与`None`的比较将导致元素对象

问题描述

显然(在未来"中)将不再使用以下内容:

Apparantly it will (in the 'future') not be possible anymore to use the following:

import numpy as np
np.array([0,1,2]) == None
> False
> FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.

这也打破了numpy数组的延迟加载模式:

This also breaks the lazy loading pattern for numpy arrays:

import numpy as np
def f(a=None):
    if a == None: 
        a = <some default value>
    <function body>

还有什么其他可能性可以让您仍然使用延迟初始化?

What other possibilities allow you to still use lazy initialization?

推荐答案

您在寻找is:

if a is None:
    a = something else

问题在于，通过使用==运算符，如果输入元素a是一个numpy数组，则numpy将尝试对元素进行明智的比较，并告知您无法对其进行比较.

The problem is that, by using the == operator, if the input element a is a numpy array, numpy will try to perform an element wise comparison and tell you that you cannot compare it.

对于a一个numpy数组，a == None给出错误，而np.all(a == None)不给出错误(但不执行您期望的操作).取而代之的是a is None将起作用，而与a的数据类型无关.

For a a numpy array, a == None gives error, np.all(a == None) doesn't (but does not do what you expect). Instead a is None will work regardless the data type of a.

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