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Rust vs Python: Choosing the Right Language for Your Data Project

Explore two popular languages in the data world: Python vs Rust. We compare syntax, performance, memory management, and much more so you can make an informed choice about which language is best suited to your project requirements.
May 2024  · 8 min read

Choosing the right programming language is an important first step for data science and data engineering projects. There are several languages to choose from, and it can be difficult to choose one. In this tutorial, we'll compare two popular languages in the data space, Rust and Python. We’ll cover how they compare on a variety of topics so you can make an informed choice about which to use for your project.

For a detailed comparison of Python vs R programming, check out our separate guide.

AI generated image of a Python and a Crab in a boxing ring - courtesy of DALL-E.

Image created using DALL-E

Python vs Rust

What are Rust and Python?

Python is an interpreted, high-level programming language known for its simplicity and readability. Created by Guido van Rossum in the late 1980s, Python emphasizes code readability and a simple, easy-to-learn syntax. These qualities make it a great choice for beginners and experienced programmers alike.

Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It has a vast ecosystem of libraries and frameworks, making it versatile for various applications, from web development and data science to automation and artificial intelligence. The documentation for Python is extensive and easy to find.

Rust is a systems programming language developed by Graydon Hoare, known for its focus on safety, speed, and concurrency. It provides memory safety without garbage collection, making it suitable for low-level systems programming while offering high-level abstractions.

Rust's syntax is influenced by C and C++, but it introduces modern features like pattern matching, algebraic data types, and ownership semantics, which help prevent common programming errors such as null pointer dereferencing and data races.

Both languages are used in the data community for different reasons. Python is the go-to for most data professionals, while Rust is more often used for cases where speed and performance need to be maximized.

Syntax and readability

Python is known for its straightforward and readable syntax, which makes it more welcoming for beginners and enables rapid development. Its simplicity and readability make it an ideal choice for projects where code clarity is paramount.

Python also has extensive libraries and frameworks, which makes it easy to efficiently tackle a wide range of tasks, from web development to scientific computing.

Here's a simple Python program that calculates π (pi) to 5 digits using the Gauss-Legendre:

import math

def calculate_pi():
    a = 1.0
    b = 1.0 / math.sqrt(2)
    t = 1.0 / 4.0
    p = 1.0

    for _ in range(5):
        a_next = (a + b) / 2.0
        b_next = math.sqrt(a * b)
        t_next = t - p * (a - a_next) ** 2
        p_next = 2.0 * p

        a = a_next
        b = b_next
        t = t_next
        p = p_next

    pi = (a + b) ** 2 / (4.0 * t)
    return round(pi, 5)

print(calculate_pi())

You can see how easy it is to follow what’s going on in this script, even if you don’t already know Python. You can see that a library is imported, and a function is created that calculates pi to 5 digits.

In contrast, Rust prioritizes safety and performance, boasting a syntax designed to enforce these principles rigorously. While Rust's syntax may initially appear more complex, it offers robust guarantees regarding code correctness, making it particularly suitable for systems programming and other performance-critical applications.

Rust's unique ownership system and compiler-enforced rules provide developers with powerful tools to manage memory safely, reducing the risk of common programming errors and enhancing overall software reliability.

Here is the same simple program that calculates π (pi) to 5 digits using the Gauss-Legendre, but this time using Rust:

fn main() {
    let mut a: f64 = 1.0;
    let mut b: f64 = 1.0 / 2.0_f64.sqrt();
    let mut t: f64 = 1.0 / 4.0;
    let mut p: f64 = 1.0;

    for _ in 0..5 {
        let a_next = (a + b) / 2.0;
        let b_next = (a * b).sqrt();
        let t_next = t - p * (a - a_next).powi(2);
        let p_next = 2.0 * p;

        a = a_next;
        b = b_next;
        t = t_next;
        p = p_next;
    }

    let pi = (a + b).powi(2) / (4.0 * t);
    println!("{:.5}", pi);
}
 

This script is more specific, if a little clunkier to read.

A big difference between this and the Python version is the declaration of variables.

