python day14

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核心编程、流畅的python

1.复习

2.生成函数进阶

例子1：yield后面的数据拿不到：，如果再增加一个g.__next__()，会报错。

def generator():
    print(123)
    yield 1
    print(456)
    yield 2
    print(789)
g = generator()
ret = g.__next__()
print("***",ret)
ret = g.__next__()
print("***",ret)

执行结果：

123
*** 1
456
*** 2

例子2：send的效果和next一样

def generator():
    print(123)
    yield 1
    print(456)
    yield 2
    print(789)
g = generator()
ret = g.__next__()
print("***",ret)
ret = g.send(None)
print("***",ret)

执行结果：

123
*** 1
456
*** 2

例子3：

#send 获取下一个值的效果和next基本一致
#只是在获取下一个值的时候，给上一yield的位置传递一个数据

def generator():
    print(123)
    content=yield 1
    print("======",content)
    print(456)
    yield 2
    print(789)
g = generator()
ret = g.__next__()
print("***",ret)
ret = g.send("hello")
print("***",ret)

执行结果：

123
*** 1
====== hello
456
*** 2

#使用send的注意事项
    # 第一次使用生成器的时候 是用next获取下一个值
    # 最后一个yield不能接受外部的值

3.生成函数进阶实例

# 获取移动平均值
# 10 20 30 10
# 10 15 20 17.5

执行代码低端的：

def average():
    sum = 0
    count = 0
    avg = 0
    num = yield
    sum += num
    count += 1
    avg = sum/count
    yield avg
avg_g = average()
avg_g.__next__()
avg1 = avg_g.send(10)
print(avg1)

执行代码高端的：

def average():
    sum = 0
    count = 0
    avg = 0
    while True:
        num = yield avg
        sum += num    # 10
        count += 1    # 1
        avg = sum/count

avg_g = average()
avg_g.__next__()
avg1 = avg_g.send(10)
avg1 = avg_g.send(20)
print(avg1)

生成器功能的装饰器：

def init(func):   #装饰器
    def inner(*args,**kwargs):
        g = func(*args,**kwargs)    #g = average()
        g.__next__()
        return g
    return inner

@init
def average():
    sum = 0
    count = 0
    avg = 0
    while True:
        num = yield avg
        sum += num    # 10
        count += 1    # 1
        avg = sum/count

avg_g = average()   #===> inner
ret = avg_g.send(10)
print(ret)
ret = avg_g.send(20)
print(ret)

流程：

面试练习题：

import os

def init(func):
    def wrapper(*args,**kwargs):
        g=func(*args,**kwargs)
        next(g)
        return g
    return wrapper

@init
def list_files(target):
    while 1:
        dir_to_search=yield
        for top_dir,dir,files in os.walk(dir_to_search):
            for file in files:
                target.send(os.path.join(top_dir,file))
@init
def opener(target):
    while 1:
        file=yield
        fn=open(file)
        target.send((file,fn))
@init
def cat(target):
    while 1:
        file,fn=yield
        for line in fn:
            target.send((file,line))

@init
def grep(pattern,target):
    while 1:
        file,line=yield
        if pattern in line:
            target.send(file)
@init
def printer():
    while 1:
        file=yield
        if file:
            print(file)

g=list_files(opener(cat(grep('python',printer()))))

g.send('/test1')

协程应用：grep -rl /dir

tail&grep

计算机移动平均值1：

def averager():
    total = 0.0
    count = 0
    average = None
    while True:
        term = yield average
        total += term
        count += 1
        average = total/count


g_avg = averager()
next(g_avg)
print(g_avg.send(10))
print(g_avg.send(30))
print(g_avg.send(5))

计算移动平均值(1)

练习题：python之路——迭代器和生成器

python3中加入了yield from

原来实现：

def generator():
    a = 'abcde'
    b = '12345'
    for i in a:
        yield i
    for i in b:
        yield i
g = generator()
for i in g:
    print(i)

改后实现的过程：

def generator():
    a = 'abcde'
    b = '12345'
    yield from a
    yield from b
#
g = generator()
for i in g:
    print(i)

4.生成器表达式和列表推导式

egg_list=['鸡蛋%s'%i for i in range(10)]    #列表推导式
print(egg_list)

等同于：

egg_list = []
for i in range(10):
    egg_list.append('鸡蛋%s'%i)
print(egg_list)

生成器表达式：

g = (i for i in range(10))

print(g)

执行结果：

<generator object <genexpr> at 0x000000000562CF10>

访问生成器：

g = (i for i in range(10))
print(g)
for i in  g:
    print(i)

生成器表达式和列表推导式的区别：

# 括号不一样
# 返回的值不一样 === 生成器几乎不占用内存

迭代器生成器专题：http://www.cnblogs.com/Eva-J/articles/7276796.html　

5.各种推导式

列表推导式

#[每一个元素或者是和元素相关的操作 for 元素 in 可迭代数据类型]    #遍历之后挨个处理
#[满足条件的元素相关的操作 for 元素 in 可迭代数据类型 if 元素相关的条件]   #筛选功能

# #30以内所有能被3整除的数

ret = [i for i in range(30) if i%3 == 0] #完整的列表推导式 g = (i for i in range(30) if i%3 == 0) #完整的生成器表达式 print(ret)

# #30以内所有能被3整除的数的平方

ret = [i*i for i in (1,2,3,4) if i%3 == 0]
ret = (i*i for i in range(30) if i%3 == 0)
print(ret)

# # 例三:找到嵌套列表中名字含有两个‘e’的所有名字

names = [['Tom', 'Billy', 'Jefferson', 'Andrew', 'Wesley', 'Steven', 'Joe'],
         ['Alice', 'Jill', 'Ana', 'Wendy', 'Jennifer', 'Sherry', 'Eva']]
ret = [name for lst in names for name in lst if name.count('e') ==2]
ret = (name for lst in names for name in lst if name.count('e') ==2)
print(ret)

#字典推导式

例一：将一个字典的key和value对调

# mcase = {'a': 10, 'b': 34}
# #{10:'a' , 34:'b'}
mcase = {'a': 10, 'b': 34}
mcase_frequency = {mcase[k]: k for k in mcase}
print(mcase_frequency)

# 例二：合并大小写对应的value值，将k统一成小写

# mcase = {'a': 10, 'b': 34, 'A': 7, 'Z': 3}
#{'a':10+7,'b':34,'z':3}
mcase = {'a': 10, 'b': 34, 'A': 7, 'Z': 3}
mcase_frequency = {k.lower(): mcase.get(k.lower(), 0) + mcase.get(k.upper(), 0) for k in mcase}
print(mcase_frequency)

#集合推导式

自带结果去重功能

squared = {x**2 for x in [1, -1, 2]}
print(squared)