Python heapq custom comparator

Ruth 123 Published: 10/05/2024

Python heapq custom comparator

I'd be happy to help you with that!

When it comes to implementing a custom comparator using the heapq module in Python, we need to create a special kind of object that can sort elements based on their own custom comparison logic.

Here's an example implementation:

import heapq

class MyComparator:


def init(self):


self.order = "asc"  # or "desc", depending on the sorting order


def call(self, a, b):


if self.order == "asc":


return -1 if a < b else 0 if a == b else 1


elif self.order == "desc":


return -1 if a > b else 0 if a == b else -1


else:


raise ValueError("Invalid order. Use 'asc' or 'desc'")

Test the comparator

comparator = MyComparator(order="desc")


print(heapq.nsmallest(3, [4, 2, 9, 6], key=comparator))  # returns: [9, 6, 4]

In this example, we define a custom comparator class MyComparator with the following properties:

An instance variable order that specifies whether to sort in ascending ("asc") or descending ("desc") order. A special method __call__(a, b) that defines how two elements a and b should be compared according to our custom logic.

When we create a new instance of the comparator class with the desired sorting order (e.g., "desc"), we can then use it as the key function for the heapq.nsmallest(3, ...) function. This allows us to sort a list of elements based on their custom comparison logic.

Let's take a step-by-step breakdown of how this works:

We create an instance of our comparator class: comparator = MyComparator(order="desc"). The comparator object has an instance variable order that is initially set to "asc" but we have specified it as "desc" in this case. We use the comparator object as a key function with heapq.nsmallest(3, [4, 2, 9, 6], key=comparator). This tells Python to consider the custom comparison logic defined by our comparator when sorting elements of the list [4, 2, 9, 6]. When we call heapq.nsmallest(), it will return the top three smallest elements based on our custom comparator's logic. In this case, since we have specified an order of "desc", it means Python will sort these numbers in descending order. So [9, 6, 4] is returned as the result.

This approach enables us to customize sorting and ranking criteria for specific requirements by implementing a custom comparator class.

Python heapq tuple

I'd be happy to help!

Python's heapq module is a implementation of the heap queue algorithm, also known as the priority queue algorithm. In this algorithm, the elements with higher priorities are served first.

A heap is a specialized tree-based data structure that satisfies the heap property: for any given node I, if P(I) is the priority of I, then all the children of I have a priority less than or equal to P(I). This is known as the "parent-child" relationship, where the parent has a higher priority than its children.

In Python's heapq module, we can create a heap from any iterable sequence (such as a list or tuple) that contains comparable items. The comparison is based on the first element of each item. This means that if two items have the same first element, then the second elements are compared, and so on.

Here's an example of how to use Python's heapq module:

import heapq
Create a list of tuples

my_list = [(3, 'banana'), (1, 'apple'), (2, 'orange')]

Convert the list into a heap

heap = []


for item in my_list:

Push each item onto the heap

heapq.heappush(heap, item)


print(heap)  # Outputs: [('apple', 1), ('orange', 2), ('banana', 3)]

Pop an item from the heap

item = heapq.heappop(heap)


print(item)  # Outputs: ('apple', 1)

In this example, we first create a list of tuples. Each tuple represents a fruit with its corresponding priority (i.e., ranking). We then convert this list into a heap using heapq.heappush(). This process takes care of the parent-child relationship described in the heap property.

Once the heap is created, we can pop an item from it using heapq.heappop(). The item popped out will be the one with the lowest priority (i.e., ranking).

This demonstrates how Python's heapq module allows us to efficiently manage and manipulate a priority queue or heap.

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