NumPy Random Numbers – Mastering Python

Getting Started with `numpy.random`

Before diving into specific functions, let’s import the necessary module:

import numpy as np

The numpy.random module (now a submodule of numpy.random) offers a wide array of functions for generating various types of random numbers. Note that numpy.random was redesigned in version 1.17. Older code using np.random directly might need adjustment.

Generating Random Numbers from Different Distributions

NumPy provides functions to sample from many common probability distributions. Here are a few examples:

Uniform Distribution

The rand() function generates random numbers from a uniform distribution over the interval [0, 1):

random_numbers = np.random.rand(5)
print(random_numbers)

random_array = np.random.rand(3, 3)
print(random_array)

To generate random numbers from a uniform distribution within a specific range (a, b), use uniform():

random_numbers_range = np.random.uniform(2, 10, 10)
print(random_numbers_range)

Normal (Gaussian) Distribution

The randn() function generates random numbers from a standard normal distribution (mean=0, standard deviation=1):

standard_normal = np.random.randn(5)
print(standard_normal)

#Generate a 2x4 array of numbers from a normal distribution with mean 5 and standard deviation 2
normal_array = np.random.normal(loc=5, scale=2, size=(2,4))
print(normal_array)

Other Distributions

NumPy offers functions for numerous other distributions, including:

randint(): Generates random integers from a specified range.
poisson(): Generates random numbers from a Poisson distribution.
binomial(): Generates random numbers from a binomial distribution.
exponential(): Generates random numbers from an exponential distribution. And many more! Consult the NumPy documentation for a complete list.

Seeding the Random Number Generator

Reproducibility is key in scientific computing. You can set a seed using seed() to ensure that the sequence of random numbers generated is the same every time you run your code:

np.random.seed(42) #Sets the seed to 42
random_numbers = np.random.rand(5)
print(random_numbers)

np.random.seed(42) #Same seed, same output
random_numbers_again = np.random.rand(5)
print(random_numbers_again)

Changing the seed will produce a different sequence.

Generating Random Integers

The randint() function is very useful for generating random integers:

#Generate 5 random integers between 1 and 10 (inclusive)
random_integers = np.random.randint(1, 11, 5)
print(random_integers)

Choosing Random Elements from Arrays

NumPy also provides functions for selecting random elements from existing arrays. choice() is particularly useful:

my_array = np.array([1, 2, 3, 4, 5])
#Choose one random element from my_array
random_element = np.random.choice(my_array)
print(random_element)

#Choose 3 random elements with replacement
random_elements_replacement = np.random.choice(my_array, size=3, replace=True)
print(random_elements_replacement)

#Choose 3 random elements without replacement
random_elements_no_replacement = np.random.choice(my_array, size=3, replace=False)
print(random_elements_no_replacement)

Shuffling Arrays

The shuffle() function randomly permutes the elements of an array in place:

my_array = np.array([1, 2, 3, 4, 5])
np.random.shuffle(my_array)
print(my_array) #The original array is modified

Remember that shuffle modifies the array directly; it doesn’t return a new shuffled array. To obtain a shuffled copy without modifying the original, you can use permutation().

my_array = np.array([1, 2, 3, 4, 5])
shuffled_array = np.random.permutation(my_array)
print(shuffled_array)
print(my_array) #Original array is unchanged

These examples provide a solid foundation for working with NumPy’s random number generation capabilities. Experiment with these functions and explore the extensive documentation to unlock the full potential of NumPy’s random number tools for your data science projects.

Getting Started with numpy.random