is that in some cases numpy allows a lot freedom, but in
other it is unnecessarily strict. Another one is exception messages (but
this is certainly subjective).
2017-03-24 19:48 GMT+03:00 Allan Haldane :
> On 03/23/2017 02:16 PM, Kirill Balunov wrote:
> > It was the first time I tried to
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 = np.
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
> wrote:
> > Hi,
> >
> > I was trying to sort
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
> code-base, not for enabling compiler
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)
scalar_input = False
Oh, sorry for noise...
With kind regards, -gdg
On Dec 12, 2017 23:05, "Robert Kern" wrote:
> On Wed, Dec 13, 2017 at 5:00 AM, Kirill Balunov
> wrote:
> >
> > On minor thing that instead of 'ret' there should be 'x'.
>
> No, `x` is the inpu
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 o
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
ther
вт, 25 июн. 2019 г. в 21:20, Cameron Blocker :
> It seems to me that the general consensus is that we shouldn't be changing
> .T to do what we've termed matrix transpose or conjugate transpose.
>
Reading through this thread, I can not say that I have the same opinion -
at first, many looked posit
Only concerns #4 from Ilhan's list.
ср, 26 июн. 2019 г. в 00:01, Ralf Gommers :
>
> []
>
> Perhaps not full consensus between the many people with different opinions
> and interests. But for the first one, arr.T change: it's clear that this
> won't happen.
>
To begin with, I must admit that
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