As the digital economy expands, the role of web scraping becomes ever more important. Read on to learn what web scraping is, how it works, and why it’s so important for data analytics.

1. Beautifulsoup Web Scraping Xml
2. Web Scraping Xml Online
3. Web Scraping Tools
4. Web Scraping Xml Download
5. Web Scraping With Python

• 2) Select CSS Path, XPath or Regex for Scraping. The Screaming Frog SEO Spider tool provides three methods for scraping data from websites. XPath – XPath is a query language for selecting nodes from an XML like document, such as HTML. This option allows you to scrape data by using XPath selectors, including attributes. CSS Path – In CSS, selectors are patterns used to select elements.
• In addition to Browser Plugins there are standalone tools such as the Oxygen XML Editor which is availabel through the Emory Software Express Website. This is a comprehensive XML editor that will allow you to parse XML and develop paths to locate specific nodes within an XML document.

The amount of data in our lives is growing exponentially. With this surge, data analytics has become a hugely important part of the way organizations are run. And while data has many sources, its biggest repository is on the web. As the fields of big data analytics, artificial intelligence and machine learning grow, companies need data analysts who can scrape the web in increasingly sophisticated ways.

How to Web Scrape Using Requests and lxml Most web scraping tutorials features either requests and beautifulsoup. What many do not know is that beautifulsoup is not an actual parser, and as such, it requires an actual parser to function.

This beginner’s guide offers a total introduction to web scraping, what it is, how it’s used, and what the process involves. We’ll cover:

Before we get into the details, though, let’s start with the simple stuff…

1. What is web scraping?

Web scraping (or data scraping) is a technique used to collect content and data from the internet. This data is usually saved in a local file so that it can be manipulated and analyzed as needed. If you’ve ever copied and pasted content from a website into an Excel spreadsheet, this is essentially what web scraping is, but on a very small scale.

However, when people refer to ‘web scrapers,’ they’re usually talking about software applications. Web scraping applications (or ‘bots’) are programmed to visit websites, grab the relevant pages and extract useful information. By automating this process, these bots can extract huge amounts of data in a very short time. This has obvious benefits in the digital age, when big data—which is constantly updating and changing—plays such a prominent role. You can learn more about the nature of big data in this post.

What kinds of data can you scrape from the web?

If there’s data on a website, then in theory, it’s scrapable! Common data types organizations collect include images, videos, text, product information, customer sentiments and reviews (on sites like Twitter, Yell, or Tripadvisor), and pricing from comparison websites. There are some legal rules about what types of information you can scrape, but we’ll cover these later on.

2. What is web scraping used for?

Web scraping has countless applications, especially within the field of data analytics. Market research companies use scrapers to pull data from social media or online forums for things like customer sentiment analysis. Others scrape data from product sites like Amazon or eBay to support competitor analysis.

Meanwhile, Google regularly uses web scraping to analyze, rank, and index their content. Web scraping also allows them to extract information from third-party websites before redirecting it to their own (for instance, they scrape e-commerce sites to populate Google Shopping).

Many companies also carry out contact scraping, which is when they scrape the web for contact information to be used for marketing purposes. If you’ve ever granted a company access to your contacts in exchange for using their services, then you’ve given them permission to do just this.

There are few restrictions on how web scraping can be used. It’s essentially down to how creative you are and what your end goal is. From real estate listings, to weather data, to carrying out SEO audits, the list is pretty much endless!

However, it should be noted that web scraping also has a dark underbelly. Bad players often scrape data like bank details or other personal information to conduct fraud, scams, intellectual property theft, and extortion. It’s good to be aware of these dangers before starting your own web scraping journey. Make sure you keep abreast of the legal rules around web scraping. We’ll cover these a bit more in section six.

3. How does a web scraper function?

So, we now know what web scraping is, and why different organizations use it. But how does a web scraper work? While the exact method differs depending on the software or tools you’re using, all web scraping bots follow three basic principles:

Beautifulsoup Web Scraping Xml

• Step 1: Making an HTTP request to a server
• Step 2: Extracting and parsing (or breaking down) the website’s code
• Step 3: Saving the relevant data locally

Now let’s take a look at each of these in a little more detail.

Step 1: Making an HTTP request to a server

As an individual, when you visit a website via your browser, you send what’s called an HTTP request. This is basically the digital equivalent of knocking on the door, asking to come in. Once your request is approved, you can then access that site and all the information on it. Just like a person, a web scraper needs permission to access a site. Therefore, the first thing a web scraper does is send an HTTP request to the site they’re targeting.

Step 2: Extracting and parsing the website’s code

Once a website gives a scraper access, the bot can read and extract the site’s HTML or XML code. This code determines the website’s content structure. The scraper will then parse the code (which basically means breaking it down into its constituent parts) so that it can identify and extract elements or objects that have been predefined by whoever set the bot loose! These might include specific text, ratings, classes, tags, IDs, or other information.

Step 3: Saving the relevant data locally

Once the HTML or XML has been accessed, scraped, and parsed, the web scraper will then store the relevant data locally. As mentioned, the data extracted is predefined by you (having told the bot what you want it to collect). Data is usually stored as structured data, often in an Excel file, such as a .csv or .xls format.

