初学Python函数的笔记整理
定义
返回单值
def my_abs(x):
if x >= 0:
return x
else:
return -x
返回多值
返回多值就是返回一个tuple
import math def move(x, y, step, angle=0): nx = x + step * math.cos(angle) ny = y - step * math.sin(angle) return nx, ny
空函数
def nop(): pass
指定默认参数
必选参数在前，默认参数在后。默认参数需指向不可变对象（默认参数值在函数定义时被计算）
def power(x, n=2):
s = 1
while n > 0:
n = n - 1
s = s * x
return s
可变参数
def calc(*numbers):
sum = 0
for n in numbers:
sum = sum + n * n
return sum
调用可变参数的函数方法
>>> calc(1, 2) 5 >>> calc() 0 >>> nums = [1, 2, 3] >>> calc(*nums) 14
关键字参数
def person(name, age, **kw): print 'name:', name, 'age:', age, 'other:', kw
调用关键字参数的方法
>>> person('Michael', 30)
name: Michael age: 30 other: {}
>>> person('Bob', 35, city='Beijing')
name: Bob age: 35 other: {'city': 'Beijing'}
>>> person('Adam', 45, gender='M', job='Engineer')
name: Adam age: 45 other: {'gender': 'M', 'job': 'Engineer'}
>>> kw = {'city': 'Beijing', 'job': 'Engineer'}
>>> person('Jack', 24, **kw)
name: Jack age: 24 other: {'city': 'Beijing', 'job': 'Engineer'}
注：
参数定义的顺序必须是：必选参数、默认参数、可变参数和关键字参数。
对于任意函数，都可以通过类似func(*args, **kw)的形式调用它，无论它的参数是如何定义的。
递归
如果一个函数在内部调用自身本身，这个函数就是递归函数。
尾递归
在函数返回的时候，调用自身本身，并且，return语句不能包含表达式。
高阶函数
- 变量可以指向函数（函数可以赋值给一个变量）
- 函数名也是变量（函数名可以赋值其他值）
- 函数可以做为函数的参数（高阶函数）
map(func, list)
map()函数接收两个参数，一个是函数，一个是序列，map将传入的函数依次作用到序列的每个元素，并把结果作为新的list返回。
>>> def f(x): ... return x * x ... >>> map(f, [1, 2, 3, 4, 5, 6, 7, 8, 9]) [1, 4, 9, 16, 25, 36, 49, 64, 81]
reduce把一个函数作用在一个序列[x1, x2, x3…]上，这个函数必须接收两个参数，reduce把结果继续和序列的下一个元素做累积计算。
reduce(f, [x1, x2, x3, x4]) #相当于： f(f(f(x1, x2), x3), x4) >>> def add(x, y): ... return x + y ... >>> reduce(add, [1, 3, 5, 7, 9]) 25
filter(func_return_bool, list)
把传入的函数依次作用于每个元素，然后根据返回值是True还是False决定保留还是丢弃该元素。
def is_odd(n): return n % 2 == 1 filter(is_odd, [1, 2, 4, 5, 6, 9, 10, 15]) # 结果: [1, 5, 9, 15]
sorted
对于两个元素x和y，如果认为x < y，则返回-1，如果认为x == y，则返回0，如果认为x > y，则返回1，
>>> sorted([36, 5, 12, 9, 21]) [5, 9, 12, 21, 36]
高阶函数用法
def reversed_cmp(x, y):
if x > y:
return -1
if x < y:
return 1
return 0
>>> sorted([36, 5, 12, 9, 21], reversed_cmp)
[36, 21, 12, 9, 5]
函数做为返回值
def lazy_sum(*args):
def sum():
ax = 0
for n in args:
ax = ax + n
return ax
return sum
>>> f = lazy_sum(1, 3, 5, 7, 9)
>>> f
<function sum at 0x10452f668>
>>> f()
25
注：每次调用lazy_sum()都会返回一个新的函数，即使传入相同的参数。
闭包
def count():
fs = []
for i in range(1, 4):
def f():
return i*i
fs.append(f)
return fs
f1, f2, f3 = count()
>>> f1()
9
>>> f2()
9
>>> f3()
9
原因是调用count的时候循环已经执行，但是f()还没有执行，直到调用其时才执行。所以返回函数不要引用任何循环变量，或者后续会发生变化的变量。
匿名函数（lambda表达式）
等价于:
def f(x): return x * x
关键字lambda表示匿名函数，冒号前面的x表示函数参数。
匿名函数做为返回值
def build(x, y): return lambda: x * x + y * y
装饰器（@func）
在代码运行期间动态增加功能的方式，称之为“装饰器”（Decorator），本质上，decorator就是一个返回函数的高阶函数。
def log(func):
def wrapper(*args, **kw):
print 'call %s():' % func.__name__
return func(*args, **kw)
return wrapper
@log
def now():
print '2013-12-25'
>>> now()
call now():
2013-12-25
#相当于执行：
now = log(now)
回到顶部
带参数的装饰器
def log(text):
def decorator(func):
def wrapper(*args, **kw):
print '%s %s():' % (text, func.__name__)
return func(*args, **kw)
return wrapper
return decorator
@log('execute')
def now():
print '2013-12-25'
#执行结果
>>> now()
execute now():
2013-12-25
#相当于执行：
>>> now = log('execute')(now)
剖析：首先执行log('execute')，返回的是decorator函数，再调用返回的函数，参数是now函数，返回值最终是wrapper函数。
import functools
def log(func):
@functools.wraps(func)
def wrapper(*args, **kw):
print 'call %s():' % func.__name__
return func(*args, **kw)
return wrapper
#对于带参函数
import functools
def log(text):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kw):
print '%s %s():' % (text, func.__name__)
return func(*args, **kw)
return wrapper
return decorator
>>> import functools
>>> int2 = functools.partial(int, base=2)
>>> int2('1000000')
64
>>> int2('1010101')
85
#相当于：
def int2(x, base=2):
return int(x, base)
max2 = functools.partial(max, 10)
相当于为max函数指定了第一个参数
max2(5, 6, 7) #相当于： max(10, 5, 6, 7)
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