我有一个Python脚本，它把一个整数列表作为输入，我需要一次处理四个整数。不幸的是，我无法控制输入，否则我将它作为一个四元素元组列表传入。目前，我以这种方式迭代它:
for i in range(0, len(ints), 4):
# dummy op for example code
foo += ints[i] * ints[i + 1] + ints[i + 2] * ints[i + 3]
不过，它看起来很像“C-think”，这让我怀疑有一种更python的方式来处理这种情况。该列表在迭代后被丢弃，因此不需要保留。也许这样会更好?
while ints:
foo += ints[0] * ints[1] + ints[2] * ints[3]
ints[0:4] = []
不过，感觉还是不太对。: - /
相关问题:在Python中如何将列表分割成大小均匀的块?
当前回答
下面是一个支持生成器的无导入chunker:
def chunks(seq, size):
it = iter(seq)
while True:
ret = tuple(next(it) for _ in range(size))
if len(ret) == size:
yield ret
else:
raise StopIteration()
使用示例:
>>> def foo():
... i = 0
... while True:
... i += 1
... yield i
...
>>> c = chunks(foo(), 3)
>>> c.next()
(1, 2, 3)
>>> c.next()
(4, 5, 6)
>>> list(chunks('abcdefg', 2))
[('a', 'b'), ('c', 'd'), ('e', 'f')]
其他回答
制作itertools很容易。Groupby工作为您获得一个iterables的iterable，而不创建任何临时列表:
groupby(iterable, (lambda x,y: (lambda z: x.next()/y))(count(),100))
不要被嵌套lambda吓跑，外部lambda只运行一次，将count()生成器和常数100放入内部lambda的作用域。
我用它来发送行块到mysql。
for k,v in groupby(bigdata, (lambda x,y: (lambda z: x.next()/y))(count(),100))):
cursor.executemany(sql, v)
在Python 3.8中，您可以使用walrus操作符和itertools.islice。
from itertools import islice
list_ = [i for i in range(10, 100)]
def chunker(it, size):
iterator = iter(it)
while chunk := list(islice(iterator, size)):
print(chunk)
In [2]: chunker(list_, 10)
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
[40, 41, 42, 43, 44, 45, 46, 47, 48, 49]
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59]
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69]
[70, 71, 72, 73, 74, 75, 76, 77, 78, 79]
[80, 81, 82, 83, 84, 85, 86, 87, 88, 89]
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]
在我的特殊情况下，我需要填充项目，以重复最后一个元素，直到它达到大小，所以我改变了这个答案，以适应我的需要。
大小为4的输入输出示例:
Input = [1,2,3,4,5,6,7,8]
Output= [[1,2,3,4], [5,6,7,8]]
Input = [[1,2,3,4,5,6,7]]
Output= [[1,2,3,4], [5,6,7,7]]
Input = [1,2,3,4,5]
Output= [[1,2,3,4], [5,5,5,5]]
def chunker(seq, size):
res = []
for el in seq:
res.append(el)
if len(res) == size:
yield res
res = []
if res:
res = res + (size - len(res)) * [res[-1]]
yield res
我从来不想填充我的块，所以这个要求是必要的。我发现在任何可迭代对象上工作的能力也是必需的。鉴于此，我决定扩展公认的答案，https://stackoverflow.com/a/434411/1074659。
如果由于需要比较和筛选填充值而不需要填充，则这种方法的性能会受到轻微的影响。然而，对于大块大小，这个实用程序是非常高性能的。
#!/usr/bin/env python3
from itertools import zip_longest
_UNDEFINED = object()
def chunker(iterable, chunksize, fillvalue=_UNDEFINED):
"""
Collect data into chunks and optionally pad it.
Performance worsens as `chunksize` approaches 1.
Inspired by:
https://docs.python.org/3/library/itertools.html#itertools-recipes
"""
args = [iter(iterable)] * chunksize
chunks = zip_longest(*args, fillvalue=fillvalue)
yield from (
filter(lambda val: val is not _UNDEFINED, chunk)
if chunk[-1] is _UNDEFINED
else chunk
for chunk in chunks
) if fillvalue is _UNDEFINED else chunks
还有另一个答案，它的优点是:
1)容易理解 2)适用于任何可迭代对象，而不仅仅是序列(上面的一些答案会阻塞文件句柄) 3)不立即将数据块加载到内存 4)不会在内存中生成对同一迭代器的块长的引用列表 5)在列表的末尾没有填充填充值
话虽如此，我还没有计算它的时间，所以它可能比一些更聪明的方法慢，而且考虑到用例，一些优势可能是无关紧要的。
def chunkiter(iterable, size):
def inneriter(first, iterator, size):
yield first
for _ in xrange(size - 1):
yield iterator.next()
it = iter(iterable)
while True:
yield inneriter(it.next(), it, size)
In [2]: i = chunkiter('abcdefgh', 3)
In [3]: for ii in i:
for c in ii:
print c,
print ''
...:
a b c
d e f
g h
Update: A couple of drawbacks due to the fact the inner and outer loops are pulling values from the same iterator: 1) continue doesn't work as expected in the outer loop - it just continues on to the next item rather than skipping a chunk. However, this doesn't seem like a problem as there's nothing to test in the outer loop. 2) break doesn't work as expected in the inner loop - control will wind up in the inner loop again with the next item in the iterator. To skip whole chunks, either wrap the inner iterator (ii above) in a tuple, e.g. for c in tuple(ii), or set a flag and exhaust the iterator.
