Python: o mecanismo por trás da compreensão da lista

Question

Ao usar a compreensão de lista ou a palavra-chave in em um contexto for loop, ou seja:

for o in X:
    do_something_with(o)

ou

l=[o for o in X]

• Como funciona o mecanismo por trás do in?
• Quais funções \ métodos dentro de X ele chama?
• Se X pode obedecer a mais de um método, qual é a precedência?
• Como escrever um X eficiente, para que a compreensão da lista seja rápida?

Answer 1

A, afaik, resposta completa e correta.

for, tanto em loops for quanto em compreensões de lista, chama iter() em X. iter() retornará um iterável se X tiver um método __iter__ ou um método __getitem__. Se implementar ambos, __iter__ será usado. Se não tiver nenhum dos dois, você obterá TypeError: 'Nothing' object is not iterable.

Isso implementa um __getitem__:

class GetItem(object):
    def __init__(self, data):
        self.data = data

    def __getitem__(self, x):
        return self.data[x]

Uso:

>>> data = range(10)
>>> print [x*x for x in GetItem(data)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Este é um exemplo de implementação de __iter__:

class TheIterator(object):
    def __init__(self, data):
        self.data = data
        self.index = -1

    # Note: In  Python 3 this is called __next__
    def next(self):
        self.index += 1
        try:
            return self.data[self.index]
        except IndexError:
            raise StopIteration

    def __iter__(self):
        return self

class Iter(object):
    def __init__(self, data):
        self.data = data

    def __iter__(self):
        return TheIterator(data)

Uso:

>>> data = range(10)
>>> print [x*x for x in Iter(data)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Como você pode ver, você precisa implementar um iterador e um __iter__ que retorna o iterador.

Você pode combiná-los:

class CombinedIter(object):
    def __init__(self, data):
        self.data = data

    def __iter__(self):
        self.index = -1
        return self

    def next(self):
        self.index += 1
        try:
            return self.data[self.index]
        except IndexError:
            raise StopIteration

Uso:

>>> well, you get it, it's all the same...

Mas então você só pode ter um iterador ativo por vez. OK, neste caso, você pode apenas fazer isso:

class CheatIter(object):
    def __init__(self, data):
        self.data = data

    def __iter__(self):
        return iter(self.data)

Mas isso é trapaça porque você está apenas reutilizando o método __iter__ de list. Uma maneira mais fácil é usar yield e transformar __iter__ em um gerador:

class Generator(object):
    def __init__(self, data):
        self.data = data

    def __iter__(self):
        for x in self.data:
            yield x

Este último é o jeito que eu recomendaria. Fácil e eficiente.

Answer 2

X deve ser iterável.Ele deve implementar __iter__() que retorna um objeto iterador;o objeto iterador deve implementar next(), que retorna o próximo item toda vez que é chamado ou gera um StopIteration se não houver próximo item.

Listas, tuplas e geradores são iteráveis.

Observe que o operador for simples usa o mesmo mecanismo.

Answer 3

Answering question's comments I can say that reading source is not the best idea in this case. The code that is responsible for execution of compiled code (ceval.c) does not seem to be very verbose for a person that sees Python sources for the first time. Here is the snippet that represents iteration in for loops:

   TARGET(FOR_ITER)
        /* before: [iter]; after: [iter, iter()] *or* [] */
        v = TOP();

        /*
          Here tp_iternext corresponds to next() in Python
        */
        x = (*v->ob_type->tp_iternext)(v); 
        if (x != NULL) {
            PUSH(x);
            PREDICT(STORE_FAST);
            PREDICT(UNPACK_SEQUENCE);
            DISPATCH();
        }
        if (PyErr_Occurred()) {
            if (!PyErr_ExceptionMatches(
                            PyExc_StopIteration))
                break;
            PyErr_Clear();
        }
        /* iterator ended normally */
        x = v = POP();
        Py_DECREF(v);
        JUMPBY(oparg);
        DISPATCH();

To find what actually happens here you need to dive into bunch of other files which verbosity is not much better. Thus I think that in such cases documentation and sites like SO are the first place to go while the source should be checked only for uncovered implementation details.

Answer 4

X must be an iterable object, meaning it needs to have an __iter__() method.

So, to start a for..in loop, or a list comprehension, first X's __iter__() method is called to obtain an iterator object; then that object's next() method is called for each iteration until StopIteration is raised, at which point the iteration stops.

I'm not sure what your third question means, and how to provide a meaningful answer to your fourth question except that your iterator should not construct the entire list in memory at once.

Answer 5

Maybe this helps (tutorial http://docs.python.org/tutorial/classes.html Section 9.9):

Behind the scenes, the for statement calls iter() on the container object. The function returns an iterator object that defines the method next() which accesses elements in the container one at a time. When there are no more elements, next() raises a StopIteration exception which tells the for loop to terminate.

Answer 6

To answer your questions:

How does the mechanism behind in works?

It is the exact same mechanism as used for ordinary for loops, as others have already noted.

Which functions\methods within X does it call?

As noted in a comment below, it calls iter(X) to get an iterator. If X has a method function __iter__() defined, this will be called to return an iterator; otherwise, if X defines __getitem__(), this will be called repeatedly to iterate over X. See the Python documentation for iter() here: http://docs.python.org/library/functions.html#iter

If X can comply to more than one method, what's the precedence?

I'm not sure what your question is here, exactly, but Python has standard rules for how it resolves method names, and they are followed here. Here is a discussion of this:

Method Resolution Order (MRO) in new style Python classes

How to write an efficient X, so that list comprehension will be quick?

I suggest you read up more on iterators and generators in Python. One easy way to make any class support iteration is to make a generator function for iter(). Here is a discussion of generators:

http://linuxgazette.net/100/pramode.html