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Wow, didn't know about np.frompyfunc, but I think it might not actually do vectorization... that would be another interesting comparison.
Okay checkout my gist!
The pandas approach on 1d array takes ..
371 µs ± 30.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
The vectorized approach on 1d array takes ..
7.14 µs ± 406 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
A list comprehension takes ..
8.7 µs ± 732 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
If anything, you have demonstrated that pandas apply is surprisingly slow.
Final comment: why use gists instead of bitbucket repos? I found it a pain to "clone" and run this locally, and it seems it will be harder to keep track of than a repo. Also what if you ever want to add another file?
@alokito I think gists are a good staging ground, perhaps? I've put code snippets up and notebooks (with outputs) up here to quickly share it with others. NBs with outputs are a bad idea in a repo. Perhaps if @wusixer amasses a collection of NBs, then that's a good time to put the stuff into a repo?
@Alokit Nice! The reason I put it in gist is that it was a quick and easy thing for me to do, and I can look up what I need by searching the names of gist (I guess you can do that in bitbucket/github too but it would require a bit more structure to make it a repo). Maybe someday I will have a repo called "little_things_Iearned_from_work" :)
This is so totally a blog post, @wusixer. Add a few more annotations - and maybe compare it to JAX's
vmap, which operates only on numerical data but still good for you to see in action.