In Python, we simply assign a variable a name and a value. We let Python decide what data type was most suitable for each variable.

In Rust, we specifically declare the data type (f64) and that the variable is mutable (mut) in addition to its name and value. This specificity sets Rust apart.

Performance characteristics

Python

Python, as an interpreted language, typically exhibits slower performance compared to compiled languages like Rust. This characteristic comes from Python's dynamic nature, where code is executed line by line by the interpreter, creating a higher overhead.

You can imagine this like someone translating a book while reading it to you. It will take you longer to get through the book, because each sentence needs to be translated first.

While Python excels in readability and ease of use, this performance limit can be a limiting factor when your project requires high computational efficiency or low latency.

Rust

Rust is a compiled language, which contributes to its superior performance. By compiling code directly to machine code (the language of computers) before execution, Rust minimizes runtime and can optimize performance more effectively. To follow our book example above this would be like getting a copy of the same book in your native language instead of translating it in real time as you read it.

This makes Rust well-suited for performance-critical tasks, such as systems programming, game development, and other applications where speed is important.

Data typing

Python

Another aspect influencing performance is the difference in their approach to data typing. Python's dynamic typing allows for flexibility in variable types, as types are inferred at runtime. This makes it easier to code in Python since it's one less thing you need to manually define.

But this flexibility comes at the cost of performance because of the need for runtime type checks and conversions. This dynamic typing can also sometimes cause problems if Python chooses a data type you don't expect. Fortunately, this latter problem can be solved through type conversions, like int() and float().

Rust

Rust uses static typing which requires explicit type declarations and enforces type safety at compile time. This means that for every variable you create, you will need to define it’s data type. While this is a bit more work for the coder, it eliminates the need for runtime type checks and enables more efficient code generation.

This static typing approach not only enhances code safety but also contributes to Rust's performance advantages, particularly in scenarios where efficient memory management and low-level optimizations are critical. This makes Rust useful for performance-sensitive applications.

Memory management

Python

Python relies on garbage collection for memory management, a process that automatically deallocates memory that is no longer in use. While garbage collection simplifies memory management for us coders, it can introduce occasional performance overhead, as the interpreter periodically pauses the program execution to reclaim memory.

This overhead may be negligible for many applications but can become noticeable in performance-sensitive scenarios or when handling large datasets.

Imagine Python's memory management as a janitorial service in a bustling office building.

The janitors (garbage collectors) roam around, periodically checking each office (memory space) for unused items (unused memory). When they find an empty office, they promptly clear it out to make space for new occupants.

While this system keeps the building tidy, the occasional pauses due to janitorial activity can disrupt the workflow of busy employees (program execution), especially during peak hours (performance-sensitive scenarios).

Rust

Rust employs a distinctive ownership system that eliminates the need for garbage collection while ensuring memory safety. By tracking ownership and borrowing rules when a program is compiled, Rust guarantees memory safety without those runtime performance penalties.

This innovative approach to memory management allows Rust programs to achieve efficient memory use and predictable performance, making it well-suited for applications where resource efficiency and performance are critical.

Rust's ownership system works less like a janitorial service and more like a well-organized warehouse. Each item (memory allocation) is tagged with a label indicating its owner (the variable), and strict rules govern how items can be borrowed or transferred between owners.

This meticulous approach ensures that every item is accounted for and properly managed, eliminating the need for “janitors” to roam the premises. As a result, the warehouse operates smoothly and efficiently, with no interruptions to its daily operations, making it ideal for handling critical applications and large datasets.

Concurrency support

Python

Concurrency is the ability of a program to execute multiple tasks simultaneously.

It's like having several chefs working in a kitchen, each handling different ingredients and cooking tasks at the same time. This is a valuable time-saving tool, especially when working on large datasets, for example when you are analyzing videos frame-by-frame.

Python's concurrency support is limited by the presence of the Global Interpreter Lock (GIL), a mechanism that prevents multiple native threads from executing Python bytecode simultaneously.

While Python offers multiprocessing as an alternative for achieving parallelism, threading remains restricted by the GIL, which can hinder performance in multi-threaded applications.

