Choosing a Language

The languages compared

Language	Style	Strengths	Typical engineering uses
Python	Interpreted, dynamic types, readable	Fast to write; huge libraries (NumPy, SciPy, pandas, Matplotlib); good glue; runs on a PC or single-board computer	Data analysis, test automation, instrument control, machine learning, prototypes
C	Compiled, static types, close to the hardware	Fast, small, predictable; runs on any microcontroller	Firmware, drivers, real-time control, performance-critical code
C++	Compiled, adds classes and templates	C’s speed with abstraction; used in robotics (ROS), games, large embedded systems	Robot software, high-performance computing, larger embedded projects
MATLAB / Octave	Interpreted, matrix-based	Excellent for math, signals and control design; many toolboxes (MATLAB is commercial, Octave is free)	Control design, signal processing, simulation, teaching
Julia	Compiled on demand, matrix-friendly	MATLAB-like syntax with C-like speed; strong for simulation and optimization	Scientific computing
Rust	Compiled, memory-safe	C-like speed without whole classes of memory bugs; growing embedded use	Safety-conscious firmware, tools
Java, C#, Go	Compiled to a virtual machine or native	Good tooling and libraries; C# is common in Windows instrument software and Unity, Go in network tools	Test benches, PC applications, servers
JavaScript / TypeScript	Runs in browsers and Node.js	Dashboards, web interfaces, simple servers	HMI-style web pages, data visualization
SQL	Query language for databases	Selecting and summarizing stored data	Production and sensor data, reports
Shell (Bash, PowerShell)	Command-line scripting	Automating file, build and deployment tasks	Batch jobs, backups, build scripts
IEC 61131-3 (ladder, ST)	PLC languages	Designed for industrial control, readable by technicians; see PLC and Logic Control	Machine and process automation

The same task in several languages

The task: add up the squares of the whole numbers from 1 to 10 (the answer is 385). Seeing it in several languages shows what changes (the punctuation and the types) and what does not (a loop, an accumulator, an answer).

# Python
total = 0
for i in range(1, 11):
    total += i * i
print(total)                      # 385; or: sum(i*i for i in range(1, 11))

# C
#include <stdio.h>
int main(void) {
    int total = 0;
    for (int i = 1; i <= 10; i++) total += i * i;
    printf("%d\n", total);        /* 385 */
    return 0;
}

% MATLAB / Octave
total = sum((1:10).^2)            % 385: no loop needed, the whole vector at once

// Rust
fn main() { let total: i32 = (1..=10).map(|i| i * i).sum(); println!("{}", total); }

// JavaScript
let total = 0; for (let i = 1; i <= 10; i++) total += i * i; console.log(total);

-- SQL (sum of squares from a table of numbers)
SELECT SUM(n * n) FROM numbers WHERE n BETWEEN 1 AND 10;

• Types: C and Rust make you declare them (and a C int can overflow); Python and JavaScript decide at run time.
• Counting: Python’s range(1, 11) stops before 11; C loops state the end condition; MATLAB counts from 1 where C, Python and JavaScript count from 0 in arrays.
• Vectors: MATLAB and NumPy do the work on a whole array at once, which is both shorter and much faster than a loop.

Speed: what it means and what it costs

Picking one: questions to ask

Question	If yes…
Does the platform dictate it? (a microcontroller, a PLC, a robot with ROS)	Use what it supports: C or C++ (or MicroPython on larger chips) for a microcontroller; ladder or structured text for a PLC; C++ or Python for ROS. See Embedded Systems
Is it analysis, plotting or a quick tool?	Python (or MATLAB if your team uses it)
Is the timing hard real-time?	C, C++ or Rust on a microcontroller or a real-time OS; not a desktop language
Will others have to maintain it?	The language they know, with tests and comments; Python is widely read
Is it a heavy simulation or numerical job?	NumPy/SciPy first; then Julia, C++ or Fortran for the slow core
Does it need a user interface?	A web page (JavaScript) or a desktop toolkit (Python Qt, C#) depending on who uses it
Is a license a problem?	Python, Octave, Julia, Rust, C and C++ are free; MATLAB and some PLC tools are licensed
Is safety involved?	Use certified tools and a safety-rated platform; see System Integration

A language you know well beats a “better” one you do not. The ideas in this guide carry over to all of them.

