我使用lambda来创建包含参数的回调。在一行中编写lambda比编写一个方法来执行相同的功能更简洁。

例如:

import imported.module

def func():
    return lambda: imported.module.method("foo", "bar")

相对于:

import imported.module

def func():
    def cb():
        return imported.module.method("foo", "bar")
    return cb

你可以用lambda做的任何事情，都可以用命名函数或列表和生成器表达式做得更好。

因此，在大多数情况下，在任何情况下您都应该只使用其中一种(可能除了在交互式解释器中编写的草稿代码)。

I started reading David Mertz's book today 'Text Processing in Python.' While he has a fairly terse description of Lambda's the examples in the first chapter combined with the explanation in Appendix A made them jump off the page for me (finally) and all of a sudden I understood their value. That is not to say his explanation will work for you and I am still at the discovery stage so I will not attempt to add to these responses other than the following: I am new to Python I am new to OOP Lambdas were a struggle for me Now that I read Mertz, I think I get them and I see them as very useful as I think they allow a cleaner approach to programming.

He reproduces the Zen of Python, one line of which is Simple is better than complex. As a non-OOP programmer reading code with lambdas (and until last week list comprehensions) I have thought-This is simple?. I finally realized today that actually these features make the code much more readable, and understandable than the alternative-which is invariably a loop of some sort. I also realized that like financial statements-Python was not designed for the novice user, rather it is designed for the user that wants to get educated. I can't believe how powerful this language is. When it dawned on me (finally) the purpose and value of lambdas I wanted to rip up about 30 programs and start over putting in lambdas where appropriate.

Lambdas通常与函数式编程风格密切相关。通过将函数应用于某些数据并合并结果来解决问题，这是谷歌用于实现其大多数算法的思想。

以函数式编程风格编写的程序很容易并行化，因此在现代多核机器中变得越来越重要。 所以简而言之，不，你不应该忘记他们。

函数这是一种非官僚化的创建函数的方法。

就是这样。例如，假设你有一个主要函数，需要对值平方。我们来看看传统的方法和的方法

传统的方法:

def main():
...
...
y = square(some_number)
...
return something

def square(x):
    return x**2

方式:

def main():
...
square = lambda x: x**2
y = square(some_number)
return something

看到区别了吗?

Lambda函数非常适合用于列表，比如列表推导式或映射。事实上，列表理解是一种使用lambda来表达自己的“python”方式。例:

>>>a = [1,2,3,4]
>>>[x**2 for x in a]
[1,4,9,16]

让我们看看每个语法元素的含义:

[]:“给我一个清单” X **2:“使用这个新诞生的函数” 对于a中的x: "into each element in a"

很方便吧?创建这样的函数。让我们用lambda重写它:

>>> square = lambda x: x**2
>>> [square(s) for x in a]
[1,4,9,16]

现在让我们使用map，这是同样的东西，但更语言中立。Maps有两个参数:

(i)一个功能

(ii)可迭代对象

给你一个列表，其中每个元素它是应用于可迭代对象的每个元素的函数。

使用map，我们会得到:

>>> a = [1,2,3,4]
>>> squared_list = map(lambda x: x**2, a)

如果你掌握了lambdas和映射，你将拥有以简洁的方式操作数据的强大能力。Lambda函数既不晦涩，也不影响代码的清晰性。不要把难的东西和新东西混为一谈。一旦你开始使用它们，你就会发现非常清楚。

我发现lambda对于执行相同功能的函数列表很有用，但适用于不同的情况。

就像Mozilla的复数规则:

plural_rules = [
    lambda n: 'all',
    lambda n: 'singular' if n == 1 else 'plural',
    lambda n: 'singular' if 0 <= n <= 1 else 'plural',
    ...
]
# Call plural rule #1 with argument 4 to find out which sentence form to use.
plural_rule[1](4) # returns 'plural'

如果你必须为所有这些定义一个函数，到最后你会疯掉的。 另外，像plural_rule_1, plural_rule_2这样的函数名也不太好。当你依赖于变量函数id时，你需要eval()它。