About the DQMP-PUG

The DQMP Python User Group (DQMP-PUG) was created in May 2019 in order to bring together Python users at the Department of Quantum Matter Physics of the University of Geneva. The group is a place to exchange ideas and learn new concepts. Whilst the group is focused on materials physics, it is open to anyone willing to join and discuss.


Meetings are held every few weeks at or near the Physics building at the University of Geneva, see here for the location.

Modus operandi

The idea of the gatherings is to exchange ideas and ask your peers for feedback on challenges that you are facing. Thus, the meetings will be split between the presentation of a new concept (in a hands-on lecture-like format), and discussions about a specific problem and ways to tackle it. If you have a specific problem you would like to ask ideas about, please write it on the GitLab Issues tracker ( https://gitlab.unige.ch/pug/user-meetings/issues ), and this way people can either answer online or might have some insights by the following gathering.

If no specific problems are presented, the discussions will follow the development of the Python Data Science Handbook that can be found for free here. Feel free to suggest any additional topics of interest :-)


When? Where? What?
12h-13h 28/10/2019 MaNEP Room Fifth meeting and roundtable, introduction to pickling and HDF5 for object storage.

A list of past meetings can be found here.

Some useful links

https://gitlab.unige.ch/pug/ - The GitLab of the user group. Feel free to use it to post your code snippets, projects and questions.

https://jakevdp.github.io/PythonDataScienceHandbook/ - The website of the Python Data Science Handbook, published online for free by the author alongside the examples and Python codes.

https://stackoverflow.com/ - Internet rule number 1: If you have a programming question, it has already been asked and answered on Stackoverflow, you just need to find the correct search terms for it.

https://www.anaconda.com/distribution/ - Anaconda python distribution, probably the best and most inclusive distribution that you can get with the least hassle..

https://www.datacamp.com/ - Free classes on Data Science in R and Python.

https://www.udacity.com/learn/python - Python basics and further links to learn Python.

https://www.codingame.com/start - Learn algorithms in any language through video game programming.

https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html - IPython interactive plot widgets, to add controls to your notebook plots

https://www.python.org/dev/peps/pep-0008/ - PEP 8 -- Style Guide for Python Code, or how to make your Python beautiful.


Please do not hesitate to contact Iaroslav Gaponenko ( iaroslav.gaponenko@unige.ch ) if you would like to help or if you have any questions.