There are many serious reasons why Python is a great language for scientific research but in this talk I will propose an alternative reason; Python is great for scientific research because of all the other non-scientific things you can do with it!

Research data can take a long time to generate, and researchers may never know when certain programming skills will be needed. Since you’re going to procrastinate on side projects anyway, using Python in those side projects is a great way to improve your skills until they are needed. Using my own experiences in computational biology research, I’ll go through how the use of Python for web scraping and data visualisation in several diversions, distractions and other side projects ultimately helped my research.

I’ll also outline how the general-purpose nature of Python can come in handy for teaching and outreach, and how packages like Django can allow for efficiently creating infrastructure around research data and analysis. There is more to research than doing research, and more to scientific programming in Python than the usual suspects in SciPy. In this talk I’ll argue that using Python for side projects and harebrained schemes is essential preparation for all of the other legitimate reasons to use Python to solve scientific problems.