Python: Array Masks

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^Downloadable code & more! This video goes through numpy array masking by showing you how to do it on a random matrix. A mask creates a matrix that has boolean values that match the mask statement. You can use the mask to examine specific parts of a matrix. Here is some syntax: np.random.random(()) and mask = A greater than 0.

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ARRAY MASKING

array masking is a powerful coding tool and I will show you how it works here.

First, so we can make matrices we import numpy as np

And today I’ll show you with a random matrix so we will import random.

First we will make a random 5x5 matrix with entries between -5 and 5.

To create a random matrix we type np.random.random(()) and insert a tuple with the size of the desired matrix

Normally np.random.random will automatically create random numbers between 0 and 1, so we will just multiply by 10 and subtract 5 to make this between -5, 5

So here is our random 5x5 matrix A

And now we will create the mask. Let’s call this mask, though you can name it anything.

We will do a mask that is anywhere A is greater than 0

When we print out the mask it will be a matrix the same size as A with boolean values in every spot. True where the value is greater than 0 and False where the value is less than 0

Once we have this mask we can type A[mask]

This will be all the values of A that satisfy the mask.

So here it is, all the positive numbers in A

And Let’s show you A again.

There you have it, that is how you do numpy array masking

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