To be honest, matplotlib seems a good contender to me (http://matplotlib.org/).
Also, what's wrong with comparing R to Pandas/Numpy ? They can only be used from within Python, right?
Edit: just realised from another comment that Pandas/Numpy can be accessed from R, too.
> To be honest, matplotlib seems a good contender to me (http://matplotlib.org/).
They're quite different, though, and I can see why many prefer ggplot. It's a declarative, domain-specific language that implements a Tufte-inspired "grammar of graphics" (hence the gg- in the name; see section 1.3 of [1], and [2,3]) for very fast and convenient interactive plotting, whereas matplotlib is just a clone of MATLIB's procedural plotting API.
[1] http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis...
[2] http://www.amazon.com/The-Grammar-Graphics-Statistics-Comput...
I've waxed lyrical about Python all over this thread, but here you have to give the medal to R. Matplotlib is one of my least favourite libraries to use, been doing it for almost 2 years, and I still spend half my time buried in the documentation trying to figure out how I'm supposed to move the legend slightly to the right or whatever.
ggplot probably has slightly less flexibility overall (mpl is monolithic), but for just doing easy things that you need 99% of the time, ggplot is king.
I am not familiar with ggplot, so I wasn't comparing them on the ground of the easiness of use, but by looking at some ggplot examples, they looked like something you can do with matplotlib, too, so I pointed that option out, too.
Absolutely nothing.
I was referring to the article title that is was an R vs. Python comparison. Python is so much more in terms of a general purpose language than R is. Similarly, R is much more in terms of stats (built-in) than Python. I just thought that it would be more accurate to call the article an R vs. Pandas/NumPy comparison.
Even though both of them need an extra plotting library to make publication quality plots. Matplotlib isn't bad by any means - and it's gotten better over the years. But R/ggplot2 produces nicer plots (IMO). I'm not sure that I'd export data from Python into R just for ggplot, but I might.
I am not that familiar with ggplot myself, but I'll give it a go as soon as I'll have the chance.
On paper perhaps, less so in application. Sure you can probably make matplotlib do everything ggplot does with enough work, but working with ggplot is just so much quicker easier and more fun.
And I say that as someone who does all his data analysis in Python.
You can try out my dev version [1] (rewrite branch). It will be nearly API compatible.
I tried the ggplot for python (ggplot.yhathq.com/) but eventually settled for seaborn (http://stanford.edu/~mwaskom/software/seaborn/). It is really quite easy to get most of the common plots that I wanted and hasn't let me down yet. The standard plots look SO much better than the standard plots of MPL without a lot of customization.
On the other hand, with ggplot, you can create a good enough chart in couple of lines of code almost for any data.
BTW, there's a ggplot port for a Python: http://ggplot.yhathq.com/