Python heapq模組，pythonheapq模組

Python heapq模組，pythonheapq模組

這個模組(build-in)實現了一個堆的資料結構，完美的解決了Top-K問題，以後解決Top-K問題的時候，直接把這個模組拿來用就可以了

注意，預設的heap是一個小頂堆！

 

heapq模組提供了如下幾個函數：

heapq.heappush(heap, item)把item添加到heap中（heap是一個列表）

heapq.heappop(heap) 把堆頂元素彈出，返回的就是堆頂

heapq.heappushpop(heap, item) 先把item加入到堆中，然後再pop，比heappush()再heappop()要快得多

heapq.heapreplace(heap, item) 先pop，然後再把item加入到堆中，比heappop()再heappush()要快得多

heapq.heapify(x) 將列表x進行堆調整，預設的是小頂堆

heapq.merge(*iterables) 將多個列表合并，並進行堆調整，返回的是合并後的列表的迭代器

heapq.nlargest(n, iterable, key=None) 返回最大的n個元素（Top-K問題）

heapq.nsmallest(n, iterable, key=None) 返回最小的n個元素（Top-K問題）

 

下面看下樣本：

import heapqimport random# Top-Kmylist = list(random.sample(range(100), 10))k = 3largest = heapq.nlargest(k, mylist)smallest = heapq.nsmallest(k, mylist)print('original list is', mylist)print('largest-'+str(k), '  is ', largest)print('smallest-'+str(k), ' is ', smallest)# heapifyprint('original list is', mylist)heapq.heapify(mylist)print('heapify  list is', mylist)# heappush & heappopheapq.heappush(mylist, 105)print('pushed heap is', mylist)heapq.heappop(mylist)print('popped heap is', mylist)# heappushpop & heapreplaceheapq.heappushpop(mylist, 130)    # heappush -> heappopprint('heappushpop', mylist)heapq.heapreplace(mylist, 2)    # heappop -> heappushprint('heapreplace', mylist)

輸出結果為：

original list is [18, 6, 10, 24, 48, 2, 9, 25, 16, 89]largest-3   is  [89, 48, 25]smallest-3  is  [2, 6, 9]original list is [18, 6, 10, 24, 48, 2, 9, 25, 16, 89]heapify  list is [2, 6, 9, 16, 48, 10, 18, 25, 24, 89]pushed heap is [2, 6, 9, 16, 48, 10, 18, 25, 24, 89, 105]popped heap is [6, 16, 9, 24, 48, 10, 18, 25, 105, 89]heappushpop [9, 16, 10, 24, 48, 130, 18, 25, 105, 89]heapreplace [2, 16, 10, 24, 48, 130, 18, 25, 105, 89]

希望能對你有所協助！