Python numbers every programmer should know

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".

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).

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.

String concatenation is mentioned twice on that page, with the same time given. The first time it has a parenthetical "(small)", the second time doesn't have it. I expect you were looking at the second one when you typed that as I would agree that you can't just label it as a constant time, but they do seem to have meant concatenating "small" strings, where the overhead of Python's object construction would dominate the cost of the construction of the combined string.

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)'