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)

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

import numpy as np

a = np.array(42)
b = np.array([1, 2, 3, 4, 5])
c = np.array([[1, 2, 3], [4, 5, 6]])
d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

#how many dimension appear in each line
print(a.ndim) #0
print(b.ndim) #1
print(c.ndim) #2
print(d.ndim) #3