As an object, will it change how numpy operates?
Sincerely Yours,
Bob
On 23/03/2016 15:22, Sebastian Berg wrote:
On Mi, 2016-03-23 at 10:02 -0400, Joseph Fox-Rabinovitz wrote:
On Wed, Mar 23, 2016 at 9:37 AM, Ibrahim EL MEREHBI
wrote:
Thanks Eric. I already checked that. It's not what I want
On Mi, 2016-03-23 at 10:02 -0400, Joseph Fox-Rabinovitz wrote:
> On Wed, Mar 23, 2016 at 9:37 AM, Ibrahim EL MEREHBI
> wrote:
> > Thanks Eric. I already checked that. It's not what I want. I think
> > I wasn't
> > clear about what I wanted.
> >
> > I want to split each column but I want to do it
On Mi, 2016-03-23 at 10:02 -0400, Joseph Fox-Rabinovitz wrote:
> On Wed, Mar 23, 2016 at 9:37 AM, Ibrahim EL MEREHBI
> wrote:
> > Thanks Eric. I already checked that. It's not what I want. I think
> > I wasn't
> > clear about what I wanted.
> >
> > I want to split each column but I want to do it
On Wed, Mar 23, 2016 at 9:37 AM, Ibrahim EL MEREHBI
wrote:
> Thanks Eric. I already checked that. It's not what I want. I think I wasn't
> clear about what I wanted.
>
> I want to split each column but I want to do it for each column and end up
> with an array. Here's the result I wish to have:
>
Thanks Eric. I already checked that. It's not what I want. I think I
wasn't clear about what I wanted.
I want to split each column but I want to do it for each column and end
up with an array. Here's the result I wish to have:
array([[[0], [1, 2, 3, 4], [5, 6, 7], [8, 9]],
[[10], [11,
Try just calling np.array_split on the full 2D array. It splits along a
particular axis, which is selected using the axis argument of
np.array_split. The axis to split along defaults to the first so the two
calls to np.array_split below are exactly equivalent.
In [16]: a = np.c_[:10,10:20,20:30]
Hello,
I have a multi-diensional array that I would like to split its columns.
For example consider,
dat = np.array([np.arange(10),np.arange(10,20), np.arange(20,30)]).T
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24],
[ 5, 15, 25]