When you use np.random.seed(0), it initializes the random number generator with a fixed value (0), so any random numbers you generate afterward (e.g., with np.random.rand() or np.random.randint()) will always be the same each time you run the code.
I’ve been using np.random.seed(0)
for a while now, especially when I want my experiments to give consistent results every time. It’s like ‘locking in’ the random number generator to start from the same spot. So, whether I’m generating random arrays or running simulations, I know the output won’t vary each time I run the code. This consistency is invaluable, especially for debugging or sharing results, everything’s reproducible.
Yeah, I totally agree with @vindhya.rddy . I always think of np.random.seed(0)
like setting a dial on a slot machine to always land on the same pattern. By initializing the random number generator to a fixed state, you ensure that functions like np.random.rand()
or np.random.randint()
produce the same sequence each time you run them. This is a game-changer for training machine learning models too, as it guarantees reproducibility in your results, which is essential when you’re comparing models or fine-tuning parameters.
I’ve used np.random.seed(0)
in almost all my data processing scripts, especially when there are random steps involved. It’s not like the number 0 is magical; any fixed number will do the job. But by setting it, you make sure that everyone running the same script will get the exact same ‘random’ values. This is crucial for testing purposes or when you’re comparing different model runs, consistency is key!