[bayesianhorse]

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...

[up_and_up]

> 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...

[adw]

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.

[MetaCosm]

The best parts of python... are C.

[nostrademons]

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

[bayesianhorse]

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

[up_and_up]

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.

[mattrobenolt]

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

[caycep]

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.

[jre]

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.