The max-heap functions avoid sign inversion when numeric priority itself is the ordering key.
Make this comfortable
Python heapq max-heap: keep highest-priority scores at the root
Operation contract
Four inspection scores are heapified and consumed from highest to lowest. The heap is a list, but its internal list order is not a sorted report; only repeated heappop_max calls establish descending output. The score represents urgency, not an object with tie-breaking metadata.
Failure boundary
If work items carry payloads, define an explicit tie-break rule so equal priorities do not compare incompatible objects. Changing an item's priority in place breaks the heap invariant. A heap gives fast access to one extreme, not constant-time arbitrary deletion or a fully sorted index.
Working program
from heapq import heapify_max, heappop_max, heappush_max
inspection_scores = [47, 82, 65]
heapify_max(inspection_scores)
heappush_max(inspection_scores, 91)
descending = []
while inspection_scores:
descending.append(heappop_max(inspection_scores))
print("priority_order", descending)Output
priority_order [91, 82, 65, 47]Costs and limits
Heap construction is O(n), each push or pop is O(log n), and popping all items is O(n log n). The heap stores O(n) values.
Common Mistakes
- The backing list is not a sorted sequence.
- Do not mutate a queued priority without restoring the heap invariant.
- Tie-breaking must be defined before storing payload records.
Connected lessons
python
heapq-native-maxheap