commit f73c61

2026-08-06 23:44:54 Niklas Polke: Create Heapq

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`/dev/null` .. `python/heapq.md`
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+	# Heapq
+	Python’s heapq module implements a min-heap using a regular list. The smallest element is always stored at index 0.
+
+	:::info
+	My usecase was to have a performant collection with a maximum size that removes the worst results to focus on the best.
+	:::
+
+	```python=
+	import heapq
+
+	class element:
+	score: int
+
+	def __lt__(self, other):
+	if not isinstance(other, element):
+	return NotImplemented
+
+	return self.score < other.score
+
+	def do_something():
+	list: list[element] = []
+
+	...
+
+	heapq.heappush(list, new_element)
+
+	heapq.heappushpop(list, new_element)
+	```
+	- 17: `heappush(heap, item)` inserts a new item while preserving the heap property.
+	- 19: `heappushpop(heap, item)` first inserts the item and then removes and returns the smallest element. It is usually more efficient than calling heappush() followed by heappop() separately.
+
+	:::warning
+	Heap elements must be mutually comparable. For custom classes, implementing __lt__() is normally sufficient, because heapq compares elements using the < operator. If two elements cannot be compared, Python raises a TypeError. A common alternative is to store tuples such as (priority, value), where the first element defines the priority.
+	:::