It's flexible, easy to use and can -with some tweaking- produce publication-quality output. It supports a wide range of backends such as PDF, SVG, PNG and a range of GUIs like Qt, so it is easy to use both for static and interactive graphs. More importantly, it plays nicely with the IPython notebook (http://ipython.org/notebook.html) and with libraries such as numpy, pandas and scikit-learn. The last point is very important because when visualizing data half of the problem is getting the data in shape, and Python offers some excellent tools for this (much better than what you would get in Javascript).
The choice of the right tool depends of course on where you want to show your plots and what kind of plots you want to generate. If you intend showing your visualizations in a web browser then D3 or a similar JS library would probably be a good choice (also have a look at Bokeh, which is becoming a very good alternative to Matplotlib). For iPhone applications there are probably native toolkits to do this, but unfortunately I have no experience there and cannot make any recommendations.
Concerning the scalability of your visualizations: To my knowledge there are not many tools that can handle more than a few 10.000 data points without becoming slow. So you will probably always have to reduce the number of data points before plotting them. In any case, showing all of your data points without prior aggregation will probably not be a good idea in most cases, performance aside. If you really need to work with a massive amounts of data points have a look at Processing, which to my understanding can handle pretty large data sets (but is not a data visualization toolkit per se). OpenGL-based approaches can also be interesting for visualizing large amounts of data points, especially but not only in 3D.
Concerning your examples:
- Gephi is a nice tool for visualizing graphs (http://gephi.github.io/), but a bit clunky to use at time.
- R has the excellent GGPlot module that can work with pretty large data sets and has specialized graphs for e.g. time series data.