If we're talking methods/tooling, I'd throw Heapy in the pile. It's a PITA to get set up in some environments but was the best tool I've used thus far for answ

If we're talking methods/tooling, I'd throw Heapy in the pile. It's a PITA to get set up in some environments but was the best tool I've used thus far for answering the question of "what objects are eating my memory" I'd also, with the _Massive_ disclaimer that I'm a MSFTie working for the Python group, mention that the visual studio IDE's Python support has a _mean_ profiler. hotpath/comparative reports/drill down/pretty pictures. That all being said I'd be curious if anyone else would echo my sentiment that maybe 85% of the python perf issues I've seen in practice (in both my own code and others, and even in light of the more sophisticated "best practices" I often read) come down to one of: - linear datastructures/algos instead of dicts/sets - deeply nested loops - excessive conversion between "stuff" (which basically falls back to #2) and/or lack of memoization. Tooling aside, it gives me a nice dose of mental conflict re: utility of algo knowlege in interviews, as someone who typically rails against it...