How to append a vector to a matrix in python

arrays, numpy, python

Solution

You're looking for `np.r_` and `np.c_`. (Think "column stack" and "row stack" (which are also functions) but with matlab-style range generations.)

Also see `np.concatenate`, `np.vstack`, `np.hstack`, `np.dstack`, `np.row_stack`, `np.column_stack` etc.

For example:

import numpy as np
m = np.zeros((10, 4))
v = np.ones((10, 1))
c = np.c_[m, v]

Yields:

array([[ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  1.]])

This is also equivalent to `np.hstack([m, v])` or `np.column_stack([m, v])`

If you're not coming from matlab, `hstack` and `column_stack` probably seem much more readable and descriptive. (And they're arguably better in this case for that reason.)

However, `np.c_` and `np.r_` have additional functionality that folks coming from matlab tend to expect. For example:

In [7]: np.r_[1:5, 2]
Out[7]: array([1, 2, 3, 4, 2])

Or:

In [8]: np.c_[m, 0:10]
Out[8]:
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  1.],
       [ 0.,  0.,  0.,  0.,  2.],
       [ 0.,  0.,  0.,  0.,  3.],
       [ 0.,  0.,  0.,  0.,  4.],
       [ 0.,  0.,  0.,  0.,  5.],
       [ 0.,  0.,  0.,  0.,  6.],
       [ 0.,  0.,  0.,  0.,  7.],
       [ 0.,  0.,  0.,  0.,  8.],
       [ 0.,  0.,  0.,  0.,  9.]])

At any rate, for matlab folks, it's handy to know about `np.r_` and `np.c_` in addition to `vstack`, `hstack`, etc.

Problem

I want to append a vector to a matrix in python. I tried `append` or `concatenate` methods but I didn't get the answer. I was previously working with Matlab and there I used this: ``` m = zeros(10, 4) % define my matrix, 10x4 v = ones(10, 1) % my vecto, 10x1 c = [m,v] % so simple! the result is: 10x5 (the vector added as the last column) ``` How can I do that in python using numpy?

Original source