Introduction and use of Python generators

The generator in python saves the algorithm, and the value will be calculated only when the value is really needed. It is a lazy evaluation.

There are two ways to create a generator.

The first method: Change [] in a list generation expression to () to create a generator:

>>> L = [x * x for x in range(10)]>>> L
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]>>> g = (x * x for x in range(10)) # Note that after changing [] to (), instead of generating a tuple, a generator>>> g264f12d38c8a93ec616de2f007802f5a at 0x1022ef630>

Second way: Use the yield keyword in the function, and the function becomes a generator.

After there is yield in the function, the execution will stop when yield is reached, and the calculation will be continued when further calculation is needed. So it doesn't matter even if the generator function has an infinite loop, it will calculate as much as it needs to count, and it won't continue counting if it doesn't need to.

def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
f = fib()
print f, next(f), next(f), next(f)
# 8f3ab7789c38b631c1b219fec2271f37 0 1 1

As in the above example, when f is output for the first time, it is a generator, and every time next is executed, it will be executed to yield a.

Of course, next() is rarely used. We can directly use a for loop to traverse a generator. In fact, the internal implementation of the for loop is to continuously call next().

The generator can avoid unnecessary calculations and improve performance; it also saves space and can realize infinite loop (infinite) data structures.

Generator syntax
Generator expression: The same as list parsing syntax, but replace the [] of list parsing with ()
What generator expressions can do List parsing can basically handle things, but when the sequence to be processed is relatively large, list parsing consumes more memory.

Generator function: If the yield keyword appears in a function, then the function is no longer an ordinary function, but a generator function.

In Python, yield is such a generator.

The operating mechanism of the yield generator:

When you ask the generator for a number, the generator will execute until the yield statement appears, and the generator will The parameters are given to you, and then the generator will not continue to run. When you ask him for the next number, he will start from the last state. Start running until the yield statement appears, give you the parameters, and then stop. This is repeated until the function exits.

Use of yield:

In Python, when you define a function and use the yield keyword, the function is a generator, and its execution will be the same as other ordinary functions. There are many differences. The function returns an object, instead of getting the result value like the return statement you usually use. If you want to get the value, you have to call the next() function

Take Fibonacci as an example:

#coding:utf8
def fib(max): #10
    n, a, b = 0, 0, 1
    while n < max: #n<10
        #print(b)
        yield b
        a, b = b, a + b

        n += 1
    return 

f = fib(10)
for i in f:
    print f

From the above description of the operating mechanism, we can know that when the program reaches the yield line, it will not continue to execute. Instead, it returns an iterator object containing the status of all parameters of the current function. The purpose is to be able to access all the parameter values ​​of the function when it is called for the second time, instead of reassigning them.

When the program is called for the first time:

def fib(max): #10
    n, a, b = 0, 0, 1
    while n < max: #n<10
        #print(b)
        yield b   #这时a,b值分别为0,1，当然，程序也在执行到这时，返回
        a, b = b, a + b

程序第二次调用时：

从前面可知，第一次调用时，a,b=0,0，那么，我们第二次调用时（其实就是调用第一次返回的iterator对象的next()方法），程序跳到yield语句处，

执行a,b = b, a+b语句，此时值变为：a,b = 0, (0+1) => a,b = 0, 1

程序继续while循环，当然，再一次碰到了yield a 语句，也是像第一次那样，保存函数所有参数的状态，返回一个包含这些参数状态的iterator对象。

等待第三次的调用....

