As a longtime user of Python's scientific stack, I understand your pain. In a practical sense, the libraries that make up the Python scientific stack -- numpy,

As a longtime user of Python's scientific stack, I understand your pain. In a practical sense, the libraries that make up the Python scientific stack -- numpy, pandas, matplolib, sklearn, etc. -- are all akin to domain-specific languages, each with its own magic behavior, quirks, and recommended best practices that one must master. It takes a long while to become proficient in all of them. The good news is that there's a large, growing community of helpful people using these tools, so you can usually find the answer to any question with a single Google search. For numpy specifically, if you are interested in getting the most out of it with the fewest possible headaches, I would recommend the following online book: http://www.labri.fr/perso/nrougier/from-python-to-numpy/ http://www.labri.fr/perso/nrougier/from-python-to-numpy/ Otherwise, please make sure to read bmarkovic's spot-on comment in this thread: https://news.ycombinator.com/item?id=14158597 https://news.ycombinator.com/item?id=14158597