Let's build a scraper to extract data from and save it to a CSV file. Now that we have mastered the components of Beautiful Soup, it's time to put our learning to use. Now that we have a feel for how to use Beautiful Soup, let's scrape a website! Beautiful Soup in Action - Scraping a Book List Sometimes the newline characters are printed, so your output may look like this as well: "\n\nHead's title\n\n\nBody's title\nline begins\n 1\n2\n3\n line ends\n\n" Your output should be like this: Head's title Let's get all the text of the HTML document: soup.get_text() The get_text() function retrieves all the text from the HTML document. Let's see how we can get it! Getting the Whole Text Sometimes, especially for less dynamic web pages, we just want the text from it. We've covered the most popular ways to get tags and their attributes. The list upon iteration, fetches the tags starting with the character b which includes and : For example: import reįor tag in soup.find_all(re. Behind the scenes, the text will be filtered using the compiled regular expression's search() method. The find() and find_all() functions also accept a regular expression instead of a string. Type this in your shell: soup.find( "a", href= True) # returns It works just like find_all() but it returns the first matching element instead of a list. What if we wanted to fetch the links embedded inside the a tags? Let's retrieve a link's href attribute using the find() option. Let's search for all a tags that have the "element" class: soup.find_all( "a", class_= "element")Īs we only have two links with the "element" class, you'll see this output: Beautiful Soup uses class_ because class is a reserved keyword in Python. We can search for tags of a specific class as well by providing the class_ argument. This image below illustrates some of the functions we can use: What makes Beautiful Soup so useful is the myriad functions it provides to extract data from HTML. In the following section, we will be covering those functions that are useful for scraping webpages. The HTML content of the webpages can be parsed and scraped with Beautiful Soup. We must scrape responsibly so we won't cause any disruption to the regular functioning of the website. A web scraper that makes too many requests can be as debilitating as a DDOS attack. Making requests to a website can cause a toll on a website's performance.We prefer to use APIs if they're available. APIs are created to provide access to data in a controlled way as defined by the owners of the data. Is there an API available already? Splendid, there's no need for us to write a scraper.We must respect websites that do not want to be scraped. Many websites also have a Terms of Use which may not allow scraping. Websites sometimes come with a robots.txt file - which defines the parts of a website that can be scraped. Don't scrape a website that doesn't want to be scraped.We must respect their labor and originality. Website owners sometimes spend a lengthy amount of time creating articles, collecting details about products or harvesting other content. Don't claim scraped content as our own.Here are some principles that a web scraper should adhere to: However, as good citizens of the internet, it's our responsibility to respect the site owners we scrape from. Web scraping is ubiquitous and gives us data as we would get with an API. This article will give you a crash course on web scraping in Python with Beautiful Soup - a popular Python library for parsing HTML and XML. While there are many libraries and frameworks in various languages that can extract web data, Python has long been a popular choice because of its plethora of options for web scraping. Web scraping is programmatically collecting information from various websites.
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