I'm working my way through a book on algorithms. One of the first challenges is the merge sort. I've seen this implemented before, but as the book suggested I coded one from just the method without looking at the code. When I looked it was very different from mine. Many online examples match the way the book does it.
I am asking about this because I want to learn the reasons this code is better, or if there are trade-offs, or things that are merely style choices, rather than just adopting the pattern of the example code.
Here's mine in Python:
A = [1,5,7,3,4,3,1,8,9,12,64,22,83,223,11] def merge(A1,A2): B1 =  while(len(A1)>0 and len(A2) > 0): if(A1 < A2): B1.append(A1.pop(0)) else: B1.append(A2.pop(0)) B1 = B1 + A1 + A2 return B1 def mergesort(B): if len(B)>1: q = len(B)//2 B1 = mergesort(B[:q]) B2 = mergesort(B[q:]) return merge(B1,B2) else: return (B) print("mergesort(A) =",mergesort(A))
Here's an example a lot like in my book and in many websites, from GeeksForGeeks
Python program for implementation of MergeSort
# Merges two subarrays of arr. # First subarray is arr[l..m] # Second subarray is arr[m+1..r] def merge(arr, l, m, r): n1 = m - l + 1 n2 = r- m # create temp arrays L =  * (n1) R =  * (n2) # Copy data to temp arrays L and R for i in range(0 , n1): L[i] = arr[l + i] for j in range(0 , n2): R[j] = arr[m + 1 + j] # Merge the temp arrays back into arr[l..r] i = 0 # Initial index of first subarray j = 0 # Initial index of second subarray k = l # Initial index of merged subarray while i < n1 and j < n2 : if L[i] <= R[j]: arr[k] = L[i] i += 1 else: arr[k] = R[j] j += 1 k += 1 # Copy the remaining elements of L, if there # are any while i < n1: arr[k] = L[i] i += 1 k += 1 # Copy the remaining elements of R, if there # are any while j < n2: arr[k] = R[j] j += 1 k += 1 # l is for left index and r is right index of the # sub-array of arr to be sorted def mergeSort(arr,l,r): if l < r: # Same as (l+r)/2, but avoids overflow for # large l and h m = (l+(r-1))/2 # Sort first and second halves mergeSort(arr, l, m) mergeSort(arr, m+1, r) merge(arr, l, m, r) # Driver code to test above arr = [12, 11, 13, 5, 6, 7] n = len(arr) print ("Given array is") for i in range(n): print ("%d" %arr[i]), mergeSort(arr,0,n-1) print ("\n\nSorted array is") for i in range(n): print ("%d" %arr[i]), # This code is contributed by Mohit Kumra
First, I notice this is manipulating the initial list in place by tracking indices, and I am creating several copies of the data.
I would suppose that I am using more memory, however the example posted lower does create temporary lists to perform the merge. But there they also use index tracking instead of destroying the two arrays with pop as they are consumed.
So again, I'm trying to learn why the differences matter. Which is more educational than just taking on this way of doing it.