创建带有随机数的2D数组的简单方法(Python) [英] Simple way of creating a 2D array with random numbers (Python)

问题描述

我知道在Python中创建一个由零组成的NxN数组的简单方法是:

I know that an easy way to create a NxN array full of zeroes in Python is with:

[[0]*N for x in range(N)]

但是，让我们假设我想通过用随机数填充数组来创建该数组:

However, let's suppose I want to create the array by filling it with random numbers:

[[random.random()]*N for x in range(N)]

这不起作用，因为创建的每个随机数都会被复制N次，因此我的数组没有NxN个唯一的随机数.

This doesn't work because each random number that is created is then replicated N times, so my array doesn't have NxN unique random numbers.

是否可以在不使用for循环的情况下单行执行此操作?

Is there a way of doing this in a single line, without using for loops?

推荐答案

您可以使用嵌套列表理解:

You could use a nested list comprehension:

>>> N = 5
>>> import random
>>> [[random.random() for i in range(N)] for j in range(N)]
[[0.9520388778975947, 0.29456222450756675, 0.33025941906885714, 0.6154639550493386, 0.11409250305307261], [0.6149070141685593, 0.3579148659939374, 0.031188652624532298, 0.4607597656919963, 0.2523207155544883], [0.6372935479559158, 0.32063181293207754, 0.700897108426278, 0.822287873035571, 0.7721460935656276], [0.31035121801363097, 0.2691153671697625, 0.1185063432179293, 0.14822226436085928, 0.5490604341460457], [0.9650509333411779, 0.7795665950184245, 0.5778752066273084, 0.3868760955504583, 0.5364495147637446]]

或使用 numpy (非stdlib，但非常流行):

Or use numpy (non-stdlib but very popular):

>>> import numpy as np
>>> np.random.random((N,N))
array([[ 0.26045197,  0.66184973,  0.79957904,  0.82613958,  0.39644677],
       [ 0.09284838,  0.59098542,  0.13045167,  0.06170584,  0.01265676],
       [ 0.16456109,  0.87820099,  0.79891448,  0.02966868,  0.27810629],
       [ 0.03037986,  0.31481138,  0.06477025,  0.37205248,  0.59648463],
       [ 0.08084797,  0.10305354,  0.72488268,  0.30258304,  0.230913  ]])

(PS，这是一个好习惯，当您要表示 list 时要说出 list 并为numpy 保留 array ndarray s.实际上有一个内置的 array 模块具有其自己的 array 类型，这样会使事情更加混乱，但是相对很少使用.)

(P.S. It's a good idea to get in the habit of saying list when you mean list and reserving array for numpy ndarrays. There's actually a built-in array module with its own array type, so that confuses things even more, but it's relatively seldom used.)

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