The Numpy library in Python comes with a number of useful functions and methods to work with and manipulate the data in arrays. In this tutorial, we will look at how to make all the negative values in a Numpy array positive with the help of some examples.
Steps to make negative values positive in Numpy
You can use boolean indexing to make all the negative values in a Numpy array positive. The following is the syntax –
# make negative values positive ar[ar < 0] = -1 * ar[ar < 0]
It replaces the negative values in the array
ar with the corresponding positive values.
Let’s now look at a step-by-step example of using this syntax –
Step 1 – Create a Numpy array
First, we will create a Numpy array that we will use throughout this tutorial.
import numpy as np # create numpy array ar = np.array([-3, -2, -1, 0, 1, 2, 3]) # display the array print(ar)
[-3 -2 -1 0 1 2 3]
Here, we used the
numpy.array() function to create a one-dimensional Numpy array containing some numbers. You can see that the array has both positive and negative values (along with a zero).
Step 2 – Make negative values positive using boolean indexing
Using boolean indexing identify the values that are less than zero and then set them to their corresponding positive values (by multiplying by
First, we will specify our boolean expression
ar < 0 and then make the array values satisfying this condition positive.
For example, let’s replace all the negative values in the above array with the corresponding positive values.
# make negative values positive ar[ar < 0] = -1*ar[ar < 0] # display the array print(ar)
[3 2 1 0 1 2 3]
The resulting array has the negative values replaced with the positive values.
To understand what’s happening here, let’s look under the hood. Let’s see what we get from the expression
ar < 0
# create a numpy array ar = np.array([-3, -2, -1, 0, 1, 2, 3]) # result of boolean expression ar < 0 ar < 0
array([ True, True, True, False, False, False, False])
We get a boolean array. The boolean values in this array represent whether a value at a particular index satisfies the given condition or not (in our case whether the element is less than 0 or not).
When we do
ar[ar < 0] = -1*ar[ar < 0], we are essentially setting the values in the array where the condition evaluates to
True to its value multiplied by -1.
You can similarly filter a Numpy array for other conditions as well.
Summary – Make negative values positive in Numpy
In this tutorial, we looked at how to replace all the negative values in a Numpy array with their corresponding positive values. The following is a short summary of the steps mentioned –
- Create a Numpy array (skip this step if you already have an array to operate on).
- Use boolean indexing to find the negative values and then make them positive
ar[ar < 0] = -1*ar[ar < 0]
You might also be interested in –
- Numpy – Make All Negative Values Zero in Array
- Numpy Array – Get All Values Smaller than a Given Value
- Filter a Numpy Array – With Examples
- Extract the Last N Elements of Numpy Array
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