In today’s highly competitive market, brands’ desires to stay ahead of the competition have introduced the need to explore new options of doing things differently, and these desires have also led to improved innovations.
The programming language, Python, with its multiple libraries, packages, modules, and frameworks, has witnessed increasing applications even as brands seek out new ways to do things like web scraping.
Python has won the right to be called the best language for web scraping, with many developers building better web crawling scripts with it.
But, why exactly is this so? Why is Python web scraping becoming such a welcoming concept, and why has Python now been considered the best language for extracting data from the web?
What is web scraping?
Web scraping is a compounding process used in collecting data from multiple websites. It is generally used by businesses that require a vast amount of information to help them make informed business decisions.
The process uses intelligent automation and a scraping tool to extract billions of data from several sources at once regularly. The data are collected in an unstructured format such as HTML and then parsed into a structured format like JSON before analyzed and put to use.
And there are several ways the collected data can be used, including for price monitoring, for developing a dynamic pricing strategy, for brand monitoring and protection, for market research, for review and price monitoring, and so on.
Why do developers choose Python over other languages for web scraping?
There are so many reasons why many developers choose Python over the other languages for building a web scraper. But we will consider the most important two:
Web scraping alone is already hard enough with millions and even billions of sites and platforms (all built differently and in different formats) to be scrapped.
Then there is the issue of having to repeat the process every so often because new information enters the web every second.
The task is mundane and done, and repeatedly, it can be even more back-breaking.
Python’s option of automation is, therefore, a considerable advantage. When built with any of the Python libraries, the web scraper can automatically extract data from target sources every day.
The code (usually in very few lines) only needs to be written just once for this to happen.
This automation saves valuable time and effort and can significantly increase the speed of data extraction.
Web scraping is usually a 2-part process that scrapes the necessary data in an unstructured format then parses or imports it in a structured format.
While some web scraping tools can perform both functions effortlessly, others can only handle the first.
A single Python web scraping script can comfortably handle both functions plus more. A web scraper built with Python can be written to scrape and append the data, then parse, import, and save it as a data frame and even visualize the extracted data with Matplotlib.
A Python library like Beautiful Soup can be used to build a tool that effectively does this regardless of the amount of data involved.
In fact, if you’re interested in trying to build a scraper yourself, here is an in-depth, step-by-step tutorial on Python web scraping.
Main benefits of Python web scraping
The following are some of the benefits of using Python web scraping tools:
- High Performance
Python tools such as Beautiful Soup and Scrapy can be easily used to develop high-performing web scrapers. These scrapers can be very efficient, fast, and easy to debug.
- Simple Syntax
Another benefit of using Python tools for web scraping is that all Python syntaxes are simple, clear, and easy to read. Meaning anyone, including beginners, can easily use them to write scraping scripts.
- Ease of Coding
There is a reason why Python is the most popular language in the world, and that is because it is the easiest to write. This applies to writing scraping scripts as well. Scripts written in Python are generally easy and quick to write, requiring only a few lines of code.
Since Python is an all-around language, its tools can build a very flexible web scraper that does more than just extract data. Python web scraping can support data extraction, parsing and importation, and even visualization — something that would be difficult with other programming languages.
Python web scraping codes are written and executed only once. After that, the scraper functions automatically, collecting an enormous amount of data every day. This takes a lot of work out of web scraping as well as saves time and energy.
Python is the best language for web scraping, and this is evident in the way web scrapers are built and developed using the many Python tools.
These Python web scraping tools generally boast high performance and are easy to code with simple and clear syntaxes.
The language’s versatility and flexibility are also practically applied in building tools that do more than extract data. Lastly, these tools are built and executed just once then allowed to run automatically subsequently.