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I think Python is at a weird place regarding data science. If you need easy-to-use tools, R libraries cover significantly more ground than Python. You don't ha
by ulucs 8y ago
I think Python is at a weird place regarding data science.
If you need easy-to-use tools, R libraries cover significantly more ground than Python. You don't have to worry about 2/3 compatibility of niche libraries. Plus the functional language forces the libraries to have similar API's. You also get to use tidyverse and ggplot2 in all of their majestic beauty. At most you have to write a "%>% do() %>%" to get around weird library API choices.
However if you are in the frontier, you could avoid so much awkward notation with an array-first language like Matlab or Julia. You have "^" instead of "np.linalg.matrix_power", "/","\" instead of "np.linalg.lstsq", the . operator and so much more life-saving tools.
- orangejewce 8y agoR has so many problems itself! Attempting to run it at any sort of scale is basically impossible. Its dependency management is atrocious. You can't thread or multi-process effectively. The amount of overhead to even install R on a system is incredible, and amazingly its documentation is even worse than Python. We use R as our foundational Data science language and it has caused nothing but immense pain for everyone attempting to wrap the packages in scalable software.