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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><h2 id="总览"><a href="#总览" class="headerlink" title="总览"></a>总览</h2><p>对以前学过的爬虫进行总结，巩固知识，也希望能帮助他人。初学爬虫的时候，看了一段视频入门，再后来走了不少弯路，究其原因是没有对爬虫有个整体性的掌握。我想整里一份框架，概括自己所学的同时，给初学者一点指导和帮助。<br><a id="more"></a><br>学习爬虫，首先需要一门编程语言来实现，我推荐python，简单易学，上手快。还要学习一些相关的python爬虫包，如requests。再来，需要了解一下HTML网页的知识，因为你需要从网页中提取信息，不了解网页是不行。学会使用抓包工具，如fiddle4，其实大部分情况下，chrome浏览器的控制台就足够了。学会反爬，没办法，现在很多网站都有反爬，不会一点技巧，难以获得信息。如果你需要将信息存入数据库的话，你还要学会操作数据库，关系型数据库，如MySQL，非关系型数据库，如mongo，redis。当然，大规模抓取的时候，少不了用一些框架，如scrapy。简单的列举如下：</p>
<ul>
<li>python</li>
<li>爬虫相关包：requests, selenium, …</li>
<li>HTML</li>
<li>fiddler4，chrome控制台</li>
<li>反爬</li>
<li>数据库：MySQL, mongo, redis, …</li>
<li>爬虫框架：scrapy, PySpider, …</li>
<li>…</li>
</ul>
<p>python和HTML知识就不介绍了，相关信息很容易查阅，下面会依次介绍一下python里面和爬虫相关的包。</p>
<h2 id="requests"><a href="#requests" class="headerlink" title="requests"></a>requests</h2><p>requests是python3里面，爬虫的核心package，详情见<a href="https://2.python-requests.org//zh_CN/latest/index.html#" target="_blank" rel="noopener">官网</a>。官网里面有详细的文档和教程。另一个相关包，selenium就不在本文介绍了。</p>
<h3 id="编码问题"><a href="#编码问题" class="headerlink" title="编码问题"></a>编码问题</h3><p>requests请求返回的结果有时候会显示为乱码，你可以指定编码。有时候，有些文本（例如表情）因为编码问题无法保存到txt，excel或者数据库，你可以使用编码后再解码的方式过滤掉。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> requests</span><br><span class="line">r = requests.get(<span class="string">'https://www.baidu.com'</span>)</span><br><span class="line">r.enconding = <span class="string">'utf8'</span> <span class="comment"># 指定编码</span></span><br><span class="line">text = r.text.encode(<span class="string">'utf8'</span>, <span class="string">'ignore'</span>).decode(<span class="string">'utf8'</span>) <span class="comment"># 编码后解码</span></span><br></pre></td></tr></table></figure></p>
<h3 id="参数传递"><a href="#参数传递" class="headerlink" title="参数传递"></a>参数传递</h3><p>传递参数一般使用字典构造，不同方法，传递参数的关键字也不同。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">payload = {<span class="string">'key1'</span>: <span class="string">'value1'</span>, <span class="string">'key2'</span>: <span class="string">'value2'</span>}</span><br><span class="line"></span><br><span class="line"><span class="comment"># GET方法</span></span><br><span class="line">r = requests.get(<span class="string">"http://httpbin.org/get"</span>, params=payload)</span><br><span class="line"></span><br><span class="line"><span class="comment"># POST方法，需要注意的是，即使是POST方法，有时候仍然会有params参数</span></span><br><span class="line">r = requests.post(<span class="string">"http://httpbin.org/post"</span>, data=payload)</span><br><span class="line"><span class="comment"># POST有时会用json作为参数</span></span><br><span class="line">r = requests.post(url, json=payload)</span><br></pre></td></tr></table></figure></p>
<h3 id="json返回"><a href="#json返回" class="headerlink" title="json返回"></a>json返回</h3><p>requests提供了json返回的便捷接口。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 返回json解码后的dict</span></span><br><span class="line">r.json()</span><br></pre></td></tr></table></figure></p>
<p>需要注意的是，如果<code>r.text</code>不是json格式，<code>r.json()</code>解码失败。但即使解码成功，也不意味这你获得了你想要的结果，有些服务器在失败的响应中返回的也是json对象，你需要验证<code>r.status_code</code>和内容。</p>
<h3 id="定制HEADER"><a href="#定制HEADER" class="headerlink" title="定制HEADER"></a>定制HEADER</h3><p>定制HEADER可以说是反爬的第一步，也是最简单的一步。很多网站会禁止机器访问，定制header是十分有必要的。header里面有很多参数，有些有用，有些就不是那么必要，看情况。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">h = {</span><br><span class="line"> <span class="string">'user-agent:'</span>: <span class="string">''</span> <span class="comment"># 可以在浏览器里面copy一下</span></span><br><span class="line">}</span><br><span class="line">r = requests.get(url, headers=headers)</span><br></pre></td></tr></table></figure></p>
