Comment by mikeckennedy
1 day ago
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.
Then you should have written that. Instead you have given more fodder for the premature optimization crowd.
I didn't tell anyone to optimize anything. I just posted numbers. It's not my fault some people are wired that way. Anytime I suggested some sort of recommendation it was to NOT optimize.
For example, from the post "Maybe we don’t have to optimize it out of the test condition on a while loop looping 100 times after all."
The literal title is "Python Numbers Every Programmer Should Know" which implies the level of detail in the article (down to the values of the numbers) is important. It is not.
It is helpful to know the relative value (costs) of these operations. Everything else can be profiled and optimized for the particular needs of a workflow in a specific architecture.
To use an analogy, turbine designers no longer need to know the values in the "steam tables", but they do need to know efficient geometries and trade-offs among them when designing any Rankine cycle to meet power, torque, and Reynolds regimes.