Concatenate two NumPy arrays vertically

Solution 1:

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,:]))

Solution 2:

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]])

Solution 3:

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.