Slashdot Mirror
Comparing R, Octave, and Python for Data Analysis
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.
So, you're linking a SlashdotBI article to the Slashdot front page?
Well then.
My work here is dung.
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
I wish there was also a column for availibility of resources for learning like: tutorials, free books, example code, etc ....
how analysts can rely on open-source software
I've done that kind of stuff at work and those criteria are NEVER how a package is selected.
If I need a commercial product I need all manner of signoffs requiring at least weeks of delay and massive IT involvement so they can insert it into windoze images automatically or whatever it is they do.
If I'm doing FOSS it just ... gets done that day. No agony. And it just works, and instead of a call center script reader in India who can only tell me to reinstall the software over and over, with FOSS the "whole internet" is my support system and they as in the whole internet know what they're doing.
Nothing about this has changed in about 15 years, so I'm not sure how this is "news". This would have been a good "news" story in the early/mid nineties.
"Science flies us to the moon. Religion flies us into buildings." - Victor Stenger
"Here is a breakdown of R, Octave and Python ..."
No there isn't - that's there is not much more than a shitty 'feature' table, too high level to be anything other than facile, which is "Based on [the author's] own user experience and research".
As an student user of all 3 I would have been interested in reading a good comparative review or explanation aimed at outsiders. This ain't it; it's just more slashvertising.
What part of "a well regulated militia" do you not understand?
Sage math http://www.sagemath.org/
There was a previous article about Julia which looked cool. I wonder how this measures up
Now that's just desperation.
Come on .. keep this shit in bi. Either it takes off or it doesn't.
The best option is to use python and R, through rpy for example.
R rocks for statistical libraries and good documentation.
Python rocks for everything else.
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.
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.