<h3 id="Cookie和简单的反爬技巧"><a href="#Cookie和简单的反爬技巧" class="headerlink" title="Cookie和简单的反爬技巧"></a>Cookie和简单的反爬技巧</h3><p>cookie是登录网站的记录，一般网站都会用cookie来限制爬虫。自动登录网站，获取cookie，大多数时候不是那么容易实现的。大多数网站都是通过<code>javascript</code>脚本验证登录，中间经历多个传参和跳转，最终才能拿到cookie，微博登录就是典型的例子。还有的，加入了验证码。这就比较麻烦了，简单的验证码还好处理，复杂的验证码，几乎没办法。有些平台提供了验证码识别的api，当然是付费的。你可以调用他们的api，实现验证码输入。<br>当然，如果你对数据量需求不大，同时，网站服务器也没有针对账号和ip进行反爬，自己手动登录，获取cookie，进行爬虫是最简单的处理方案。针对账号反爬，会限制同一账户短时间内的过多访问，如果需要短时间大量爬取数据，多账号是必不可少的。ip限制也是类似，可以通过代理池解决。如果需要的数据量不大，控制访问速度就行了。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line">cookie = {</span><br><span class="line"> <span class="string">'key'</span>: <span class="string">'value'</span></span><br><span class="line">}</span><br><span class="line">r = requests.get(url, cookies=cookie)</span><br><span class="line"><span class="comment"># 代理</span></span><br><span class="line">proxies = {</span><br><span class="line"> <span class="string">"http"</span>: <span class="string">"http://10.10.1.10:3128"</span>,</span><br><span class="line"> <span class="string">"https"</span>: <span class="string">"http://10.10.1.10:1080"</span>,</span><br><span class="line">}</span><br><span class="line">r = requests.get(<span class="string">"http://example.org"</span>, proxies=proxies)</span><br></pre></td></tr></table></figure></p>
<p>关于cookie获取，还有个需要注意的地方。在实现自动登录获取cookie的时候，有些教程会建议使用session的会话方式记住cookie，这种方式有时可行，有的时候不行。究其根本，是在登录过程中进行了多次跳转，多次设置cookie，session的方式没能成功的设置最后的cookie。这种时候，需要自己去分析登录过程中的参数传递和cookie设置。有些小伙伴，可能在此过程中找不到cookie，发现<code>r.cookies</code>是空的，或者没有自己要的cookies，可以在返回的请求头<code>r.headers</code>里面找找看，它的set-cookie可能就是你需要的。<code>r.request.headers</code>也可以试试，如果你某些参数设置错误，你的cookie可能会进到里面，在另外一篇博文里面看到的，具体是哪篇想不起来了。</p>
<h2 id="信息提取"><a href="#信息提取" class="headerlink" title="信息提取"></a>信息提取</h2><p>从HTML中提取信息方法很多，可以用re，xpath，BeautifulSoup，等等。re适用于任何文本的提取，就不说了。xpath和BeautifulSoup网上的教程也有很多，建议两种方法都掌握，都很简单。</p>
<h3 id="xpath基础语法"><a href="#xpath基础语法" class="headerlink" title="xpath基础语法"></a>xpath基础语法</h3><table>
<thead>
<tr>
<th>表达式</th>
<th>描述 </th>
</tr>
</thead>
<tbody>
<tr>
<td>nodename</td>
<td>选取此节点的所有子节点。</td>
</tr>
<tr>
<td>/</td>
<td>从根节点选取。</td>
</tr>
<tr>
<td>//</td>
<td>从匹配选择的当前节点选择文档中的节点，而不考虑它们的位置。</td>
</tr>
<tr>
<td>.</td>
<td>选取当前节点。</td>
</tr>
<tr>
<td>..</td>
<td>选取当前节点的父节点。</td>
</tr>
<tr>
<td>@</td>
<td>选取属性。</td>
</tr>
</tbody>
</table>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> lxml <span class="keyword">import</span> etree</span><br><span class="line"><span class="keyword">import</span> request</span><br><span class="line"></span><br><span class="line">r = requests.get(<span class="string">'https://www.baidu.com'</span>)</span><br><span class="line">html = etree.HTML(r.text)</span><br><span class="line">result = etree.tostring(html)</span><br><span class="line"><span class="comment"># tostring()方法即可输出修正后的HTML代码，但是结果是bytes类型</span></span><br><span class="line">print(result.decode(<span class="string">'utf-8'</span>))</span><br><span class="line">result = html.xpath(<span class="string">'//li'</span>)</span><br><span class="line"><span class="comment"># 返回的是list类型，每个元素是element类型，选取什么元素，就用基础语法进行组合就好，节点内文本使用text()</span></span><br><span class="line">html.xpath(<span class="string">'//li[@class="li"]/a/text()'</span>)</span><br><span class="line"><span class="comment"># 属性有多个值就需要用 contains() 函数</span></span><br><span class="line">html.xpath(<span class="string">'//li[contains(@class, "li") and @name="item"]/a/text()'</span>)</span><br></pre></td></tr></table></figure>
