Why does NumPy's random function seemingly display a pattern in its generated values?

I was playing around with NumPy and Pillow and came across an interesting result that apparently showcases a pattern in NumPy random.random() results.

Here a sample of the full code for generating and saving 100 of these images (with seed 0), the above are the first four images generated by this code.

import numpy as np
from PIL import Image

np.random.seed(0)
img_arrays = np.random.random((100, 256, 256, 3)) * 255
for i, img_array in enumerate(img_arrays):
    img = Image.fromarray(img_array, "RGB")
    img.save("{}.png".format(i))

The above are four different images created using PIL.Image.fromarray() on four different NumPy arrays created using numpy.random.random((256, 256, 3)) * 255 to generate a 256 by 256 grid of RGB values in four different Python instances (the same thing also happens in the same instance).

I noticed that this only happens (in my limited testing) when the width and height of the image is a power of two, I am not sure how to interpret that.

Although it may be hard to see due to browser anti-aliasing (you can download the images and view them in image viewers with no anti-aliasing), there are clear purple-brown columns of pixels every 8th column starting from the 3rd column of every image. To make sure, I tested this on 100 different images and they all followed this pattern.

What is going on here? I am guessing that patterns like this are the reason that people always say to use cryptographically secure random number generators when true randomness is required, but is there a concrete explanation behind why this is happening in particular?