該模塊提供了堆排序算法的實(shí)現(xiàn)。堆是二叉樹，最大堆中父節(jié)點(diǎn)大于或等于兩個(gè)子節(jié)點(diǎn)，最小堆父節(jié)點(diǎn)小于或等于兩個(gè)子節(jié)點(diǎn)。

創(chuàng)建堆

heapq有兩種方式創(chuàng)建堆， 一種是使用一個(gè)空列表，然后使用heapq.heappush()函數(shù)把值加入堆中，另外一種就是使用heap.heapify(list)轉(zhuǎn)換列表成為堆結(jié)構(gòu)

import heapq

# 第一種

"""

函數(shù)定義：

heapq.heappush(heap, item)

- Push the value item onto the heap, maintaining the heap invariant.

heapq.heappop(heap)

- Pop and return the smallest item from the heap, maintaining the heap invariant.

If the heap is empty, IndexError is raised. To access the smallest item without popping it, use heap[0].

"""

nums = [2, 3, 5, 1, 54, 23, 132]

heap = []

for num in nums:

heapq.heappush(heap, num) # 加入堆

print(heap[0]) # 如果只是想獲取最小值而不是彈出，使用heap[0]

print([heapq.heappop(heap) for _ in range(len(nums))]) # 堆排序結(jié)果

# out: [1, 2, 3, 5, 23, 54, 132]

# 第二種

nums = [2, 3, 5, 1, 54, 23, 132]

heapq.heapify(nums)

print([heapq.heappop(nums) for _ in range(len(nums))]) # 堆排序結(jié)果

# out: [1, 2, 3, 5, 23, 54, 132]

heapq 模塊還有一個(gè)heapq.merge(*iterables) 方法，用于合并多個(gè)排序后的序列成一個(gè)排序后的序列， 返回排序后的值的迭代器。

類似于sorted(itertools.chain(*iterables))，但返回的是可迭代的。

"""

函數(shù)定義：

heapq.merge(*iterables)

- Merge multiple sorted inputs into a single sorted output (for example, merge timestamped entries from multiple log files). Returns an iterator over the sorted values.

- Similar to sorted(itertools.chain(*iterables)) but returns an iterable, does not pull the data into memory all at once, and assumes that each of the input streams is already sorted (smallest to largest).

"""

import heapq

num1 = [32, 3, 5, 34, 54, 23, 132]

num2 = [23, 2, 12, 656, 324, 23, 54]

num1 = sorted(num1)

num2 = sorted(num2)

res = heapq.merge(num1, num2)

print(list(res))

訪問堆內(nèi)容

堆創(chuàng)建好后，可以通過`heapq.heappop() 函數(shù)彈出堆中最小值。

import heapq

nums = [2, 43, 45, 23, 12]

heapq.heapify(nums)

print(heapq.heappop(nums))

# out: 2

# 如果需要所有堆排序后的元素

result = [heapq.heappop(nums) for _ in range(len(nums))]

print(result)

# out: [12, 23, 43, 45]

如果需要?jiǎng)h除堆中最小元素并加入一個(gè)元素，可以使用heapq.heaprepalce() 函數(shù)

import heapq

nums = [1, 2, 4, 5, 3]

heapq.heapify(nums)

heapq.heapreplace(nums, 23)

print([heapq.heappop(nums) for _ in range(len(nums))])

# out: [2, 3, 4, 5, 23]

獲取堆最大或最小值

如果需要獲取堆中最大或最小的范圍值，則可以使用heapq.nlargest() 或heapq.nsmallest() 函數(shù)

"""

函數(shù)定義：

heapq.nlargest(n, iterable[, key])?

- Return a list with the n largest elements from the dataset defined by iterable.

- key if provided, specifies a function of one argument that is used to extract a comparison key from each element in the iterable: key=str.lower

- Equivalent to: sorted(iterable, key=key, reverse=True)[:n]

"""

import heapq

nums = [1, 3, 4, 5, 2]

print(heapq.nlargest(3, nums))

print(heapq.nsmallest(3, nums))

"""

輸出：

[5, 4, 3]

[1, 2, 3]

"""

這兩個(gè)函數(shù)還接受一個(gè)key參數(shù)，用于dict或其他數(shù)據(jù)結(jié)構(gòu)類型使用

import heapq

from pprint import pprint

portfolio = [

{'name': 'IBM', 'shares': 100, 'price': 91.1},

{'name': 'AAPL', 'shares': 50, 'price': 543.22},

{'name': 'FB', 'shares': 200, 'price': 21.09},

{'name': 'HPQ', 'shares': 35, 'price': 31.75},

{'name': 'YHOO', 'shares': 45, 'price': 16.35},

{'name': 'ACME', 'shares': 75, 'price': 115.65}

]

cheap = heapq.nsmallest(3, portfolio, key=lambda s: s['price'])

expensive = heapq.nlargest(3, portfolio, key=lambda s: s['price'])

pprint(cheap)

pprint(expensive)

"""

輸出：

[{'name': 'YHOO', 'price': 16.35, 'shares': 45},

{'name': 'FB', 'price': 21.09, 'shares': 200},

{'name': 'HPQ', 'price': 31.75, 'shares': 35}]

[{'name': 'AAPL', 'price': 543.22, 'shares': 50},

{'name': 'ACME', 'price': 115.65, 'shares': 75},

{'name': 'IBM', 'price': 91.1, 'shares': 100}]

"""

heapq應(yīng)用

實(shí)現(xiàn)heap堆排序算法

>>> def heapsort(iterable):

... h = []

... for value in iterable:

... heappush(h, value)

... return [heappop(h) for i in range(len(h))]

...

>>> heapsort([1, 3, 5, 7, 9, 2, 4, 6, 8, 0])

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

該算法和sorted(iterable) 類似，但是它是不穩(wěn)定的。

堆的值可以是元組類型，可以實(shí)現(xiàn)對(duì)帶權(quán)值的元素進(jìn)行排序。

>>> h = []

>>> heappush(h, (5, 'write code'))

>>> heappush(h, (7, 'release product'))

>>> heappush(h, (1, 'write spec'))

>>> heappush(h, (3, 'create tests'))

>>> heappop(h)

(1, 'write spec')

參考

總結(jié)

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