A scientist goes to PyCon 2015

It’s already been two months, and I still haven’t posted about going to PyCon in Montreal. I had a wonderful experience! Many thanks to the PSF and PyLadies, whose travel grant brought the cost down into the realm of the feasible for me.

PyCon is an extremely well-run conference, run by a community that emphasizes a welcoming attitude. There’s a visible science presence (much more general than the topics you’d see at SciPy, of course), and an impressive 30% of speakers were women. I came away from it with many new ideas, got to talk with countless Python people, met many members of the geospatial community, including Sean Gillies, the author of such useful libraries as Shapely, Fiona and Rasterio, who turned out to be lovely. Also, two very nice gentlemen from the National Snow and Ice Datacenter (my pleasure!), serendipitously, as I used some NSIDC data in my presentation. Right, I gave a talk (on using satellite data to make maps, understandable without a remote sensing background), which was well received. I’ve embedded it below, and you can get the slides on speakerdeck here :

Indeed, all the talks are available in a YouTube channel and on pyvideo.org.

I’ve learnt tons by watching talks from past PyCons. It’s one of the best pass-times to do in the evening.  So I thought I’d put together a quick “PyCon highlights for the pythonic scientist”, with links to the relevant videos. A few notes of caution:

  • These are not my best-of PyCon talks. Some talks that were excellent I left aside in favour of some that have a clearer utility for someone working in scientific research.
  • Most of these are 30 min talks. Some are 45 min. The ones that are marked as “3h” were tutorials, and may be somewhat tedious to watch — except if you really want to learn about a topic in-depth, in which case you’ll be happy they exist. Otherwise, skip!
  • I organized them roughly by topic area and added annotations. If you only have time for a few, my suggestion is to start with the ones with the asterisk. (Again, not because they’re necessarily the best, but because I think you get a lot of reward for your time investment).

Science topics

(In no particular order.)


Becoming a better Python programmer

(The hard ones are at the end.)


Understanding Python internals


Philosophy, ethics and community


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