<p>您可以使用嵌套列表理解:</p>
<pre><code>>>> N = 5
>>> import random
>>> [[random.random() for i in range(N)] for j in range(N)]
[[0.9520388778975947, 0.29456222450756675, 0.33025941906885714, 0.6154639550493386, 0.11409250305307261], [0.6149070141685593, 0.3579148659939374, 0.031188652624532298, 0.4607597656919963, 0.2523207155544883], [0.6372935479559158, 0.32063181293207754, 0.700897108426278, 0.822287873035571, 0.7721460935656276], [0.31035121801363097, 0.2691153671697625, 0.1185063432179293, 0.14822226436085928, 0.5490604341460457], [0.9650509333411779, 0.7795665950184245, 0.5778752066273084, 0.3868760955504583, 0.5364495147637446]]
</code></pre>
<p>或者使用<code>numpy</code>(非stdlib但非常流行):</p>
<pre><code>>>> import numpy as np
>>> np.random.random((N,N))
array([[ 0.26045197, 0.66184973, 0.79957904, 0.82613958, 0.39644677],
[ 0.09284838, 0.59098542, 0.13045167, 0.06170584, 0.01265676],
[ 0.16456109, 0.87820099, 0.79891448, 0.02966868, 0.27810629],
[ 0.03037986, 0.31481138, 0.06477025, 0.37205248, 0.59648463],
[ 0.08084797, 0.10305354, 0.72488268, 0.30258304, 0.230913 ]])
</code></pre>
<p>(p.S.当你指的是<code>list</code>时,养成说<code>list</code>的习惯是一个好主意,并为numpy <code>ndarray</code>保留<code>array</code>。实际上有一个内置的<code>array</code>模块,它有自己的<code>array</code>类型,这样会更加混乱,但它相对很少使用。)</p>