MATLAB vs. Python: Comparing Speed and Syntax

Written by Coursera Staff • Updated on

Explore the speed and syntax nuances in the MATLAB versus Python debate with this comprehensive guide for making informed choices in scientific computing.

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MATLAB is a computing platform with its own programming language, whereas Python is a general-purpose programming language with frameworks and libraries to extend its functionality. You’ll find similar applications for both and commonly compared differences between the two, such as speed and syntax.

When comparing MATLAB vs. Python, it’s helpful to remember that both MATLAB and Python have their strengths and weaknesses, and the decision of which to choose likely depends on the actual needs and requirements of your project, individual preferences, and the type of tasks you’re working with.

MATLAB vs. Python: understanding the basics

To understand a little more about the overall landscape and basics of each, let's compare how each environment works, some common applications for both, and key features and components that extend functionality and use. 

MATLAB overview 

MATLAB, which is short for matrix laboratory, is a technical computing programming language and environment designed for tasks such as numerical computing, simulations, engineering, data analysis, and visualization.

Developed by mathematician Cleve Molar, who co-founded MathWorks, MATLAB is commonly used in academia, engineering, and industry for signal processing, image processing, control systems, robotics, and much more. It is popular for these tasks as it features built-in functions and toolboxes tailored for different scientific and engineering applications.

Python overview 

Python is a high-level programming and scripting language with an active and growing community. Guido van Rossum developed Python in 1990 as an object-oriented, dynamic, cross-platform programming language that runs on most major hardware platforms and operating systems. The language has maintained its popularity and reputation as a versatile and easy-to-use language. As a result, Python has gained popularity in various fields, including web development, machine learning, AI, and scientific computing.

Python is best known for its extensive libraries and frameworks extending its functionality, making it an ideal option for a wider array of applications beyond scientific computing. Due to its versatility, ease of use, and appeal to a wider range of users, Python is a great option for tasks like science, data analysis, and machine learning.

MATLAB vs. Python speed

When considering speed, the choice between MATLAB and Python depends on the requirements of your task and factors like implementation and optimization strategies.

Comparing MATLAB versus Python based on speed isn’t always a comparison of which is faster all around but which is faster for certain applications. Some tasks, such as complex numerical computations, are best suited for MATLAB, while tasks like program development might be more efficient and faster to achieve in Python. 

MATLAB's high-speed numerical computation 

MathWorks developed MATLAB for numerical computing and scientific programming, implementing many optimizations like add-on programs and specialized toolboxes to extend its functionality and performance. In MATLAB, operations on matrices and arrays are efficient, making it an excellent choice for tasks that deal with heavy numerical computation. For example, it’s faster to develop algorithms with MATLAB than in other traditional languages since you don’t have to take steps such as compiling code, declaring variables, or allocating memory.  Engineers and scientists working with large datasets or complex mathematical models tend to appreciate MATLAB's speed in tasks like crunching numbers.

MATLAB's specialized libraries can offer a performance advantage for computationally intensive tasks that rely heavily on matrix operations. For example, MATLAB is a matrix-based language operating on whole matrices and arrays; other programming languages work with one number at a time. 

Python's fast and efficient program development 

Because Python is an object-oriented language, program development in Python is faster than other high-level programming languages and more streamlined when working across multiple platforms or with different languages; however, due to its interpreted nature, it is slower when it comes to larger, more complex tasks.

Helpful libraries and frameworks have helped boost Python's numerical performance. Libraries like NumPy and SciPy implement functionalities in C and Fortran, making it easier to work with mathematical-based tasks that require crunching numbers. Python frameworks like TensorFlow and PyTorch are other examples of ways to work in Python with other numerical-based tasks. These frameworks shorten machine and deep learning tasks that may otherwise take longer. And the use of just-in-time (JIT) compilation through libraries like Numba and Just-In-Time Compilation (PyPy) helps to improve Python's execution speed.

MATLAB vs. Python syntax

Python treats everything as an object, while MATLAB treats everything as an array. The fundamental technical difference influences many other aspects of syntactic variations between the two.

