python 【第三篇】:函数及参数

函数背景

在学习函数之前,一直遵循:面向过程编程:

根据业务逻辑从上到下实现功能,其往往用一长段代码来实现指定功能,开发过程中最常见的操作就是粘贴复制,也就是将之前实现的代码块复制到现需功能处,如下:

 while True:
if cpu利用率 > 90%:
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 if 硬盘使用空间 > 90%:
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 if 内存占用 > 80%:
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接

分析可以看出如下:

1.除了判断条件是不同之处外,所有的其他信息都是相同的。

2.能不能将这些代码归并成一个,减少重复的代码?

3.函数?

 def 发送邮件(内容)
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 while True: if cpu利用率 > 90%:
发送邮件('CPU报警') if 硬盘使用空间 > 90%:
发送邮件('硬盘报警') if 内存占用 > 80%:

思考如下:

1.相同之处被单独放到了一个函数里面,减少了代码量,增强了可读性。

2.是不是之后如果遇到发邮件的需求,可以直接将这个函数直接copy复用,或者调用呢?增强了复用性。

3.函数里面传了参数,参数如何用?

函数式编程和面向过程编程的区别:

  • 面向过程:根据需求一行一行垒代码!代码逻辑从上往下、并且功能都是一个完了之后下一个才能执行。代码重复、不易修改重用性差!
  • 函数式:将某功能代码封装到函数中,日后便无需重复编写,仅调用函数即可
  • 面向对象:对函数进行分类和封装,让开发“更快更好更强...”

函数的定义

函数式编程最重要的是增强代码的重用性和可读性。

1 def 函数名(参数):
2
3 ...
4 函数体
5 ...

函数的定义主要有如下要点:

  • def:表示函数的关键字
  • 函数名:函数的名称,日后根据函数名调用函数
  • 函数体:函数中进行一系列的逻辑计算,如:发送邮件、计算出 [11,22,38,888,2]中的最大数等...
  • 参数:为函数体提供数据
  • 返回值:当函数执行完毕后,可以给调用者返回数据,默认返回None。

1、返回值

函数是一个功能块,该功能到底执行成功与否,需要通过返回值来告知调用者。

 def 发送短信():

     发送短信的代码...

     if 发送成功:
return True
else:
return False while True: # 每次执行发送短信函数,都会将返回值自动赋值给result
# 之后,可以根据result来写日志,或重发等操作 result = 发送短信()
if result == False:
记录日志,短信发送失败...

2.参数

参数,参数,参数,重要的事情说三遍,但是这是啥米东西?

 复制代码

 def CPU报警邮件()
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 def 硬盘报警邮件()
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 def 内存报警邮件()
#发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 while True: if cpu利用率 > 90%:
CPU报警邮件() if 硬盘使用空间 > 90%:
硬盘报警邮件() if 内存占用 > 80%:
内存报警邮件()

无参数示例

 def 发送邮件(邮件内容)

     #发送邮件提醒
连接邮箱服务器
发送邮件
关闭连接 while True: if cpu利用率 > 90%:
发送邮件("CPU报警了。") if 硬盘使用空间 > 90%:
发送邮件("硬盘报警了。") if 内存占用 > 80%:
发送邮件("内存报警了。")

有参数实例

函数中三个不同的函数:

  • 普通参数
  • 默认参数
  • 动态参数

普通参数:

普通参数的传递并没有个数和数据类型的限制,可以传递字符串,数字,列表和字典。也不限定个数,需要注意的是:函数需要多少参数,调用的时候就要按照它定义时的顺序和数据类型传递过去。

可以是任意类型:

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# ######### 定义函数 ######### 

# name 叫做函数func的形式参数,简称:形参
def func(name):
print name # ######### 执行函数 #########
# 'test123' 叫做函数func的实际参数,简称:实参
func('test123')

默认参数:

默认参数顾名思义就是本身就带一个默认的值;

1.比如你去给人打工,中午吃饭的时候东家提供饭菜自然好,如果不提供你是不是得自己带点(可能不好吃),饿着的话肚子会不干的。

同理如果传递了这个参数,那么我们就使用传递过来的参数,如果不传递就使用默认的参数;

2.如果东家比较大方,管饭、管酒、管汤那岂不快哉,即便自己带饭也得带个黄瓜西红柿的吧,不能干吃馒头吧!

