Listing 2.50: heapq_heappop.py import heapq Once the heap is organized correctly, use heappop() to remove the element with the lowest value. The result of building a list in heap order one item at a time is the same as building an unordered list and then calling heapify(). Listing 2.49: heapq_heapify.py import heapq If the data is already in memory, it is more efficient to use heapify() to rearrange the items of the list in place. When heappush() is used, the heap sort order of the elements is maintained as new items are added from a data source. Listing 2.48: heapq_heappush.py import heapq There are two basic ways to create a heap: heappush() and heapify(). Output.write(str(n).center(col_width, fill)) Row = int(math.floor(math.log(i + 1, 2)))Ĭol_width = int(math.floor(total_width / columns)) Listing 2.47: heapq_showtree.py import mathĭef show_tree(tree, total_width=36, fill=' '): The heap output is printed using heapq_showtree.py. Listing 2.46: heapq_heapdata.py # This data was generated with the random module. The examples in this section use the data in heapq_heapdata.py. Python’s heapq module implements a min-heap. A min-heap requires that the parent be less than or equal to its children.

This layout makes it possible to rearrange heaps in place, so it is not necessary to reallocate as much memory when adding or removing items.Ī max-heap ensures that the parent is larger than or equal to both of its children.

Binary heaps can be represented using a list or array organized so that the children of element N are at positions 2* N+1 and 2* N+2 (for zero-based indexes). Learn More Buy 2.4 heapq: Heap Sort AlgorithmĪ heap is a tree-like data structure in which the child nodes have a sort-order relationship with the parents. Python 3 Standard Library by Example, The

Priority queue python 2.4.3.