As the digital economy expands, the role of web scraping becomes more important. Read on to learn what web scraping is, how it works, and why it's so important for data analysis.
The amount of data in our lives is growing exponentially. With this rise, data analytics has become an extremely important part of the way businesses are run. And while data comes from many sources, its largest repository is on the Internet. As big data analytics, artificial intelligence, and machine learning grow, companies need data analysts who can explore the web in increasingly sophisticated ways.
This beginner's guide provides a comprehensive introduction to web scraping, what it is, how to use it, and the process. We treat:
- What is web scraping?
- What is web scraping used for?
- How does a web scraper work?
- How to scrape the web (step by step)
- What tools can you use to scrape the web?
- What else do you need to know about web scraping?
- in summary
Before we get into the details though, let's start with the simple things...
1. What is web scraping?
Web scraping (or data scraping) is a technique for collecting content and data from the Internet. This data is usually stored in a local file so it can be edited and analyzed as needed. If you've ever copied and pasted content from a website into an Excel spreadsheet, that's essentially what web scraping is, but to a very small extent.
However, when people talk about "web scrapers", they are usually referring to software applications. Web scraping applications (or "bots") are programmed to visit websites, find relevant pages, and extract useful information. By automating this process, these bots can extract large amounts of data in a very short time. This has obvious benefits in the digital age, where big data, which is constantly updated and changing, plays such a prominent role. CanLearn more about the nature of big data in this post.
What types of data can be extracted from the Internet?
If there is data on a website, it can theoretically be thrown away! Common types of data businesses collect include images, videos, text, product information, customer reviews and ratings (on sites like Twitter, Yell, or Tripadvisor), and prices from comparison sites. There are some legal rules about what types of information you can collect, but we'll cover those later.
2. What is web scraping used for?
Web scraping has countless applications, especially in the field of data analysis. Market research companies use scrapers to extract data from social media or online forums for things like analyzing customer sentiment. Others pull data from product sites like Amazon or eBay to help with competitor analysis.
Google now regularly uses web scraping to analyze, rank, and index your content. Web scraping also allows them to extract information from third-party websites before redirecting it to their own (for example, scraping e-commerce websites to populate Google Shopping).
Many companies also do contact scaling, i. h search the internet for contact information to be used for marketing purposes. If you've ever given a company access to your contacts in exchange for using their services, then you've given them permission to do just that.
There are few restrictions on how web scraping can be used. It basically depends on how creative you are and what your end goal is. From real estate listings to weather data to SEO audits, the list is almost endless!
However, it should be noted that web scraping also has a dark side. Bad players often extract data such as bank details or other personal information to carry out fraud, fraud, intellectual property theft, and extortion. It's good to be aware of these dangers before starting your own web scraping journey. Make sure you stay up to date on web scraping legislation. We'll cover this in a bit more detail in section six.
3. How does a web scraper work?
We now know what web scraping is and why different organizations use it.But how does a web scraper work?While the exact method will vary depending on the software or tools used, all web scraping bots follow three basic principles:
- Step 1 - Send an HTTP request to a server
- Step 2 - Extract and analyze (or break down) the code of the website
- Step 3: Local storage of the relevant data
Now let's take a closer look at these.
Step 1 - Send an HTTP request to a server
When you, as an individual, visit a website through your browser, you send what is called an HTTP request. This is basically the digital equivalent of knocking on the door and asking permission to enter. Once your application is approved, you will be able to access this website and all the information it contains. Just like a person, a web scraper needs permission to access a website. Therefore, the first thing a web scraper does is send an HTTP request to the targeted website.
Step 2: Extract and analyze the code of the website
Once a website grants access to a scraper, the bot can read and extract the website's HTML or XML code. This code determines the structure of the content of the website. The scraper then parses the code (which basically means breaking it down into its components) so that it can identify and extract elements or objects predefined by whoever unleashed the bot. This may include specific text, grades, classes, labels, IDs, or other information.
Step 3: Local storage of the relevant data
Once the HTML or XML has been accessed, scraped and parsed, the web scraper stores the relevant data locally. As mentioned above, you predefine the data pulled (after telling the bot what to collect). The data is usually stored as structured data, often in an Excel file, e.g. B. in .csv or .xls format.
Once these steps are completed, you will be able to use the data for its intended purposes. Easy right? And it's true… these three stepsAgainMake data scraping look easy. In reality, however, the process is performed not just once, but countless times. This brings with it a number of problems that need to be resolved. For example, poorly coded scrapers can send too many HTTP requests, which can crash a website. Each website also has different rules about what bots can and cannot do. Running web scraping code is just part of a more complex process. Let's see that now.
4. How to scrape the mesh (step by step)
Okay, we understand what a web scraping bot does. But there's more to it than just running code and hoping for the best! In this section we cover all the steps you need to take. The exact method of performing these steps will depend on the tools you use, so we'll focus on the basics (non-technical).
Step One: Find the URLs you want to scrape
It may seem obvious, but the first thing you need to do is figure out which website you want to scrape. For example, if you're researching customer book reviews, you might want to pull relevant data from sites like Amazon, Goodreads, or LibraryThing.
Step two: check the page
Before programming your web scraper, you need to determine what it will scrape. When you right-click anywhere on a website interface, you have the option to "Inspect Element" or "View Page Source". This will display the back-end code of the website that will be read by the scraper.
