Merge two sorted arrays in Python using heapq

Given two sorted arrays, the task is to merge them in a sorted manner. Examples:

Input :  arr1 = [1, 3, 4, 5]  
         arr2 = [2, 4, 6, 8]
Output : arr3 = [1, 2, 3, 4, 4, 5, 6, 8]

Input  : arr1 = [5, 8, 9]  
         arr2 = [4, 7, 8]
Output : arr3 = [4, 5, 7, 8, 8, 9]

This problem has existing solution please refer Merge two sorted arrays link. We will solve this problem in python using heapq.merge() in a single line of code. 

Implementation:

Python3

# Function to merge two sorted arrays
from heapq import merge

def mergeArray(arr1,arr2):
    return list(merge(arr1, arr2))

# Driver function
if __name__ == "__main__":
    arr1 = [1,3,4,5]
    arr2 = [2,4,6,8]
    print (mergeArray(arr1, arr2))

Output

[1, 2, 3, 4, 4, 5, 6, 8]

The time complexity  is O(n log n).

The space complexity  is O(n).

Properties of heapq module ?

This module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm. To create a heap, use a list initialized to [], or you can transform a populated list into a heap via function heapify().The following functions are provided:

• heapq.heappush(heap,item) : Push the value item onto the heap, maintaining the heap invariant.
• heapq.heappop(heap) : Pop and return the smallest item from the heap, maintaining the heap invariant. If the heap is empty, IndexError is raised. To access the smallest item without popping it, use heap[0].
• heapq.heappushpop(heap, item) : Push item on the heap, then pop and return the smallest item from the heap. The combined action runs more efficiently than heappush() followed by a separate call to heappop().
• heapq.heapify(x) : Transform list x into a heap, in-place, in linear time.
• heapq.merge(*iterables) : Merge multiple sorted inputs into a single sorted output (for example, merge timestamped entries from multiple log files). Returns an iterator over the sorted values.