在 Python 中将列表的列表转换为元组

发布于 2024-08-13 19:24:45 字数 475 浏览 4 评论 0原文

我有一个列表列表(用简单的列表理解生成):

>>> base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]
>>> base_lists

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

我想将整个列表转换为包含列表中所有值的元组,即:

resulting_tuple = (1,1,1,2,1,3,1,4,1,5,2,1,2,2,2,3,2,4,2,5)

最有效的方法是什么? (通过列表理解生成相同元组的方法也是一个可以接受的答案。)我已经在此处和 Python 文档中查看了答案,但是我一直无法找到合适的答案。

编辑:

非常感谢所有回答的人!

I have a list of lists (generated with a simple list comprehension):

>>> base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]
>>> base_lists

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

I want to turn this entire list into a tuple containing all of the values in the lists, i.e.:

resulting_tuple = (1,1,1,2,1,3,1,4,1,5,2,1,2,2,2,3,2,4,2,5)

What would the most effective way to do this be? (A way to generate this same tuple with list comprehension would also be an acceptable answer.) I've looked at answers here and in the Python documentation, however I have been unable to find a suitable one.

EDIT:

Many thanks to all who answered!

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寻找一个思念的角度 2024-08-20 19:24:46
tuple(x for sublist in base_lists for x in sublist)

编辑:请注意,由于base_lists如此短,genexp(具有无限可用内存)速度很慢。考虑以下文件 tu.py

base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]

def genexp():
  return tuple(x for sublist in base_lists for x in sublist)

def listcomp():
  return tuple([x for sublist in base_lists for x in sublist])

def withsum():
  return tuple(sum(base_lists,[]))

import itertools as it

def withit():
  return tuple(it.chain(*base_lists))

现在:

$ python -mtimeit -s'import tu' 'tu.genexp()'
100000 loops, best of 3: 7.86 usec per loop
$ python -mtimeit -s'import tu' 'tu.withsum()'
100000 loops, best of 3: 5.79 usec per loop
$ python -mtimeit -s'import tu' 'tu.withit()'
100000 loops, best of 3: 5.17 usec per loop
$ python -mtimeit -s'import tu' 'tu.listcomp()'
100000 loops, best of 3: 5.33 usec per loop

当列表较长时(即,当性能确实很重要时),情况会有所不同。例如,将 100 * 放在定义 base_lists 的 RHS 上:

$ python -mtimeit -s'import tu' 'tu.genexp()'
1000 loops, best of 3: 408 usec per loop
$ python -mtimeit -s'import tu' 'tu.withsum()'
100 loops, best of 3: 5.07 msec per loop
$ python -mtimeit -s'import tu' 'tu.withit()'
10000 loops, best of 3: 148 usec per loop
$ python -mtimeit -s'import tu' 'tu.listcomp()'
1000 loops, best of 3: 278 usec per loop

因此对于长列表,只有 withsum 是性能灾难 - 其他的都在大致相同,尽管 itertools 显然具有优势,并且列表推导式(当有足够的内存可用时,因为它总是在微基准测试中;-)比 Genexps 更快。

使用 1000 *,genexp 减慢约 10 倍(相对于 100 *),withit 和 listcomp 减慢约 12 倍,withsum 减慢约 180 倍(withsum 为 < code>O(N squared),而且在这个大小上它开始遭受严重的堆碎片的困扰)。

tuple(x for sublist in base_lists for x in sublist)

Edit: note that, with base_lists so short, the genexp (with unlimited memory available) is slow. Consider the following file tu.py:

base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]

def genexp():
  return tuple(x for sublist in base_lists for x in sublist)

def listcomp():
  return tuple([x for sublist in base_lists for x in sublist])

def withsum():
  return tuple(sum(base_lists,[]))

import itertools as it

def withit():
  return tuple(it.chain(*base_lists))

Now:

$ python -mtimeit -s'import tu' 'tu.genexp()'
100000 loops, best of 3: 7.86 usec per loop
$ python -mtimeit -s'import tu' 'tu.withsum()'
100000 loops, best of 3: 5.79 usec per loop
$ python -mtimeit -s'import tu' 'tu.withit()'
100000 loops, best of 3: 5.17 usec per loop
$ python -mtimeit -s'import tu' 'tu.listcomp()'
100000 loops, best of 3: 5.33 usec per loop

When lists are longer (i.e., when performance really matters) things are a bit different. E.g., putting a 100 * on the RHS defining base_lists:

$ python -mtimeit -s'import tu' 'tu.genexp()'
1000 loops, best of 3: 408 usec per loop
$ python -mtimeit -s'import tu' 'tu.withsum()'
100 loops, best of 3: 5.07 msec per loop
$ python -mtimeit -s'import tu' 'tu.withit()'
10000 loops, best of 3: 148 usec per loop
$ python -mtimeit -s'import tu' 'tu.listcomp()'
1000 loops, best of 3: 278 usec per loop

so for long lists only withsum is a performance disaster -- the others are in the same ballpark, although clearly itertools has the edge, and list comprehensions (when abundant memory is available, as it always will be in microbenchmarks;-) are faster than genexps.

Using 1000 *, genexp slows down by about 10 times (wrt the 100 *), withit and listcomp by about 12 times, and withsum by about 180 times (withsum is O(N squared), plus it's starting to suffer from serious heap fragmentation at that size).

娜些时光,永不杰束 2024-08-20 19:24:46
from itertools import chain
base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]

print tuple(chain(*base_lists))
from itertools import chain
base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]

print tuple(chain(*base_lists))
巨坚强 2024-08-20 19:24:46
>>> sum(base_lists,[])
[1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5]
>>> tuple(sum(base_lists,[]))
(1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5)
>>> sum(base_lists,[])
[1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5]
>>> tuple(sum(base_lists,[]))
(1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5)
山川志 2024-08-20 19:24:46

resulting_tuple = tuple(base_lists 中 l 的项目,l 中的项目)

resulting_tuple = tuple(item for l in base_lists for item in l)

旧瑾黎汐 2024-08-20 19:24:46
>>> arr=[]
>>> base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]
>>> [ arr.extend(i) for i in base_lists ]
[None, None, None, None, None, None, None, None, None, None]
>>> arr
[1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5]
>>> tuple(arr)
(1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5)
>>> arr=[]
>>> base_lists = [[a, b] for a in range(1, 3) for b in range(1, 6)]
>>> [ arr.extend(i) for i in base_lists ]
[None, None, None, None, None, None, None, None, None, None]
>>> arr
[1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5]
>>> tuple(arr)
(1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5)
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