blocks|key|695729|text|我写了一个测试程序。它显示完成您的任务只需要1秒。所以，你想怎么写就怎么写吧，它不会成为你的瓶颈。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|695730|#!/usr/bin/env+python3
#+-*-+coding:+utf-8+-*-
#+Xiang+Wang+@+2018-05-23+16:49:00

import+time
import+random

start+=+time.time()

for+i+in+range(1000000):
++++random.randint(1,+1000000)

end+=+time.time()

print("total+time:+{}".format(end-start))|code-block|syntax|javascript|695731|​|695732|📷|atomic|offset|length|695733|695734|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/3d2262f9cb563801d09ceb461f2e86d2.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

I wrote a test program. It showed that it only cost 1 second to complete your task. So just write whatever way you want, it will not be your bottleneck.

<pre><code>#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Xiang Wang @ 2018-05-23 16:49:00

import time
import random

start = time.time()

for i in range(1000000):
 random.randint(1, 1000000)

end = time.time()

print("total time: {}".format(end-start))
</code></pre>

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

blocks|key|3969791|text|如果您使用的是numpy，那么使用random.random_integers一次性生成所需的所有值会更有效。python和numpy都使用Mersenne+Twister。更多信息：Differences+between+numpy.random+and+random.random+in+Python|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3969792|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.random_integers.html#numpy.random.random_integers|1|https://stackoverflow.com/questions/7029993/differences-between-numpy-random-and-random-random-in-python^0|H|M|H|M|0|2K|1O|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]]|D|@$9|S|A|T|1|U]|$9|V|A|W|1|X]]|E|$]]|$1|F|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]|N|$5|I|J|K|E|$L|O]]]]

If you are using numpy, it is more efficient to generate all the values you need at once with <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.random_integers.html#numpy.random.random_integers" rel="nofollow noreferrer"><code>random.random_integers</code></a>. Both python and numpy use Mersenne Twister. More info: <a href="https://stackoverflow.com/questions/7029993/differences-between-numpy-random-and-random-random-in-python">Differences between numpy.random and random.random in Python</a>

blocks|key|3969816|text|numpy在生成随机数的大样本(数组)时效率更高。例如,|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3969817|In+[10]:+%25timeit+np.random.randint(1,1000000,+1000000)
5.14+ms+±+64.1+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)

In+[11]:+%25timeit+[random.choice(range(1,1000000))+for+_+in+range(1000000)]
1.01+s+±+14.4+ms+per+loop+(mean+±+std.+dev.+of+7+runs,+1+loop+each)|code-block|syntax|javascript|3969818|此外，有关如何执行计时测试的信息，请参阅How+can+I+time+a+code+segment+for+testing+performance+with+Pythons+timeit?。当您使用time.clock()时，您至少应该尝试多次重复该操作，然后计算平均时间。更可取的做法是使用timeit进行定时测试。此外，正如其他人在评论中提到的那样，print()比随机数生成花费的时间要长得多，所以您的计时测试主要是测量print()的工作速度。相反，您应该这样做：|3969819|In+[12]:+repeat+=+1000000
++++...:+t0+=+time.clock()
++++...:+for+_+in+range(repeat):
++++...:+++++np.random.randint(1,+1000000)
++++...:+t1+=+time.clock()
++++...:+print((t1+-+t0)+/+repeat)
1.3564629999999908e-06

In+[13]:+repeat+=+1000000
++++...:+t2+=+time.clock()
++++...:+for+_+in+range(repeat):
++++...:+++++random.choice(range(1,+1000000))
++++...:+t3+=+time.clock()
++++...:+print((t3+-+t2)+/+repeat)
1.0206699999999956e-06|3969820|因此，对于单个数字，numpy平均只比内置随机数生成器慢大约35%25。然而，之前的测试表明，在生成大样本时，numpy的速度要快得多。|3969821|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/q/2866380/8033585^0|0|5|0|0|2R|C|42|6|4X|7|5X|7|K|22|0|0|0|A|5|1H|5|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|M|3|N|5|H|7|1E|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1F|8|@$9|1G|A|1H|B|C]|$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1K|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]]]

