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php website crawling library

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php website crawling library

<?php
header("Content-Type: text/html; charset=UTF-8");
require("phpQuery.php");
$hj = QueryList::Query('http://mobile.csdn.net/',array("title"=>array('.unit h1','text')));
//dump($hj->data);
$data = QueryList::Query('http://cms.querylist.cc/bizhi/453.html',array(
    'image' => array('img','src')
    ))->data;
//
$data = QueryList::Query('http://cms.querylist.cc/google/list_1.html',array(
    'link' => array('a','href')
    ))->data;
$page = 'http://cms.querylist.cc/news/566.html';
$reg = array(
    'title' => array('h1','text'),
    'date' => array('.pt_info','text','-span -a',function($content){
        $arr = explode(' ',$content);
        return $arr[0];
    }),
    'content' => array('.post_content','html','a -.content_copyright -script',function($content){
     
            $doc = phpQuery::newDocumentHTML($content);
            $imgs = pq($doc)->find('img');
            foreach ($imgs as $img) {
                $src = 'http://cms.querylist.cc'.pq($img)->attr('src');
                $localSrc = 'w/'.md5($src).'.jpg';
                $stream = file_get_contents($src);
                file_put_contents($localSrc,$stream);
                pq($img)->attr('src',$localSrc);
            }
            return $doc->htmlOuter();
    })
    );
$rang = '.content';
$ql = QueryList::Query($page,$reg,$rang);
$data = $ql->getData();
dump($data);

supports crawling websites and crawling. It is very powerful. It is a server-side open source project based on PHP. It allows PHP developers to easily process DOM document content, such as obtaining the headline information of a news website. What's more interesting is that it uses the idea of ????jQuery. You can process the page content just like using jQuery to get the page information you want.

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A practical guide to crawling HTML tabular data from an ASP.NET website

27 Aug 2025

This article aims to provide a clear and efficient solution for crawling table data from dynamic ASP.NET websites. By simulating the website's POST request, bypass the use of Selenium and directly obtain the HTML source code containing the tabular data. Combined with BeautifulSoup and Pandas libraries, data parsing, cleaning and extraction are realized, and finally presented in an easy-to-read table. This method is suitable for scenarios where data is required to be automatically captured by such websites, and can effectively improve the efficiency and stability of data acquisition.