Python 为什么pickle加载numpy数组的速度比numpy快得多？

我在发布问题之前已经检查过了。 从那里的答案来看，我们可以认为

numpy

应该能够更快地使用

ndarrays

但是看看这些实验

我们测试的功能：

import numpy as np
import pickle as pkl

a = np.random.randn(1000,5)

with open("test.npy", "wb") as f:
    np.save(f, a)

with open("test.pkl", "wb") as f:
    pkl.dump(a,f)    

def load_with_numpy(name):
    for i in range(1000):
        with open(name, "rb") as f:
            np.load(f)
def load_with_pickle(name):
    for i in range(1000):
        with open(name, "rb") as f:
            pkl.load(f)

%timeit load_with_numpy("test.npy")
296 ms ± 1.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit load_with_pickle("test.pkl")
28.2 ms ± 994 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

实验结果：

import numpy as np
import pickle as pkl

a = np.random.randn(1000,5)

with open("test.npy", "wb") as f:
    np.save(f, a)

with open("test.pkl", "wb") as f:
    pkl.dump(a,f)    

def load_with_numpy(name):
    for i in range(1000):
        with open(name, "rb") as f:
            np.load(f)
def load_with_pickle(name):
    for i in range(1000):
        with open(name, "rb") as f:
            pkl.load(f)

%timeit load_with_numpy("test.npy")
296 ms ± 1.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit load_with_pickle("test.pkl")
28.2 ms ± 994 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

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为什么会这样？

如果不访问您正在使用的文件，我们无法复制这些内容，而且您还没有显示文件创建过程。此外，即使您向我们显示的内容也无法实际工作。例如，

load\u with_pickle

试图使用一个从未导入或定义过的非限定的

load

函数。@user2357112修复了增加

a

的大小，一旦它有100000个元素，我从numpy中看到了更好的性能。@user2699是的，现在我明白了。在我链接的主题中，有人说pickle使用numpy保存和加载numpy数组，但这似乎不是真的