search
HomeBackend DevelopmentPython Tutorial详解Django中的form库的使用

Django带有一个form库,称为django.forms,这个库可以处理我们本章所提到的包括HTML表单显示以及验证。 接下来我们来深入了解一下form库,并使用她来重写contact表单应用。

Django的newforms库

在Django社区上会经常看到django.newforms这个词语。当人们讨论django.newforms,其实就是我们本章里面介绍的django.forms。

改名其实有历史原因的。 当Django一次向公众发行时,它有一个复杂难懂的表单系统:django.forms。后来它被完全重写了,新的版本改叫作:django.newforms,这样人们还可以通过名称,使用旧版本。 当Django 1.0发布时,旧版本django.forms就不再使用了,而django.newforms也终于可以名正言顺的叫做:django.forms。

表单框架最主要的用法是,为每一个将要处理的HTML的``

`` 定义一个Form类。 在这个例子中,我们只有一个``
`` ,因此我们只需定义一个Form类。 这个类可以存在于任何地方,甚至直接写在`` views.py`` 文件里也行,但是社区的惯例是把Form类都放到一个文件中:forms.py。在存放`` views.py`` 的目录中,创建这个文件,然后输入:

from django import forms

class ContactForm(forms.Form):
  subject = forms.CharField()
  email = forms.EmailField(required=False)
  message = forms.CharField()

这看上去简单易懂,并且很像在模块中使用的语法。 表单中的每一个字段(域)作为Form类的属性,被展现成Field类。这里只用到CharField和EmailField类型。 每一个字段都默认是必填。要使email成为可选项,我们需要指定required=False。

让我们钻研到Python解释器里面看看这个类做了些什么。 它做的第一件事是将自己显示成HTML:

>>> from contact.forms import ContactForm
>>> f = ContactForm()
>>> print f
<tr><th><label for="id_subject">Subject:</label></th><td><input type="text" name="subject" id="id_subject" /></td></tr>
<tr><th><label for="id_email">Email:</label></th><td><input type="text" name="email" id="id_email" /></td></tr>
<tr><th><label for="id_message">Message:</label></th><td><input type="text" name="message" id="id_message" /></td></tr>

为了便于访问,Django用``

默认输出按照HTML的格式,另外有一些其它格式的输出:

>>> print f.as_ul()
<li><label for="id_subject">Subject:</label> <input type="text" name="subject" id="id_subject" /></li>
<li><label for="id_email">Email:</label> <input type="text" name="email" id="id_email" /></li>
<li><label for="id_message">Message:</label> <input type="text" name="message" id="id_message" /></li>
>>> print f.as_p()
<p><label for="id_subject">Subject:</label> <input type="text" name="subject" id="id_subject" /></p>
<p><label for="id_email">Email:</label> <input type="text" name="email" id="id_email" /></p>
<p><label for="id_message">Message:</label> <input type="text" name="message" id="id_message" /></p>

请注意,标签

的开闭合标记没有包含于输出当中,这样你就可以添加额外的行或者自定义格式。

这些类方法只是一般情况下用于快捷显示完整表单的方法。 你同样可以用HTML显示个别字段:

>>> print f['subject']
<input type="text" name="subject" id="id_subject" />
>>> print f['message']
<input type="text" name="message" id="id_message" />

Form对象做的第二件事是校验数据。 为了校验数据,我们创建一个新的对Form象,并且传入一个与定义匹配的字典类型数据:

>>> f = ContactForm({'subject': 'Hello', 'email': 'adrian@example.com', 'message': 'Nice site!'})

一旦你对一个Form实体赋值,你就得到了一个绑定form:

>>> f.is_bound
True

调用任何绑定form的is_valid()方法,就可以知道它的数据是否合法。 我们已经为每个字段传入了值,因此整个Form是合法的:

>>> f.is_valid()
True

如果我们不传入email值,它依然是合法的。因为我们指定这个字段的属性required=False:

>>> f = ContactForm({'subject': 'Hello', 'message': 'Nice site!'})
>>> f.is_valid()
True

但是,如果留空subject或message,整个Form就不再合法了:

>>> f = ContactForm({'subject': 'Hello'})
>>> f.is_valid()
False
>>> f = ContactForm({'subject': 'Hello', 'message': ''})
>>> f.is_valid()
False

你可以逐一查看每个字段的出错消息:

>>> f = ContactForm({'subject': 'Hello', 'message': ''})
>>> f['message'].errors
[u'This field is required.']
>>> f['subject'].errors
[]
>>> f['email'].errors
[]

每一个邦定Form实体都有一个errors属性,它为你提供了一个字段与错误消息相映射的字典表。

>>> f = ContactForm({'subject': 'Hello', 'message': ''})
>>> f.errors
{'message': [u'This field is required.']}

最终,如果一个Form实体的数据是合法的,它就会有一个可用的cleaned_data属性。 这是一个包含干净的提交数据的字典。 Django的form框架不但校验数据,它还会把它们转换成相应的Python类型数据,这叫做清理数据。

>>> f = ContactForm({subject': Hello, email: adrian@example.com, message: Nice site!})
>>> f.is_valid()
True
>>> f.cleaned_data
{message': uNice site!, email: uadrian@example.com, subject: uHello}

我们的contact form只涉及字符串类型,它们会被清理成Unicode对象。如果我们使用整数型或日期型,form框架会确保方法使用合适的Python整数型或datetime.date型对象。

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

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 vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

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 for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

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.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

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 for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

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.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

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.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

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 vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

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.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools