blocks|key|96865|text|您可以使用random.sample通过一次调用生成列表：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|96866|import+random
my_randoms+=+random.sample(range(100),+10)|code-block|syntax|javascript|96867|它生成从0到99+(包括0到99)范围内的数字。如果你想要1到100，你可以使用这个(感谢@martineau指出了我的复杂解决方案)：|96868|my_randoms+=+random.sample(range(1,+101),+10)|96869|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3.8/library/random.html#random.sample^0|5|D|5|D|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

You could use <a href="https://docs.python.org/3.8/library/random.html#random.sample" rel="noreferrer"><code>random.sample</code></a> to generate the list with one call:
<pre><code>import random
my_randoms = random.sample(range(100), 10)
</code></pre>
That generates numbers in the (inclusive) range from 0 to 99. If you want 1 to 100, you could use this (thanks to @martineau for pointing out my convoluted solution):
<pre><code>my_randoms = random.sample(range(1, 101), 10)
</code></pre>

blocks|key|96854|text|import+random
my_randoms+=+[random.randrange(1,+101,+1)+for+_+in+range(10)]|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|96855|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>import random
my_randoms = [random.randrange(1, 101, 1) for _ in range(10)]
</code></pre>

blocks|key|313712|text|修复print语句的缩进|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|313713|import+random

my_randoms=[]
for+i+in+range+(10):
++++my_randoms.append(random.randrange(1,101,1))

print+(my_randoms)|code-block|syntax|javascript|313714|entityMap^0|2|5|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Fix the indentation of the <code>print</code> statement

<pre><code>import random

my_randoms=[]
for i in range (10):
 my_randoms.append(random.randrange(1,101,1))

print (my_randoms)
</code></pre>

blocks|key|313765|text|xrange()不适用于3.x版本。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|313766|对于指定时间间隔内的整数，numpy.random.randint().tolist()是一个很好的选择：|313767|+#[In]:
import+numpy+as+np
np.random.seed(123)+#option+for+reproducibility
np.random.randint(low=0,+high=100,+size=10).tolist()

#[Out:]

[66,+92,+98,+17,+83,+57,+86,+97,+96,+47]|code-block|syntax|javascript|313768|你也有用于浮点数的np.random.uniform()：|313769|#[In]:
np.random.uniform(low=0,+high=100,+size=10).tolist()

#[Out]:
[69.64691855978616,
28.613933495037948,
22.68514535642031,
55.13147690828912,
71.94689697855631,
42.3106460124461,
98.07641983846155,
68.48297385848633,
48.09319014843609,
39.211751819415056]|313770|entityMap^0|0|8|0|D|V|0|0|9|J|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|V|8|@$9|W|A|X|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|Y|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|Z|8|@$9|10|A|11|B|C]]|D|@]|E|$]]|$1|O|3|P|5|J|7|12|8|@]|D|@]|E|$K|L]]|$1|Q|3|-4|5|6|7|13|8|@]|D|@]|E|$]]]|R|$]]

<code>xrange()</code> will not work for 3.x.

<code>numpy.random.randint().tolist()</code> is a great alternative for integers in a specified interval:

<pre><code> #[In]:
import numpy as np
np.random.seed(123) #option for reproducibility
np.random.randint(low=0, high=100, size=10).tolist()

#[Out:]

[66, 92, 98, 17, 83, 57, 86, 97, 96, 47]
</code></pre>

You also have <code>np.random.uniform()</code> for floats:

<pre><code>#[In]:
np.random.uniform(low=0, high=100, size=10).tolist()

#[Out]:
[69.64691855978616,
28.613933495037948,
22.68514535642031,
55.13147690828912,
71.94689697855631,
42.3106460124461,
98.07641983846155,
68.48297385848633,
48.09319014843609,
39.211751819415056]
</code></pre>

blocks|key|96875|text|import+random

a=[]
n=int(input("Enter+number+of+elements:"))

for+j+in+range(n):
+++++++a.append(random.randint(1,20))

print('Randomised+list+is:+',a)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|96876|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>import random

a=[]
n=int(input("Enter number of elements:"))

for j in range(n):
 a.append(random.randint(1,20))

print('Randomised list is: ',a)
</code></pre>

blocks|key|96961|text|这里没有提到的random模块中的一个随机列表生成器是random.choices|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|96962|my_randoms+=+random.choices(range(0,+100),+k=10)|code-block|syntax|javascript|96963|它类似于random.sample，但有替代功能。传递的序列不必是一个范围；它甚至不一定是数字。下面的方法同样适用：|96964|my_randoms+=+random.choices(['a','b'],+k=10)|96965|如果我们比较运行时，在随机列表生成器中，无论要创建的列表大小如何，random.choices都是最快的。然而，对于较大的列表/数组，numpy选项要快得多。因此，例如，如果要创建一个随机列表/数组来分配给pandas+DataFrame列，那么使用np.random.randint是最快的选择。|96966|​|96967|📷|atomic|96968|96969|用于生成上述图的代码：|96970|import+perfplot
import+numpy+as+np
import+random