With these steps complete, you’re ready to start using the data for your intended purposes. Easy, eh? And it’s true…these three steps do make data scraping seem easy. In reality, though, the process isn’t carried out just once, but countless times. This comes with its own swathe of problems that need solving. For instance, badly coded scrapers may send too many HTTP requests, which can crash a site. Every website also has different rules for what bots can and can’t do. Executing web scraping code is just one part of a more involved process. Let’s look at that now.

4. How to scrape the web (step-by-step)

OK, so we understand what a web scraping bot does. But there’s more to it than simply executing code and hoping for the best! In this section, we’ll cover all the steps you need to follow. The exact method for carrying out these steps depends on the tools you’re using, so we’ll focus on the (non-technical) basics.

Step one: Find the URLs you want to scrape

It might sound obvious, but the first thing you need to do is to figure out which website(s) you want to scrape. If you’re investigating customer book reviews, for instance, you might want to scrape relevant data from sites like Amazon, Goodreads, or LibraryThing.

Step two: Inspect the page

Before coding your web scraper, you need to identify what it has to scrape. Right-clicking anywhere on the frontend of a website gives you the option to ‘inspect element’ or ‘view page source.’ This reveals the site’s backend code, which is what the scraper will read.

Step three: Identify the data you want to extract

If you’re looking at book reviews on Amazon, you’ll need to identify where these are located in the backend code. Most browsers automatically highlight selected frontend content with its corresponding code on the backend. Your aim is to identify the unique tags that enclose (or ‘nest’) the relevant content (e.g. <div> tags).

Step four: Write the necessary code

Once you’ve found the appropriate nest tags, you’ll need to incorporate these into your preferred scraping software. This basically tells the bot where to look and what to extract. It’s commonly done using Python libraries, which do much of the heavy lifting. You need to specify exactly what data types you want the scraper to parse and store. For instance, if you’re looking for book reviews, you’ll want information such as the book title, author name, and rating.

Step five: Execute the code

Once you’ve written the code, the next step is to execute it. Now to play the waiting game! This is where the scraper requests site access, extracts the data, and parses it (as per the steps outlined in the previous section).

Step six: Storing the data

After extracting, parsing, and collecting the relevant data, you’ll need to store it. You can instruct your algorithm to do this by adding extra lines to your code. Which format you choose is up to you, but as mentioned, Excel formats are the most common. You can also run your code through a Python Regex module (short for ‘regular expressions’) to extract a cleaner set of data that’s easier to read.

Now you’ve got the data you need, you’re free to play around with it.Of course, as we often learn in our explorations of the data analytics process, web scraping isn’t always as straightforward as it at first seems. It’s common to make mistakes and you may need to repeat some steps. But don’t worry, this is normal, and practice makes perfect!

5. What tools can you use to scrape the web?

We’ve covered the basics of how to scrape the web for data, but how does this work from a technical standpoint? Often, web scraping requires some knowledge of programming languages, the most popular for the task being Python. Luckily, Python comes with a huge number of open-source libraries that make web scraping much easier. These include:

BeautifulSoup

BeautifulSoup is another Python library, commonly used to parse data from XML and HTML documents. Organizing this parsed content into more accessible trees, BeautifulSoup makes navigating and searching through large swathes of data much easier. It’s the go-to tool for many data analysts.

Scrapy

Scrapy is a Python-based application framework that crawls and extracts structured data from the web. It’s commonly used for data mining, information processing, and for archiving historical content. As well as web scraping (which it was specifically designed for) it can be used as a general-purpose web crawler, or to extract data through APIs.

Pandas

Web Scraping Xml Online

Pandas is another multi-purpose Python library used for data manipulation and indexing. It can be used to scrape the web in conjunction with BeautifulSoup. The main benefit of using pandas is that analysts can carry out the entire data analytics process using one language (avoiding the need to switch to other languages, such as R).

Parsehub

A bonus tool, in case you’re not an experienced programmer!Parsehub is a free online tool (to be clear, this one’s not a Python library) that makes it easy to scrape online data. The only catch is that for full functionality you’ll need to pay. But the free tool is worth playing around with, and the company offers excellent customer support.

There are many other tools available, from general-purpose scraping tools to those designed for more sophisticated, niche tasks. The best thing to do is to explore which tools suit your interests and skill set, and then add the appropriate ones to your data analytics arsenal!

6. What else do you need to know about web scraping?

We already mentioned that web scraping isn’t always as simple as following a step-by-step process. Here’s a checklist of additional things to consider before scraping a website.

Have you refined your target data?

When you’re coding your web scraper, it’s important to be as specific as possible about what you want to collect. Keep things too vague and you’ll end up with far too much data (and a headache!) It’s best to invest some time upfront to produce a clear plan. This will save you lots of effort cleaning your data in the long run.

Have you checked the site’s robots.txt?

Each website has what’s called a robot.txt file. This must always be your first port of call. This file communicates with web scrapers, telling them which areas of the site are out of bounds. If a site’s robots.txt disallows scraping on certain (or all) pages then you should always abide by these instructions.

Have you checked the site’s terms of service?

