Have you ever wanted to extract a lot of information from a website?
Depending on how much data you’re looking to extract, copying and pasting might not do the job.
Computing specialized uk / ˈweb ˌskreɪ.pɪŋ / us / ˈweb ˌskreɪ.pɪŋ / (also data scraping) another name for scraping (= the activity of taking information from a website or computer screen and putting it into an ordered document on a computer): Web scraping can be useful in the context of data science projects. Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format. Data displayed by most websites can only be viewed using a web browser.
Here’s where web scraping can help. Today we will define web scraping and go over how it differs from other similar terms.
What is web scraping?
Feb 12, 2021 ScrapingBee is a web scraping API that allows you to scrape the web without getting blocked. We offer both classic (data-center) and premium (residentials) proxies so you will never get blocked again while scraping the web. Jun 22, 2020 Web scraping requires two parts namely the crawler and the scraper. The crawler is an artificial intelligence algorithm that browses the web to search the particular data required by following the links across the internet. The scraper, on the other hand, is a specific tool created to extract the data from the website. Web scraping appears as an aggressive exercise that does not respect any ethical or legal norms. This is the reason people find it difficult to perceive web scraping in a positive light. At times, it’s simply the manner in which web scraping happens that people find offensive.
Web scraping refers to the process of extracting data from a website on to a format that is more convenient for the user.
Web scraping can be done manually, however, automated tools are usually preferred due to speed, convenience, and lower costs.
These automated tools are known as Web Scrapers. These can have different sets of features depending on the kind of website you’re looking to scrape.
Want to learn more about web scraping? Check our in-depth guide on web scraping and what it is used for.
Web Scraping vs Web Crawling
Web Scraping is often confused with Web Crawling. However, they refer to different processes.
Web Crawling refers to the process of using bots and spiders to read, scrape and store all the data from a website. Most commonly, this technique is used by search engines to build their site indexes. This is how Google can tell if what pages contain the information you’re looking for.
If you want to learn more, read our guide on the differences between web scraping and web crawling.
Web Scraping vs Data Mining
Web Scraping is also often confused with Data Mining. Again, in this case, both refer to separate processes.
Data Mining refers to the process of in-depth analysis of massively large databases.
These analyses can be advanced enough to require the use of complex algorithms and AI.
While Web Scraping can be used to build the data sets that will be analyzed via Data Mining, they still stand as separate processes.
Want to learn more? Read our in-depth article about the differences between Web Scraping and Data Mining.
Web Scraping vs API
Lastly, Web Scraping is often also confused with APIs. After all, they do refer to very similar processes.
An API (Application Programming Interface) refers to a set of procedures and protocols that allow for the access of data from a specific database. For example, a weather forecast agency can build an API for news agencies and developers to access weather data and build their own tools or content.
In cases where an API might not be available, a web scraper can help you in accessing the data you want via extraction.
Want to learn more? Read our article about the differences between APIs and Web Scraping.
Getting Started with Web Scraping
Now that you know the basics of web scraping, you might be interested in starting your first web scraping project.
While it might sound intimidating, web scraping is way easier than you think. It can also be done with free tools.
In fact, we’ve put together a step-by-step tutorial on how to scrape data from any website for free.
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.
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!
How Does A Web Scraper Work
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 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 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 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).
Web Scraper Api
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.
Web Scraper Software
Have you checked the site’s robots.txt?
Web Scraper Meaning Definition
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.
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: