Why Python Is Popular, and Where It Is Used

Part I · Module 1 · Chapter 1.3 · Why Python Is Popular, and Where It Is Used

1. Where It Came From, Briefly

Python was created by Guido van Rossum, who began work on it in late 1989 in the Netherlands. His stated motivation was practical: he needed something above the level of C for system-administration work, but the shell was not enough either. He wanted the gap filled.

It has been open source from the beginning, which is why it is on nearly every machine you will meet and why nobody has to buy a licence to run your script.

That is the entire history you need. It is genuinely useful only for one reason: the gap Python was designed to fill — more structure than a shell script, far less ceremony than C — is exactly the gap most engineering automation sits in. The language is popular in that role because it was built for it.

2. Four Properties That Explain Most of It

It reads like what it does

Python code is unusually close to a description of the steps in English. Not identical — it is still a formal language with exact rules — but close enough that a colleague who does not know Python can often follow the shape of what a script does.

That matters far more in engineering than it sounds. Most automation is read many more times than it is written, usually by someone who did not write it and is trying to work out why it did something surprising at 3 a.m.

A large standard library ships with it

When you install Python you get a substantial collection of ready-made capability with it: reading and writing files, working with file paths, handling CSV and JSON data, launching other programs, searching text with patterns. Nothing to download, nothing to add to a build.

This is sometimes called batteries included, and it is a real advantage on a locked-down corporate or compute-farm machine where installing extra packages is a request rather than a command. Chapter 13.4 in Part I catalogues the parts of it you will actually use.

It runs essentially everywhere

The same file generally runs on a laptop, a Linux compute server and a build machine without modification. For automation that has to run overnight on a machine you are not sitting at, that portability is the difference between a tool and a demo.

There is very little ceremony

This is the one that is hardest to appreciate before you have used something else. Compare what has to exist before a program can print one line of text:

#include <stdio.h>

int main(void) {
    printf("Simulation complete\n");
    return 0;
}

print("Simulation complete")

You are not expected to understand either of these yet. Look only at the quantity of scaffolding. The C version needs an include line, a function with a specific name, a declared return type, an explicit newline character and a return value — all of which you must get right before the one interesting line will run at all.

None of that is a criticism of C. That scaffolding buys precision and control that Python does not offer, and Part II of the C track on this platform exists because those properties matter for hardware work. It is simply a different trade: C makes you say more, and gives you more exact control over what happens. Python makes you say less, and handles the details itself.

For automation — where the job is to launch a tool, read its output and report a result — saying less is usually the better trade.

3. Where It Is Used

Python became a default in a lot of places, and the list is worth seeing because it explains why the skill transfers:

• Scientific and numerical work — data analysis, simulation post-processing, research code.
• System administration and infrastructure — the automation that keeps servers and build systems running.
• Web services — a large share of the internet's back-end logic.
• Test and hardware engineering — instrument control, automated test equipment, lab automation.
• Semiconductor and EDA flows — regression management, log and report analysis, build and release automation. Several EDA tools embed a Python interpreter directly.

That last one is the reason this course exists, and it is what Part II is entirely about. But notice that the first four are not VLSI at all. The Python you learn in Part I is ordinary, transferable Python — it is the same language whichever of those rows you end up in.

4. What Python Is Bad At

A fair account has to include this, otherwise you will reach for the wrong tool later.

• Raw speed. Python is considerably slower than C for computation-heavy work. For automation this rarely matters, because the time is spent waiting for a simulator rather than in your script. For a tight numerical loop it matters a great deal.
• Tight resource control. If you need to know exactly how many bytes something occupies or exactly when memory is released, Python deliberately hides that from you. That is the wrong property for firmware.
• Catching mistakes before running. Python will happily start running a file that contains a mistake it cannot detect in advance, and fail partway through. Languages with stricter up-front checking catch more before anything executes. Part I Module 14 is about handling that reality rather than pretending it away.

5. Exercise

Exercise 1 — count the scaffolding. Look again at the two blocks in §2. Without understanding either language, list every line in the C version that exists only to make the program legal rather than to do the actual job. How many of the six lines are left?

Exercise 2 — pick the wrong tool. For each task, decide whether Python's properties make it a good or a poor fit, and name which property decides it: (a) collecting results from 600 overnight simulations; (b) code running inside a microcontroller with 8 KB of memory; (c) reading a 2 GB log and counting error lines; (d) a numerical routine that must finish in under a millisecond.

Exercise 3 — one sentence. "Batteries included" was described as mattering specifically on a corporate or compute-farm machine. Why would it matter less on your own laptop?

6. Summary

• Python was built to fill a specific gap — more structure than a shell script, far less ceremony than C. Most engineering automation sits in exactly that gap.
• Four properties explain the adoption: it reads close to English, a large standard library ships with it, it runs nearly everywhere, and there is very little scaffolding between you and a working program.
• Less ceremony is a trade, not a victory. C makes you say more and gives you exact control; Python makes you say less and handles the details. Each wins different jobs.
• It is used far beyond semiconductors — science, infrastructure, web, test engineering. The Python you learn in Part I is ordinary transferable Python.
• It is genuinely bad at some things: raw speed, tight resource control, and catching mistakes before the program runs.

That is the last chapter of context. Chapter 1.4 takes the one step deeper into how Python runs your file that makes later error messages make sense — and then Chapter 1.5 puts you in front of a real interpreter.