Find the best Python libraries for web scraping with our list of top picks. Extract data from websites quickly and easily to meet your project needs.
Web scraping seems a little more nefarious than it really is. Essentially, the process of web scraping (also called “web harvesting”) involves extracting data from websites. For example, your company may need to collect stock prices, sports statistics, real estate data, product information, leads, contacts, authors, band names, song titles, or addresses and use this information to better refine and promote your products. This task may seem incredibly time-consuming or difficult, but with the help of the top Python web scraping libraries, you can achieve this goal simply and quickly.
After going through the web scraping process, you can import the data into spreadsheets, databases, and even APIs. This process is exponentially easier than collecting the data manually. Even better, since programming languages like Python support web scraping tools, you can integrate the task directly into your programs. By doing this, you no longer need to manually perform a web scraping task and then integrate the data into your programs.
Thanks to these libraries, the web scraping process is part of the package. And because there are so many Python development companies around the world, you won't have any problem finding a team to build these apps if your in-house teams aren't successful.
Of course, there is web scraping software. However, using these applications and scripts would require your teams to work considerably harder to integrate them into your application. This is why you should consider one of these Python web scraping libraries.
Why is it important to choose the right Python library for web scraping?
One of the main reasons you might want to choose one library over another is simply the output it will produce. Some libraries export the extracted data in CSV or Excel spreadsheet formats, while others export in JSON. If your plan is to use the data with an API, your only option may be a library that exports in JSON format; otherwise, you will have to spend considerable time developing another tool for your application that will automate the process of converting CSV or Excel data to JSON. This can be complicated and not always reliable.
If you want to create web applications that are not only efficient but also reliable, selecting the right Python web scraping library will be critical. With that in mind, let's take a look at the best Python web scraping tools available.
The Web Page Scraping Process
Web scrapers work like this:
- A person or an application feeds a URL into the web scraper.
- The web scraper extracts all content from web pages or just the specific information it is configured to extract.
- The web scraper then processes the copied data and generates it in CSV, Excel or JSON format that can be used by a person or an application.
Although it seems like a simple process, the actual data extraction is quite complex, especially if you just want to extract specific data. And depending on the size of the website you are copying, the process may take a while.
Things to Consider When Choosing a Python Library
As you might expect, web scraping isn't exactly black and white. There are things you should consider.
- Legality: Although web scraping itself is not illegal, you should be very careful about the data you collect. You don't want to find yourself (or your company) in a situation where proprietary or protected data is copied and used for other purposes, as this could cause problems. Because of this, you want to ensure that your Python web scraping tools are being used for legitimate and legal purposes.
- Output Format: As we already mentioned, you want to select Python libraries that will output the extracted data in a format that you can use.
- Open source: When choosing your Python library, you may need to consider whether or not you will need one released under an open source license. You don't want to rely too heavily on open source libraries to create proprietary and closed source software... at least not without giving back to the open source community.
- Still in development: Sometimes a library is created for a specific purpose and then abandoned. When looking for a new Python library, make sure the one you select is still in active development; otherwise, you could end up with broken web apps and no way to fix them.
- Community: When looking for a Python library, be sure to restrict your search to only those with active and supportive communities; otherwise, you may have to troubleshoot the problems yourself.
Now that you understand what web scraping is and the issues to consider, let's dive into the most popular Python web scraping libraries on the market.
Top 4 web scraping libraries (Python)
Keep in mind that not all web scraping libraries are created equal. So, make sure you choose the one that best suits your project, your company and the data you need to extract.
#1 Beautiful Soup
Beautiful Soup is the best library on this list for beginners because it simply extracts data (from HTML or XML documents) and turns it into a Python object. Because of this, Beautiful Soup can be implemented in minutes.
This library makes it easy to extract data using tags, classes, IDs, names or other HTML attributes. And since Beautiful Soup can be easily installed on any Debian-based operating system with apt-get or any operating system that supports Python (using the pip installer), you will have no problem getting this library up and running.
