Back to Blog

Web Scraping for Data Analysis: How to Extract & Analyze Data Like a Pro

---

Data analysis is a core function for every business department. From inventory managers to finance managers, all executives use data analysis to improve their workflows and make key decisions. Even the strategic planners for businesses choose data analysis as the key to competitive and price intelligence. For all data analysis endeavors of firms, access to quality data is essential. From their own data collected at various business touchpoints to that of the competitors, data is the primary fuel that ignites data analysis.

How do businesses get this data? Web scraping is the go-to method for collecting high-volume, enterprise-grade data.

Web scraping for data analysis is the automated process of extracting data from websites, online platforms, databases, and social media. Price monitoring, competitor tracking, market intelligence, and business analysis are key use cases of web scraping. Businesses extract data from their competitors’ products, including descriptions, promotions, and customer reviews, to gain insights that inform better decision-making.

As web scraping has become a critical function in modern data analysis, businesses must learn how to do it effectively. It requires technical expertise in data scraping tools, proper infrastructure, and adherence to best practices. For reaping the full benefits of web scraping for data analysis, it must be done professionally, systematically, and ethically.

In this article, we’ll explore how to extract and analyze data like a pro.

What is Web Scraping For Data Analysis?

You may be using Power BI or Tableau for data analysis, or you may be using AI data analytics models. However, all these data analytics tools are powered by ‘data’. Therefore, as data is at the core of data analysis, collecting it is a critical business function. In this context, web scraping is also essential as it is the method of extracting and tabulating data for analysis.

Web scraping (also known as data scraping or web crawling) is the use of custom-built software bots to extract information from websites. The web scraping software market size is projected to exceed $3.52 billion by 2037.

How Does Web Scraping Work?

Web scraping is extracting data from the web (websites, webpages, social media pages, listing platforms, e-stores, etc.). This process is carried out by programmed scrapers (bots) that can access websites and scrape the data. These bots send an HTTP request to a target site, retrieve the raw HTML of the pages, and then parse that HTML to grab the specific data points you need (for example, product names and prices on a page).

Web scraping automates what would otherwise be a tedious and time-consuming task of copying and pasting data.

To scrape effectively, you need a basic understanding of how websites deliver content (i.e, Website architecture – HTML, CSS, JS).

• HTML is the structure and content of the page (the text, links, and data you want are usually embedded in HTML tags).
• CSS is the styling (how things look) – usually irrelevant to scrapers except when it hides or alters content visibility.
• JavaScript (JS) adds interactivity and can dynamically load data after the initial page load.

Web scraping involves sending the scrapers to target websites to extract text, images, descriptions, prices, or any other data present on the target web pages. Common data points extracted include textual content (product descriptions, prices, headlines, and reviews), numerical data (ratings and stock counts), images (URLs or binary data for photos), and metadata such as links or tags.

In the section below, we outline how to web scrape data for analysis, just as experts do.

Step by Step Extraction and Analysis of Data Like a Pro

Setting Up Your Environment

You will also need to set up a proper development environment for your scraping project, including the scraper scripts, libraries, and dependencies.

Ready to start scraping?

First, you’ll need the right tools and environment.

Web scraping can be done in virtually any programming language, but Python is the undisputed leader in web scraping due to its simplicity and powerful scraping libraries.

Most data scraping professionals utilize Python with libraries such as Beautiful Soup for parsing HTML, Scrapy for building large-scale crawlers, or Selenium for automating browser interactions.

Next comes the running environment for scrapers. When setting up your environment, consider whether to run scrapers locally or in the cloud.

For small projects, running scrapers on a local server is fine. However, for large-scale scraping, cloud environments are preferred due to their reliability and scalability.

Lastly, set up a proper development environment for your scraping project, including the libraries and dependencies.

Custom Building Robust Web Scrapers

For reliable enterprise-grade web scraping, you will need robust web scrapers.

