[python] Concatenate two NumPy arrays vertically

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?

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 = 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)

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.