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.

Scrapingdog is a web-based tool for scraping the elements of a website along with its content. Its built-in scraper returns the data of a webpage as raw HTML. The tool provides an API that gives a more structured output of your data. Jan 05, 2021 Web scraping, also called web data extraction, refers to the technique of harvesting data from a web page through leveraging the patterns in the page’s underlying code. It can be used to collect unstructured information from websites for processing and storage in a structured format.

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.

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:

• 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

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.

Free Web Scraping Software

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.

Web Scraping Programs For Beginners

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

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:

By Ida Jessie Sagina, Scrapeworks.

If there’s anything that I’ve learned in content creation over the past year, it’s that no matter how good your piece of content is, without strategic promotion and marketing it isn’t going to add the intended value to anyone, be it the readers or the company I work for.

Though promoting on social media and company website counts, if my blog or whitepaper reaches a highly-qualified list of readers who’ll find the content truly useful then you couldn’t find a more gratified writer than me! So how am I going to build that golden list for every content I develop? The Web is a huge mine of thoughts and interests expressed by diverse people and collecting data from this wealth of information could help me spot the right audience - a process familiarly known as web scraping.

Well, I could outsource the entire scraping job to a managed services company but my coding and tools-exploration instincts cultivated during my 3 year-stint as a cyber techie in a leading software development company, got the better of me. I decided to get my hands dirty with the ins and outs of web scraping and the number of options I had knocked me out.

Armed with my study of the web scraping landscape, I’ve categorized all the available options I was able to find and the unique features of popular web scraping tools found in the market that appeals to different audience segments.

Before jumping straight to the web scraping tools, it’s important to determine how you are going to harvest web data and that’s dependent on the purpose, your levels of curiosity and the resources you have in hand.

So first, pick the right web scraping approach

Based on my outlook, web scraping is majorly done in the following ways -

1. Build your very own scraper from scratch

This is for code-savvy folks who love experimenting with site layouts and tackle blockage problems and are well-versed in any programming language like Python, R or Perl. Just like their routine programming for any data science project, a student or researcher can easily build their scraping solution with open-source frameworks like Python-based Scrapy or the rvest package, RCrawler in R.

2. Developer-friendly tools to host efficient scrapers

Web scraping tools suitable for developers mostly, where they can construct custom scraping agents with programming logic in a visual manner. You can equate these tools to the Eclipse IDE for Java EE applications. Provisions to rotate IPs, host agents, and parse data are available in this range for personalization.

3. DIY Point-and-click web scraping tools for the no-coders

Website Scraping Programs

To the self-confessed non-techie with no coding knowledge, there’s a bunch of visually appealing point and click tools that help you build sales list or populate product information for your catalog with zero manual scripting.

4. Outsourcing the entire web scraping project

For enterprises that look for extensively scaled scraping or time-pressed projects where you don’t have a team of developers to put together a scraping solution, web scraping services come to the rescue.

If you are going with the tools, then here are the advantages and drawbacks of popular web scraping tools that fall in the 2nd and 3rd category.

DIY point-and-click web scraping tools for the no-coders

Truly a killer in the DIY tools category, Import.io provides a way for anyone with a web data need to extract information with a very user-friendly, intuitive, and interactive interface. The cloud-based scraping platform can structure data found behind images, login screen and hundreds of web pages with absolutely no coding. Monitoring website changes and the ability to integrate with a number of reporting tools and apps make it a great option for enterprises with a pressing scraping need.

Pros:

1. A simple and light-weight UI that works well for non-coders looking to build their list of prospects or track price changes.
2. It’s a viable option for scraping at a reasonable speed efficiently from different websites concurrently.

Cons:

If this sounds like your Aha product then there should be just one thing stopping you from trying it - the PRICE! While they had adopted a freemium model earlier, it’s no longer available (basic plan begins at $299/month) and scraping more pages equals scraping more dollars off your pocket.

Earlier called CloudScrape, Dexi.io is another visually stunning extraction automation tool positioned for commercial purposes and is available as a hassle-free browser app. Dexi has provisions for creating robots that can work as an extractor or crawler or perform ETL data cleansing tasks after extraction in the form of Dexi Pipes. The powerful scraping tool gives suggestions after data selection on the webpage for intelligent extraction features that resolves pagination issues, performs extraction in a loop and takes screenshots of web pages.

