Here in my own word I understand in this way; You are given an integer list of array then, you scan all numbers in your minds. At the first this question have to come to your mind. You look at the elements, then you pick out the smallest numbers then compare with the first position(index). Then you swap them together. this process is repeatedly goes through the logic until order(sorting) satisfied.

given a list for array numbers: [3, 2, 4, 1] what we've to do is by using selection sort and sort the numbers.

For instance: look at the following.
[5, 2, 4, 1]
Divide
[5, 2] [4, 1]
Divide again
[5] [2] [4] [1]
Merging sort happens here👇
[2, 5] [1, 4]
Final merge
[1, 2, 4, 5]

def merge(left, right):
result = []
i = 0
j = 0

# Compare elements from both arrays
while i < len(left) and j < len(right):
if left[i] <= right[j]:
result.append(left[i])
i += 1
else:
result.append(right[j])
j += 1

# Add remaining elements
result.extend(left[i:])
result.extend(right[j:])

return result

-NumPy is a Python library.
-NumPy is used for working with arrays.
-NumPy is short for "Numerical Python".

In Python we have lists that serve the purpose of arrays, but they are slow to process.
The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy.
Arrays are very frequently used in data science & also ML, where speed and resources are very important.

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.
This behavior is called locality of reference in computer science.
This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures

C:\Users\Your Name>pip install numpy

import numpy
arr = numpy.array([1,2,3,4,5])
print(arr)