Concatenation of 2 1D `numpy` Arrays Along 2nd Axis

Question

Executing

import numpy as np
t1 = np.arange(1,10)
t2 = np.arange(11,20)

t3 = np.concatenate((t1,t2),axis=1)

results in a

Traceback (most recent call last):

  File "<ipython-input-264-85078aa26398>", line 1, in <module>
    t3 = np.concatenate((t1,t2),axis=1)

IndexError: axis 1 out of bounds [0, 1)

why does it report that axis 1 is out of bounds?

Solution 1

Your title explains it - a 1d array does not have a 2nd axis!

But having said that, on my system as on @Oliver W.s, it does not produce an error

In [655]: np.concatenate((t1,t2),axis=1)
Out[655]: 
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 12, 13, 14, 15, 16, 17, 18,
       19])

This is the result I would have expected from axis=0:

In [656]: np.concatenate((t1,t2),axis=0)
Out[656]: 
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 12, 13, 14, 15, 16, 17, 18,
       19])

It looks like concatenate ignores the axis parameter when the arrays are 1d. I don't know if this is something new in my 1.9 version, or something old.

For more control consider using the vstack and hstack wrappers that expand array dimensions if needed:

In [657]: np.hstack((t1,t2))
Out[657]: 
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 12, 13, 14, 15, 16, 17, 18,
       19])

In [658]: np.vstack((t1,t2))
Out[658]: 
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9],
       [11, 12, 13, 14, 15, 16, 17, 18, 19]])

Solution 2

This is because of Numpy's way of representing 1D arrays. The following using reshape() will work:

t3 = np.concatenate((t1.reshape(-1,1),t2.reshape(-1,1),axis=1)

Explanation: This is the shape of the 1D array when initially created:

t1 = np.arange(1,10)
t1.shape
>>(9,)

'np.concatenate' and many other functions don't like the missing dimension. Reshape does the following:

t1.reshape(-1,1).shape
>>(9,1) 

Solution 3

If you need an array with two columns you can use column_stack:

import numpy as np
t1 = np.arange(1,10)
t2 = np.arange(11,20)
np.column_stack((t1,t2))

Which results

[[ 1 11]
 [ 2 12]
 [ 3 13]
 [ 4 14]
 [ 5 15]
 [ 6 16]
 [ 7 17]
 [ 8 18]
 [ 9 19]]

Solution 4

You better use a different function of Numpy called numpy.stack.
It behaves like MATLAB's cat.

The numpy.stack function doesn't require the arrays to have the dimension they are concatenated along.

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