Boolean Array in Python

In this article, you will learn how to create Boolean arrays and how to use them in your code.

What is a Boolean Array?

We all know arrays are collections of contiguous elements of the same type. Boolean arrays specifically store Boolean values ('true' and 'false').

Example:

Boolean arrays can be created using various techniques and libraries such as NumPy and built-in Python data structures. These arrays are commonly employed in logical operations, filtering data, and masking during data manipulation.

The concept of true and false values

Truthy Values:

• True: True is a Boolean data type that always evaluates to true, which means it is considered true in Python.
• Non-zero Numbers: Any non-zero numeric value, including integers, floats, and complex numbers, is considered truthy. For example, 1, 2.5, and -3.14 are all true.
• Non-empty Sequences: Non-empty sequences such as lists, tuples, sets, and strings are true.
• Non-empty Containers: Non-empty dictionaries and user-defined objects with __bool__() or __len__() methods that return True are truthy.
• Custom Objects: If a custom object defines a __bool__() or __len__() method that returns a positive integer or True, it will be considered true.

False Values:

• False: False is a Boolean value that is always considered falsy.
• Zero: It is important to note that the number 0, whether an integer or a float, is considered falsy.
• Empty Sequences: Empty sequences such as lists, tuples, sets, and strings are considered falsy.
• Empty Containers: Empty dictionaries are considered false when evaluated in a Boolean context.
• None: The unique value None is considered falsy.
• Custom Objects: If a custom object has a __bool__() or __len__() method that returns False or 0, it will be considered falsy.

Creation of a Boolean Array

1. Using list comprehension

You can create a Boolean array from a Python list using list comprehensions. For example:

Example

Output:

 [True, True, False, True, False] 

Explanation

In this example, the Boolean array is_even indicates if each element in the data is even.

2. Using NumPy:

The NumPy library offers robust tools for creating and manipulating Boolean arrays. Here's an example of using NumPy to create a Boolean array:

Program

Output:

 [False True False True False] 

Explanation

The is_even array will have the same length as data, with True values where the elements are even and False where they are not.

3. Using bool function

Example

Small Example program to showcase the values that are considered true or false.

Program

Output:

 [False, True, True, True, False, True, True] 

Explanation

• None is converted to False.
• Numeric values other than 0 are converted to True.
• The Boolean value True remains True.
• Non-empty strings are converted to True.
• The numeric value 0 is converted to False.

After executing the code, Boolean_Array will hold Boolean values indicating the truthiness of each element in list1.

4. Using astype

NumPy provides a method called astype() that allows you to modify the data type of a NumPy array. This method will enable you to specify a new data type for the elements of the array. When you use astype(), a new array is returned with the selected data type, and the original array remains unchanged.

Program

Output:

 [False True True True False False] 

Explanation

The code creates a NumPy array arr with elements [0, 1, 2, 3, 0, 0] and then converts it to a Boolean NumPy array truthiness using the astype(bool) method. This conversion sets each element to False if it equals 0 and True for all other non-zero values.

5. Using dtype

When you explicitly specify the dtype as bool while creating a Boolean NumPy array in NumPy, the array will consist of bool data type elements. The bool data type represents Boolean values, which can only have two possible values: True or False.

Program

Output:

 [ True True True] 

Explanation

The code creates a NumPy array called "arr" with elements [1.0, 2.0, 3.0]. The data type of the array is explicitly set to bool using the "dtype" argument. When you set the data type to bool, NumPy interprets non-zero values as True and zero as False. It means that any element that is not zero in the array will be considered as True, and the zero element will be considered as False.

Program

Output:

 [[ True True False True] [ True True True True] [ True False True True]] 

Explanation

The code creates a NumPy array named A with a shape of (3, 4) and a Boolean data type. The values in the array are explicitly cast to Booleans based on the provided values. The output displays the boolean representation of the values in the original array, where non-zero values are converted to True and zero values are converted to False.

6. Using logical operations

You can perform logical operations on existing Boolean arrays to create new ones.

1. logical_and():This function performs element-wise logical AND between multiple arrays, returning a Boolean array where each element is True if all corresponding elements in the input arrays are True and False otherwise.
2. logical_or():This function performs element-wise logical OR between two or more arrays, resulting in a Boolean array with True for any corresponding input elements that are True.
3. logical_not():This function performs element-wise logical negation on a single array, returning a Boolean array with negated elements corresponding to the input array.

Program

Output:

 Logical And: [ True False False False] Logical Or: [ True True True False] Logical Not: [False False True True] 

Explanation

The logical_and array will contain the element-wise logical AND of arr1 and arr2, meaning it will be True only where both arr1 and arr2 have True values.

The logical_or array will contain the element-wise logical OR of arr1 and arr2, meaning it will be True where either arr1 or arr2 has a True value.

The logical_not array will contain the element-wise logical NOT of arr1, which will invert the Boolean values in arr1.

7. Using relational operators

NumPy offers a simple and convenient way to apply relational operators element-wise to an array. You can create a Boolean array by using these operators:

Program

Output:

 [False False False True True] 

Explanation

In this example program, when the relational operator > is applied to each element in the NumPy array arr, a Boolean array called bool_array is generated. The values in bool_array are True for elements greater than 3 and False for elements less than or equal to 3.

Conclusion

Boolean arrays, created using NumPy, are essential in programming for data analysis, logical operations, and conditional control.

Boolean arrays are a crucial concept in Python, frequently utilized for logical operations and data filtering. Whether you are working with standard Python lists or more advanced data structures like NumPy arrays, it is vital to understand Boolean arrays for many programming and data analysis tasks.

In summary, having a solid grasp of Boolean arrays is essential to be a proficient Python programmer.