What's the simplest way to extend a numpy array in 2 dimensions?

Question

I have a 2d array that looks like this:

XX
xx

What\'s the most efficient way to add an extra row and column:

xxy
xxy
yyy
         


        

Answer 1

You can use:

>>> np.concatenate([array1, array2, ...]) 

e.g.

>>> import numpy as np
>>> a = [[1, 2, 3],[10, 20, 30]]
>>> b = [[100,200,300]]
>>> a = np.array(a) # not necessary, but numpy objects prefered to built-in
>>> b = np.array(b) # "^
>>> a
array([[ 1,  2,  3],
       [10, 20, 30]])
>>> b
array([[100, 200, 300]])
>>> c = np.concatenate([a,b])
>>> c
array([[  1,   2,   3],
       [ 10,  20,  30],
       [100, 200, 300]])
>>> print c
[[  1   2   3]
 [ 10  20  30]
 [100 200 300]]

~-+-~-+-~-+-~

Sometimes, you will come across trouble if a numpy array object is initialized with incomplete values for its shape property. This problem is fixed by assigning to the shape property the tuple: (array_length, element_length).

Note: Here, 'array_length' and 'element_length' are integer parameters, which you substitute values in for. A 'tuple' is just a pair of numbers in parentheses.

e.g.

>>> import numpy as np
>>> a = np.array([[1,2,3],[10,20,30]])
>>> b = np.array([100,200,300]) # initialize b with incorrect dimensions
>>> a.shape
(2, 3)
>>> b.shape
(3,)
>>> c = np.concatenate([a,b])

Traceback (most recent call last):
  File "", line 1, in 
    c = np.concatenate([a,b])
ValueError: all the input arrays must have same number of dimensions
>>> b.shape = (1,3)
>>> c = np.concatenate([a,b])
>>> c
array([[  1,   2,   3],
       [ 10,  20,  30],
       [100, 200, 300]])