diff options
Diffstat (limited to 'Lib/heapq.py')
| -rw-r--r-- | Lib/heapq.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/Lib/heapq.py b/Lib/heapq.py index cc61294cc8..dec15aec9c 100644 --- a/Lib/heapq.py +++ b/Lib/heapq.py @@ -1,5 +1,3 @@ -# -*- coding: latin-1 -*- - """Heap queue algorithm (a.k.a. priority queue). Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for @@ -34,7 +32,7 @@ maintains the heap invariant! __about__ = """Heap queues -[explanation by François Pinard] +[explanation by François Pinard] Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for all k, counting elements from 0. For the sake of comparison, @@ -187,6 +185,8 @@ def nlargest(n, iterable): Equivalent to: sorted(iterable, reverse=True)[:n] """ + if n < 0: + return [] it = iter(iterable) result = list(islice(it, n)) if not result: @@ -203,6 +203,8 @@ def nsmallest(n, iterable): Equivalent to: sorted(iterable)[:n] """ + if n < 0: + return [] if hasattr(iterable, '__len__') and n * 10 <= len(iterable): # For smaller values of n, the bisect method is faster than a minheap. # It is also memory efficient, consuming only n elements of space. @@ -214,11 +216,10 @@ def nsmallest(n, iterable): pop = result.pop los = result[-1] # los --> Largest of the nsmallest for elem in it: - if los <= elem: - continue - insort(result, elem) - pop() - los = result[-1] + if elem < los: + insort(result, elem) + pop() + los = result[-1] return result # An alternative approach manifests the whole iterable in memory but # saves comparisons by heapifying all at once. Also, saves time |