yield is simply a generator. A generator is a function that remembers the position in the function body when it last returned. The second (or nth) call to a generator function jumps to the middle of the function, leaving all local variables unchanged from the previous call.
The generator is a function
All parameters of the function will be retained
When this function is called for the second time
The parameters used are retained from the previous time.
The generator also "remembers" it in the flow control Constructing a
generator doesn’t just “remember” its data state. The generator also "remembers" its position within the flow control construct (in imperative programming, this construct is not just a data value). Continuity is still relatively general since it lets you jump arbitrarily between execution frames without always returning to the immediate caller's context (as with generators).
The operating mechanism of the yield generator
When you ask the generator for a number, the generator will execute until the yield statement appears. The generator will give you the parameters of yield, and then the generator will not continue to run. When you ask him for the next number, he will start running from the last state until the yield statement appears, give you the parameters, and then stop. Repeat this until the function exits.
Example: Python permutation, combination generator
#Generate full permutation
def perm(items, n=None): if n is None: n = len(items) for i in range(len(items)): v = items[i:i+1] if n == 1: yield v else: rest = items[:i] + items[i+1:] for p in perm(rest, n-1): yield v + p
#Generate combination
def comb(items, n=None): if n is None: n = len(items) for i in range(len(items)): v = items[i:i+1] if n == 1: yield v else: rest = items[i+1:] for c in comb(rest, n-1): yield v + c a = perm('abc') for b in a: print b break print '-'*20 for b in a: print b
The results are as follows:
102 pvopf006 ~/test> ./generator.py
abc
--- ------------------
acb
bac
bca
cab
cba
As you can see, after the first loop break, the generator does not continue is executed, and the second loop is executed after the first loop

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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