MATLAB vs Python – Differences you must know?

MATLAB vs Python: Differences you must know

Authored By: Ankita Prajapati

MATLAB is a multi-paradigm programming language and environment created by MathWorks. Python is a object oriented programming language created by Guido Van Rossum.

MATLAB is a low- level language while Python is a High- level language that is open source.

Read the differences between MATLAB vs Python.

Matlab is a great tool for manipulatng matrices, visualising data, applying algorithms, and creating user interface. On the other hand, programming styles supported by Python include procedural, object-oriented, and functional programming. The best feature of Python, aside from its clean syntax and code readability qualities.

Nature

MATLAB is a proprietary, closed-source, commercial product. Therefore, in order to utilize it, you must buy it.

You must pay additional fees for each additional MATLAB toolbox you wish to install and use. Aside from the cost, it’s important to remember that MATLAB has a very small user base because it was created specifically for MathWorks.

Python is a completely free open-source programming language, in contrast to MATLAB. Python’s source code is available for download and modification to best fit your needs.

Syntax

The syntax of MATLAB and Python differs significantly on a technical level.

Python handles everything as a general object, as contrast to MATLAB’s treatment of everything as an array. For example, strings in MATLAB can be either arrays of strings or arrays of characters,

whereas strings in Python are denoted by a special object called “str.” Another illustration of the syntax differences between MATLAB and Python is the fact that in MATLAB, anything that comes before the percent sign (%) is considered a comment. In contrast, Python comments often come after the hash sign (#). 

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IDE

 A variable explorer is on the right, there is a directory listing on the left, and there is a console in the center where you can type commands.

However, Python lacks a built-in development environment by default. Users must select an IDE that matches their requirements. Spyder and JupyterLab are two IDEs included with the well-known Python package Anaconda that perform similarly to the MATLAB IDE. 

Tools

A group of specialized tools are typically included with programming languages to assist a variety of user needs, from modelling scientific data to creating machine learning models. 

The development process is streamlined, accelerated, and made easier with integrated technologies. 

MATLAB may not have a large number of libraries, but its standard library does offer integrated toolkits to deal with difficult scientific and computational problems. The nicest part about MATLAB toolkits is that professionals create them, have them thoroughly tested, and have them well-documented for use in engineering and science activities. 

The toolkits are made to work well together and smoothly with GPUs and parallel computing environments. Additionally, as they are updated concurrently, you obtain versions of the tools that are completely compatible. 

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Regarding Python, each of its libraries has a wide variety of beneficial modules for various programming requirements and frameworks. NumPy, SciPy, PyTorch, OpenCV Python, Keras, TensorFlow, Matplotlib, Theano, Requests, and NLTK are a few of the top Python libraries. 

Python gives developers the flexibility and independence to create Python-based software tools (such GUI toolkits) in order to expand the language’s capabilities because it is an open-source programming language. 

Libraries

A group of specialized tools are typically included with programming languages to assist a variety of user needs, from modeling scientific data to creating machine learning models.

The development process is streamlined, accelerated, and made easier with integrated technologies.

MATLAB may not have a large number of libraries, but its standard library does offer integrated toolkits to deal with difficult scientific and computational problems.

The nicest part about MATLAB toolkits is that professionals create them, have them thoroughly tested, and have them well-documented for use in engineering and science activities.

The toolkits are made to work well together and smoothly with GPUs and parallel computing environments. Additionally, as they are updated concurrently, you obtain versions of the tools that are completely compatible.

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Conclusion

Even though it has a vibrant community and top-notch standard packages, Python falls short of MATLAB in one area: the Simulink Toolbox.

With a graphical user interface, this toolbox expands MATLAB’s modelling and signal processing capabilities. Python doesn’t have a graphical user interface that can carry out these sophisticated tasks. 

In general, MATLAB and Python are both top-notch tools. One can conduct a wide range of generic procedures, whilst another can execute specialized jobs (MATLAB). 

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