<h3 id="BeautifulSoup的select"><a href="#BeautifulSoup的select" class="headerlink" title="BeautifulSoup的select"></a>BeautifulSoup的select</h3><p><a href="https://beautifulsoup.readthedocs.io/zh_CN/v4.4.0/" target="_blank" rel="noopener">BeautifulSoup</a>支持最常用的CSS selectors，通过<code>select</code>方法调用。当然，BeautifulSoup也有自己的方法，使用起来也用方便。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> bs4 <span class="keyword">import</span> BeautifulSoup</span><br><span class="line">r = requests.get(<span class="string">'https://www.baidu.com'</span>)</span><br><span class="line">soup = BeautifulSoup(r.text, <span class="string">'html.parser'</span>)</span><br><span class="line">soup.select(<span class="string">'title'</span>)</span><br></pre></td></tr></table></figure></p>
<p>下表来自w3schools的<a href="https://www.w3schools.com/cssref/css_selectors.asp" target="_blank" rel="noopener">CSS Selector Reference</a>，只拿了一部分，完整表格参见网址，可能有部分不支持。</p>
<table>
<thead>
<tr>
<th>Selector</th>
<th>Example</th>
<th>Example description</th>
</tr>
</thead>
<tbody>
<tr>
<td>.class</td>
<td>.intro</td>
<td>Selects all elements with class=”intro”</td>
</tr>
<tr>
<td>.class1.class2</td>
<td><code><div class="name1 name2">...</div></code></td>
<td>Selects all elements with both name1 and name2 set within its class attribute</td>
</tr>
<tr>
<td>.class1 .class2</td>
<td><code><div class="name1"><div class="name2">...</div></div></code></td>
<td>Selects all elements with name2 that is a descendant of an element with name1</td>
</tr>
<tr>
<td>#id</td>
<td>#firstname</td>
<td>Selects the element with id=”firstname”</td>
</tr>
<tr>
<td>*</td>
<td>*</td>
<td>Selects all elements</td>
</tr>
<tr>
<td>element</td>
<td>p</td>
<td>Selects all <code><p></code> elements</td>
</tr>
<tr>
<td>element.class</td>
<td>p.intro</td>
<td>Selects all <code><p></code> elements with class=”intro”</td>
</tr>
<tr>
<td>element,element</td>
<td>div, p</td>
<td>Selects all <code><div></code> elements and all <code><p></code> elements</td>
</tr>
<tr>
<td>element element</td>
<td>div p</td>
<td>Selects all <code><p></code> elements inside <code><div></code> elements</td>
</tr>
<tr>
<td>element>element</td>
<td>div > p</td>
<td>Selects all <code><p></code> elements where the parent is a <code><div></code> element</td>
</tr>
<tr>
<td>element+element</td>
<td>div + p</td>
<td>Selects all <code><p></code> elements that are placed immediately after <code><div></code> elements</td>
</tr>
<tr>
<td>element1~element2</td>
<td>p ~ ul</td>
<td>Selects every <code><ul></code> element that are preceded by a <code><p></code> element</td>
</tr>
<tr>
<td>[attribute]</td>
<td>[target]</td>
<td>Selects all elements with a target attribute</td>
</tr>
<tr>
<td>[attribute=value]</td>
<td>[target=_blank]</td>
<td>Selects all elements with target=”_blank”</td>
</tr>
<tr>
<td>[attribute~=value]</td>
<td>[title~=flower]</td>
<td>Selects all elements with a title attribute containing the word “flower”</td>
</tr>
<tr>
<td>[attribute|=value]</td>
<td>[lang|=en]</td>
<td>Selects all elements with a lang attribute value starting with “en”</td>
</tr>
<tr>
<td>[attribute^=value]</td>
<td>a[href^=”https”]</td>
<td>Selects every <code><a></code> element whose href attribute value begins with “https”</td>
</tr>
