GeeksforGeeks
geeksforgeeks.org › python › python-heapq-heapify-method
Python heapq.heapify() Method - GeeksforGeeks
July 23, 2025 - The heapq.heapify() function rearranges the elements in the list to make it a valid min-heap.
Python
docs.python.org › 3 › library › heapq.html
heapq — Heap queue algorithm
To create a heap, use a list initialized as [], or transform an existing list into a min-heap or max-heap using the heapify() or heapify_max() functions, respectively.
Videos
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Heap - Data Structures in Python #6 - YouTube
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Heaps & Priority Queues - Heapify, Heap Sort, Heapq Library - DSA ...
Heaps & Priority Queues in Python
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GeeksforGeeks
geeksforgeeks.org › python › heap-queue-or-heapq-in-python
Heap queue or heapq in Python - GeeksforGeeks
... import heapq h = [10, 20, 15, 30, 40] heapq.heapify(h) # Appending an element heapq.heappush(h, 5) print(h) # Pop the smallest element from the heap min = heapq.heappop(h) print("Smallest:", min) print(h)
Published 2 weeks ago
CodeSignal
codesignal.com › learn › courses › understanding-and-using-trees-in-python › lessons › unraveling-heaps-theory-operations-and-implementations-in-python
Theory, Operations, and Implementations in Python
The "Heapify" method is an intriguing function used to rearrange elements in heap data structures. It assists in preserving the heap property within the heap. In Python, this operation can be executed using the heapify() function.
TutorialsPoint
tutorialspoint.com › python_data_structure › python_heaps.htm
Python - Heaps
A heap is created by simply using a list of elements with the heapify function. In the below example we supply a list of elements and the heapify function rearranges the elements bringing the smallest element to the first position.
Medium
medium.com › @hs_pedro › implementing-a-heap-in-python-1036e759e0eb
Implementing a Heap in Python. Heap is an elegant data structure that… | by Pedro Soares | Medium
December 20, 2021 - Example Input: Start: 2, End: 5 Start: 3, End: 7 Start: 8, End: 9 ... On Python’s standard library, the API for heaps can be found in heapq module. For instance, one can heapify an array (min-heap, in this case) by doing:
Real Python
realpython.com › python-heapq-module
The Python heapq Module: Using Heaps and Priority Queues – Real Python
July 18, 2022 - For example, a[2], which is 3, is less than a[2*2 + 2], which is 7. As you can see, heapify() modifies the list in place but doesn’t sort it. A heap doesn’t have to be sorted to satisfy the heap property.
Reddit
reddit.com › r/leetcode › python heapify
r/leetcode on Reddit: Python heapify
June 3, 2024 -
Does heapify take an unordered array and convert it into a heap in O(n) time ? I think this is specific to Python and there is some fancy math on why.
But in other languages like JavaScript for example, is n log n, this makes sense to me since you are adding n elements and each takes log n time. And how you have a sorted heap.
Top answer 1 of 2
8
You can see some good answers here https://stackoverflow.com/questions/9755721/how-can-building-a-heap-be-on-time-complexity But yes heapify() is O(n). Creating an empty heap and adding elements one at a time is O(nlogn). One thing to note is a heap is not sorted.
2 of 2
1
std::make_heap from C++ is also O(N). In fact, the standard even guarantees a specific constant factor - at most 3N comparisons to make a heap of N elements.
Interviewcrunch
interviewcrunch.com › python › advanced-data-structures › heap
Heap | InterviewCrunch: Coding Interviews Broken Down
Or we can initialized the list with existing items, and then use heapify() to rearrange the list into the heap:
FavTutor
favtutor.com › blogs › heap-in-python
Heap in Python: Min & Max Heap Implementation (with code)
April 21, 2023 - Here is the Python implementation with full code for Max Heap: def max_heapify(A,k): l = left(k) r = right(k) if l < len(A) and A[l] > A[k]: largest = l else: largest = k if r < len(A) and A[r] > A[largest]: largest = r if largest != k: A[k], ...
Top answer 1 of 9
166
According to the example from the documentation, you can use tuples, and it will sort by the first element of the tuple:
>>> h = []
>>> heappush(h, (5, 'write code'))
>>> heappush(h, (7, 'release product'))
>>> heappush(h, (1, 'write spec'))
>>> heappush(h, (3, 'create tests'))
>>> heappop(h)
(1, 'write spec')
So if you don't want to (or can't?) do a __cmp__ method, you can manually extract your sorting key at push time.
Note that if the first elements in a pair of tuples are equal, further elements will be compared. If this is not what you want, you need to ensure that each first element is unique.
2 of 9
114
heapq sorts objects the same way list.sort does, so just define a method __cmp__() within your class definition, which will compare itself to another instance of the same class:
def __cmp__(self, other):
return cmp(self.intAttribute, other.intAttribute)
Works in Python 2.x.
