Which programming language should you choose for your next business project?

In many situations, the right answer would be Python. Python is a hyper-popular programming language that can be used to build anything from a calculator to a robust website backend. With that said, Python can be lackluster for some applications.

Here’s where Rust comes in. Rust is obviously not the only alternative to Python, but it possesses certain features that make it highly preferable in certain environments.

Rust is a relatively young programming language. Rust has won over developers, researchers, and hobbyists with its emphasis on performance, efficiency, and security. Rust’s adoption is still in its infancy, but the language promises to become the prime tool for speed- and safety-oriented projects.

So, in the context of the team’s needs, would Python be able to satisfy the requirements? Or should you instead consider incorporating Rust in the team’s IT workflows?

To answer these questions, we need to better understand what makes these programming languages unique!

If you happen to be looking for a programming language for a data science project, read our guide detailing the most popular statistical languages. This guide will help you come up with a structured approach to choosing a programming language, and it will also introduce you to the pros and cons of several languages, including Python, R, and MATLAB.

What Is Python?

Python in a nutshell:

  • Possesses hundreds of thousands of libraries.
  • Retains a simple and easily readable syntax.
  • Demonstrates a very beginner-friendly environment.
  • Exhibits flexibility and can be used in virtually any situation.
  • Reveals its popularity in machine learning, deep learning, data science, and scientific computing.
  • Displays slow performance (mitigated in packages that are implemented in fast programming languages).

Python is a general-purpose programming language that emphasizes simplicity and clarity in coding. According to the Stack Overflow 2021 Developer Survey, Python is the third most popular programming language out there, with 48.24% of respondents using it extensively in their projects.

The straightforward syntax of Python has made it the go-to language for beginner programmers. What’s more, Python’s ecosystem contains hundreds of thousands of libraries (packages) for virtually any need. These libraries include modules for web development, game development, GUI (graphical user interface) building, web scraping, scientific computing, and artificial intelligence.

The Python programming language is an especially strong player in the industries of machine learning and deep learning. Existing off-the-shelf packages hide away the math-intensive implementation details of AI algorithms, allowing anyone to build and use complex models to enhance their business operations.

What Is Rust?

Rust in a nutshell:

  • Demonstrates excellent performance (on par with C and C++).
  • Heavily emphasizes memory safety.
  • Establishes strict rules to make code secure, safe, and bug-free.
  • Has tens of thousands of libraries.
  • Relatively difficult to learn due to the C++-like syntax and unique coding rules.

Released in 2010, Rust is still in the early stages of adoption and is no match to Python when it comes to popularity. Only about 7.03% of developers use Rust, according to the Stack Overflow 2021 Developer Survey.

But despite its low market share, Rust is the most loved programming language of them all, with 86.98% of developers wishing to continue using it in their projects. Moreover, Rust has maintained this status for six years in a row!

The reason for the admiration is simple – Rust, albeit difficult to adopt due to its syntax and programming rules, has brilliant documentation, excellent performance, and makes developers more conscious about their code’s quality and safety. The end product of Rust’s business value is code that executes quickly, is secure, and has a minimum number of bugs.

As a general-purpose programming language, Rust – like Python – can again serve as the foundation for any project. It has libraries for mathematical computations, web development, game development, and text processing, among other things.

Additionally, Rust has recently found increased application in research. More and more researchers are choosing to program their scientific projects in Rust – primarily due to the language’s performance and efficiency.

Rust vs Python – Comparison of the Languages’ Key Features

Now that we have a clear idea of the features and purpose of Rust and Python, we can stack them up against each other. Below, we’ll compare the languages from the standpoints of their:

  • Security.
  • Performance.
  • Ecosystems.
  • Ease of use.
  • Documentation and community.

Security

Rust is stronger than Python in the area of memory safety and secure programming. Python is no slouch either, but Rust, unlike Python, implements a set of strict rules to keep developers from crafting unsafe code.

Rust is engineered with memory safety and security in mind. The standard implementation of Rust is even called Safe Rust which clearly showcases the intentions of Rust’s creators with the language.

