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使用Python的Scrapy框架编写web爬虫的简单示例

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Release： 2016-06-10 15:14:59
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 在这个教材中,我们假定你已经安装了Scrapy。假如你没有安装,你可以参考这个安装指南。

我们将会用开放目录项目(dmoz)作为我们例子去抓取。

这个教材将会带你走过下面这几个方面:

  •     创造一个新的Scrapy项目
  •     定义您将提取的Item
  •     编写一个蜘蛛去抓取网站并提取Items。
  •     编写一个Item Pipeline用来存储提出出来的Items

Scrapy由Python写成。假如你刚刚接触Python这门语言,你可能想要了解这门语言起,怎么最好的利用这门语言。假如你已经熟悉其它类似的语言,想要快速地学习Python,我们推荐这种深入方式学习Python。假如你是新手,想从开始使用Python学习,可以尝试去看看非程序员Python资源列表。

创造一个项目

在你要抓取之前,首先要建立一个新的Scrapy项目。然后进去你的存放代码目录,执行如下命令。

scrapy startproject tutorial
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它将会创建如下的向导目录:

复制代码 代码如下:

tutorial/
scrapy.cfg
tutorial/
__init__.py
items.py
pipelines.py
settings.py
spiders/
__init__.py
...

这是一些基本信息:

  • scrapy.cfg: 项目的配置文件。
  • tutorial/: 项目的python模块, 在这里稍后你将会导入你的代码。
  • tutorial/items.py: 项目items文件。
  • tutorial/pipelines.py: 项目管道文件。
  • tutorial/settings.py: 项目配置文件。
  • tutorial/spiders/: 你将要放入你的spider到这个目录中。


定义我们的Item

Items是装载我们抓取数据的容器。它们工作像简单的Python字典,它提供更多的保护,比如对未定义的字段提供填充功能防止出错。

它们通过创建scrapy.item.Item类来声明并定义它们的属性作为scrapy.item.Field 对象,就像是一个对象关系映射(假如你不熟悉ORMs,你将会看见它是一个简单的任务).

我们将需要的item模块化,来控制从demoz.org网站获取的数据,比如我们将要去抓取网站的名字,url和描述信息。我们定义这三种属性的域。我们编辑items.py文件,它在向导目录中。我们Item类看起来像这样。


from scrapy.item import Item, Field
 
class DmozItem(Item):
 title = Field()
 link = Field()
 desc = Field()
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这个看起来复杂的,但是定义这些item能让你用其他Scrapy组件的时候知道你的item到底是什么

我们第一个Spider

Spiders是用户写的类,它用来去抓取一个网站的信息(或者一组网站) 。
我们定义一个初始化的URLs列表去下载,如何跟踪链接,如何去解析这些页面的内容去提取 items.创建一个Spider,你必须是scrapy.spider.BaseSpider的子类, 并定义三个主要的,强制性的属性。

名字: Spider的标识. 它必须是唯一的, 那就是说,你不能在不同的Spiders中设置相同的名字。

开始链接:Spider将会去爬这些URLs的列表。所以刚开始的下载页面将要包含在这些列表中。其他子URL将会从这些起始URL中继承性生成。

parse() 是spider的一个方法, 调用时候传入从每一个URL传回的Response对象作为参数。response是方法的唯一参数。

这个方法负责解析response数据和提出抓取的数据(作为抓取的items),跟踪URLs

parse()方法负责处理response和返回抓取数据(作为Item对象) 和跟踪更多的URLs(作为request的对象)

这是我们的第一个Spider的代码;它保存在moz/spiders文件夹中,被命名为dmoz_spider.py：

from scrapy.spider import BaseSpider
 
class DmozSpider(BaseSpider):
 name = "dmoz"
 allowed_domains = ["dmoz.org"]
 start_urls = [
  "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
  "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
 ]
 
 def parse(self, response):
  filename = response.url.split("/")[-2]
  open(filename, 'wb').write(response.body)
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爬

