有一个库可以完全满足你的需要:

import more_itertools
list(more_itertools.windowed([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],n=3, step=3))

Out: [(1, 2, 3), (4, 5, 6), (7, 8, 9), (10, 11, 12), (13, 14, 15)]

我最终使用的解决方案(保持简单):

def sliding_window(items, size):
    return [items[start:end] for start, end
            in zip(range(0, len(items) - size + 1), range(size, len(items) + 1))]

不用说，项目序列需要是可切片的。使用索引并不理想，但考虑到其他选项，这似乎是最不坏的选择……这也可以很容易地更改为生成器:只需替换[…]和……

我喜欢t ():

from itertools import tee, izip

def window(iterable, size):
    iters = tee(iterable, size)
    for i in xrange(1, size):
        for each in iters[i:]:
            next(each, None)
    return izip(*iters)

for each in window(xrange(6), 3):
    print list(each)

给:

[0, 1, 2]
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]

更新

Kelly发现这是一个重复的答案。但我在这里留下这个作为反例，因为我包含了一个毫无意义的最小值。

所以如果你想用min来避免IndexError，没有必要，range会帮你处理这种情况。

旧的答案

奇怪的是，当n > len(l)返回[]时，下面的句柄在语义上是正确的。

>>> l = [0, 1, 2, 3, 4]

>>> n = 2
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1], [1, 2], [2, 3], [3, 4]]
>>>
>>> n = 3
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2], [1, 2, 3], [2, 3, 4]]
>>>
>>> n = 4
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2, 3], [1, 2, 3, 4]]
>>>
>>> n = 5
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> [[0, 1, 2, 3, 4]]
>>>
>>> n = 10 # n > len(l)
>>> [l[i: i + min(n, len(l)-i)] for i in range(len(l)-n+1)]
>>> []

在旧版本的Python文档中有一个itertools示例:

from itertools import islice

def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result
    for elem in it:
        result = result[1:] + (elem,)
        yield result

文档中的那个更简洁一点，我想它使用了itertools来达到更好的效果。

如果你的迭代器是一个简单的列表/元组，用指定的窗口大小滑动它的简单方法是:

seq = [0, 1, 2, 3, 4, 5]
window_size = 3

for i in range(len(seq) - window_size + 1):
    print(seq[i: i + window_size])

输出:

[0, 1, 2]
[1, 2, 3]
[2, 3, 4]
[3, 4, 5]

深度学习中滑动窗口数据的优化函数

def SlidingWindow(X, window_length, stride):
    indexer = np.arange(window_length)[None, :] + stride*np.arange(int(len(X)/stride)-window_length+4)[:, None]
    return X.take(indexer)

应用于多维数组

import numpy as np
def SlidingWindow(X, window_length, stride1):
    stride=  X.shape[1]*stride1
    window_length = window_length*X.shape[1]
    indexer = np.arange(window_length)[None, :] + stride1*np.arange(int(len(X)/stride1)-window_length-1)[:, None]
    return X.take(indexer)