perfplot.show(
++++setup=lambda+n:+n,
++++kernels=[
++++++++lambda+n:+[random.randint(0,+n*2)+for+x+in+range(n)],
++++++++lambda+n:+random.sample(range(0,+n*2),+k=n),+
++++++++lambda+n:+[random.randrange(n*2)+for+i+in+range(n)],
++++++++lambda+n:+random.choices(range(0,+n*2),+k=n),+
++++++++lambda+n:+np.random.rand(n),+
++++++++lambda+n:+np.random.randint(0,+n*2,+size=n),
++++++++lambda+n:+np.random.choice(np.arange(n*2),+size=n),
++++],
++++labels=['random_randint',+'random_sample',+'random_randrange',+'random_choices',+
++++++++++++'np_random_rand',+'np_random_randint',+'np_random_choice'],
++++n_range=[2+**+k+for+k+in+range(17)],
++++equality_check=None,
++++xlabel='~n'
)|96971|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/95bec795beaffc24177f3defed506c06.png|imageAlt^0|7|6|R|E|0|0|4|D|0|0|X|E|3H|H|0|0|0|1|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|19|8|@$9|1A|A|1B|B|C]|$9|1C|A|1D|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1E|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1F|8|@$9|1G|A|1H|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1I|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1J|8|@$9|1K|A|1L|B|C]|$9|1M|A|1N|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1O|8|@]|D|@]|E|$]]|$1|S|3|T|5|U|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|V|3|R|5|6|7|1T|8|@]|D|@]|E|$]]|$1|W|3|X|5|6|7|1U|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|H|7|1V|8|@]|D|@]|E|$I|J]]|$1|10|3|-4|5|6|7|1W|8|@]|D|@]|E|$]]]|11|$12|$5|13|14|15|E|$16|17|18|-4]]]]

The one random list generator in the <code>random</code> module not mentioned here is <code>random.choices</code>:
<pre><code>my_randoms = random.choices(range(0, 100), k=10)
</code></pre>
It's like <code>random.sample</code> but with replacement. The sequence passed doesn't have to be a range; it doesn't even have to be numbers. The following works just as well:
<pre><code>my_randoms = random.choices(['a','b'], k=10)
</code></pre>
If we compare the runtimes, among random list generators, <code>random.choices</code> is the fastest no matter the size of the list to be created. However, for larger lists/arrays, numpy options are much faster. So for example, if you're creating a random list/array to assign to a pandas DataFrame column, then using <code>np.random.randint</code> is the fastest option.
<a href="https://i.stack.imgur.com/tfPUs.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tfPUs.png" alt="enter image description here" /></a>
Code used to produce the above plot:
<pre><code>import perfplot
import numpy as np
import random

perfplot.show(
 setup=lambda n: n,
 kernels=[
 lambda n: [random.randint(0, n*2) for x in range(n)],
 lambda n: random.sample(range(0, n*2), k=n), 
 lambda n: [random.randrange(n*2) for i in range(n)],
 lambda n: random.choices(range(0, n*2), k=n), 
 lambda n: np.random.rand(n), 
 lambda n: np.random.randint(0, n*2, size=n),
 lambda n: np.random.choice(np.arange(n*2), size=n),
 ],
 labels=['random_randint', 'random_sample', 'random_randrange', 'random_choices', 
 'np_random_rand', 'np_random_randint', 'np_random_choice'],
 n_range=[2 ** k for k in range(17)],
 equality_check=None,
 xlabel='~n'
)
</code></pre>

blocks|key|96878|text|my_randoms+=+[randint(n1,n2)+for+x+in+range(listsize)]|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|96879|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>my_randoms = [randint(n1,n2) for x in range(listsize)]
</code></pre>

Create a 'list' called my_randoms of 10 random numbers between 0 and 100.

This is what I have so far:

<pre><code>import random
my_randoms=[]
for i in range (10):
 my_randoms.append(random.randrange(1, 101, 1))
 print (my_randoms)
</code></pre>

Unfortunately Python's output is this:

<pre><code>[34]
[34, 30]
[34, 30, 75]
[34, 30, 75, 27]
[34, 30, 75, 27, 8]
[34, 30, 75, 27, 8, 58]
[34, 30, 75, 27, 8, 58, 10]
[34, 30, 75, 27, 8, 58, 10, 1]
[34, 30, 75, 27, 8, 58, 10, 1, 59]
[34, 30, 75, 27, 8, 58, 10, 1, 59, 25]
</code></pre>

It generates the 10 numbers like I ask it to, but it generates it one at a time. What am I doing wrong?

Python: Random numbers into a list

创建一个名为my_randoms的‘列表’，其中包含10个介于0和100之间的随机数。这就是我到目前为止所知道的：import randommy_randoms=[]for i in range (10):    my_randoms.append(random.randrange(1, 101, 1))    print (my_randoms)不幸的是，Python的输出是这样的：[34][3

问Python:列表中的随机数
EN

回答 7

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