The difference is clear: MATLAB is the easiest and most productive computing environment for engineers and scientists. It includes the MATLAB language, the only top programming language dedicated to mathematical and technical computing.
In contrast, Python is a general-purpose programming language requiring add-on libraries for performing even basic mathematics.
"With MATLAB, I can code and debug a new capability much faster than with other languages. Cutting our development time in half with MATLAB was instrumental in our ability to meet a tight timeline. Our customer was convinced that I’d worked 70 hours each week when he saw the results."
Bancroft Henderson, EMSolutions
Engineers and scientists need a programming language that expresses matrix and array mathematics directly instead of through generalized programming constructs.
Matrix math in Python requires function calls, not natural operators, and you must keep track of the differences between scalars, 1-D arrays, and 2-D arrays. Even in the simplest Python code this can be difficult, as the hidden mistake in the following example shows. The same example written in the MATLAB language shows that MATLAB is the more natural way to express computational mathematics.
Ultimately, linear algebra in MATLAB looks like linear algebra in a textbook. The same is true for data analytics, signal and image processing, control design, and other applications.
This is why more than 1,800 textbooks teach using MATLAB.
Python functions are typically designed and documented by advanced programmers for other experienced programmers. Python development environments for scientific computing lack the reliability and integration of the MATLAB desktop.
Everything about MATLAB is designed specifically for engineers and scientists:
“As a process engineer I had no experience with neural networks or machine learning. I worked through the MATLAB examples to find the best machine learning functions for our predictive metrology use case. I couldn’t have done this in C or Python—it would’ve taken too long to find, validate, and integrate the right packages.”
Emil Schmitt-Weaver, ASML
A programming language must be combined with specialized tools that support what you need to do, whether you’re modeling economic data, analyzing an image sequence, or driving a robot. Those tools need to work, and they need to work together.
Python relies on community-authored packages for scientific and engineering functionality. Python packages vary widely in quality and capabilities. Each package has its own stand-alone documentation, putting the burden on you to piece together a solution.
Unlike Python, MATLAB toolboxes offer professionally developed, rigorously tested, field-hardened, and fully documented functionality for scientific and engineering applications. Toolboxes are designed to work with each other, and they integrate with parallel computing environments, GPUs, and automatic C code generation. They are updated together, so you never struggle with incompatible library versions.
We need to filter our data, look at poles and zeroes, run nonlinear optimizations, and perform numerous other tasks. In MATLAB, those capabilities are all integrated, robust, and commercially validated.
Borislav Savkovic, lead data scientist, BuildingIQ
Python doesn’t offer integrated workflow apps for scientific and engineering applications, requiring custom programming instead. This slows down discovery and exploration, especially for highly iterative workflows.
MATLAB apps let you start working right away. These interactive applications combine direct access to large collections of algorithms with immediate visual feedback. You can try a new surface fitting algorithm, filter design technique, or machine learning classification algorithm and see right away how it works with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work.
Major engineering and scientific challenges require broad coordination across teams to take ideas to implementation. Every handoff along the way risks adding errors and delays.
Unlike Python, MATLAB can help teams through their entire workflow:
Out of the box, MATLAB is faster than Python for common technical computing tasks in statistics, engineering calculations, and data visualization. The following table and chart illustrate benchmark results.
Python code requires overlapping and conflicting add-ons to get performance benefits such as just-in-time compilation and explicit parallel programming. These solutions tend to be incomplete or targeted at advanced programmers.
MATLAB does the hard work of making your code fast. Math operations are distributed across your computer’s cores, library calls are heavily optimized, and all code is just-in-time compiled. You can run your algorithms in parallel by changing for-loops into parallel for-loops, which entails simply adding the three characters “par” to “for”, or by changing standard arrays into GPU arrays. You can run your parallel algorithms in an infinitely scalable public or private cloud with no code changes.
Engineers and scientists trust MATLAB to send a spacecraft to Pluto, match transplant patients with organ donors, or just compile a report for management. This trust is built on impeccable numerics stemming from the strong roots of MATLAB in the numerical analysis research community.
A team of MathWorks engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.
Capability |
MATLAB |
Python |
|---|---|---|
Mathematical syntax Natural syntax makes programs easier to write and read, improving communication and reducing mistakes. |
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Ease of learning for scientific computing Programming is a means to an end for most engineers and scientists. An easily learned program keeps the focus on the domain, not the programming. |
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Capability |
MATLAB |
Python |
|---|---|---|
Scientific data visualization Easy visualization lets you see not only the result of your analysis or algorithm but also the behavior of your program. |
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Toolboxes that just work Well-designed add-ons provide essential building blocks for specialized applications. Don't waste time trying to piece together and maintain disparate add-ons. |
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Capability |
MATLAB |
Python |
|---|---|---|
Out-of-the-box performance Scientific computing requires rapid iteration, which is best achieved with programs that without additional optimization. |
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Parallel processing and big data for non-experts You shouldn't have to become an expert in parallel programming just because your problems are too big to run quickly on a single processor. |
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Capability |
MATLAB |
Python |
|---|---|---|
Scientific computing desktop An excellent development environment can accelerate common tasks by putting everything you need in front of you. |
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Documentation Documentation needs to be comprehensive, comprehensible, and available at your fingertips. |
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Apps Scientific and engineering tasks are performed more quickly and easily with a graphical interface than with programming. |
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Capability |
MATLAB |
Python |
|---|---|---|
Desktop application development and deployment Allowing non-developers to build custom applications enables the spread of complex technical applications. |
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Embedded code generation Automatic C and C++ code generation reduces the time, defect rates, and cost required to move algorithms from research to production |
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Hardware integration Hardware is essential to many computer-based engineering and scientific workflows, both as a source of data and as a target for algorithms. |
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Model-Based Design Scientific computing work often supports larger design projects. Integration with downstream tools minimizes the risk associated with hand-offs. |
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Capability |
MATLAB |
Python |
|---|---|---|
Professional, real-time support Reliable, real-time support enables users to get the right answer quickly. |
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Widespread adoption in academia Organizations can rely on the MATLAB skills of recent graduates. |
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