Numpy Tutorial
Creating NumPy Array
NumPy Array Manipulation
Matrix in NumPy
Operations on NumPy Array
Reshaping NumPy Array
Indexing NumPy Array
Arithmetic operations on NumPy Array
Linear Algebra in NumPy Array
NumPy and Random Data
Sorting and Searching in NumPy Array
Universal Functions
Working With Images
Projects and Applications with NumPy
The numpy.random.choice() function is a useful utility to generate random samples from a given 1-D array. It's a versatile function that lets you generate single or multiple random elements with or without replacement.
numpy.random.choice() can be used for generating random samples from a 1-D array or a range of numbers. It also allows specifying probabilities associated with each entry for non-uniform random sampling.
Start by importing the necessary library:
import numpy as np
numpy.random.choice():Choose a single item from a range:
# Choose a random item between 0 (inclusive) and 5 (exclusive) print(np.random.choice(5)) # Output: Random number between 0 and 4
Choose a single item from a list:
items = ['apple', 'banana', 'cherry', 'date'] print(np.random.choice(items)) # Output: Randomly selected fruit
You can also specify the size parameter to choose multiple items:
# Choose 3 random items from the list with replacement print(np.random.choice(items, size=3)) # Example Output: ['apple', 'banana', 'apple']
If you don't want to choose the same item more than once, set the replace parameter to False:
# Choose 3 random items from the list without replacement print(np.random.choice(items, size=3, replace=False)) # Example Output: ['cherry', 'banana', 'apple']
You can provide a probability distribution using the p parameter:
# Choose 3 items with specified probabilities probabilities = [0.5, 0.1, 0.1, 0.3] print(np.random.choice(items, size=3, p=probabilities)) # 'apple' has a higher chance to be selected
p parameter should sum to 1, or else you'll get an error.replace parameter is set to False, you can't select more items than are available in the array without getting an error.The numpy.random.choice() function offers a lot of flexibility in generating random samples. Whether you need uniform or non-uniform sampling, with or without replacement, this function has got you covered. It's especially useful in simulations, games, or any application where random selection is essential.
NumPy's random module provides functions for random element selection from arrays.
import numpy as np
# Create an array
elements = np.array([1, 2, 3, 4, 5])
# Randomly select an element
random_element = np.random.choice(elements)
print("Randomly Selected Element:")
print(random_element)
random.choice() examples:The random.choice() function in NumPy allows you to randomly select elements from an array.
# Assuming 'elements' array is already defined
# Randomly select multiple elements
random_elements = np.random.choice(elements, size=3, replace=False)
print("Randomly Selected Elements:")
print(random_elements)
The random.choice() function is versatile and can sample random elements from an array with or without replacement.
# Assuming 'elements' array is already defined
# Sample random elements with replacement
sample_with_replacement = np.random.choice(elements, size=3, replace=True)
# Sample random elements without replacement
sample_without_replacement = np.random.choice(elements, size=3, replace=False)
print("Sample with Replacement:")
print(sample_with_replacement)
print("\nSample without Replacement:")
print(sample_without_replacement)
choice function:You can use the p parameter to define probability distributions for element selection.
# Assuming 'elements' array is already defined
# Probability distributions for element selection
probabilities = [0.1, 0.2, 0.3, 0.2, 0.2]
random_elements_with_probs = np.random.choice(elements, size=3, p=probabilities)
print("Random Elements with Probabilities:")
print(random_elements_with_probs)
NumPy's random.choice() function allows for customization, such as specifying axis and replacement.
# Assuming 'elements' array is already defined
# Customizing random sampling
random_samples = np.random.choice(elements, size=(2, 3), replace=True, axis=0)
print("Random Samples with Replacement:")
print(random_samples)
random.choice() vs random.sample():While both functions provide random sampling, random.choice() is more flexible for NumPy arrays.
# Assuming 'elements' array is already defined
# Using random.choice() for random sampling
random_elements_choice = np.random.choice(elements, size=3, replace=False)
# Using random.sample() for random sampling
random_elements_sample = np.random.sample(elements, 3)
print("Random Elements using random.choice():")
print(random_elements_choice)
print("\nRandom Elements using random.sample():")
print(random_elements_sample)
NumPy's random.choice() is an efficient way to sample random elements, especially from large arrays.
# Assuming 'elements' array is already defined
# Efficient random sampling
random_samples_efficient = np.random.choice(elements, size=5, replace=True)
print("Efficient Random Sampling:")
print(random_samples_efficient)
The p parameter in random.choice() allows weighted random sampling.
# Assuming 'elements' array is already defined
# Weighted random sampling
weights = [0.1, 0.2, 0.3, 0.2, 0.2]
weighted_random_elements = np.random.choice(elements, size=3, p=weights)
print("Weighted Random Elements:")
print(weighted_random_elements)
You can apply random choice to both 1D and 2D arrays using random.choice().
# Assuming 'elements_2d' is a 2D array
# Random choice from 1D array
random_choice_1d = np.random.choice(elements, size=3, replace=False)
# Random choice from 2D array
random_choice_2d = np.random.choice(elements_2d, size=(2, 3), replace=True)
print("Random Choice from 1D Array:")
print(random_choice_1d)
print("\nRandom Choice from 2D Array:")
print(random_choice_2d)