Python Web Scraping Tools: Advantages And Disadvantages Simply Explained
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Web scraping seems to have become a technique widely used to retrieve and obtain data from the web. People begin to develop or use a number of different tools to attain their targets. This is usually split into two factions: software and resources. This article will present you with a straightforward explanation of 3 powerful web scraping tools in the python environment.
Python Web Scraping Tools
There are several established tools in the Python ecosystem for implementing a web scraping project:
We’ll go into the advantages and disadvantages of the three technologies below.
Web scraping with Scrapy
The Python web scraping tool Scrapy uses an HTML parser to extract information from the page’s HTML source code. The following scheme results for web scraping with scrapy:
URL → HTTP request → HTML → Scrapy
The core concept of the scraper development with Scrapy is the “Web Spider” called scrapers. These are small programs based on Scrapy. Each spider is programmed to scrape a specific website and shimmy from side to side like the eponymous spider. Object-oriented programming is used here: Each spider is its own Python class.
In addition to the actual Python package, the Scrapy installation comes with a command line tool. The spiders are controlled via this scrapy shell. Existing spiders can also be uploaded to the Scrapy Cloud. There the spiders are carried out with a schedule. This means that even large sites can be scrapped without having to use your own computer or your home internet connection. Alternatively, you can set up your own web scraping server with the open source software Scrapyd.
Scrapy is a mature platform for performing web scraping with Python. The architecture of the tool is based on the needs of professional projects. Scrapy contains an integrated pipeline for processing the scraped data. The page fetch in Scrapy is asynchronous; this means that several pages can be downloaded in parallel. Thus, Scrapy is well suited for scraping projects with a high volume of pages to be processed.
Web scraping with Selenium
The free software Selenium is a framework for automated software tests of web applications. Actually developed for testing websites and web apps, the Selenium WebDriver can also be used with Python to scrape websites. Although Selenium itself is not written in Python, the software’s functionality can be accessed from Python.
Unlike Scrapy or BeautifulSoup, Selenium does not operate on the page’s HTML source code. Instead, the page loads in a browser with no user interface. The browser interprets the source text of the page and creates a Document Object Model (DOM) from it. This standardized interface allows the testing of user interactions. a. Simulate clicks and fill out forms automatically. The resulting changes to the page are reflected in the DOM. The following scheme results for web scraping with Selenium:
URL → HTTP request → HTML → Selenium → DOM
URL → HTTP-Request → HTML → Selenium → DOM → HTML → Scrapy / BeautifulSoup
Web scraping with BeautifulSoup
BeautifulSoup is the oldest of the Python web scraping tools featured. As with Scrapy, it is an HTML parser. The result is the following scheme for web scraping with BeautifulSoup:
URL → HTTP request → HTML → BeautifulSoup
The most well known Python HTML parser seems to be BeautifulSoup, however I find it slow, difficult to use (compared to XPath), often parses HTML inaccurately, and significantly – the original author has lost interest in further developing it.– Expert Opinion
Unlike Scrapy, Scraper development with BeautifulSoup does not require object-oriented programming. Instead, a scraper is written as a simple script. Thus, BeautifulSoup is probably the easiest way to fish specific information out of the “soup of the day”.
Python Web Scraping Tools Compared
Each of the three tools presented has its advantages and disadvantages. We have clearly summarized these for you:
|Easy to learn||★★★||★||★★★|
|Readout dynamic content||★★||★★★||★|
|Realize complex applications||★★★||★||★★|
|Robustness against HTML errors||★★||★||★★★|
|Optimized for scraping performance||★★★||★||★|
Now, which tool should you use for your project? In a nutshell, go with BeautifulSoup if you want to speed up development or if you just want to familiarize yourself with Python and web scraping. With Scrapy, demanding web scraping applications can be implemented in Python – provided you have the appropriate know-how. Use Selenium if your primary goal is to scrape dynamic content with Python.