Adventures in Machine Learning

Mastering Randomness in Python: Generating Unique and Excluded Numbers

Python is a versatile programming language used by developers around the world. One of the most essential features of Python is generating random numbers.

The random module in Python makes generating random numbers quite easy. While generating random numbers in Python, there are two significant issues to consider: ensuring unique random numbers and excluding specific numbers.

Fortunately, Python provides multiple solutions to these issues. 1.

Generate N Unique Random Numbers within a Range in Python:

One common use case is generating unique random numbers in a specified range. Python provides an easy way to generate unique random numbers in a range using the random.sample() method.

The random.sample() method selects unique elements from a list without replacement. Therefore, it is possible to generate N unique random numbers within a specified range using the random.sample() function.

Example:

Suppose we want to generate five unique random numbers between 1 and

50. We can use the random.sample() method as follows:

“`python

import random

random.sample(range(1,

51),

5)

“`

Output:

“`python

[4

6, 32, 39, 17, 9]

“`

In the above example, the random.sample() function selects five unique random numbers from the range(1,

51) list. Therefore, the output is a list of five random numbers, each between 1 and

50.

Alternatively, we can achieve the same result using the random.shuffle() method. The random.shuffle() method shuffles a list randomly.

Therefore, we can first generate a list of numbers in a range, shuffle them, and then select the first N elements to obtain N unique random numbers in a specified range. Example:

Suppose we want to generate five unique random numbers between 1 and

50.

We can use the random.shuffle() method as follows:

“`python

import random

l = list(range(1,

51))

random.shuffle(l)

l[:

5]

“`

Output:

“`python

[2, 29, 31, 44, 40]

“`

In the above example, we first create a list of numbers in the range of 1 to

50 using the list() and range() functions. The random.shuffle() method then shuffles the list randomly, and the first five items of the shuffled list are selected using slicing, to generate five unique random numbers between 1 and

50.

2. Generate Random Number in Range Excluding Some Numbers:

In some cases, it is necessary to generate a random number within a range, excluding specific numbers.

For instance, suppose we want to generate a random number between 1 and 10, excluding the numbers 3 and 7. Python provides multiple solutions to this problem.

Solution 1: Using List Comprehension and random.choice() Method:

One way to generate a random number within a range, excluding specific numbers, is using list comprehension and the random.choice() method. We first create a list of numbers that are within the range but exclude the numbers we want to exclude, and then use the random.choice() method to select a random number from the list.

Example:

Suppose we want to generate a random number between 1 and 10, excluding the numbers 3 and 7. We can use the above approach as follows:

“`python

import random

range_list = [x for x in range(1, 11) if x not in [3, 7]]

print(random.choice(range_list))

“`

Output:

“`python

6

“`

In the above example, we use list comprehension to generate a list of numbers within the range of 1 to 10, but exclude the numbers 3 and 7. We then use the random.choice() method to select a random number from the list, which yields a random number between 1 and 10, excluding the numbers 3 and 7.

Solution 2: Using Set Objects and random.choice() Method:

Another way to generate a random number within a range, excluding specific numbers, is using set objects and the random.choice() method. We can create a set object with the numbers in a range, exclude the numbers we want to exclude, and then use the random.choice() method to select a random number from the set.

Example:

Suppose we want to generate a random number between 1 and 10, excluding the numbers 3 and 7. We can use the above approach as follows:

“`python

import random

range_set = set(range(1, 11)) – set([3, 7])

print(random.choice(list(range_set)))

“`

Output:

“`python

5

“`

In the above example, we create a set object with the numbers within the range of 1 to 10, and then remove the numbers 3 and 7 from the set. We then use the random.choice() method to select a random number from the set, yielding a random number between 1 and 10, excluding the numbers 3 and 7.

Conclusion:

Python provides various approaches to generating random numbers within a range while ensuring that they are unique and excluding specific numbers. The choice of approach depends on the specific needs of the task at hand.

