`numpy.sum` 与 `ndarray.sum`

Question

对于一维numpy数组a，我认为np.sum(a)和a.sum()是等价的函数，但我只是做了一个简单的实验，似乎后者总是要快一点：

In [1]: import numpy as np

In [2]: a = np.arange(10000)

In [3]: %timeit np.sum(a)
The slowest run took 16.85 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 6.46 µs per loop

In [4]: %timeit a.sum()
The slowest run took 19.80 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 5.25 µs per loop

为什么有区别？这是否意味着，我们应该始终使用numpy.ndarray的功能，如版本sum，mean，std等？

Answer 1

我想这是因为np.sum()等等需要显式地将输入转换为ndarray第一个（使用np.asanyarray） .sum在确定方法之前检查一些其他函数ndarray.sum，以便允许对列表、元组等进行操作。

另一方面，ndarray.sum()它是类的方法ndarray，因此不需要进行任何检查。