Python中的heapq模块提供了一种堆队列heapq类型,这样实现堆排序等算法便相当方便,
这里我们就来详解Python中heapq模块的用法,需要的朋友可以参考下
heapq 模块提供了堆算法。heapq是一种子节点和父节点排序的树形数据结构。
这个模块提供heap[k] <= heap[2k+1] and heap[k] <= heap[2k+2]。
为了比较不存在的元素被人为是无限大的。heap最小的元素总是[0]。
打印 heapq 类型
import math
import random
from cStringIO import StringIO
def show_tree(tree, total_width=36, fill=' '):
output = StringIO()
last_row = -1
for i, n in enumerate(tree):
if i:
row = int(math.floor(math.log(i+1, 2)))
else:
row = 0
if row != last_row:
output.write('\n')
columns = 2**row
col_width = int(math.floor((total_width * 1.0) / columns))
output.write(str(n).center(col_width, fill))
last_row = row
print output.getvalue()
print '-' * total_width
print
return
data = random.sample(range(1,8), 7)
print 'data: ', data
show_tree(data)
打印结果
data: [3, 2, 6, 5, 4, 7, 1]
3
2 6
5 4 7 1
-------------------------
heapq.heappush(heap, item)
push一个元素到heap里, 修改上面的代码
heap = []
data = random.sample(range(1,8), 7)
print 'data: ', data
for i in data:
print 'add %3d:' % i
heapq.heappush(heap, i)
show_tree(heap)
打印结果
data: [6, 1, 5, 4, 3, 7, 2]
add 6:
6
------------------------------------
add 1:
1
6
------------------------------------
add 5:
1
6 5
------------------------------------
add 4:
1
4 5
6
------------------------------------
add 3:
1
3 5
6 4
------------------------------------
add 7:
1
3 5
6 4 7
------------------------------------
add 2:
1
3 2
6 4 7 5
------------------------------------
根据结果可以了解，子节点的元素大于父节点元素。而兄弟节点则不会排序。
heapq.heapify(list)
将list类型转化为heap, 在线性时间内, 重新排列列表。
print 'data: ', data
heapq.heapify(data)
print 'data: ', data
show_tree(data)
打印结果
data: [2, 7, 4, 3, 6, 5, 1]
data: [1, 3, 2, 7, 6, 5, 4]
1
3 2
7 6 5 4
------------------------------------
heapq.heappop(heap)
删除并返回堆中最小的元素, 通过heapify() 和heappop()来排序。
data = random.sample(range(1, 8), 7)
print 'data: ', data
heapq.heapify(data)
show_tree(data)
heap = []
while data:
i = heapq.heappop(data)
print 'pop %3d:' % i
show_tree(data)
heap.append(i)
print 'heap: ', heap
打印结果
data: [4, 1, 3, 7, 5, 6, 2]
1
4 2
7 5 6 3
------------------------------------
pop 1:
2
4 3
7 5 6
------------------------------------
pop 2:
3
4 6
7 5
------------------------------------
pop 3:
4
5 6
7
------------------------------------
pop 4:
5
7 6
------------------------------------
pop 5:
6
7
------------------------------------
pop 6:
7
------------------------------------
pop 7:
------------------------------------
heap: [1, 2, 3, 4, 5, 6, 7]
可以看到已排好序的heap。
heapq.heapreplace(iterable, n)
删除现有元素并将其替换为一个新值。
data = random.sample(range(1, 8), 7)
print 'data: ', data
heapq.heapify(data)
show_tree(data)
for n in [8, 9, 10]:
smallest = heapq.heapreplace(data, n)
print 'replace %2d with %2d:' % (smallest, n)
show_tree(data)
打印结果
data: [7, 5, 4, 2, 6, 3, 1]
1
2 3
5 6 7 4
------------------------------------
replace 1 with 8:
2
5 3
8 6 7 4
------------------------------------
replace 2 with 9:
3
5 4
8 6 7 9
------------------------------------
replace 3 with 10:
4
5 7
8 6 10 9
------------------------------------
heapq.nlargest(n, iterable) 和 heapq.nsmallest(n, iterable)
返回列表中的n个最大值和最小值
data = range(1,6)
l = heapq.nlargest(3, data)
print l # [5, 4, 3]
s = heapq.nsmallest(3, data)
print s # [1, 2, 3]
PS：一个计算题
构建元素个数为 K=5 的最小堆代码实例:
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Author: kentzhan
#
import heapq
import random
heap = []
heapq.heapify(heap)
for i in range(15):
item = random.randint(10, 100)
print "comeing ", item,
if len(heap) >= 5:
top_item = heap[0] # smallest in heap
if top_item < item: # min heap
top_item = heapq.heappop(heap)
print "pop", top_item,
heapq.heappush(heap, item)
print "push", item,
else:
