本文实例讲述了python生成器generator用法。分享给大家供大家参考。具体如下:
使用yield,可以让函数生成一个结果序列,而不仅仅是一个值
例如:
def countdown(n): print "counting down" while n>0: yield n #生成一个n值 n -=1 >>> c = countdown(5) >>> c.next() counting down 5 >>> c.next() 4 >>> c.next() 3
next()调用生成器函数一直运行到下一条yield语句为止,此时next()将返回值传递给yield.而且函数将暂停中止执行。再次调用时next()时,函数将继续执行yield之后的语句。此过程持续执行到函数返回为止。
通常不会像上面那样手动调用next(), 而是使用for循环,例如:
>>> for i in countdown(5): ... print i ... counting down 5 4 3 2 1
next(), send()的返回值都是yield 后面的参数, send()跟next()的区别是send()是发送一个参数给(yield n)的表达式,作为其返回值给m, 而next()是发送一个None给(yield n)表达式, 这里需要区分的是,一个是调用next(),send()时候的返回值,一个是(yield n)的返回值,两者是不一样的.看输出结果可以区分。
def h(n): while n>0: m = (yield n) print "m is "+str(m) n-=1 print "n is "+str(n) >>> p= h(5) >>> p.next() 5 >>> p.next() m is None n is 4 4 >>> p.send("test") m is test n is 3 3
希望本文所述对大家的Python程序设计有所帮助。

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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