numpy.random.choice() in Python

numpy.random.choice() function allows you to randomly select elements from an array. It’s a part of NumPy's random module and is widely used for sampling with or without replacement, shuffling data, simulations and bootstrapping.

Example:

import numpy as np a = [10, 20, 30, 40] res = np.random.choice(a) print(res) 

40 

Explanation: A single random element is selected from the array. In this case, 40 was picked randomly (your output may vary since it's random).

Syntax

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

Parameters:

• a: 1D array-like or int. If int n, samples from np.arange(n).
• size: Number of samples (int or tuple). Default is one value.
• replace: If True, samples with replacement. Default is True.
• p: List of probabilities associated with a. Must sum to 1.

Returns: A single value or an array of values based on sampling rules.

Examples

1. Pick one value

import numpy as np res= np.random.choice([1, 2, 3]) print(res) 

3 

2. Pick multiple values (with replacement)

import numpy as np res= np.random.choice([10, 20, 30], size=2) print(res) 

[30 30] 

3. Sample without replacement

import numpy as np res= np.random.choice(['a', 'b', 'c'], size=2, replace=False) print(res) 

['a' 'c'] 

4. Use custom probabilities

import numpy as np res= np.random.choice(['red', 'green'], size=3, p=[0.2, 0.8]) print(res) 

['green' 'green' 'green'] 

6. Multi-dimensional output

import numpy as np res= np.random.choice([0, 1], size=(2, 3)) print(res) 

[[1 1 1] [0 0 0]]