Heapq vs SortedList: Which to Choose in Python?

What will you learn?

Discover the nuances between heapq and sortedlist in Python, and gain insights into selecting the ideal option based on distinct scenarios.

Introduction to Problem and Solution

When faced with the decision of opting for heapq or sortedlist, it’s imperative to weigh critical factors such as time complexity, memory utilization, and the specific requisites of your project.

In Python, heapq presents a binary heap implementation facilitating efficient insertion and retrieval of the smallest element. Conversely, sortedlist furnishes a sorted list data structure with logarithmic time complexity for most operations.


# Importing heapq module
import heapq

# Creating a min-heap using heapq
h = []
heapq.heappush(h, 5)
heapq.heappush(h, 2)
heapq.heappush(h, 7)

print("Using heapq:")
while h:

# Importing SortedList from sortedcontainers module
from sortedcontainers import SortedList

# Creating a sorted list using SortedList
s = SortedList([5, 2, 7])

print("\nUsing SortedList:")
for value in s:

# Copyright PHD


  1. Begin by importing essential modules – heapq for min-heap creation and SortedList from sortedcontainers for sorted list generation.
  2. For heapq, demonstrate min-heap creation by pushing elements onto it followed by popping them off in ascending order.
  3. For SortedList, initialize it with an unsorted list of values and iterate through to exhibit ordered output.
    HeapQ vs SortedList Comparison:

    Which is faster – HeapQ or SortedList?

    In general, HeapQ excels in push-pop operations due to its binary heap structure compared to SortedList, which maintains order post each operation.

    When to use HeapQ over SortedList?

    Opt for HeapQ when swift access to the smallest element is crucial or when priority queue functionalities like heapsort or Dijkstra’s algorithm optimizations are required.

    Are there scenarios favoring SortedList over HeapQ?

    Consider utilizing SortedList when frequent maintenance of elements in sorted order along with efficient insertions or deletions without constant data restructuring is needed.

    Can duplicate elements be stored in both HeapQ and SortedList?

    Yes, both structures support duplicate elements since they are list-based but retain unique properties concerning sorting/ordering rules.

    Do these structures impose limitations on supported data types?

    While both can manage various data types permitted by Python lists (integers, strings), ensure custom objects implement comparison methods if used as elements within these structures for proper functionality during sorting/insertion operations.


    The choice between HeapQ or SortedList hinges on your specific requirements – whether prioritizing speed in accessing minimum values (HeapQ) or maintaining a consistently ordered collection (SortedList). Evaluate factors like operation complexities and memory usage before finalizing your decision.

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