对 current_datetime 的一次赋值操作:
def current_datetime(request): now = datetime.datetime.now() return render_to_response('current_datetime.html', {'current_date': now})
很多时候,就像在这个范例中那样,你发现自己一直在计算某个变量,保存结果到变量中(比如前面代码中的 now ),然后将这些变量发送给模板。 尤其喜欢偷懒的程序员应该注意到了,不断地为临时变量和临时模板命名有那么一点点多余。 不仅多余,而且需要额外的输入。
如果你是个喜欢偷懒的程序员并想让代码看起来更加简明,可以利用 Python 的内建函数 locals() 。它返回的字典对所有局部变量的名称与值进行映射。 因此,前面的视图可以重写成下面这个样子:
def current_datetime(request): current_date = datetime.datetime.now() return render_to_response('current_datetime.html', locals())
在此,我们没有像之前那样手工指定 context 字典,而是传入了 locals() 的值,它囊括了函数执行到该时间点时所定义的一切变量。 因此,我们将 now 变量重命名为 current_date ,因为那才是模板所预期的变量名称。 在本例中, locals() 并没有带来多 大 的改进,但是如果有多个模板变量要界定而你又想偷懒,这种技术可以减少一些键盘输入。
使用 locals() 时要注意是它将包括 所有 的局部变量,它们可能比你想让模板访问的要多。 在前例中, locals() 还包含了 request 。对此如何取舍取决你的应用程序。

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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