Read flat list into multidimensional array/matrix in python

Use numpy.reshape:

>>> import numpy as np
>>> data = np.array( [0, 2, 7, 6, 3, 1, 4, 5] )
>>> shape = ( 2, 4 )
>>> data.reshape( shape )
array([[0, 2, 7, 6],
       [3, 1, 4, 5]])

You can also assign directly to the shape attribute of data if you want to avoid copying it in memory:

>>> data.shape = shape

If you dont want to use numpy, there is a simple oneliner for the 2d case:

group = lambda flat, size: [flat[i:i+size] for i in range(0,len(flat), size)]

And can be generalized for multidimensions by adding recursion:

import operator
def shape(flat, dims):
    subdims = dims[1:]
    subsize = reduce(operator.mul, subdims, 1)
    if dims[0]*subsize!=len(flat):
        raise ValueError("Size does not match or invalid")
    if not subdims:
        return flat
    return [shape(flat[i:i+subsize], subdims) for i in range(0,len(flat), subsize)]

For those one liners out there:

>>> data = [0, 2, 7, 6, 3, 1, 4, 5]
>>> col = 4  # just grab the number of columns here

>>> [data[i:i+col] for i in range(0, len(data), col)]
[[0, 2, 7, 6],[3, 1, 4, 5]]

>>> # for pretty print, use either np.array or np.asmatrix
>>> np.array([data[i:i+col] for i in range(0, len(data), col)]) 
array([[0, 2, 7, 6],
       [3, 1, 4, 5]])