For example, MATLAB's syntax is ideal for mathematical modeling and linear algebra, offering a clear and concise representation of mathematical concepts. Comparatively, Python provides a general or all-purpose approach that’s more suitable for a broader range of applications with its readability, versatility, and extensive ecosystem. 

MATLAB's clean and intuitive syntax

MATLAB has a simple and intuitive syntax for data analysis and numerical computing. One reason is that it uses a scripting language that resembles mathematical notation. The built-in functions, add-on programs, and applications provide additional syntax support. The code structure revolves around matrices, and its graphical and command-line interfaces make MATLAB quite versatile for mathematical, scientific, and engineering applications.

MATLAB's simple syntax is ideal for quick development and creating prototypes. MATLAB is a popular option in academic and research work because it allows you to easily write short and clear instructions for things like multiplying matrices and creating plots. It’s also popular in industries like AI, as it’s often used to achieve accurate computations and a range of development tools for AI development. 

Python's versatility and readability

Python, known for its readability and versatility, adopts a more general-purpose syntax that extends beyond scientific computing. The language's ease of readability comes from its use of indentation for code blocks, creating a cleaner, more expressive syntax that closely resembles how people read text. Designed as beginner-friendly, Python’s syntax presents a straightforward, clear coding style.

Python's syntax can extend to include automation, web development, and other domains. This versatility in syntax has contributed to its widespread use across different types of industries. While Python may not look exactly like math symbols like MATLAB does, its way of writing code creates a balance between being easy to understand and allowing for expressive coding instructions.

Comparing integration and interoperability

MATLAB's integration with Simulink and specialized toolboxes makes it an ideal choice for certain engineering applications. On the other hand, Python's vast ecosystem and interoperability work well with a broader range of applications and more collaborative-based tasks and projects.

MATLAB: integration with Simulink and toolboxes

MATLAB features an integrated, graphical modeling environment that supports interoperability with other programming languages through its add-on products and specialized toolboxes. Similar to libraries and frameworks in Python, MATLAB features Simulink and a collection of specialized toolboxes to extend its functionality and offer additional tools designed for specific domains.


MATLAB integrates with the add-on product Simulink, a block diagram graphical modeling environment for simulating multidomain physical systems. This integration allows you to use MATLAB algorithms in models and export simulation results to MATLAB for additional analysis. Simulink has its own set of customizable block libraries, a graphical editor, and solvers for modeling and simulating dynamic systems. As a result, Simulink is especially valuable in engineering disciplines for creating simulations and system-level modeling.

To extend the functionality of Simulink, MATLAB offers additional products or toolboxes. Some of these products and/or toolboxes include:

  • Simulink Coder is a toolbox that works within Simulink to generate C and C++ code from Simulink models. You can use the generated code in embedded systems or integrate code into existing external applications.

  • Simulink Compiler allows users to create standalone executable applications from Simulink models. It can also convert Simulink models into standalone executables, functional mockup units (FMUs), or web apps.

  • Simulink Control Design is a tool for designing, analyzing, and testing control systems modeled in Simulink. It provides the tools necessary to automatically adjust different types of control systems, such as PID controllers, to adapt to changes or disturbances in the system.

  • SimEvents adds discrete-event simulation capabilities to Simulink, allowing users to model and simulate events in systems with scheduling, queuing, and route modeling.

  • Simscape enables the efficient physical modeling and simulation of multidomain systems and is used for modeling hydraulic actuators, electric motors, and other physical systems within the Simulink environment.


A major advantage of MATLAB is toolboxes, which feature specialized collections of functions and programs called M-files. You can use these M-files, generated in MATLAB code, to address specific class problems. In other words, these toolboxes provide pre-built functions and algorithms tailored to specific domains, enhancing productivity for engineers and scientists working in those areas. MATLAB’s specialized toolboxes and extensions allow for various applications, including image processing, control systems, signal processing, and much more. Some MATLAB toolboxes include the following:

  • The Signal Processing Toolbox adds various functions and tools specifically designed for analyzing, preprocessing, and manipulating uniform and nonuniform signals, making it easier to visualize trends and results. It is ideal for AI model training and more.

