45
votes

I couldn't find any working Python 3.3 mergesort algorithm codes, so I made one myself. Is there any way to speed it up? It sorts 20,000 numbers in about 0.3-0.5 seconds

def msort(x):
    result = []
    if len(x) < 2:
        return x
    mid = int(len(x)/2)
    y = msort(x[:mid])
    z = msort(x[mid:])
    while (len(y) > 0) or (len(z) > 0):
        if len(y) > 0 and len(z) > 0:
            if y[0] > z[0]:
                result.append(z[0])
                z.pop(0)
            else:
                result.append(y[0])
                y.pop(0)
        elif len(z) > 0:
            for i in z:
                result.append(i)
                z.pop(0)
        else:
            for i in y:
                result.append(i)
                y.pop(0)
    return result
28
You should not pop from lists, as that will unecessarily shift the array elements over and over. You should avoid changing the list anyway when iterating over it. - poke
Also, there is probably nothing specific to Python 3.3 in an ordinary implementation of mergesort so you can just Google for "python mergesort" and use any implementation you find, even if it is for older versions. For instance, this one: geekviewpoint.com/python/sorting/mergesort - Tamás
The question is too old but isn't it using more memory for result array merge sort already uses double memory of array to sort it we are again producing the array in result. - siddhesh

28 Answers

16
votes

You can initialise the whole result list in the top level call to mergesort:

result = [0]*len(x)   # replace 0 with a suitable default element if necessary. 
                      # or just copy x (result = x[:])

Then for the recursive calls you can use a helper function to which you pass not sublists, but indices into x. And the bottom level calls read their values from x and write into result directly.

That way you can avoid all that poping and appending which should improve performance.

73
votes

The first improvement would be to simplify the three cases in the main loop: Rather than iterating while some of the sequence has elements, iterate while both sequences have elements. When leaving the loop, one of them will be empty, we don't know which, but we don't care: We append them at the end of the result.

def msort2(x):
    if len(x) < 2:
        return x
    result = []          # moved!
    mid = int(len(x) / 2)
    y = msort2(x[:mid])
    z = msort2(x[mid:])
    while (len(y) > 0) and (len(z) > 0):
        if y[0] > z[0]:
            result.append(z[0])
            z.pop(0)
        else:
            result.append(y[0])
            y.pop(0)
    result += y
    result += z
    return result

The second optimization is to avoid popping the elements. Rather, have two indices:

def msort3(x):
    if len(x) < 2:
        return x
    result = []
    mid = int(len(x) / 2)
    y = msort3(x[:mid])
    z = msort3(x[mid:])
    i = 0
    j = 0
    while i < len(y) and j < len(z):
        if y[i] > z[j]:
            result.append(z[j])
            j += 1
        else:
            result.append(y[i])
            i += 1
    result += y[i:]
    result += z[j:]
    return result

A final improvement consists in using a non recursive algorithm to sort short sequences. In this case I use the built-in sorted function and use it when the size of the input is less than 20:

def msort4(x):
    if len(x) < 20:
        return sorted(x)
    result = []
    mid = int(len(x) / 2)
    y = msort4(x[:mid])
    z = msort4(x[mid:])
    i = 0
    j = 0
    while i < len(y) and j < len(z):
        if y[i] > z[j]:
            result.append(z[j])
            j += 1
        else:
            result.append(y[i])
            i += 1
    result += y[i:]
    result += z[j:]
    return result

My measurements to sort a random list of 100000 integers are 2.46 seconds for the original version, 2.33 for msort2, 0.60 for msort3 and 0.40 for msort4. For reference, sorting all the list with sorted takes 0.03 seconds.

