Numpy’s key feature is its ability to generate arrays of various shapes and sizes, as well as perform operations on those arrays efficiently. In this case, we want to create a random vector of size 10 using NumPy and replace the maximum value in that vector with 0. There are many methods and techniques available to perform this. let’s discuss a few:

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

The `where()`

function is a powerful tool in NumPy that allows us to search for elements in an array that meet a certain condition, and return their indices as a tuple of arrays. `max()`

is a function in NumPy that returns the maximum value in an array or along a specified axis.

You can use `random.randint()`

to create a random vector and `where()`

to find the index of the maximum value, then replace the max value with 0 using index.

Have a look to the example given below:

#### 2. Using "argsort()" and Indexing :

The `argsort()`

function in numpy is a method that returns the indices that would sort an array in ascending order. It is a useful function for sorting an array, while still keeping track of the original index positions of the sorted elements.

You can also use `random.uniform()`

to create a random vector and `argsort()`

to find the index of the maximum value, then replace the max value with 0.

#### 3. Using "argmax()" function :

The `argmax()`

function in NumPy returns the indices of the maximum values along a given axis of an array. It returns the index or indices of the maximum value(s) in the array.

The `argmax()`

function takes one required argument, which is the array to be searched, and one optional argument, `axis`

.

You can use `random.rand()`

to create a random vector and `argmax()`

to find the index of the maximum value. Lets understand this through an example