Python numpy.square与** [英] Python numpy.square vs **

问题描述

numpy.square和在Numpy数组上使用**运算符之间是否有区别?

Is there a difference between numpy.square and using the ** operator on a Numpy array?

据我所见，它产生了相同的结果.

From what I can see it yields the same result.

执行效率有何不同?

澄清示例:

In [1]: import numpy as np

In [2]: A = np.array([[2, 2],[2, 2]])

In [3]: np.square(A)
Out[3]: 
array([[4, 4],
       [4, 4]])

In [4]: A ** 2
Out[4]: 
array([[4, 4],
       [4, 4]])

推荐答案

您可以检查执行时间以获取清晰的图片

You can check the execution time to get clear picture of it

In [2]: import numpy as np
In [3]: A = np.array([[2, 2],[2, 2]])
In [7]: %timeit np.square(A)
1000000 loops, best of 3: 923 ns per loop
In [8]: %timeit A ** 2
1000000 loops, best of 3: 668 ns per loop

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