擅长:python、mysql、java
<p>使用内置的<code>sum</code>函数与使用<code>numpy.sum</code>或数组的<code>sum</code>方法不同。在</p>
<p>对于>;1d数组,python的<code>sum</code>将给出截然不同的结果:</p>
<pre><code>In [1]: import numpy as np
In [2]: x = np.arange(100).reshape(10, 10)
In [3]: x
Out[3]:
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
In [4]: sum(x)
Out[4]: array([450, 460, 470, 480, 490, 500, 510, 520, 530, 540])
In [5]: x.sum()
Out[5]: 4950
In [6]: np.sum(x)
Out[6]: 4950
</code></pre>
<p>这是因为python的sum基本上是对对象上的for循环求和。在</p>
<p>在一个>;1d数组上循环将返回沿第一个轴的切片。E、 g</p>
^{pr2}$
<p>在本例中,Python的<code>sum</code>有效地给出了列的总和(即<code>row1 + row2 + row3 ...</code>)</p>