Probability states that you’re familiar with the importance of harvesting data from the internet for myriad reasons. However, full-service web scraping solutions can get quite pricey. Running a prebuilt tool can be more economical, but their budget versions are severely limited. To save the most money possible, you should consider using free libraries to build your own web scraper.
There are a lot of options out there for multiple programming languages. Some are more beginner-friendly than others, particularly Python-based ones. Let’s go over some of the popular ones while covering what language they’re for and a little information about them.
But first, here are some things to keep in mind when you’re configuring a web scraper.
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Best Practices When Web Scraping
Regardless of which language and library you choose to use, there are a few universal rules to follow when setting up your scraper for optimal results:
- Most important of all: use a rotating proxy! Ideally, you should go with residential IPs, but a datacenter proxy may be sufficient for your needs.
- Avoid using high-risk or suspicious IP geolocations for your target data.
- Set unique user agents for your requests, or use headless browsers.
- Set a believable native referral source. Just how often do you directly type in the exact sub-domain in your browser to go to an exact part of a website, instead of navigating through the site to get there?
- Set rate limits on your requests, ideally respecting the target site’s robots.txt settings.
- Run your threads asynchronously. A constant stream of requests with the same time gap in between them while running parallel with each other is effortlessly detectable bot activity.
- Avoid using obvious red flag search operators. Making things too precise is not organic traffic.
Also read: Top 5 Best Rotating Residential Proxies
Why Do I Need A Proxy When Web Scraping?
You’re surely familiar with the sheer quantity of anti-bot measures in place across the internet. We’ve all dealt with more than our fair share of annoying CAPTCHAs. A pool of rotating IPs takes care of the majority of the effort in masking the fact that all of your requests are coming from a program instead of a human user.
CAPTCHAs are one of the most common anti-bot measures on the internet, designed to differentiate between human users and automated bots by challenging users with tasks that are easy for humans but difficult for machines.
Source: von Ahn, L., Blum, M., & Langford, J. (2004). Telling humans and computers apart automatically. Communications of the ACM, 47(2), 56-60.
As datacenter proxies are readily detectable and are commonly attributed to botting, residential IPs are the way to go. Residential proxies are much more convincing when you’re trying to resemble organic traffic. This is, of course, what you should be aiming for to get the most reliable web scraping results.
Now, to get to the subject at hand: free libraries to build your own scraper.
Also read: Datacenter Proxies Use Cases
Free Libraries to Build Your Own Scraper
Since we all have different preferences and requirements, it’s pretty hard to pin down exactly what makes a particular library ideal for your use case. What I can do to help, though, is give you a list of options with some information about them so you can make an informed decision.
Without further ado, and in no particular order, let’s begin going through the free libraries to build your own web scraper!
Scrapy
Language: Python
Scrapy is one of the leading open-source Python libraries that offers great scalability for web scraping. It can handle all of the complicated components of crawling and scraping, at the cost of not being very beginner-friendly.
It is very widely used, and it is largely considered one of the top-tier libraries out there. Thanks to this, there is extensive documentation available with tons of tutorials to get you started.
As to why it’s considered one of the best, well, the fact that benchmark tests put it up to 20 times faster than other equivalent tools should give you some idea as to why. The extensive number of modules for scraping and parsing, complete with exacting customizations for both, certainly helps.
BeautifulSoup
Language: Python
While Scrapy isn’t beginner-friendly, BeautifulSoup most definitely is. When you don’t need the precision and power of Scrapy, BeautifulSoup will provide you with an easy means of parsing HTML.
Similar to Scrapy, BeautifulSoup is thoroughly tested and well-documented after years of use.
Selenium
Language: Python
Selenium was originally developed for automated web testing. It automates web browser activity but has been adapted for web scraping use as well. With a solid built-in parser, it loads and reads JavaScript, unlike Scrapy and BeautifulSoup.
If you’ll be building your scraper in Python and you know that you’ll be pulling target data requiring JavaScript access, you should consider using Selenium.
Cheerio
Language: JavaScript (NodeJS)
Cheerio has a similar API to jQuery. If you’re already familiar with jQuery and are looking to parse HTML, you’re all set.
It’s fast, flexible, and a favored library for web scraping with JavaScript.
