NumPy is a powerful library for scientific computing in Python that provides a convenient way to perform mathematical operations on large, multi-dimensional arrays and matrices. Finding the first ‘n’ largest value(s) from a NumPy array is a common task in data analysis and machine learning. There are several ways to accomplish this task using NumPy, which are mentioned below:

#### 1. Using “np.sort()” function:

- Imports the NumPy library and creates a 1D NumPy array.
- Sets a variable
`n`

to 3. - Code sorts the input array in ascending order using
`np.sort()`

and reverses the sorted array using`[::-1]`

. - Code then selects the first
`n`

(3) elements of the reversed sorted array using`[:n]`

and stores the result in a new array. - Finally, the new array is printed to the console.

This code returns the `n`

largest values of the input array, sorted in **descending order**. This is **slower** compared to the second method as this function first sorts the elements and then fetches the required element(s).

#### 2. Using “np.partition()” function:

- Imports the NumPy library and creates a 1D NumPy array.
- Sets a variable
`n`

to 3. - Code partitions the input array using
`np.partition()`

, with the`-n index`

indicating that the right partition should contain the`n`

largest values. - Code then selects the last
`n`

elements of the right partition using`[-n:]`

and stores the result in a new array. - Finally, the new array is printed to the console.

This code returns the n largest values of the input array, sorted in **ascending order**.