Here are some tips for building scrapers like a pro:

• Clean, maintainable code: Organize the code logically, using functions or classes for distinct tasks (e.g., login handling, page parsing), and employ clear naming conventions. This makes it easier to update when the target site changes.
• Error handling and retry logic: Scraping at scale requires your scrapers to handle various network, server, and request-related issues, including timeouts and other errors. Design a scraper that can handle it properly. Robust scrapers are fault-tolerant.
• Use proxies and rotate IP addresses and user agents: To avoid getting blocked, you should not repeatedly hit a website from a single IP address, even if you’re doing it at scale. Professional scrapers use proxy pools – a list of IP addresses (proxy servers) that they route requests through. By rotating proxies, the load is distributed. Using them can drastically improve your scraper’s success rate on sites with anti-bot measures.
• Automation and scheduling: Whether you need data daily, hourly, or in real-time, a robust scraper can run automatically to meet your needs. By automating scrapers, you ensure data is collected at optimal times. You can add artificial intelligence to these scrapers to automate the scheduling as needed.
• Dynamic Website Handling: Another challenge is distinguishing between static and dynamic websites.
  • Some websites are static, meaning all the data is present in the HTML sent from the server, and a scraper can immediately find the data in the source.
  • Dynamic sites, however, send an HTML framework and use JS to fetch data (often from additional API calls) and populate the page afterward.
• Resilience to site changes: To build resilience, try to write parsing logic that’s not overly specific when possible (e.g., rely on stable identifiers or text around the data). Some advanced strategies include writing tests for your scraper (using sample HTML pages to ensure your parsing functions still find what they expect) or even using machine learning to locate data points by context (beyond fixed XPaths).

Adhering to the Best Web Scraping Techniques

• Basic HTML parsing: You fetch a page’s HTML via an HTTP GET request and then parse that HTML to extract data. Parsing can be done by searching for specific HTML tags or patterns.
• Using APIs when available: Many websites offer official APIs that provide data in a structured format (such as JSON), intended for developers. If an API exists for your target data, it can be a much easier and more robust option than scraping the website’s HTML.
• Handling navigation and pagination: Often, the data you need isn’t all on one page. For example, a product listing might span 50 pages, or a forum has multiple discussion pages. A good scraper handles pagination – automatically clicking “Next” or altering the URL to move through pages.
• Scraping JavaScript-rich content: Dynamic websites that heavily use JavaScript (for example, sites that load content on scroll or after user interactions) require more advanced tactics. One way is to use a headless browser that can run the page’s JavaScript and then provide the final rendered HTML.
• Dealing with logins and sessions: Sometimes, data is behind a login. You need a login to access data and sessions. You can still scrape these, but you have to handle the authentication. The simplest method is to use an API token or basic HTTP authentication; however, you’ll usually need to mimic the login form.
• Handling CAPTCHAs and anti-bot challenges: CAPTCHAs are those annoying "I am not a robot" tests (image selections, puzzles, etc.) that websites use to thwart automated access. If your scraper triggers a CAPTCHA, it’s a sign the site identified you as a bot. Avoiding CAPTCHAs is preferable by rotating IP addresses, using human-like browsing patterns, and making fewer requests at a time.

Adhering to the Best Practices of Legal and Ethical Scraping

Here are key considerations for responsible scraping:

• Check robots.txt: Websites often provide a robots.txt file that outlines the allowed access for bots. This isn’t the law, but it’s an honor system that polite scrapers follow. If robots.txt disallows scraping certain sections, you should avoid those. Many businesses programmatically check this file before scraping a site.
• Honor Terms of Service: Always review the target site’s Terms of Service (ToS) or user agreement. In short, if a site says “no scraping,” either get explicit permission or choose a different data source. It’s not worth the risk.
• Rate limiting and server impact: Ethical scraping ensures that it does not harm the performance of the target website. Bombarding a server with rapid requests can degrade the site for other users or trigger an IP ban for your scraper. Introducing delays between requests or throttling the number of requests per second is a good practice.
• Data privacy and consent: Public data is generally fair game for scraping, but personal data is a different matter. Therefore, avoid scraping any sensitive personal data (such as emails, phone numbers, health information, or financial information) unless you have a legal right to do so.
• Implement backoff logic: If a site starts responding with errors or too many requests, slow down or stop. And if a website provides an official API for data access, use it instead of scraping HTML. Using the official channel is not only often easier but also entirely within their allowed usage policies.

Conclusion

Using web scraping helps extract valuable insights from the data found online. Whether it's your competitor's websites or third-party databases available online, you can extract insights by scraping them. To minimize efforts, costs, and errors, it is essential to adhere to best practices for web scraping in data analysis.

From building scrapers with the right technical competencies to complying with ethical data extraction guidelines, with the steps mentioned in this article, you can extract and analyze data like a pro.

If you want a data scraping expert to do it for you, connect with AI-powered data scraping services providers at Scraping Intelligence.

---