Python效率:列表与元组 [英] Python efficiency: lists vs. tuples

问题描述

我有中等数量的基础对象.

I have a medium-amount of base objects.

这些基础对象将被放入集合中，并且这些集合将被杂乱地排列:已排序，截断等.

These base objects will be put in collections, and these collections will be munged around: sorted, truncated, etc.

不幸的是， n 足够大，以至于内存消耗有些令人担忧，并且速度越来越令人担忧.

Unfortunately, the n is large enough that memory consumption is slightly worrisome, and speed is getting concerning.

我的理解是，由于对元组进行了重复数据删除，因此它们的内存效率略高.

My understanding is that tuples are slightly more memory-efficient, since they are deduplicated.

无论如何，我想知道在Python 2.6/2.7中列表与元组的cpu/内存权衡是什么.

Anyway, I would like to know what the cpu/memory tradeoffs of lists vs. tuples are in Python 2.6/2.7.

推荐答案

如果您有一个元组和具有相同元素的列表，则该元组将占用较少的空间.由于元组是不可变的，因此您无法对其进行排序，添加等.我建议您观看

If you have a tuple and a list with the same elements, the tuple takes less space. Since tuples are immutable, you can't sort them, add to them, etc. I recommend watching this talk by Alex Gaynor for a quick intro on when to choose what datastructure in Python.

更新:再考虑一下，您可能希望研究优化对象的空间使用，例如，通过 namedtuple 实例代替实际对象.这可能会带来更大的节省，因为您有N个收藏夹，并且(大概)只有几个收藏夹出现在其中. namedtuple特别棒.查看 Raymond Hettinger的讲话.

UPDATE: Thinking about it some more, you may want to look into optimizing the space usage of your objects, e.g., via __slots__ or using namedtuple instances as proxies instead of the actual objects. This would likely lead to much bigger savings, since you have N of them and (presumbaly) only a few collections in which they appear. namedtuple in particular is super awesome; check out Raymond Hettinger's talk.

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