Python学习笔记2—内置函数

时间:2021-07-29 10:23:54

函数的使用

官方文档:https://docs.python.org/2/library/functions.html

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" 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查看帮助

>>> help(abs)
Help on built-in function abs in module __builtin__: abs(...)
abs(number) -> number Return the absolute value of the argument.
(END)

按q退出。

实例:

divmod()

>>> divmod(5,2)   #表示5除以2的商和余数
(2, 1)
>>> divmod(9,2)
(4, 1)

round()

>>> round(3.3456,3)   #保留3位小数,进行4舍五入
3.346

raw_input()

类似于bash中的read,注意它的输出都是Str类型的。

>>> name=raw_input("How old are you?")
How old are you?15
>>> type(name)
<type 'str'>
>>> print name
15

 ord()

求某个字符对应的ASSIC码值

>>> ord("a")
97
>>> ord("b")
98
>>> ord("A")
65

 hasattr()

判断一个对象是否有某一中属性

>>> a=[1,2]
>>> hasattr(a,'__iter__') #判断是否可迭代
True
>>> hasattr(3,'__iter__')
False

 range()

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

 enumerate

中文翻译是枚举的意思,

>>> seasons = ['Spring', 'Summer', 'Fall', 'Winter']
>>> list(enumerate(seasons))
[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]
>>> list(enumerate(seasons, start=1))
[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]

lambda

lambda arg1, arg2, ...argN : expression using arguments

>>> lamb = [ lambda x:x,lambda x:x**2,lambda x:x**3,lambda x:x**4 ]
>>> for i in lamb:
... print i(3),
...
3 9 27 81

 map

map(func,seq)

func 是一个函数,seq 是一个序列对象。在执行的时候,序列对象中的每个元素,按照从左到右的顺序,依次被取出来,并塞入到 func 那个函数里面,并将 func 的返回值依次存到一个 list 中。

 

>>> items=[1,2,3,4]
>>> def sqr(x):return x**2
...
>>> map (sqr,items)
[1, 4, 9, 16] >>> map(lambda x: x**2, items) #使用lambda
[1, 4, 9, 16] >>> [ x**2 for x in items ] #使用list解析
[1, 4, 9, 16]

reduce

map是上下运算,reduce 是横着逐个元素进行运算。

>>> reduce(lambda x,y: x+y,[1,2,3,4,5])
15

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" alt="" />

filter

filter(function, iterable)

>>> numbers = range(-5,5)
>>> numbers
[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
>>> filter(lambda x: x>0, numbers)
[1, 2, 3, 4] >>> [x for x in numbers if x>0] #与上面那句等效
[1, 2, 3, 4