, "Robert Kern" <robert.k...@gmail.com> wrote:
> On Wed, Dec 13, 2017 at 5:00 AM, Kirill Balunov <kirillbalu...@gmail.com>
> wrote:
> >
> > On minor thing that instead of 'ret' there

Hi,
I was trying to sort an array (N, 3) by rows, and firstly come with this
solution:
N = 100
arr = np.random.randint(-100, 100, size=(N, 3))
dt = np.dtype([('x', int),('y', int),('z', int)])
*arr.view(dtype=dt).sort(axis=0)*
Then I found another way using lexsort function
*:*
*idx =

h is 1D. 1D arrays do not have an axis=1. You actually
> want to iterate over the columns, so np.lexsort(a.T) is the correct
> phrasing of that. No idea about the speed difference.
>
>-Joe
>
> On Fri, Oct 20, 2017 at 6:00 AM, Kirill Balunov <kirillbalu...@gmail.com>
>

Currently in docstring the description of dtype argument for np.array says
this:
dtype : data-type, optional
> The desired data-type for the array. If not given, then the type will
> be determined as the minimum type required to hold the objects in the
> sequence. This argument can

What considerations formed the basis for choosing the next type promotion
behavior in numpy:
In[2] : a = np.array([10], dtype=np.int64)
b = np.array([10], dtype=np.uint64)
(a+b).dtype
Out[2]: dtype('float64')
Why the `object` dtype was not chosen for the resulting dtype? Are