itertools中的chain和chain.from_iterable有什么区别？

71

我在互联网上找不到任何有效的示例，可以看到它们之间的区别以及为什么要选择一个而不是另一个。

python  iterator  itertools 
— 用户1994660
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Answers:

86

第一个带有0个或多个参数，每个参数都是可迭代的，第二个带有一个参数，该参数有望产生可迭代的：

from itertools import chain

chain(list1, list2, list3)

iterables = [list1, list2, list3]
chain.from_iterable(iterables)

但iterables可以是产生可迭代对象的任何迭代器：

def gen_iterables():
    for i in range(10):
        yield range(i)

itertools.chain.from_iterable(gen_iterables())

使用第二种形式通常是方便的情况，但是由于它懒惰地遍历输入可迭代对象，因此这也是链接无限数量的有限迭代器的唯一方法：

def gen_iterables():
    while True:
        for i in range(5, 10):
            yield range(i)

chain.from_iterable(gen_iterables())

上面的示例将为您提供一个可迭代的迭代器，该迭代器将产生一个循环的数字模式，该模式将永远不会停止，但永远不会消耗比单个range()调用所需的内存更多的内存。

— 马亭皮特斯
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4
我还是不明白。您能给我输出差异和用例在实际情况下在哪里使用吗
— user1994660 2013年
14
@ user1994660：没有输出差异。这是输入差异。它使使用某些输入变得更容易。
— 马丁·彼得斯
@ user1994660：我在此答案中使用第二种形式。
— 马丁·彼得斯
1
@ user1994660：运行此代码：# Return an iterator of iterators def it_it(): return iter( [iter( [11, 22] ), iter( [33, 44] )] ) print( list(itertools.chain.from_iterable(it_it())) ) print( list(itertools.chain(it_it())) ) print( list(itertools.chain(*it_it())) ) 第一个最好。第二个没有到达嵌套的迭代器，它返回迭代器，而不是所需的数字；第三个产生正确的输出，但并不是完全懒惰：“ *”强制创建所有迭代器。对于这个愚蠢的输入，没关系。
— ToolmakerSteve
1
请注意，如果iterables不太大，您也可以这样做itertools.chain(*iterables)
— balki 2014年
9

我找不到任何有效的示例……在这里我可以看到它们[chain和chain.from_iterable]之间的区别以及为什么要选择一个而不是另一个

公认的答案是彻底的。对于那些寻求快速申请的人，请考虑展平几个列表：

list(itertools.chain(["a", "b", "c"], ["d", "e"], ["f"]))
# ['a', 'b', 'c', 'd', 'e', 'f']

您可能希望稍后再使用这些列表，因此可以对列表进行迭代： 

iterable = (["a", "b", "c"], ["d", "e"], ["f"])

尝试

但是，将iterable传入chain会产生不平坦的结果：

list(itertools.chain(iterable))
# [['a', 'b', 'c'], ['d', 'e'], ['f']]

为什么？您传入了一项（元组）。 chain分别需要每个列表。

解决方案

如果可能，您可以解开可迭代的文件：

list(itertools.chain(*iterable))
# ['a', 'b', 'c', 'd', 'e', 'f']

list(itertools.chain(*iter(iterable)))
# ['a', 'b', 'c', 'd', 'e', 'f']

更一般地，使用.from_iterable（因为它也可以与无限迭代器一起使用）：

list(itertools.chain.from_iterable(iterable))
# ['a', 'b', 'c', 'd', 'e', 'f']

g = itertools.chain.from_iterable(itertools.cycle(iterable))
next(g)
# "a"
— pylang
source
7

他们做非常相似的事情。对于少量的可迭代对象itertools.chain(*iterables)，itertools.chain.from_iterable(iterables)执行类似的操作。

关键的优势from_iterables在于能够处理大量（可能无限）的可迭代对象，因为在调用时不需要全部可用。

— 比坚
source
有人知道*操作员是否iterables开箱吗？
— Rotareti
1
@Rotareti，是的，它确实延迟解压缩（一次解包），但是在这种情况下itertools.chain(*iterables)是一个函数调用。所有参数必须在调用时出现。
— BiGYAN
这是真的？从CPython代码来看，似乎是相同的stackoverflow.com/a/62513808/610569
— alvas
@alvas尝试将元素数量更改为非常大；在10_000到1_000_000的范围内，您会发现from_iterables速度越来越快。
— BiGYAN
3

