Nice numbers and it's always worth to know an order of magnitude. But these charts are far away from what "every programmer should know".

Great reference overall, but some of these will diverge in practice: 141 bytes for a 100 char string won’t hold for non-ASCII strings for example, and will change if/when the object header overhead changes.

I doubt list and string concatenation operate in constant time, or else they affect another benchmark. E.g., you can concatenate two lists in the same time, regardless of their size, but at the cost of slower access to the second one (or both).

More contentiously: don't fret too much over performance in Python. It's a slow language (except for some external libraries, but that's not the point of the OP).

Counterintuitively: program in python only if you can get away without knowing these numbers.

When this starts to matter, python stops being the right tool for the job.

I doubt there is much to gain from knowing how much memory an empty string takes. The article or the listed numbers have a weird fixation on memory usage numbers and concrete time measurements. What is way more important to "every programmer" is time and space complexity, in order to avoid designing unnecessarily slow or memory hungry programs. Under the assumption of using Python, what is the use of knowing that your int takes 28 bytes? In the end you will have to determine, whether the program you wrote meats the performance criteria you have and if it does not, then you need a smarter algorithm or way of dealing with data. It helps very little to know that your 2d-array of 1000x1000 bools is so and so big. What helps is knowing, whether it is too much and maybe you should switch to using a large integer and a bitboard approach. Or switch language.

Python programmers don't need to know 85 different obscure performance numbers. Better to really understand ~7 general system performance numbers.

It's important to know that these numbers will vary based on what you're measuring, your hardware architecture, and how your particular Python binary was built.

For example, my M4 Max running Python 3.14.2 from Homebrew (built, not poured) takes 19.73MB of RAM to launch the REPL (running `python3` at a prompt).

The same Python version launched on the same system with a single invocation for `time.sleep()`[1] takes 11.70MB.

My Intel Mac running Python 3.14.2 from Homebrew (poured) takes 37.22MB of RAM to launch the REPL and 9.48MB for `time.sleep`.

My number for "how much memory it's using" comes from running `ps auxw | grep python`, taking the value of the resident set size (RSS column), and dividing by 1,024.

1: python3 -c 'from time import sleep; sleep(100)'

Initially I thought how efficient strings are... but then I understood how inefficient arithmetic is. Interesting comparison but exact speed and IO depend on a lot of things, and unlikely one uses Mac mini in production so these numbers definitely aren't representative.

The titles are oddly worded. For example -

  Collection Access and Iteration
  How fast can you get data out of Python’s built-in collections? Here is a dramatic example of how much faster the correct data structure is. item in set or item in dict is 200x faster than item in list for just 1,000 items!

It seems to suggest an iteration for x in mylist is 200x slower than for x in myset. It’s the membership test that is much slower. Not the iteration. (Also for x in mydict is an iteration over keys not values, and so isn’t what we think of as an iteration on a dict’s ‘data’).

Also the overall title “Python Numbers Every Programmer Should Know” starts with 20 numbers that are merely interesting.

That all said, the formatting is nice and engaging.

Yeah... No. I've 10+ years of python under my belt and I might have had need for this kind of micro optimizations in like 2 times most

int is larger than float, but list of floats is larger than list of ints

Then again, if you're worried about any of the numbers in this article maybe you shouldn't be using Python at all. I joke, but please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum.

What would be the explanation for an int taking 28 bytes but a list of 1000 ints taking only 7.87KB?

tfa mentions running benchmark on a multi-core platform, but doesn't mention if benchmark results used multithreading.. a brief look at the code suggests not

Every Python programmer should be thinking about far more important things than low level performance minutiae. Great reference but practically irrelevant except in rare cases where optimization is warranted. If your workload grows to the point where this stuff actually matters, great! Until then it’s a distraction.

This is really weird thing to worry about in python. But is also misleading; Python int is arbitrary precision, they can take up much more storage and arithmetic time depending in their value.

A meta-note on the title since it looks like it’s confusing a lot of commenters: The title is a play on Jeff Dean’s famous “Latency Numbers Every Programmer Should Know” from 2012. It isn’t meant to be interpreted literally. There’s a common theme in CS papers and writing to write titles that play upon themes from past papers. Another common example is the “_____ considered harmful” titles.

Why? If those micro benchmarks mattered in your domain, you wouldn't be using python.

That's a long list of numbers that seem oddly specific. Apart from learning that f-strings are way faster than the alternatives, and certain other comparisons, I'm not sure what I would use this for day-to-day.

After skimming over all of them, it seems like most "simple" operations take on the order of 20ns. I will leave with that rule of thumb in mind.

Author here.

Thanks for the feedback everyone. I appreciate your posting it @woodenchair and @aurornis for pointing out the intent of the article.

The idea of the article is NOT to suggest you should shave 0.5ns off by choosing some dramatically different algorithm or that you really need to optimize the heck out of everything.

In fact, I think a lot of what the numbers show is that over thinking the optimizations often isn't worth it (e.g. caching len(coll) into a variable rather than calling it over and over is less useful that it might seem conceptually).

Just write clean Python code. So much of it is way faster than you might have thought.

My goal was only to create a reference to what various operations cost to have a mental model.

This is AI slop.