Matplotlib Recipes for your graphical soups @ PyCon 5

The term **Data Visualization** (a.k.a. *data viz*) refers to any visual
representation of data that is *algorithmically drawn* [[Illinsky & Steele, 2011]][0].
In particular, *data viz* is easy to regenerate (with different data); it is often aesthetically barren;
and it is relatively data-rich, usually representing large volume of data [[Illinsky & Steele, 2011]][0].

Data viz approaches are further distinguished in two distinct categories, namely *static* and *interactive*
visualizations. Static visualizations can offer only precomposed *views* of data, so multiple static views
are often needed to present a variety of perspectives on the same information [[Murray, 2013]][1].
On the other hand, (dynamic) interactive visualization tools allow the users to customize the way they
want to *view* the plotted data.

[Matplotlib](http://matplotlib.org) is a very powerful data visualization library, being so far the *de-facto*
standard data viz library for **Python** code.

Matplotlib offers a wide variety of 2D and 3D plots, graphics and maps (`matplotlib.basemap`) typically useful
in case of (but not limited to) data analysis in scientific contexts.
All the generated plots may be further customized (e.g.,
*annotating charts with LateX formulas*, *changing fonts and colors* ) in order to make them perfectly
suited for the different requirements we may have.

Last but not least, recent versions of the library started supporting creation of animated graphics and charts
(i.e., `matplotlib.animations`)

Most of the charts provided out-of-the-box by the library belongs to the *static data viz* category.
However, the new **MLPD3** [project](http://mpld3.github.io) aims at integrating Matplotlib with the
[**D3js**](http://d3js.org), the popular Javascript library to generate data-driven web pages.

This talk is going to present the main features and capabilities provided by the
`matplotlib` library in a very **recipe-oriented** and practical fashion.
The main goal of the talk is to emphasize *when* and *how* the different Matplotlib charts could be effectively
used, depending on the considered problem to tackle.

To this end, several code recipes will be discussed during the presentation.
Recipes using `mpld3` and `matplotlib.animations` for the *interactive data viz* will be presented as well,
and will conclude the talk

This talk assumes a good knowledge of the Python language.
Basic knowledge of `numpy` and `scipy` is also a plus.

[0]: http://www.amazon.it/Designing-Data-Visualizations-Julie-Steele/dp/1449312284/ "Designing Data Visualization"
[1]: http://www.amazon.it/Interactive-Data-Visualization-Scott-Murray/dp/1449339735/ "Interactive Data Visualization for the Web"

Transcript

1. Matplotlib Recipes for your graphical “soups” Valerio Maggio valerio.maggio@gmail.com
2. Data Visualisation (a.k.a. data viz) refers to any visual representation of data that is algorithmically drawn [Illinsky & Steele, 2011].
3. Data Visualisation is • easy to regenerate (with different data); • often aesthetically barren; • relatively data-rich • usually representing large volume of data
4. Visualising Data the Pythonic way
5. • matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala MATLAB® or Mathematica®†), web application servers, and six graphical user interface toolkits.
6. Data Viz Static Dynamic
7. Static Dynamic
8. • Very Pythonic • Nice API • Full featured • Very useful for scientific stuff • Integration with numpy and scipy • Anyway... Not for Science only!!
9. at a glance
10. Let’s see 
 some examples
11. Plotting curves
12. Plotting curves
13. Plotting curves with numpy
14. Plotting multiple curves
15. Plotting multiple curves
16. (Sub) Plotting curves
17. (Sub) Plotting curves
18. Changing Aspect Ratio
19. Plotting Grid
20. Plotting Grid
21. Plotting Annotations
22. Plotting Annotations
23. Plotting in log scale
24. Plotting in log scale
25. What about the other charts?
26. Scatter Plots
27. Scatter Plots Colormaps
28. Scatter Plots Colormaps
29. 3D Scatter Plots
30. 3D Scatter Plots
31. 3D Bar Charts
32. 3D Bar Charts
33. 2D Bar Charts
34. 2D Bar Charts
35. Bar Chart xkcd
36. Bar Chart xkcd
37. what we missed • Histograms • special cases of BarCharts for Probability Distributions • Horizontal BarCharts (barh) • Pie Charts • Box Plots • Path Plots • Stream Plots • …. http://matplotlib.org/gallery.html
38. Wait a minute…
39. Matplotlib Animations examples: @jakevdp
40. Animations in 4 steps
41. Animations
42. More Tricky Example
44. Thanks for your kind attention Valerio Maggio valerio.maggio@gmail.com