Skip to main content

Heap Implementation for Python

Project description

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

Read more here for the background story.

Why?

Less code.

What are heaps good for anyway?

When you need the smallest item of a large list—fast and with no overhead.

How?

Let’s suppose you have a heap, you can use pop to get its smallest item.

from xheap import Heap

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

Heapsort works this way.

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 RemovalHeap.remove is supposed to do.

from xheap import RemovalHeap

heap = RemovalHeap(['A', 'D', 'B', 'H', 'E', 'C', 'L', 'J', 'I'])
heap.remove('L')

Can I specify the order of the heap?

Just imagine two heaps of the very same set of items but you need different sorting for each heap. Or you need a max-heap instead of a min-heap. That is what OrderHeap is designed for:

from xheap import OrderHeap

items = [date(2016, 1, 1), date(2016, 1, 2),  date(2016, 1, 3),  date(2016, 1, 4)]

day_heap = OrderHeap(items, key=lambda date: date.day)
day_heap.peek()      # returns date(2016, 1, 1)

weekday_heap = OrderHeap(items, key=lambda date: date.weekday())
weekday_heap.peek()  # returns date(2016, 1, 4)

What about both remove+order?

No problem. Use XHeap.

If you wonder why there are 4 distinct heap implementations, it’s a matter of speed. Each additional feature slows a heap down. Thus, you could always use XHeap but beware of the slowdown.

Checking Heap Invariant

A heap is just a list. So, if you tinker with it, you can check whether its 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

  • no slowdown if you don’t need more than a simple heap

  • removal possible

  • custom orders possible

  • works with Python2 and Python3

Bad

  • no drawbacks discovered so far ;-)

  • needs fix/work:

    • item wrapper which allows duplicate items

    • decrease-key+increase-key: another missing use-case of heapq

    • merge heaps

  • ideas are welcome :-)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xheap-0.17.tar.gz (4.5 kB view hashes)

Uploaded Source

Built Distribution

xheap-0.17-py2.py3-none-any.whl (3.7 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page