Generating random data in python fitting given function

I want to generate data in python as if it was some experimental points. I want to get noisy exponential decrease, with noise and it's errors normally distributed. Like this picture, but exponential: noisy polynomial data. Will it be okay if I simply take exponential curve and add some Gaussian noise to it and also generate random errors like this

import numpy as np
errors = np.random.normal(0,1,100)

Or it can be done in a more intellectual way?