28
votes

Code from MIT course. (with generic cooperator )

import operator


def merge(left, right, compare):
    result = []
    i, j = 0, 0
    while i < len(left) and j < len(right):
        if compare(left[i], right[j]):
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1
    while i < len(left):
        result.append(left[i])
        i += 1
    while j < len(right):
        result.append(right[j])
        j += 1
    return result


def mergeSort(L, compare=operator.lt):
    if len(L) < 2:
        return L[:]
    else:
        middle = int(len(L) / 2)
        left = mergeSort(L[:middle], compare)
        right = mergeSort(L[middle:], compare)
        return merge(left, right, compare)
21
votes
def merge_sort(x):

    if len(x) < 2:return x

    result,mid = [],int(len(x)/2)

    y = merge_sort(x[:mid])
    z = merge_sort(x[mid:])

    while (len(y) > 0) and (len(z) > 0):
            if y[0] > z[0]:result.append(z.pop(0))   
            else:result.append(y.pop(0))

    result.extend(y+z)
    return result
11
votes

Take my implementation

def merge_sort(sequence):
    """
    Sequence of numbers is taken as input, and is split into two halves, following which they are recursively sorted.
    """
    if len(sequence) < 2:
        return sequence

    mid = len(sequence) // 2     # note: 7//2 = 3, whereas 7/2 = 3.5

    left_sequence = merge_sort(sequence[:mid])
    right_sequence = merge_sort(sequence[mid:])

    return merge(left_sequence, right_sequence)

def merge(left, right):
    """
    Traverse both sorted sub-arrays (left and right), and populate the result array
    """
    result = []
    i = j = 0
    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
    result += left[i:]
    result += right[j:]

    return result

# Print the sorted list.
print(merge_sort([5, 2, 6, 8, 5, 8, 1]))
7
votes

As already said, l.pop(0) is a O(len(l)) operation and must be avoided, the above msort function is O(n**2). If efficiency matter, indexing is better but have cost too. The for x in l is faster but not easy to implement for mergesort : iter can be used instead here. Finally, checking i < len(l) is made twice because tested again when accessing the element : the exception mechanism (try except) is better and give a last improvement of 30% .

def msort(l):
    if len(l)>1:
        t=len(l)//2
        it1=iter(msort(l[:t]));x1=next(it1)
        it2=iter(msort(l[t:]));x2=next(it2)
        l=[]
        try:
            while True:
                if x1<=x2: l.append(x1);x1=next(it1)
                else     : l.append(x2);x2=next(it2)
        except:
            if x1<=x2: l.append(x2);l.extend(it2)
            else:      l.append(x1);l.extend(it1)
    return l
6
votes

Loops like this can probably be speeded up:

for i in z:
    result.append(i)
    z.pop(0)

Instead, simply do this:

result.extend(z)

Note that there is no need to clean the contents of z because you won't use it anyway.

5
votes

A longer one that counts inversions and adheres to the sorted interface. It's trivial to modify this to make it a method of an object that sorts in place.

import operator

class MergeSorted:

    def __init__(self):
        self.inversions = 0

    def __call__(self, l, key=None, reverse=False):

        self.inversions = 0

        if key is None:
            self.key = lambda x: x
        else:
            self.key = key

        if reverse:
            self.compare = operator.gt
        else:
            self.compare = operator.lt

        dest = list(l)
        working = [0] * len(l)
        self.inversions = self._merge_sort(dest, working, 0, len(dest))
        return dest

    def _merge_sort(self, dest, working, low, high):
        if low < high - 1:
            mid = (low + high) // 2
            x = self._merge_sort(dest, working, low, mid)
            y = self._merge_sort(dest, working, mid, high)
            z = self._merge(dest, working, low, mid, high)
            return (x + y + z)
        else:
            return 0

    def _merge(self, dest, working, low, mid, high):
        i = 0
        j = 0
        inversions = 0

        while (low + i < mid) and (mid + j < high):
            if self.compare(self.key(dest[low + i]), self.key(dest[mid + j])):
                working[low + i + j] = dest[low + i]
                i += 1
            else:
                working[low + i + j] = dest[mid + j]
                inversions += (mid - (low + i))
                j += 1

        while low + i < mid:
            working[low + i + j] = dest[low + i]
            i += 1

        while mid + j < high:
            working[low + i + j] = dest[mid + j]
            j += 1

        for k in range(low, high):
            dest[k] = working[k]

        return inversions


msorted = MergeSorted()

