NumPy Random
Codes With Pankaj
Introduction
we’ll explore the concept of random numbers in Python and learn how to generate random integers, floats, and arrays using the NumPy library. We’ll cover both pseudo-random numbers and truly random numbers, emphasizing the practical use of pseudo-random numbers in most applications.
What is a Random Number?
A random number is not necessarily different every time; rather, it means something that cannot be predicted logically. In the context of computers, which operate based on programs and algorithms, true randomness is challenging to achieve. Instead, we often work with pseudo-random numbers, generated through algorithms that produce seemingly random sequences.
Pseudo Random and True Random
Computers use algorithms to generate random numbers, making them predictable. Such numbers are termed pseudo-random. True randomness, on the other hand, involves obtaining data from an external source, such as keystrokes, mouse movements, or network data. While true random numbers have specific applications, many scenarios, including most programming tasks, can be effectively addressed using pseudo-random numbers.
Generating Pseudo-Random Numbers with NumPy
NumPy, a powerful numerical computing library in Python, provides a random module for working with random numbers.
Generate Random Integer
To generate a random integer between 0 and 100, we can use the randint() method.
from numpy import random
x = random.randint(100)
print(x)Generate Random Float
To generate a random float between 0 and 1, we use the rand() method.
from numpy import random
x = random.rand()
print(x)Generate Random Array
NumPy allows us to generate random arrays of integers or floats.
Random Integer Array
To create a 1-D array with random integers, we use randint() with the size parameter.
from numpy import random
x = random.randint(100, size=(5))
print(x)To generate a 2-D array, we adjust the size parameter accordingly.
from numpy import random
x = random.randint(100, size=(3, 5))
print(x)Random Float Array
For random float arrays, we use the rand() method similarly.
from numpy import random
x = random.rand(5)
print(x)Adjusting the size parameter allows us to create 2-D float arrays as well.
from numpy import random
x = random.rand(3, 5)
print(x)Generate Random Number From Array
The choice() method enables us to generate random values based on an array of values.
Single Value
from numpy import random
x = random.choice([3, 5, 7, 9])
print(x)Array of Values
To generate a 2-D array with random values from a given array, we use the size parameter.
from numpy import random
x = random.choice([3, 5, 7, 9], size=(3, 5))
print(x)Random Data Distribution
Random Distribution
A random distribution is a set of random numbers that follows a specific probability density function. The probability density function describes the likelihood of each value occurring in the distribution. NumPy’s random module provides the choice() method, allowing us to generate random numbers based on defined probabilities.
Generating Random Data Distribution
Example: Generating a 1-D Array
Let’s generate a 1-D array containing 100 values, where each value can be 3, 5, 7, or 9. We’ll set the probability for each value using the p parameter in the choice() method.
from numpy import random
# Define values and their probabilities
values = [3, 5, 7, 9]
probabilities = [0.1, 0.3, 0.6, 0.0]
# Generate a 1-D array with 100 values based on probabilities
x = random.choice(values, p=probabilities, size=(100))
print(x)In this example, the probability for the value 3 is set to 0.1, for 5 is set to 0.3, for 7 is set to 0.6, and for 9 is set to 0.0. The sum of all probability values should be 1. The resulting array represents a random distribution based on the defined probabilities.
Example: Generating a 2-D Array
We can extend the example to generate a 2-D array with multiple rows and columns.
from numpy import random
# Define values and their probabilities
values = [3, 5, 7, 9]
probabilities = [0.1, 0.3, 0.6, 0.0]
# Generate a 2-D array with 3 rows and 5 columns based on probabilities
x = random.choice(values, p=probabilities, size=(3, 5))
print(x)Adjust the size parameter to control the shape of the array. In this case, we have 3 rows and 5 columns. The resulting 2-D array reflects the random distribution specified by the given probabilities.
Random Permutations
Shuffling Arrays
Shuffling means changing the arrangement of elements in-place, directly within the array. The shuffle() method in the NumPy random module allows us to achieve this.
Example: Shuffling an Array
Let’s randomly shuffle the elements of an array using the shuffle() method.
from numpy import random
import numpy as np
# Create an array
arr = np.array([1, 2, 3, 4, 5])
# Shuffle the array in-place
random.shuffle(arr)
# Print the shuffled array
print(arr)The shuffle() method modifies the original array, changing the order of its elements.
Generating Permutations of Arrays
The permutation() method in the NumPy random module generates a random permutation of the elements in an array.
Example: Generating a Random Permutation
Let’s generate a random permutation of the elements in an array using the permutation() method.
from numpy import random
import numpy as np
# Create an array
arr = np.array([1, 2, 3, 4, 5])
# Generate a random permutation of the array
permuted_arr = random.permutation(arr)
# Print the random permutation
print(permuted_arr)The permutation() method returns a new array representing a random permutation of the elements in the original array. The original array remains unchanged.