<code>numpy</code> is more efficient when generating large samples (arrays) of random numbers. For example,

<pre><code>In [10]: %timeit np.random.randint(1,1000000, 1000000)
5.14 ms ± 64.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [11]: %timeit [random.choice(range(1,1000000)) for _ in range(1000000)]
1.01 s ± 14.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
</code></pre>

In addition, see <a href="https://stackoverflow.com/q/2866380/8033585">How can I time a code segment for testing performance with Pythons timeit?</a> on how to perform timing tests. When you use <code>time.clock()</code>, you should at least try to repeat the operation multiple times and then compute mean time. It is more advisable to use <code>timeit</code> for timing tests. Also, as others have mentioned in the comments, <code>print()</code> takes significantly longer that random number generation, so your timing test is mostly measuring how fast <code>print()</code> works. Instead, you should do something like this:

<pre><code>In [12]: repeat = 1000000
 ...: t0 = time.clock()
 ...: for _ in range(repeat):
 ...: np.random.randint(1, 1000000)
 ...: t1 = time.clock()
 ...: print((t1 - t0) / repeat)
1.3564629999999908e-06

In [13]: repeat = 1000000
 ...: t2 = time.clock()
 ...: for _ in range(repeat):
 ...: random.choice(range(1, 1000000))
 ...: t3 = time.clock()
 ...: print((t3 - t2) / repeat)
1.0206699999999956e-06
</code></pre>

So, for a single number, <code>numpy</code> is on average just about 35% slower than built-in random number generator. However, previous tests show that when generating large samples, <code>numpy</code> is significantly faster.

blocks|key|189228|text|random.getrandbits似乎比其他random模块工具要快得多。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|189229|%25timeit+random.randint(0,1000000)+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
799+ns+±+2.45+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+1000000+loops+each)

%25timeit+random.choice(range(0,1000000))+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
742+ns+±+13.1+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+1000000+loops+each)

%25timeit+random.getrandbits(20)++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
83.9+ns+±+1.61+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+10000000+loops+each)|code-block|syntax|javascript|189230|有关更多信息，请阅读以下内容：https://eli.thegreenplace.net/2018/slow-and-fast-methods-for-generating-random-integers-in-python/|189231|entityMap|0|LINK|mutability|MUTABLE|url|https://eli.thegreenplace.net/2018/slow-and-fast-methods-for-generating-random-integers-in-python/^0|0|I|N|6|0|0|F|2Q|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|10|8|@]|D|@$9|11|A|12|1|13]]|E|$]]|$1|M|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

<code>random.getrandbits</code> seems to be much faster than other <code>random</code> module tools.
<pre><code>%timeit random.randint(0,1000000) 
799 ns ± 2.45 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%timeit random.choice(range(0,1000000)) 
742 ns ± 13.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%timeit random.getrandbits(20) 
83.9 ns ± 1.61 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
</code></pre>
Read this for further information:
<a href="https://eli.thegreenplace.net/2018/slow-and-fast-methods-for-generating-random-integers-in-python/" rel="nofollow noreferrer">https://eli.thegreenplace.net/2018/slow-and-fast-methods-for-generating-random-integers-in-python/</a>

What is the absolute fastest way of achieving this? I will be doing this over 1 million times per day so I want maximum efficiency.

With numpy (avg after 20 runs 0.0001679009692429128)

<pre><code>t0 = time.clock()
print(np.random.randint(1,1000000))
t1 = time.clock()
print (t1-t0)
</code></pre>

With random (avg: 0.0000920492372555262)

<pre><code>t2 = time.clock()
print(random.choice(range(1,1000000)))
t3 = time.clock()
print (t3-t2)
</code></pre>

To my surprise random was consistently faster than numpy. Is there a faster way?

Fastest way to get a random number between 1 and 1million in python

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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实现这一目标的绝对最快的方法是什么？我每天会做超过一百万次，所以我想要最大的效率。使用numpy ( 20次后平均运行0.0001679009692429128次)t0 = time.clock()print(np.random.randint(1,1000000))t1 = time.clock()print (t1...

问在python中获取介于1到100万之间的随机数的最快方法
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问在python中获取介于1到100万之间的随机数的最快方法EN