Python, what is it?
An older, dynamically typed language created in 1991, Python hadn’t immediately jumped to the soaring popularity it enjoys now. Interestingly even as recently as 2017, it was still the 4th most used language, though already then, numerous Python libraries and unparalleled assistance with data analysis and scientific computing made it a competitive choice. So, what exactly does it offer?
The creators of Python themselves state that it is a high-level, interpreted language with a focus on object-oriented programming. Furthermore, Python is often outlined as a prime choice in many areas, like big data processing and data science. As a whole, Python is described as being:
A beginner-friendly language whose simplicity doesn’t take away from its usefulness.
Easily readable. Being void of any semicolons and brackets allows even inexperienced programmers to not get lost upon looking at it.
Extensible as it allows some Python code blocks to be written in other languages.
Able to run on multiple hardware platforms while using the same interface.
A high-level language. When creating programs in Python, there's no need to remember architecture.
One of the primary choices for AI and machine learning applications.
Useful for GUI. Python offers plenty of support for Graphical User Interface in the form of libraries, such as Tkinter and PyQt5.
A language full of helpful libraries. This can translate to a significant amount of time saved as you can avoid writing every single line of code yourself.
Code from different languages can be used with Python’s source code and vice versa.
Open source and free. Such a feature is immediately attractive to anyone interested as there are no monetary barriers to prevent you from trying it out.
What is Golang?
The younger of the two, created in 2009, Go, a procedural language, seemingly has a lot of catching up to do. Interestingly, developed by Google, it was built with a particular goal in mind, “to facilitate the construction of its backend services.” However, anyone who’s encountered Go before, you’ll be aware that Go’s usage has moved far beyond its initial purpose.
When deciding if Go’s the appropriate language for your projects, consider these vital features:
It’s a new language built by Google in an age of cloud computing. Created with specific, modern, and technological goals in mind.
Considered a portable language that's optimized for microservice architecture.
It can compile to a single binary, ensuring the need for virtual machines and dependencies is non-existent.
Completely open-source.
Clear and easy readability.
Has the ability to be drastically quicker than Python.
Efficient and commonly used in container technologies. Docker was written in Go and is considered among the top software containers.
Supports concurrency through goroutines.
Contains and supports powerful libraries, though the standard library is much smaller than Python’s.
Advantages of Python and Go
In order to analyze how each language can benefit you the most, a table for Python or Golang has been made below:
Python
Beginner friendly.
Incredibly versatile when compared to Go. Offers a large variety of libraries and frameworks.
Old language, which allowed for the establishment of in-depth communities and high-degree of error-solving knowledge.
Compared to other popular languages, it is rather easy to learn.
Dynamically typed
Object-oriented, has support for functional concepts.
Requires less code than Go.
Contains automated garbage collection and typing.
Open source.
Go
It is significantly faster than Python; performance is comparable to C++ and Java.
Some coding skills are transferable if you’ve used C# or C++.
Contains automated garbage collection and typing as well.
Well-scaled and highly supportive of concurrency (goroutines only take up 2kb memory).
Currently, it is the most in-demand language for employers.
Open-source.
Has excellent concurrency patterns.
Able to easily tune CPU and memory utilization with powerful profiling tools.
Disadvantages of Go vs Python
While the advantages of both programming languages strongly outnumber the weaknesses, it is still essential to discuss the limitations of Python vs Golang:
Python
Go
More time-consuming than Python, especially when boilerplate code is concerned.
Being a young language means less experience, i.e. less support when solving errors.
Python vs Go Performance/Speed
We’ve mentioned that Go beats Python in performance, but why is that the case? Once again, let’s re-iterate the point of both languages having varied purposes. Go, unlike Python, was explicitly designed with speed in mind. Thus, in most benchmarks, Go is seriously outperforming it. Interestingly, Go is so much quicker that it even reached faster results when compared to Java, a language that is widely considered to already be more rapid than Python.
To further analyze the differing performances of Go vs Python, see this stack overflow example. Within it, a program counts to 1 billion; Go’s script finishes it in less than a second, while Python takes entire minutes because it lacks the concept of natively typed variables. Python has to recognize the type of variables each time, meaning no static typing. Be advised, though, that this is true for the reference implementation of Python, which is CPython. The other implementations might not have this problem at all (pypy, jython, nuitka).
Therefore, if a developer’s requirements are performance-conscious, then Golang might be the more attractive language.
Python vs Go for Scraping
A simple test in 2021 was conducted to experiment and see what Go vs Python performance was within public web data scraping. The test scraped stock ticket prices from Yahoo. When Python was considered, the BeautifulSoup library was chosen as it allowed for parsed, structured data scraping.
With Go, Goquery with goroutines for multithreading was the pick as the optimal approach to web scraping. As the name suggests, Goquery is a library that is very similar to Jquery.
Two sets were done for the public web data scrapers, one being 2000 URLs on seven threads and the other 500 URLs on five threads. Somewhat unsurprisingly, the results were staggeringly unequal. On average, Golang’s performance was more than twice as fast when compared to Python.
It truly is rather clear that Go is significantly more suited for most public web data scraping needs if your needs are performance-based. Furthermore, such a conclusion comes as no surprise, as within the performance/speed section, it was outlined that Go is made with speedy performance in mind. For further reading, discover more about Golang web scraping here.
If you feel that building a web scraper from scratch is overwhelming, we offer an all-in-one public data gathering solution – Web Scraper API. You can try it for free and figure out if it's a suitable option for your public data collection projects.
Python vs Go for Scalability
When considering Python or Golang, scalability can be easily among the top concerns. However, similarly to speed, there is a clear victor between the two languages. Once again, like with performance, Go was built with scalability in mind.
By default, the lightness of its goroutines ensures that going from small to large scale will still prevent your memory from being overburdened.
Python vs Go for Machine Learning
Python is considered to be the top machine learning language. Machine learning (ML) requires easy, simple, but effective solutions as huge data sets are the primary use case for applying ML. Python's easy structure, simple syntax, and high-efficiency work wonders in such a case. Thus the question may arise, why should you consider Golang for ML when an already existing, popular, and effective language exists?
The answer lies in the primary advantages of Go. Like Python, it is easy to learn and simple to use. Still, unlike Python, its performance is better, handles concurrent programming well, and compiles down to a single binary, meaning, when deploying, dependency management is unnecessary. Of course, it also has limitations, yet some of them are less applicable to ML; therefore, with a handful of benefits being tailored to some ML applications, Go can be, at times, a consideration for machine learning.
Conclusion
While in some use cases, when comparing Go and Python, there is a winner, it probably becomes apparent that choosing between the two isn’t as simple as it could initially appear. Both languages were built with different strengths in mind, which is abundantly clear when we look at the above-discussed use cases. Crucially, some strengths or weaknesses may be more or less relevant depending entirely on your project requirements. Nevertheless, the critical conclusion is that while Go is unlikely to replace Python completely, it has become a serious consideration when speed, scalability, scraping, and possibly ML is concerned.
