如何获得浮动范围之间的随机数？

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问题：如何获得浮动范围之间的随机数？

randrange(start, stop)只接受整数参数。那么，如何在两个浮点值之间获得一个随机数呢？

randrange(start, stop) only takes integer arguments. So how would I get a random number between two float values?

>>> random.uniform(1.5, 1.9)
1.8733202628557872

>>> random.uniform(1.5, 1.9)
1.8733202628557872

random.uniform(a, b)似乎是您要寻找的。从文档：

返回一个随机浮点数N，使得a <= N <= b表示a <= b，b <= N <= a表示b <a。

random.uniform(a, b) appears to be what your looking for. From the docs:

Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.

See here.

如果您想生成一个随机浮点数，该浮点数的右边是N个数字，则可以执行以下操作：

round(random.uniform(1,2), N)

第二个参数是小数位数。

if you want generate a random float with N digits to the right of point, you can make this :

round(random.uniform(1,2), N)

the second argument is the number of decimals.

最常见的是，您将使用：

import random
random.uniform(a, b) # range [a, b) or [a, b] depending on floating-point rounding

如果需要，Python可提供其他发行版。

如果已经numpy导入，则可以使用其等效项：

import numpy as np
np.random.uniform(a, b) # range [a, b)

同样，如果需要其他发行版，请numpy提供与python相同的发行版，以及许多其他发行版。

Most commonly, you’d use:

import random
random.uniform(a, b) # range [a, b) or [a, b] depending on floating-point rounding

Python provides other distributions if you need.

If you have numpy imported already, you can used its equivalent:

import numpy as np
np.random.uniform(a, b) # range [a, b)

Again, if you need another distribution, numpy provides the same distributions as python, as well as many additional ones.