<p>在您的示例中,<code>arange</code>在<code>[1,limit+1]</code>范围内生成等间距的1D数组。在</p>
<p>现在假设你想要一个均匀分布的多维数组。然后可以使用<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html" rel="nofollow">^{<cd1>}</a>生成2D数组的每个组件。将<code>arange</code>的结果转换为一个带有<code>list()</code>的python列表,使其成为<code>ndarray</code>构造函数的参数的正确格式。在</p>
<p>这取决于你的目的。当你处理数学的时候。分析,您需要的可能是网格:</p>
<pre><code>>>> np.mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
</code></pre>
<p><a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.mgrid.html" rel="nofollow">More here.</a></p>
<p>编辑:
发布代码后:
正如DSM所提到的,<code>np.vectorize</code>是一个很好的方法。<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html" rel="nofollow">From doc</a></p>
^{pr2}$
<blockquote>
<p>Generalized function class.</p>
<p>Define a vectorized function which takes a nested sequence of objects
or numpy arrays as inputs and returns a numpy array as output. The
vectorized function evaluates pyfunc over successive tuples of the
input arrays like the python map function, except it uses the
broadcasting rules of numpy.</p>
</blockquote>