Industrial languages and G-code

• PLC languages (IEC 61131-3): ladder diagram, function block diagram, structured text (a Pascal-like language), instruction list and sequential function chart. They run in a fixed scan cycle on rugged hardware and are readable by maintenance technicians (see PLC and Logic Control).
• G-code and CNC macros: a list of motion and machine commands for CNC machines and 3D printers (G1 X50 Y20 F1200); machine macros add variables and loops (see CNC Machining).
• Robot languages: vendor languages (RAPID, KRL, URScript, Karel) and the common Python and C++ interfaces of ROS (see Robotics Tools).
• HDL (Verilog, VHDL) describe hardware for FPGAs: a different way of thinking, since everything happens in parallel.
• Spreadsheet formulas are a programming language too: for small jobs a spreadsheet with clear units is the right tool; move to code when you need loops, tests or versions.

Using more than one language

• Python for the top level, C for the hot spot: write in Python, find the slow part with a profiler (see Software Practice, Testing and Debugging), then replace it with NumPy, a compiled extension (Cython, pybind11, ctypes calling a C library) or Numba.
• Python on the PC, C on the microcontroller, talking over a serial link or a network. The PC side logs, plots and sends commands; the chip does the real-time work (see Data, Files and Parsing).
• One data format between them (CSV, JSON, a defined binary frame) with the units and byte order written down (see Bits, Bytes and Data Formats).
• Shell scripts glue the steps (run the simulation, convert, plot, archive).

Setting up your tools

Step	Python	C / C++
1. Install	Python 3 from python.org or Anaconda / Miniforge; check python --version	A compiler: GCC or Clang (MSYS2, MinGW or WSL on Windows), or the chip vendor’s toolchain
2. Project folder	A virtual environment: python -m venv .venv, then activate it	A src folder and a Makefile or CMake file
3. Libraries	pip install numpy scipy matplotlib pandas; record them with pip freeze > requirements.txt	Headers and libraries; a package manager (vcpkg, Conan) for larger projects
4. Editor	VS Code (with the Python extension), PyCharm, or Jupyter for exploration	VS Code, CLion, Eclipse, or the vendor IDE (STM32CubeIDE, MPLAB X)
5. Version control	git init; commit small changes	The same
6. Run and debug	python script.py; the editor’s debugger; print while exploring	gcc -Wall -Wextra -O2 main.c -o main; GDB or the IDE debugger

Learning a language efficiently

• Learn by solving a real problem of your own: parse a log file, plot a test result, control an LED. A project you care about teaches faster than exercises.
• Read other people’s code in the libraries you use; it is the best guide to idioms.
• Type the examples; do not paste them. Change a number and predict the result before you run it.
• Learn the debugger early: stepping through a loop shows what the code really does.
• Keep a small file of snippets (reading a CSV, a serial port, a plot, a filter) that you reuse.
• Read the documentation for the library function before you write your own; a numerical routine in a good library is almost always better than a quick one you write.

Related

• Programming Languages: the overview of this guide.
• Python for Engineers and C and C++ Essentials: the two languages most engineers use.
• Software Practice, Testing and Debugging: git, tests and profiling for any language.
• Embedded Systems and PLC and Logic Control: languages tied to their platforms.

FAQ

Python or C: which should I learn first?

Python if you want to analyze data, automate tests or prototype quickly; C if you want microcontrollers and firmware. After one, the other takes weeks, not months.

Is MATLAB still worth learning?

In control design, signal processing and many universities and industries, yes, and Simulink has no free equal. For general work Python with NumPy, SciPy and Matplotlib does most of it for free, and Octave runs much MATLAB code.

Is Python too slow for engineering?

Not if you use NumPy: the heavy loops run in compiled code. Pure Python loops are slow, but few analysis scripts are limited by them; profile before you worry.

Should I learn Rust?

It is worth knowing about for new embedded and tool projects where memory safety matters, but C and Python still cover most of the code you will meet today.

References

• Python Software Foundation, The Python Tutorial and Python documentation; NumPy and SciPy documentation.
• Kernighan, B. W. and Ritchie, D. M., The C Programming Language; Stroustrup, B., A Tour of C++.
• Stack Overflow Developer Survey and the TIOBE index (popularity of languages): trends, not engineering advice.

Last reviewed: October 2026. Speed figures are rough orders of magnitude for illustration: measure your own code.