MATLAB Vs Python: Difference Between Matlab & Python [2021]

One of the trending debates in the scientific and computing community is that is MATLAB vs. Python. Often, people in the scientific community talk about transitioning from MATLAB to Python.

While MATLAB is a robust computing environment for mathematical or technical computing operations involving arrays, matrices, and linear algebra, Python is also gaining popularity in the computing area. This is because Python incorporates MATLAB’s computational power and facilitates speedy and easy development of scientific applications. However, there are some key differences between MATLAB and Python.

In this article, we’ll explore the differences between MATLAB and Python.

MATLAB vs. Python: What are they?


MATLAB is both a commercial numerical computing environment and programming language. In fact, it is one of the most advanced and well-designed programming language for computing. In the late 1970s, Cleve Moler began the development of MATLAB. It is a multi-paradigm computing environment and language developed by MathWorks.

It is an excellent tool for matrix manipulations, data plotting, implementing algorithms, and developing user interfaces. Even though MATLAB is designed primarily for numerical computation functions, it allows for symbolic computation using the MuPAD symbolic engine.  


Python is an open-source, high-level, general-purpose programming language. It was developed by Guido van Rossum and released in 1991. Simplicity lies at the core of Python, and hence, it uses the OOP approach to help developers write precise and logical code for small and large projects.

Python supports multiple programming paradigms, such as procedural programming, OOP, and functional programming. Apart from its neat syntax and code readability features, Python’s best aspect is that it comes equipped with a host of standard libraries for accomplishing different programming and computing tasks.

MATLAB vs. Python: The key differences

Let’s look at some of the main differences between MATLAB and Python:


MATLAB is closed-source software and a proprietary commercial product. Thus, you need to purchase it to be able to use it. For every additional MATLAB toolbox you wish to install and run, you need to incur extra charges. The cost aspect aside, it is essential to note that since MATLAB is specially designed for MathWorks, its user base is quite limited. Also, if MathWorks were to ever go out of business, MATLAB would lose its industrial importance. 

Unlike MATLAB, Python is an open-source programming language, meaning it is entirely free. You can download and install Python and make alterations to the source code to best suit your needs. Due to this reason, Python enjoys a bigger fan following and user base. Naturally, the Python community is pretty extensive, with hundreds and thousands of developers contributing actively to enrich the language continually. As we stated earlier, Python offers numerous free packages, making it an appealing choice for developers worldwide.


The most notable technical difference between MATLAB and Python lies in their syntax. While MATLAB treats everything as an array, Python treats everything as a general object. For instance, in MATLAB, strings can either be arrays of strings or arrays of characters, but in Python, strings are denoted by a unique object called “str.” Another example highlighting the difference between MATLAB and Python’s syntax is that in MATLAB, a comment is anything that starts after the percent sign (%). In contrast, comments in Python typically follow the hash symbol (#).


MATLAB boasts of having an integrating development environment. It is a neat interface with a console located in the center where you can type commands, while a variable explorer lies on the right, you’ll find a directory listing on the left.

On the other hand, Python does not include a default development environment. Users need to choose an IDE that fits their requirement specifications. Anaconda, a popular Python package, encompasses two different IDEs – Spyder and JupyterLab – that function as efficiently as the MATLAB IDE. 


Programming languages are usually accompanied by a suite of specialized tools to support a wide range of user requirements, from modeling scientific data to building ML models. Integrated tools make the development process easier, quicker, and more seamless.

Although MATLAB does not have a host of libraries, its standard library includes integrated toolkits to cover complex scientific and computational challenges. The best thing about MATLAB toolkits is that experts develop them, rigorously tested, and well-documented for scientific and engineering operations. The toolkits are designed to collaborate efficiently and also integrate seamlessly with parallel computing environments and GPUs. Moreover, since they are updated together, you get fully-compatible versions of the tools.

As for Python, all of its libraries contain many useful modules for different programming needs and frameworks. Some of the best Python libraries include NumPy, SciPy, PyTorch, OpenCV Python, Keras, TensorFlow, Matplotlib, Theano, Requests, and NLTK. Being an open-source programming language, Python offers the flexibility and freedom to developers to design Python-based software tools (like GUI toolkits) for extending the capabilities of the language. 

Read: 15 Interesting MATLAB Project Ideas & Topics For Beginners


Despite having an active community and excellent standard packages, Python fails to match up to MATLAB in one particular area – the Simulink Toolbox. This toolbox extends MATLAB’s capabilities for signal processing and modeling in a graphical interface. Python lacks a graphical interface that can perform these advanced functions. 

Overall, both MATLAB and Python are excellent tools. While one is designed for specific tasks (MATLAB), another can perform a wide variety of generic operations. 

If you’re interested to learn more about MATLAB, machine learning, and its relevant topics, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

Lead the AI Driven Technological Revolution

Enroll Today

Leave a comment

Your email address will not be published.

Accelerate Your Career with upGrad

Our Popular Machine Learning Course