Python基础3-进阶语法

class Fib(object):  
    def __init__(self, num):
        self.a, self.b = 0, 1
        self.num = num

    def __iter__(self):       
        return self           

    def __next__(self):
        self.a, self.b = self.b, self.a + self.b 
        if self.a > self.num: 
            raise StopIteration()
        return self.a

fib = Fib(10)
for i in fib:
    print(i) # 1 1 2 3 5 8

import itertools

#1 count(1)从1开始创建一个无限的迭代器
num = itertools.count(1)
for i in num:
    if i > 5:
        break
    print(i, end=' ') # 1 2 3 4 5 

#2 cycle()把传入的序列无限重复下去
alpha = itertools.cycle('ABC') 
n = 0
for i in alpha:
    if n > 6:
        break
    n += 1
    print(i, end=' ') # A B C A B C A

#3 repeat()把一个元素无限重复下去，第二个参数可以限定重复次数
alpha = itertools.repeat('A', 3)
for i in alpha:
    print(i, end=' ') # A A A 

#4 takewhile()根据条件截取出一个有限的序列
num = itertools.count(1)
part = itertools.takewhile(lambda x: x < 5, num)
print(list(part)) # [1, 2, 3, 4]

#5 chain()把一组迭代对象连接起来
for i in itertools.chain('ABC', 'DEF'):
    print(i, end=' ') # A B C D E F 

#6 groupby()把迭代器中相邻的重复元素挑出来放在一起
for key, group in itertools.groupby('ABBCCC'):
    print(key, list(group))
# A ['A']
# B ['B', 'B']
# C ['C', 'C', 'C']

#7 groupby()的挑选规则通过函数完成,只要作用于函数的两个元素的返回值相等，就被认为是一组，而函数返回值作为组的key
for key, group in itertools.groupby('AaBBbcCcC', lambda c: c.upper()):
    print(key, list(group))
# A ['A', 'a']
# B ['B', 'B', 'b']
# C ['c', 'C', 'c', 'C']

#1 使用推导式构建生成器
g = (x * x for x in range(5))

for i in g:
    print(i) # 0 1 4 9 16

#2 使用yield关键字构建生成器
def g():
    for i in range(5):
        yield i * i

for i in g():
    print(i) # 0 1 4 9 16

#1 闭包
def lazy_sum(*args):
    def sum():
        _sum = 0
        for n in args:
            _sum += n
        return _sum
    return sum

f = lazy_sum(1,2,3,4,5)
print(f()) # 15

#2 循环变量的当前值未被捕获：
def count():
    fs = []
    for i in range(1, 4):
        def f():
            return i*i
        fs.append(f) 
    return fs

f1, f2, f3 = count() 
print(f1(), f2(), f3()) # 9 9 9

#3 循环变量的当前值被捕获：
def count():
    fs = []
    for i in range(1, 4):
        def f(_i=i):       
            return _i*_i
        fs.append(f)    
    return fs

f1, f2, f3 = count()    
print(f1(), f2(), f3()) # 1 4 9

#4 利用闭包实现计数器，每次调用返回递增整数：
def createCounter():
    fs = [0]
    def counter():
        fs[0] = fs[0] + 1
        return fs[0]
    return counter

f = createCounter()
for i in range(5):
    print(f()) # 1 2 3 4 5

@decorate
def target():
    print('running target()')
等价于：
def target():
    print('running target()')
target = decorate(target)      # target赋值为了装饰器函数的返回值

import functools
import time

#1 装饰器 
def log(func):
    @functools.wraps(func)
    def wrapper(*args, **kw):
        print(func.__name__) 
        return func(*args, **kw)
    return wrapper

@log
def func():
    print('Hello')

func()
# func
# Hello

#2 传入参数的装饰器
def log(text):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kw):
            print(text, func.__name__)
            return func(*args, **kw)
        return wrapper
    return decorator

@log('execute')
def func():
    print('Hello')

func()
# execute func
# Hello

#3 打印函数的执行时间
def metric(fn):
    @functools.wraps(fn)
    def wrapper(*args, **kw):
        start = time.time()
        f = fn(*args, **kw)  
        print(time.time() - start)
        return f
    return wrapper

@metric
def func():
    sum = 0
    for i in range(1000000):
        sum += i*i*i*i
        sum -= i*i*i*i
    print('sum: ', sum)

func()
# sum:  0
# 0.25935

#4 装饰器类
class logit(object): 
    def __init__(self, msg):
        self.msg = msg
        print('init:', self.msg)

    def __call__(self, func):
        def wrapper(*args, **kw):       
            print(func.__name__) 
            print(self.msg)
            return func(*args, **kw)
        return wrapper

