python vs numpy中的布尔值和类型检查 [英] boolean and type checking in python vs numpy

问题描述

我今天在python if子句中遇到了意外的结果:

I ran into unexpected results in a python if clause today:

import numpy
if numpy.allclose(6.0, 6.1, rtol=0, atol=0.5):
    print 'close enough'  # works as expected (prints message)

if numpy.allclose(6.0, 6.1, rtol=0, atol=0.5) is True:
    print 'close enough'  # does NOT work as expected (prints nothing)

经过一番摸索(例如，此问题，尤其是

After some poking around (i.e., this question, and in particular this answer), I understand the cause: the type returned by numpy.allclose() is numpy.bool_ rather than plain old bool, and apparently if foo = numpy.bool_(1), then if foo will evaluate to True while if foo is True will evaluate to False. This appears to be the work of the is operator.

我的问题是:为什么numpy具有自己的布尔类型，鉴于这种情况，最佳实践是什么?在上面的示例中，我可以写if foo:来获得预期的行为，但是我喜欢更严格的if foo is True:，因为它从返回True中排除了2和[2]之类的东西，有时还不包括显式类型检查是可取的.

My questions are: why does numpy have its own boolean type, and what is best practice in light of this situation? I can get away with writing if foo: to get expected behavior in the example above, but I like the more stringent if foo is True: because it excludes things like 2 and [2] from returning True, and sometimes the explicit type check is desirable.

推荐答案

为什么numpy有自己的布尔类型

why does numpy have its own boolean type

空间和速度. Numpy将事物存储在紧凑的数组中；如果它可以将布尔值放入单个字节中，则将尝试.使用Python对象很难做到这一点，因为您必须存储引用，这会大大降低计算速度.

Space and speed. Numpy stores things in compact arrays; if it can fit a boolean into a single byte it'll try. You can't easily do this with Python objects, as you have to store references which slows calculations down significantly.

在上面的示例中，我可以放弃编写if foo:来获得预期的行为，但是如果foo为True，我喜欢更严格的方法:因为它排除了2和[2]之类的返回True的功能，有时还不包括显式的类型检查是可取的.

I can get away with writing if foo: to get expected behavior in the example above, but I like the more stringent if foo is True: because it excludes things like 2 and [2] from returning True, and sometimes the explicit type check is desirable.

好吧，不要那样做.

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