If I understood correctly what you're asking, you have a case where numpy did not convert array of arrays into 2d array. This can happen when your arrays are not of the same size. Example:

Automatic conversion to 2d array:

import numpy as np
a = np.array([np.array([1,2,3]),np.array([2,3,4]),np.array([6,7,8])])
print a

Output:

>>>[[1 2 3]
    [2 3 4]
    [6 7 8]]

No automatic conversion (look for the change in the second subarray):

import numpy as np
b = np.array([np.array([1,2,3]),np.array([2,3,4,5]),np.array([6,7,8])])
print b

Output:

>>>[array([1, 2, 3]) array([2, 3, 4, 5]) array([6, 7, 8])]

I found a couple of ways of converting an array of arrays to 2d array. In any case you need to get rid of subarrays which have different size. So you will need a mask to select only "good" subarrays. Then you can use this mask with list comprehensions to recreate array, like this:

import numpy as np

a = np.array([np.array([1,2,3]),np.array([2,3,4,5]),np.array([6,7,8])])
mask = np.array([True, False, True])

c = np.array([element for (i,element) in enumerate(a) if mask[i]])

print a
print c

Output:

>>>>[array([1, 2, 3]) array([2, 3, 4, 5]) array([6, 7, 8])]
>>>>[[1 2 3]
     [6 7 8]]

Or you can delete "bad" subarrays and use vstack(), like this:

import numpy as np

a = np.array([np.array([1,2,3]),np.array([2,3,4,5]),np.array([6,7,8])])
mask = np.array([True, False, True])

d = np.delete(a,np.where(mask==False))
e = np.vstack(d)

print a
print e

Output:

>>>>[array([1, 2, 3]) array([2, 3, 4, 5]) array([6, 7, 8])]
>>>>[[1 2 3]
     [6 7 8]]

I believe second method would be faster for large arrays, but I haven't tested the timing.

def nest_list(list1,rows, columns):    
        result=[]               
        start = 0
        end = columns
        for i in range(rows): 
            result.append(list1[start:end])
            start +=columns
            end += columns
        return result

for:

 list1=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
nest_list(list1,4,4)

Output:

[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]

np.array() is even more powerful than what unutbu said above. You also could use it to convert a list of np arrays to a higher dimention array, the following is a simple example:

aArray=np.array([1,1,1])

bArray=np.array([2,2,2])

aList=[aArray, bArray]

xArray=np.array(aList)

xArray's shape is (2,3), it's a standard np array. This operation avoids a loop programming.

Try this:

>>> import numpy as np
>>> a = np.array([[[1,2],[3,4],[5,6]],[[7,8],[9,10],[11,12]]])
>>> a
array([[[ 1,  2],
        [ 3,  4],
        [ 5,  6]],

       [[ 7,  8],
        [ 9, 10],
        [11, 12]]])
>>> a.reshape(4,-1)
array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])


>>> a.reshape(1,-1)
array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]])

I think you should use numpy, which is purpose-built for working with matrices/arrays, rather than a list of lists. That would look like this:

>>> import numpy as np
>>> list_ = [0,1,2,3]
>>> a = np.array(list_).reshape(2,2)
>>> a
array([[0, 1],
       [2, 3]])
>>> a.shape
(2, 2)

Avoid calling a variable list as it shadows the built-in name.

If you really want a matrix, you might be better off using numpy. Matrix operations in numpy most often use an array type with two dimensions. There are many ways to create a new array; one of the most useful is the zeros function, which takes a shape parameter and returns an array of the given shape, with the values initialized to zero:

>>> import numpy
>>> numpy.zeros((5, 5))
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

Here are some other ways to create 2-d arrays and matrices (with output removed for compactness):

numpy.arange(25).reshape((5, 5))         # create a 1-d range and reshape
numpy.array(range(25)).reshape((5, 5))   # pass a Python range and reshape
numpy.array([5] * 25).reshape((5, 5))    # pass a Python list and reshape
numpy.empty((5, 5))                      # allocate, but don't initialize
numpy.ones((5, 5))                       # initialize with ones

numpy provides a matrix type as well, but it is no longer recommended for any use, and may be removed from numpy in the future.