I'm slowly moving my elaborate data-cleaning system from R to python. I never got the hang of R packages and ended up rolling my own "import" scheme, but now it all seems like I've passed some critical complexity point.
I'm still doing all of my model estimation in R, and data.table + ggplot2 is still my go-to solution for interactive exploration and plotting, but I'm putting more and more of the process-csv, write-hdf5, remove out-of-bounds values, backfill, stratified sampling/bootstrap, etc., logic into python.
I may end up moving more of the estimation itself into python, now that statsmodels and sklearn are more mature, but R does that pretty well.
I've not tried Julia, but I have experimented with Java for some of the data-processing, but it's never seemed worth it, over python. Basic CSV processing is faster in python than in Java (although the python version uses much more CPU), so I haven't felt the need. (I'm probably limited more by disk bandwidth than CPU, but I've not done the tests required to prove that.)
Julia seems nice, but it seems intent on copying all the questionable design choices of matlab, rather than using ideas from the numpy/kdb+/apl worlds.
I'm still doing all of my model estimation in R, and data.table + ggplot2 is still my go-to solution for interactive exploration and plotting, but I'm putting more and more of the process-csv, write-hdf5, remove out-of-bounds values, backfill, stratified sampling/bootstrap, etc., logic into python.
I may end up moving more of the estimation itself into python, now that statsmodels and sklearn are more mature, but R does that pretty well.
I've not tried Julia, but I have experimented with Java for some of the data-processing, but it's never seemed worth it, over python. Basic CSV processing is faster in python than in Java (although the python version uses much more CPU), so I haven't felt the need. (I'm probably limited more by disk bandwidth than CPU, but I've not done the tests required to prove that.)
Julia seems nice, but it seems intent on copying all the questionable design choices of matlab, rather than using ideas from the numpy/kdb+/apl worlds.