[Python crawler] Python crawler introductory tutorial & precautions

1 Introduction

With the rapid development of the Internet, network data has become an important source for people to obtain information. As an important means of obtaining network data, crawler technology has attracted more and more attention. Among many programming languages, Python has become the first choice for crawler development because of its advantages such as ease of learning and use, rich libraries, and active community. This article will take you into the world of Python crawlers, allowing you to master this technology from entry to advanced.

2 Python crawler basics

2.1 Python environment configuration

First, you need to make sure that the Python environment is installed on your computer. You can download and install the latest version of Python from the Python official website. In addition, in order to facilitate code debugging and running, you also need to install an integrated development environment (IDE), such asPyCharm, Spyderetc.

2.2 Introduction to crawler library

In Python, there are many libraries for crawler development, the most commonly used ones are Requests, BeautifulSoup, Scrapy, etc. The Requests library is used to send HTTP requests, the BeautifulSoup library is used to parse HTML or XML data, and the Scrapy library is a complete crawler framework that provides rich functions and APIs.

2.3 Practical crawler combat: obtaining web page data

The following is a simple Python crawler program that uses the Requests library to obtain web page data:

import requests  
  
url = 'https://www.example.com'  
response = requests.get(url)  
if response.status_code == 200:  
    print(response.text)  
else:  
    print('Failed to fetch the page.')

This code obtains the content of the https://www.example.com web page by sending a GET request. You can modify the URL as needed to obtain data from other web pages.

3 Python crawler advanced

3.1 Parse HTML data

After obtaining the web content, we need to extract the required data from it. HTML data can be easily parsed using the BeautifulSoup library:

from bs4 import BeautifulSoup  
  
html_doc = """  
<html><head><title>The Dormouse's story</title></head>  
<body>  
<p class="title"><b>The Dormouse's story</b></p>  
<p class="story">Once upon a time there were three little sisters; and their names were  
<a href="http://example.com/elsie" class="sister" id="link1">Elsie</a>,  
<a href="http://example.com/lacie" class="sister" id="link2">Lacie</a> and  
<a href="http://example.com/tillie" class="sister" id="link3">Tillie</a>;  
and they lived at the bottom of a well.</p>  
<p class="story">...</p>  
"""  
soup = BeautifulSoup(html_doc, 'html.parser')  
  
# 查找所有 class 为 "sister" 的标签  
sisters = soup.find_all('a', class_='sister')  
for sister in sisters:  
    print(sister.text)  # 输出每个 sister 的文本内容

This codeParses HTML data through the BeautifulSoup library, finds all tags with class "sister", and outputs their text content . You can modify the parameters of BeautifulSoup as needed to implement more complex parsing logic.

3.2 Avoid being banned or banned

When developing a crawler, you need to comply with the crawler protocol and laws and regulations of the website. In order to avoid being banned or blocked, you can take the following measures:Set a reasonable crawling frequency, use User Agent to simulate browser behavior, set Cookies, etc. In addition, you can also use Scrapy's built-in functions to implement operations such as IP proxy and request current limiting.

3.3 Handling JavaScript and dynamically loading data

Many websites use JavaScript to dynamically load data to achieve a better user experience. If you directly use the Requests library to obtain web page content, you may not be able to obtain complete data due to the lack of JavaScript. At this time, you canuse the Selenium library to simulate browser behavior, execute JavaScript and obtain dynamically loaded data.

from selenium import webdriver  
  
# 创建浏览器驱动  
driver = webdriver.Chrome()  
  
# 打开网页  
driver.get('https://www.example.com')  
  
# 等待页面加载完毕  
driver.implicitly_wait(10)  
  
# 获取页面内容  
html_doc = driver.page_source  
  
# 关闭浏览器驱动  
driver.quit()

        This code uses the Selenium library to open a web page and waits for the page to load to obtain the page content. You can modify the code as needed to implement more complex processing logic.

3.4 Data storage and processing

After data is obtained, it usually needs to be stored and processed. You can use Python's database library (such as sqlite3, MySQLdb, etc.) to store data into the database. For processing large amounts of data, you can use Python's Pandas library for data cleaning, analysis, and visualization.

4 Notes and FAQs

Comply with the website crawler agreement: When using crawler technology to obtain data,should comply with the website’s crawler agreement and terms of use to avoid damaging the website server Cause excessive burden or infringe the rights of the website.
Handling anti-crawling mechanisms: Many websites will adopt anti-crawling mechanisms to prevent crawler access, such as setting IP restrictions, detecting User Agents, etc. Therefore, when using crawler technology, you need to deal with these anti-crawling mechanisms to avoid being banned from the website.
Pay attention to data updates: Website data may be updated at any time, so when using crawler technology to obtain data, you need to pay attention to the timeliness of the data. If you need to obtain real-time data, the crawler should be updated regularly.
Reasonable use of proxy IP and cookies: When crawling large amounts of data, using proxy IP and cookies can avoid being banned. At the same time, it is also necessary to set the proxy IP and the frequency of use of cookies reasonably to avoid excessive burden on the server.
Pay attention to network security: When using crawler technology,you need to pay attention to network security issues to avoid security issues such as hacker attacks or data leaks due to crawler program vulnerabilities< a i=2>.
Handling abnormal situations: When using crawler technology, you may encounter various abnormal situations, such as network connection failure, timeout, etc. Therefore, when writing a crawler program, you need to handle these exceptions to ensure the stability and availability of the program.
Pay attention to legal issues: When using crawler technology,you need to pay attention to legal issues to avoid legal disputes caused by violating the law. At the same time, you also need to abide by the laws, regulations and terms of use of the website to ensure that your behavior is legal and compliant.

5 Summary

Python crawler technology is a powerful tool that can help us automatically obtain and process website data. In this article, we introduce the basic concepts and common tools of Python crawlers, including the Requests library, BeautifulSoup library, etc. At the same time, we also introduced through practical cases how to use Python crawlers to crawl website article lists, user information, and product information, and save them to local files or databases. Finally, we summarized the issues and common problems you need to pay attention to when using Python crawlers.

When using Python crawler technology, we need to abide by the website's crawler agreement and terms of use to avoid placing an excessive burden on the website server or infringing on the rights of the website. At the same time, we need to pay attention to data updates, reasonable use of proxy IP and cookies, pay attention to network security, handle abnormal situations, and pay attention to legal issues, etc. Only in this way can we better use Python crawler technology to obtain and process website data to facilitate our work and life.