Loading Beautiful Soup into a Python application is as simple as using one line like this:
from BeautifulSoup bs4 import
Key Features // Product Highlights
- Greatly simplifies extracting data from websites.
- Free and open source.
- It has a thriving and active community.
- Able to beautify data output.
| PROS | CONS |
| Very shallow learning curve. | It only offers basic functionalities. |
| Allows the extraction of specific data. | It only searches the content of your source URL and nothing else. |
| Allows developers to create their own scraping parameters. | Does not edit or save data. |
| It can only output in HTML and XML formats. | Difficult to use for larger scratches without getting your IP banned. |
#2 shaved
Scrapy is Beautiful Soup's biggest competitor. The main difference between the two is that Scrapy is considered more of a complete data extraction tool. This Python web scraping library handles everything from sending requests to implementing proxies, extracting and exporting data.
Scrapy also includes the necessary data extraction tools, called selectors, which simplifies the process of choosing the necessary data categories to extract. While Beautiful Soup is used for very simplistic web scraping, Scrapy can be used for much more complex processes such as automation testing and even data mining.
Key Features // Product Highlights
- Scrapy is a complete library for data extraction, so there is no need to employ more tools for the process.
- You can automatically resume scrapes when you encounter errors (like 404 errors).
- It can be used to create web spiders that will automatically extract data from a page that has been updated.
- Includes the ability to accelerate scraping speed.
- Can execute multiple requests in parallel.
| PROS | CONS |
| Includes tools for data post-processing. | It's not as flexible as Beautiful Soup. |
| It makes it easier to better organize the extracted data to meet your needs. | Does not work with JavaScript. |
| It can output in CSV, JSON and XML formats. | More challenging to install than Beautiful Soup. |
| Steeper learning curve. |
#3 Selenium
One of the most popular Python libraries, Selenium is a great tool for extracting dynamic content rendered via JavaScript. This cross-platform tool can render HTML, CSS and JavaScript and extract only what you need.
Selenium also makes it possible to mimic user interactions by coding keyboard and mouse actions into your application, which can be very useful when dealing with interactive and dynamic websites.
Selenium uses a web driver to generate a browser instance and load the target web page. It then uses CSS and XPath locators to find and extract content from the configured HTML elements.
Key Features // Product Highlights
- Supports multiple web browsers.
- Offers multilingual compatibility.
- Web elements are easily configured and identified.
- Supports dynamic content scraping.
- Open code.
- Cross-platform
| PROS | CONS |
| It can imitate the most popular browsers. | Requires more system resources. |
| Works with content generated by JavaScript. | Steeper learning curve. |
| Mimicking user interaction means you can extract data that other scrapers can't. | It can only output in HTML or XML. |
#4 Mechanical Soup
Mechanical Soup is not a fork of Soup Bonita. This library was inspired by a combination of Mechanize, Python requests, and Beautiful Soup. What Mechanize does is simplify the process of automating human behavior on a website to scrape web pages and extract data that would normally require input. Essentially, Mechanize is the best of Beautiful Soup and Selenium.
Key Features // Product Highlights
- It makes it possible to automate human interaction on a web page to more easily extract data that would otherwise not be available for extraction.
- Can fill web forms through a simple script.
- Automatically handles redirects.
| PROS | CONS |
| It makes it easy to extract just the web pages you want from a website. | It does not have a built-in method for handling data. |
| Similar to Beautiful Soup, so it's quite easy to learn. | I can't work with JavaScript. |
| It can only output in HTML and XML. |
Conclusion
Your business depends on data, which means collecting this information is a key factor in your success. With the right type and amount of data, your business will be better able to grow, change, and market to an ever-changing audience.
Using the best Python libraries to extract data efficiently can be an excellent option for this, as long as you follow Python best practices and make sure you are collecting data legally.
If you liked this article, check out one of our other Python articles.
- Diving into Django's REST framework
- Is Django the web framework for perfectionists?
- The 5 best websites created with Python
- What can a good Python developer do to help your company?
- How to hire a Python programmer
Source: BairesDev