In 3.x use:
def __lt__(self, other):
return self.intAttribute < other.intAttribute
Educative
educative.io › answers › what-is-the-heapqheapify-module-in-python
What is the heapq.heapify() module in Python?
In the above code, we define a list of Person objects called person_lst and construct a heap of Person objects using the heapify() method.
Medium
cleverzone.medium.com › exploring-pythons-heapq-module-b0c9d131545c
Exploring Python's heapq Module - Cleverzone
June 17, 2024 - The heapified list is [1, 3, 9, 7, 5]. Heapreplace — The heapreplace function is called with the new item 2. It first pops the smallest item 1 from the heap, then pushes the new item 2 onto the heap, and the heap is adjusted.
Pythontic
pythontic.com › algorithms › heapq › heapify
The heapify function of heapq module in Python | Pythontic.com
The method heapify() of heapq module in Python, takes a Python list as parameter and converts the list into a min heap.
Medium
medium.com › @allan.sioson › max-heapify-build-max-heap-and-heapsort-algorithm-in-python-42c4dec70829
Max-Heapify, Build-Max-Heap, and Heapsort Algorithm | by Allan A. Sioson | Medium
October 17, 2023 - Initially, we let the current last node be the last item in array A. We can then repeatedly swap the key of the root with the key of the current last node and then use the Max-Heapify algorithm on the slice of array A from index 0 up to the ...
Top answer 1 of 4
42
In Python 2.X and 3.x, heaps are supported through an importable library, heapq. It supplies numerous functions to work with the heap data structure modelled in a Python list. Example:
>>> from heapq import heappush, heappop
>>> heap = []
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> for item in data:
heappush(heap, item)
>>> ordered = []
>>> while heap:
ordered.append(heappop(heap))
>>> ordered
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> data.sort()
>>> data == ordered
True
You can find out more about Heap functions: heappush, heappop, heappushpop, heapify, heapreplace in heap python docs.
2 of 4
17
Here's another variation based on Sedgewick
The heap is represented internally in an array where if a node is at k, it's children are at 2*k and 2*k + 1. The first element of the array is not used, to make the math more convenient.
To add a new element to the heap, you append it to the end of the array and then call swim repeatedly until the new element finds its place in the heap.
To delete the root, you swap it with the last element in the array, delete it and then call sink until the swapped element finds its place.
swim(k):
while k > 1 and less(k/2, k):
exch(k, k/2)
k = k/2
sink(k):
while 2*k <= N:
j = 2*k
if j < N and less(j, j+1):
j++
if not less(k, j):
break
exch(k, j)
k = j
Here's a visualization of heap insert, inserting the first 15 letters of the alphabet: [a-o]
APXML
apxml.com › courses › data-structures-algorithms-ml › chapter-5-heaps-priority-queues-ml › python-heapq
Python heapq Module for Heap Operations
data = [50, 20, 80, 10, 30, 5, 90, 45] print(f"Original list: {data}") heapq.heapify(data) # Transform list into a heap in-place print(f"Heapified list: {data}") print(f"Smallest item: {data[0]}") # Expected output (heapified list order might vary, smallest is first): # Original list: [50, ...
Roman Glushko
romaglushko.com › home › blog › heapify ✌️
Heapify ✌️ - Blog by Roman Glushko
May 16, 2021 - from typing import List class PriorityQueue: """ Represents the heap and preserves the heap property during adding/removing elements """ items: List[int] def __init__(self, items: List[int]): self.items = self.build_heap(items) def build_heap(self, items: List[int]) -> List[int]: """ Turn an unsorted array into a heap """ items_count = len(items) for i in range(items_count // 2, -1, -1): items = self.heapify(items, i) return items def heapify(self, items: List[int], node_idx: int, root_idx: int = 0) -> List[int]: """ Check and fix violations of the heap property recursively """ items_count = len(items) largest_idx = node_idx # formulas for zero-indexed arrays left_child_idx = 2 * (node_idx - root_idx) + 1 + root_idx right_child_idx = 2 * (node_idx - root_idx) + 2 + root_idx # is the left child node bigger than parent node?
GeeksforGeeks
geeksforgeeks.org › python › min-heap-in-python
Min Heap in Python - GeeksforGeeks
1 week ago - Python · from heapq import heapify, heappush, heappop heap = [] heapify(heap) heappush(heap, 10) heappush(heap, 30) heappush(heap, 20) heappush(heap, 400) print("Head value of heap : "+str(heap[0])) print("The heap elements : ") for i in heap: print(i, end = ' ') print("\n") element = heappop(heap) print("The heap elements : ") for i in heap: print(i, end = ' ') Output ·
Plain English
python.plainenglish.io › heapify-in-linear-time-114a15487ba1
Heapify in Linear Time | Python in Plain English - PlainEnglish.io
April 25, 2022 - push: O(log(n)), heapq.heappush(heap: List[Any], k: int) in Python · pop: O(log(n)), heapq.heappop(heap: List[Any], k: int) heapify or build_heap: O(n), heapq.heapify(heap: List[Any])