Setting Seed for Reproducibility in NumPy Random Generator

Reproducibility is crucial when working with random number generation, especially in scientific computing and machine learning. By setting a seed, you ensure that your random sequences can be reproduced exactly in future runs.

NumPy's modern Generator class allows you to easily set a seed via the default_rng() function, enabling consistent results across experiments and debugging sessions.

Key Benefits of Setting Seeds

Consistency: Run your code multiple times and get the same random values.
Debugging: Helps to reproduce and fix issues related to randomness.
Experiment Tracking: Easier to document and share results that depend on randomness.

Understanding how to set and use seeds effectively is a foundational skill when working with randomness in NumPy.

Generating Random Numbers with a Seed

Sometimes, you want to get the same random numbers every time you run your code. This is called reproducibility. To do this, you set a seed when creating the random number generator. If you use the same seed, you will get the same random numbers each time.

python

import numpy as np

# Create two random generators with the same seed
rng = np.random.default_rng(42)
rng1 = np.random.default_rng(42)

# Generate random integers from 0 to 10
random_value = rng.integers(low=0, high=11, size=5)
random_value2 = rng1.integers(low=0, high=11, size=5)

print("Random numbers from rng:", random_value)
print("Random numbers from rng1:", random_value2)

import numpy as np

# Create two random generators with the same seed
rng = np.random.default_rng(42)
rng1 = np.random.default_rng(42)

# Generate random integers from 0 to 10
random_value = rng.integers(low=0, high=11, size=5)
random_value2 = rng1.integers(low=0, high=11, size=5)

print("Random numbers from rng:", random_value)
print("Random numbers from rng1:", random_value2)

What’s Happening Here?

We create two random generators (rng and rng1) using the same seed number 42.
Both generators produce the same list of random numbers because they start from the same seed.
This is useful when you want your results to be the same every time, like when testing or sharing your code.

Output

Random numbers from rng: [ 8  1  5  0  6]
Random numbers from rng1: [ 8  1  5  0  6]

Random numbers from rng: [ 8  1  5  0  6]
Random numbers from rng1: [ 8  1  5  0  6]

💡 Tip: Use a seed when you want to repeat experiments exactly or share your results with others.

Generating Random Numbers Without a Seed

When you don’t set a seed, each random number generator starts with a different starting point. This means that even if you create two generators right after each other, they will produce different random numbers every time you run your code.

python

import numpy as np

# Create two random generators without seeds
rng = np.random.default_rng()
rng1 = np.random.default_rng()

# Generate 5 random integers from each generator
random_values = rng.integers(low=0, high=11, size=5)
random_values1 = rng1.integers(low=0, high=11, size=5)

print("Random numbers from rng:", random_values)
print("Random numbers from rng1:", random_values1)

import numpy as np

# Create two random generators without seeds
rng = np.random.default_rng()
rng1 = np.random.default_rng()

# Generate 5 random integers from each generator
random_values = rng.integers(low=0, high=11, size=5)
random_values1 = rng1.integers(low=0, high=11, size=5)

print("Random numbers from rng:", random_values)
print("Random numbers from rng1:", random_values1)

What’s Happening Here?

We create two random generators, rng and rng1, both without specifying a seed.
Each generator produces different random numbers because they start from different random states.
This is useful when you want true randomness and don’t need to repeat the same results.

Output

Random numbers from rng: [ 2 10  0  4  7]
Random numbers from rng1: [ 9  3  8  1  5]

Random numbers from rng: [ 2 10  0  4  7]
Random numbers from rng1: [ 9  3  8  1  5]

💡 Tip: Without a seed, you get fresh, different random numbers every time you run your code.

Why Use a Seed?

A seed is like a starting point or a secret key for the random number generator. Using a seed ensures that your random numbers are reproducible — meaning you get the same results every time you run your code.

This reproducibility is important in many situations:

Debugging: If your program behaves unexpectedly, having the same random numbers makes it easier to find and fix bugs.
Sharing and Collaboration: Others can run your code and get the exact same results, which is crucial in research and teamwork.
Experimentation: You can run experiments multiple times with the same data and conditions to compare results fairly.

However, if you want fresh randomness each time — like in games or simulations where unpredictability is key — you should not use a seed.

💡 Tip: Think of a seed as a “recipe” that creates the same batch of random numbers every time you follow it.

Frequently Asked Questions

What is a random seed in NumPy?

A seed is a fixed starting point for the random number generator in NumPy. Using the same seed ensures that you get the same random numbers every time you run the code.

How do I set a seed in NumPy?

You can set a seed using np.random.default_rng(seed). For example, np.random.default_rng(42) creates a reproducible random number generator.

Why should I use a seed in random number generation?

Using a seed makes your results reproducible. This is important for debugging, sharing code, or running consistent experiments.

Will two generators with the same seed produce the same numbers?

Yes, if you create two generators with the same seed using np.random.default_rng(seed), they will generate the same sequence of random numbers.

What happens if I don’t set a seed in NumPy?

If you don’t set a seed, NumPy uses system entropy or the current time to generate randomness. This means you’ll get different random values each time you run the code.

What's Next?

Up next, we’ll dive into Sorting in NumPy — a key technique for organizing data efficiently.