# Numpy – Make All Negative Values Positive

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.

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```import numpy as np

# create numpy array
ar = np.array([-3, -2, -1, 0, 1, 2, 3])
# display the array
print(ar)```

Output:

`[-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 `-1`).

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)```

Output:

`[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```

Output:

`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 –

1. Create a Numpy array (skip this step if you already have an array to operate on).
2. Use boolean indexing to find the negative values and then make them positive `ar[ar < 0] = -1*ar[ar < 0]`

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