Python has grown to become one of the most popular programming languages. It is a versatile, high-level programming language used in machine learning applications, web development, data analytics and visualization, game development, web scraping, and more. This versatility emanates from the fact that it is a general-purpose language. Its popularity is partly because of its many libraries that help programmers avoid writing code from scratch.

Why Python Is Best Suited For Web Scraping?

Notably, Python is the best-suited language for web scraping, thanks to the available libraries. From Beautiful Soup and requests to Selenium and Scrapy, Python offers resources that greatly help in the web scraping process.

At its core, web scraping, also known as web data extraction, refers to extracting content and information from websites. This process is primarily and conveniently undertaken automatically through web scrapers that are essentially scripts created by writing code, mainly in Python.

However, the process can also be conducted manually. Whenever you copy a group of words from a website with the aim of pasting it on a file stored on your computer, you have, knowingly or unknowingly, performed web scraping at a small scale. If you intended to collect more text from hundreds or thousands of websites and webpages, manual web data extraction would be unnecessarily time-consuming. This is where Python web scraping comes in.

Python Web Scraping Libraries

In addition to Scrapy, a Python framework, there are four main Python web scraping libraries, namely:

1. Requests
2. Beautiful Soup
3. lxml
4. Selenium
5. Scrapy (not a library)

1. Python Requests Library

The Python Requests library is used to create different types of HTTP requests. This means that it forms the foundational layer of web scraping. Importantly, though, this library does not convert the HTML data (the code file sent) into a readable format in a process known as parsing. It also has another downside – it cannot extract data from websites purely written using JavaScript.

2. Beautiful Soup

Beautiful Soup is a parsing library. It is used to convert HTML and XML files into readable formats such as Unicode. However, it does not request this data from websites. This means that the Beautiful Soup library is used alongside the Requests library.

3. Lxml

Like Beautiful Soup, lxml is a parsing Python web scraping library that can convert both HTML and XML files into readable formats. However, its ability to parse data relies heavily on the HTML design. Therefore, it cannot work with poorly designed HTML.

4. Selenium

As a web driver, Selenium can be used to visit websites, click links, open a new webpage, log in to websites such as social media platforms, and more. In addition, it can render entire webpages as it runs JavaScript code. This makes Selenium a useful tool because, by rendering pages, it provides content for parsing. As such, it is used alongside parsing libraries.

5. Scrapy

Scrapy is a complete Python web scraping solution. Importantly, it is not a library; it is instead a Python framework. It can be used to send requests, maintain user sessions, follow links, including redirected links, download HTML data, parse the HTML data, and, finally, convert the data into a structured format for storage as a .csv file.

How Python Web Scraping Works?

The libraries described above are used to create a script that sends requests, maintains user sessions, downloads HTML data, parses the data, and, finally, converts it into a format that can be analyzed. Simply put, Python web scraping can, therefore, be thought of as the automated process of retrieving data from websites using tools created by writing Python code.

It is worth noting that some of the libraries, such as Selenium, are only suited for small-scale web scraping applications. This is because Selenium renders entire webpages, which makes it slow.

How Python Web Scraping Can Enrich Your Business?

The quick and automated data retrieval achieved through Python web scraping can be beneficial in many ways. For instance, you can use it for:

1. Price comparison in order to develop a pricing strategy.
2. Competition analysis.
3. Lead generation by ethically extracting phone numbers and email addresses from websites.
4. Reputation monitoring, which helps you establish what customers and news outlets write about your products and services.
5. Market research, especially when you intend to scale or enter a new market.

Conclusion

Python libraries make it easy to create a tool with which you can extract valuable data from websites. Some of the data you can retrieve include competitors’ prices, potential customers’ emails, mobile phones, and more. With the data, you can reach more people, improve your services, develop a pricing strategy, and more. Simply put, python web scraping is bound to skyrocket and enrich your business.

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