python function programming return function anonymous function decorator partial function

Return function

The return object of a function can be a function, which is not executed immediately when it returns, but is executed when the return object is called

def lazy_sum(*args):
    def sum():
        ax = 0
        for n in args:
            ax = ax + n
        return ax
    return sum
>>> f1 = lazy_sum(1, 3, 5, 7, 9)
>>> f2 = lazy_sum(1, 3, 5, 7, 9)
>>> f1==f2
False

When the lazy_sumfunction sumis returned , the relevant parameters and variables are stored in the returned function, which is called Closure.

Try to avoid referencing loop variables in the closure, otherwise problems may occur

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

#解决办法 是另外定义一个函数 固定住参数
def count():
    def f(j):
        def g():
            return j*j
        return g
    fs = []
    for i in range(1, 4):
        fs.append(f(i)) # f(i)立刻被执行，因此i的当前值被传入f()
    return fs
>>> f1, f2, f3 = count()
>>> f1()
1
>>> f2()
4
>>> f3()
9
#
def createCounter():
    num = 0
    def counter():
        nonlocal num
        num=num+1
        return num
    return counter
# 测试:
counterA = createCounter()
print(counterA(), counterA(), counterA(), counterA(), counterA()) # 1 2 3 4 5
counterB = createCounter()
if [counterB(), counterB(), counterB(), counterB()] == [1, 2, 3, 4]:
    print('测试通过!')
else:
    print('测试失败!')

Use the closure to return a counter function, each time it is called, it returns an incrementing integer;

nonlocal means global variable

Anonymous function

An anonymous function has only one expression, so you don’t need to worry about function name conflicts and call it directly; lambda x, x represent parameters;

>>> list(map(lambda x: x * x, [1, 2, 3, 4, 5, 6, 7, 8, 9]))
[1, 4, 9, 16, 25, 36, 49, 64, 81]

L = list(filter(lambda n: n % 2 == 1, range(1, 20)))
print(L)#打印奇数
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]

Decorator

def log(func):
    def wrapper(*args, **kw):
        print('call %s():' % func.__name__)
        return func(*args, **kw)
    return wrapper
#@语法，把decorator置于函数的定义处
@log
def now():
    print('2015-3-25')

>>> now()
call now():
2015-3-25

wrapper()The parameter definition of the function is (*args, **kw), therefore, the wrapper()function can accept calls with arbitrary parameters. In the wrapper()function, first print the log, and then call the original function.

More complicated usage:

def log(text):
    def decorator(func):
        def wrapper(*args, **kw):
            print('%s %s():' % (text, func.__name__))
            return func(*args, **kw)
        return wrapper
    return decorator

#__name__等属性复制到wrapper()
import functools
def log(func):
    @functools.wraps(func)
    def wrapper(*args, **kw):
        print('call %s():' % func.__name__)
        return func(*args, **kw)
    return wrapper

Design decorator, it can act on any function, and print the execution time of the function: 

import time, functools
def metric(func):
    @functools.wraps(func)
    def wrapper(*args, **kw):
        s = time.time()
        x = func(*args, **kw)
        e = time.time()
        print('%s executed in %s ms' % (func.__name__, (e-s)*1000.0))
        return x
    return wrapper
# 测试
@metric
def fast(x, y):
    time.sleep(0.0012)
    return x + y;

@metric
def slow(x, y, z):
    time.sleep(0.1234)
    return x * y * z;

f = fast(11, 22)
s = slow(11, 22, 33)
if f != 33:
    print('测试失败!')
elif s != 7986:
    print('测试失败!')

Partial function

functools.partialThe role of is to fix certain parameters of a function (that is, set the default value), return a new function, and call this new function easier.

>>> import functools
>>> int2 = functools.partial(int, base=2)
>>> int2('1000000')
64
>>> int2('1010101')
85

Reference source: https://www.liaoxuefeng.com/wiki/1016959663602400/1017454145929440