In NumPy, the indices of the non-zero elements of a vector can be obtained using various methods. This can be particularly useful in many applications, such as image processing, where we may need to isolate and work with only the non-zero pixels in an image. By quickly identifying the indices of these elements, we can perform various operations on them, such as filtering, thresholding, or segmentation, leading to faster and more efficient processing of the data. In this thread, we will explore how to find find indices of non-zero elements using NumPy’s array.

#### 1. Using "where()" function:

The `where()`

function in NumPy is a powerful tool for performing conditional operations on arrays. It allows you to specify a condition, and returns the indices of the elements in the input array that satisfy that condition.

let’s see the example given below to gain better umderstanding:

The above code creates a NumPy array and uses the `where()`

function to find the indices of the non-zero elements. The resulting `indices`

variable contains a tuple of arrays, with the first (and only) array containing the indices of the non-zero elements. The output is displayed using the `print()`

function

#### 2. Using "argwhere()" function:

The `argwhere()`

function in NumPy is a variation of the `where()`

function that returns the indices of all elements in an array that satisfy a given condition, not just the non-zero elements

Here is an example for you given below:

The above code creates a NumPy array and uses the `argwhere()`

function to find the indices of all elements that are non-zero. The resulting `indices`

variable contains a two-dimensional array with each row representing the indices of an element that satisfies the condition. The output is displayed using the `print()`

function.