This is one of the reasons NumPy has moved toward advising users to create a random number generator for specific tasks (or to even pass around when you need parts to be reproducible). Your Mom Doesn’t Work Here Data Scientists, Your Variable Names Are a Mess.
#NUMPY RANDOM PERMUTE CODE#
For the most part, you will only need to ensure you use the same random numbers for specific parts of your code (like tests or functions).
Some imported packages or other scripts could reset the global random seed to another random seed with np.ed(another_number), which may lead to undesirable changes to your output and your results becoming unreproducible. Using np.ed(number) sets what NumPy calls the global random seed, which affects all uses to the np.random.* module. The problem comes in larger projects or projects with imports that could also set the seed. For the most part, this is true and for many projects, you may not need to worry about this. You may be looking at the above example and thinking, “so what’s the problem?” You can create reproducible calls, which means that all random numbers generated after setting the seed will be the same on any machine. The Problem With NumPy’s Global Random Seed More From Our Python Experts 5 Ways to Write More Pythonic Code For example, look at the following: > np.ed(2021) Setting the seed means the next random call is the same it sets the sequence of random numbers such that any code that produces or uses random numbers (with NumPy) will now produce the same sequence of numbers. If you need to prove this to yourself, you can enter the above code on your Python setup. If you want somebody to be able to reproduce your projects, you can set the seed with the following code snippet: > np.ed(2021) We can see how this works: > import numpy as npĪrray()Īs you can see, two calls to the function lead to two completely different answers. If you look up tutorials using np.random you see many of them using np.ed to set the seed for reproducible work.
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