np.full(size，0)与np.zeros(size)与np.empty() [英] np.full(size, 0) vs. np.zeros(size) vs. np.empty()

问题描述

如果您要选择以下三种用零初始化数组的方式中的一种，那么您会选择哪一种?为什么?

If you were to choose one of the following three ways of initializing an array with zeros which one would you choose and why?

my_arr_1 = np.full(size, 0) 

或

my_arr_2 = np.zeros(size)

或

my_arr_3 = np.empty(size)
my_arr_3[:] = 0

推荐答案

由于其名称，我会使用np.zeros.我永远不会使用第三个成语，因为

I'd use np.zeros, because of its name. I would never use the third idiom because

1. 它需要两个语句而不是一个表达式，并且

1. it takes two statements instead of a single expression and

对于NumPy员工来说，最难优化.实际上，在NumPy中 1.10，尽管对索引进行了所有优化，但np.zeros仍然是最快的选择:

it's harder for the NumPy folks to optimize. In fact, in NumPy 1.10, np.zeros is still the fastest option, despite all the optimizations to indexing:

>>> %timeit np.zeros(1e6)
1000 loops, best of 3: 804 µs per loop
>>> %timeit np.full(1e6, 0)
1000 loops, best of 3: 816 µs per loop
>>> %timeit a = np.empty(1e6); a[:] = 0
1000 loops, best of 3: 919 µs per loop

更大的数组，可与@John Zwinck的结果进行比较:

Bigger array for comparison with @John Zwinck's results:

>>> %timeit np.zeros(1e8)
100000 loops, best of 3: 9.66 µs per loop
>>> %timeit np.full(1e8, 0)
1 loops, best of 3: 614 ms per loop
>>> %timeit a = np.empty(1e8); a[:] = 0
1 loops, best of 3: 229 ms per loop

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