Implement Merge-Sort in python
Published: 2024-04-19 22:38:24

---
title: Implement Merge-Sort in python
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date: 
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canonical_url: https://www.pynerds.com/data-structures/implement-merge-sort-in-python/
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merge sort implementation

```

#merge sort

#the merge algorithm
def merge(L1, L2, L):
  i = 0
  j = 0
  while i+j < len(L):
    if j == len(L2) or (i < len(L1) and L1[i] < L2[j]):
       L[i+j] = L1[i]
       i += 1
    else:
       L[i+j] = L2[j]
       j += 1

#main function
def merge_sort(L):

  n = len(L)
  if n < 2:
     return # list is already sorted

  # divide
  mid = n // 2 #midpoint
  L1 = L[0:mid] # the first half
  L2 = L[mid:n] # the second half
 
  # conquer with recursion
  merge_sort(L1) # sort first sub-list
  merge_sort(L2) # sort second sub-list

  # merge result
  merge(L1, L2, L) 

#example
L = [7, 5, 1, 4, 2, 8, 0, 9, 3, 6]
print('Before: ', L)
merge_sort(L)
print('After: ', L)
```

 

The merge sort algorithm uses the **divide and conquer**  technique to efficiently sort a list of elements.

Divide and conquer involves breaking down a larger problem into smaller more manageable sub-problems, solving the sub-problems and then merging their solutions to solve the original problem.  In the case with merge-sort, the algorithm breaks down the list into smaller sub-lists, sorts those sub-lists recursively, and then merges the sorted sub-lists back together until the entire list is sorted

Merge sort has  time complexity of `O(nlogn)` in both average and worst case scenarios, this makes it one of the fastest sorting algorithms and consequently, one of the most sought after.

Merge sort is stable, meaning that it preserves the relative order of equal elements in the original list.

## Algorithm Description

Assuming we have a list **L** , the basic steps followed by merge sort are as highlighted below:

1. **divide ** - If **L** has zero or one element, it is already sorted, immediately return **L**. Otherwise, divide L's elements between two sub-lists, **L<sub>1</sub>** and **L<sub>2</sub>**.  Where, **L1** contains first half of  the elements and **L2** the rest..
2. **conquer** - Recursively sort sub-lists **L1** and **L2**.
3. **merge** - Combine the elements in a sorted state and put them back to` L`.

## Example

Consider if we want to sort the following list using merge sort.

![Unsorted list of values](https://res.cloudinary.com/dgc50iktu/image/upload/v1713216606/9b3e857f-527b-4712-a0b9-c7e1bb6b164c_dnv04j.png)

In the first step of merge sort, the list will be recursively divided into individual sub-lists. This means that the list will be split into two sub-lists and then each of the sub-lists will be split into two more sub-lists, and so on until each sub-list contains only a single element. The following figure demonstrates the splitting phase:

![Splitting phase of merge sort](https://res.cloudinary.com/dgc50iktu/image/upload/v1713216606/41d39c84-54c7-4c3b-adbe-1dd42e2ca83a_uyn9rb.png)

After the divide step is over, we need to sort each sub-list and then recursively merge it with the other sorted sub-lists. This is known as the **"conquer" step**.

At the beginning of the conquer step, each sub-lists is made up of a single element. Logically, a list with only one element is already sorted. Sublists with more than a single elements will have to be sorted before they are combined.

![conquer step in merge sort](https://res.cloudinary.com/dgc50iktu/image/upload/v1713216605/8c4ea10f-2b79-404b-9654-0dedb84a7859_ptxj6b.png)

## Merge sort implementation

In this section, we will implement the merge sort algorithm using functions.

For the sake of clarity, we will split the algorithm into two functions, ` merge()` and `merge_sort()`. The `merge() `function will be responsible for combining two already sorted sub-lists into a single sorted list. The `merge_sort()` function is where the main logic of the algorithm will go.

