python–generator generator_python code automatic generator

3. The following is an example of a simple generator function

It generates a sequence of natural numbers starting from 0:

return

numbers = natural_numbers()

Isn’t it so difficult that numbers should not be equal to the return value None of the function natural_numbers?

Although there is an explicit return statement (there may be no return), when python compilation is executed to the yield statement, the function natural_numbers will be labeled with a generator, so calling the generator function natural_numbers can return a generator object and save it to numbers.

def natural_numbers():
    n = 0
    while True:
        yield n
        n + = 1
    return
numbers = natural_numbers()

Obtain the values in the sequence sequentially through the iterator-next() method:

Neither yield nor return is the return value of the generator function. Only when I use the next function on the generator object will the generator function body start running.

print(next(numbers)) # Output: 0
print(next(numbers)) # Output: 1
print(next(numbers)) # Output: 2

Since the generator object is an iterator, it can be used in a for loop, for example:

for n in natural_numbers():
    if n > 10:
        break
    print(n)

4. Generator function execution logic

• There can be multiple yield statements in the same generator function.

for i in range(5):
    yield i #The yield statement is executed 5 times.

• When traversing the generator function object, the execution of the function will be interrupted when it reaches the yield statement, and the value of the yield expression will be returned.
• When the object is traversed again, the function continues execution from the previous yield breakpoint position and executes to the next yield statement.
• Repeat the previous step until the function ends normally or a return statement is encountered.
• The return statement can still terminate the function, but the return value of the return statement cannot be obtained.
• return will cause the current function to return and execution cannot continue. The generator object has no iterable depth at this point.
• Continue to use the next() method and throw a StopIteration exception.
• If the function does not have an explicit return statement, and if the generator function is executed to the end (equivalent to executing return None), a StopIteration exception will also be thrown.

 def gen():
        print('line 1')
        yield 1
        print('line 2')
        yield 2
        print('line 3')
        return 3
        yield 4

5.next()

• If you directly use the next() method to call the generator function itself, then a generator object will be regenerated each time, and it will not be possible to continuously traverse a certain generator object downwards.

 next(gen()) # line 1
    next(gen()) # line 1
    
    ```
    ```
    line 1
    1
    line 1
    1   
    

• Therefore, a variable needs to be used to receive the generator object returned by gen().

 g = gen()
    print(g)
    
    ```
    ```
    <generator object gen at 0x000001C19302C228>
    

• Use the next method to traverse the generator object one by one and output normally.

 print(next(g)) # line 1
    print(next(g)) # line 2
    

 line 1
   1
   line 2
   2
   

• But if you continue to use next() to traverse, the interpreter will report an error, StopIteration, indicating that the traversal has exceeded the limit. The function has returned at return 3 and will not be executed until the subsequent yield.

 print(next(g)) # StopIteration
    

 line 3
   
   -------------------------------------------------- -----------------------
   
       StopIteration Traceback (most recent call last)
       <ipython-input-91-9757af9844f9> in <module>
       ----> 1 print(next(g)) # StopIteration
       
       StopIteration: 3
       
       StopIteration: 3
   

6. Function

• Generators can be used to process large data sets, reducing memory usage. In addition, generators can be used for lazy calculations, which only calculate when needed, thus improving the efficiency of the program.
• The generator function can be used as an interval to interrupt the execution of the loop body. Each time the generator object is called, the function loop body will continue to run once.
• Infinite loop:

def counter():
i = 0
while True:
i+=1
yield i
def inc(c):
return next(c)
c = counter()
print(inc(c))

• counter:

def inc():
def counter():
i = 0
while True:
i+=1
yield i
c = counter()
return lambda : next(c)
foo = inc()
print(foo()) # 1
print(foo()) # 2
print(foo()) # 3

• Dealing with recursion problems: The original infinite loop recursion will cause the interpreter to report an error when the maximum recursion depth is exceeded. However, after inserting the yield statement, the loop body is stuck, and the function loop body will be executed only once when the generator object is called.

def fib():
x = 0
y=1
while True:
yield y
x, y = y, x + y
foo = fiber()
for _ in range(5):
print(next(foo))

7. Generator function syntax sugar-yield from

• Yield from is a new syntax that appeared in Python 3.3

• yield from iterable is syntactic sugar of the form for item in iterable: yield item

def inc():
for x in range(1000):
yield x

def inc():
yield from range(1000) ## Syntax sugar for interpreter optimization

-END-

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