The two keynote talks were particularly inspiring. On Saturday, Marian Petre reported on her studies of how people in general and scientists in particular develop software. The first part of her presentation was about how "expert" design and implement software, the definition of an expert being someone who produces software that actually works, is finished on time, and doesn't exceed the planned budget. The second part was about the particularities of software development in science. But perhaps the most memorable quote of the keynote was Marian's reply to a question from the audience of how to deal with unreasonable decisions coming from technically less competent managers. She recommended to learn how to manage management - a phrase that I heard repeated several times during the discussions along the conference.
The Sunday keynote was given by Fernando Perez. As was to be expected, IPython was his number one topic and there was a lot of new stuff to show off. I won't mention all the new features in the recently released version 0.11 because they are already discussed in detail elsewhere. What I find even more exciting is the new Web notebook interface, available only directly from the development site at github. A notebook is an editable trace of an interactive session that can be edited, saved, stored in a repository, or shared with others. It contains inputs and outputs of all commands. Inputs are cells that can consist of more than one line. Outputs are by default what Python prints to the terminal, but IPython provides a mechanism for displaying specific types of objects in a special way. This allows to show images (in particular plots) inline, but also to turn SymPy expressions into mathematical formulas typeset in LaTeX.
A more alarming aspect of Fernando's keynote was his statistical analysis of contributions to the major scientific libraries of the Python universe. In summary, the central packages are maintained by a grand total of about 25 people in their spare time. This observation caused a lot of debate, centered around how to encourage more people to contribute to this fundamental work.
Among the other presentations, as usual mostly of high quality, the ones that impressed me most were Andrew Straw's presentation of ROS, the Robot Operating System, Chris Myers' presentation about SloppyCell, and Yann Le Du's talk about large-scale machine learning running on a home-made GPU cluster. Not to forget the numerous posters with lots of more interesting stuff.
For the first time, EuroSciPy was complemented by domain-specific satellite meetings. I attended PyPhy, the Python in Physics meeting. Physicists are traditionally rather slow in accepting new technology, but the meeting showed that a lot of high-quality research is based on Python tools today, and that Python has also found its way into physics education at various universities.
Finally, conferences are good also because of what you learn during discussions with other participants. During EuroSciPy, I discovered a new scientific journal called Open Research Computation , which is all about software for scientific research. Scientific software developers regularly complain about the lack of visibility and recognition that their work receives by the scientific community and in particular by evaluation and grant attribution committees. A dedicated journal might just be what we need to improve the situation. I hope this will be a success.