Ask HN: Python or R?

I don't use R. I use [pandas] / [statsmodels] in Python. I don't do particularly special stats though.

I read this [blog] earlier and as a result I don't think I'll bother to learn R unless I have to.

[pandas]: http://pandas.pydata.org/

[statsmodels]: http://statsmodels.sourceforge.net/

[blog]: http://www.talyarkoni.org/blog/2013/11/18/the-homogenization...

What's the problem you're trying to solve? It's hard for anyone to give useful advice without any more context.

That said, I recommend whichever language is easiest for you. I use R and have not fully learned Python, so I have an obvious bias. If you're performing complicated statistical analysis, I'd recommend R, but for more traditional programming, I get the impression that Python interfaces more efficiently with other languages.

I use both. I like R for its charting capability and the sheer amount of packages for different use cases. I use Python to pre-process data and get it into because it is a lot easier than in R. Also Scikit-learn, NumPy and Pandas are really nice.

As everyone says, depends on what you are trying to do.

For all interactive/exploratory analysis, for statistical graphics, for more advanced statistics, for most statistics-related research work in general, I would definitely pick R out of these two.

If statistics is only a small part of the application, if you already know exactly what you have to do (i.e. no data exploration), if you have to do a lot of web/text processing -- probably Python.

Also, check which one has more/better packages related to what you are doing.

For some stats projects I would go with something else entirely though.

They're not really directly comparable. Python is an general purpose programming language, R is a statistical processing tool.

You could compare R to Matlab, or R to python library with similar scope (numpy or pandas).

It really all boils down to what problem you're trying to solve, what kind of analysis you're trying to do, and what the performance requirements are. For basic stats, R and Python will be comparable in terms of library availability and functionality. If you start getting into more specialized and/or esoteric statistics, you will find more R packages (libraries) than you will Python libraries.

I use both for my data analysis (though I'm not doing anything too fancy these days). R is great at statistics (that's what it was designed for) but a bit of a terrible programming language. So I combine the two, do all the IO stuff etc in Python and run the actual statistical analysis in R (RPy is a great Python interface to R).