numpy与random的seed函数是否需在各模块设置？主模块设置后子模块呢？

1. Do you need to set seed in every module for numpy and random? #

Nope, you don't need to set the seed in every module. Here's why:

• Both numpy.random (from NumPy) and Python's standard random module rely on global state for their random number generators (RNGs). When you set the seed in one part of your program, it updates the RNG's state globally—meaning every other part of the same process using that specific RNG will follow the same sequence of random values.
• Critical note: These are two entirely separate RNGs! Setting np.random.seed() won't affect the standard random module, and vice versa. So if you need reproducibility for both, you have to set seeds for both once, but you don't need to repeat this setup across modules.

2. If I set seeds in the main module (foo.py), does bar.py (which uses both RNGs) need to set seeds too? #

Absolutely not—you shouldn't re-set the seed in bar.py if you want consistent, reproducible results.

Let me walk through a concrete example to show how this works:

foo.py #

import numpy as np
import random
import bar

if __name__ == "__main__":
    # Set seeds once at the program start
    np.random.seed(42)
    random.seed(42)
    
    print("Foo - first np random:", np.random.rand(1))
    bar.get_bar_np_random()
    print("Foo - second np random:", np.random.rand(1))
    
    print("\nFoo - first standard random:", random.random())
    bar.get_bar_std_random()
    print("Foo - second standard random:", random.random())

bar.py #

import numpy as np
import random

def get_bar_np_random():
    print("Bar - np random:", np.random.rand(1))

def get_bar_std_random():
    print("Bar - standard random:", random.random())

When you run foo.py, you'll see the output follows a continuous sequence for each RNG—because bar.py uses the same global RNG state initialized in foo.py. If you added np.random.seed(42) inside bar.py, it would reset the state mid-program, breaking the sequence and making your results unpredictable.

A quick best practice: For full reproducibility, set both seeds once at the very start of your program (usually in the main module). Avoid setting seeds in other modules unless you intentionally want to reset the RNG state for a specific, isolated task.

One last tip: NumPy now recommends using explicit Generator or RandomState objects (instead of the global np.random module) for better control. This lets you create independent RNGs per module if needed, but that's a separate approach from relying on global seed settings.

内容的提问来源于stack exchange，提问作者user592419