How to Create a Boolean Mask in a 2D Numpy Array Using a 1D Reference Array

Discover how to efficiently generate a boolean mask from a 2D Numpy array based on a 1D reference array without using loops.
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Create a Boolean Mask in a 2D Numpy Array with a 1D Reference Array

Problem Overview

Let's say you have a 2D array called test, shaped like this:

[[See Video to Reveal this Text or Code Snippet]]

And a 1D reference array called ref:

[[See Video to Reveal this Text or Code Snippet]]

The goal is to create a boolean mask where elements in test match any of the values in ref. To visualize, you may be looking for a result that looks like this:

[[See Video to Reveal this Text or Code Snippet]]

However, you want to achieve this without looping through the elements manually.

Fortunately, Numpy provides a highly efficient function called isin. This function is specifically designed for checking membership, and it can easily generate the boolean mask we need.

Code Example

Here's how you can implement this solution in Python:

[[See Video to Reveal this Text or Code Snippet]]

Output

When you run the code above, you will receive the following output:

[[See Video to Reveal this Text or Code Snippet]]

This outcome indicates that the boolean mask correctly identifies where elements from the test array match those in the ref array.

Conclusion

Feel free to experiment with your own arrays and explore the vast functionalities that Numpy offers for array manipulation! If you have more questions or topics you want to discuss, leave a comment below.