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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFor new NumPy code, create a random-number generator with rng = np.random.default_rng(seed), then call the method that matches the result you need. Use rng.random() for a float from 0 up to (but not including) 1, rng.integers() for integer ranges, and size to request arrays. A seed helps reproduce a run, but NumPy does not guarantee identical Generator output across versions.
Start with NumPy’s Generator
Import NumPy and create a Generator using default_rng. The default BitGenerator is PCG64. After that, draw values through the methods on rng, rather than relying on the legacy module-level random interface.
import numpy as np
rng = np.random.default_rng(seed=42)
Choose a method by the kind of sample you need:
rng.random()produces a uniform float in[0.0, 1.0).rng.integers(low, high)produces an integer in a range whose lower bound is included and upper bound is excluded by default.rng.standard_normal()produces samples from the standard normal distribution.rng.choice()selects from a set of values, while permutation methods can reorder values.
Generate random floats and integers
Floats from zero to one
rng.random() returns one float in the half-open interval [0.0, 1.0): zero is possible, but 1.0 is not. For example:
u = rng.random()
Integer ranges and the exclusive upper bound
By default, rng.integers(low, high) includes low but excludes high. Thus rng.integers(0, 10) can return 0 through 9, not 10. This half-open convention is useful when a range represents positions or indices, since the upper limit can equal the length of a sequence.
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# Five integers: each is 0 through 9
ids = rng.integers(low=0, high=10, size=5)
If you want to include the upper endpoint, pass endpoint=True:
# Integers from 0 through 10, including 10
values = rng.integers(low=0, high=10, endpoint=True, size=5)
Make a random array with size
The size argument controls the output shape. An integer requests a one-dimensional array with that many values; a tuple specifies each dimension. If size is omitted or set to None, a Generator method returns a scalar value.
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# One-dimensional array of five integers
ids = rng.integers(0, 10, size=5)
# Two-dimensional array of uniform floats, shape (3, 3)
matrix = rng.random((3, 3))
# 1,000 samples from the standard normal distribution
noise = rng.standard_normal(size=1000)
These calls show the requested shapes and distributions; their particular random values vary. The normal example is not restricted to the interval used by rng.random().
Set a seed for reproducible work
Passing a seed to default_rng initializes the generator. Reusing the same seed is useful for reproducing a run when the relevant NumPy implementation and execution conditions are unchanged:
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rng = np.random.default_rng(seed=42)
first_run = rng.random(5)
rng = np.random.default_rng(seed=42)
repeat_run = rng.random(5)
Do not treat that as a permanent promise of identical output across NumPy versions. The Generator documentation explicitly makes no version-compatibility guarantee for its bit stream; algorithms can evolve, so output may change between releases. If you need to preserve a result for a particular workflow, record the NumPy version and the seed along with the other relevant inputs.
For applications that need robust seed material, NumPy recommends large positive seed values and points to Python’s secrets.randbits for obtaining a 128-bit seed. A seed does not make NumPy’s output cryptographically secure.
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Create random streams for parallel tasks
Parallel workers should not all be initialized with the same small seed, which can lead to repeated streams. NumPy provides stream-spawning mechanisms for deriving child generators from a common starting point. SeedSequence.spawn creates child seed sequences; Generator.spawn offers a convenience interface for creating child generators. NumPy describes the resulting streams as independent with very high probability, not as an unconditional guarantee.
import numpy as np
root = np.random.default_rng(42)
worker_rngs = root.spawn(4)
# Give one child generator to each worker
worker_values = [worker_rng.random(5) for worker_rng in worker_rngs]
If you instead derive worker seeds from a root seed and worker IDs, make the IDs deterministic and unique, as NumPy advises. The identity assigned to a worker should not accidentally be reused for a different stream.
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Generator versus legacy RandomState
Generator is NumPy’s improved replacement for new random-number work, while RandomState remains available for compatibility with existing code. The key differences for everyday use are:
| Use case | Modern API | Legacy API |
|---|---|---|
| Intended use | Generator for new code |
RandomState for backward compatibility |
| Construction | np.random.default_rng(seed) |
Legacy RandomState initialization |
| Integer draw method | Generator.integers() |
RandomState.randint(); module-level np.random.randint() belongs to the legacy interface |
| Cross-version stream guarantee | No version compatibility guarantee is promised for Generator’s bit stream | Not stated in the cited NumPy migration documentation |
For a new integer draw, use rng.integers(). Existing code using RandomState or np.random.randint() has not disappeared, but the modern workflow makes the generator explicit and easier to manage.
Do not use NumPy random numbers for security
NumPy’s documentation says its pseudo-random number generators are designed for statistical modeling and simulation and “are not suitable for security or cryptographic purposes.” For security-sensitive random values, use Python’s secrets module instead.
Official NumPy references
- NumPy random sampling
- NumPy Generator
- Bit generators and parallel stream guidance
- What is new or different in NumPy’s random interface
- Legacy
numpy.random.randintreference
These links point to NumPy’s stable documentation; version-sensitive details should be checked against the documentation for the NumPy version in use.
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