Back in 2012, I carried out a survey to find out which Python, NumPy, and
SciPy versions scientists are currently using for their daily work, in order
to better understand which versions should be supported. The main finding was that a large fraction of people have
reasonably up-to-date Python installations, although virtually no-one was
using Python 3 for daily work.
This year, I decided to repeat the experiment: last January
I advertised a survey which asked users to provide information
about their Python installation(s) for research/production work, as well as
more general information about their Python experience, which packages they
used regularly, why they are not using Python 3 if they were still using
Python 2, and so on.
There is a lot to be learned from this data, and there is no way that I can
cover all results in a single blog post, so instead I will focus only on a
few points in this post, and will write several more posts over the next
couple of weeks to highlight various other results.
For this post, I thought it would be fun to take a look specifically at what
Python versions users in the scientific Python community are using, and in
particular, the state of Python 3 adoption. I am making an anonymized
and cleaned-up version of the subset of the data used in this post in this GitHub repository, and will add to the data over time with future blog posts.