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)
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)