Uses

>>> l = [5, 2, 3, 1, 4]
>>> s = msorted(l)
>>> s
[1, 2, 3, 4, 5]
>>> msorted.inversions
6

>>> l = ['e', 'b', 'c', 'a', 'd']
>>> d = {'a': 10,
...      'b': 4,
...      'c': 2,
...      'd': 5,
...      'e': 9}
>>> key = lambda x: d[x]
>>> s = msorted(l, key=key)
>>> s
['c', 'b', 'd', 'e', 'a']
>>> msorted.inversions
5

>>> l = [5, 2, 3, 1, 4]
>>> s = msorted(l, reverse=True)
>>> s
[5, 4, 3, 2, 1]
>>> msorted.inversions
4

>>> l = ['e', 'b', 'c', 'a', 'd']
>>> d = {'a': 10,
...      'b': 4,
...      'c': 2,
...      'd': 5,
...      'e': 9}
>>> key = lambda x: d[x]
>>> s = msorted(l, key=key, reverse=True)
>>> s
['a', 'e', 'd', 'b', 'c']
>>> msorted.inversions
5
3
votes

Here is the CLRS Implementation:

def merge(arr, p, q, r):
    n1 = q - p + 1
    n2 = r - q
    right, left = [], []
    for i in range(n1):
        left.append(arr[p + i])
    for j in range(n2):
        right.append(arr[q + j + 1])
    left.append(float('inf'))
    right.append(float('inf'))
    i = j = 0
    for k in range(p, r + 1):
        if left[i] <= right[j]:
            arr[k] = left[i]
            i += 1
        else:
            arr[k] = right[j]
            j += 1


def merge_sort(arr, p, r):
    if p < r:
        q = (p + r) // 2
        merge_sort(arr, p, q)
        merge_sort(arr, q + 1, r)
        merge(arr, p, q, r)


if __name__ == '__main__':
    test = [5, 2, 4, 7, 1, 3, 2, 6]
    merge_sort(test, 0, len(test) - 1)
    print test

Result:

[1, 2, 2, 3, 4, 5, 6, 7]
3
votes

Many have answered this question correctly, this is just another solution (although my solution is very similar to Max Montana) but I have few differences for implementation:

let's review the general idea here before we get to the code:

  • Divide the list into two roughly equal halves.
  • Sort the left half.
  • Sort the right half.
  • Merge the two sorted halves into one sorted list.

here is the code (tested with python 3.7):

def merge(left,right):
    result=[] 
    i,j=0,0
    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
    result.extend(left[i:]) # since we want to add each element and not the object list
    result.extend(right[j:])
    return result

def merge_sort(data):
    if len(data)==1:
        return data
    middle=len(data)//2
    left_data=merge_sort(data[:middle])
    right_data=merge_sort(data[middle:])
    return merge(left_data,right_data)


data=[100,5,200,3,100,4,8,9] 
print(merge_sort(data))
2
votes

here is another solution

class MergeSort(object):
    def _merge(self,left, right):
        nl = len(left)
        nr = len(right)
        result = [0]*(nl+nr)
        i=0
        j=0
        for k in range(len(result)):
            if nl>i and nr>j:
                if left[i] <= right[j]:
                    result[k]=left[i]
                    i+=1
                else:
                    result[k]=right[j]
                    j+=1
            elif nl==i:
                result[k] = right[j]
                j+=1
            else: #nr>j:
                result[k] = left[i]
                i+=1
        return result

    def sort(self,arr):
        n = len(arr)
        if n<=1:
            return arr 
        left = self.sort(arr[:n/2])
        right = self.sort(arr[n/2:] )
        return self._merge(left, right)
def main():
    import random
    a= range(100000)
    random.shuffle(a)
    mr_clss = MergeSort()
    result = mr_clss.sort(a)
    #print result