The safety of Rust is based on several unique features – including ownership and the borrow checker – whose function is to essentially make sure that variables don’t escape the scope they are intended to be used in. This helps secure data and prevents memory leaks and bugs.

Unlike Python, Rust does not have garbage collection. Instead, Rust performs memory management via its safety features. Additionally, Rust doesn’t compile code it deems unsafe – whenever its best practices are violated, the language throws an error message describing what went wrong.

This “handholding” can make Rust seem rather clunky to developers who are used to more traditional programming logic. However, once mastered, Rust offers a secure coding experience with few to no bugs.

This is not to say that Python is unsafe. As a high-level language, Python handles memory allocation and deallocation behind the scenes, leaving few opportunities for introducing vulnerabilities to code or triggering memory leaks. However, Python doesn’t have an equivalent to Rust’s strict coding ruleset and thus requires more care from users in terms of security.

Performance

Rust is significantly faster and more efficient than Python. However, many Python packages are actually written in other, faster programming languages, which may somewhat compensate for the performance differences in some applications.

Performance is another important “selling point” of the Rust programming language. Rust offers performance comparable to that of C and C++. In fact, in some applications, Rust often performs computations faster than C/C++!

Python, on the other hand, is quite notorious for its sluggishness. As an interpreted programming language, Python is one of the slower programming languages in the industry, and it’s lightyears behind Rust in terms of performance. Rust often achieves tenfold faster execution than Python!

With all that said, Python’s performance heavily depends on which library package is used. Take matrix calculations, for example –standard Python lists appear to be horribly inefficient at them. However, if the team were to use statistical libraries such as SciPy or NumPy instead, substantially better performance could be achieved. That’s because these Python packages (and many others) are implemented in faster low-level languages, which allows users to enjoy good-to-better performance while remaining in Python’s intuitive environment.

In the end, Rust will likely outperform Python in most situations, but Python can also be a viable choice when speed is important – it all depends on the applications and the library packages used.

Ecosystem

Python’s ecosystem is richer, offering packages for a wide range of applications such as machine learning/deep learning, data science, statistics, scientific computing, data visualization, and web development.

Python possesses a wealth of library packages intended for use in an extensive array of industries ranging from artificial intelligence to app building. PyPi, Python’s package index, contained over 300,000 packages as of September 2021, while Crates.io (Rust’s package registry) had about 67,000 packages (called “crates” in Rust-speak).

Python is particularly prominent in machine learning/deep learning thanks to popular packages such as TensorFlow (developed by Google), Keras an API built on top of TensorFlow to facilitate its use), PyTorch (developed by Facebook), and scikit-learn. These libraries are unaffected by Python’s low performance because their backends aren’t written exclusively in Python – many of them are implemented in C++, C, or CUDA (an API that allows developers to use NVIDIA graphics processing units for general-purpose computing).

Rust’s packages also enable the language to be used virtually for anything and everything, but it’s weaker than Python in certain areas. Python is arguably unparalleled in AI and is strong in data science & statistics, and its immense treasury of packages can help organizations build the basis for any project.

That said if we consider the specific benefits of Rust – namely, memory safety and excellent performance – the smaller ecosystem may not seem problematic. When speed is a concern, it may be worth it to code in Rust, even if this requires some learning and implementing algorithms from scratch.

Intuitiveness and ease of use

Python is a more beginner-friendly programming language and is a far better choice for those new to programming (as long as we don’t consider the differences in the use cases of Python and Rust).

Python’s syntax is clean and simple, which significantly lessens the language’s learning curve.

Most notably, Python requires minimal punctuation (e.g., semicolons are not required to end statements) and doesn’t require users to specify the type of variables when declaring them. The language also makes heavy use of indentation to determine code execution order and improve readability.

Combined, these features lower the entry barrier to Python, which is one of the reasons for the language’s insane popularity. However, the reliance on indentation can make one’s programming experience in Python frustrating – one extra space in the code can cause errors that may be difficult to fix.

