# Numpy – Set All Non Zero Values to Zero

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 set all the non-zero values in a Numpy array to zero with the help of some examples.

## Steps to set all non zero values to zero in Numpy

You can use boolean indexing to set all the non-zero values in a Numpy array to zero. The following is the syntax –

```# set non-zero values to zero
ar[ar != 0] = 0```

It replaces the non-zero values in the array `ar` with `0`.

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)```

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 non-zero values zero using boolean indexing

Using boolean indexing identify the values that are not equal to zero and then set them to `0`.

📚 Data Science Programs By Skill Level

Introductory

Intermediate ⭐⭐⭐

🔎 Find Data Science Programs 👨‍💻 111,889 already enrolled

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

First, we will specify our boolean expression `ar != 0` (which finds the non-zero values in the array) and then set values satisfying this condition to zero.

```# set non-zero values to zero
ar[ar != 0] = 0
# display the array
print(ar)```

Output:

`[0 0 0 0 0 0 0]`

The resulting array has all the non-zero values replaced with zero. Now all the elements in the array are zero.

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,  True,  True,  True])`

We get a boolean array. The boolean values in this array represent whether a value at a particular index satisfies the given condition (in our case whether the element is not equal to zero or not).

When we do `ar[ar != 0] = 0`, we are essentially setting the values in the array where the condition evaluates to `True` to `0`.

You can similarly filter a Numpy array for other conditions as well.

## Summary – Set non-zero values to `0` in Numpy

In this tutorial, we looked at how to replace all the non-zero values in a Numpy array with 0 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 non-zero values and then set them to zero –
`ar[ar != 0] = 0`

You might also be interested in –