NumPy ndarray.all() vs np.all(ndarray) vs all(ndarray) [英] NumPy ndarray.all() vs np.all(ndarray) vs all(ndarray)

问题描述

Python/NumPy 中的三个all"方法有什么区别?性能差异的原因是什么?ndarray.all() 真的是三个中最快的吗?

What is the the difference between the three "all" methods in Python/NumPy? What is the reason for the performance difference? Is it true that ndarray.all() is always the fastest of the three?

这是我运行的计时测试:

Here is a timing test that I ran:

In [59]: a = np.full(100000, True, dtype=bool)

In [60]: timeit a.all()
The slowest run took 5.40 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 5.24 µs per loop

In [61]: timeit all(a)
1000 loops, best of 3: 1.34 ms per loop

In [62]: timeit np.all(a)
The slowest run took 5.54 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 6.41 µs per loop

推荐答案

np.all(a) 和 a.all() 的区别很简单:

The difference between np.all(a) and a.all() is simple:

• 如果 a 是 numpy.array 那么 np.all() 将简单地调用 a.all().
• 如果 a 不是 numpy.array，np.all() 调用会将其转换为 numpy.array 然后调用 a.all().a.all() 另一方面会失败，因为 a 不是 numpy.array，因此可能没有 all 方法.

• If a is a numpy.array then np.all() will simply call a.all().
• If a is not a numpy.array the np.all() call will convert it to an numpy.array and then call a.all(). a.all() on the other hand will fail because a wasn't a numpy.array and therefore probably has no all method.

np.all 和 all 的区别比较复杂.

The difference between np.all and all is more complicated.

• all 函数适用于任何可迭代对象(包括 list、sets、generators、...).np.all 仅适用于 numpy.arrays(包括所有可以转换为 numpy 数组的东西，即 lists 和 元组s).
• np.all 处理具有指定数据类型的 array，这使得在比较 != 0 时非常有效.all 但是需要为每个项目评估 bool ，这要慢得多.
• 使用 python 函数处理数组非常慢，因为数组中的每个项目都需要转换为 python 对象.np.all 不需要进行这种转换.

• The all function works on any iterable (including list, sets, generators, ...). np.all works only for numpy.arrays (including everything that can be converted to a numpy array, i.e. lists and tuples).
• np.all processes an array with specified data type, that makes it pretty efficient when comparing for != 0. all however needs to evaluate bool for each item, that's much slower.
• processing arrays with python functions is pretty slow because each item in the array needs to be converted to a python object. np.all doesn't need to do that conversion.

请注意，时间还取决于您的 a 的类型.如果您处理 python 列表 all 可以更快地处理相对较短的列表.如果你处理一个数组，np.all 和 a.all() 在几乎所有情况下都会更快(除了 object 数组，但我不会走那条路，那是疯子).

Note that the timings depend also on the type of your a. If you process a python list all can be faster for relativly short lists. If you process an array, np.all and a.all() will be faster in almost all cases (except maybe for object arrays, but I won't go down that path, that way lies madness).

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