Summary of usage of python yield and yield from

yield function:

Note: The next() method of generator is next() in python 2, but in python 3 it is __next__() [next is preceded and followed by two underscores]

Turn a function into a generator. The function with yield is no longer an ordinary function. That is: a function with yield is a generator. It is different from an ordinary function. Generating a generator looks like a function call, but will not execute any function code until next() is called on it (it will be automatically called in a for loop next()) starts execution. Although the execution flow is still executed according to the flow of the function, every time a yield statement is executed, it will be interrupted and an iteration value will be returned. The next execution will continue from the next statement of yield. It looks like a function is interrupted several times by yield during normal execution, and each interruption returns the current iteration value through yield.

The benefits of yield are obvious. By rewriting a function as a generator, you gain the ability to iterate. Compared with using an instance of a class to save the state to calculate the value of the next next(), not only the code is concise, but the execution process is also simpler. Extraordinarily clear.

Use print to print the Fibonacci sequence - basic version

#!/usr/bin/env python
# -*- coding: utf-8 -*-def fab(max):
    n , a, b = 0, 0 , 1
    while n < max:
        print(b)
        a, b = b, a + b
        n = n + 1if __name__ == '__main__':
    fab(6)  # 1 1 2 3 5 8

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Use yield to print the Fibonacci sequence - upgrade Version

#!/usr/bin/env python
# -*- coding: utf-8 -*-def fab(max):
    n , a, b = 0, 0 , 1
    while n < max:
        yield b
        a, b = b, a + b
        n = n + 1if __name__ == '__main__':
    for n in fab(6): # 1 1 2 3 5 8
        print(n)

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How to determine whether a function is a special generator function

#!/usr/bin/env python
# -*- coding: utf-8 -*-from inspect import isgeneratorfunction

def fab(max):
    n , a, b = 0, 0 , 1
    while n < max:
        yield b
        a, b = b, a + b
        n = n + 1if __name__ == '__main__':
    f1 = fab(3)
    # True fab是一个generator function
    print(isgeneratorfunction(fab))

    # False fab(3)不是一个generator function
    # 而fab(3)是调用fab返回的一个generator    print(isgeneratorfunction(fab(3)))

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Use yield to read large files

#!/usr/bin/env python
# -*- coding: utf-8 -*-def read_file(fpath):
    BLOCK_SIZE = 100
    with open(fpath, "rb") as f:
        while True:
            block = f.read(BLOCK_SIZE)
            if block:
                yield block            else:
                returnif __name__ == '__main__':
    fpath = "/home/exercise-python3.7.1/vote/mysite/mysite/polls/test.txt"
    read_gen = read_file(fpath)

    print(read_gen.__next__())
    print(read_gen.__next__())
    print(read_gen.__next__())
    print(read_gen.__next__())

    # for循环会自动调用generatr的__next__()方法，故输出效果同如上的4个print  【内容较短，4个print就将test.txt中的内容输出完了】    for data in read_gen:
        print(data)

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Comparison of usage between yield and yield from

Use yield to splice iterable objects

#!/usr/bin/env python
# -*- coding: utf-8 -*-if __name__ == '__main__':
    astr = "ABC"
    alist = [1, 2, 3]
    adict = {"name": "wangbm", "age": 18}
    # generate
    agen = (i for i in range(4, 8))

    def gen(*args, **kw):
        for item in args:
            for i in item:
                yield i

    new_list = gen(astr, alist, adict, agen)
    print(list(new_list))
    # ['A', 'B', 'C', 1, 2, 3, 'name', 'age', 4, 5, 6, 7]

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Use yield from to splice iterable objects Iterative object

#!/usr/bin/env python
# -*- coding: utf-8 -*-if __name__ == '__main__':
    astr = "ABC"
    alist = [1, 2, 3]
    adict = {"name": "wangbm", "age": 18}
    # generate
    agen = (i for i in range(4, 8))

    def gen(*args, **kw):
        for item in args:
            yield from item

    new_list = gen(astr, alist, adict, agen)
    print(list(new_list))
    # ['A', 'B', 'C', 1, 2, 3, 'name', 'age', 4, 5, 6, 7]

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Conclusion:
Comparing the above two methods, it can be seen that adding an iterable object after yield from can convert each item in the iterable object The elements yield one by one. Compared with yield, the code is more concise and the structure is clearer.

Related learning recommendations: python video tutorial

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