5 Secure Ways to Generate Random Numbers in Python

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πŸ’‘ Problem Formulation: When building applications, especially ones dealing with security and cryptography, there’s often a need for truly unpredictable random numbers. For instance, an application may require a cryptographically secure random token as a password reset link. Using Python’s built-in modules, developers can generate random numbers that are suitable for security-sensitive applications.

Method 1: Using the secrets Module

The secrets module is designed for generating cryptographically strong random numbers suitable for managing data such as passwords, account authentication, security tokens, and related secrets. It’s included in Python’s standard library and is an easy-to-use solution for most security needs.

Here’s an example:

import secrets

# Generate a secure random number between 0 and 99
secure_num = secrets.randbelow(100)

Output: 42 (Note: Output will vary every time you run it)

This code snippet imports the secrets module and then generates a secure random number between 0 and 99. The secrets.randbelow(100) function provides a random number that is less than the provided upper bound, suitable for any application needing an unpredictable result.

Method 2: Using the os.urandom Function

The os module’s urandom function returns a string of random bytes from an OS-specific randomness source. This source is typically suitable for cryptographic use, making the produced bytes unpredictable and secure.

Here’s an example:

import os

# Get 16 bytes of secure random data
random_bytes = os.urandom(16)

Output: b’\xebr\x1d\xe0\x03S\xd5\x9fL\xbd\xd2<\x1e\x85\xed\xd6'

In this example, we import the os library and use the os.urandom function to generate 16 bytes of secure random data. The output is displayed as a byte string, with each byte represented by an escaped sequence suitable for cryptographic operations.

Method 3: Using the random.SystemRandom Class

The random module’s SystemRandom class uses sources provided by the operating system to generate random numbers. This class provides the same interface as random.Random, but uses os.urandom to generate the underlying random numbers, making it cryptographically safe.

Here’s an example:

import random

# Create a SystemRandom instance
system_random = random.SystemRandom()

# Use the instance to generate a secure random floating point between 0 and 1
secure_float = system_random.random()

Output: 0.41907358279680914 (Note: Output will vary every time you run it)

The code creates an instance of random.SystemRandom which taps into the underlying OS’s secure random number generator. We then generate a secure random floating-point number between 0 and 1. This is useful when floating-point precision is required!

Method 4: Using numpy with random.SystemRandom

While not a part of the standard library, the numpy library can be used alongside random.SystemRandom to generate arrays of secure random numbers efficiently, which can be useful in scientific computing that also requires secure randomness.

Here’s an example:

import numpy as np
import random

# Create a SystemRandom instance
system_random = random.SystemRandom()

# Use the instance to generate a secure random array
secure_array = np.array([system_random.random() for _ in range(10)])

Output: [0.27643876, 0.80126587, …, 0.78321354] (Note: Output will vary every time you run it)

This snippet uses a list comprehension to generate a list of secure random numbers, then converts it to a numpy array. This approach combines the cryptographic strength of random.SystemRandom with the array-manipulation capabilities of numpy.

Bonus One-Liner Method 5: Generating a Secure Random Integer with secrets

For a quick and easy way to generate a secure random integer within a specified range, you can use a one-liner that leverages the secrets module.

Here’s an example:

import secrets

# One-liner to generate a random integer from 0 to 99999
secure_rand_int = secrets.randbelow(100000)

Output: 42517 (Note: Output will vary every time you run it)

The above code is efficient for generating an integer within a specified range, using secrets.randbelow to produce a secure random number. It’s particularly handy when an application requires a unique identifier or nonce that’s also secure.


  • Method 1: secrets Module. Easy to use for most security-related needs. Limited to basic functionality provided by the module.
  • Method 2: os.urandom Function. Generates a string of secure random bytes suitable for cryptographic use. Requires additional steps to convert bytes into other numeric types.
  • Method 3: random.SystemRandom Class. Cryptographically secure version of random.Random, offering versatile random number generation. Not as straightforward or optimized for array generation.
  • Method 4: numpy with random.SystemRandom. Efficient generation of secure random number arrays but requires an external library.
  • Bonus Method 5: secrets for Random Integers. Quick one-liner for generating secure random integers within a range. Simplicity may be a limiting factor for more complex tasks.