Python numpy.random.choice method example

Python numpy.random.choice example to generate a random sample from a given 1-D array:

numpy.random.choice method is used to generate a random sample from a 1-D array. It gets the random sample from a 1-D array and returns that random sample.

In this post, we will learn how to use numpy.random.choice() with examples.

Definition of numpy.random.choice():

numpy.random.choice() method is defined as like below:

choice(arr, size, replace, p)

Here,

arr is 1-D array-like or int. If we pass an integer, it takes the np.arrange value for that integer.
size is optional. It is used to define the output shape. By default it is None and it returns a single value. It can be a int or tuple of ints.
replace is optional boolean value. It defines whether replacement is supported or not. By default, it is True, i.e. a value of arr can be selected multiple times.
p is 1-D array-like optional parameter. It is the probabilities associated with each entries.

Example of random.choice():

Let’s pass one integer value as the first param:

from numpy import random as r

print(r.choice(3, 4))

It will take 3 as numpy.arrange(3) and it will print output as like below:

[2 0 1 0]

Example with replacement False:

We can create a random sample without replacement if we add replace = False parameter.

from numpy import random as r

print(r.choice(3, 3, replace=False))

It will print one output as like below:

[0 2 1]

Error while replace=False:

It will throw ValueError is the size is greater than the given sample size.

from numpy import random as r

print(r.choice(3, 4, replace=False))

It will throw ValueError:

ValueError: Cannot take a larger sample than population when 'replace=False'

Using probability:

We can pass the p parameter to add probabilities to the entries:

from numpy import random as r

print(r.choice(4, 2,  p=[0.1, 0.2, 0.5, 0.2]))

It will print something as like below:

[2, 1]

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