Using MATLAB with Python

Bridging Two Engineering Worlds

MATLAB has long powered numerical computing and model-based design in engineering-heavy industries, offering built-in toolboxes for control systems, signal processing, and simulation that Python historically required piecing together via libraries like NumPy, SciPy, and specialized packages. Python, however, exploded in adoption for its versatility, zero licensing cost, and leadership in artificial intelligence and machine learning.

Recent MATLAB releases shifted the calculus. In R2025a (May 2025) and subsequent updates through R2025b, MathWorks delivered smoother two-way integration: automatic conversions between MATLAB tables and Python Pandas DataFrames, full support for Python datatypes in data exchange, direct execution of Python code within Simulink models, and streamlined packaging of MATLAB algorithms as Python modules. These changes reduce friction for engineers who need MATLAB's precision for domain-specific tasks but Python's ecosystem for scaling AI models or cloud deployment.

The stakes are concrete in industries under digital transformation pressure. Automotive and aerospace firms, for instance, rely on MATLAB/Simulink for certified simulations of autonomous driving or electrification systems, yet integrate Python-trained neural networks for perception or optimization. Poor interoperability previously meant manual data wrangling or duplicate tooling, inflating development costs and timelines—sometimes by quarters in fast-moving markets. Licensing remains a pain point: MATLAB's annual fees can exceed $10,000 per user in large teams, pushing some R&D groups toward Python-only strategies that sacrifice validated legacy code.

Less discussed are the trade-offs in hybrid approaches. Teams gain flexibility but inherit complexity—managing Python environments compatible with specific MATLAB releases (e.g., Python 3.9–3.12 in R2025b), ensuring cross-tool validation for safety-critical applications, and bridging skill gaps as younger engineers arrive fluent in Python but not MATLAB. Counterarguments exist: MATLAB's debugger, interactive figures, and performance in matrix-heavy computations still outperform equivalents in many Python setups for prototyping. Yet Python's momentum—evident in its near-58% developer usage versus MATLAB's low single digits in 2025 surveys—suggests the long-term direction unless MathWorks' integration keeps pace.

With the topic gaining renewed focus in early 2026, organizations are evaluating these tools against tightening deadlines for AI-embedded products and cost controls in uncertain economic conditions.