Numpy, scikit (learn/image), panda, and probably tensorflow (never used that one). Tbf numpy+pandas is like using a mathlab that also have easy way to do http

Numpy, scikit (learn/image), panda, and probably tensorflow (never used that one). Tbf numpy+pandas is like using a mathlab that also have easy way to do http requests/web scraping, so there's that. I didn't really do datascience in 6 years now (except that one time I demo'ed our jupyterlab+dask setup vs a classic Hadoop), but to me those are the clear reasons. Also the first time I used python I was doing a CTF and had to add between 70 and 130 NOP (randomly) before the payload. I think I now could do that in perl and awk, but at the time it was a python one-liner and I do think python was the easiest to debug between the three, hence was the best solution.