Did you know it takes about 17,000 CPU instructions1 to print("Hello") in Python? And that it takes ~2 billion of them to import seaborn? Since writting this I have upgraded Cirron to substract its own overhead; it now measures print at ~9,000 instructions. ↩
Did you know it takes about 17,000 CPU instructions to print("Hello") in Python? And that it takes ~2 billion of them to import a module?
Same for me. I have used Python for most things since the late 1990s. Love Python. Have always hated the poor performance... but in my case mostly it was good enough. When it was not good enough, I wrote C code.
Python is good for problems where time to code is the limiting factor. It sucks for compute bound problems where time to execute is the limiting factor. Most problems in my world are time to code limited but some are not.
I get that... I'm not a developer, I'm a network engineer but I use a lot of python in my day to day operations. I always took python to be the "code for non-coders" which made it infinitely more approachable than some of the other languages.
I'm not running the F1 grand prix over here, I'm driving to get groceries, so what if it's not the fastest thing out there. Close enough is good enough for me. And in my experience that's what people are using python for, daily driving.
People use Python a lot as a Matlab, Excel/VBA, or R alternative. That was my use for many years. Some of these are compute focused problems and if the dataset is large enough and the computations complex enough then speed can be an issue.
As far as loading packages and printing. Who cares. These are not computationally intensive and are typically IO bound.