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Python heapq.heappush() Method - GeeksforGeeks

Python heapq.heappush() Method

Last Updated : 11 Mar, 2025

The heapq.heappush() function in Python is used to push an element onto a heap while maintaining the heap property. This function is useful when dealing with priority queues or implementing efficient sorting algorithms.

Example:

Python


 import heapq

# Create an empty list that will represent the heap
h = []

# Use heappush to add elements to the heap
heapq.heappush(h, 10)
heapq.heappush(h, 5)
heapq.heappush(h, 20)
heapq.heappush(h, 15)

# Print the heap after adding elements
print(h)

Explanation:

The heappush() function ensures that the heap property is maintained, meaning the smallest element is always at the root (index 0).
After pushing the values 10, 5, 20, and 15, the heap is automatically rearranged to maintain the heap property.

Syntax of heappush() method

heapq.heappush(heap, item)

Parameters

heap: The list representing the heap (must be a valid heap structure).
item: The element to be pushed onto the heap.

Return Value

This function does not return anything. It modifies the heap in-place by inserting the new element while maintaining the heap property.

Examples of heappush() method 1. Using heappush() to Insert Elements in a Min-Heap Python


 import heapq

# Create an empty heap
h = []

# Push elements onto the heap
heapq.heappush(h, 5)
heapq.heappush(h, 1)
heapq.heappush(h, 8)
heapq.heappush(h, 3)

print("Min-Heap:", h)

Output

Min-Heap: [1, 3, 8, 5]

Explanation:

The heappush() function is used to insert the values 5, 1, 8, and 3 into the heap.
The heap automatically rearranges itself to maintain the min-heap property, where the smallest element is always at the root (index 0).

2. Using heappush() in a Max-Heap

Since heapq only supports min-heaps, we can simulate a max-heap by pushing negative values.

Python


 import heapq

h = []

# Push elements as negative values to simulate max-heap
heapq.heappush(h, -5)
heapq.heappush(h, -1)
heapq.heappush(h, -8)
heapq.heappush(h, -3)

# Convert back to positive for correct order
max_heap = [-x for x in h]

print("Max-Heap:", max_heap)

Output

Max-Heap: [8, 3, 5, 1]

Explanation:

Elements are pushed onto the heap as negative values (-5, -1, -8, -3) to simulate the behavior of a max-heap.
The heappush() function inserts the negative values, maintaining the min-heap property on the negative values, which results in a simulated max-heap.
After inserting all elements, the heap is converted back to positive values using list comprehension ([-x for x in h]) to display the correct max-heap order.

3. Using heappush() in a Priority Queue

heapq.heappush() is commonly used in priority queues, where elements are inserted based on their priority.

Python


 import heapq

# List of tuples (priority, task)
pq= []

# Push elements (priority, task)
heapq.heappush(pq, (2, "Task A"))
heapq.heappush(pq, (1, "Task B"))
heapq.heappush(pq, (3, "Task C"))

print("Priority Queue:", pq)

Output

Priority Queue: [(1, 'Task B'), (2, 'Task A'), (3, 'Task C')]

Explanation:

Elements are pushed onto the heap as tuples, where the first value is the priority (lower values indicate higher priority) and the second value is the task.
The heappush() function ensures that the queue is sorted according to the priority, with the element having the lowest priority value at the root.

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