Good ways to “expand” a numpy ndarray?

Question

Are there good ways to \"expand\" a numpy ndarray? Say I have an ndarray like this:

[[1 2]
 [3 4]]

And I want each row to contains more ele

Answer 1

You should use np.column_stack or append

import numpy as np

p = np.array([ [1,2] , [3,4] ])

p = np.column_stack( [ p , [ 0 , 0 ],[0,0] ] )

p
Out[277]: 
array([[1, 2, 0, 0],
       [3, 4, 0, 0]])

Append seems to be faster though:

timeit np.column_stack( [ p , [ 0 , 0 ],[0,0] ] )
10000 loops, best of 3: 61.8 us per loop

timeit np.append(p, [[0,0],[0,0]],1)
10000 loops, best of 3: 48 us per loop

And a comparison with np.c_ and np.hstack [append still seems to be the fastest]:

In [295]: z=np.zeros((2, 2), dtype=a.dtype)

In [296]: timeit np.c_[a, z]
10000 loops, best of 3: 47.2 us per loop

In [297]: timeit np.append(p, z,1)
100000 loops, best of 3: 13.1 us per loop

In [305]: timeit np.hstack((p,z))
10000 loops, best of 3: 20.8 us per loop

and np.concatenate [that is a even a bit faster than append]:

In [307]: timeit np.concatenate((p, z), axis=1)
100000 loops, best of 3: 11.6 us per loop