This entry explains what is Python random module and how you can use it to generate pseudo-random numbers. We will also touch on how to improve the randomness of the random number generation.

# Random Module

In Python, the `random`

module provides functions for generating random numbers and selecting random elements from sequences. Here are a few examples:

### Generating random numbers

`random.random()`

: Returns a random float number between 0 and 1 (inclusive of 0, but exclusive of 1).`random.randint(a, b)`

: Returns a random integer between`a`

and`b`

(inclusive of both`a`

and`b`

).`random.uniform(a, b)`

: Returns a random float number between`a`

and`b`

(inclusive of both`a`

and`b`

).

### Selecting random elements

`random.choice(seq)`

: Returns a random element from the sequence`seq`

.`random.sample(seq, k)`

: Returns a list of`k`

unique random elements from the sequence`seq`

.`random.shuffle(seq)`

: Randomly shuffles the elements of the sequence`seq`

in place.

### Setting the random seed

You can set the seed for the random number generator using the `random.seed()`

function. If you set the same seed value, you can ensure that the generator produces the same sequence of random numbers every time you run your program.

Here’s an example of how to use some of these functions:

import random # Generate a random float between 0 and 1 print(random.random()) # Generate a random integer between 1 and 10 print(random.randint(1, 10)) # Generate a random float between 1.0 and 5.0 print(random.uniform(1.0, 5.0)) # Select a random element from a list fruits = ["apple", "banana", "cherry"] print(random.choice(fruits)) # Select 3 unique random elements from a list colors = ["red", "green", "blue", "yellow", "orange"] print(random.sample(colors, 3)) # Shuffle a list of numbers numbers = [1, 2, 3, 4, 5] random.shuffle(numbers) print(numbers)

Note that the `random`

module is a pseudo-random number generator, which means that it produces numbers that appear to be random, but are actually generated using a deterministic algorithm. If you need truly random numbers (e.g. for cryptographic purposes), you should use a different module, such as `secrets`

or `os.urandom()`

.

# What is Seed

In the context of a random number generator, a “seed” is a value used to initialize the generator’s internal state. The initial state determines the sequence of random numbers that the generator will produce.

By using the same seed value, you can ensure that the generator produces the same sequence of random numbers every time you run your program. This can be useful for testing and debugging, as well as for applications that require deterministic behavior.

For example, consider the following Python code:

import random # Set the seed to a specific value random.seed(42) # Generate three random numbers print(random.random()) print(random.random()) print(random.random())

In this code, we first set the seed value to 42 using the `random.seed()`

function. We then generate three random numbers using the `random.random()`

function. Because we used the same seed value each time, the generator will produce the same sequence of random numbers every time we run the program.

If we were to change the seed value, the sequence of random numbers would also change. This can be useful if we want to produce different sequences of random numbers for different runs of our program.

It’s important to note that while setting the seed can ensure deterministic behavior, it doesn’t guarantee that the sequence of numbers produced by the generator will be truly random. Random number generators are only “pseudo-random”, meaning that they produce numbers that appear to be random, but are actually generated using a deterministic algorithm.

In summary, the seed is a value used to initialize the internal state of a random number generator, and can be used to ensure deterministic behavior in programs that use random numbers.

# Ways to Generate More Random Numbers

Random number generators in Python, like most programming languages, are based on deterministic algorithms that use a seed value to generate a sequence of seemingly random numbers. However, depending on the application, you may need to generate numbers that are more “random” than the default sequence produced by the random number generator.

Here are a few techniques for generating more “random” numbers:

- Use a better random number generator: The default random number generator in Python,
`random`

, is a basic pseudorandom number generator that is suitable for many applications but may not be suitable for cryptographic or security-related tasks. In these cases, you may need to use a more advanced random number generator, such as`os.urandom()`

or the`secrets`

module, which are specifically designed for these applications. - Use a better random seed: The seed value used to initialize the random number generator can have a significant impact on the randomness of the generated sequence. If the seed value is predictable or not sufficiently “random”, the resulting sequence may be predictable or not sufficiently “random”. To generate a better seed value, you can use sources of randomness such as system entropy, the current time, or user input.
- Use multiple sources of randomness: To increase the randomness of the generated sequence, you can combine multiple sources of randomness. For example, you can use a combination of the current time, the process ID, and the output of a cryptographic hash function to generate a seed value.
- Use a hardware-based random number generator: For the highest level of randomness, you can use a hardware-based random number generator, such as those found in some CPUs or external devices. These generators are based on physical processes that are inherently unpredictable, such as thermal noise or radioactive decay, and can produce truly random numbers.

Overall, generating “more random” numbers can be a complex topic that depends on the specific application and security requirements. In general, it’s a good idea to use a well-vetted library or module that is specifically designed for generating random numbers in a secure and reliable way.

# Use OS Module’s random() to generate Better Random Numbers

This code below uses os.urandom to generate a random byte string

import os # Generate a random byte string of length 8 rand_bytes = os.urandom(8) # Print the byte string as a hexadecimal string print(rand_bytes.hex())

In this code, we use the `os.urandom()`

function to generate a random byte string of length 8. The `hex()`

method is then used to convert the byte string to a hexadecimal string, which is more human-readable.

Note that `os.urandom()`

generates cryptographically secure random bytes that are suitable for use in cryptographic applications, such as generating encryption keys or passwords. It uses the operating system’s random number generator, which is designed to produce highly unpredictable numbers.

It’s important to note that `os.urandom()`

returns a byte string, not a number, so if you need to use the random value as a number, you will need to convert it using a function like `int.from_bytes()`

. For example:

import os # Generate a random integer between 0 and 255 rand_int = int.from_bytes(os.urandom(1), byteorder='big') # Print the random integer print(rand_int)

In this code, we use `os.urandom()`

to generate a random byte string of length 1, which represents a single byte. We then use `int.from_bytes()`

to convert the byte string to an integer. The `byteorder='big'`

argument specifies that the byte string should be interpreted as a big-endian integer, which means that the most significant byte is first. This code will generate a random integer between 0 and 255.