I want to concatenate three 1 dimensional numpy arrays (x1, x2, x3) to one array X (3 columns). I already tried the concatenate function but I think I am doing something wrong. At least I got an error message:
I tried the following:
X = np.concatenate([x1, x2, x3], axis = 1)
As well as:
X = np.concatenate((x1, x2, x3), axis = 1)
Both times I got an error:
Error: IndexError: axis 1 out of bounds [0, 1)
How to use the concatenate function correct? Or is there a better way to do it?
I'd do it this way:
np.column_stack((x1, x2, x3))
To me this is more expressive, does what you want, and has an intuitive name with one less argument required.
You have to use numpy.vstack. Try:
import numpy as np
X = np.vstack([x1, x2, x3])
The size of x1, x2 and x3 must be the same.
The correct way to use concatenate is to reshape the arrays so they are (n,1) 2d array. Here's what np.column_stack does
In [222]: x1=np.arange(3);x2=np.arange(10,13);x3=np.arange(20,23)
In [230]: ll=[x1,x2,x3]
In [231]: np.concatenate([np.array(i, copy=False, ndmin=2).T for i in ll], axis=1)
Out[231]:
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22]])
Though I think this is more readable:
In [233]: np.concatenate([i[:,None] for i in ll],axis=1)
Out[233]:
array([[ 0, 10, 20],
[ 1, 11, 21],
[ 2, 12, 22]])
np.vstack does
In [238]: np.concatenate([np.atleast_2d(i) for i in ll],axis=0)
Out[238]:
array([[ 0, 1, 2],
[10, 11, 12],
[20, 21, 22]])
but in this case requires a further transpose to get columns.
Numpy has a stack function (since NumPy 1.10). This allows you to concatenate along any dimension, as long as it makes sense (eg can't concatenate 1-d arrays along 3rd dimension).
For example, pairing up the elements of two 1-D arrays:
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4])
>>> b = np.array([-1, -2, -3, -4])
>>> np.stack((a, b), 1)
array([[ 1, -1],
[ 2, -2],
[ 3, -3],
[ 4, -4]])
(Note the input argument is a tuple of np.arrays)
axis=1. You have to to turn the arrays into 2d first. That's what all the answers do before usingconcatenate.