Numpy.copy not working as intended for random sampling

I have an issue with numpy.copy not working as intended.

When using it as following, it still references the change back in my original list, which I want to avoid:

test = np.copy(random.sample(population_list, 2))
test[0][0][0][0] = 1.1111

If I print out population list after the test assignment, it does replace the value in that position of population_list with 1.1111. My goal is to sample from the list, and then make some changes to those samples, without affecting the initial list.

As further info, my population_list is a list of lists, where the first element is a numpy matrix:

print(type(population_list))
print(type(population_list[0]))
print(type(population_list[0][0]))
print(type(population_list[0][0][0][0]))

list
list
numpy.ndarray
numpy.float64