Concatenate two NumPy arrays vertically

Question

I tried the following:

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

However, I'd expect at least that one result looks like this

array([[1, 2, 3],
       [4, 5, 6]])

Why is it not concatenated vertically?

weird !!! You can use `np.vstack((a,b))` for this purpose (in case you don't know it) — Abid Rahman K, Feb 19 '14 at 17:42
Guys, sorry for the stupid comment, but why do you use brackets twice in case of vstack? — Dmitry Isakov, Aug 11 '20 at 18:05
@DmitryIsakov Don't worry, it's not a stupid comment. numpy does this because the one required argument when using `vstack` is a tuple. In other words, `np.vstack((a,b))` is the same as doing `np.vstack(tup=(a,b))`. See here: https://numpy.org/doc/stable/reference/generated/numpy.vstack.html — Ian, Sep 25 '20 at 16:15
@DmitryIsakov assuming of course that you were asking about parentheses `( )` and not square brackets `[ ]` — Ian, Sep 25 '20 at 16:23

gg349 · Accepted Answer · 2019-02-08T13:43:16.373

112

Because both a and b have only one axis, as their shape is (3), and the axis parameter specifically refers to the axis of the elements to concatenate.

this example should clarify what concatenate is doing with axis. Take two vectors with two axis, with shape (2,3):

a = np.array([[1,5,9], [2,6,10]])
b = np.array([[3,7,11], [4,8,12]])

concatenates along the 1st axis (rows of the 1st, then rows of the 2nd):

np.concatenate((a,b), axis=0)
array([[ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11],
       [ 4,  8, 12]])

concatenates along the 2nd axis (columns of the 1st, then columns of the 2nd):

np.concatenate((a, b), axis=1)
array([[ 1,  5,  9,  3,  7, 11],
       [ 2,  6, 10,  4,  8, 12]])

to obtain the output you presented, you can use vstack

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

You can still do it with concatenate, but you need to reshape them first:

np.concatenate((a.reshape(1,3), b.reshape(1,3)))
array([[1, 2, 3],
       [4, 5, 6]])

Finally, as proposed in the comments, one way to reshape them is to use newaxis:

np.concatenate((a[np.newaxis,:], b[np.newaxis,:]))

edited Feb 08 '19 at 13:43

answered Feb 19 '14 at 17:42

gg349

21,996
5
54
64

are you sure reshaping will work? It didn't work for me. – Abid Rahman K Feb 19 '14 at 17:44
9

Try `np.concatenate([a[None,:],b[None,:]])` – wim Feb 19 '14 at 17:44
1

yes it does. Maybe you ran `a.reshape(1,3)` without assigning it, instead of `a=a.reshape(1,3)`? – gg349 Feb 19 '14 at 17:46
strange. I suppose you then did `d=b.reshape(1,3)`? Still, `concatenate((c,d))` works here. – gg349 Feb 19 '14 at 17:49
Actually this implementation is not particularly intutive. At some (suprisingly basic) points numpy does not make sense to me. – toom Feb 25 '14 at 14:52
So why doesn't Numpy raise an exception? It's clearly a contradiction in terms when we explicitly request `axis=1`, when it is non-existent. – Evgeni Sergeev Mar 10 '17 at 05:41
`np.newaxis` is `None` (quite literally) and makes its intent clearer than `None` -- `a[np.newaxis, :]`. – BallpointBen Apr 24 '18 at 15:39
2

Please edit the answer changing `vstack((a,b))` to `np.vstack((a,b))` – Employee Dec 06 '18 at 15:03

score 34 · Answer 2 · answered Apr 12 '17 at 14:05

If the actual problem at hand is to concatenate two 1-D arrays vertically, and we are not fixated on using concatenate to perform this operation, I would suggest the use of np.column_stack:

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

score 11 · Answer 3 · answered Apr 28 '15 at 07:31

A not well known feature of numpy is to use r_. This is a simple way to build up arrays quickly:

import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.r_[a[None,:],b[None,:]]
print(c)
#[[1 2 3]
# [4 5 6]]

The purpose of a[None,:] is to add an axis to array a.

score 6 · Answer 4 · answered Mar 11 '17 at 17:45

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

works just as well as

np.array([[1,2,3], [4,5,6]])

Regardless of whether it is a list of lists or a list of 1d arrays, np.array tries to create a 2d array.

But it's also a good idea to understand how np.concatenate and its family of stack functions work. In this context concatenate needs a list of 2d arrays (or any anything that np.array will turn into a 2d array) as inputs.

np.vstack first loops though the inputs making sure they are at least 2d, then does concatenate. Functionally it's the same as expanding the dimensions of the arrays yourself.

np.stack is a new function that joins the arrays on a new dimension. Default behaves just like np.array.

Look at the code for these functions. If written in Python you can learn quite a bit. For vstack:

return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)

Concatenate two NumPy arrays vertically

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