Generate each column of the numpy array with random number from different range

Question

How to generate a numpy array such that each column of the array comes from a uniform distribution within different ranges efficiently? The following code uses two for loop which is slow, is there any matrix-style way to generate such array faster? Thanks.

import numpy as np
num = 5
ranges = [[0,1],[4,5]]
a = np.zeros((num, len(ranges)))
for i in range(num):
    for j in range(len(ranges)):
        a[i, j] = np.random.uniform(ranges[j][0], ranges[j][1])

Answer 1

What you can do is produce all random numbers in the interval [0, 1) first and then scale and shift them accordingly:

import numpy as np
num = 5
ranges = np.asarray([[0,1],[4,5]])
starts = ranges[:, 0]
widths = ranges[:, 1]-ranges[:, 0]
a = starts + widths*np.random.random(size=(num, widths.shape[0]))

So basically, you create an array of the right size via np.random.random(size=(num, widths.shape[0])) with random number between 0 and 1. Then you scale each value by a factor corresponding to the width of the interval that you actually want to sample. Finally, you shift them by starts to account for the different starting values of the intervals.