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A Screen Scraping Tutorial Provided By Semalt

When it comes to scraping web content, it's common to search the internet for a screen scraping tutorial. There are times when the information you want can only be accessed only through an API (Application Programming Language), and in some cases, you may want to use a screen scraping tool or opt for a Python library to accomplish your tasks.

In this screen scraping tutorial, we will discuss the best and most famous Python libraries and will learn about the different components of a web page.

The Components Of A Webpage:

When you visit a web page, your browser will send a request to the web server. This request is known as GET request, and the server will send back the files that will tell your web browser how to render the pages for you. There are four main components of a web page: HTML, CSS, JS, and Images. HTML contains the main content of a page, and CSS is used to add styles to a page and makes it look appealing, charming and attractive. On the other hand, JavaScript or JS files are used to add interactivity to a web page, and the images are used to make a site look professional and better than the others. The best image formats are PNG and JPG – both of these formats are suitable for webmasters and image curators and allow them to give an interactive look to their web documents.

Different Python libraries for screen scraping:

1. Requests

It is the most famous and one of the best Python libraries. Requests is written by Kenneth Reitz and used to build different web applications and data scrapers.

2. Scrapy

Scrapy is so far the most powerful and useful Python library for your screen scraping tasks. You don't need to have the technical knowledge to use this library because Scrapy automates the web scraping tasks and saves your time and energy to an extent.

3. wxPython

It is a GUI toolkit for Python and is a good alternative to Scrapy. However, this Python library is not as common as Scrapy and BeautifulSoup.

4. Pandas

Pandas is primarily a Python package that is designed to work with "relational" and "labeled" data samples. Pandas is a perfect way to scrape content from the internet and is known for its marvelous data manipulation visualization and aggregation.

5. Matplotlib

In this screen scraping tutorial, you will also learn about Matplotlib, which is a SciPy Stack core package and a popular Python library. Matplotlib is tailored for the screen scraping tasks and generates powerful visualizations with ease. It is a good alternative to Scrapy and can be used individually or in combination with NumPy, Pandas, and SciPy. However, Matplotlib is a low-level library, meaning that you will have to write sophisticated codes to reach an advanced level of data extraction and visualization.

6. BeautifulSoup

Just like Requests and Scrapy, BeautifulSoup is a popular Python library that is used for parsing both HTML and XML documents (including non-closed tags). It helps create a parse tree for the parsed pages that can be used to scrape data from HTML.

All these Python libraries are used for screen scraping tasks and extract useful data from the above-mentioned components of a webpage.

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