为了使你的spider工作, 到项目的顶级目录让后运行:

scrapy crawl dmoz
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crawl dmoz命令使spider去爬dmoz.org网站的信息。你将会得到如下类似的信息:

2008-08-20 03:51:13-0300 [scrapy] INFO: Started project: dmoz
2008-08-20 03:51:13-0300 [tutorial] INFO: Enabled extensions: ...
2008-08-20 03:51:13-0300 [tutorial] INFO: Enabled downloader middlewares: ...
2008-08-20 03:51:13-0300 [tutorial] INFO: Enabled spider middlewares: ...
2008-08-20 03:51:13-0300 [tutorial] INFO: Enabled item pipelines: ...
2008-08-20 03:51:14-0300 [dmoz] INFO: Spider opened
2008-08-20 03:51:14-0300 [dmoz] DEBUG: Crawled <http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/> (referer: <None>)
2008-08-20 03:51:14-0300 [dmoz] DEBUG: Crawled <http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> (referer: <None>)
2008-08-20 03:51:14-0300 [dmoz] INFO: Spider closed (finished)
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注意那些行包含[dmoz], 它和我们的spider相关。你能够看见每行初始化的URL日志信息。因为这些URLs是起始页面,所以他们没有引用referrers。 所以在每行的末尾部门,你能看见(referer: ).

但是有趣的是,在我们的parse方法作用下,两个文件被创建: Books and Resources, 它保航两个URLs的内容
刚刚发生了什么事情?

Scrapy为每一个start_urls创建一个scrapy.http.Request对象,并将爬虫的parse 方法指定为回调函数。

这些Request首先被调度,然后被执行,之后通过parse()方法,将scrapy.http.Response对象被返回,结果也被反馈给爬虫。

提取Items
选择器介绍

我们有多种方式去提取网页中数据。Scrapy 使用的是XPath表达式,通常叫做XPath selectors。如果想了解更多关于选择器和提取数据的机制,可以看看如下教程XPath selectors documentation.

这里有一些表达式的例子和它们相关的含义:

