# Get random boolean in Python

## What are Boolean Values?

Python has a built-in data type Boolean that represents True and False values. It has only these two values. We can also consider True and False as 1 and 0, respectively. If we convert 0 or 1 to Boolean, then we get True or False only.

This tutorial will demonstrate how to generate random Boolean values in Python.

## Ways to Generate Random Boolean in Python

Random numbers are very instrumental in programming. We tend to use them for generating test cases or simulations. There are several methods associated with random number generation in Python. We will discuss how to generate random Boolean values.

### Using the `random.getrandbits()` function

The `getrandbits` method of the `random` module returns the integers or numbers with a specified number of bits. The number of bits returned is placed as an argument or a parameter of this function.

For example,

Output:

True

In the above example,

• We pass one as the parameter of the `getrandbits()` function. This will generate either 0 or 1.
• Then, we use the `bool()` function to convert this randomly generated integer to a Boolean value, i.e., True or False and displayed it.

### Using the `random.choice()` function

With the `random.choice()` function, we can randomly select an element from a given sequence like a list.

To generate random Boolean values using this function, we will create a list of two elements, True and False and then randomly select a value from this list.

For example,

Output:

True

We use the `bool()` function in the above example also. This function ensures that the final value is of type `bool`.

### Using the `random.random()` function

The `random.random()` function generates a random float number between 0 and 1. We can use it to generate random Boolean values.

For example,

Output:

False

In the above code,

• We generate a random float value using the `random.random()` function.
• Then, we compare this value with 0.5. If the value is greater than 0.5, it will return True, or it will return False.
• This way, by comparing it with 0.5, we get a random Boolean value.

### Using the `random.randint()` function

This method is similar to the previously discussed `random.getrandbits()` method.

We will use the `random.randint()` function to get a random integer, 0 or 1. Then we convert this to Boolean using the `bool()` function.

For example,

Output:

True

### Using the `numpy.random.choice()` function

The `numpy` module has a variety of random functions to generate random arrays of specific sizes. The `numpy.random.choice()` function returns a random set of samples in a one-dimensional array that returns a random `numpy` array. This method will create a random Boolean array in this method.

For example,

Output:

[ True True False True False]

In the above example,

• We generate an array of random Boolean values having a total of five elements.
• The `map()` function applies the `bool()` method to all the elements of the array.
• The `list()` function converts the map object to a list.

### Using the `numpy.random.rand()` function

The `numpy.random.rand()` function generates an array of the given size with random float values. We will follow a similar logic as discussed in the `random.random()` method.

For example,

Output:

[False False True True True]

### Using the `numpy.random.randint()` function

This function will create an array of random integers between a given range.

We will generate an array of random integers, either 0 or 1, and convert them to Boolean values using the `bool()` function.

For example,

Output:

[ True True False True False]

### Using the `faker.pybool()` function

The `faker` library can generate various types of fake test data. We first create an object using the `Faker()` constructor and the `pybool` function to get a random Boolean value.

For example,

Output:

True

## Conclusion

In this article, we discussed how to generate random Boolean values in Python. Different methods involving the `random`, `numpy`, and `faker()` module were discussed. The functions of the `numpy` module are more hopeful in generating arrays of such values.

import_contacts

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