使用通用视图的方法是在URLconf文件中创建配置字典,然后把这些字典作为URLconf元组的第三个成员。
例如,下面是一个呈现静态“关于”页面的URLconf:
from django.conf.urls.defaults import * from django.views.generic.simple import direct_to_template urlpatterns = patterns('', (r'^about/$', direct_to_template, { 'template': 'about.html' }) )
一眼看上去似乎有点不可思议,不需要编写代码的视图! 它和第八章中的例子完全一样:direct_to_template视图仅仅是直接从传递过来的额外参数获取信息并用于渲染视图。
因为通用视图都是标准的视图函数,我们可以在我们自己的视图中重用它。 例如,我们扩展 about例子,把映射的URL从 /about//修改到一个静态渲染 about/.html 。 我们首先修改URL配置以指向新的视图函数:
from django.conf.urls.defaults import * from django.views.generic.simple import direct_to_template **from mysite.books.views import about_pages** urlpatterns = patterns('', (r'^about/$', direct_to_template, { 'template': 'about.html' }), **(r'^about/(\w+)/$', about_pages),** )
接下来,我们编写 about_pages 视图的代码:
from django.http import Http404 from django.template import TemplateDoesNotExist from django.views.generic.simple import direct_to_template def about_pages(request, page): try: return direct_to_template(request, template="about/%s.html" % page) except TemplateDoesNotExist: raise Http404()
在这里我们象使用其他函数一样使用 direct_to_template 。 因为它返回一个HttpResponse对象,我们只需要简单的返回它就好了。 这里唯一有点棘手的事情是要处理找不到模板的情况。 我们不希望一个不存在的模板导致一个服务端错误,所以我们捕获TemplateDoesNotExist异常并且返回404错误来作为替代。
这里有没有安全性问题?
眼尖的读者可能已经注意到一个可能的安全漏洞: 我们直接使用从客户端浏览器得到的数据构造模板名称(template="about/%s.html" % page )。乍看起来,这像是一个经典的 目录跨越(directory traversal) 攻击(详情请看第20章)。 事实真是这样吗?
完全不是。 是的,一个恶意的 page 值可以导致目录跨越,但是尽管 page 是 从请求的URL中获取的,但并不是所有的值都会被接受。 这就是URL配置的关键所在: 我们使用正则表达式 \w+ 来从URL里匹配 page ,而 \w 只接受字符和数字。 因此,任何恶意的字符 (例如在这里是点 . 和正斜线 / )将在URL解析时被拒绝,根本不会传递给视图函数。

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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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