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?
NumPy: vstack() function The vstack() function is used to stack arrays in sequence vertically (row wise). This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). The arrays must have the same shape along all but the first axis.
C = vertcat( A1,A2,…,An ) concatenates A1 , A2 , … , An vertically. vertcat is equivalent to using square brackets for vertically concatenating arrays. For example, [A; B] is equal to vertcat(A,B) when A and B are compatible arrays.
Joining Arrays Using Stack FunctionsWe can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking. We pass a sequence of arrays that we want to join to the stack() method along with the axis.
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,:]))
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]])
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
.
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