The random module in Python is quite powerful, and developers can leverage it to build complex systems that require randomness. Additional Resources:

Generating random numbers is an essential task in data science, machine learning, statistics, and game development.

Python provides several built-in modules for generating random numbers, such as random, numpy.random, and secrets. In addition, there are third-party libraries such as faker, which generate realistic fake data, and pydash, which provides functional programming constructs for Python.

Here are some additional resources on generating random numbers in Python. 1.

NumPy Random:

The numpy.random module provides functions for generating random numbers from various distributions, such as normal, binomial, uniform, and poisson. NumPy is a popular numerical computing library that provides efficient arrays and matrices operations in Python.

NumPy Random provides a suite of fast, vectorized random number generators that can generate random numbers in large quantities. Example:

“`python

import numpy as np

# Generate random numbers from a normal distribution

random_numbers = np.random.normal(0, 1, 10)

print(random_numbers)

“`

Output:

“`python

array([-0. 6

6271414, -0.

52

6

52727, 0.0894097 , 0.9

6944

523, -1.9

620737 ,

1.77

674228, 0.1111

59

6

5, 0.109

6

6838, 0. 62290198, 0.277

6709 ])

“`

In the above example, we use the numpy.random.normal() method to generate ten random numbers from a normal distribution with the mean of 0 and standard deviation of 1.

2. Random.org:

Random.org is a website that provides a true random number generator service that generates numbers based on atmospheric noise.

The randomness of the generated numbers is guaranteed by the randomness of atmospheric noise. The random.org API allows developers to integrate true random numbers into their applications.

Example:

“`python

import requests

url = “https://www.random.org/integers/”

params = {

“num”:

5,

“min”: 0,

“max”: 100,

“col”: 1,

“base”: 10,

“format”: “plain”,

“rnd”: “new”

}

response = requests.get(url, params=params)

print(response.text)

“`

Output:

“`

33

50

4

5

81

24

“`

In the above example, we use the requests module to make a GET request to the random.org API and request five integers between 0 and 100. The response is a string of five random numbers separated by newlines.

3. Randomness in Games:

Randomness plays a critical role in game development, where unpredictable events can make games more exciting and challenging.

Games use various techniques to generate random numbers, depending on the requirements. For instance, some games use physical processes, such as rolling dice or shuffling cards, while others use computer-generated random numbers.

Python provides several libraries for game development, such as Pygame, Arcade, and PyOpenGL. These libraries incorporate randomness into their game development frameworks.

Example (using Pygame):

“`python

import random

import pygame

pygame.init()

display_width = 800

display_height =

600

game_display = pygame.display.set_mode((display_width, display_height))

pygame.display.set_caption(“Random Numbers in Game Development”)

def generate_random_color():

return random.randint(0, 2

5

5), random.randint(0, 2

5

5), random.randint(0, 2

5

5)

game_exit = False

while not game_exit:

for event in pygame.event.get():

if event.type == pygame.QUIT:

game_exit=True

random_color = generate_random_color()

game_display.fill(random_color)

pygame.display.update()

pygame. quit()

quit()

“`

Output:

This code generates a Pygame window that displays a random RGB color every frame update. The generate_random_color() function generates a random RGB tuple using the random.randint() method, and the main while loop repeatedly sets the display to a random color.

Conclusion:

Generating random numbers is essential in various applications such as data science, statistics, machine learning, and game development. Python provides various built-in modules and third-party libraries that can generate random numbers efficiently and reliably.

By leveraging the power of these modules and libraries, developers can create truly random experiences for users. In conclusion, generating random numbers is a crucial task in various fields such as game development, data science, machine learning, and statistics.

Python provides multiple built-in modules and third-party libraries that can generate random numbers efficiently and reliably, such as random, numpy.random, and random.org. While generating random numbers in Python, developers must ensure that the numbers generated are unique and exclude specified numbers when necessary, and Python provides several solutions to these issues.

By leveraging the power of these modules and libraries, developers can create truly random experiences for users. Overall, generating random numbers in Python is a powerful tool that should be in the arsenal of every developer.

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