if __name__ == '__main__':
    main()

and here is run time for list with 100000 elements:

real    0m1.073s
user    0m1.053s
sys         0m0.017s
2
votes
def merge(l1, l2, out=[]):
    if l1==[]: return out+l2
    if l2==[]: return out+l1
    if l1[0]<l2[0]: return merge(l1[1:], l2, out+l1[0:1])
    return merge(l1, l2[1:], out+l2[0:1])
def merge_sort(l): return (lambda h: l if h<1 else merge(merge_sort(l[:h]), merge_sort(l[h:])))(len(l)/2)
print(merge_sort([1,4,6,3,2,5,78,4,2,1,4,6,8]))
2
votes
def merge(x):
    if len(x) == 1:
        return x
    else:
        mid = int(len(x) / 2)
        l = merge(x[:mid])
        r = merge(x[mid:])
    i = j = 0
    result = []
    while i < len(l) and j < len(r):
        if l[i] < r[j]:
            result.append(l[i])
            i += 1
        else:
            result.append(r[j])
            j += 1
    result += l[i:]
    result += r[j:]
    return result
2
votes

A little late the the party, but I figured I'd throw my hat in the ring as my solution seems to run faster than OP's (on my machine, anyway):

# [Python 3]
def merge_sort(arr):
    if len(arr) < 2:
        return arr
    half = len(arr) // 2
    left = merge_sort(arr[:half])
    right = merge_sort(arr[half:])
    out = []
    li = ri = 0  # index of next element from left, right halves
    while True:
        if li >= len(left):  # left half is exhausted
            out.extend(right[ri:])
            break
        if ri >= len(right): # right half is exhausted
            out.extend(left[li:])
            break
        if left[li] < right[ri]:
            out.append(left[li])
            li += 1
        else:
            out.append(right[ri])
            ri += 1
    return out

This doesn't have any slow pop()s, and once one of the half-arrays is exhausted, it immediately extends the other one onto the output array rather than starting a new loop.

I know it's machine dependent, but for 100,000 random elements (above merge_sort() vs. Python built-in sorted()):

merge sort: 1.03605 seconds
Python sort: 0.045 seconds
Ratio merge / Python sort: 23.0229
2
votes
def mergeSort(alist):
    print("Splitting ",alist)
    if len(alist)>1:
        mid = len(alist)//2
        lefthalf = alist[:mid]
        righthalf = alist[mid:]

        mergeSort(lefthalf)
        mergeSort(righthalf)

        i=0
        j=0
        k=0
        while i < len(lefthalf) and j < len(righthalf):
            if lefthalf[i] < righthalf[j]:
                alist[k]=lefthalf[i]
                i=i+1
            else:
                alist[k]=righthalf[j]
                j=j+1
            k=k+1

        while i < len(lefthalf):
            alist[k]=lefthalf[i]
            i=i+1
            k=k+1

        while j < len(righthalf):
            alist[k]=righthalf[j]
            j=j+1
            k=k+1
    print("Merging ",alist)

alist = [54,26,93,17,77,31,44,55,20]
mergeSort(alist)
print(alist)
2
votes

After implementing different versions of solution, I finally made a trade-off to achieve these goals based on CLRS version.

Goal

  • not using list.pop() to iterate values
  • not creating a new list for saving result, modifying the original one instead
  • not using float('inf') as sentinel values
def mergesort(A, p, r):
    if(p < r):
        q = (p+r)//2
        mergesort(A, p, q)
        mergesort(A, q+1, r)
        merge(A, p, q, r)
def merge(A, p, q, r):
    L = A[p:q+1]
    R = A[q+1:r+1]
    i = 0
    j = 0
    k = p
    while i < len(L) and j < len(R):
        if(L[i] < R[j]):
            A[k] = L[i]
            i += 1
        else:
            A[k] = R[j]
            j += 1
        k += 1
    if i < len(L):
        A[k:r+1] = L[i:]
if __name__ == "__main__":
    items = [6, 2, 9, 1, 7, 3, 4, 5, 8]
    mergesort(items, 0, len(items)-1)
    print items
    assert items == [1, 2, 3, 4, 5, 6, 7, 8, 9]

