Informative Guide From Semalt On How To Scrape Sites In Python

The importance of data extraction cannot be ignored! There are different ways, techniques, methods, and software to extract information from websites. APIs and Python are probably the best and most powerful techniques to collect and scrape data.

Web scraping in Python:

Web scraping is the practice of extracting data from different web pages. This technique mainly focuses on the transformation of a raw or unstructured data (HTML formats) into an organized one (spreadsheets and database). We can perform different web scraping tasks using Python-based libraries.

Python is a high-level programming language created by Guido van Rossum. It features an automatic memory management system and a dynamic system to extract data. Python supports different programming paradigms, such as imperative, procedural, functional and object-oriented.

Libraries required for data extraction:

You can find a large number of Python libraries that help extract data from websites easily. However, Urllib2 and BeautifulSoup are two distinctive libraries or modules to get benefited from.

1. Urllib2:

This Python library is used to fetch data from different URLs. It can define functions and classes of a page and helps undertake various web scraping tasks at a time. It is useful to extract information from websites with cookies, authentication, and redirects.

2. BeautifulSoup:

BeautifulSoup is an incredible way to pull data from various websites and blogs. It is suitable for programmers, developers, and coders and helps them extract data from tables, short paragraphs, long paragraphs, lists, and charts. Once the data is scraped, you can use BeautifulSoup's filters to improve its quality. BeautifulSoup 4 is the best and latest version to scrape web documents, HTML pages, and PDF files.

Scraping HTML text with Python:

Besides BeautifulSoup and Urllib2 have several options to scrape HTML text:

  • Scrapy
  • Mechanize
  • Scrapemark

When you carry out web scraping tasks, it is important to get familiar with HTML tags. You can learn how to scrape information from both HTML text and HTML tags with BeautifulSoup and Python. Some useful HTML tags are described below:

  • HTML links that are defined with a <a> tag.
  • HTML tables that are defined with <Table> and <tr>. The rows are divided into different data patterns with tag.
  • The HTML lists start with <ul> (unordered) and <ol> (ordered) tags.


The codes written in BeautifulSoup are more robust than codes written in regular expressions. Thus, you can implement the BeautifulSoup codes to scrape data from both basic and dynamic websites easily. If you are looking for a suitable tool, Scrapy is the right option for you. This Python-based software helps collect, scrape and organize data in a matter of minutes.