It's all about trade-offs. Here are a few reasons why one might care about performance in their Python code:

1. Performance is often more tied to the code than to the interpreter - an O(n³) algorithm in blazing fast C won't necessarily perform any better than an O(nlogn) algorithm in Python.
2. Just because this particular Python code isn't particularly performance constrained doesn't mean you're okay with it taking twice as long.
3. Rewriting a large code base can be very expensive and error-prone. Converting small, very performance-sensitive parts of the code to a compiled language while keeping the bulk of the business logic in Python is often a much better value proposition.

These are also performance benefits one can get essentially for free with linter rules.

Anecdotally: in my final year of university I took a computational physics class. Many of my classmates wrote their simulations in C or C++. I would rotate between Matlab, Octave and Python. During one of our labs where we wrote particle simulations, I wrote and ran Octave and Python simulations in the time it took my classmates to write their C/C++ versions, and the two fastest simulations in the class were my Octave and Python ones, respectively. (The professor's own sim came in third place). The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don't think I could have explained why their code was so slow compared to mine).

Honestly most people use Python because it has fantastic libraries. They optimize it because the language is middling, but the libraries are gorgeous

ETA: This might double post because my Internet sucks right now, will fix when I have a chance

This is my two cents as someone in the industry.

Because, while you don't want to nitpick on each instruction cycle, sometimes the code runs millions of times and each microsecond adds up.

Keep in mind that people use this kind of things for work, serving real world customers who are doing their work.

Yes, the language itself is not optimal even by design, but its easy to work with, so they are making it worth a while. There's no shortage of people who can work with it. It is easy to develop and maintain stuff with it, cutting development cost. Yes, we're talking real businesses with real resource constraints.

You may want to beneficiate from little performance boost even though you mostly don't need it and still need python's advantages. Being interested in performance isnt always looking for the very best performance there is out of any language, it can also be using little tips to go a tiny bit faster when you can.

Yes, Python is the wrong choice if performance is your top priority.

But here's another perspective: why leave easy performance wins on the table? Especially if the cost is simpler code that works as you probably wanted anyway with both None and []?

Python is great if you want a really fast development cycle, because the code is generally quite simple and it's "fast enough." Any wins for "fast enough" is appreciated, because it delays me needing to actually look into little performance issues. It's pretty easy for me to write a simple regex to fix this cose (s/if len\((\w+)\) == 0:/if not \1:/), and my codebase will be slightly faster. That's awesome! I could even write up a quick pylint or ruff rule to catch these cases for developers going forward (if there isn't one already).

If I'm actively tweaking things in my Python code to get a little better performance, you're right, I should probably just use something else (writing a native module is probably a better use of time). But the author isn't arguing that you should do that, just that, in this case, if not foo is preferred over if len(foo) == 0 for technical reasons, and I'll add that it makes a ton of sense for readability reasons as well.

Here are some other simple wins:

• [] and {} instead of list() and dict() - the former copy constants, whereas the latter actually constructs things; oh, and you save a few chars
• use list comprehensions instead of regular loops - list comprehensions seem to be faster due to not needing to call append (and less code)
• use built-ins when you can - they're often implemented in native code

I consider each of those cleaner Python code anyway, because they're less code, just as explicit, and use built-in language features instead of reinventing the wheel.

It comes down to the question "Is YOUR C++ code faster than Python?" (and of course the reverse).

I've built a SCADA from scratch and performance requirements are low to begin with, seeing as it's all network bound and real world objects take time to react, but I'm finding everything is very timely.

A colleague used SQLAlchemy for a similar task and got abysmal performance. No wonder, it's constantly querying the DB for single results.

I have the same question. I prefer other languages. I use G'MIC for image processing over Python and C++.

I’ve worked on a library that’s Python because the users of said library are used to Python.

The original version of the project made heavy use of numpy, so the actual performance sensitive code was effectively C++ and fourtran, which is what numpy is under the hood.

We eventually replaced the performance sensitive part of the code with Rust (and still some fourtran because BLAS) which ended up being about 10x faster.

The outermost layer of code is still Python though.