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 :
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).
(In no particular order.)
- * [fast scientific arrays with numpy] Jake VanderPlas – Losing your Loops Fast Numerical Computing with NumPy
- [visualisation] Sarah Bird – Interactive data for the web – Bokeh for web developers
- [genetics] Titus Brown – How to interpret your own genome using (mostly) Python
- [neural nets] Melanie Warrick – Neural Nets for Newbies
- * [combinatorics] Viviane Pons – Experimental pure mathematics using Sage
- [linguistics & machine learning] Michelle Fullwood – Grids, Streets and Pipelines: Building a linguistic street map with scikit-learn
- [text analysis] Adam Palay – “Words, words, words”: Reading Shakespeare with Python
- [using geospatial satellite data] Chris Waigl – Satellite mapping for everyone
- [machine learning — 3h] Jake VanderPlas – Machine Learning with Scikit-Learn (I)
- [machine learning — 3h] Olivier Grisel – Machine Learning with Scikit-Learn (II)
- [data science — 3h] Brandon Rhodes – Pandas From The Ground Up
Becoming a better Python programmer
(The hard ones are at the end.)
- * [good style] Raymond Hettinger – Beyond PEP 8: Best practices for beautiful intelligible code
- [variable assignment] Ned Batchelder – Facts and Myths about Python names and values
- * [software engineering] Nina Zakharenko – Technical Debt: The code monster in everyone’s closet
- [debugging] Clayton Parker – So you think you can PDB?
- [usability] Katie Cunningham – Usability Testing on the Cheap
- [multiple inheritance] Raymond Hettinger – Super considered super!
- [concurrency] David Beazley – Python Concurrency From the Ground Up: LIVE!
Understanding Python internals
- [Python processes] Ying Li – Where in your RAM is “python san_diego.py”?
- [interpreter] Allison Kaptur – Bytes in the Machine: Inside the CPython interpreter
- [Python source code] Allison Kaptur – Exploring is never boring: understanding CPython without reading the code
Philosophy, ethics and community
- * [against the “rockstar” fallacy] Keynote – Jacob Kaplan-Moss
- [social responsibility] Glyph – The Ethical Consequences Of Our Collective Activities
- * [healthy teams] Kate Heddleston – How our engineering environments are killing diversity (and how we can fix it)