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Is Python a Legitimate Data Analysis Tool?

Posted by Soulskill on from the one-end-it-is dept.

Back in May we discussed using Python, R, and Octave as data analysis tools, and compared the relative strength of each. One point of contention was whether Python could be considered a legitimate tool for such work. Now, Bei Lu writes while Python on its own may be lacking, Python with packages is very much up to the task: "My passion with Python started with its natural language processing capability when paired with the Natural Language Toolkit (NLTK). Considering the growing need for text mining to extract content themes and reader sentiments (just to name a few functions), I believe Python+packages will serve as more mainstream analytical tools beyond the academic arena." She also discusses an emerging set of solutions for R which let it better handle big data.

6 of 67 comments (clear)

  1. It Works by mrsquid0 · · Score: 4, Insightful

    Python may not be a legitimate data analysis tool, but it is widely used for data analysis, and it gives the right results. For the most part that is what really matters.

    --
    Just because you are paranoid does not mean that no-one is out to get you.
    1. Re:It Works by mcgrew · · Score: 5, Insightful

      Python is a language. It's a tool to build other tools with, including data analysis tools.

      --
      Free Martian Whores!
    2. Re:It Works by ceoyoyo · · Score: 4, Insightful

      What does "legitimate data analysis tool" mean? MatLab was included in the comparison, and MatLab is more of an engineering tool. The built in (excuse me, optional paid for) stats library is pretty limited.

      R is great for doing statistical analysis, but it's not great for doing things like image analysis. Without additional libraries R isn't nearly as good as it is with libraries either.

  2. Re:Call me old fashioned by ceoyoyo · · Score: 5, Interesting

    It depends how complicated the math is.

    I wrote a general linear model in Python because I was unhappy with the existing ones and I wanted an intimate knowledge of how it worked. I wrote most of a general linear mixed model, but then decided it wasn't worth the time and just used the one in R via RPy2. Then it turned out the one built into R was too slow, so I upgraded to the one in the lme R package. That exists because a lot of smart people use R.

    But sure, if your "data analysis" involves multiplication and maybe a t-test or two, it doesn't really matter what you use.

  3. Use what works by hawguy · · Score: 5, Insightful

    Since people do use python for data analysis (hence the data analysis related packages that are available), of course it's legitimate.

    Just like how when you're standing on the roof and you need to pound in a couple nails, that heavy pair of pliers in your pocket is a legitimate tool. It may not be the best tool for the job, the best tool might be a pneumatic nail gun, but if all you have with you and what you know how to use is pliers, then that's the right tool. Why spend time and money learning some other "more appropriate" language (or buying an air compressor and nail gun) when you already have a tool at your fingertips that will do what you need.

    As your needs grow you might need to find another more appropriate tool, but if you can get the job done with Python, why bother searching for the "perfect" tool?

    Depending on your needs, sh, awk, sed, sort, and uniq may be all the tools you need - many log parsing, analysis and reporting programs have been writing with those tools, often ingesting more rows of data per day than many small business BI systems.

  4. Python can do anything by Anonymous Coward · · Score: 5, Funny

    http://xkcd.com/353/