On Tue, Jul 20, 2010 at 10:35 AM, Gael Varoquaux
wrote:
> On Tue, Jul 20, 2010 at 10:24:56AM -0400, Skipper Seabold wrote:
>> Will one of the stack functions do? I take it your a looks something like
>
>> a = [np.arange(1000), np.arange(1000), np.arange(1000)]
>
>> np.all(np.vstack(a) == np.conca
On Tue, Jul 20, 2010 at 10:24:56AM -0400, Skipper Seabold wrote:
> Will one of the stack functions do? I take it your a looks something like
> a = [np.arange(1000), np.arange(1000), np.arange(1000)]
> np.all(np.vstack(a) == np.concatenate([a_[None] for a_ in a]))
> # True
Works only for 1D arra
On Tue, Jul 20, 2010 at 7:24 AM, Skipper Seabold wrote:
> On Tue, Jul 20, 2010 at 5:11 AM, Gael Varoquaux
> wrote:
>> Is there in numpy a function that does:
>>
>> np.concatenate([a_[np.newaxis] for a_ in a])
>>
>> ?
>>
>> ie: add a dimension in front and stack along this dimension, just like
On Tue, Jul 20, 2010 at 5:11 AM, Gael Varoquaux
wrote:
> Is there in numpy a function that does:
>
> np.concatenate([a_[np.newaxis] for a_ in a])
>
> ?
>
> ie: add a dimension in front and stack along this dimension, just like
>
> np.array(a)
>
> would do, but more efficient.
>
> This is som
Is there in numpy a function that does:
np.concatenate([a_[np.newaxis] for a_ in a])
?
ie: add a dimension in front and stack along this dimension, just like
np.array(a)
would do, but more efficient.
This is something that do all the time. Am I the only one?
Cheers,
Gaël
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