Scraping is simply a process of extracting data.When we do scraping or extracting data or feeds from the web (like from web-pages or websites), it is termed as web-scraping. This tutorial will teach you about the basics of web scraping by using a practical example. More specifically, we'll scrape job postings from the PythonJobs website using Python's BeautifulSoup library. Table of Contents. You can skip to a specific section of this Python web scraping tutorial using the table of contents below: Why Use Web Scraping? The good news is that Python web scraping libraries like Beautiful Soup can automate the collection of data from websites. Codecademy has a new course introducing you to the basics of webscraping and Beautiful Soup. In this article, we will walk through an example of how to use Beautiful Soup to collect MLB player stats from the 2018 season. At this point, you should see a list of requests, the top one being the actual site - and that will be our focus, because it contains the data with the identity we can use for Python and BeautifulSoup to scrape it Right click the site request (the top one), hover over copy, and then copy as cURL.
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- Web Scraping With Beautifulsoup
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- Web Scraping Python Beautifulsoup Selenium
- Beautiful Soup Web Scraping Python Example
APIs are not always available. Sometimes you have to scrape data from a webpage yourself. Luckily the modules Pandas and Beautifulsoup can help!
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Web scraping
Pandas has a neat concept known as a DataFrame. A DataFrame can hold data and be easily manipulated. We can combine Pandas with Beautifulsoup to quickly get data from a webpage.
If you find a table on the web like this:
We can convert it to JSON with:
And in a browser get the beautiful json output:
Converting to lists
Rows can be converted to Python lists.
We can convert it to a dataframe using just a few lines:
Pretty print pandas dataframe
You can convert it to an ascii table with the module tabulate.
This code will instantly convert the table on the web to an ascii table:
This will show in the terminal as:
In this article, we will see how to extract structured information from web-page leveraging BeautifulSoup and CSS selectors.
WebScraping with BeautifulSoup
Pulling the HTML out
BeautifulSoup is not a web scraping library per se. It is a library that allows you to efficiently and easily pull out information from HTML. In the real world, it is often used for web scraping projects.
So, to begin, we'll need HTML. We will pull out HTML from the HackerNews landing page using the requests python package.
Parsing the HTML with BeautifulSoup
Now that the HTML is accessible we will use BeautifulSoup to parse it. If you haven't already, you can install the package by doing a simple pip install beautifullsoup4. In the rest of this article, we will refer to BeautifulSoup4 as BS4.
We now need to parse the HTML and load it into a BS4 structure.
This soup object is very handy and allows us to easily access many useful pieces of information such as:
Targeting DOM elements
You might begin to see a pattern in how to use this library. It allows you to quickly and elegantly target the DOM elements you need.
If you need to select DOM elements from its tag (<p>, <a>, <span>, ….) you can simply do soup.<tag> to select it. The caveat is that it will only select the first HTML element with that tag.
For example if I want the first link I just have to do
This element will also have many useful methods to quickly extract information:
This is a simple example. If you want to select the first element based on its id or class it is not much more difficult:
And if you don't want the first matching element but instead all matching elements, just replace find with find_all.
This simple and elegant interface allows you to quickly write short and powerful Python snippets.
For example, let's say that I want to extract all links in this page and find the top three links that appear the most on the page. All I have to do is this:
Advanced usage
BeautifulSoup is a great example of a library that is both easy to use and powerful.
You can do much more to select elements using BeautifulSoup. Although we won't cover those cases in this article, here are few examples of advanced things you can do:
- Select elements with regexp
- Select elements with a custom function (links that have Google in them for example)
- Iterating over siblings elements
We also only covered how to target elements but there is also a whole section about updating and writing HTML. Again, we won't cover this in this article.
Let's now talk about CSS selectors.
CSS selectors
Why learn about CSS selectors if BeautifulSoup can select all elements with its pre-made method?
Well, you'll soon understand.
Hard dom
Sometimes, the HTML document won't have a useful class and id. Selecting elements with BS4 without relying on that information can be quite verbose.
For example, let's say that you want to extract the score of a post on the HN homepage, but you can't use class name or id in your code. Here is how you could do it:
Not that great right?
If you rely on CSS selectors, it becomes easier.
This is much clearer and simpler, right? Of course, this example artificially highlights the usefulness of the CSS selector. But, you will quickly see that the DOM structure of a page is more reliable than the class name.
Easily debuggable
Beautifulsoup Web Scraping Python Github
Another thing that makes CSS selectors great for web scraping is that they are easily debuggable. I'll show you how. Open Chrome, then open your developers’ tools, (left-click -> “Inspect”), click on the document panel, and use “Ctrl-F or CMD-F” to be in search mode.
In the search bar, you'll be able to write any CSS expression you want, and Chrome will instantly find all elements matching it.
Iterate over the results by pressing Enter to check that you are correctly getting everything you need.
What is great with Chrome is that it works the other way around too. You can also left-click on an element, click “Copy -> Copy Selector”, and your selector will be pasted in your clipboard.
Web Scraping With Beautifulsoup
Powerful
CSS selectors, and particularly pseudo-classes, allow you to select any elements you want with one simple string.
Child and descendants
You can select direct child and descendant with:
And you can mix them together:
This will totally work.
Siblings
This one is one of my favorites because it allows you to select elements based on the elements on the same level in the DOM hierarchy, hence the sibling expression.
To select all p coming after an h2 you can use the h2 ~ p selector (it will match two p). You can also use h2 + p if you only want to select p coming directly after an h2 (it will match only one p)
Attribute selectors
Attribute selectors allow you to select elements with particular attributes values. So, p[data-test='foo'] will match
Position pseudo classes
If you want to select the last p inside a section, you can also do it in “pure” CSS by leveraging position pseudo-classes. For this particular example, you just need this selector: section p:last-child(). If you want to learn more about this, I suggest you take a look at this article
Maintainable code
I also think that CSS expressions are easier to maintain. For example, at ScrapingBee, when we do custom web scraping tasks all of our scripts begins like this:
Beautiful Soup Web Scraping Python Interview
This makes it easy and quick to fix scripts when DOM changes appear. The laziest way to do it is to simply copy/paste what Chrome gives you when you left-click on an element. If you do this, be careful, Chrome tends to add a lot of useless selectors when you use this trick. So do not hesitate to clean them up a bit before using them in your script.
Conclusion
In the end, everything you do with pure CSS selectors you can do it with BeautifulSoup4. But, I think choosing the former is the best way to go.
I hoped you liked this article about web scraping in Python and that it will make your life easier.
Web Scraping Python Beautifulsoup Selenium
If you'd like to read more about web scraping in Python do not hesitate to check out our extensive Python web scraping guide.
You might also be interested by our XPath tutorial
Happy Scraping,
Beautiful Soup Web Scraping Python Example
Pierre de Wulf