另一种查看方式：

chain(iterable1, iterable2, iterable3, ...) 用于当您已经知道自己有什么可迭代项时，因此可以将它们编写为这些逗号分隔的参数。

chain.from_iterable(iterable) 用于从另一个iterable中获取您的iterable（例如iterable1，iterable2，iterable3）。

— 纳维加
source
2

扩展@ martijn-pieters答案

尽管在迭代过程中对内部项目的访问保持不变，但在实现方面还是明智的，

  • itertools_chain_from_iterable（即chain.from_iterable在Python中）和
  • chain_new（即chain在Python中）

在CPython实现中，都是chain_new_internal的鸭子类型

使用chain.from_iterable(x)，x有可迭代的地方是否有优化的好处？主要目的是最终消耗平整的物料清单？

我们可以尝试使用以下方法进行基准测试：

import random
from itertools import chain
from functools import wraps
from time import time

from tqdm import tqdm

def timing(f):
    @wraps(f)
    def wrap(*args, **kw):
        ts = time()
        result = f(*args, **kw)
        te = time()
        print('func:%r args:[%r, %r] took: %2.4f sec' % (f.__name__, args, kw, te-ts))
        return result
    return wrap

def generate_nm(m, n):
    # Creates m generators of m integers between range 0 to n.
    yield iter(random.sample(range(n), n) for _ in range(m))
    

def chain_star(x):
    # Stores an iterable that will unpack and flatten the list of list.
    chain_x = chain(*x)
    # Consumes the items in the flatten iterable.
    for i in chain_x:
        pass

def chain_from_iterable(x):
    # Stores an iterable that will unpack and flatten the list of list.
    chain_x = chain.from_iterable(x)
    # Consumes the items in the flatten iterable.
    for i in chain_x:
        pass


@timing
def versus(f, n, m):
  f(generate_nm(n, m))

P / S：正在运行基准测试...等待结果。

结果

chain_star，m = 1000，n = 1000

for _ in range(10):
    versus(chain_star, 1000, 1000)

[出]：

func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6494 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6603 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6367 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6350 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6296 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6399 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6341 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6381 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6343 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 1000, 1000), {}] took: 0.6309 sec

chain_from_iterable，m = 1000，n = 1000

for _ in range(10):
    versus(chain_from_iterable, 1000, 1000)

[出]：

func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6416 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6315 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6535 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6334 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6327 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6471 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6426 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6287 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6353 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 1000, 1000), {}] took: 0.6297 sec

chain_star，m = 10000，n = 1000

func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2659 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2966 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2953 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.3141 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2802 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2799 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2848 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.3299 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.2730 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 10000, 1000), {}] took: 6.3052 sec

chain_from_iterable，m = 10000，n = 1000

func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.3129 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.3064 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.3071 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2660 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2837 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2877 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2756 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2939 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2715 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 10000, 1000), {}] took: 6.2877 sec

chain_star，m = 100000，n = 1000

func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.7874 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 63.3744 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.5584 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 63.3745 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.7982 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 63.4054 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.6769 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.6476 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 63.7397 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 100000, 1000), {}] took: 62.8980 sec

chain_from_iterable，m = 100000，n = 1000

for _ in range(10):
    versus(chain_from_iterable, 100000, 1000)

[出]：

func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7227 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7717 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7159 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7569 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7906 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.6211 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.7294 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.8260 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.8356 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 100000, 1000), {}] took: 62.9738 sec

chain_star，m = 500000，n = 1000

for _ in range(3):
    versus(chain_from_iterable, 500000, 1000)

[出]：

func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 500000, 1000), {}] took: 314.5671 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 500000, 1000), {}] took: 313.9270 sec
func:'versus' args:[(<function chain_star at 0x7f5c7188ef28>, 500000, 1000), {}] took: 313.8992 sec

chain_from_iterable，m = 500000，n = 1000

for _ in range(3):
    versus(chain_from_iterable, 500000, 1000)

[出]：

func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 500000, 1000), {}] took: 313.8301 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 500000, 1000), {}] took: 313.8104 sec
func:'versus' args:[(<function chain_from_iterable at 0x7f5c7188eb70>, 500000, 1000), {}] took: 313.9440 sec
— 阿尔瓦斯
source
0

另一种查看方式是使用chain.from_iterable

当您拥有一个可迭代的可迭代对象（例如嵌套可迭代对象（或复合可迭代对象））并且将链用于简单可迭代对象时

— Chyanit Singh
source
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