heapq.heappush(heap, item)
print "push", item,
pass
print heap
pass
print heap
print "sort"
heap.sort()
print heap
例:
import heapq
# 接受 list tuple 中包含的int和str型dict
# 也接受中文,不过不清楚是怎么对比的
nums = [1,2,3,213,23123,213,21,33,434,53,342,344,-324]
# nums = ['2017-12-22', '2014-12-22', '2016-03-22', '2015-04-22', '2017-04-25', '2017-05-22',]
# nums = ['p9','s8','a0','9z','0z']
# nums = ['我','人','有','的','这']
# print(heapq.nlargest(2,nums)) # 返回 nums 中包含2个最大的元素的列表
# print(heapq.nsmallest(3,nums)) # 返回 nums 中包含3个最小的元素的列表
#结果
# [23123, 434]
# [-324, 1, 2]
# ['2017-12-22', '2017-05-22']
# ['2014-12-22', '2015-04-22', '2016-03-22']
# ['s8', 'p9']
# ['0z', '9z', 'a0']
# ['这', '的']
# ['人', '我', '有']
info = [
{'name':'a0','time':'2014-09-23','price':231.2},
{'name':'0b','time':'2017-03-23','price':231.2},
{'name':'z1','time':'2019-09-23','price':231.2},
{'name':'2z','time':'2010-09-23','price':231.2},
{'name':'2a','time':'2017-12-23','price':231.2}
]
l_names = heapq.nsmallest(2,info,key=lambda di:di['name'])
l_times = heapq.nsmallest(2,info,key=lambda di:di['time'])
l_prices = heapq.nsmallest(2,info,key=lambda di:di['price'])
b_names = heapq.nlargest(2, info, key=lambda di: di['name'])
b_times = heapq.nlargest(2, info, key=lambda di: di['time'])
b_prices = heapq.nlargest(2, info, key=lambda di: di['price'])
# print(l_names, b_names,l_times, b_times,l_prices,b_prices,sep='\n')
# [{'price': 231.2, 'name': '0b', 'time': '2017-03-23'}, {'price': 231.2, 'name': '2a', 'time': '2017-12-23'}]
# [{'price': 231.2, 'name': 'z1', 'time': '2019-09-23'}, {'price': 231.2, 'name': 'a0', 'time': '2014-09-23'}]
# [{'price': 231.2, 'name': '2z', 'time': '2010-09-23'}, {'price': 231.2, 'name': 'a0', 'time': '2014-09-23'}]
# [{'price': 231.2, 'name': 'z1', 'time': '2019-09-23'}, {'price': 231.2, 'name': '2a', 'time': '2017-12-23'}]
# [{'price': 231.2, 'name': 'a0', 'time': '2014-09-23'}, {'price': 231.2, 'name': '0b', 'time': '2017-03-23'}]
# [{'price': 231.2, 'name': 'a0', 'time': '2014-09-23'}, {'price': 231.2, 'name': '0b', 'time': '2017-03-23'}]
# 堆数据结构
heapq.heapify(nums)
for i in range(len(nums)):
print(heapq.heappop(nums)) #取出最小的元素
'''
-324
1
2
3
21
33
53
213
213
342
344
434
23123
'''
实现一个优先级队列
import heapq
class PriorityQueue:
""
def __init__(self):
""
self._queue = []
self._index = 0
def push(self,item,priority):
print(item,-priority)
print('+'*20)
heapq.heappush(self._queue,(-priority,self._index,item))
self._index +=1
print(self._queue,self._index)
def pop(self):
print(self._queue)
return heapq.heappop(self._queue)[-1]
class Item:
def __init__(self,name):
self.name = name
def __repr__(self):
# print(r'Item({!r})'.format(self.name))
return 'Item({!r})'.format(self.name)
q = PriorityQueue()
q.push(Item('foo'),1)
q.push(Item('bar'),5)
q.push(Item('spam'),4)
q.push(Item('grok'),100)
print('_'*20)
print(q.pop())
print(q.pop())
print(q.pop())
print(q.pop())
# Item('foo') - 1
# ++++++++++++++++++++
# [(-1, 0, Item('foo'))]
# 1
# Item('bar') - 5
# ++++++++++++++++++++
# [(-5, 1, Item('bar')), (-1, 0, Item('foo'))]
# 2
# Item('spam') - 4
# ++++++++++++++++++++
# [(-5, 1, Item('bar')), (-1, 0, Item('foo')), (-4, 2, Item('spam'))]
# 3
# Item('grok') - 100
# ++++++++++++++++++++
# [(-100, 3, Item('grok')), (-5, 1, Item('bar')), (-4, 2, Item('spam')), (-1, 0, Item('foo'))]
# 4
# ____________________
# [(-100, 3, Item('grok')), (-5, 1, Item('bar')), (-4, 2, Item('spam')), (-1, 0, Item('foo'))]
# Item('grok')
# [(-5, 1, Item('bar')), (-1, 0, Item('foo')), (-4, 2, Item('spam'))]
# Item('bar')
# [(-4, 2, Item('spam')), (-1, 0, Item('foo'))]
# Item('spam')
# [(-1, 0, Item('foo'))]
# Item('foo')
FROM: http://www.jb51.net/article/87552.htm