python - `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，不过我只是做了一个简单的实验，似乎后者总是快一点:

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

为什么会有差异？这是否意味着我们应该始终使用 numpy.ndarray 版本的函数，例如 sum、mean、std、等等？

Answer 1

我想这是因为 np.sum() 之类的需要先将输入显式转换为 ndarray(使用 np .asanyarray) checks a few other .sum functions在确定 ndarray.sum 方法之前，以便允许对列表、元组等进行操作

另一方面，ndarray.sum() 是 ndarray 类的一个方法，因此不需要进行任何检查。