Complete code steps for a simple python crawler: 1. Import the required libraries; 2. Specify the URL of the target web page; 3. Send a request to the target web page and obtain the HTML content of the page; 4. Use "BeautifulSoup" to parse HTML content; 5. Use CSS selectors or XPath to locate the data that needs to be crawled according to the structure and needs of the target web page; 6. Process the acquired data; 7. Save the data to a file or database; 8. Exception handling and logging
The operating environment of this tutorial: Windows 10 system, python version 3.11.2, dell g3 computer.
To write a simple Python crawler complete code, you can follow the following steps:
1. Import the required libraries:
import requests from bs4 import BeautifulSoup
2. Specify the target web page URL:
url = "https://example.com"
3. Send a request to the target web page and obtain the HTML content of the page:
response = requests.get(url) html_content = response.content
4. Use BeautifulSoup to parse the HTML content:
soup = BeautifulSoup(html_content, 'html.parser')
5. According to the target web page The structure and needs, use CSS selectors or XPath to locate the data that needs to be crawled:
data = soup.select('css选择器')
6. Process the acquired data:
for item in data: # 进行数据处理或存储等操作
7. Save the data to a file or database:
# 保存数据到文件 with open('data.txt', 'w') as file: for item in data: file.write(item.text + '\n') # 保存数据到数据库 import sqlite3 conn = sqlite3.connect('data.db') cursor = conn.cursor() for item in data: cursor.execute("INSERT INTO table_name (column_name) VALUES (?)", (item.text,)) conn.commit() conn.close()
8. Exception handling and logging:
try: # 执行爬取代码 except Exception as e: # 处理异常 print("出现异常:" + str(e)) # 记录日志 with open('log.txt', 'a') as file: file.write("出现异常:" + str(e) + '\n')
The above is a complete code example of a simple Python crawler, which you can modify and extend according to actual needs. Of course, this is just a basic framework, and more processing may be involved in practice, such as anti-crawler measures, multi-threading or asynchronous processing, etc.
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