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!!