同理默认参数可以有多个,但是必须放在所有的参数后边,要不就会报错!原因是:他的参数赋值是一个一个的赋值。如果提供了默认值的形参,你默认一定要往后排序为了就是你给那些没有陪默认值的参数 !

 def argvtest(argv1,argv2 = 'aaa',argv3 = 'bbb'):
print 'argv1:',argv1
print 'argv2:',argv2
print 'argv3:',argv3 argvtest('a1',argv3 = 'a2') 输出的内容:
argv1: a1
argv2: aaa
argv3: a2

动态参数:

动态参数一:

def func(*args) 接受多个参数,内部自动构造元组,序列前加*,避免内部构造元组。

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" 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结果:

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" alt="" />

 #!/usr/bin/env python
# -*- coding:utf-8 -*-
#
# def f1(a):
# print a,type(a) # f1(123)
# f1('aaa')
# f1([11,22,33,44]) #动态参数一
def f1(*a):
#a = (123)单个值也会组成元祖
#a = (123,456)多个传入值也会组成元祖。
print a,type(a) li = [1,2,3,4,5,6]
li1 =(77,88,99)
#各种类型传入,结果类型都是组成元祖
f1(123)
f1(123,456)
f1('aaa')
f1(111,'ssss',(33,44),[11,22,33,44],{'k1':1,'k2':2}) #如果传入的是列表,或者元祖,都会被当成新元祖的元素
print
f1(li)
f1(li1) #如果想输入的列表或者元祖成为单个的元素,在前面加*就会按照列表或者元祖的多个元素当参数传入
print
f1(*li)
f1(*li1)

动态参数1实例

结论:

1.接受多个参数

2.不管你传入什么类型的实参,最后都会组合成元祖。

3.序列前面加*,可以避免内部构造元祖

动态参数二:

def func(**kwargs) 接收多个参数,内部自动构造字典,序列前加**,直接传递字典

aaarticlea/png;base64,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" alt="" />

结果:

aaarticlea/png;base64,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" alt="" />

结论:

1.接受多个参数

2.不管你传入什么类型的实参,最后都会组合成字典。

3.序列前面加**,可以避免内部构造字典

动态参数三:

def func(*args,**kwargs):接受多个参数,既可以自动构造元组,又可以自动构造字典,传入*和**可避免内部构造元祖和字典。

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" alt="" />

结果:

aaarticlea/png;base64,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" alt="" />

代码:

 def f2(*a,**aa):
print a,type(a)
print aa,type(aa)
li = [1,2,3,4,5,6]
li1 ={'k3':111,'k4':'aaaa'}
f2(11,22,33)
print '***********'
f2(k1=123,k2='aaa')
print '***********'
f2(11,22,33,k1=123,k2='aaa')
print '***********'
f2(*li,**li1)

结论:

1.这个被称为万能参数,可以接受各种类型的参数;

2.当你只传入多个元素,但不包含k-v模式,参数会被组合成元祖,字典值存在但是为空;

3.当你只传入多个元素,只包含k-v模式,参数会被组合成字典,元祖存在但是为空;

4.*参数必须在**参数前面。

局部变量和全局变量

全局变量:可以在任何地方调用。
局部变量:在对应的生成中可以被调用,比如在函数里面生成的,就只能在函数内部调用。
 p = 'alex' #全局变量
def func1():
#局部变量
a = 123
p = 'eric'
print a def fun2():
#局部变量
a = 456
print p
print a func1()
func2()

总结:

建议规范:全局变量都大写,局部变量全小写。
1.声明在函数外面的是全局变量;
2.全局变量可以被函数调用;
3.在函数内部修改全局变量是不生效的,只是相当于又生成了一个和全局变量同名字的新局部变量,之后在函数内部调用,只会是局部变量的值生效;
4.如果想修改全局变量的值,必须得在你修改之前加一global p 这样就可以修改了。

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