Puppeteer
Language: JavaScript (NodeJS)
Puppeteer is Google’s headless Chrome API that grants precise control to NodeJS devs. The Google Chrome team is creating and maintaining it in an open-source format.
Like Selenium, it is a go-to for data that is gated behind JavaScript.
Just keep in mind that it can be an absolute resource hog for the host machine. When you don’t need a full-on browser, you should probably consider a different tool.
Kimura
Language: Ruby
As yet another open-source web scraping framework, Kimura is the leading popular Ruby library. It plays nice with PhantomJS, both headless Chrome and headless Firefox, and also normal GET requests.
It has some solid configuration options and has some similar syntax as Scrapy.
Goutte
Language: PHP
Goutte is an open-source PHP web crawling framework ideal for pulling HTML and XML data. As it is designed with simplicity in mind, it’s the most no-nonsense library on this list.
When you want to get a wee bit more advanced, it integrates smoothly with Guzzle for more customization.
Also read: Web Scraping With Proxies
FAQs
Q1. Is Beautiful Soup good for web scraping?
Oh, absolutely! Beautiful Soup is awesome for web scraping, especially if you’re just getting started or working on simpler projects. Let me tell you why.
Beautiful Soup makes it super easy to navigate and manipulate HTML. You can pull out specific pieces of data—like titles, links, or tables—without too much hassle. Import it using
from **bs4 import BeautifulSoup**
Websites often have messy, imperfect HTML, but Beautiful Soup is pretty forgiving. It can parse even poorly structured HTML, which means your scraper won’t break every time a page has some wonky code.
If you’re just starting out with scraping, Beautiful Soup is a great tool to learn. It’s straightforward and gives you a lot of flexibility. And if you’re an advanced user, it’s still a solid tool for smaller, focused scraping projects.
Once you’ve fetched the webpage using a library like requests, you can use Beautiful Soup to parse the HTML. For example, after your bs4 import BeautifulSoup, you can use it to sift through the HTML structure, find elements by tag names, classes, or even specific attributes, and then easily get the extracted data you need.
While Beautiful Soup is awesome for parsing and navigating HTML, it’s not the fastest tool out there if you’re scraping really large datasets or complex websites. For larger projects, you might want to pair it with something like Scrapy or use it alongside tools that handle JavaScript-heavy pages like Selenium.
Q2. How do you scrape HTML content?
Scraping HTML content is actually pretty straightforward once you know the basics! Let me walk you through it, step by step, and show you how it’s done.
First, go to the website you want to scrape and inspect the page. Right-click on the element you’re interested in (like a title, price, or image) and select “Inspect” in your browser. This will open up the developer tools, where you can see the HTML elements that make up the page. This is where you’ll find the tags, classes, or IDs you’ll use to target specific parts of the page.
You’ll need to use Python for this, so let’s start by importing two main libraries: requests (for fetching the webpage) and BeautifulSoup (for parsing the HTML).
import requests from bs4 import BeautifulSoup
Now, we use requests to get the webpage’s content.
response = requests.get(‘https://example.com’)
webpage = response.content
Once you’ve fetched the HTML, you’ll need to parse it using BeautifulSoup. Now you can navigate through the HTML elements and pick out what you need!
Q3. Does web scraping need coding?
Yes, web scraping does need a bit of coding, but it’s not as intimidating as it sounds—especially when you break it down into small steps. If you can write a few lines of code, you’ll be able to scrape data in no time!
To scrape a website, you need to write some code that:
- Fetches the webpage (gets the HTML).
- Extracts the data you want from the HTML.
Luckily, Python makes this really simple, and you only need two main libraries:
- requests (to fetch the webpage)
- BeautifulSoup (to parse the HTML and find the data)
Before you start, you’ll need to install these libraries. Open your terminal or command prompt and run:
pip install requests
pip install beautifulsoup4
Most of the time, yes—coding gives you control over exactly what data you want and how to get it. But if you’re not into coding, there are web scraping tools that let you scrape data with minimal or no coding at all. However, these tools might not give you the flexibility that writing code does.
Also read: Well Paid Web Scraping Projects
Conclusion
There is no perfect web scraping tool library out there. They all have their own strengths and weaknesses, while also giving us freedom of choice over what programming language to use.
This list makes it easier to choose which one of the free libraries to build your own web scraper with. All that’s left is to grab a trustworthy proxy so you can get started on web scraping and data parsing right away.
