SoFunction
Updated on 2025-04-13

Guide to efficiently obtaining network data using Python

The basic concept of web crawler

The workflow of a web crawler usually includes the following steps:

  1. Send a request: Send HTTP request to the target website to obtain the content of the web page.
  2. Analyze the web page: Analyze the obtained web page content and extract the required data.
  3. Store data: Store the extracted data locally or in the database.

Common library introduction

  • Requests: Used to send HTTP requests and obtain web page content.
  • BeautifulSoup: Used to parse HTML and XML documents and extract data.
  • Scrapy: A powerful crawler framework that provides complete crawler development tools.
  • Selenium: Used to simulate browser operations and process pages that require JavaScript rendering.

Install the library

First, you need to install these libraries, you can use the following command:

pip install requests beautifulsoup4 scrapy selenium

Requests and BeautifulSoup crawler development

Send a request

Use the Requests library to send HTTP requests to get web page content.

import requests

url = ''
response = (url)

print(response.status_code)  # Print the response status codeprint()  # Print web page content

Analyze the web page

Use BeautifulSoup to parse the obtained web page content.

from bs4 import BeautifulSoup

soup = BeautifulSoup(, '')
print()  # Print web page title

Extract data

Extract the required data through various methods of BeautifulSoup.

# Extract all linkslinks = soup.find_all('a')
for link in links:
    print(('href'))
    
# Extract specific contentcontent = ('div', {'class': 'content'})
print()

Store data

Stores the extracted data into a local file or database.

with open('', 'w', encoding='utf-8') as f:
    for link in links:
        (('href') + '\n')

Scrapy for advanced crawler development

Scrapy is a powerful crawler framework suitable for complex crawler tasks.

Create a Scrapy project

First, create a Scrapy project:

scrapy startproject myproject

Define Item

existDefine the data structure to be extracted in the file:

import scrapy

class MyprojectItem():
    title = ()
    link = ()
    content = ()

Write Spider

existspidersCreate a Spider in the directory to define the crawling logic:

import scrapy
from  import MyprojectItem

class MySpider():
    name = 'myspider'
    start_urls = ['']

    def parse(self, response):
        for article in (''):
            item = MyprojectItem()
            item['title'] = ('h2::text').get()
            item['link'] = ('a::attr(href)').get()
            item['content'] = ('::text').get()
            yield item

Running crawler

Run the following command in the project directory to start the crawler:

scrapy crawl myspider -o

Selenium handles dynamic web pages

For web pages that require JavaScript rendering, Selenium can be used to simulate browser operations.

Install Selenium and browser drivers

pip install selenium

Download and install the corresponding browser driver (such as chromedriver).

Use Selenium to get web content

from selenium import webdriver

# Create a browser objectdriver = (executable_path='/path/to/chromedriver')

# Visit the web page('')

# Get web contenthtml = driver.page_source
print(html)

# Close the browser()

Combined with BeautifulSoup to analyze dynamic web pages

soup = BeautifulSoup(html, '')
print()

Deal with anti-crawling measures

Many websites will take anti-crawling measures, and the following are some common solutions:

Set request header

Simulate browser requests and set request headers such as User-Agent.

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 = (url, headers=headers)

Using a proxy

Send requests through proxy servers to avoid IP blocking.

proxies = {'http': 'http://your_proxy', 'https': 'https://your_proxy'}
response = (url, headers=headers, proxies=proxies)

Add delay

Add random delays to simulate human browsing behavior and avoid triggering anti-crawling mechanisms.

import time
import random

((1, 3))

Using browser automation tools

Tools such as Selenium can simulate human browsing behavior and bypass some anti-crawling measures.

Actual case: Crawling news websites

Target website

Choose to crawl a simple news site such as /.

Send requests and parse web pages

import requests
from bs4 import BeautifulSoup

url = '/'
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 = (url, headers=headers)

soup = BeautifulSoup(, '')

Extract news titles and links

articles = soup.find_all('a', {'class': 'storylink'})
for article in articles:
    title = 
    link = ('href')
    print(f'Title: {title}\nLink: {link}\n')

Store data

with open('', 'w', encoding='utf-8') as f:
    for article in articles:
        title = 
        link = ('href')
        (f'Title: {title}\nLink: {link}\n\n')

Summarize

This article introduces in detail the basic concepts, common libraries, data extraction methods and anti-crawling measures and countercrawling strategies of Python network crawlers. Basic crawling tasks can be easily implemented with Requests and BeautifulSoup, the Scrapy framework is suitable for complex crawler development, while Selenium can handle dynamic web pages. The specific examples show how to efficiently obtain network data and provide a way to deal with anti-crawling measures. Mastering these technologies can help everyone better collect and analyze data in actual projects.

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