  • The Optimization Toolbox helps users more efficiently solve quadratic, conic, linear, integer, and nonlinear problems. The toolbox provides a variety of algorithms and tools for solving optimization issues in different domains, which extends the capability of the MATLAB numeric computing environment.

  • The Control System Toolbox provides functions for control engineering, a branch of engineering that controls various systems. The toolbox allows for systematic analysis, design, and tuning of linear control of systems.

  • The Fuzzy Logic Toolbox handles “fuzzy” concepts that involve uncertain or imprecise information, such as human reasoning and linguistic variables (computing with words) rather than numbers. It makes it easier to work with real-world problems and tasks involving a lot of variability and ambiguity. The Toolbox finds applications in various fields, from consumer products to industrial process control and medical instrumentation.

Through Simulink and various toolboxes in MATLAB, you can leverage existing code or libraries written in different languages within MATLAB, which enhances its versatility and integration capabilities. These features make it possible for different programs to work together and share and reuse code across various language environments. You can call other high-level programming languages, including Python, from MATLAB with the MATLAB Compiler SDK. It’s important to mention that, unlike Python, you must purchase add-on packages like this one and ones like it.

Python: broad ecosystem and interoperability through libraries and frameworks

A major perk of Python is its vast ecosystem of various libraries and frameworks, which enhance its functionality, versatility, and interoperability.

In Python, many different libraries and frameworks extend the functionality and range of tasks possible in the Python ecosystem. These libraries and frameworks provide you with special tools, structures, and functions that help streamline the development process and enhance the capabilities of applications you build and design in Python. For example:

  • For scientific computing and engineering capabilities, NumPy (Numerical Python), SciPy (Scientific Python), pandas, and Matplotlib are fundamental libraries in Python ideal for numerical computing and data analysis.

  • When working with machine learning and deep learning tasks, you can rely on libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn.

  • Python's versatility also extends beyond scientific domains to web development, with popular frameworks such as Django, Flask, and web2py. 

The interoperability of Python with other languages is another advantage for users. Python can seamlessly integrate with C, C++, Fortran, and other higher-level programming languages, allowing you to access and use existing libraries and frameworks written in those languages without modifying your code. Python’s interoperability is especially helpful when working with specialized libraries or legacy code that might not have Python equivalents.

Differences in community and support 

MATLAB's extensive use in academia and industry is ideal for users who need specialized support in engineering and scientific disciplines. However, it is proprietary, which means all code is owned and licensed, and users can’t build or add to its functionality. Python is not. Python is open-source and free, which means you can create, build, and add to the language. As a result, Python's active and growing open-source community is ideal for someone who desires a diverse and collaborative environment with a bit more freedom.

MATLAB: A strong presence in academia and beyond

MATLAB is rooted and widely used in academia but extends beyond to include applications in various industries such as physics, engineering, and finance. Many universities and research institutions use MATLAB as a tool for teaching and research due to its ease of use and built-in functions. Outside of education and academia, engineers and scientists often use MATLAB on projects that involve analyzing data, mathematical modeling, app building, developing algorithms, and more.

Although it lacks some of the freedoms of Python when it comes to collaborating and creating code, MATLAB provides support to users through forums, blogs, an AI chat playground, discussion boards, and more through MATLAB Central, a community-based hub with helpful resources.

Python: an active open-source community

Python boasts one of the world's largest and most active open-source communities. The community spans diverse areas and applications, including scientific computing, web development, data science, and machine learning. The collaborative nature of the Python community encourages knowledge sharing, contributing to the continuous improvement of libraries and frameworks.

For scientific computing and data analysis, the Python community can access forums like Stack Overflow, mailing lists, and extensive documentation. Python's open ecosystem allows for and encourages innovation and creativity, as users benefit from a wide range of tools and resources.

Getting started with Coursera

Ready to get your start in computer programming or scientific and data science computing? Enroll in an online course such as MATLAB Programming for Engineers and Scientists Specialization or Python for Data Science, AI & Development, offered on Coursera. You can learn more about each language and choose which to use for your needs, skill level, goals, and more.

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