Pros:

1. There are no tough set-up routines that you’ve got to follow. Sign up and the browser app opens for you to create your robot. Their awesome support team will help you with the bot creation in case you hit a roadblock.
2. For a commercial tool, the standard plan priced at $119/month (for small projects) is very reasonable and the professional plan would be apt for a larger business needs.

Cons:

1. The concept of add-ons in Dexi.io though attractive at first becomes a handful to maintain as the add-ons increase and so does the cost for any add-on in the store.
2. There are slight murmurs and grunts about the product documentation which I believe Dexi folks can easily fix.

The blue Octo promises data at your fingertips with no programming at all and they’ve really got it. Within just 2 years of their launch, Octoparse has gone through 7 revised versions tweaking their scraping workflow with the feedback received from users. It’s got an intuitive point-and-click interface that supports infinite scrolling, log-in authentication, multi-format data export and supports unlimited pages per crawl in its free plan(yes, you heard that right!).

Pros:

1. Scheduled crawling features and provision for unlimited web pages per crawl make it an ideal choice for price monitoring scenarios.
2. Features provided in their free plan are more than enough if you are looking for an effective one-time, off-the-shelf solution with good user guide documentation. Also, precise extraction of data can be achieved with their in-built XPath and Regex tools.

Cons:

1. Octoparse is yet to add pdf-data extraction and image extraction features (just image URL is fetched) so calling it a complete web data extraction tool would be a tall claim.
2. Customer support is not great for the product and timely responses are not to be expected.

A desktop app that offers a graphical interface to select and extract the data of your choice from Javascript and AJAX pages as well and is supported by Windows, Mac OS X, and Linux. It can scrape through nested comments, maps, images, calendars, and pop-ups too. They’ve also got a browser-based extension to launch your scrape instantly and the tutorials out there are of great help.

Pros:

1. ParseHub has a rich UI and pulls data from many tricky areas of a website, unlike other scrapers.
2. Developers can play with ParseHub’s RestfulAPI for good data access after they are happy with the one-off scrape.

Cons:

1. The purported free plan from ParseHub looks painful by limiting number of scraped pages to 200 and just 5 projects in all. Plus, their paid versions begin at a whopping $149 per month which sounds way overboard especially for one-time scrapes.
2. Speed at which scrape is performed needs to be vastly improved which also slows down the rate at which large volume scrape is done.

Outwit technologies offers a simple, no-fancy GUI which was initially offered as a Firefox add-on (legacy version still available but with no feature updates) and now comes as a freely downloadable software that can be upgraded to Light and Pro version. With no programming skills, Outwit Hub can extract and export links, email addresses, RSS news and data tables to CSV, HTML, Excel or SQL databases. Their other products like Outwit Images and documents fetch images and docs from websites to your local drives.

Pros:

1. It’s a flexible and powerful option for people looking to source contacts and is priced appropriately beginning at $69 for the basic one-time standalone application purchase.
2. The “Fast Scrape” feature is a nice add to quickly scrape data from a list of URLs that you feed Outwit.

Cons:

1. Outwit’s aptness for repeated, high volume scrapes is questionable and their documentation and tutorials definitely need a lift.
2. The product lacks a point-and-click interface so first time users may need to go through random Youtube tutorials before their scrape venture.

A visual web scraping software with a macro designer component to develop a scraping project flowchart by looking at the website alongside the same screen. The Python-based tool can be run on both Windows and Mac OS machines with good Regex support. FMiner has advanced data extraction features like captcha solving, post-extraction data refining options and allows you to embed python code to run tasks on target websites.

Pros:

Being multi-platform and a software feasible for both the no-code as well as the developer community, FMiner is powerful for data harvesting from complex site layouts.

Cons:

1. The visual interface isn’t very appealing and efforts need to be put in to construct a proper scraping workflow (think flowcharts and connectors). You need to know your way around defining data elements with XPath expressions
2. After a 15-day trial, you are forced to purchase at least the basic software version which is priced at $168 with no scheduling, email reporting or JS support. Btw, how active are they in keeping their product updated? Not so sure as there’s no news on recent improvements in FMiner.

Next, we examine Developer-Friendly Web scraping Tools.