# python之排序操作及heapq模块

sorted(iterable, *, key=None, reverse=False)

``````list1=[1,6,4,3,9,5]
list2=['12','a6','4','c34','b9','5']

print(sorted(list1))    #[1, 3, 4, 5, 6, 9]
print(sorted(list2))    #['12', '4', '5', 'a6', 'b9', 'c34']
#总结上面两种排序：字符串排序根据元素首字符的ASCII比较进行排序，
#数字类型按照大小排序，数字不能混合排序

list3=[
{'name':'jim','age':23,'price':500},
{'name':'mase','age':23,'price':600},
{'name':'tom','age':25,'price':2000},
{'name':'alice','age':22,'price':300},
{'name':'rose','age':21,'price':2400},
]

print(sorted(list3,key=lambda s:(s['age'],s['price'])))
#[{'name': 'rose', 'age': 21, 'price': 2400}, {'name': 'alice', 'age': 22, 'price': 300}, {'name': 'jim', 'age': 23, 'price': 500}, {'name': 'mase', 'age': 23, 'price': 600}, {'name': 'tom', 'age': 25, 'price': 2000}]

operator模块中的方法itemgetter
>>> itemgetter(1)('ABCDEFG')
'B'
>>> itemgetter(1,3,5)('ABCDEFG')
('B', 'D', 'F')
>>> itemgetter(slice(2,None))('ABCDEFG')
'CDEFG

print(sorted(list3,key=itemgetter('age','price')))    #结果同上但效率会比较高
``````

heapq(Python内置的模块)

``````__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge',
'nlargest', 'nsmallest', 'heappushpop']``````

nlargest与nsmallest,通过字面意思可以看出方法大致的作用，接下来动手测验

``````nlargest(n, iterable, key=None)
nsmallest(n, iterable, key=None)
#n:查找个数    iterable:可迭代对象    key：同sorted

list1=[1,6,4,3,9,5]
list2=['12','a6','4','c34','b9','5']
list3=[
{'name':'jim','age':23,'price':500},
{'name':'mase','age':23,'price':600},
{'name':'tom','age':25,'price':2000},
{'name':'alice','age':22,'price':300},
{'name':'rose','age':21,'price':2400},
]

from operator import itemgetter
import heapq

print(heapq.nlargest(len(list1),list1))
print(heapq.nlargest(len(list2),list2))
print(heapq.nlargest(len(list3),list3,key=itemgetter('age','price')))
#以上代码输出结果同sorted

print(heapq.nsmallest(len(list1),list1))
print(heapq.nsmallest(len(list2),list2))
print(heapq.nsmallest(len(list3),list3,key=itemgetter('age','price')))
#结果是降序
[1, 3, 4, 5, 6, 9]
['12', '4', '5', 'a6', 'b9', 'c34']
[{'name': 'rose', 'age': 21, 'price': 2400}, {'name': 'alice', 'age': 22, 'price': 300}, {'name': 'jim', 'age': 23, 'price': 500}, {'name': 'mase', 'age': 23, 'price': 600}, {'name': 'tom', 'age': 25, 'price': 2000}]``````

heappush,heappop,heapify,heapreplace,heappushpop

``````heapify：对序列进行堆排序，
heappush:在堆序列中添加值
heappop:删除最小值并返回
heappushpop:添加并删除堆中最小值且返回，添加之后删除
heapreplace:添加并删除队中最小值且返回，删除之后添加

nums=[54,23,64.,323,53,3,212,453,65]
heapify(nums)    #先进行堆排序
print(heappop(nums))    #3
print(heappush(nums,50))    #添加操作，返回None
print(heappushpop(nums,10))    #由于是添加后删除，所以返回10
print(heappop(nums))    #23
print(heapreplace(nums,10))    #和heappushpop，返回50
print(nums)    #[10, 53, 54, 65, 323, 64.0, 212, 453]``````

merge：合并多个序列

``````list1 = [1, 2, 3, 4, 5, 12]
set1 = {2, 3, 9, 23, 54}
s = list(merge(list1,set1))
print(s)    #[1, 2, 2, 3, 3, 4, 5, 9, 12, 54, 23]
#发现输出结果不仅进行了合并，还进行了排序，有意思哈，可是换个代码测验，你再看一下

list1 = [31, 2, 83, 24, 5, 12]
set1 = {2, 83, 9, 23, 54}
s = list(merge(list1,set1))
print(s)    #[2, 9, 31, 2, 83, 24, 5, 12, 83, 54, 23]
#你们肯定想这是什么鬼，一点都没有头绪，其实经过我的多次测验，还是有规律的，但是由于没有什么作用就不大篇幅说明了，喜欢刨根问题的小伙伴可以尝试自己思考一下。``````

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