使用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]])

如果要避免在内存中复制data的shape属性，也可以直接将其赋值：

>>> data.shape = shape

如果你不想使用numpy，有一个简单的2d例子：

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

并且可以通过添加递归来推广到多维：

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)]

对于那些俏皮话：

>>> 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]])

如果没有Numpy，我们也可以做如下..

l1 = [1,2,3,4,5,6,7,8,9]

def convintomatrix(x):

    sqrt = int(len(x) ** 0.5)
    matrix = []
    while x != []:
        matrix.append(x[:sqrt])
        x = x[sqrt:]
    return matrix

print (convintomatrix(l1))

[list(x) for x in zip(*[iter(data)]*shape[1])]


（在搜索如何工作时找到this post）