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.
What is Python web scraping?
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.
What is Python web scraping?: Web Scraping Applications
They are:
#1. Sentiment Analysis
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.
#2. Market Research
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.
#3. Technological Research
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.
#4. Machine Learning
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.
Selenium Python Web Scraping
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.
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:
- Clicking buttons
- Populating forms with data
- Scrolling the page
- Taking screenshots
- Executing your own, custom JavaScript code
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.
Selenium Python Web Scraping: A Step-by-Step Guide to Selenium Web Scraping
Let’s dig into web scraping with Selenium and Python!
Pre-Requisites:
- Set up a Python environment.
- Install Selenium v4. If you have Conda or Anaconda set up, then using the pip package installer would be the most efficient method for Selenium installation. Simply run this command (on the Anaconda prompt or directly on the Linux terminal):
- Download the latest WebDriver for the browser you wish to use, or install webdriver_manager by running the command. Also, install BeautifulSoup:
Steps In Details
Step 1: Import the Required Packages.
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.
Step 2: Obtain the version of ChromeDriver compatible with the browser.
Step 3: Take the user input to obtain the URL of the website to be scraped, and web scrape the page.
The user input for this example is https://www.browserstack.com/guide/how-AI-in-visual-testing-is-evolving. The driver is used to obtain this URL, and a wait command is used to allow the page to load. The current URL method is then used to ensure that the correct URL is being accessed.
Step 4: Use BeautifulSoup to parse the HTML content obtained.
Selenium web scraped HTML content is parsed and converted into a soup object. Following that, user input is collected for a keyword for which the article’s body will be searched. “Data” is the keyword in this example. Using regex, all instances of the word “data” are found in the soup object’s body tags. Finally, the text found in the soup object’s title tag is extracted.
Step 4: Store the collected data in a text file.
Open a text file titled article_scraping.txt with codecs. Write the title of the article into the file, followed by this number, and add all instances of the keyword within the article. Finally, add the number of matches found for the keyword in the article. Then close the file and exit the driver.
Python Web Scraping Library & Tools
There is an abundance of Python libraries available for web scraping. But how do you decide which one to use for your specific project? Which Python library offers the most flexibility? I’ll try to answer these questions here using the popular Python web scraping tools and libraries for web scraping that I believe every enthusiast should be aware of.
Python Web Scraping Library & Tools: Examples
Here are the seven most popular Python tools and libraries for web scraping that every data professional must be familiar with.
#1. Requests (HTTP for Humans)
Let’s begin with the most fundamental Python library for web scraping. ‘Requests’ allows us to send HTML requests to the website’s server in order to retrieve data from its pages. The first and most important step in web scraping is to obtain the HTML content of a web page. Requests is a Python library for making various HTTP requests such as GET, POST, and so on.
Its motto is HTTP for humans because of its simplicity and ease of use. This is the most fundamental yet essential library for web scraping. The Requests library does not parse the retrieved HTML data. To do so, we’ll need libraries like lxml and Beautiful Soup.
#2. Lxml
We know that the request library cannot parse HTML from a web page. As a result, we need lxml, a high-performance, lightning-fast, production-quality HTML and XML parsing Python library. It combines the speed and power of element trees with Python’s simplicity. When we want to scrape large datasets, it works well. The use of requests and LXML is common in web scraping. It also supports data extraction from HTML using XPath and CSS selectors. Let’s look at the benefits and drawbacks of the lxml Python library.
Advantages:
- Faster than the majority of existing parsers
- Light-weight
- Elements and trees are used.
- The Pythonic API
Disadvantages
- It does not work well with poorly designed HTML
- The official documentation is not very beginner-friendly
#3. Beautiful Soup
BeautifulSoup is possibly the most popular Python web scraping library. It generates a parse tree that you need to parse HTML and XML documents. Beautiful Soup automatically converts incoming and outgoing documents to Unicode and UTF-8. One of the main reasons why the Beautiful Soup library is so popular is that it is simple to use and ideal for beginners.
Beautiful Soup can be combined with other parsers, such as XML. But all of this convenience comes at a price: it is slower than LXML. It is slower than pure XML, even when using XML as a parser. The Beautiful Soup library has many functions and works well with poorly designed HTML. The combination of beautiful soups and requests is quite common in the industry.
#4. Selenium
All of the Python libraries we’ve discussed thus far have one limitation: we can’t easily scrape data from dynamically populated websites. This occurs because some of the data on the page is loaded using JavaScript. In other words, if the page is not static, the previously mentioned Python libraries struggle to scrape the data from it. Selenium comes into play here. Selenium is a Python library designed to automate web application testing.
Although it was not meant for web scraping at first, the data science community quickly changed that! It is a web driver designed to render web pages, but it is set apart by this feature. Selenium excels in situations where other libraries are unable to run JavaScript. It can click on a page, fill out forms, scroll the page, and do a variety of other things.
#5. Scrapy
Now it’s time to meet Scrapy, the king of Python web scraping libraries! Scrapy is more than just a library; it is an entire web scraping framework created by Scrapinghub co-founders Pablo Hoffman and Shane Evans. It is a full-fledged web scraping solution that handles all of the grunt work for you. Scrapy offers spider bots that can crawl and extract data from multiple websites.
Scrapy allows you to create spider bots and host them on Scrapy Hub or as an API. This saves us a lot of time and increases our efficiency, which is something we should all strive for. Scrapy’s functionality can also be enhanced by adding plugins. Although Scrapy cannot handle JavaScript like Selenium, you can combine it with Splash, a lightweight web browser library.
#6. Urllib3
Urllib3 is a popular Python web scraping library that can quickly extract data from HTML documents or URLs, similar to the Python requests library. It has over 165,866,058 downloads. The Python package urllib request allows you to retrieve URLs. The URL open method has a simple user interface and can retrieve URLs via a variety of protocols.
It also offers a more complex user interface for dealing with issues such as simple authentication, cookies, and proxies. Although the urllib in Python 3 differs from that in Python 2, they both perform the same functions. urllib allows you to execute any GET and POST requests, as well as parse data and modify headers.
Is Python good for web scraping?
Python is an excellent choice for developers to build web scrapers
How long does it take to learn Python web scraping?
Depending on your Python knowledge and how much time you’re allocating to learn this skill, it could take anywhere from two days to two years.
Why is web scraping illegal?
Legal issues have developed around web scraping because some businesses don’t appreciate having their data scraped.
How hard is web scraping?
If the website is complex and dynamic and uses advanced techniques such as AJAX, web scraping can become quite challenging.
Can I make money by web scraping?
Yes, a web scraper at the top of his career can earn up to $131,500 annually.
Conclusion
Finally, web scraping with selenium is an effective method for extracting data from websites. This is because it allows you to automate the data collection process, which saves you a lot of time and effort. Imagine the satisfaction that comes with using Selenium, you can interact with websites as if you were a human user, extracting data more efficiently.
References
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