If you are familiar with the standard sorting algorithms like bubble sort and merge sort, you are probably also familiar with the problem of sorting large arrays in memory. With the help of **Merge Sort Algorithm in **Python, your data will be sorted in place, which can be a speedy process. It combines multiple smaller sorted collections into one larger sorted collection.

In this article, we will look at the **M****erge Sort algorithm in Python**. This well-known algorithm is used to sort data and is very easy to implement. We will cover the basic concepts behind merge sort, including how it works, its advantages and disadvantages, and how to implement it in Python.

Table of Contents

## What is Merge Sort Algorithm in Python?

Merge sort is a data sorting algorithm that is used to organize a list of items in ascending or descending order. The algorithm works by first arranging the items in a sequence, and then merging the sequences together until the desired order is achieved.

## Advantages of Merge Sort Algorithm

Merge sort lists can be helpful in a variety of scenarios, such as when you need to find a specific item quickly or when you have large amounts of data that must be organized in an efficient way. Additionally, merge sort is often faster than other sorting algorithms and it tends to produce smaller memory footprints.

Merge Sort also has the ability to return duplicate items so that they are placed at the end of the list instead of near the beginning. This can help avoid problems caused by overlapping ranges in your data.

### Algorithm

- 1 + (last – first)/2 We use this formula to determine the array’s center index.
- Then divide the array into two halves.
- MergeSort(array, 2nd, middle) We use this formula to merge sort the first halve of the array if left > right return mid= (left+right)/2
- MergeSort(array, middle + 1, last) We use this formula to merge sort the second halve of the array

## Implementation:

Merge sort is a practical sorting algorithm that can be used to solve problems in data collections where the goal is to distribute items into ascending or descending order. Merge sort is one of the most typically used algorithms for merging two sorted lists. This algorithm sorts arrays by comparing their elements and placing them into ascending or descending order according to their values. The complexity of this procedure depends on the size and importance of the two original lists. If you are dealing with small lists, then the merge sort may be able to run in linear time.

```
from turtle import right
def mergeSort(array):
if len(array) > 1:
middle = len(array)//2
Left = array[:middle]
right = array[middle:]
mergeSort(Left)
mergeSort(right)
i = j = k = 0
while i < len(Left) and j < len(right):
if Left[i] < right[j]:
array[k] = Left[i]
i += 1
else:
array[k] = right[j]
j += 1
k += 1
while i < len(Left):
array[k] = Left[i]
i += 1
k += 1
while j < len(right):
array[k] = right[j]
j += 1
k += 1
def printList(array):
for i in range(len(array)):
print(array[i], end=" ")
print()
if __name__ == '__main__':
array = [12, 11, 13, 90, 7, 78]
print("Given array is", end="\n")
printList(array)
mergeSort(array)
print("Sorted array is: ", end="\n")
printList(array)
```

#### Output

Also learn, how to sort a dictionary in python

## Disadvantages of Merge Sort Algorithm

Merge sort is a well-known sorting algorithm that has several advantages, but it also has some disadvantages. One disadvantage is the time it takes to execute the algorithm. It can take quite a bit to sort an array using this technique, so be sure you have enough memory available on your computer if you plan on using this approach.

Another disadvantage is that merge sort may not perform as well as other algorithms when multiple data types are involved. For example, if you have numbers and strings in your sorted list, merge sorts will likely perform worse than quicksort or Selection Sort because they do not allow duplicate elements to remain in the exact location at once.

## Faqs

### What is the time complexity of merge sort in Python?

MergeSort’s time complexity is O(n*Log n) in all three situations (worst, average, and best), since the mergesort always divides the array into two halves and takes linear time to merge them.

### How can merge sort be improved?

For small subarrays, use insertion sort. By dealing with small instances differently, we may enhance the majority of recursive algorithms. A conventional mergesort implementation’s run time will be improved by 10 to 15% by switching to insertion sort for tiny subarrays.

### Is merge sort top down or bottom up?

Your mileage may vary when it comes to the length of the program, but top-down merge sort might be made shorter than bottom-up if stability isn’t needed.

### Is merge sort iterative?

Because it does not change the relative order of elements of the same value in the input, iterative merge sort is an example of a stable sorting algorithm.

### What is the recurrence relation for merge sort?

For Merge Sort, there is a recurring relation. When we are processing the whole array of N elements, T(N) refers to the total number of comparisons between array pieces in Merge Sort. What 2*T(N/2) refers to is the divide stage’s Merge Sort of two sections of the array.

## Conclusion

In this article, we explained the key characteristics of merge sort and how it is used to sort data in lists or arrays. The **m****erge sort algorithm in Python** is quite simple and easy to implement. The complexity only comes when you must maintain an array with multiple values of the same type. In this case, you can speed up the sorting process by employing a binary search technique. We hope that this article has cleared your doubts on how to sort lists or arrays using merge sort in Python.