Despite this limitation, Python's extensive ecosystem of libraries and frameworks provides solutions for various concurrency scenarios, allowing developers to leverage asynchronous programming techniques to improve responsiveness in applications.

If you are interested in concurrent execution in Python, I highly recommend reading this tutorial on the Global Interpreter Lock first. It will give you a conceptual understanding of the GIL in Python.

Rust

Rust provides native support for concurrency and parallelism without the constraints imposed by Python's GIL.

Rust's concurrency model ensures thread safety and prevents data races, allowing you to write concurrent code with confidence. This native support for concurrency makes Rust well-suited for applications where parallel execution is essential, such as high-performance computing or distributed systems.

In general, Rust is better and faster at concurrency than Python.

Ecosystem and libraries

The network of resources, tools, libraries, frameworks, and communities associated with a programming language greatly enhance the programming experience. They offer pre-built solutions, frameworks, and modules that streamline development processes and solve common problems across various domains.

Access to robust ecosystems and libraries enables you to leverage existing code, accelerate development cycles, and focus on solving specific challenges rather than reinventing the wheel. So let’s compare the ecosystem and libraries available to Python and Rust coders.

Python

Python's ecosystem offers an extensive collection of libraries and frameworks tailored to various domains, including data analysis, machine learning, web development, and scientific computing.

With libraries like NumPy, Pandas, TensorFlow, and Django, Python offers robust solutions for a wide range of tasks. This rich ecosystem makes Python useful across diverse industries, making it a go-to language for rapid prototyping, research, and production-grade applications.

You can learn more about the top Python libraries in a separate article.

Rust

On the other hand, while Rust's ecosystem continues to grow, it does not yet rival Python's breadth, particularly in domains such as data analysis and machine learning.

However, Rust does boast libraries like Tokio for asynchronous I/O, Rocket for web development, Serde for data serialization, and many more. The Rust ecosystem fosters a community focused on building high-performance, reliable software, making it a compelling choice for projects requiring both speed and safety.

Python vs Rust comparison table

For ease of reference, we’ve compiled the information above into a handy reference table, showing a comparison of Rust vs Python:

Feature

Python

Rust

Syntax and Readability

Straightforward and readable syntax, welcoming for beginners, enables rapid development. Syntax demonstrates simplicity and ease of understanding.

Syntax designed to enforce safety and performance, initially more complex. Offers robust guarantees regarding code correctness. Syntax highlights specificity and data type declaration.

Performance Characteristics

Interpreted language, typically exhibits slower performance due to higher overhead.

Compiled language, superior performance, minimizes runtime, optimizes performance effectively.

Data Typing

Dynamic typing allows flexibility but incurs runtime type checks and conversions.

Static typing requires explicit type declarations, enhances code safety, eliminates runtime type checks.

Memory Management

Relies on garbage collection, simplifies memory management, but introduces occasional performance overhead.

Employs an ownership system, eliminates the need for garbage collection, ensures memory safety without runtime performance penalties.

Concurrency Support

Limited by the Global Interpreter Lock (GIL), offers multiprocessing for parallelism, extensive ecosystem provides solutions for concurrency scenarios.

Native support for concurrency and parallelism without GIL constraints, ensures thread safety, prevents data races.

Ecosystem and Libraries

Extensive collection of libraries and frameworks for various domains like data analysis, machine learning, web development, and scientific computing.

Growing ecosystem, strong in domains requiring performance and safety, notable libraries for asynchronous I/O, web development, and data serialization.

Rust vs Python: Use Cases and Applications

Python's versatility shines through its broad spectrum of applications, making it a favorite among developers in various fields.

For data analysis and visualization, Python offers powerful analysis and visualization tools, allowing analysts and researchers to work with large datasets efficiently.

Python offers strong machine learning and artificial intelligence frameworks that allow you to build sophisticated models and applications. It is also widely used for web scraping, data extraction and the creation of scripts to automate mundane tasks, ranging from file manipulation to system administration. This broad range of Python uses makes it a useful starting point in most, high level applications.