Reference

[1] Book: CLRS

[2] https://github.com/gzc/CLRS/blob/master/C02-Getting-Started/exercise_code/merge-sort.py

1
votes

Try this recursive version

def mergeList(l1,l2):
    l3=[]
    Tlen=len(l1)+len(l2)
    inf= float("inf")
    for i in range(Tlen):
        print   "l1= ",l1[0]," l2= ",l2[0]
        if l1[0]<=l2[0]:
            l3.append(l1[0])
            del l1[0]
            l1.append(inf)
        else:
            l3.append(l2[0])
            del l2[0]
            l2.append(inf)
    return l3

def main():
    l1=[2,10,7,6,8]
    print mergeSort(breaklist(l1))

def breaklist(rawlist):
    newlist=[]
    for atom in rawlist:
        print atom
        list_atom=[atom]
        newlist.append(list_atom)
    return newlist

def mergeSort(inputList):
    listlen=len(inputList)
    if listlen ==1:
        return inputList
    else:
        newlist=[]
        if listlen % 2==0:
            for i in range(listlen/2):
                newlist.append(mergeList(inputList[2*i],inputList[2*i+1]))
        else:
            for i in range((listlen+1)/2):
                if 2*i+1<listlen:
                    newlist.append(mergeList(inputList[2*i],inputList[2*i+1]))
                else:
                    newlist.append(inputList[2*i])
        return  mergeSort(newlist)

if __name__ == '__main__':
    main()
1
votes
    def merge(a,low,mid,high):
        l=a[low:mid+1]
        r=a[mid+1:high+1]
        #print(l,r)
        k=0;i=0;j=0;
        c=[0 for i in range(low,high+1)]
        while(i<len(l) and j<len(r)):
            if(l[i]<=r[j]):

                c[k]=(l[i])
                k+=1

                i+=1
            else:
                c[k]=(r[j])
                j+=1
                k+=1
        while(i<len(l)):
            c[k]=(l[i])
            k+=1
            i+=1

        while(j<len(r)):
            c[k]=(r[j])
            k+=1
            j+=1
        #print(c)  
        a[low:high+1]=c  

    def mergesort(a,low,high):
        if(high>low):
            mid=(low+high)//2


            mergesort(a,low,mid)
            mergesort(a,mid+1,high)
            merge(a,low,mid,high)

    a=[12,8,3,2,9,0]
    mergesort(a,0,len(a)-1)
    print(a)
1
votes

If you change your code like that it'll be working.

def merge_sort(arr):
    if len(arr) < 2:
        return arr[:]
    middle_of_arr = len(arr) / 2
    left = arr[0:middle_of_arr]
    right = arr[middle_of_arr:]
    left_side = merge_sort(left)
    right_side = merge_sort(right)
    return merge(left_side, right_side)

def merge(left_side, right_side):
    result = []
    while len(left_side) > 0 or len(right_side) > 0:
        if len(left_side) > 0 and len(right_side) > 0:
            if left_side[0] <= right_side[0]:
                result.append(left_side.pop(0))
            else:
                result.append(right_side.pop(0))
        elif len(left_side) > 0:
            result.append(left_side.pop(0))
        elif len(right_side) > 0:
            result.append(right_side.pop(0))
    return result

arr = [6, 5, 4, 3, 2, 1]
# print merge_sort(arr)
# [1, 2, 3, 4, 5, 6]
1
votes

The following code pops at the end (efficient enough) and sorts inplace despite returning as well.

def mergesort(lis):
    if len(lis) > 1:
        left, right = map(lambda l: list(reversed(mergesort(l))), (lis[::2], lis[1::2]))
        lis.clear()
        while left and right:
            lis.append(left.pop() if left[-1] < right[-1] else right.pop())
        lis.extend(left[::-1])
        lis.extend(right[::-1])
    return lis
1
votes

Glad there are tons of answers, I hope you find this one to be clear, concise, and fast.

