CODEWITHSUNDEEP
pythonnumpy
juniper-
juniper-

Reputation: 6582

numpy array in array resize

Say I make a weird little array:

>>> a = np.array([[[1,2,3],4],[[4,5,6],5]])
>>> a
array([[[1, 2, 3], 4],
       [[4, 5, 6], 5]], dtype=object)

And then take a the first column as a slice:

>>> b = a[:,0]
>>> b
array([[1, 2, 3], [4, 5, 6]], dtype=object)
>>> b.shape
(2,)

Say I now want to reshape b so that its shape is (2,3):

>>> b.reshape((-1,3))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged

I presume that numpy is treating each array in b as an object rather than an array in and of itself. The question is, is there a good way of doing the desired resize?

Upvotes: 4

Views: 10995

Answers (2)

lucasg
lucasg

Reputation: 11012

In your particular example, you could use numpy.vstack : 

import numpy as np


a = np.array([[[1,2,3],4],[[4,5,6],5]])
b = a[:,0]

c = np.vstack(b)
print c.shape # (2,3)

EDIT : Since your array a is not a real matrix but a collection of arrays (as pointed by wim ), you can also do the following : 

   b = np.array([ line for line in a[:,0]])
   print b.shape #(2,3)

Upvotes: 3

wim
wim

Reputation: 363566

You can not change the shape of b in place, but you create a copy of the desired shape with np.vstack(b). I guess you probably already knew this much though.

Note that you did not make an array in the first column of a, if you examine type(a[0,0]) you will see that you actually have a list there. i.e. your slice a[:,0] is actually a column vector of two list objects, it isn't (and was never) an array in and of itself.

Upvotes: 2

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