Argsort in numpy python module numpy tutorial part 17

# numpy tutorial part 17: understanding `argsort`

in this tutorial, we will explore the `argsort()` function from the numpy library, which is used to return the indices that would sort an array. this is particularly useful when you want to sort an array but also need to keep track of the original indices.

## what is `argsort`?

the `argsort()` function returns the indices that would sort an array. this means that instead of returning the sorted values, it returns the indices of the elements in the original array that would result in a sorted array if you were to use those indices to index into the original array.

### syntax

#### parameters:
- **a**: array-like input data.
- **axis**: axis along which to sort. by default, it sorts along the last axis.
- **kind**: sorting algorithm to use. options include `'quicksort'`, `'mergesort'`, `'heapsort'`, and `'stable'`.
- **order**: if `a` is an array with fields, this argument specifies which field to compare first.

#### returns:
- an array of indices that would sort the input array.

## example usage of `argsort`

let's look at some examples to understand how `argsort` works in practice.

### example 1: simple 1d array

**output:**

### example 2: 2d array

in a multi-dimensional array, `argsort` can work along a specified axis.

**output:**

### example 3: sorting with different `kind`

you can specify the sorting algorithm using the `kind` parameter.

**output:**

### conclusion

the `argsort()` function in numpy is a powerful tool for sorting arrays while retaining the indices of the original data. it provides flexibility in choosing the sorting algorithm and allows for sorting across different dimensions of multi-dimensional arrays. use `argsort` when you need both sorted data and the ability to reference the original indices.

in this tutorial, we covered the basics of using `argsort`, including examples with both 1d and 2d arrays, and we discussed the `kind` parameter for different sorting algorithms ...

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