Rust, in contrast, has a syntax that resembles C and C++. Due to this, Rust will be familiar to many people, and those who despise Python’s design choices may favor Rust’s syntax as well.

But no matter what you think about Python’s design philosophy, Rust is most certainly more difficult to learn. Not only is this because of the syntax, but the memory-safe programming rules of Rust will likely make beginners scratch their heads in confusion very often.

One thing to keep in mind here – Rust’s error messages are actually more useful than Python’s in some cases. As an example, if you mistype a variable name in Rust, the compiler will throw an exception (as expected) and will hint that a variable with a name similar to what has been typed exists. In Python, you get a generic error that just tells you that a variable name is not defined.

But overall, Python is easier to get started with, and many of you will likely find Python code more visually pleasing.

Documentation and community

Both Rust and Python have excellent documentation and thriving communities, but there are more resources available for Python due to its popularity and adoption.

Neither of the languages lacks in the availability of educational resources. Python and Rust have well-developed, in-depth standard documentation, and their [communities ](Rust Communities “Rust Communities”)are extremely friendly to beginners. No matter which language you select for your project, you won’t experience a shortage of information, though Python is, all-in-all, better covered.

But considering how much smaller Rust is than Python, the Rust team’s monumental efforts in documenting their language deserve praise. Rust’s documentation is neatly and logically organized into sections covering aspects of Rust-like, its package manager Cargo, the basic functionality of the language, and its foundational features and concepts. The Rust Compiler Error Index is a particularly helpful resource that contains Rust’s error codes along with detailed explanations and examples of bad and working codes. The Error Index is interactive as well, with users being able to run, edit, and debug the presented code in their web browsers.

So, although Rust’s market penetration is no match to that of Python, its outstanding documentation makes the onboarding process quite smooth. Nonetheless, keep in mind that getting to grips with Rust may take much longer than with Python.

When To Use Rust? When To Use Python?

The Rust vs Python clash shows no declared winner – each language has distinct advantages that make it suitable for specific applications. To summarize the strengths and weaknesses of Rust and Python, some of the best use cases for each language are outlined in the following table.

Use Rust If:Use Python If:
The team is working with large datasets and need high performanceThe team is building a project for AI, statistics, or data science purposes
The team is building a polished app with few to no bugsThe team needs to deploy a working product fast
The team requires an extra degree of securityThe team values code readability and simplicity

In summary, this table suggests:

  1. Rust is a great fit for projects where performance and memory safety are top priorities. It’s also a wonderful choice if you need to build a complex product with very few bugs.
  2. Python is a great fit for projects aimed at solving AI, data science, or statistics problems. Its clean and intuitive syntax allows those with limited experience in programming to get a product up and running in-a-minute timeframe.

Next Steps

When trying to pick a programming language for your project, don’t get caught up in the eternal debates about the “best language in the world.” Programming languages are just tools intended to help us achieve specific goals, and no universal language can meet any and all of our needs.

Choosing a programming language comes down to understanding your project’s requirements and making sure that your toolset will be able to support you on your way to your goals. Be flexible, don’t fixate on a programming language only because it’s likable or popular, and ensure that you have a versatile arsenal of instruments (whether programmatic or not) to achieve your objectives!

If your team members are interested in a quick taste of either language, take a look at these free, self-paced introductions to Python or Rust.

References:

Curcio, K., Navarro, T., Malucelli, A., & Reinehr, S. (2018). Requirements engineering: A systematic mapping study in agile software development. Journal of Systems and Software, 139, 32-50.

Kasauli, R., Liebel, G., Knauss, E., Gopakumar, S., & Kanagwa, B. (2017, September). Requirements engineering challenges in large-scale agile system development. In 2017 IEEE 25th International Requirements Engineering Conference (RE) (pp. 352-361). IEEE.

Vogelsang, A., & Borg, M. (2019, September). Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (pp. 245-251). IEEE.

Zamudio, L., Aguilar, J. A., Tripp, C., & Misra, S. (2017, July). A requirements engineering techniques review in agile software development methods. In International Conference on Computational Science and Its Applications (pp. 683-698). Springer, Cham.