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。

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.

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)。

使用lambdas的一个有用的例子是提高长列表推导式的可读性。 在这个例子中，loop_dic是为了清晰起见的缩写，但是假设loop_dic非常长。如果你只是使用一个包含i的普通值，而不是该值的lambda版本，你会得到一个NameError。

>>> lis = [{"name": "Peter"}, {"name": "Josef"}]

>>> loop_dic = lambda i: {"name": i["name"] + " Wallace" }
>>> new_lis = [loop_dic(i) for i in lis]

>>> new_lis
[{'name': 'Peter Wallace'}, {'name': 'Josef Wallace'}]

而不是

>>> lis = [{"name": "Peter"}, {"name": "Josef"}]

>>> new_lis = [{"name": i["name"] + " Wallace"} for i in lis]

>>> new_lis
[{'name': 'Peter Wallace'}, {'name': 'Josef Wallace'}]

我经常使用它，主要是作为空对象或将参数部分绑定到函数。

下面是一些例子:

实现空对象模式:

{
    DATA_PACKET: self.handle_data_packets
    NET_PACKET: self.handle_hardware_packets
}.get(packet_type, lambda x : None)(payload)

对于参数绑定:

假设我有以下API

def dump_hex(file, var)
    # some code
    pass

class X(object):
    #...
    def packet_received(data):
        # some kind of preprocessing
        self.callback(data)
    #...

然后，当我不想快速转储接收到的数据到一个文件，我这样做:

dump_file = file('hex_dump.txt','w')
X.callback = lambda (x): dump_hex(dump_file, x)
...
dump_file.close()

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。