@logit('log')
def func():
    print("Hello")

func()
# init: log
# func
# log
# Hello

from functools import lru_cache

@lru_cache(maxsize=3)  # 最多缓存3个结果
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))  # 第10个斐波那契数，结果将被缓存
print(fibonacci(5))   # 第5个斐波那契数，结果将被缓存
print(fibonacci(10))  # 缓存命中，直接返回结果，不执行函数调用

from functools import singledispatch

@singledispatch
def my_func(arg):
    print("Default:", arg)

@my_func.register(int)
def _(arg):
    print("Integer:", arg)

@my_func.register(str)
def _(arg):
    print("String:", arg)

@my_func.register(list)
def _(arg):
    print("List:", arg)

# 调用函数
my_func(10)          # 输出 "Integer: 10"
my_func("hello")     # 输出 "String: hello"
my_func([1, 2, 3])   # 输出 "List: [1, 2, 3]"
my_func(3.14)        # 输出 "Default: 3.14"

#1 基于类，定义enter和exit方法
class File(object):
    def __init__(self, name, method):
        self.file = open(name, method)

    def __enter__(self):
        return self.file

    #exit返回True清空处理异常，否则抛出异常
    def __exit__(self, type, val, trace): 
        self.file.close()
        return True

with File('test.py', 'r') as f:
    f.undefined_function()
    print('Hello')  # 这里不执行了

#2 基于生成器
from contextlib import contextmanager

@contextmanager
def open_file(name):
    f = open(name, 'w')
    yield f
    f.close()

with open_file('test.txt') as f:
    f.write('hello!')

#3 首尾代码固定时：
@contextmanager
def tag(name):
    print("<%s>" % name)
    yield
    print("</%s>" % name)

with tag("h1"):
    print("hello world")
# <h1>
# hello world
# </h1>

from functools import partial

int2 = partial(int, base=2)
print(int2('110'))       # 6

max10 = partial(max, 10) # 把10加入比较
print(max10(1,2,3))      # 10

# 计算阶乘
from functools import reduce
from operator import mul

def fact(n):
	return reduce(lambda a, b: a*b, range(1, n+1))

def fact(n):
	return reduce(mul, range(1, n+1))

>>> metro_data = [
... ('Tokyo', 'JP', 36.933, (35.689722, 139.691667)),
... ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),
... ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),
... ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),
... ('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833)),
... ]
>>>
>>> from operator import itemgetter
>>> for city in sorted(metro_data, key=itemgetter(1)):
... print(city)
...
('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833))
('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889))
('Tokyo', 'JP', 36.933, (35.689722, 139.691667))
('Mexico City', 'MX', 20.142, (19.433333, -99.133333))
('New York-Newark', 'US', 20.104, (40.808611, -74.020386))

>>> cc_name = itemgetter(1, 0)
>>> for city in metro_data:
... print(cc_name(city))
...
('JP', 'Tokyo')
('IN', 'Delhi NCR')
('MX', 'Mexico City')
('US', 'New York-Newark')
('BR', 'Sao Paulo')

>>> from collections import namedtuple
>>> LatLong = namedtuple('LatLong', 'lat long') 
>>> Metropolis = namedtuple('Metropolis', 'name cc pop coord') 
>>> metro_areas = [Metropolis(name, cc, pop, LatLong(lat, long)) for name, cc, pop, (lat, long) in metro_data]
>>> metro_areas[0]
Metropolis(name='Tokyo', cc='JP', pop=36.933, coord=LatLong(lat=35.689722, long=139.691667))
>>> metro_areas[0].coord.lat 
35.689722
>>> from operator import attrgetter
>>> name_lat = attrgetter('name', 'coord.lat') 
>>>
>>> for city in sorted(metro_areas, key=attrgetter('coord.lat')): 
... print(name_lat(city)) 
...
('Sao Paulo', -23.547778)
('Mexico City', 19.433333)
('Delhi NCR', 28.613889)
('Tokyo', 35.689722)
('New York-Newark', 40.808611)

>>> from operator import methodcaller
>>> s = 'The time has come'
>>> upcase = methodcaller('upper')
>>> upcase(s)
'THE TIME HAS COME'
>>> hiphenate = methodcaller('replace', ' ', '-')
>>> hiphenate(s)
'The-time-has-come'