让我用一段代码来演示，这段代码摘自一个关于SO的“函数式”Python问题的答案

Python:

def grandKids(generation, kidsFunc, val):
  layer = [val]
  for i in xrange(generation):
    layer = itertools.chain.from_iterable(itertools.imap(kidsFunc, layer))
  return layer

Haskell:

grandKids generation kidsFunc val =
  iterate (concatMap kidsFunc) [val] !! generation

这里的主要区别是Haskell的标准库具有用于函数式编程的有用函数:在本例中是iterate、concat和(!!)

让我用一段代码来演示，这段代码摘自一个关于SO的“函数式”Python问题的答案

Python:

def grandKids(generation, kidsFunc, val):
  layer = [val]
  for i in xrange(generation):
    layer = itertools.chain.from_iterable(itertools.imap(kidsFunc, layer))
  return layer

Haskell:

grandKids generation kidsFunc val =
  iterate (concatMap kidsFunc) [val] !! generation

这里的主要区别是Haskell的标准库具有用于函数式编程的有用函数:在本例中是iterate、concat和(!!)

Guido对此有很好的解释。以下是最相关的部分:

I have never considered Python to be heavily influenced by functional languages, no matter what people say or think. I was much more familiar with imperative languages such as C and Algol 68 and although I had made functions first-class objects, I didn't view Python as a functional programming language. However, earlier on, it was clear that users wanted to do much more with lists and functions. ... It is also worth noting that even though I didn't envision Python as a functional language, the introduction of closures has been useful in the development of many other advanced programming features. For example, certain aspects of new-style classes, decorators, and other modern features rely upon this capability. Lastly, even though a number of functional programming features have been introduced over the years, Python still lacks certain features found in “real” functional programming languages. For instance, Python does not perform certain kinds of optimizations (e.g., tail recursion). In general, because Python's extremely dynamic nature, it is impossible to do the kind of compile-time optimization known from functional languages like Haskell or ML. And that's fine.

我从中得出两点结论:

该语言的创造者并不真正认为Python是一种函数式语言。因此，可能会看到“函数式”的特性，但不太可能看到任何明确的功能。 Python的动态特性抑制了您在其他函数式语言中看到的一些优化。当然，Lisp和Python一样是动态的(如果不是更动态的话)，所以这只是部分解释。

上面没有提到的另一个原因是，许多内置类型的内置函数和方法修改了对象，但不返回修改后的对象。如果返回这些修改后的对象，将使函数代码更干净、更简洁。例如，如果some_list.append(some_object)返回附加some_object的some_list。

In addition to other answers, one reason Python and most other multi-paradigm languages are not well suited for true functional programming is because their compilers / virtual machines / run-times do not support functional optimization. This sort of optimization is achieved by the compiler understanding mathematical rules. For example, many programming languages support a map function or method. This is a fairly standard function that takes a function as one argument and a iterable as the second argument then applies that function to each element in the iterable.

不管怎样，map(foo()， x) * map(foo()， y)和map(foo()， x * y)是一样的。后者实际上比前者快，因为前者执行两个副本，而后者执行一个副本。

更好的函数式语言能够识别这些基于数学的关系，并自动执行优化。不致力于函数式范式的语言可能无法优化。

对于这个问题(以及答案)，有一点非常重要: 函数式编程到底是什么，它最重要的特性是什么? 我将尝试给出我的观点:

Functional programming is a lot like writing math on a whiteboard. When you write equations on a whiteboard, you do not think about an execution order. There is (typically) no mutation. You don't come back the day after and look at it, and when you make the calculations again, you get a different result (or you may, if you've had some fresh coffee :)). Basically, what is on the board is there, and the answer was already there when you started writing things down, you just haven't realized what it is yet.

函数式编程与此很相似;你不需要改变，只需要评估 方程(或者在这种情况下，“程序”)，并找出答案是什么。这个项目 还在那里，没有改变。数据也是一样。

I would rank the following as the most important features of functional programming: a) referential transparency - if you evaluate the same statement at some other time and place, but with the same variable values, it will still mean the same. b) no side effect - no matter how long you stare at the whiteboard, the equation another guy is looking at at another whiteboard won't accidentally change. c) functions are values too. which can be passed around and applied with, or to, other variables. d) function composition, you can do h=g·f and thus define a new function h(..) which is equivalent to calling g(f(..)).

这个列表是按我的优先顺序排列的，所以参考透明度是最重要的， 而且没有副作用。

现在，如果你浏览python并检查该语言和库的支持程度， 并保证，这些方面，然后你就可以很好地回答你自己的问题。