Rust's strengths lie in its performance and safety, making it most suitable for systems programming and performance-critical applications. Its focus on memory safety and zero-cost abstractions makes it an excellent choice for building low-level systems where performance and reliability are paramount.

The speed and memory safety of Rust makes it well-suited for tasks requiring high-performance algorithms and data processing, appealing to developers tackling computationally intensive tasks.

Rust's minimal runtime and low-level control make it a compelling option for embedded systems and IoT applications, where resource efficiency and reliability are crucial. Similarly, its concurrency features and performance optimizations make it a great option for building high-performance backend services.

Below is a summary of the use cases of the two programming languages:

Python

Rust

Web Scraping

Systems Programming

Data Analysis and Visualization

Network Services Development

Machine Learning Model Development

Embedded Systems Development

Integration and Interoperability

With Python’s ease of use and Rust’s performance value, you may wonder if you can leverage the two of them together for your project. Well, you’re in luck!

Python and Rust integrate well with their support for foreign function interfaces and library bindings. These interoperability features empower you to leverage the strengths of both languages in the same project so you can build efficient, reliable software systems that capitalize on the unique capabilities of each language.

To get the best of both worlds, focus on using Rust for performance-critical components, such as computationally intensive algorithms or low-level system interactions, and use Python for higher-level logic, such as business logic, data processing pipelines, or interface design.

By strategically integrating Rust and Python, you can create hybrid applications that deliver optimal performance, maintainability, and scalability, catering to a wide range of use cases and requirements.

Another way is to integrate premade libraries into your Python code that are written in Rust. You may have used a Python library that was written in Rust and not even known it!

For example, Polars is a popular DataFrame library that has been gaining traction in the data science community in recent years. One of the reasons why Polars has garnered so much attention is its performance capabilities. This is because it’s written in Rust!

Learn more about polars with this introduction to polars tutorial or this comparison between polars and pandas.

Preferred IDEs

Though both Python and Rust can be used directly from the terminal, many programmers prefer to use an IDE. So, which IDEs support Python and Rust?

When it comes to Python development, especially for data scientists and engineers, several local IDEs stand out for their robust features and extensive plugin ecosystems.

One popular choice is PyCharm, which offers powerful code analysis, debugging tools, and integration with popular data science libraries. Other popular IDEs include Spyder and VS Code.

For Rust development, IDE support has been steadily improving, with several options offering features for data-related projects.

VS Code stands out as a versatile and extensible IDE with strong support for Rust through plugins like Rust Analyzer and Rust Language Server.

Since VS Code also supports Python, you could easily use one IDE for both languages! Another option worth considering is IntelliJ IDEA with the Rust plugin, which is another IDE from JetBrains', similar to Pycharm.

There are also online playgrounds where you can try out code with minimal investment. Try out Kaggle for Python or Rust Playground for Rust.

DataCamp’s DataLab also supports several languages, such as Python, SQL, and R. Learn more about DataLab’s launch and key features!

Personally, I prefer to use online playgrounds for quick, one-off, proof-of-concept scripts. When I want to create something more permanent, I tend to prefer a local IDE.

Rust vs Python: Which Should You Choose?

So, at the end of the day, which programming language should you choose for your project? Well, the answer depends on your project requirements, development goals, and familiarity with each language.

Here are a few things to help you decide:

Consider your project’s requirements

Performance-critical tasks

If your project involves performance-critical tasks such as systems programming, low-level optimization, or high-performance backend services, Rust is likely the better choice due to its compiled nature, strong performance characteristics, and memory safety guarantees.

Rapid prototyping and data analysis

For tasks that prioritize rapid prototyping, data analysis, or machine learning, Python shines with its simple syntax, extensive libraries, and vibrant ecosystem. Python's ease of use and versatility make it well-suited for exploratory data analysis, building machine learning models, and developing web applications, particularly if you are already well-versed in the language.

