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Comparing R, Octave, and Python for Data Analysis

Posted by Soulskill on from the data-analysis-just-wants-to-be-free dept.

Here is a breakdown of R, Octave and Python, and how analysts can rely on open-source software and online learning resources to bring data-mining capabilities into their companies. The article breaks down which of the three is easiest to use, which do well with visualizations, which handle big data the best, etc. The lack of a budget shouldn't prevent you from experiencing all the benefits of a top-shelf data analysis package, and each of these options brings its own set of strengths while being much cheaper to implement than the typical proprietary solutions.

4 of 61 comments (clear)

  1. Did I seriously miss something? by ACK!! · · Score: 4, Informative

    The whole article was not much more than a high level review. The graphic naturally draws attention to the parameters the writer wanted to cover but he did not back up his graphic with any sort of serious textual review of what he felt were the weaknesses or advantages of the different programming language at least not in any detail.

    --
    ACK /ak/ interj. 2. [from the comic strip "Bloom County"] An exclamation of surprised disgust, esp. i
  2. Julia? by Chrisq · · Score: 3, Informative

    There was a previous article about Julia which looked cool. I wonder how this measures up

  3. I don't understand by utkonos · · Score: 4, Informative

    This article compares three languages that have different purposes. R's purpose is statistical analysis and visualization. Octave is a general mathematical analysis and visualization language. Python is a generalist language that has it's own focuses on code readability among other things.

    These languages also have a target audience. R is for statisticians and scientists. Octave is for mathematicians, and Python is for programmers.

  4. Python does have data.frame.. by csirac · · Score: 3, Informative

    Through pandas, for a start. The SciPy/NumPy stack is quite nifty, I'm especially interested in how to apply it for working with irregular time series data.

    Not to say anybody should ditch R, I still support our researchers most weeks at work in using it. But it's not as clear-cut as you seem to think it is, especially in terms of memory efficiency.