Python is used as a general-purpose programming language that allows developers to express concepts in fewer lines of code. This is the main reason why Python was created in the first place.
You can find comparisons on the internet that Python is like a chameleon of the programming world. Well, it is not a lie. Python is used for pretty much anything you would need, from building web apps to data analysis. Python’s creators gave attention to its syntax and code readability, so now Python is everywhere, and you may not realize how widespread it is.
In this article, we will explain what Python can do, what it is mostly used for, and the most important part – why is it the most popular programming language for web scraping. Also, we will compare Python with other programming languages in terms of web scraping.
What is Python used for?
As you can now understand, Python is used in many different fields. We will single out the most important areas, which simply would not be the same without Python. So, what can you do with Python?
Web development
Python is excellent for using on back end web development projects because it has pre-built libraries and web frameworks such as Pyramid, Flask, and Django. They noticeably shorten time-consuming tasks developers spend on projects. Additionally, these libraries and frameworks provide generic functionality that can be changed to create application-specific software.
Machine Learning
Machine Learning has become more robust in recent years, and Python is commonly used for machine learning development. The main reason for this is that Python is stable, flexible, and has specific machine learning libraries and frameworks such as SciKit-Learn and TensorFlow. Developers focus on solving machine learning problems rather than focusing on the technical nuances of the programming language.
Artificial Intelligence
Python is commonly used for Artificial Intelligence (AI) development. Various Python libraries perfectly fit to work with AI, such as PyTorch, Theano, and Keras. Furthermore, Python makes it more comfortable to handle complex systems because it allows developers to express concepts in fewer code lines. What is essential, Python is one of the most popular programming languages to work with data management. Properly managing data in AI is crucial as it is a fuel of Artificial Intelligence technologies.
Data science
Python is mostly used in data science because it is a flexible and open-sourced language. Even a beginner data analyst can easily learn Python because it has a large selection of libraries for data manipulation. Theano, Matplotlib, SciPy – these are only a few libraries of many that data analysts can use to improve their work with Python.
Video game development
To fully answer the question of what is Python used for, it is essential to mention that Python is a suitable choice for video game development. Python is used in this field as it has clear and simply readable syntax. Furthermore, Python has a set of modules designed for writing video games, such as PyGame. It is worth mentioning specific libraries like Pyglet, frameworks like PyKyra. These tools make developers’ tasks much easier.
Web scraping
Python is widely used for web scraping
If you need to start writing code for web scraping, it is definitely worth it to learn Python. The best part is that Python, compared to other programming languages, is easy to learn, clear to read, and simple to write in.
What are Python advantages for web scraping?
Diverse libraries. Python has a fantastic collection of libraries such as BeautifulSoup, Selenium, lxml, and much more. These libraries are a perfect fit for web scraping and, also, for further work with extracted data. You will find more information about these libraries below.
Easy to use. To put it simply, Python is easy to code. Of course, it is wrong to believe that you would easily write a code for web scraping without any programming knowledge. But, compared to other languages, it is much easier to use as you do not have to add semicolons like “;” or curly-brackets “{}” everywhere. Many developers agree that this is the reason why Python is less messy. Furthermore, Python syntax is clear and easy to read. Developers can simply navigate between different blocks in the code.
Saves time. As you probably know, web scraping was created to simplify time-consuming tasks like collecting vast amounts of data manually. Using Python for web scraping is similar because you are able to write a little bit of code that completes a large task. Python saves a bunch of developers’ time.
Community. As Python is one of the most popular programming languages, it also has a very active community. Developers are sharing their knowledge on various questions, so if you are struggling while writing the code, you can always search for help.
Python has a very active community
Python libraries used for web scraping
Powerful frameworks and libraries, explicitly built for web scraping, are the main reason why Python is a popular choice for data extraction. We will take a closer look at all the essential libraries that makes every developer’s web scraping tasks much easier.
Selenium. The primary purpose of Selenium is to test web applications. However, it is not limited to do just that as you can use Selenium for web scraping. It automates script processes because, for web scraping, the script needs to interact with a browser to perform repetitive tasks like clicking, scrolling, etc. If you are interested in web scraping with Selenium, check out our other blog posts.
BeautifulSoup. BeautifulSoup is widely used for parsing the HTML files. According to their documentation, BeautifulSoup library is precisely built for pulling data out of HTML and XML files. It saves developers hours or even days of work. If you would like to know more about this library, check out our intro tutorial: using Python and BeautifulSoup to parse data.
Pandas. According to their official site, Pandas in web scraping is used for data manipulation and analysis. Pandas features include flexible reshaping and pivoting of data sets, reading and writing data between in-memory data structures and different formats, aggregating or transforming data, etc.
