在你的第二种方法中，我将通过这样做进入下一组4人:

ints = ints[4:]

然而，我还没有做过任何绩效评估，所以我不知道哪种方法更有效。

话虽如此，我通常会选择第一种方法。这并不漂亮，但这通常是与外部世界接触的结果。

因为没有人提到它，这里有一个zip()解决方案:

>>> def chunker(iterable, chunksize):
...     return zip(*[iter(iterable)]*chunksize)

只有当你的序列长度总是能被块大小整除时，它才有效，或者如果后面的块不能整除，你就不关心它。

例子:

>>> s = '1234567890'
>>> chunker(s, 3)
[('1', '2', '3'), ('4', '5', '6'), ('7', '8', '9')]
>>> chunker(s, 4)
[('1', '2', '3', '4'), ('5', '6', '7', '8')]
>>> chunker(s, 5)
[('1', '2', '3', '4', '5'), ('6', '7', '8', '9', '0')]

或者使用itertools。返回一个迭代器而不是一个列表:

>>> from itertools import izip
>>> def chunker(iterable, chunksize):
...     return izip(*[iter(iterable)]*chunksize)

填充可以固定使用@ΤΖΩΤΖΙΟΥ的答案:

>>> from itertools import chain, izip, repeat
>>> def chunker(iterable, chunksize, fillvalue=None):
...     it   = chain(iterable, repeat(fillvalue, chunksize-1))
...     args = [it] * chunksize
...     return izip(*args)

我希望通过将迭代器从列表中删除，我不是简单地复制列表的一部分。生成器可以被切片，它们将自动仍然是一个生成器，而列表将被切片成1000个条目的大块，这是较低的效率。

def iter_group(iterable, batch_size:int):
    length = len(iterable)
    start = batch_size*-1
    end = 0
    while(end < length):
        start += batch_size
        end += batch_size
        if type(iterable) == list:
            yield (iterable[i] for i in range(start,min(length-1,end)))
        else:
            yield iterable[start:end]

用法:

items = list(range(1,1251))

for item_group in iter_group(items, 1000):
    for item in item_group:
        print(item)

使用小的函数和东西真的不吸引我;我更喜欢使用切片:

data = [...]
chunk_size = 10000 # or whatever
chunks = [data[i:i+chunk_size] for i in xrange(0,len(data),chunk_size)]
for chunk in chunks:
    ...

首先，我将它设计为将字符串拆分为子字符串以解析包含十六进制的字符串。 今天我把它变成复杂的，但仍然简单的生成器。

def chunker(iterable, size, reductor, condition):
    it = iter(iterable)
    def chunk_generator():
        return (next(it) for _ in range(size))
    chunk = reductor(chunk_generator())
    while condition(chunk):
        yield chunk
        chunk = reductor(chunk_generator())

参数:

明显的

Iterable是任何包含/生成/迭代输入数据的Iterable /迭代器/生成器， 当然，大小是你想要得到的块的大小，

更有趣的

reductor is a callable, which receives generator iterating over content of chunk. I'd expect it to return sequence or string, but I don't demand that. You can pass as this argument for example list, tuple, set, frozenset, or anything fancier. I'd pass this function, returning string (provided that iterable contains / generates / iterates over strings): def concatenate(iterable): return ''.join(iterable) Note that reductor can cause closing generator by raising exception. condition is a callable which receives anything what reductor returned. It decides to approve & yield it (by returning anything evaluating to True), or to decline it & finish generator's work (by returning anything other or raising exception). When number of elements in iterable is not divisible by size, when it gets exhausted, reductor will receive generator generating less elements than size. Let's call these elements lasts elements. I invited two functions to pass as this argument: lambda x:x - the lasts elements will be yielded. lambda x: len(x)==<size> - the lasts elements will be rejected. replace <size> using number equal to size

more-itertools包有一个分块方法，它可以做到这一点:

import more_itertools
for s in more_itertools.chunked(range(9), 4):
    print(s)

打印

[0, 1, 2, 3]
[4, 5, 6, 7]
[8]

Chunked返回列表中的项。如果你更喜欢迭代对象，可以使用ichunked。