Master Modern Web Scraping: From Classic Tools to DeepSeek AI Integration

Overview

The article compares mainstream web‑scraping tools, tracing the evolution from traditional methods to innovative applications that combine large language models like DeepSeek.

Traditional Data Crawling Techniques

Static pages are typically scraped using Python's

requests

library together with

BeautifulSoup

, while dynamic pages require browser automation tools such as

Selenium

. Traditional crawlers rely on manual analysis of HTML and CSS selectors, which limits reusability and struggles with JavaScript‑rendered content. Anti‑scraping measures like IP blocking often necessitate proxy pools and header spoofing.

Main Scraping Tools Comparison

Popular open‑source tools (requests + BeautifulSoup, Selenium) are suitable for small‑scale static or dynamic sites but have a learning curve for large‑scale distributed crawling. Commercial, low‑code solutions provide visual interfaces and built‑in anti‑captcha mechanisms but may lack flexibility and can be costly.

DeepSeek for Data Extraction

Large‑model technologies enable natural‑language specifications of scraping rules, eliminating the need to hand‑write parsers. By describing the target data, DeepSeek can generate extraction scripts and directly analyze results.

Using BrightData for Industry Data

BrightData offers residential, data‑center and mobile proxies, along with tools like Web Unlocker, Scraping Browser and Web Scraper API. Combined with Selenium, these services help bypass captchas, IP blocks and fingerprinting, ensuring compliance and high‑quality data collection.

Registration steps, account creation, and trial credit acquisition are illustrated with screenshots.

Step‑by‑Step DeepSeek Script Generation

The workflow defines the scraping goal, installs required Python packages, and provides HTML selectors to the model. DeepSeek then outputs a complete Python script that fetches book titles and prices from

https://books.toscrape.com

and saves them to an Excel file.

<code>#default > div > div > div > div > section > div:nth-child(2) > ol > li:nth-child(1) > article > h3 > a</code>

<code>You are a web scraping expert utilizing Python’s BeautifulSoup library and any necessary automation tools.</code><code>I want you to generate a detailed step‑by‑step script to scrape https://books.toscrape.com;</code><code>1. Install any necessary Python packages.</code><code>2. Extract the book title using selector #default > div.container-fluid.page > ... > a and the book price using selector #default > div.container-fluid.page > ... > p.price_color.</code><code>3. Store the list of titles and prices in an Excel file.</code><code>4. Scrape the first page only.</code>

<code># Step 1: Install required packages</code><code># Run these commands in your terminal:</code><code># pip install beautifulsoup4 requests pandas openpyxl</code>

<code>import requests</code><code>from bs4 import BeautifulSoup</code><code>import pandas as pd</code><code>def scrape_books():</code><code>    url = 'https://books.toscrape.com/'</code><code>    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}</code><code>    response = requests.get(url, headers=headers)</code><code>    if response.status_code != 200:</code><code>        print(f'Failed to retrieve page. Status code: {response.status_code}')</code><code>        return</code><code>    soup = BeautifulSoup(response.text, 'html.parser')</code><code>    books = soup.select('ol.row li.col-xs-6')</code><code>    book_list = []</code><code>    for book in books:</code><code>        title_tag = book.select_one('h3 a')</code><code>        title = title_tag.get('title', '').strip()</code><code>        price_tag = book.select_one('p.price_color')</code><code>        price = price_tag.get_text().replace('£', '').strip() if price_tag else ''</code><code>        if title and price:</code><code>            book_list.append({'Title': title, 'Price': price})</code><code>    if book_list:</code><code>        df = pd.DataFrame(book_list)</code><code>        df.to_excel('books_data.xlsx', index=False)</code><code>        print('Successfully saved data to books_data.xlsx')</code><code>    else:</code><code>        print('No books found on the page')</code><code>if __name__ == '__main__':</code><code>    scrape_books()</code>

Images illustrating the workflow are included throughout the article.