Lofkin

What do you think of bokeh: https://github.com/DataWookie/MonthOfJulia

Does this work on windows? Rapydscript does not IIRC

Call the libraries with Rpy2, or build models using a DAG of functions and distributions with pymc 3

Have you seen numba? Compiles numerical python approaching fortran speeds

Nuitka is already faster than Cpython and it seems a majority of the speed work has yet to be done.

Julia will when it can compile to web assembly

Awesome!

A while back there was some talk of native excel-python integration. Any chance that is in the pipes, or is MS sticking to R in that sense?

Sweet, new Microsoft is awesome!

Pyston seems to be catching up to PyPy fairly quickly ...not sure if there is anything transferable to pyjion, but if you haven't done so might be worth checking out.

Also for getting around the GIL, PyParallel seems to have a really interesting (and seemingly successful approach: http://pyparallel.org/

Its being worked on by one of the Guy's at continuum...and It's definitely in continuum's best interest to help python's future so I'm sure they wouldn't mind collab.

Exciting work! Is pyjion exploratory or is MS determined to see this through?

What sort of diet and supplements?

Nuitka?

For now...

Not a problem for Julia: https://github.com/Keno/Cxx.jl

What about when Julia gets those things?

Thoughts on Julia?

Solved here: http://www.ibis-project.org/index.html

Makes sense. Thanks for both your open source work and your reply!

Also, any plans for the stats ecosystem? (dataframe, dashboards, imputation, data cleaning etc)

It is still a bit wanting.

"However, it gives us the ability to do some really amazing things with our array infrastructure going forward."

What kind of things? Super curious.

The GIL issue has been solved without breaking backwards compat: http://pyparallel.org/

It just need further dev work and acceptance into py3

Sure!

If you like it, can you please blog about it (and post on hn)? Needs exposure outside the python data community.

Your example is trivially parallelized with some decorators from the dask library.

Example here: http://dask.pydata.org/en/latest/imperative.html

Have you seen this: http://dask.pydata.org/en/latest/

They pretty much already did, but with 3.x: http://pyparallel.org/

Better multitasking and concurrency syntax, with task scheduling and out of core support here: http://dask.pydata.org/

Try this: http://dask.pydata.org/

You can use python, call out to R libraries with Rpy2 and speed up loops to faster than Julia and around native Speed with numba

Try numba in python. Its usually fortran fast and often faster than Julia.

Any thoughts on what this guy is saying?

Check this out for frontend in python: https://github.com/zoofIO/flexx