blocks|key|1344144|text|12000个元素在Python中不算什么……实际上，元素的数量可以达到Python解释器在系统中的内存大小。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1344145|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

12000 elements is nothing in Python... and actually the number of elements can go as far as the Python interpreter has memory on your system.

blocks|key|1344215|text|我要说的是，你只会受到可用内存总量的限制。显然，数组越大，对其进行的操作所需的时间就越长。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1344216|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I'd say you're only limited by the total amount of RAM available. Obviously the larger the array the longer operations on it will take.

blocks|key|1344308|text|当然可以。实际上，你可以很容易地看到：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1344309|l+=+range(12000)
l+=+sorted(l,+reverse=True)|code-block|syntax|javascript|1344310|在我的机器上运行这些代码行需要：|1344311|real++++0m0.036s
user++++0m0.024s
sys++0m0.004s|1344312|但正如其他人所说的那样，确实如此。数组越大，操作速度就越慢。|1344313|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Sure it is OK. Actually you can see for yourself easily:

<pre><code>l = range(12000)
l = sorted(l, reverse=True)
</code></pre>

Running the those lines on my machine took:

<pre><code>real 0m0.036s
user 0m0.024s
sys 0m0.004s
</code></pre>

But sure as everyone else said. The larger the array the slower the operations will be.

blocks|key|1344388|text|在随意编写的代码中，我创建了包含数百万个元素的列表。我相信Python的列表实现只受系统内存大小的限制。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1344389|此外，不管列表的大小如何，列表方法/函数都应该继续工作。|1344390|如果您关心的是性能，那么研究一下NumPy这样的库可能是值得的。|offset|length|1344391|entityMap|0|LINK|mutability|MUTABLE|url|http://numpy.scipy.org/^0|0|0|G|5|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@$F|S|G|T|1|U]]|A|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]]]

In casual code I've created lists with millions of elements. I believe that Python's implementation of lists are only bound by the amount of memory on your system. 

In addition, the list methods / functions should continue to work despite the size of the list. 

If you care about performance, it might be worthwhile to look into a library such as <a href="http://numpy.scipy.org/" rel="noreferrer">NumPy</a>.

blocks|key|1560028|text|在Effbot上描述了Performance+characteristics+for+lists。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1560029|Python列表实际上是作为向量实现的，以便快速随机访问，所以容器基本上会容纳内存中有多少空间就有多少项。(列表中包含的指针需要空间，所指向的对象也需要内存空间。)|1560030|附加的是O(1)+(分期恒定复杂度)，然而，插入到序列中间/从序列中间删除将需要O(n)+(线性复杂度)重新排序，这将随着列表中元素的数量而变慢。|style|CODE|1560031|您的排序问题更加细微，因为比较操作可能需要无限的时间。如果您正在执行非常慢的比较，将需要很长时间，尽管这不是Python's+list+data+type的错。|1560032|反转只需要交换列表中的所有指针所需的时间(必须是O(n)+(线性复杂度)，因为您需要触摸每个指针一次)。|1560033|entityMap|0|LINK|mutability|MUTABLE|url|http://effbot.org/zone/python-list.htm#performance|1|http://docs.python.org/library/stdtypes.html#sequence-types-str-unicode-list-tuple-buffer-xrange^0|B|11|0|0|0|4|4|14|4|0|1I|N|1|0|O|4|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|12|8|@$A|13|B|14|H|I]|$A|15|B|16|H|I]]|9|@]|C|$]]|$1|J|3|K|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]]|C|$]]|$1|L|3|M|5|6|7|1B|8|@$A|1C|B|1D|H|I]]|9|@]|C|$]]|$1|N|3|-4|5|6|7|1E|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|Q|R|S|C|$T|W]]]]

<a href="http://effbot.org/zone/python-list.htm#performance" rel="nofollow noreferrer">Performance characteristics for lists</a> are described on Effbot.

Python lists are actually implemented as vector for fast random access, so the container will basically hold as many items as there is space for in memory. (You need space for pointers contained in the list as well as space in memory for the object(s) being pointed to.)

Appending is <code>O(1)</code> (amortized constant complexity), however, inserting into/deleting from the middle of the sequence will require an <code>O(n)</code> (linear complexity) reordering, which will get slower as the number of elements in your list.

Your sorting question is more nuanced, since the comparison operation can take an unbounded amount of time. If you're performing really slow comparisons, it will take a long time, though it's no fault of <a href="http://docs.python.org/library/stdtypes.html#sequence-types-str-unicode-list-tuple-buffer-xrange" rel="nofollow noreferrer">Python's list data type</a>.

Reversal just takes the amount of time it required to swap all the pointers in the list (necessarily <code>O(n)</code> (linear complexity), since you touch each pointer once).

blocks|key|1560144|text|列表数量没有限制。导致错误的主要原因是RAM。请升级内存大小。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1560145|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

There is no limitation of list number. 
The main reason which causes your error is the RAM. 
Please upgrade your memory size.

blocks|key|1560281|text|我在Python3.7.0b5+(v3.7.0b5:abb8802389，May+31+2018，01:54:01)+MSC+v.1913+64位(AMD64)+win32上得到了这个|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1560282|​|1560283|📷|atomic|offset|length|1560284|1560285|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/d291534106e3efa595e6deef1f4cf36e.png|imageAlt^0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@$G|V|H|W|1|X]]|A|$]]|$1|I|3|C|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q|R|-4]]]]

I got this from here on a x64 bit system:
Python 3.7.0b5 (v3.7.0b5:abb8802389, May 31 2018, 01:54:01) [MSC v.1913 64 bit (AMD64)] on win32

<a href="https://i.stack.imgur.com/4vUY9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/4vUY9.png" alt="enter image description here"></a>

blocks|key|1560398|text|它因系统不同而不同(取决于RAM)。找出答案的最简单方法是|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1560399|import+six+six.MAXSIZE+9223372036854775807根据documentation，这也给出了list和dict的最大大小|offset|length|style|CODE|1560400|entityMap|0|LINK|mutability|MUTABLE|url|https://pythonhosted.org/six/#six.MAXSIZE^0|0|0|16|1R|4|1W|4|18|D|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@$D|R|E|S|F|G]|$D|T|E|U|F|G]|$D|V|E|W|F|G]]|9|@$D|X|E|Y|1|Z]]|A|$]]|$1|H|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]]]

It varies for different systems (depends on RAM). The easiest way to find out is

<code>import six
six.MAXSIZE
9223372036854775807
</code>
This gives the max size of <code>list</code> and <code>dict</code> too ,as per the <a href="https://pythonhosted.org/six/#six.MAXSIZE" rel="noreferrer">documentation</a>

In Python, how big can a list get? I need a list of about 12000 elements. Will I still be able to run list methods such as sorting, etc?

How Big can a Python List Get?

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Python

在Python中，列表可以变得多大？我需要一个大约有12000个元素的列表。我还能运行列表方法吗，比如排序等？

问Python列表可以变得多大？
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