Suppose I have a numpy array c constructed as follows:

a = np.zeros((2,4))
b = np.zeros((2,8))
c = np.array([a,b])

I would have expected c.shape to be (2,1) or (2,) but instead it is (2,2). Additionally, what I want to do is concatenate a column vector of ones onto a, but by accessing it through c in the following way:

c0 = c[0] # I would have expected this to be 'a'
np.concatenate((np.ones((c0.shape[0], 1)), c0), axis=1)

This of course doesn't work because c[0] does not equal a as I expected, and I get

ValueError: all the input arrays must have same number of dimensions

I need some way to have an array (or list) of pairs, each pair component being a numpy array, and I need to access the first array in the pair in order to concatenate a column vector of ones to it. My application is machine learning and my data will be coming to me in the format described, but I need to modify the data at the start in order to add a bias element to it.

EDIT: I'm using Python 2.7 and Numpy 1.8.2

I believe what you want to use is hstack:

a = np.zeros((2,4))  # 4 column vectors of length 2
b = np.ones((2,1))   # 1 column vector of length 2c = np.hstack((a, b))
print c
# [[ 0.  0.  0.  0.  1.]
#  [ 0.  0.  0.  0.  1.]]

Regarding the problem concatenating your a and b: This cannot be done in a obvious way. Concatenation means stacking on top of each other in an additional dimension. Your data does not fit on one another though...