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Comment by bayesianhorse | Hacker News Reader

I've heard this several times. But the success stories are mostly concerned with smaller parts under heavy load. Go still is a lot harder to work with than Python if you know Python and its tools well...

With Go you don't have anything comparable to Django, Numpy, Pandas...

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> With Go you don't have anything comparable to Django, Numpy, Pandas...

Given the age of the language I would assume building out its associated toolchain is only a matter of time. Python didnt ship with Django, Numpy, Pandas...

It will be a matter of years. I for one, am developing stuff today.

As are most of us lol. I would say if you want to use golang for a serious project and need additional libs you have to ready to write it yourself, which grants the opportunity to give back to the community and contribute to open source etc.

Python is rapidly becoming the lingua franca of data science; but there the vast majority of your inner loop isn't Python (numpy, scipy, pandas, numba, theano; C, Fortran, assembler, Cython, LLVM, CUDA...).

Basically Python is the glue language data people wanted, it turns out.

The best parts of python... are C.

The most-frequently-executed parts of Python are C. The best part is being able to glue all those together easily.

This is very true. We're definitely looking at Go for "services", not as something to replace our entire Django app with.

Will this change with time? I know numpy, pandas and matplotlib are likely relatively large projects, but my assumption is someone will likely put together data analysis / matrix math libraries to perform some of these functions.

I think Julia (http://julialang.org/) might be a better alternative to Scientific Python than Go. I'm not sure you can get the same flexibility/expressiveness you get in Numpy/R/Matlab in Go.

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