The count_nonzero() function allows you to determine the number of non-zero values in a given array.

Let us discuss.

## Function Syntax

The count_nonzero() function can be expressed as shown below:

## Parameters

The function parameters are as follows:

- a – refers to the input array to count the non-zero values.
- axis – specifies along which axis to count the non-zero values.

## Return Value

The function then returns the number of non-zero values in the array along the specified axis.

Note: if the axis is set to None, the function will flatten the array and return the total number of non-zero values in the entire array.

## Example #1

Consider the example code provided below:

import numpy as np

arr = np.array([0,1,2,3,0,4,5,0,6,7,8,9])

print(f"total elements: {arr.size}")

print(f"total non-zero: {np.count_nonzero(arr)}")

In the example code above, we have a one-dimensional array containing three zero values.

We then use the arr.size property to get the total number of elements in the array and the count_nonzero() function to get the number of non-zero elements.

The code above should return:

total non-zero: 9

## Example #2

The example below shows the count_nonzero() function with a 2D array along the zero axis.

print(f"total elements: {arr_2d.size}")

print(f"total non-zero: {np.count_nonzero(arr_2d, axis=0)}")

In this case, we have a 2D array with three zero elements. The function should determine the number of non-zero values along the zero axis and return the output as shown below:

total non-zero: [0 3 3 3]

## Example #3

The same operation can be said along the one axis. An example illustration is as shown in the code below:

print(f"total elements: {arr_2d.size}")

print(f"total non-zero: {np.count_nonzero(arr_2d, axis=1)}")

The above code should return:

total non-zero: [3 3 3]

## Termination

With the help of this guide, you are now familiar with the NumPy count_nonzero() function and how to use it in your NumPy arrays.

Thanks for reading!!