Concurrency and parallelism

When concurrency and parallelism are crucial for your project, Rust's native support for concurrency without the limitations of the Global Interpreter Lock (GIL) in Python makes it a compelling option. Rust's ownership model ensures thread safety and prevents data races, allowing for safe concurrent execution without sacrificing performance.

Resource efficiency and safety

If your project demands efficient memory management and strong safety guarantees, Rust's ownership system offers a compelling advantage. By eliminating the need for garbage collection and ensuring memory safety at compile time, Rust minimizes runtime overhead and reduces the risk of memory-related errors.

Learning curve

If you don’t already know the languages, consider the learning curve associated with each. Rust usually has a steeper learning curve due to its emphasis on memory safety and ownership. If you already know C or C++, however, learning Rust won’t be so jarring. Meanwhile, Python's simplicity and readability, as well as the abundant learning resources available, can mean a gentler learning curve, especially for beginners.

What if I only want to learn one?

If you're new to the data space and looking to learn a programming language to get started, I recommend learning Python. Its simplicity, readability, and extensive ecosystem make it an excellent entry point for beginners.

Python's popularity in the data science and machine learning communities also means abundant learning resources, tutorials, and support are readily available. But it’s not just an easy language to learn; it’s also a powerhouse of functionality, which makes the time you spend learning it a good investment.

There’s a lot that Python can do, and learning that language first is an excellent choice.

However, if you have the time and inclination to explore deeper into systems programming, performance optimization, or building highly concurrent applications, learning Rust can be a valuable addition to your skill set.

Rust's emphasis on safety, performance, and control opens up opportunities to work on projects where reliability and efficiency are paramount.

Feature

Rust

Python

Syntax and Readability

Features a syntax that emphasizes safety and performance, with a steeper learning curve.

Known for its simple and readable syntax, making it beginner-friendly and conducive to rapid development.

Performance Characteristics

Compiled language offering high performance.

Interpreted language, generally slower than compiled languages.

Memory Management

Employs a unique ownership system that ensures memory safety without the need for garbage collection, leading to efficient memory management.

Utilizes garbage collection for memory management, which can lead to occasional performance overhead.

Concurrency Support

Offers robust support for concurrency and parallelism, allowing safe concurrent execution without the need for a GIL.

Limited concurrency support due to the Global Interpreter Lock, which restricts multi-threading.

Ecosystem and Libraries

While growing, Rust's ecosystem for data-related tasks is not as mature as Python's.

Boasts a vast ecosystem of libraries and frameworks for data analysis, machine learning, web scraping, and more.

Where Can I Learn Python and Rust?

For aspiring learners and enthusiasts looking to delve into Python and Rust, a variety of online platforms offer comprehensive resources and interactive environments for experimentation.

DataCamp offers a variety of curated courses and interactive coding exercises to help you learn Python. Check out DataCamp’s Python Programming track or this guide to learn Python like a pro.

I also highly recommend this software engineering in Python course, which not only teaches you how to use Python, but also covers more thoroughly several of the topics we’ve discussed here.

If you are interested in learning Rust, I recommend learning directly from the source: the Rust website. They offer several methods of learning, including a book, courses, code examples, and documentation to help you jumpstart your Rust journey. You can also ask questions of the Rust community to help guide your learning.

Of course the best learning comes from practice. Try your hand at simple scripts in an online IDE or work on contributing to open source projects to level up your skills.

Conclusion

Rust and Python both have their strengths and are suitable for different parts of data and development projects. Python excels in rapid development, data analysis, and machine learning, while Rust offers performance, safety, and control, making it ideal for systems programming and performance-critical applications. Experimenting with both languages can help you make informed decisions about which to use based on your project requirements and constraints.

Ready to get started learning these languages? Install Python or Rust and get started!

If you’re looking to further your learning, check out Data Management Concepts or Introduction to Data Security to level up your coding skills. And check out this article on Mojo, a language that seeks to combine the best of Python and Rust into one language.


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Author
Amberle McKee

I am a PhD with 13 years of experience working with data in a biological research environment. I create software in several programming languages including Python, MATLAB, and R. I am passionate about sharing my love of learning with the world.

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