o_18ufhmfmq19t513t3lgmn5l1qa8a.pdf

CHAPTER 10 ■ BATTERIES INCLUDED 225
The heapreplace function is not quite as commonly used as the others. It pops the smallest
element off the heap and then pushes a new element onto it. This is more efficient than a
heappop followed by a heappush:
>>> heapreplace(heap, 0.5)
0
>>> heap
[0.5, 1, 5, 3, 2, 7, 9, 8, 4, 6]
>>> heapreplace(heap, 10)
0.5
>>> heap
[1, 2, 5, 3, 6, 7, 9, 8, 4, 10]
The remaining two functions of the heapq module, nlargest(n, iter) and
nsmallest(n, iter), are used to find the n largest or smallest elements, respectively, of any
iterable object iter. You could do this by using sorting (for example, using the sorted function)
and slicing, but the heap algorithm is faster and more memory-efficient (and, not to mention,
easier to use).
Deques (and Other Collections)
Double-ended queues, or deques, can be useful when you need to remove elements in the
order in which they were added. In Python 2.4, the collections module was added, which
contains the deque type.
■Note As of Python 2.4, the collections module only contains the deque type. Possible future additions
are B-trees and Fibonacci heaps.
A deque is created from an iterable object (just like sets) and has several useful methods:
>>> from collections import deque
>>> q = deque(range(5))
>>> q.append(5)
>>> q.appendleft(6)
>>> q
deque([6, 0, 1, 2, 3, 4, 5])
>>> q.pop()
5
>>> q.popleft()
6
>>> q.rotate(3)
>>> q
deque([2, 3, 4, 0, 1])
>>> q.rotate(-1)
>>> q
deque([3, 4, 0, 1, 2])