Thank you

import math

def merge_array(ar1, ar2):
    c, i, j= [], 0, 0

    while i < len(ar1) and j < len(ar2):
        if  ar1[i] < ar2[j]:
            c.append(ar1[i])
            i+=1
        else:
            c.append(ar2[j])
            j+=1     
    return c + ar1[i:] + ar2[j:]

def mergesort(array):
    n = len(array)
    if n == 1:
        return array
    half_n =  math.floor(n/2)  
    ar1, ar2 = mergesort(array[:half_n]), mergesort(array[half_n:])
    return merge_array(ar1, ar2)
0
votes

This is very similar to the "MIT" solution and a couple others above, but answers the question in a little more "Pythonic" manner by passing references to the left and right partitions instead of positional indexes, and by using a range in the for loop with slice notation to fill in the sorted array:

def merge_sort(array):
    n = len(array)
    if n > 1:
        mid = n//2
        left = array[0:mid]
        right = array[mid:n]
        print(mid, left, right, array)
        merge_sort(left)
        merge_sort(right)
        merge(left, right, array)

def merge(left, right, array):
    array_length = len(array)
    right_length = len(right)
    left_length = len(left)
    left_index = right_index = 0
    for array_index in range(0, array_length):
        if right_index == right_length:
            array[array_index:array_length] = left[left_index:left_length]
            break
        elif left_index == left_length:
            array[array_index:array_length] = right[right_index:right_length]
            break
        elif left[left_index] <= right[right_index]:
                array[array_index] = left[left_index]
                left_index += 1
        else:
            array[array_index] = right[right_index]
            right_index += 1

array = [99,2,3,3,12,4,5]
arr_len = len(array)
merge_sort(array)
print(array)
assert len(array) == arr_len

This solution finds the left and right partitions using Python's handy // operator, and then passes the left, right, and array references to the merge function, which in turn rebuilds the original array in place. The trick is in the cleanup: when you have reached the end of either the left or the right partition, the original array is filled in with whatever is left over in the other partition.

0
votes
#here is my answer using two function one for merge and another for divide and 
 #conquer 
l=int(input('enter range len'))      
c=list(range(l,0,-1))
print('list before sorting is',c)
def mergesort1(c,l,r):    
    i,j,k=0,0,0
    while (i<len(l))&(j<len(r)):
        if l[i]<r[j]:
            c[k]=l[i]
            i +=1            
        else:
            c[k]=r[j]
            j +=1
        k +=1
    while i<len(l):
        c[k]=l[i]
        i+=1
        k+=1
    while j<len(r):
        c[k]=r[j]
        j+=1
        k+=1
    return c   
def mergesort(c):
    if len(c)<2:
        return c
    else:
        l=c[0:(len(c)//2)]
        r=c[len(c)//2:len(c)]
        mergesort(l)
        mergesort(r)
    return    mergesort1(c,l,r)   
0
votes
def merge(arr, p, q, r):
    left = arr[p:q + 1]
    right = arr[q + 1:r + 1]
    left.append(float('inf'))
    right.append(float('inf'))
    i = j = 0
    for k in range(p, r + 1):
        if left[i] <= right[j]:
            arr[k] = left[i]
            i += 1
        else:
            arr[k] = right[j]
            j += 1


def init_func(function):
    def wrapper(*args):
        a = []
        if len(args) == 1:
            a = args[0] + []
            function(a, 0, len(a) - 1)
        else:
            function(*args)
        return a

    return wrapper


@init_func
def merge_sort(arr, p, r):
    if p < r:
        q = (p + r) // 2
        merge_sort(arr, p, q)
        merge_sort(arr, q + 1, r)
        merge(arr, p, q, r)
if __name__ == "__main__":
    test = [5, 4, 3, 2, 1]
    print(merge_sort(test))

