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, 12, 13, 14], [15, 16, 17], [18, 19]],
[[20], [21, 21, 23, 24], [25, 26, 27], [28, 29]]], dtype=object)
Sincerely Yours,
Bob
On 23/03/2016 14:17, Eric Moore wrote:
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]
In [17]: np.array_split(a, [2,5,8])
Out[17]:
[array([[ 0, 10, 20],
[ 1, 11, 21]]), array([[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24]]), array([[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27]]), array([[ 8, 18, 28],
[ 9, 19, 29]])]
In [18]: np.array_split(a, [2,5,8], 0)
Out[18]:
[array([[ 0, 10, 20],
[ 1, 11, 21]]), array([[ 2, 12, 22],
[ 3, 13, 23],
[ 4, 14, 24]]), array([[ 5, 15, 25],
[ 6, 16, 26],
[ 7, 17, 27]]), array([[ 8, 18, 28],
[ 9, 19, 29]])]
Eric
On Wed, Mar 23, 2016 at 9:06 AM, Ibrahim EL MEREHBI
<bobmerh...@gmail.com <mailto:bobmerh...@gmail.com>> wrote:
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],
[ 6, 16, 26],
[ 7, 17, 27],
[ 8, 18, 28],
[ 9, 19, 29]])
I already can split one column at a time:
np.array_split(dat[:,0], [2,5,8])
[array([0, 1]), array([2, 3, 4]), array([5, 6, 7]), array([8, 9])]
How can I extend this for all columns and (overwrite or) have a
new multi-dimensional array?
Thank you,
Bob
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