Python for Engineers — The Complete Guide: Free Calculations, Plots and Automation
Python is a free, open-source programming language, and today probably the most useful tool an engineer can learn beyond their dedicated software. It is not a single "engineering program" — it is a base with thousands of libraries on top, and many free (and even commercial) engineering programs can be controlled from it.
Python
Python Software Foundation
The language behind most modern engineering computation, and its full ecosystem.
Get it from the vendorLibraries worth knowing
- NumPy — arrays and matrices, the basis of all numerical computing.
- SciPy — differential equations, optimisation, integration, interpolation and statistics.
- pandas — data tables: reading Excel and CSV, filtering, grouping and summarising measurements.
- matplotlib — quality plots for reports.
- SymPy — symbolic maths: differentiation, integration and solving equations analytically.
- Jupyter — a notebook combining code, results, plots and explanations — ideal for documented engineering calculations.
Common engineering uses
Processing measurement and lab test results, automating repeated calculations that used to live in a spreadsheet, producing tables and reports, reading and writing Excel files (openpyxl), and controlling engineering software: OpenSees through OpenSeesPy, CoolProp for fluid properties, FreeCAD and CadQuery for modelling, and Gmsh for meshing.
OpenSees
Pacific Earthquake Engineering Research Center, UC Berkeley
Nonlinear and dynamic structural analysis driven by scripts, from the earthquake engineering world.
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CoolProp
CoolProp Project
Thermophysical property data for over a hundred fluids, callable from any language.
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FreeCAD
FreeCAD Project
Open-source parametric 3D modeller with workbenches for solid modelling, FEM, drafting and BIM.
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CadQuery
CadQuery Project
Parametric CAD scripted in Python, on a proper solid-modelling kernel.
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Gmsh
Gmsh Project
FEA mesh generator with built-in geometry and scripting — standard infrastructure for open numerical work.
Get it from the vendorHow to start
1. Install Python from the official site, or a distribution that comes with the scientific libraries. 2. Work in Jupyter or an editor such as Pyzo. 3. Start from a real problem at work — a calculation table you keep repeating — rather than a general course. 4. Learn NumPy and pandas before anything else.
Pyzo
Pyzo Project
A lightweight Python environment for scientific and engineering work.
Get it from the vendorWhat's missing
There is no ready graphical interface — you have to write code. There is no Simulink and no single unified environment like MATLAB, and at first managing versions and libraries can be confusing. And a calculation submitted to authorities needs its code documented and checked like any other calculation.
Who it suits
Every engineer who finds themselves repeating the same calculation, anyone processing a lot of data, researchers and students — and especially those working with open tools who want to connect them.
Alternatives
If you already work in MATLAB — GNU Octave. For block simulation — Scilab. For symbolic maths in a graphical interface — wxMaxima.
GNU Octave
GNU Project
Numerical computing in MATLAB-compatible syntax — matrices, equations, signals and plots.
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Scilab
Dassault Systèmes / Scilab Enterprises
Numerical computing with Xcos — block-diagram simulation of dynamic systems alongside scripting.
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wxMaxima — Symbolic Algebra
Maxima Project / wxMaxima
Graphical front end to the Maxima computer algebra system — derivatives, integrals and equations in mathematical notation.
Get it from the vendorBottom line
Learning Python pays for itself quickly: it is free, it runs everywhere, and it connects almost every other engineering tool.