Cleanup heapq docs
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@ -63,45 +63,16 @@ The following functions are provided:
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Pop and return the smallest item from the *heap*, and also push the new *item*.
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The heap size doesn't change. If the heap is empty, :exc:`IndexError` is raised.
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This is more efficient than :func:`heappop` followed by :func:`heappush`, and
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can be more appropriate when using a fixed-size heap. Note that the value
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returned may be larger than *item*! That constrains reasonable uses of this
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routine unless written as part of a conditional replacement::
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if item > heap[0]:
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item = heapreplace(heap, item)
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This one step operation is more efficient than a :func:`heappop` followed by
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:func:`heappush` and can be more appropriate when using a fixed-size heap.
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The pop/push combination always returns an element from the heap and replaces
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it with *item*.
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Example of use:
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>>> from heapq import heappush, heappop
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>>> heap = []
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>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
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>>> for item in data:
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... heappush(heap, item)
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...
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>>> ordered = []
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>>> while heap:
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... ordered.append(heappop(heap))
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...
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>>> print ordered
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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>>> data.sort()
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>>> print data == ordered
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True
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Using a heap to insert items at the correct place in a priority queue:
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>>> heap = []
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>>> data = [(1, 'J'), (4, 'N'), (3, 'H'), (2, 'O')]
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>>> for item in data:
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... heappush(heap, item)
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...
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>>> while heap:
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... print heappop(heap)[1]
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J
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O
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H
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N
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The value returned may be larger than the *item* added. If that isn't
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desired, consider using :func:`heappushpop` instead. Its push/pop
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combination returns the smaller of the two values, leaving the larger value
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on the heap.
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The module also offers three general purpose functions based on heaps.
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@ -152,6 +123,35 @@ values, it is more efficient to use the :func:`sorted` function. Also, when
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functions.
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Basic Examples
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--------------
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A `heapsort <http://en.wikipedia.org/wiki/Heapsort>`_ can be implemented by
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pushing all values onto a heap and then popping off the smallest values one at a
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time::
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>>> def heapsort(iterable):
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... 'Equivalent to sorted(iterable)'
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... h = []
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... for value in iterable:
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... heappush(h, value)
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... return [heappop(h) for i in range(len(h))]
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...
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>>> heapsort([1, 3, 5, 7, 9, 2, 4, 6, 8, 0])
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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Heap elements can be tuples. This is useful for assigning comparison values
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(such as task priorities) alongside the main record being tracked::
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>>> h = []
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>>> heappush(h, (5, 'write code'))
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>>> heappush(h, (7, 'release product'))
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>>> heappush(h, (1, 'write spec'))
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>>> heappush(h, (3, 'create tests'))
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>>> heappop(h)
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(1, 'write spec')
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Priority Queue Implementation Notes
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-----------------------------------
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