


Basic Web Scraping Techniques Using Python Requests and BeautifulSoup
Jul 05, 2025 am 02:57 AMThe basic method of using Python for web crawling is to combine requests and BeautifulSoup, first send a request to get HTML, and then parse and extract data. 1. After installing the library, use requests.get() to get the web page content and handle exceptions; 2. BeautifulSoup parses HTML, locates elements through find_all(), class name, ID, etc. and extracts text or links; 3. Set headers to simulate browser access, and adds delays to avoid triggering anti-crawling mechanisms.
Directly answer the title question: Using Python for web crawling, the most basic and common method is to combine the two libraries: requests and BeautifulSoup. They are simple and practical to use together, and are suitable for data extraction of most static pages.

1. Installation and basic procedures
To start web crawling, you must first install the necessary libraries:

pip install requests beautifulsoup4
The whole process is roughly divided into three steps:
- Use
requests
to send requests to get web page content (HTML) - Parsing HTML with
BeautifulSoup
- Extract the required data, such as title, paragraph or link
The most important thing in this step is to ensure that the page content can be obtained normally. Sometimes it will fail due to server restrictions or network problems, so it is recommended to add an exception, such as:

import requests url = 'https://example.com' try: response = requests.get(url) response.raise_for_status() # If the status code is not 200, an exception will be thrown except requests.RequestException as e: print(f"Request failed: {e}")
2. How to locate and extract data
After getting the HTML content, the next step is to parse the structure. You can use BeautifulSoup
to find tags, class names, or IDs.
Common practices:
- Find all child nodes under a tag:
.find_all()
- Filter elements by class name:
soup.find_all('div', class_='your-class')
- Extract text content:
.get_text()
- Get the link address:
.get('href')
For example, I want to extract all the titles and links in a news list page:
from bs4 import BeautifulSoup soup = BeautifulSoup(response.text, 'html.parser') for item in soup.find_all('h2', class_='post-title'): title = item.get_text() link = item.find('a')['href'] print(title, link)
It should be noted here that the HTML structures of different websites vary greatly. It is best to manually check the web source code to confirm the structure, and do not blindly write the selector.
3. Avoid being blocked or triggering anti-crawling mechanism
Although this is just a basic crawling technique, the anti-crawler problem cannot be completely ignored. Many websites will respond to frequent requests, such as returning verification codes, blocking IP, etc.
A few simple but effective suggestions:
Add
headers
to simulate browser access:headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' } response = requests.get(url, headers=headers)
Add random delays between requests to avoid continuous access too fast:
import time import random time.sleep(random.uniform(1, 3))
Don't send too many requests, especially during the testing phase, keeping single-threaded and slow-paced safer.
These measures cannot be 100% anti-climbing, but they are enough in the basic crawling scenario.
Basically that's it. Although the combination of Requests BeautifulSoup is simple, it is OK to deal with most static pages. There is no need for too complex logic, the key is to be familiar with the HTML structure and CSS selector writing.
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