In addition to the robots.txt, you should review a website’s terms of service (TOS). While the two should align, this is sometimes overlooked. The TOS might have a formal clause outlining what you can and can’t do with the data on their site. You can get into legal trouble if you break these rules, so make sure you don’t!

Are you following data protection protocols?

Just because certain data is available doesn’t mean you’re allowed to scrape it, free from consequences. Be very careful about the laws in different jurisdictions, and follow each region’s data protection protocols. For instance, in the EU, the General Data Protection Regulation (GDPR) protects certain personal data from extraction, meaning it’s against the law to scrape it without people’s explicit consent.

Are you at risk of crashing a website?

Big websites, like Google or Amazon, are designed to handle high traffic. Smaller sites are not. It’s therefore important that you don’t overload a site with too many HTTP requests, which can slow it down, or even crash it completely. In fact, this is a technique often used by hackers. They flood sites with requests to bring them down, in what’s known as a ‘denial of service’ attack. Make sure you don’t carry one of these out by mistake! Don’t scrape too aggressively, either; include plenty of time intervals between requests, and avoid scraping a site during its peak hours.

Be mindful of all these considerations, be careful with your code, and you should be happily scraping the web in no time at all.

Web Scraping Tools

7. In summary

In this post, we’ve looked at what data scraping is, how it’s used, and what the process involves. Key takeaways include:

• Web scraping can be used to collect all sorts of data types: From images to videos, text, numerical data, and more.
  
• Web scraping has multiple uses: From contact scraping and trawling social media for brand mentions to carrying out SEO audits, the possibilities are endless.
  
• Planning is important: Taking time to plan what you want to scrape beforehand will save you effort in the long run when it comes to cleaning your data.
  
• Python is a popular tool for scraping the web: Python libraries like Beautifulsoup, scrapy, and pandas are all common tools for scraping the web.
  
• Don’t break the law: Before scraping the web, check the laws in various jurisdictions, and be mindful not to breach a site’s terms of service.
  
• Etiquette is important, too: Consider factors such as a site’s resources—don’t overload them, or you’ll risk bringing them down. It’s nice to be nice!

Data scraping is just one of the steps involved in the broader data analytics process. To learn about data analytics, why not check out our free, five-day data analytics short course? We can also recommend the following posts:

If you are interested in web-scraping like I am, it is very useful, if not essential, to know something about XML. XML stands for Extensible Markup Language and it was designed to transport and store data while HTML was designed to display data. XML separates data from HTML and simplifies data sharing and data transport since it stores data in plain text format and provides a software- and hardware-independent way of storing data.

Next I will summarize what I have learned about XML from [1]. From the web-scraping point of view, I think the most relevant section is the one about XML tree structure, while the sections about naming practices and attribute vs. element debate is here only to give a better background on XML. This is naturally not intended to be a comprehensive review of the subject and this post is subject to future changes as I learn more about XML and about which parts of XML knowledge are useful for web-scraping.

XML tree structure

Following is a valid XML structure extracted from [1] that will be used as an example.

• First, XML tags are not predefined, you must define your own tags. In our example above, the tags bookstore and book doesn’t have any predefined meaning and were chosen by the developer of the XML example. The name of the tags are usually associated with an intrinsic meaning that are related to the kind of data that the XML structure are supposed to hold. In this example, it is quite clear that inside the bookstore element, there will be different book elements, and that each book element have a title element, an author element, and so on.
• XML tags are case sensitive.
• XML elements must have a closing tag, unlike HTML.
• XML documents must contain a root element. This element is the parent of all other elements. The terms parent, child, and sibling are used to describe the relationships between elements. Parent elements have children and children on the same level are called siblings. All elements can have children. So, in the example above bookstore is the root element, book is the child of bookstore. title, author, year and price are siblings and children of book.
• All elements can have text content and attributes, just like in HTML. So, the title element of the first book has “Everyday Italian” as text content and attribute lang that assumes the value “en”.
• XML attribute values must be quoted. So lang=en would be incorrect, the correct form being lang='en'.
• Comments in XML: <!-- This is a comment -->

XML Naming Rules and Best Naming Practices

1. XML elements must follow these naming rules:
   • Names can contain letters, numbers, and other characters
   • Names cannot start with a number or punctuation character
   • Names cannot start with the letters xml (or XML, or Xml, etc)
   • Names cannot contain spaces
2. Make names descriptive. Names with an underscore separator are nice: <first_name>, <last_name>
3. Names should be short and simple, like this: <book_title>; not like this: <the_title_of_the_book>.

XML Elements vs. Attributes

Take a look at the following examples:

Both examples provide the same information but in the first example sex is an attribute while in the last, sex is an element. Attributes are handy in HTML but in XML the advice is to avoid them and use elements instead.

Some of the problems with using attributes are:

• attributes cannot contain multiple values (elements can)
• attributes cannot contain tree structures (elements can)
• attributes are not easily expandable (for future changes)
• attributes are difficult to read and maintain.

Web Scraping Xml Download

In general, use elements for data and use attributes for information that is not relevant to the data.

References:

Web Scraping With Python

[1] XML tutorial from w3schools.