“for"循环中的 i = i + 1 和 i += 1 有什么区别? [英] What is the difference between i = i + 1 and i += 1 in a 'for' loop?

问题描述

我今天发现了一件奇怪的事情，想知道是否有人可以解释这里的区别?

I found out a curious thing today and was wondering if somebody could shed some light into what the difference is here?

import numpy as np

A = np.arange(12).reshape(4,3)
for a in A:
    a = a + 1

B = np.arange(12).reshape(4,3)
for b in B:
    b += 1

在运行每个 for 循环后，A 没有改变，但 B 已添加到每个元素.我实际上使用 B 版本在 for 循环中写入初始化的 NumPy 数组.

After running each for loop, A has not changed, but B has had one added to each element. I actually use the B version to write to a initialized NumPy array within a for loop.

推荐答案

区别在于一个修改数据结构本身(就地操作)b += 1 而另一个只是 重新分配变量a = a + 1.

The difference is that one modifies the data-structure itself (in-place operation) b += 1 while the other just reassigns the variable a = a + 1.

只是为了完整性:

x += y 并不总是进行就地操作，有(至少)三个例外:

x += y is not always doing an in-place operation, there are (at least) three exceptions:

• 如果 x 没有实现 __iadd__ 方法，则 x += y 语句只是 x = x + y 的简写.如果 x 类似于 int，就会出现这种情况.

• If x doesn't implement an __iadd__ method then the x += y statement is just a shorthand for x = x + y. This would be the case if x was something like an int.

如果 __iadd__ 返回 NotImplemented，Python 会回退到 x = x + y.

If __iadd__ returns NotImplemented, Python falls back to x = x + y.

__iadd__ 方法理论上可以实现为不工作.不过这样做真的很奇怪.

The __iadd__ method could theoretically be implemented to not work in place. It'd be really weird to do that, though.

碰巧你的 b 是 numpy.ndarray ，它实现了 __iadd__ 并返回自身，所以你的第二个循环修改了原始数组-地方.

As it happens your bs are numpy.ndarrays which implements __iadd__ and return itself so your second loop modifies the original array in-place.

您可以在 模拟数字类型"的 Python 文档中阅读更多相关内容.

调用这些 [__i*__] 方法来实现增广算术赋值(+=, -=, *=、@=、/=、//=、%=、**=, <<=, >>=, &=, ^=, |=).这些方法应该尝试就地执行操作(修改 self)并返回结果(可以是但不一定是 self).如果未定义特定方法，则增广赋值回退到正常方法.例如，如果 x 是具有 __iadd__() 方法的类的实例，则 x += y 等价于 x = x.__iadd__(y) .否则，将考虑 x.__add__(y) 和 y.__radd__(x)，与对 x + y 的评估一样.在某些情况下，增强赋值可能会导致意外错误(请参阅 为什么 a_tuple[i] += ["item"] 当加法起作用时引发异常?)，但这种行为实际上是数据模型的一部分.

These [__i*__] methods are called to implement the augmented arithmetic assignments (+=, -=, *=, @=, /=, //=, %=, **=, <<=, >>=, &=, ^=, |=). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an __iadd__() method, x += y is equivalent to x = x.__iadd__(y) . Otherwise, x.__add__(y) and y.__radd__(x) are considered, as with the evaluation of x + y. In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += ["item"] raise an exception when the addition works?), but this behavior is in fact part of the data model.

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