Many useful algorithms are recursive in structure: to solve a given problem, they call themselves recursively one or more times to deal with closely related subproblems.
These algorithms typically follow a divide-and-conquer approach: they break the problem into several subproblems that are similar to the original problem but smaller in size, solve the subproblems recursively, and then combine these solutions to create a solution to the original problem.

The divide-and-conquer paradigm involves three steps at each level of the recursion:

  • Divide the problem into a number of subproblems that are smaller instances of the same problem.
  • Conquer the subproblems by solving them recursively. If the subproblem sizes are small enough, however, just solve the subproblems in a straightforward manner.
  • Combine the solutions to the subproblems into the solution for the original problem.

The merge sort algorithm closely follows the divide-and-conquer paradigm. Intuitively, it operates as follows.

  • Divide: Divide the n-element sequence to be sorted into two subsequences of n=2 elements each.
  • Conquer: Sort the two subsequences recursively using merge sort.
  • Combine: Merge the two sorted subsequences to produce the sorted answer.

here is a basic code in python

import sys
SENTINEL = sys.maxint

def merge(input\_array,first,middle,last):
   n1 = middle - first + 1
   n2 = last - middle

   L = [None for t in range(n1+1)]
   R = [None for t in range(n2+1)]

   for i in range(n1):
       L[i] = input\_array[first + i - 1]
   for j in range(n2):
       R[j] =input\_array[middle + j]

   L[n1] = SENTINEL
   R[n2] = SENTINEL

   i = 0
   j = 0 
   for k in range(first-1,last): 
       if L[i] <= R[j]:
           input\_array[k] = L[i]
           i = i + 1
           input\_array[k] = R[j]
           j = j + 1

def mergeSort(input\_array,first,last):
   if first < last:
       middle =  int((first + last)/2) 
       mergeSort(input\_array,middle + 1,last)

arr = [5,2,4,7,1,3,2,6]

print arr

Hope this helps ;)