I've been working to get Haskell approved at my place of employment for 1.5 years but getting the US Gov't to change is rather hard; I've had to sit the "tech"

I've been working to get Haskell approved at my place of employment for 1.5 years but getting the US Gov't to change is rather hard; I've had to sit the "tech" people down and explain that javascript != java... with that baseline, explaining 'Why Haskell' is non-trivial. Come September, after 1.5 years of effort, I should have 'all the FOSS'. Until then I have to wait. It's worth noting that iPython Notebook + pandas vs. cassava + conduits (even with iHaskell Notebook) serve very different ends. If I need to explore how to do something, I'd use Haskell. But I'm still in phase 2 (phase 1: collect underpants) and I've yet to find anything as powerful and flexible as the iPython Notebook + pandas + hdf5 stack that also just works. I can just move faster with that stack than anything else I've ever seen. That being said, I'm knowingly deferring bugs to the runtime -- 'tis the cost of python. If you're unfamiliar with pandas, the "quick vignette" here[0] is decent enough. The reason pandas is awesome, IMO, isn't actually because pandas is awesome (which it is) but because it's embedded in a full language. Julia, R, etc... can do the same stuff (maybe faster), but I wouldn't also want to program, say, a production web-app in them (though I have high hopes for Julia). Fuzzy parsing on dates: pandas by default uses dateutil[1] which is both awesome and slow. 10GB CSV... : yeah... it's "fast for python" but pandas is admittedly doing a lot in that time, namely putting it into a data structure that is very friendly to time series analysis. [0] http://pandas.pydata.org/ http://pandas.pydata.org/ [1] https://labix.org/python-dateutil https://labix.org/python-dateutil