How to use Selenium to crawl web page data in Python
1. What is Selenium
Web crawler is a very useful technique in Python programming, which allows you to automatically obtain data on web pages.
Selenium is an automated testing tool that can simulate user operations in the browser, such as clicking buttons, filling out forms, etc. Unlike commonly used crawler libraries such as BeautifulSoup and requests, Selenium can handle content dynamically loaded by JavaScript. Therefore, Selenium is a very suitable choice for data that needs to be obtained by simulating user interaction.
2. Install Selenium
To use Selenium, you first need to install it. You can use the pip command to install the Selenium library:
pip install selenium
After the installation is complete, you also need to download a browser driver that works with Selenium. This article uses the Chrome browser as an example. You need to download the ChromeDriver corresponding to your Chrome browser version. Download address: sites.google.com/a/chromium.…
After downloading and decompressing, put the chromedriver.exe file into a suitable location and remember the location. We will need it later. used in the code.
3. Crawl web page data
The following is a simple example. We will use Selenium to crawl a web page and output the page title.
from selenium import webdriver # 指定chromedriver.exe的路径 driver_path = r"C:\path\to\chromedriver.exe" # 创建一个WebDriver实例,指定使用Chrome浏览器 driver = webdriver.Chrome(driver_path) # 访问目标网站 driver.get("https://www.example.com") # 获取网页标题 page_title = driver.title print("Page Title:", page_title) # 关闭浏览器 driver.quit()
4. Simulate user interaction
Selenium can simulate various user operations in the browser, such as clicking buttons, filling out forms, etc. The following is an example where we will use Selenium to perform login operations on a website:
from selenium import webdriver from selenium.webdriver.common.keys import Keys driver_path = r"C:\path\to\chromedriver.exe" driver = webdriver.Chrome(driver_path) driver.get("https://www.example.com/login") # 定位用户名和密码输入框 username_input = driver.find_element_by_name("username") password_input = driver.find_element_by_name("password") # 输入用户名和密码 username_input.send_keys("your_username") password_input.send_keys("your_password") # 模拟点击登录按钮 login_button = driver.find_element_by_xpath("//button[@type='submit']") login_button.click() # 其他操作... # 关闭浏览器 driver.quit()
By combining various functions of Selenium, you can write a powerful web crawler to crawl data on various websites. However, please note that when crawling, you must abide by the robots.txt regulations of the target website and respect the website's data scraping policy. In addition, too frequent crawling may burden the website and even trigger the anti-crawling mechanism, so it is recommended to reasonably control the crawling speed.
5. Handling dynamically loaded content
For some websites with dynamically loaded content, we can use the explicit waiting and implicit waiting mechanisms provided by Selenium to ensure that the elements on the web page have been loaded. .
1. Explicit waiting
Explicit waiting refers to setting a specific waiting condition and waiting for an element to meet the condition within a specified time.
from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC driver_path = r"C:\path\to\chromedriver.exe" driver = webdriver.Chrome(driver_path) driver.get("https://www.example.com/dynamic-content") # 等待指定元素出现,最多等待10秒 element = WebDriverWait(driver, 10).until( EC.presence_of_element_located((By.ID, "dynamic-element-id")) ) # 操作该元素... driver.quit()
2. Implicit waiting
Implicit waiting is to set a global waiting time. If the element does not appear within this time, an exception will be thrown.
from selenium import webdriver driver_path = r"C:\path\to\chromedriver.exe" driver = webdriver.Chrome(driver_path) # 设置隐式等待时间为10秒 driver.implicitly_wait(10) driver.get("https://www.example.com/dynamic-content") # 尝试定位元素 element = driver.find_element_by_id("dynamic-element-id") # 操作该元素... driver.quit()
The above is the detailed content of How to use Selenium to crawl web page data in Python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



PHP and Python have their own advantages and disadvantages, and the choice depends on project needs and personal preferences. 1.PHP is suitable for rapid development and maintenance of large-scale web applications. 2. Python dominates the field of data science and machine learning.

Efficient training of PyTorch models on CentOS systems requires steps, and this article will provide detailed guides. 1. Environment preparation: Python and dependency installation: CentOS system usually preinstalls Python, but the version may be older. It is recommended to use yum or dnf to install Python 3 and upgrade pip: sudoyumupdatepython3 (or sudodnfupdatepython3), pip3install--upgradepip. CUDA and cuDNN (GPU acceleration): If you use NVIDIAGPU, you need to install CUDATool

Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.

Enable PyTorch GPU acceleration on CentOS system requires the installation of CUDA, cuDNN and GPU versions of PyTorch. The following steps will guide you through the process: CUDA and cuDNN installation determine CUDA version compatibility: Use the nvidia-smi command to view the CUDA version supported by your NVIDIA graphics card. For example, your MX450 graphics card may support CUDA11.1 or higher. Download and install CUDAToolkit: Visit the official website of NVIDIACUDAToolkit and download and install the corresponding version according to the highest CUDA version supported by your graphics card. Install cuDNN library:

Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.

When selecting a PyTorch version under CentOS, the following key factors need to be considered: 1. CUDA version compatibility GPU support: If you have NVIDIA GPU and want to utilize GPU acceleration, you need to choose PyTorch that supports the corresponding CUDA version. You can view the CUDA version supported by running the nvidia-smi command. CPU version: If you don't have a GPU or don't want to use a GPU, you can choose a CPU version of PyTorch. 2. Python version PyTorch

MinIO Object Storage: High-performance deployment under CentOS system MinIO is a high-performance, distributed object storage system developed based on the Go language, compatible with AmazonS3. It supports a variety of client languages, including Java, Python, JavaScript, and Go. This article will briefly introduce the installation and compatibility of MinIO on CentOS systems. CentOS version compatibility MinIO has been verified on multiple CentOS versions, including but not limited to: CentOS7.9: Provides a complete installation guide covering cluster configuration, environment preparation, configuration file settings, disk partitioning, and MinI

CentOS Installing Nginx requires following the following steps: Installing dependencies such as development tools, pcre-devel, and openssl-devel. Download the Nginx source code package, unzip it and compile and install it, and specify the installation path as /usr/local/nginx. Create Nginx users and user groups and set permissions. Modify the configuration file nginx.conf, and configure the listening port and domain name/IP address. Start the Nginx service. Common errors need to be paid attention to, such as dependency issues, port conflicts, and configuration file errors. Performance optimization needs to be adjusted according to the specific situation, such as turning on cache and adjusting the number of worker processes.
