How to Build a Web Scraper With Python: Step-by-Step Tutorial
Mastering web scraping allows you access to static or real-time data for market research, lead generation, price monitoring, and more. Countless opportunities lie ahead. Still, one critical barrier continues to hold many back.
Indeed, you are spot on! Language choice is the barrier. With programming languages like C#, Java, or Ruby, one has to spend a lot of time figuring out how to resolve certain scraping challenges. Some are even forced to build custom tools.
Fortunately, Python offers a balance of simplicity, flexibility, and a rich ecosystem of pre-built web scraping tools. That’s why I’ve prepared a tutorial to help you get started with building web scrapers with Python.
Building a Web Scraper With Python: Comprehensive Walkthrough
For this guide, I’ll not focus much on how to write web scraping Python code to extract data from various websites. I’m focusing on the fundamentals that will get you going no matter the Python tools or libraries you decided to use.
Once you are done exploring this guide, you should be in the position to understand the essence of certain steps as you begin this journey to becoming a Python master. Dig in!
- Establish a clear purpose or objective
Even though web scraping is the process of extracting data from websites of varied nature, you don’t delve directly into the process. You must have a clear reason as to why you want to scrape a certain website.
First, think of the problem at hand and the data you need to solve the problem. For instance you may want to compare competitor pricing. So, what do you need in this case? Yes, a list of your competitor’s prices.
With a clear reason or purpose in mind and understanding of the data you need, assess how many times you need the data. This will impact how you design your scraper.
Requiring data regularly will see you design a script in a way that does not overwhelm the target website. Also, you’ll need proxies to avoid IP bans.
Lastly, once you have defined the reason, type of data, and frequency, lock in on a specific target website.
- Examine the layout of the target website
While the basic structure of websites includes HTML (HyperText Markup Language) and CSS (Cascading Style Sheets), the way websites load their content varies.
Some are built using HTML and CSS only, allowing you to obtain all the data you need on a single request. These types of websites are known as static websites.
Other websites are dynamic. Compared to static sites, dynamic sites rely on JavaScript for real-time data loading.
To determine whether you are about to scrape a dynamic or static site, open the target website in a select browser. Then, navigate to the browser’s developer tools and inspect the site’s web pages.
If the target website is static, most of the data you want to scrape should be in the site’s HTML or source code. A dynamic website should have minimal data or place holders within its HTML or source code.
Alternatively, to categorize the website in question, head to your browser’s settings and temporarily disable JavaScript. If your site is static, the opened web page’s content should remain fully visible or intact. Otherwise, if it is a dynamic website, some content may be missing or the page may request you to switch JavaScript on.
Overall, examining the target site’s structure helps with knowing what web scraping tools you’ll need.
- Specify the tools you’ll need and configure your scripting workspace
As highlighted, Python grants you access to an array of pre-built web scraping tools.
If you want to scrape a static website, your options include Python libraries such as Requests and BeautifulSoup.
While the Requests library is used to fetch the raw HTML of a static page, BeautifulSoup parses raw HTML. Parsing means turning it into a structure you can navigate to extract specific data.
For a dynamic website, you have libraries such as Playwright, Selenium, and Puppeteer. These libraries work like your browser does. They can send requests to the target site, receive the target data, parse it, and extract it.
After selecting the appropriate tools, download and install the latest version of Python from their official website. Then, install your select libraries, too. After installing Python and the necessary library, you should be ready to write your first web scraping script.
- Configure your script to fetch, parse content, and store it
Now to the coding part. Based on your select libraries, write a script to send a request to the target website. This is similar to how you input a URL on your browser and have your browser send a request to a specific site’s servers.
After sending a request, the script should be able to receive raw HTML or handle JavaScript rendering by waiting for all JavaScript content to load before capturing it. Once done, the script should finally be able to extract the data and save or store it in a defined format.
Hack the coding part by following web scraping with Python coding examples. If you get stuck while using a specific Python web scraping library, go through its documentation.
Finally, while in the process of navigating various web scraping approaches and optimizing your scraper, remember to scrape ethically.
Don’t overwhelm websites with requests or attempt to gain access to private or protected data. Doing this may result in legal action or issues.
Wrapping up!
With the advancement in web scraping tactics, businesses are investing heavily to gain real-time insights into what their competitors are up to, what customers think about their brands, and more. However, the advancements are selective.
For instance, web scraping with languages such as Java or C# is likely to limit you unlike scraping with Python. Due to its simplicity, versatility, and extensive library support, Python has seen a significant growth in applications in the web scraping space.
Use this guide to get started on a journey to mastering web scraping with Python. Not forgetting, before you decide to scrape a website, always review its terms of service and robots.txt files to determine what data you are allowed to extract.
