Learning Haskell Data Analysis

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Detecting and managing mangled encodings? That line of code was demonstrative. The actual book uses 'Data. IsSpace' which properly handled it. I'm not affiliated with the book but you probably deterred people from buying it. IanCal on July 30, Well for one the line shouldn't really be one of the first bits of advertising for the book if the author also knows it's wrong.

The second example is taken from the GitHub repo for the book though, and is exactly the same type of error. Quite possibly, but I think with good reason. I don't know what's in the book but I'm concerned it won't contain things like a discussion of what whitespace is and is not, how to decide what you should do for your data and when isSpace might not do what you really need.

I can't review it properly but at least one bit of code in the repo looks dangerous and one bit on the website looks dangerous. I agree that line of code was a turn-off because of its oversimplification. Why demonstrate with something completely unrepresentative of the actual contents, then? Great, thanks for the link. Char isAlpha, isLower, isSpace import qualified Data.

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Text as T giving you e. Important note for non-Haskellers: the library OP is talking about is part of the standard base libraries usually distributed with GHC. This is a repost of a book we published in I have bought that book some years ago and wouldn't do so again. It was very disappointing to see neither a focus on Haskell nor on data analysis.

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It scratches both topics but covers only very elementary things. The content is mostly short receipts that were to me of no value at the time. For people interested in the topics I'd recommend to buy some other good books, on Haskell and data analysis separately. Serious question: Is there any reason why somebody would use Haskell for data analysis when there is also R and Python - which are perfect for that job - except for that the respective person happens to be a Haskell expert anyway?

I use all three, depending on the task. Haskell is compiled and relatively speedy, as well as being great for writing custom parsers plus in a lot of situations it parallelizes very easily. One thing I find Haskell to be really useful for is coercing large unstructured datasets into a format that is easier to feed into Python. For instance, I once had to write a parser for the data coming off of a digitizer being fed by a rather complicated radiation detector array. It's in an obscure and somewhat bizarre binary format that is pretty tedious to work with because it involves a lot of state in the parser, plus the files are pretty enormous they describe every radioactive particle hitting every detector in the array over several hours of measurement.

My colleague wrote a horribly tedious script in Python to parse it that was complicated and agonizingly slow, but I was able to write a very natural line Haskell program in a few hours that was several orders of magnitude faster as well as much more robust.

Best Books for Learning Haskell Programming | Hacker News Books

I was just massaging the data to feed into Python, but it was far far easier to do in Haskell. The knowledge gained through his experience as an analyst for the MPMP as well as other companies was turned into a data analysis course for undergraduates at the University of Mississippi. This course was taught using the languages of Python and R.

Joel Grus: Learning Data Science Using Functional Python

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Learning Haskell Data Analysis

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