Comparing R, Octave, and Python for Data Analysis 61
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.
Did I seriously miss something? (Score:5, Informative)
Julia? (Score:4, Informative)
I don't understand (Score:5, Informative)
These languages also have a target audience. R is for statisticians and scientists. Octave is for mathematicians, and Python is for programmers.
Python does have data.frame.. (Score:4, Informative)
Through pandas [sourceforge.net], 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.