Python vs MATLAB: A Comparative Study for Learners

In recent years, Python and MATLAB have emerged as two of the world's most popular software tools. Due to their versatile applications, both languages have attracted the attention of a wide range of learners. Consequently, in this article, we will be performing a comparative study on the features of Python and MATLAB, exploring the advantages and disadvantages of both technologies to decide which one is better suited for the needs of a particular learner. We will cover the main aspects of both languages, highlighting the top six differences that you should be aware of before making a decision.

What is Python?

Python is an open-source programming language that can be used for almost anything. It is an open-source programming language. Hence you can access it and delve into its inner workings to discover how it works. You can even build packages for it. This is one of the primary advantages of Python programming over MATLAB programming.

Guido van Rossum, Python's former Benevolent Dictator for Life, invented Python in the late 1980s and initially implemented it in December 1989. Python has only risen in popularity since its debut. It is routinely ranked as one of the most popular languages in the world today.

It has a large and expanding functionality. It is suitable for both simple jobs that novices may be interested in and sophisticated tasks that organizations may require. As developers produce additional libraries and functions, companies utilize Python for web development, data science, scientific computing, artificial intelligence, computer science, and other applications.

What is MATLAB?

In contrast to Python, MATLAB code is proprietary, which means it is owned and licensed for a fee by someone else. It is a high-level programming language that is commonly used for numerical computation, visualization, and data analysis.

Its beginnings are at colleges and universities, which is where you'll find the majority of MATLAB users today. It was developed in the 1970s as a teaching aid for University of New Mexico students.

What Are Its Key Advantages?

You do not need any specialized skills to make use of MATLAB. To get started with MATLAB, you don't need much programming experience; it's a rather intuitive language.

What are The Top 6 Differences Between MATLAB and Python?

Below are the notable differences between MATLAB and Python:

• Ease of Language Usage

Python and MATLAB are programming languages that may be used interactively (one command at a time) or to create large-scale applications. Scripting, procedural, and object-oriented programming are all supported by both languages.

Python has mostly supplanted Java as the primary language for individuals learning how to program since it is simple to learn and can be used for a wide range of programming activities. Many engineers and scientists use MATLAB as their first (and often only) programming language because it is matrix algebra, and array orientation makes it simple to learn and apply to engineering and scientific problem-solving. Apps and other interactive applications create MATLAB code automatically, lowering the barrier to access even more.

• The Difference in Technical Computing: 

Python and MATLAB are two powerful programming languages in the field of technical computing. They are both used for the same type of work, such as numerical analysis, data visualization, and scientific computation. 

When it comes to syntax and readability, Python is often easier to read and understand than MATLAB. Python also provides a more modern, object-oriented approach to coding than MATLAB’s procedural-style approach. This makes it easier for developers with experience with other object-oriented languages to make the switch to Python. Additionally, the free availability of Python makes it much more accessible than MATLAB, a paid, proprietary product. 

MATLAB has an advantage in terms of speed. Its dedicated numerical libraries and built-in functions for certain operations allow it to run code much faster than Python. Additionally, MATLAB is better suited for interactive data analysis and graphically-intensive tasks due to its built-in plotting tools and interactive GUI.

• User Base:

Python and MATLAB have huge user bases, while MATLAB's user community predominantly comprises engineers and scientists. LinkedIn searches find around 7.6 million Python users and 4.1 million MATLAB users as of May 2022.

• Support and Documentation:

Most Python users rely on Stack Overflow and other similar sites for help. With so many people globally, it is simple to discover or create an online conversation that may assist in answering almost any query. Python and most libraries have documentation available via docstrings and individual webpages.

MATLAB licenses offer free live support, so users may call or contact a MATLAB specialist for assistance with their unique project. 

Additional free support from MathWorks experts throughout the world can assist users in determining the best method to combine existing technologies for their application, research, or teaching. MATLAB Answers is a MATLAB-specific online question-and-answer forum comparable to Stack Overflow. MATLAB features an integrated documentation system that combines professionally written documentation for all MATLAB and Simulink products, user-authored package assistance, and extra online resources like MATLAB Answers and videos.

• Tools:

MATLAB includes various standard libraries as well as several more specialized and specialist libraries, the majority of which have been tested and created by specialists. Third-party libraries can also be downloaded through the MATLAB file exchange.

The following are some of the most popular and widely used MATLAB libraries:

• Simulink: Modelling, simulation, and analysis of dynamic systems, such as control and communication systems.
• Image Processing Toolbox: Analyse and manipulate digital pictures.
• Signal Processing Toolbox: Filtering, spectral analysis, and signal feature extraction.
• Optimization Toolbox: Linear, quadratic, and nonlinear programming are all part of it.
• The Statistics and Machine Learning Toolbox: It includes hypothesis testing, regression analysis, and clustering.

While not all Python libraries can claim to have been created and tested by specialists, it compensates for the lack of proprietary solutions with a vast array of open-source libraries for practically every programming necessity or framework interaction.

The following are the most popular community-created libraries:

• NumPy: A Python library for scientific computing that supports big, multi-dimensional arrays and matrices as well as a wide set of mathematical functions to work on them.
• Pandas: A data analysis and manipulation package that provides data structures for storing and accessing huge datasets effectively.
• Matplotlib: A plotting package that may be used to create visualizations such as charts, graphs, and histograms.
• Scikit-learn: A machine learning package that includes algorithms for classification, regression, and clustering, as well as tools for data preparation and model assessment.
• TensorFlow: A machine learning open-source library developed by Google that is frequently used for deep learning applications such as neural networks.
• Django: A Python web framework for creating web applications.
• Flask: A micro web framework for constructing Python web applications.

• In Comparison to Cost:

Although Python and most Python libraries are free to download and use, many users access Python via a commercial subscription. Paid services assist IT organizations in managing the risks associated with open-source software use, such as security, licensing, and access control. Paid services also give access to packaged or hosted distributions, which alleviate most of the agony associated with establishing a Python installation with suitable library versions.

MATLAB is not free but more accessible and less expensive than most people believe. MATLAB is available to around 8 million individuals through their school, research institution, or job, including most colleges worldwide that provide engineering and scientific degrees. Institutions choose to invest in MATLAB because they see the benefit of offering purpose-built tools to their engineers and scientists.

Conclusion

MATLAB is often preferred for its built-in engineering features, while Python is one of the fastest-growing coding languages and is expanding rapidly in use. While both tools can easily be used for numerical computations, CFD analysis SolidWorks, and mechanical engineering applications, there are some key differences in each language’s capabilities and features. For both novice and experienced learners, Skill-Lync provides high-quality courses in Python and MATLAB. To get complete course details, connect with our experts.