Common problems and solutions for crawler programming in Python

Common problems and solutions for crawler programming in Python

Introduction:
With the development of the Internet, the importance of network data has become increasingly prominent. Crawler programming has become an essential skill in fields such as big data analysis and network security. However, crawler programming not only requires a good programming foundation, but also requires facing various common problems. This article will introduce common problems of crawler programming in Python, and provide corresponding solutions and specific code examples. I hope this article can help readers better master crawler programming skills.

1. Access restrictions on target websites
During the crawler programming process, the target website may have set up a series of anti-crawler mechanisms, such as limiting request frequency, banning illegal robots, etc. To overcome these limitations, the following measures can be taken:
1. Set request header information: To simulate normal browser behavior, you can set request header information such as User-Agent and Referer to make the request look more like it is initiated by the user. .

import requests

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',
    'Referer': 'http://www.example.com'
}

response = requests.get(url, headers=headers)

2. Use proxy IP: By using a proxy server, you can hide your real IP address to avoid being banned by the target website. You can find some available proxy IPs on the Internet and set the proxy using the proxies parameter of the requests library.

import requests

proxies = {
    'http': 'http://111.11.111.111:8080',
    'https': 'http://111.11.111.111:8080'
}

response = requests.get(url, proxies=proxies)

3. Use Cookies: Some websites use Cookies to identify whether they are robots. Cookie information can be passed using the cookies parameter of the requests library.

import requests

cookies = {
    'name': 'value'
}

response = requests.get(url, cookies=cookies)

2. Dynamic loading and asynchronous loading data acquisition
Many websites now use dynamic loading or asynchronous loading to obtain data. For such websites, we need to simulate the behavior of the browser. retrieve data. The following methods can be used:
1. Use Selenium WebDriver: Selenium is an automated testing tool that can simulate browser behavior, including clicks, inputs and other operations. Selenium WebDriver can achieve dynamic loading and asynchronous loading of data acquisition.

from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get(url)

# 使用WebDriverWait等待数据加载完毕
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

locator = (By.XPATH, '//div[@class="data"]')
data = WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator)).text

2. Analyze Ajax requests: Open the Chrome browser developer tools, select the Network panel, refresh the page, observe the data format and parameters of the request, and then use the requests library to simulate sending Ajax requests.

import requests

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',
    'Referer': 'http://www.example.com',
    'X-Requested-With': 'XMLHttpRequest'
}

response = requests.get(url, headers=headers)

3. Data analysis and extraction
In crawler programming, data analysis and extraction is a very critical step. Common data formats include HTML, JSON, XML, etc. The following will introduce the parsing methods of these common data formats:
1.HTML parsing: You can use the BeautifulSoup library in Python to parse HTML documents and use selectors or XPath Expressions extract the required data.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, 'html.parser')

# 使用选择器提取数据
data = soup.select('.class')

2.JSON parsing: Use Python’s built-in json library to parse data in JSON format.

import json

data = json.loads(response.text)

3. XML parsing: The xml library, ElementTree library, etc. in Python can be used to parse data in XML format.

import xml.etree.ElementTree as ET

tree = ET.fromstring(xml)
root = tree.getroot()

# 提取数据
data = root.find('tag').text

Summary:
Crawler programming is a complex and challenging task, but with adequate preparation and learning, we can overcome the difficulties and problems. This article introduces common problems of crawler programming in Python and gives corresponding solutions and code examples. I hope this content can help readers better master the skills and methods of crawler programming. In practice, different methods can also be flexibly applied to solve problems according to the actual situation.

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