Scientific research or data science need rapid experimentation and building intuitions from data. Yet, in academia or in the industry, the code must live on to be useful for future enquiries or in production. Always experimenting yet writing production-ready robust code may seem a conundrum. However it shares a lot with agile or extreme programming techniques. It is an interesting test bed of programming practices.
I will explore simple, and less simple, practices that I have encountered in my research for fast turn around and consolidation of code. I will discuss how these considerations led to the design of scikit-learn, that enables easy machine learning, yet is used in production. Finally, I will mention some scikit-learn gems, new or forgotten.

A gentle introduction into the world of hobby electronics. Robots, art, gadgets, monitoring, tools, toys, home appliances, wearables -- with microcontrollers you can make anything. And now, that MicroPython is here, you can easily learn to program them.

RevDB is an experimental "reverse debugger" for Python, similar to UndoDB-GDB or LL for C. You run your program once, in "record" mode, producing a log file; once you get buggy behavior, you start the reverse-debugger on the log file. It gives an (improved) pdb-like experience, but it is replaying your program exactly as it ran---all input/outputs are replayed from the log file instead of being redone.

The main point is that you can then go backward as well as forward in time: from a situation that looks really buggy you can go back and discover how it came to be. You also get "watchpoints", which are very useful to find when things change. Watchpoints work both forward and backward.

I will show on small examples how you can use it, and also give an idea about how it works. It is based on PyPy, not CPython, so you need to ensure your program works on PyPy in the first place (but chances are that it does).

For years, lawyers and attorneys working in Switzerland have sensed that cases of migrants appealing deportation weren't treated equally by the 24 judges of the Swiss Federal Administrative Court.

To explore this claim we scraped the 30'000 verdicts of the court since 2007 from the court's data base. Then used the Python libraries pandas, glob and regular expressions to explore the data.

A lot of people think that Python is a really simple and straightforward language. Python hides a lot of peculiarities very well, but for the sake of this talk we will try to uncover them.

I will be explaining how the whole process of tokenizing -> parsing -> ast creation -> bytecode works and will use odd Python code to show the internals. Do you think `++4;` is valid Python? Or how about `0jif.1else-2`? There's no spaces in it. Go figure! "Edge cases" will help us understand the inner workings of Python.

We will be looking into how modules, classes and instances are really just fancy dictionaries and how importing is really nothing else than storing a module into a dictionary (`sys.modules`).

There's a lot of things we can learn from diving deep into the details of our beloved languages.

How we designed and built back-end servers for MMO games on the web like Thunder Run (www.thunderrun.com), using Python to handle 100,000+ monthly players. How to take advantage of the Twisted library and asynchronous I/O for low-latency networking.

New technologies like Docker, Kubernetes and Openshift make it much easier to run python applications on multiple servers for redundancy and load-balancing. In this talk I will explain how the 100% open-source Docker, Kubernetes and Openshift work and how to run python applications on this stack. I will show two or more live examples how to combine them with Git into a complete continuous delivery pipeline.

Python 3 removes a lot of the confusion around Unicode handling in Python, but that by no means fixes everything. Different locales and writing systems have unique behaviours that can trip you up. Here's some of the worst ones and how to handle them correctly.

This talk will be about the usage of Python inside of Sensirion, a hardware company producing sensors. We will see where and how we rely on Python and how the usage evolved from collections of small Python scripts in each department to a stack of gerrit, Jenkins and devpi to develop, test and deploy Python packages to 100+ non software engineers in the lab.