Result would be

[1, 2, 3, 4, 5]
0
votes
from run_time import run_time
from random_arr import make_arr

def merge(arr1: list, arr2: list):
    temp = []
    x, y = 0, 0
    while len(arr1) and len(arr2):
        if arr1[0] < arr2[0]:
            temp.append(arr1[0])
            x += 1
            arr1 = arr1[x:]
        elif arr1[0] > arr2[0]:
            temp.append(arr2[0])
            y += 1
            arr2 = arr2[y:]
        else:
            temp.append(arr1[0])
            temp.append(arr2[0])
            x += 1
            y += 1
            arr1 = arr1[x:]
            arr2 = arr2[y:]

    if len(arr1) > 0:
        temp += arr1
    if len(arr2) > 0:
        temp += arr2
    return temp

@run_time
def merge_sort(arr: list):
    total = len(arr)
    step = 2
    while True:
        for i in range(0, total, step):
            arr[i:i + step] = merge(arr[i:i + step//2], arr[i + step//2:i + step])
        step *= 2
        if step > 2 * total:
            return arr

arr = make_arr(20000)
merge_sort(arr)
# run_time is 0.10300588607788086
0
votes

Here is my attempt at the recursive merge_sort function in python. Note, this is my first python class and my first encounter with this problem so please bear with me if my code is rough, but it works.

def merge_sort(S):
    temp = []

    if len(S) < 2:
        return S

    split = len(S) // 2
    left = merge_sort(S[:split])
    right = merge_sort(S[split:])

    finale = temp + merge(left, right)

    return finale


def merge(left, right):

    holder = []

    while len(left) > 0 and len(right) > 0:

        if left[0] < right[0]:

            holder.append(left[0])
            del left[0]

        elif left[0] > right[0]:

            holder.append(right[0])
            del right[0]

    if len(left) > 0:

        holder.extend(left)

    elif len(right) > 0:

        holder.extend(right)

    return holder

0
votes
def splitArray(s):
    return s[:len(s)//2], s[len(s)//2:]

# the idea here is i+j should sum to n as you increment i and j, 
# but once out of bound, the next item of a or b is infinity 
# therefore, the comparison will always switch to the other array
def merge(a, b, n):
    result = [0] * n
    a = a + [float('inf')]
    b = b + [float('inf')]
    result = [0] * n
    i, j = 0, 0
    for k in range(0, n):
        if a[i] < b[j]:
            result[k] = a[i]
            i+=1
        else:
            result[k] = b[j]
            j+=1
    return result

def mergeSort(items):
    n = len(items)
    baseCase = []
    if n == 0:
        return baseCase
    if n == 1:
        baseCase.append(items[0])
        return baseCase
    if n == 2:
        if items[0] < items[1]:
            baseCase.append(items[0])
            baseCase.append(items[1])
            return baseCase
        else:
            baseCase.append(items[1])
            baseCase.append(items[0])
            return baseCase
    left, right = splitArray(items)
    sortedLeft = mergeSort(left)
    sortedRight = mergeSort(right)
    return merge(sortedLeft,sortedRight,n)

# 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]),

arr = mergeSort(arr)
print ("\n\nSorted array is")
for i in range(n):
    print ("%d" %arr[i]),
0
votes
def merge_sort(l):
    if len(l) == 1:
        if len(n)> 0:
            for i in range(len(n)):
                if n[i] > l[0]:
                    break
            else:
                i = i+1
            n.insert(i, l[0])
        else:
            n.append(l[0])
    else:
        p = len(l)//2
        a = l[:p]
        b = l[p:]
        merge_sort(a)
        merge_sort(b)

m = [3,5,2,4,1]
n = []
merge_sort(m)
print(n)