合并不同长度的python列表 [英] Merge python lists of different lengths

问题描述

我正在尝试合并两个python列表，其中它们在给定索引处的值将在新列表中形成一个列表(元素).例如:

I am attempting to merge two python lists, where their values at a given index will form a list (element) in a new list. For example:

merge_lists([1,2,3,4], [1,5]) = [[1,1], [2,5], [3], [4]]

我可以迭代此函数以组合更多列表.最有效的方法是什么?

I could iterate on this function to combine ever more lists. What is the most efficient way to accomplish this?

在测试了我之前选择的答案后，我意识到我还有其他标准和更普遍的问题.我还想合并包含列表 或 值的列表.例如:

Upon testing the answer I had previously selected, I realized I had additional criteria and a more general problem. I would also like to combine lists containing lists or values. For example:

merge_lists([[1,2],[1]] , [3,4]) = [[1,2,3], [1,4]]

在这种情况下，当前提供的答案会生成更高维度的列表.

The answers currently provided generate lists of higher dimensions in cases like this.

推荐答案

一种选择是使用itertools.zip_longest(在python 3中):

One option is to use itertools.zip_longest (in python 3):

from itertools import zip_longest    

[[x for x in t if x is not None] for t in zip_longest([1,2,3,4], [1,5])]
# [[1, 1], [2, 5], [3], [4]]

如果您喜欢设置:

[{x for x in t if x is not None} for t in zip_longest([1,2,3,4], [1,5])]
# [{1}, {2, 5}, {3}, {4}]

---

在python 2中，使用itertools.izip_longest:

from itertools import izip_longest    

[[x for x in t if x is not None] for t in izip_longest([1,2,3,4], [1,5])]
#[[1, 1], [2, 5], [3], [4]]

---

更新处理稍微复杂一点的情况:

---

Update to handle the slightly more complicated case:

def flatten(lst):

    result = []
    for s in lst:
        if isinstance(s, list):
            result.extend(s)
        else:
            result.append(s)

    return result

这可以很好地处理以上两种情况:

This handles the above two cases pretty well:

[flatten(x for x in t if x is not None) for t in izip_longest([1,2,3,4], [1,5])]
# [[1, 1], [2, 5], [3], [4]]

[flatten(x for x in t if x is not None) for t in izip_longest([[1,2],[1]] , [3,4])]
# [[1, 2, 3], [1, 4]]

请注意，即使此方法适用于以上两种情况，但由于情况会变得非常复杂，因此它仍然可以在更深层的嵌套结构下分解.有关更一般的解决方案，请参见此处.

Note even though this works for the above two cases, but it can still break under deeper nested structure, since the case can get complicated very quickly. For a more general solution, you can see here.

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