我怀疑lambda不会消失。 请参阅Guido关于最终放弃尝试删除它的帖子。也请参阅冲突概要。

你可以看看这篇文章，了解更多Python函数特性背后的交易历史: http://python-history.blogspot.com/2009/04/origins-of-pythons-functional-features.html

奇怪的是，最初引入lambda和其他函数特性的map、filter和reduce函数在很大程度上已经被列表推导式和生成器表达式所取代。事实上，在Python 3.0中，reduce函数已从内置函数列表中删除。(但是，没有必要投诉lambda、map或filter的删除:它们被保留了。: -)

我个人的意见是:就清晰度而言，lambda值不了多少。通常有一个更清晰的不包含的解。

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

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.

我怀疑lambda不会消失。 请参阅Guido关于最终放弃尝试删除它的帖子。也请参阅冲突概要。

你可以看看这篇文章，了解更多Python函数特性背后的交易历史: http://python-history.blogspot.com/2009/04/origins-of-pythons-functional-features.html

奇怪的是，最初引入lambda和其他函数特性的map、filter和reduce函数在很大程度上已经被列表推导式和生成器表达式所取代。事实上，在Python 3.0中，reduce函数已从内置函数列表中删除。(但是，没有必要投诉lambda、map或filter的删除:它们被保留了。: -)

我个人的意见是:就清晰度而言，lambda值不了多少。通常有一个更清晰的不包含的解。

我使用lambdas来避免代码重复。这样函数就容易理解了 例如:

def a_func()
  ...
  if some_conditon:
     ...
     call_some_big_func(arg1, arg2, arg3, arg4...)
  else
     ...
     call_some_big_func(arg1, arg2, arg3, arg4...)

我用一个临时变量替换它

def a_func()
  ...
  call_big_f = lambda args_that_change: call_some_big_func(arg1, arg2, arg3, args_that_change)
  if some_conditon:
     ...
     call_big_f(argX)
  else
     ...
     call_big_f(argY)