Assume you want to scrape competitor websites for information about their pricing pages. What are you going to do? Manually copying and pasting data is time-consuming, ineffective, and error-prone. Python allows you to easily automate it. In this article, we will learn how to use Python’s tools and libraries to perform Selenium web scraping. Selenium is an open-source automated testing framework for web applications you need to validate across multiple browsers and platforms. Jason Huggins, a ThoughtWorks software engineer, invented it in 2004.<\/p>\n\n\n\n
Web scraping is the process of extracting and processing large amounts of data from the internet using a program or algorithm. Scraping data from the web is a useful skill to have, whether you are a data scientist, engineer, or anyone who analyzes large amounts of datasets. If you find data on the web but cannot download it directly, web scraping with Python is a skill you can use to extract the data into a useful format you can import.<\/p>\n\n\n\n
They are: <\/p>\n\n\n\n
While most websites used for sentiment analysis, such as social media websites, have APIs that allow users to access data, this is not always sufficient. Web scraping is often more appropriate for obtaining data in real time about information, conversations, research, and trends.<\/p>\n\n\n\n
E-commerce sellers can track products and pricing across multiple platforms to conduct market research on consumer sentiment and competitor pricing. This enables very efficient monitoring of competitors and price comparisons to maintain a clear view of the market.<\/p>\n\n\n\n
You need data for self-driving cars, face recognition, and recommendation engines. Web scraping is one of the most convenient and widely used methods for obtaining valuable information from reputable websites.<\/p>\n\n\n\n
While sentiment analysis is a well-known machine learning algorithm, it is not the only one. However, one thing all machine learning algorithms have in common is the massive amount of data you need to train them. Machine learning drives research, technological progress, and overall growth in all fields of learning and innovation. In turn, web scraping can provide highly accurate and dependable data collection for these algorithms.<\/p>\n\n\n\n
Selenium Python web scraping refers to a collection of open-source projects for browser automation. It supports bindings for all major programming languages, including our favorite, Python. To control web browsers such as Chrome, Firefox, and Safari, the Selenium API employs the WebDriver protocol. Selenium can control both a locally installed browser instance and one running on a remote machine over the network.<\/p>\n\n\n\n
Selenium was originally designed (over 20 years ago!) for cross-browser, end-to-end testing (acceptance tests). In the meantime, it is primarily seen as a general browser automation platform (e.g., for taking screenshots), which, of course, includes the purpose of web crawling and web scraping. Nothing beats a real person “talking” to a website. Selenium provides a wide range of ways to interact with sites, such as:<\/p>\n\n\n\n
Using Selenium Webdriver Browser Automation, you can collect all of the necessary data for web scraping. Selenium crawls the target URL webpage at scale and collects data. This article will show you how to use Selenium to perform web scraping.<\/p>\n\n\n\n
Let’s dig into web scraping with Selenium and Python!<\/p>\n\n\n\n
Selenium is required to perform web scraping and automate the Chrome browser that we will be using. This is because Selenium uses the webdriver protocol, the manager is imported to obtain ChromeDriver compatible with the browser version being used. BeautifulSoup is required as an HTML parser in order to parse the HTML content that we scrape. Re is imported in order to match our keyword using regex. To write to a text file, codecs are used.<\/p>\n\n\n\n