Requests (HTTP for Humans). This library is used for making various types of HTTP requests like GET, POST. Python Requests library retrieves only static content of the page. This library does not parse the HTML data extracted from web sites. However, requests library can be used for basic web scraping tasks.
lxml. This library is similar to BeautifulSoup because developers use lxml for processing XML and HTML files in the Python language. Check out our lxml tutorial for more information.
Python has an amazing collection of libraries
Why you should use Python
Now that we know what is Python good for, it should be easier to understand its appeal, especially for web scraping. Python is the most popular programming language for web scraping because it can handle almost all processes related to data extraction smoothly. However, there are other languages that can be used by developers for web scraping such as Ruby, C ++, PHP.
All of these languages have their pros and cons compared to Python, so let’s compare them in terms of web scraping.
Python vs Ruby
Ruby programming language is similar to Python, as its idea is simplicity and productivity. It is an interpreted, high-level, and general-purpose programming language. Ruby has a syntax that is easy to follow and handy for writing compared to other programming languages.
Ruby is mostly used for building web applications. Of course, it is not the only use case as Ruby is a suitable choice for web scraping. Ruby has specific libraries (gems) for web scraping, such as NokoGiri and HTTParty. These gems help developers to build perfectly functioning web scrapers.
NokoGiri is a Ruby gem that offers XML, HTML, SAX, and Reader parsers with the support of XPath or CSS3 selectors. HTTParty is intended for sending HTTP requests to the pages where required data is.
As Ruby is an eligible choice for web scraping, it has its own cons compared to Python:
Ruby is slower than Python. You should know that both of these languages are in a category of interpreted languages. It means that Ruby and Python are slower than compiled languages such as C++. However, Python has other advantages compared to C++ that makes it more suitable in terms of web scraping. To sum up, Python is still faster than Ruby, and high performance is essential in web scraping.
Ruby is not that widely used as Python. For beginners in web scraping, it could be hard to locate good documentation. Also, it could be complicated to find help when struggling with coding.
Python vs C++
C++ is a programming language for general purposes. This language is widely used to develop operating systems, video games, browsers, and other complex systems where the hardware level coding is required. It is one of the most popular programming languages worldwide.
However, if you need to choose a programming language for web scraping, Python is a better choice than C++, especially if you are a beginner in web scraping.
C++ is not the best choice for any web-related programming, as it is a static programming language. With a dynamic programming language like Python, coding for web scraping is much more comfortable.
Compared to Python, C++ programming language is hard to learn. It is a better choice to save time and learn Python for web scraping as its primary purpose is to allow developers to express concepts in fewer code lines. However, C++ is an amazing programming language. It is more worth it to learn and use C++ for coding more complex challenges, where developers are unable to do their tasks without this language.
If needed, C++ can be suitable for data extraction. Even if C++ programming language is not recommended to set up a crawler, libcurl solves this problem as developers use this library to fetch URLs. Also, as C++ is one of the compiled programming languages, it will offer you high performance and speed.
Python vs PHP
PHP is an open-source general-purpose scripting language. It is a widely-used programming language for web development.
PHP has high-quality web scraping libraries such as Goutte, cURL, HTTPful, and much more. Goutte library provides APIs to crawl websites and scrape data from the HTML and XML responses. The question what is cURL is comprehensively answered in one of our previous articles but, in short, cURL is one of the most popular libraries for making HTTP requests with PHP from web pages. HTTPful is a PHP library, and it helps to make the HTTP format more readable for developers. You can also use cURL with proxy, which you can read more about in our blog post.
Although, developers rarely choose PHP for web scraping because it is not easy to write a web crawler program. The task scheduling and other problems could be uncomfortable while using PHP language for web scraping. If you are a beginner in web scraping, we recommend you to choose Python as it is less complicated and more comfortable to learn than PHP.
Developers choose their programming language depending on their skills and tasks
Conclusion
To sum up, Python is used in many different fields, such as web development, machine learning, data science, video game development, and, most importantly, web scraping.
Python is a perfect fit for building web scrapers and extracting data as it has a large selection of libraries, and an active community to search for help if you have issues with coding. One of the most important parts why use Python for web scraping is that Python is easy to learn, clear to read, and simple to write in.
There are other programming languages for web scraping, such as Ruby, C++, PHP, and much more. All of these languages have their pros and cons in terms of web scraping. However, developers choose their programming language depending on their skills and tasks.
If you are interested in a Python web scraping tutorial, how to scrape images with Python, or concurrency vs. parallelism in Python, check out our other blog posts.
In addition, we offer a 1-week free trial for our web intelligence solutions, such as SERP scraper, so don't miss out on a chance to try them out and see whether they suit your needs.
About the author
Iveta Vistorskyte
Lead Content Manager
Iveta Vistorskyte is a Lead Content Manager at Oxylabs. Growing up as a writer and a challenge seeker, she decided to welcome herself to the tech-side, and instantly became interested in this field. When she is not at work, you'll probably find her just chillin' while listening to her favorite music or playing board games with friends.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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