Python Heapq

The smallest element is at the leaf nodes.

Each parent node is larger than its children.

The smallest element is always at the root.

Elements are stored in a random order.

heapq.pop()

heapq.insert()

heapq.heappush()

heapq.add()

It only adds an element to the heap.

It pops the largest element from the heap.

It pops the smallest element and adds a new element.

It merges two heaps into one.

heapq.nlargest()

heapq.getlargest()

heapq.maxelements(n)

heapq.retrievelargest()

It supports random access to elements.

It allows efficient sorting of all elements.

It is not thread-safe.

It requires more memory than linked lists.

heapq.combine()

heapq.merge()

heapq.concat()

heapq.join()

It adds a new element to the heap.

It returns the largest element in the heap.

It removes and returns the smallest element in the heap.

It checks the size of the heap.

Heaps can be implemented using binary trees only.

Heaps support random access to elements efficiently.

Heaps do not allow duplicate elements.

Heaps can be implemented using lists in Python.

O(1)

O(n)

O(log n)

O(n log n)

Using heappop() followed by heappush()

Using heapq.replace()

Using heappushpop()

Using heapq.merge()