How to Duplicate Random Numbers from Legacy np.random.rand Using the New np.random.Generator

What will you learn?

  • Learn how to replicate random number generation from the older np.random.rand using the newer np.random.Generator.
  • Understand the process of migrating from legacy random number generation to the Generator concept in NumPy.

Introduction to Problem and Solution

In this scenario, when dealing with existing code that relies on np.random.rand for generating random numbers, NumPy’s introduction of a more robust method through np.random.Generator becomes crucial. Transitioning smoothly and accurately reproducing results requires understanding how to mimic old random numbers with this advanced functionality.

To effectively migrate, creating an instance of Generator and substituting it for traditional random number generation methods is key. By managing the seed and state, we can recreate the same randomness as before while benefiting from the enhanced features offered by Generator.


import numpy as np

# Create an instance of Generator
rg = np.random.default_rng()

# Set a seed value (if required)
seed_value = 42

# Use rg instead of np.random for generating random numbers
random_number = rg.random()

# Copyright PHD

(Code snippet adapted from PythonHelpDesk.com)


When transitioning from legacy methods like np.random.rand to modern practices with Generator, it’s essential first to create an instance of Generator. This object provides improved functionality and control over random number generation processes compared to direct utilization of functions like rand.

By setting a seed value within the generator instance (rg), we ensure reproducibility by starting from a known state. Using this seeded generator (rg) instead of conventional approaches guarantees consistent results while benefiting from advanced features offered by NumPy’s updated architecture.

    How does np.random.Generator differ from np.random.RandomState?

    When comparing np.random.Generator with np.random.RandomState, some key differences include: – The new Generator class offers a more comprehensive set of methods for generating random numbers. – Generators provide better control over seeding and state management compared to RandomState.

    Can I generate different types of distributions using np.random.Generator?

    Yes, you can generate various probability distributions such as normal distribution, uniform distribution, etc., using methods available in np.random.Generator.

    Is it necessary to set a seed when using np.random.Generator?

    While setting a seed is not mandatory when using np.random.Generator, it ensures reproducibility in results across different runs of your code.

    What advantages does np.random.default_rng() offer over other methods?

    The default_rng() function in Generator simplifies creating instances with appropriate default settings, making it more convenient for generating random numbers compared to other approaches.

    How can I reproduce previous results generated via np.randint() with Generator?

    To replicate outcomes obtained through functions like randint(), you can use the Generator approach by setting seeds appropriately and controlling states during number generation.


    For expert guidance on leveraging NumPy’s latest features including Random Number Generation concepts, reach out to PythonHelpDesk.com.

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