<tr>
<td>[attribute$=value]</td>
<td>a[href$=”.pdf”]</td>
<td>Selects every <code><a></code> element whose href attribute value ends with “.pdf”</td>
</tr>
<tr>
<td>[attribute*=value]</td>
<td>a[href*=”w3schools”]</td>
<td>Selects every <code><a></code> element whose href attribute value contains the substring “w3schools”</td>
</tr>
</tbody>
</table>
<h2 id="数据库"><a href="#数据库" class="headerlink" title="数据库"></a>数据库</h2><p>我主要使用的数据库还是MySQL，mongo和redis使用很少，它们使用起来都不困难，python都提供了相应的包进行操作。<code>pymysql</code>是python里面用来操作mysql数据库的包，详情参见<a href="https://pymysql.readthedocs.io/" target="_blank" rel="noopener">官网</a>。操作很简单，下面是官网的例子。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pymysql.cursors</span><br><span class="line"></span><br><span class="line"><span class="comment"># Connect to the database</span></span><br><span class="line">connection = pymysql.connect(host=<span class="string">'localhost'</span>,</span><br><span class="line"> user=<span class="string">'user'</span>,</span><br><span class="line"> password=<span class="string">'passwd'</span>,</span><br><span class="line"> db=<span class="string">'db'</span>,</span><br><span class="line"> charset=<span class="string">'utf8mb4'</span>,</span><br><span class="line"> cursorclass=pymysql.cursors.DictCursor)</span><br><span class="line"></span><br><span class="line"><span class="keyword">try</span>:</span><br><span class="line"> <span class="keyword">with</span> connection.cursor() <span class="keyword">as</span> cursor:</span><br><span class="line"> <span class="comment"># Create a new record</span></span><br><span class="line"> sql = <span class="string">"INSERT INTO `users` (`email`, `password`) VALUES (%s, %s)"</span></span><br><span class="line"> cursor.execute(sql, (<span class="string">'webmaster@python.org'</span>, <span class="string">'very-secret'</span>))</span><br><span class="line"></span><br><span class="line"> <span class="comment"># connection is not autocommit by default. So you must commit to save</span></span><br><span class="line"> <span class="comment"># your changes.</span></span><br><span class="line"> connection.commit()</span><br><span class="line"></span><br><span class="line"> <span class="keyword">with</span> connection.cursor() <span class="keyword">as</span> cursor:</span><br><span class="line"> <span class="comment"># Read a single record</span></span><br><span class="line"> sql = <span class="string">"SELECT `id`, `password` FROM `users` WHERE `email`=%s"</span></span><br><span class="line"> cursor.execute(sql, (<span class="string">'webmaster@python.org'</span>,))</span><br><span class="line"> result = cursor.fetchone()</span><br><span class="line"> print(result)</span><br><span class="line"><span class="keyword">finally</span>:</span><br><span class="line"> connection.close()</span><br></pre></td></tr></table></figure></p>
<h2 id="爬虫框架"><a href="#爬虫框架" class="headerlink" title="爬虫框架"></a>爬虫框架</h2><p>实际上，小规模的爬取数据根本用不上框架。爬虫框架一般用来进行大规模爬取，搭建一个爬虫工程。我之前就用了scrapy搭建了一个抓取题库的项目，主要是网站上的选择题，存入数据库。scrapy是异步框架，但是没有实现多进程，需要自己分配爬取队列，来实现多进程的爬取。如果要进行分布式爬取，那建立一个爬取队列的数据库的很有必要的，保证每个框架间的队列是唯一的，不做重复爬取。爬虫框架使用起来很简单，教程很多，不展开了。</p>
<h2 id="结尾"><a href="#结尾" class="headerlink" title="结尾"></a>结尾</h2><p>上面写的这些内容几乎都有尝试过，也有部分很少使用，或者没有经历。但就我这样十分业余的水平来说，应当是可堪用了。</p>
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<div style="text-align:center;color: #ccc;font-size:14px;">-------------本文结束<i class="fa fa-paw"></i>感谢您的阅读-------------</div>
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