Step Three: Identify the data you want to extract
When you look at book reviews on Amazon, you need to identify where they are in the back code. Most browsers automatically highlight selected frontend content with the appropriate code on the backend. Its goal is to identify the unique tags that enclose (or "nest") the relevant content (for example, <div> tags).
Step Four – Write the required code
Once you've found the right Nest tags, you'll need to integrate them into your favorite scraping software. Basically this tells the bot where to look and what to extract. Python libraries are commonly used to do this and do most of the heavy lifting. You must specify exactly what types of data you want the scraper to parse and store. For example, when you search for book reviews, you want information like the book title, author name, and rating.
Step Five: Run the Code
Once you've written the code, the next step is to run it. Play the waiting game now! Here, the scraper requests access to the site, extracts the data and analyzes it (following the steps described in the previous section).
Sixth step: save the data
After you extract, analyze, and collect the relevant data, you need to store it. You can tell 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 from PythonModulo Regex(short for "regular expressions") to extract a cleaner data set that is easier to read.
Now you have the data you need and you can play with it. Of course, as we often learn in our explorationsthe data analysis process, Web scraping is not always as easy as it seems. It is common to make mistakes and you may have to repeat some steps. But don't worry, this is normal and practice makes perfect!
We've covered the basics of searching for data on the web, but how does it work from a technical standpoint? Often, web scraping requires some knowledge of programming languages, being the most popular for this taskPiton. Fortunately, Python includes a large number ofopen source librarieswhich make web scraping much easier. These include:
good soupis another Python library that is commonly used to parse data from XML and HTML documents. BeautifulSoup organizes this parsed content into more accessible trees, making it much easier to navigate and search through large amounts of data. It is the tool of choice for many data analysts.
ruggedis a Python-based application framework that crawls and extracts structured data from the web. It is widely used for data mining, information processing, and archiving of historical content. In addition to web scraping (which it is specifically designed for), it can be used as a general purpose web crawler or to extract data via APIs.
pandasis another general purpose Python library used for data manipulation and indexing. It can be used to scrape the web together with BeautifulSoup. The main benefit of using pandas is that analysts can perform the entire data analysis process in a single language (and not have to switch to other languages like R).
A bonus tool if you are not an experienced programmer!Parsehubis a free online tool (to be clear, this is not a python library) that makes it easy to scrape data online. The only drawback is that you have to pay for the full functionality. However, 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 demanding niche jobs. The best way to do this is to explore which tools fit your interests and skills, and then add the right ones to your data analysis arsenal!
6. What else do you need to know about web scraping?
We have already mentioned that web scraping is not always as easy as following a step by step process. Here is a checklist of additional things to consider before scraping a website.
Have you refined your target dates?
When coding your web scraper, it's important to be as specific as possible about what you want to collect. If you keep things too vague, you'll end up with too much data (and a headache!). It is best to invest some time in advance to create a clear plan. This saves you a lot of effort.cleaning your datain the long run.
Have you checked the site's robots.txt file?
Every website has a file called robot.txt. This should always be your first port of call. This file communicates with web scrapers and tells them which areas of the website are prohibited. If a website's robots.txt file prohibits scraping on certain (or all) pages, you should always follow those instructions.
Have you consulted the conditions of use of the web?
Do you follow privacy protocols?
Just because certain dates are available doesn't mean you can scratch them off without consequences. Be very careful with the laws in the different jurisdictions and follow the privacy protocols of each region. In the EU, for example, the General Data Protection Regulation (GDPR) protects certain personal data from extraction, which means that it is illegal to delete it without an individual's explicit consent.
Is there a risk of a website being blocked?
Big websites like Google or Amazon are designed to handle high traffic. Smaller sites are not. Therefore, it is important that you do not overload a website with too many HTTP requests, which can slow it down or even crash it completely. In fact, this is a technique commonly used by hackers. They flood websites with requests to remove them, known as a "denial of service" attack. Be careful not to accidentally run any of these! Also, don't scratch too aggressively; Allow enough time gaps between requests and avoid scraping a website during peak hours.
Keep all these considerations in mind, be careful with your code, and you should be happily scraping the web in no time.
7. In summary
In this post, we take a look at what data scraping is, how it is used, and what the process involves. Key findings include:
- Web scraping can be used to collect all sorts of types of data:From images to videos, text, numerical data and more.
- Web scraping has multiple uses:From contact scraping and finding brand mentions on social media to running SEO audits, the possibilities are endless.
- Planning is important:Taking the time up front to plan what you want to scrape will save you trouble in the long run when it comes to cleaning up your data.
- Python is a popular web scraping tool:Python libraries like Beautifulsoup, scrapy, and pandas are common tools for web scraping.
- Do not break the law:Before you scrape the web, find out about the laws in different jurisdictions and be careful not to violate a website's terms of service.
- The label is also important:Consider factors such as a website's resources: don't overload it or you risk taking it down. It's good to be good!
Data scraping is just one step in the larger process of data analysis. For more information on data analysis, please visit ourfree short 5-day data analysis course? We can also recommend the following publications:
- Where to find free data sets for your next project
- What is data quality and why is it important?
- Quantitative vs. Qualitative Data: What's the Difference?