.choice()

In the random module of NumPy, the .choice() method generates a random sample from a specified 1-D array. It is commonly used in simulations, random sampling, and testing scenarios where randomness is required.

Syntax

numpy.random.choice(a, size=None, replace=True, p=None)

Parameters:

• a: 1-D array-like or int. If an integer n is provided, the array [0, 1, ..., n-1] is used as the input.
• size (Optional): The number of samples to draw. If None, a single sample is returned.
• replace (Optional): Determines whether sampling is with or without replacement.
  • If True (default), an element can be selected multiple times.
  • If False, each element can only be selected once.
• p (Optional): The probabilities associated with each element in a. Must sum to 1. If not specified, each element has an equal probability of being selected.

Return value:

In NumPy, the .choice() function returns a randomly selected sample or an array of randomly selected samples from the provided array a.

• If size is None, it returns a single randomly selected value.
• If size is specified, it returns an array of random selections, where the length of the array is equal to size. The selections can either be with or without replacement, depending on the replace parameter.

Example

The example below shows how to randomly select elements from an array:

import numpy as np

result = np.random.choice([10, 20, 30, 40], size=2, replace=False)

print(result)

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A possible output of this code can be:

[20 30]

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The code above randomly selects 2 different elements from the array [10, 20, 30, 40] without replacement.

Codebyte Example

In this codebyte example, we sample elements based on custom probabilities:

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Note: The output may differ every time you run it, as the selection is random. The probability distribution influences how often each item is chosen. Note: The probability distribution influences how often each item is chosen, but since replace=True, elements may be selected more than once.