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 "<pyshell#191>", line 1, in <module>
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]])