  • /html/head/title: 选择元素,在HTML文档的<head>元素里</li> <li> /html/head/title/text(): 选择<title>元素里面的文本</li> <li> //td: 选择所有的<td>元素</li> <li> //div[@class="mine"]: 选择所有的div元素里面class属性为mine的</li> </ul> <p>这里有许多的例子关于怎么使用XPath,可以说XPath表达式是非常强大的。如果你想要学习更多关于XPath,我们推荐如下教程this XPath tutorial.</p> <p>为了更好使用XPaths, Scrapy提供了一个XPathSelector类,它有两种方式, HtmlXPathSelector(HTML相关数据)和XmlXPathSelector(XML相关数据)。如果你想使用它们,你必须实例化一个Response对象.</p> <p>你能够把selectors作为对象,它代表文件结构中的节点。所以,第1个实例的节点相当于root节点,或者称为整个文档的节点。</p> <p>选择器有三种方法(点击方法你能够看见完整的API文档)。</p> <ul> <li> select(): 返回选择器的列表,每一个select表示一个xpath表达式选择的节点。</li> <li> extract(): 返回一个unicode字符串 ,该字符串XPath选择器返回的数据。</li> <li> re() : 返回unicode字符串列表,字符串作为参数由正则表达式提取出来。</li> </ul> <p><br /> <strong>在Shell里面使用选择器</strong></p> <p>为了更加形象的使用选择器,我们将会使用Scrapy shell,它同时需要你的系统安装IPython (一个扩展的Python控制台)。</p> <p>如果使用shell,你必须到项目的顶级目录上,让后运行如下命令:</p> <p>scrapy shell http://www.dmoz.org/Computers/Programming/Languages/Python/Books/</p> <p>shell将会显示如下的信息</p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> [ ... Scrapy log here ... ] [s] Available Scrapy objects: [s] 2010-08-19 21:45:59-0300 [default] INFO: Spider closed (finished) [s] hxs <HtmlXPathSelector (http://www.dmoz.org/Computers/Programming/Languages/Python/Books/) xpath=None> [s] item Item() [s] request <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> [s] response <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> [s] spider <BaseSpider 'default' at 0x1b6c2d0> [s] xxs <XmlXPathSelector (http://www.dmoz.org/Computers/Programming/Languages/Python/Books/) xpath=None> [s] Useful shortcuts: [s] shelp() Print this help [s] fetch(req_or_url) Fetch a new request or URL and update shell objects [s] view(response) View response in a browser In [1]: </pre><div class="contentsignin">Copy after login</div></div> </p> <p>当shell装载之后,你将会得到一个response的本地变量。所以你输入reponse.body,你能够看见response的body部分或者你能够输入response.headers,你能够看见reponse.headers部分。</p> <p>shell同样实例化了两个选择器,一个是HTML(在hvx变量里),一个是XML(在xxs变量里)。所以我们尝试怎么使用它们:</p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> In [1]: hxs.select('//title') Out[1]: [<HtmlXPathSelector (title) xpath=//title>] In [2]: hxs.select('//title').extract() Out[2]: [u'<title>Open Directory - Computers: Programming: Languages: Python: Books</title>'] In [3]: hxs.select('//title/text()') Out[3]: [<HtmlXPathSelector (text) xpath=//title/text()>] In [4]: hxs.select('//title/text()').extract() Out[4]: [u'Open Directory - Computers: Programming: Languages: Python: Books'] In [5]: hxs.select('//title/text()').re('(\w+):') Out[5]: [u'Computers', u'Programming', u'Languages', u'Python'] </pre><div class="contentsignin">Copy after login</div></div> </p> <p><strong>提取数据Extracting the data</strong></p> <p>现在我们开始尝试在这几个页面里提取真正的信息。</p> <p>你能够在控制台里面输入response.body,检查源代码里面的XPaths是否与预期相同。然而,检查原始的HTML代码是一件非常枯燥乏味的事情。假如你想让你的工作变的简单,你使用Firefox扩展的插件例如Firebug来做这项任务。更多关于介绍信息请看Using Firebug for scraping和Using Firefox for scraping。</p> <p>当你检查了页面源代码之后,你将会发现页面的信息放在一个<ul>元素里面,事实上,确切地说是第二个<ul>元素。</p> <p>所以我们选择每一个<li>元素使用如下的代码：<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> hxs.select('//ul/li') </pre><div class="contentsignin">Copy after login</div></div> </p> <p>网站的描述信息可以使用如下代码:<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> hxs.select('//ul/li/text()').extract() </pre><div class="contentsignin">Copy after login</div></div> </p> <p>网站的标题:<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> hxs.select('//ul/li/a/text()').extract() </pre><div class="contentsignin">Copy after login</div></div> </p> <p>网站的链接:<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> hxs.select('//ul/li/a/@href').extract() </pre><div class="contentsignin">Copy after login</div></div> </p> <p>如前所述，每个select()调用返回一个selectors列表，所以我们可以结合select()去挖掘更深的节点。