关于sorted与sort的比较
Another difference is that the list.sort() method is only defined for lists. In contrast, the sorted() function accepts any iterable.
>>> sorted({1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'})
[1, 2, 3, 4, 5]
list,sort只能用于列表，而sorted可以接收任何迭代器
关于key
Starting with Python 2.4, both list.sort() and sorted() added a key parameter to specify a function to be called on each list element prior to making comparisons.
key参数用于知名一个函数，这个函数对于每一个元素被调用，与前一个进行比较。
For example, here's a case-insensitive string comparison:
>>> sorted("This is a test string from Andrew".split(), key=str.lower)
['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
字符串.split() 默认分隔符将字符串分成若干片段：空格，回车等
str.lower() 小写形式
The value of the key parameter should be a function thattakes a single argument and returns a key to use for sorting purposes. This technique is fast because the key function is called exactly once for each input record.
A common pattern is to sort complex objects using some of the object's indices as a key. For example:
>>> student_tuples = [
('john', 'A', 15),
('jane', 'B', 12),
('dave', 'B', 10),
]
>>> sorted(student_tuples, key=lambda student: student[2]) # sort by age
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
关于lambda
http://www.cnblogs.com/wanpython/archive/2010/11/01/1865919.html
下面举几个python lambda的例子吧
1单个参数的：
g = lambda x:x*2
print g(3)
结果是6
2多个参数的：
m = lambda x,y,z: (x-y)*z
print m(3,1,2)
结果是4
本例中定义了一个传入参数为student ，返回值是传入函数中的第3个元素的函数。
The same technique works for objects with named attributes. For example:
>>> class Student:
def __init__(self, name, grade, age):
self.name = name
self.grade = grade
self.age = age
def __repr__(self):
return repr((self.name, self.grade, self.age))
>>> student_objects = [
Student('john', 'A', 15),
Student('jane', 'B', 12),
Student('dave', 'B', 10),
]
>>> sorted(student_objects, key=lambda student: student.age) # sort by age
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
The operator module has itemgetter, attrgetter, and starting in Python 2.6 a methodcaller function.
Using those functions, the above examples become simpler and faster.
>>> from operator import itemgetter, attrgetter
>>> sorted(student_tuples, key=itemgetter(2))
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
>>> sorted(student_objects, key=attrgetter('age'))
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age:
>>> sorted(student_tuples, key=itemgetter(1,2))
[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
>>> sorted(student_objects, key=attrgetter('grade', 'age'))
[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
关于reverse
Both list.sort() and sorted() accept a reverse parameter with a boolean value. This is using to flag descending sorts. For example, to get the student data in reverse age order:
>>> sorted(student_tuples, key=itemgetter(2), reverse=True)
[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
>>> sorted(student_objects, key=attrgetter('age'), reverse=True)
[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
Starting with Python 2.2, sorts are guaranteed to be stable. That means that when multiple records have the same key, their original order is preserved.
>>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)]
>>> sorted(data, key=itemgetter(0))
[('blue', 1), ('blue', 2), ('red', 1), ('red', 2)]
Notice how the two records for 'blue' retain their original order so that ('blue', 1) is guaranteed to precede ('blue', 2).
This wonderful property lets you build complex sorts in a series of sorting steps. For example, to sort the student data by descending grade and then ascending age, do the age sort first and then sort again using grade:
>>> s = sorted(student_objects, key=attrgetter('age')) # sort on secondary key
>>> sorted(s, key=attrgetter('grade'), reverse=True) # now sort on primary key, descending
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]