 

通过上面的分析，可以一次类推的展示了yield的详细运行过程了！

通过使用生成器的语法，可以免去写迭代器类的繁琐代码，如，上面的例子使用迭代类来实现，代码如下：

#coding:UTF8

class Fib:  
    def __init__(self, max):  
        self.max = max
        print self.max
    def __iter__(self):  
        self.a = 0  
        self.b = 1 
        self.n = 0 
        return self  
    def next(self):  
        fib = self.n  
        if fib >= self.max:  
            raise StopIteration  
        self.a, self.b = self.b, self.a + self.b  
        self.n += 1
        return self.a
    
f = Fib(10)
for i in f:
    print i

yield 与 return

在一个生成器中，如果没有return，则默认执行到函数完毕时返回StopIteration；

 

 如果遇到return,如果在执行过程中 return，则直接抛出 StopIteration 终止迭代。

如果在return后返回一个值，会直接报错，生成器没有办法使用return来返回值。

 

生成器支持的方法（借鉴别人的例子，感觉蛮好的）

     close(...)
 |      close() -> raise GeneratorExit inside generator.
 |  
 |  next(...)
 |      x.next() -> the next value, or raise StopIteration
 |  
 |  send(...)
 |      send(arg) -> send 'arg' into generator,
 |      return next yielded value or raise StopIteration.
 |  
 |  throw(...)
 |      throw(typ[,val[,tb]]) -> raise exception in generator,
 |      return next yielded value or raise StopIteration.

 

close()

手动关闭生成器函数，后面的调用会直接返回StopIteration异常。

#coding:UTF8

def fib():
    yield 1
    yield 2
    yield 3

f = fib()
print f.next()
f.close()
print f.next()

 

send()

生成器函数最大的特点是可以接受外部传入的一个变量，并根据变量内容计算结果后返回。
这是生成器函数最难理解的地方，也是最重要的地方，

def gen():
    value=0
    while True:
        receive=yield value
        if receive=='e':
            break
        value = 'got: %s' % receive
 
g=gen()
print(g.send(None))     
print(g.send('aaa'))
print(g.send(3))
print(g.send('e'))

执行流程：

1. 通过g.send(None)或者next(g)可以启动生成器函数，并执行到第一个yield语句结束的位置。此时，执行完了yield语句，但是没有给receive赋值。yield value会输出初始值0注意：在启动生成器函数时只能send(None),如果试图输入其它的值都会得到错误提示信息。

2. 通过g.send(‘aaa’)，会传入aaa，并赋值给receive，然后计算出value的值，并回到while头部，执行yield value语句有停止。此时yield value会输出”got: aaa”，然后挂起。

3. 通过g.send(3)，会重复第2步，最后输出结果为”got: 3″

4. 当我们g.send(‘e’)时，程序会执行break然后推出循环，最后整个函数执行完毕，所以会得到StopIteration异常。

最后的执行结果如下：

0
got: aaa
got: 3
Traceback (most recent call last):
  File "1.py", line 15, in <module>
    print(g.send(&#39;e&#39;))
StopIteration

　

throw()

用来向生成器函数送入一个异常，可以结束系统定义的异常，或者自定义的异常。
throw()后直接跑出异常并结束程序，或者消耗掉一个yield，或者在没有下一个yield的时候直接进行到程序的结尾。

def gen():
    while True: 
        try:
            yield 'normal value'
            yield 'normal value 2'
            print('here')
        except ValueError:
            print('we got ValueError here')
        except TypeError:
            break
 
g=gen()
print(next(g))
print(g.throw(ValueError))
print(next(g))
print(g.throw(TypeError))

执行流程：

1. print(next(g))：会输出normal value，并停留在yield ‘normal value 2’之前。

2. 由于执行了g.throw(ValueError)，所以会跳过所有后续的try语句，也就是说yield ‘normal value 2’不会被执行，然后进入到except语句，打印出we got ValueError here。然后再次进入到while语句部分，消耗一个yield，所以会输出normal value。

3. print(next(g))，会执行yield ‘normal value 2’语句，并停留在执行完该语句后的位置。

4. g.throw(TypeError)：会跳出try语句，从而print(‘here’)不会被执行，然后执行break语句，跳出while循环，然后到达程序结尾，所以跑出StopIteration异常。

　　

 

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