## Problem Formulation

Given a NumPy array consisting of Boolean values. How to convert it to an integer array?

- Convert each
`True`

value to integer`1`

, and - Convet each
`False`

value to integer`0`

.

Here’s an example Boolean array:

>>> import numpy as np >>> a = np.array([True, False]) >>> a array([ True, False])

What you want is the following integer array:

>>> a array([1, 0])

Let’s examine some methods to accomplish this easily.

## Method 1: Don’t Do It

Python internally represents Booleans using integers 1 and 0 for `True`

and `False`

, respectively. This means that you may not even need to convert your Boolean array to an integer array because all integer operations already work on the Boolean array as well!

For example, you can add a Boolean array and an integer array and the result is an integer array already. In the following code snippet, you add the array `[True False False]`

to the array `[3 2 1]`

and obtain the result `[4 2 1]`

because each `True`

value is represented by integer 1 and each `False`

value is represented by integer 0.

>>> a = np.array([True, False, False]) >>> b = np.array([3, 2, 1]) >>> a + b array([4, 2, 1])

Okay, still here? 😉 If you actually want to convert your Boolean array to an integer array, read on! 🙂

## Method 2: Multiplication with 1

Python NumPy uses broadcasting to allow array multiplication with a scalar. The scalar is then multiplied with every single value in the array. Data types of the scalar and the array are combined on a best-fit basis.

This means that you can simply ** multiply your Boolean array with an integer** to obtain an array of integers! In particular, if you multiply with integer 1, each Boolean

`True`

is transformed to integer 1 and `False`

to integer 0. For example, the expression `1 * np.array([True, False])`

results in the integer array `[1 0]`

. Here’s the same example as a code snippet:

>>> import numpy as np >>> a = np.array([True, False]) >>> a * 1 array([1, 0])

You can see that Python creates a new integer array from the result of the multiplication of the Boolean array and the scalar 1.

This is the simplest way to convert a Boolean array to an integer array and it is what I would do if I actually had to convert the array—and couldn’t just work with the Boolean array in the first place. 😉

## Method 3: Use array.astype(int)

Arguably, this is the most standard canonical way to do the job.

To convert a Boolean array a to an integer array, use the `a.astype(int)`

method call. The single argument `int`

specifies the desired data type of each array item. NumPy converts on a best-effort basis. If the original array only contains Booleans, it’ll result in an array of integers so that each `True`

value converts to integer `1`

and each `False`

value to integer `0`

.

This strategy is shown in the following code snippet:

>>> import numpy as np >>> a = np.array([True, False]) >>> a.astype(int) array([1, 0])

You can see that the method returns a new array of integers rather than working on an existing array. The original array remains unchanged:

>>> a array([ True, False])

This is a clean, readable, and canonical way to convert a Boolean array to an integer array in NumPy. And every reliable source would recommend this method as the best one. Yet, in this particular problem instance, I think the above two methods 1 and 2 are superior if applicable to your specific scenario. No conversion is better than a redundant one, don’t you think?

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While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

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