The pop/push combination always returns an element from the heap and replaces Heappush() and can be more appropriate when using a fixed-size heap. This one step operation is more efficient than a heappop() followed by If the heap is empty, Inde圎rror is raised. Pop and return the smallest item from the heap, and also push the new item.
Transform list x into a heap, in-place, in linear time. The combined action runs more efficiently than heappush()įollowed by a separate call to heappop(). Push item on the heap, then pop and return the smallest item from the Smallest item without popping it, use heap. Pop and return the smallest item from the heap, maintaining the heap Push the value item onto the heap, maintaining the heap invariant. The following functions are provided: heapq. Populated list into a heap via function heapify(). To create a heap, use a list initialized to, or you can transform a Surprises: heap is the smallest item, and heap.sort() maintains the These two make it possible to view the heap as a regular Python list without Item, not the largest (called a “min heap” in textbooks a “max heap” is moreĬommon in texts because of its suitability for in-place sorting). This makes the relationship between the index for a nodeĪnd the indexes for its children slightly less obvious, but is more suitable The API below differs from textbook heap algorithms in two aspects: (a) We use Smallest element is always the root, heap. The interesting property of a heap is that its For the sake of comparison, non-existing elements areĬonsidered to be infinite. Heap <= heap and heap <= heap for all k, countingĮlements from zero. This implementation uses arrays for which Heaps are binary trees for which every parent node has a value less than orĮqual to any of its children. This module provides an implementation of the heap queue algorithm, also known
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