The merge() function

```

#L is the list while L1 and L2 are the sublists.

def merge(L1, L2, L):
  i = j = 0
  while i+j < len(L):
    if j == len(L2) or (i < len(L1) and L1[i] < L2[j]):
       L[i+j] = L1[i]
       i += 1

    else:
       L[i+j] = L2[j]
       j += 1

#example
L = [2, 7, 4, 0, 6, 5]
L1 = [2, 4, 7]
L2 = [0, 6, 5]

merge(L1, L2, L)
print(L)
```

The `merge()` function above takes in two sorted sub-lists(`L1` and` L2`) and puts their elements back to the original list(`L`) in a sorted state. It does so by determining from which sub-list the next item should be taken before inserting the picked item back to list `L`. For clarity, let us look at the function statement by statement:

1. `i = j = 0` - This statement initializes two distinct variables, `i` and `j` each with a value of `0`. The two variables` i`, and `j `represents the position of the current element in `L1 `and `L2`, respectively.
2. `while i+j < len(L):` -  Loop until the value of` i + j` is greater than the length of the list. Note that at this point, the merge operation will be complete.
3. `if j == len(L2) or (i < len(L1) and L1[i] < L2[j]):
	  L[i+j] = L1[i]
	  i += 1 `  
`else:
	  L[i+j] = L2[j]
	  j += 1`  
  
 -The `j == len(L2)` condition checks if there is elements to be picked in `L2`, remember that` j `represents the position of the current element in `L2`, thus if it is equal to `len(L2)`, it means that we have reached the end of `L2`, so we pick the next element from `L1` and insert it to` L`.  
 -Due to the presence of `or`, the second condition i.e `(i < len(L1) and L1[i] < L2[j]) `will only be tested if the first condition, i.e `(j == len(L2))` fails,  it means that `L2` still has element .  So we pick and insert into` L` the smaller of the sub-lists' current element and then increment either `i `or` j `depending on whose sub-list's element was picked.  
 -The `else `statement also caters for when `L1 `has no more elements.

 After implementing the `merge(`) function, the implementation of the `merge_sort() `function become easier.

 implement` merge_sort()`

```

#the implementation of merge() is omitted, refer to the previous snippet.

def merge_sort(L):

  n = len(L)
  if n < 2:
     return # list is already sorted

  # divide
  mid = n // 2 #midpoint
  L1 = L[0:mid] # the first half
  L2 = L[mid:n] # the second half
 
  # conquer
  merge_sort(L1) # recursively sort first sub-list
  merge_sort(L2) # recursively sort second sub-list

  # merge
  merge(L1, L2, L) 

#usage example
L = [99, 9, 0, 2, 1, 0, 1, 100, -2, 8, 7, 4, 3, 2]
merge_sort(L)
print(L)
```

[-2, 0, 0, 1, 1, 2, 2, 3, 4, 7, 8, 9, 99, 100]

The` merge_sort() `function does not return any value because the sorting happens to the original list.

The entire merge sort implementation is as shown below:

```

#merge sort implementation

def merge(L1, L2, L):
  i = 0
  j = 0
  while i+j < len(L):
    if j == len(L2) or (i < len(L1) and L1[i] < L2[j]):
       L[i+j] = L1[i]
       i += 1
    else:
       L[i+j] = L2[j]
       j += 1

def merge_sort(L):

  n = len(L)
  if n < 2:
     return # list is already sorted

  # divide
  mid = n // 2 #midpoint
  L1 = L[0:mid] # the first half
  L2 = L[mid:n] # the second half
 
  # conquer with recursion
  merge_sort(L1) # sort first sub-list
  merge_sort(L2) # sort second sub-list

  # merge result
  merge(L1, L2, L) 

#usage example
L = [99, 9, 0, 2, 1, 0, 1, 100, -2, 8, 7, 4, 3, 2]
merge_sort(L)
print(L)
```

## Complexity analysis

The time complexities of merge sort with a list of size `n`, are as outlined below:

- **Best case** - `O(nlogn`) , this happens when the input list is already sorted.
- **Average case** -` O(nlogn)`, when the input list is randomly sorted.
- **Worst case** - `O(nlogn)`, when the input list is reverse sorted.

#### Space complexity

Merge sort has a space complexity of` O(n)` due to the space occupied by the sub-lists.

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