我们将会用到这些特性，所以:<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> sites = hxs.select('//ul/li') for site in sites: title = site.select('a/text()').extract() link = site.select('a/@href').extract() desc = site.select('text()').extract() print title, link, desc Note </pre><div class="contentsignin">Copy after login</div></div> </p> <p><br /> 如果想了解更多的嵌套选择器,可以参考Nesting selectors和Working with relative XPaths相关的Selectors文档<br /> 将代码添加到我们spider中:</p> <p> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> from scrapy.spider import BaseSpider from scrapy.selector import HtmlXPathSelector class DmozSpider(BaseSpider): name = "dmoz" allowed_domains = ["dmoz.org"] start_urls = [ "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/", "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/" ] def parse(self, response): hxs = HtmlXPathSelector(response) sites = hxs.select('//ul/li') for site in sites: title = site.select('a/text()').extract() link = site.select('a/@href').extract() desc = site.select('text()').extract() print title, link, desc </pre><div class="contentsignin">Copy after login</div></div> </p> <p>现在我们再次抓取dmoz.org，你将看到站点在输出中被打印 ，运行命令:<br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> scrapy crawl dmoz </pre><div class="contentsignin">Copy after login</div></div> </p> <p><strong>使用我们的 item</strong></p> <p>Item对象是自定义python字典;使用标准字典类似的语法,你能够访问它们的字段(就是以前我们定义的属性) <br /> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> >>> item = DmozItem() >>> item['title'] = 'Example title' >>> item['title'] 'Example title' </pre><div class="contentsignin">Copy after login</div></div> </p> <p>Spiders希望将抓取的数据放在 Item对象里。所以,为了返回我们抓取的数据,最终的代码要如下这么写 :</p> <p> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> from scrapy.spider import BaseSpider from scrapy.selector import HtmlXPathSelector from tutorial.items import DmozItem class DmozSpider(BaseSpider): name = "dmoz" allowed_domains = ["dmoz.org"] start_urls = [ "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/", "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/" ] def parse(self, response): hxs = HtmlXPathSelector(response) sites = hxs.select('//ul/li') items = [] for site in sites: item = DmozItem() item['title'] = site.select('a/text()').extract() item['link'] = site.select('a/@href').extract() item['desc'] = site.select('text()').extract() items.append(item) return items </pre><div class="contentsignin">Copy after login</div></div> <strong>Note<br /> </strong></p> <p>你能够找到完整功能的spider在dirbot项目里,同样你可以访问https://github.com/scrapy/dirbot</p> <p>现在重新抓取dmoz.org网站:</p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> [dmoz] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> {'desc': [u' - By David Mertz; Addison Wesley. Book in progress, full text, ASCII format. Asks for feedback. [author website, Gnosis Software, Inc.\n], 'link': [u'http://gnosis.cx/TPiP/'], 'title': [u'Text Processing in Python']} [dmoz] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> {'desc': [u' - By Sean McGrath; Prentice Hall PTR, 2000, ISBN 0130211192, has CD-ROM. Methods to build XML applications fast, Python tutorial, DOM and SAX, new Pyxie open source XML processing library. [Prentice Hall PTR]\n'], 'link': [u'http://www.informit.com/store/product.aspx&#63;isbn=0130211192'], 'title': [u'XML Processing with Python']} </pre><div class="contentsignin">Copy after login</div></div> </p> <p><strong><br /> 存储抓取的数据</strong></p> <p>最简单的方式去存储抓取的数据是使用Feed exports,使用如下的命令:</p> <p> <br /> </p> <div class="jb51code"> <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:py;"> scrapy crawl dmoz -o items.json -t json </pre><div class="contentsignin">Copy after login</div></div> <p>它将会产生一个items.json文件,它包含所有抓取的items(序列化的JSON)。</p> <p>在一些小的项目里(例如我们的教程中),那就足够啦。然而,假如你想要执行更多复杂的抓取items,你能够写一个 Item Pipeline。 因为在项目创建的时候，一个专门用于Item Pipelines的占位符文件已经随着项目一起被建立，目录在tutorial/pipelines.py。如果你只需要存取这些抓取后的items的话，就不需要去实现任何的条目管道。 <br> </p> </div> </div> </div> <div style="height: 25px;"> <div class="wzconBq" style="display: inline-flex;"> <span>Related labels：</span> <div class="wzcbqd"> <a onclick="hits_log(2,'www',this);" href-data="https://www.php.cn/search?word=python" target="_blank">python</a> </div> </div> <div style="display: inline-flex;float: right; color:#333333;">source：php.cn</div> </div> <div class="wzconOtherwz"> <a href="https://www.php.cn/faq/159729.html" title="使用Python脚本来控制Windows Azure的简单教程"> <span>Previous article：使用Python脚本来控制Windows Azure的简单教程</span> </a> <a href="https://www.php.cn/faq/159732.html" title="在Python下利用OpenCV来旋转图像的教程"> <span>Next article：在Python下利用OpenCV来旋转图像的教程</span> </a> </div> <div class="wzconShengming"> <div class="bzsmdiv">Statement of this Website</div> <div>The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. 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