# Tag: matplotlib

It was quite a bit that I wanted to have a go to play with the ipython notebook, but I wanted to do it with something that was quite interesting and useful.

The IPython Notebook is a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document

– from the docs

This means that you can document your process and or exploration using Markdown, which is than beautiful rendered in html, and have also python code executed, with the graphs that are going to be embedded and will stay in the document.

I think it’s a very valuable tool, in particular when you are doing exploratory work, because the process of discovery can be documented and written down, and it a great way to write interactive tutorials.

For example, in this notebook, I’ve plotted the Probability Density Function of several statistical distributions, to have an idea how they are shaped, and which one to pick as base when creating new bayesian model.

You can see how it looks like on nbviewer

Gg2plot is an amazing library to plot and it’s available for R to create stunning graphs. GGplot2 takes a different approach from the classic library, and instead of offering a classic line/points approach permits to combine these elements (example), which is a similar root took by D3js. If you are using the scientific python stack (matplotlib, numpy, scipy, ipython) you have the very good matplotlib to plot and have all your graph app.

For example a bunch of sin and cosine generated by the following code:

https://gist.github.com/mattions/5330631

look like this:

Instead if we set up a ggplot2 style, the graph looks like this:

You may prefer one or the other. Anyway if you like the last one, just download this matplotlibrc and save it as `~/.matplotlib/matplotlibrc`, and all your graph will have that style as default.

The matplotlibrc has been inspired by this post, I’ve just updated with the latest matplotlibrc from matplotlib 1.2.1 version.

Have fun!

Edit: Bonus plot, code in the gist.

I’ve just added a CC BY-NC-SA 3.0 license to my (one year old) presentation about scipy and numpy and imported the code and the stuff on github, so it’s easier for people to download it (and hopefully the link will not go down, after I’ll leave the EBI)

I’ll put it also here, so you can have a look.

[slideshare id=2512270&doc=pylab-091116123248-phpapp02]