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Heap Implementation for Python

Project description

It’s like heapq (blazingly fast) but object-oriented + more features.

Why?

Less code.

What are heaps good for anyway?

They are fast when you need the smallest/biggest item of big collections - Runtime: O(log n).

How?

Suppose you have a heap, then use pop get the smallest one. Heapsort works this way.

heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.pop()  # returns A
heap.pop()  # returns B
heap.pop()  # returns C
...

Can I insert an item?

Indeed and it’s as fast as pop. Use push for insertion.

heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.push('Z')

Can I remove an item from the middle of a heap?

Yes, that’s what remove is supposed to do.

heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.remove('L')     # returns index: 6

A heap is basically a list. So, if you know the index of the item, you can use pop instead.

heap = Heap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.pop(6)          # returns item: L

Max-Heap or Min-Heap?

You define the order of items. Just imagine two heaps of the very same set of items but you need different sorting for each heap. So, you define what min and max means, via cmp.

items = [date(2015, 1, 1), date(2015, 1, 2),  date(2015, 1, 3)]
order1 = Heap(items, cmp=lambda x, y: x.day <= y.day)
order2 = Heap(items, cmp=lambda x, y: x.weekday() >= y.weekday())

Checking Heap Invariant

If you tinker with a heap you can check whether the heap invariant still holds:

heap = Heap([4, 3, 7, 6, 1, 2, 9, 8, 5])
heap[3] = 10           # I know what I am doing here
heap.check_invariant() # but better check... ooops

Conclusion

Good

  • uses C implementation if available (i.e. fast)

  • object-oriented

  • can remove items from within the heap

  • can remove items with unknown index

  • sorting defined per heap (falls back to Pythonic <=)

  • works with Python2 and Python3

Bad

  • no drawbacks discovered so far ;)

  • needs fix:

    • decrease-key and increase-key seem to be another important missing use-case of heapq; so, I will dig into that as well

    • merge heaps

  • ideas are welcome :-)

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