問(wèn)題描述

我可以在 Python 中重置迭代器/生成器嗎?我正在使用 DictReader 并希望將其重置為文件的開頭.

Can I reset an iterator / generator in Python? I am using DictReader and would like to reset it to the beginning of the file.

推薦答案

我看到很多答案建議 itertools.tee，但這忽略了文檔中的一個(gè)重要警告:

I see many answers suggesting itertools.tee, but that's ignoring one crucial warning in the docs for it:

此迭代工具可能需要大量輔助存儲(chǔ)(取決于如何需要大量臨時(shí)數(shù)據(jù)存儲(chǔ)).一般來(lái)說(shuō)，如果一個(gè)迭代器使用之前的大部分或全部數(shù)據(jù)另一個(gè)迭代器啟動(dòng)，它更快使用 list() 而不是 tee().

This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee().

基本上，tee 是為這樣的情況設(shè)計(jì)的 - 相反，他們說(shuō)的是同一個(gè)附近"(一些項(xiàng)目彼此落后或領(lǐng)先).不適合OP的從頭重做"的問(wèn)題.

Basically, tee is designed for those situation where two (or more) clones of one iterator, while "getting out of sync" with each other, don't do so by much -- rather, they say in the same "vicinity" (a few items behind or ahead of each other). Not suitable for the OP's problem of "redo from the start".

L = list(DictReader(...)) 另一方面是非常合適的，只要字典列表可以舒適地放入內(nèi)存中.可以隨時(shí)使用 iter(L) 制作一個(gè)新的從頭開始的迭代器"(非常輕量級(jí)和低開銷)，并部分或全部使用而不影響新的或現(xiàn)有的；其他訪問(wèn)模式也很容易獲得.

L = list(DictReader(...)) on the other hand is perfectly suitable, as long as the list of dicts can fit comfortably in memory. A new "iterator from the start" (very lightweight and low-overhead) can be made at any time with iter(L), and used in part or in whole without affecting new or existing ones; other access patterns are also easily available.

正如幾個(gè)答案正確指出的那樣，在 csv 的特定情況下，您還可以 .seek(0) 底層文件對(duì)象(一種相當(dāng)特殊的情況).盡管目前確實(shí)有效，但我不確定是否已記錄并保證.僅對(duì)于真正巨大的 csv 文件可能值得考慮，其中我推薦的 list 因?yàn)橐话惴椒〞?huì)占用太大的內(nèi)存.

As several answers rightly remarked, in the specific case of csv you can also .seek(0) the underlying file object (a rather special case). I'm not sure that's documented and guaranteed, though it does currently work; it would probably be worth considering only for truly huge csv files, in which the list I recommmend as the general approach would have too large a memory footprint.

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