<p>你的建议很快,但清晰的形式可以提高可读性:</p>
<pre><code>for i in range(c.shape[-1]):
print c[:,:,i]
</code></pre>
<p>或者,更好(更快、更普遍、更明确):</p>
<pre><code>for i in range(c.shape[-1]):
print c[...,i]
</code></pre>
<p>然而,上面第一种方法的速度似乎是<code>swapaxes()</code>方法的两倍:</p>
<pre><code>python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' \
'for r in c.swapaxes(2,0).swapaxes(1,2): u = r'
100000 loops, best of 3: 3.69 usec per loop
python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' \
'for i in range(c.shape[-1]): u = c[:,:,i]'
100000 loops, best of 3: 6.08 usec per loop
python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' \
'for r in numpy.rollaxis(c, 2): u = r'
100000 loops, best of 3: 6.46 usec per loop
</code></pre>
<p>我想这是因为<code>swapaxes()</code>不复制任何数据,而且<code>c[:,:,i]</code>的处理可以通过通用代码完成(处理<code>:</code>被更复杂的片段替换的情况)。</p>
<p>但是请注意,更明确的第二个解决方案<code>c[...,i]</code>既清晰又快速:</p>
<pre><code>python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' \
'for i in range(c.shape[-1]): u = c[...,i]'
100000 loops, best of 3: 4.74 usec per loop
</code></pre>