python的几个概念
1、函数在传递实参的时候是传递的是引用而不是从内存中重新赋相同值给形参。
2、函数名带圆括号和不带圆括号。函数名带圆括号是函数的调用,而函数名代表的是函数体。
3、函数返回值,在函数没有返回值的时候默认是None。
4、python 中的False:0、False、[]、{}、()、''、""。空列表、空字典、空字符创、空元组都是False.
二:内置函数
1:callable()该函数检测对象是否可以被调用。不常用这个函数。
2:chr()是把数字转换成对应的字母。
ord()是把对应的字母转成数字。
3、exec('print(2);import os;print os.dir')执行一连串命令;eval('2*2+3')执行表达式
4、dir(list)、help(list);dir显示函数方法和函数、help的帮助信息更详细
5、装饰器
语法:@函数名
自动执行outer()函数并且将下面的函数f1当做参数传递。
将outer函数的返回值,重新赋值给f1。
执行顺序:
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" alt="" />
python执行顺序是,从上到下,先将outer函数加载到内存,然后把函数f1加载到内存。在执行f1的时候,由于f1被outer装饰,f1作为实参赋值给outer函数的形参func,然后将outer函数的返回函数inner赋值给f1,
所以执行inner函数,因为到func()的调用时候,执行f1的旧函数的函数体,也就是7,执行完在执行第8步,如果f1有返回值,那么inner函数将返回相同的返回值。
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