PyCon has come and gone. PyCon remains my favorite conference each year, not just because of the quality of the topics discussed, but because of the people. Each year I meet old friends and new who are working on interesting problems and enjoy sharing their knowledge.
I’ve been watching the talks I’ve missed online, and here’s an curated list of what I find interesting. I have a lot of links to share, so I’ll break them up to make it easier.
Updated on (2015-05-11): The links to presentation videos were moved from blip.tv over to pyvideo.org. I can no longer find the video for the presentation, “Running ultra large telescopes in Python”, which is a shame. In this case, I link to the presentation abstract.
These talks are both educational and just plain fun.
Using Python 3 to Build a Cloud Computing Service for My Superboard II » David Beazley is on a roll (his talks about Python’s GIL at previous PyCons were educational and fun). This talk is pure nerd fun.
Exhibitions of Atrocity » Coding atrocity, that is. Mike Pirnat displays his most embarrassing Python mistakes so you don’t repeat them. Humorous and insightful.
Running ultra large telescopes in Python » General science nerdery. Features live video of the presenter controlling a telescope array using an IPython interactive shell.
Talks About Python Itself
API Design: Lessons Learned » Raymond Hettinger, a Python core committer, on how the Python’s APIs have evolved and what was learned along the way. I always learn something new at his talks.
Fun with Python’s Newer Tools » Raymond Hettinger again. Did you know how useful named tuples are? Because I didn’t before I saw this talk.
Useful Namespaces: Context Managers and Decorators » Jack Diedrich is a Python core committer. I enjoy his talks because he focuses on a few topics, and his slides are stripped down to only what you need to understand the concept he’s presenting.
Hidden Treasures in the Standard Library » Doug Hellman talks about some modules you may have missed in the standard library. Doug is the creator and maintainer of the Python Module of the Week, for which he deserves many thanks.
Everything you wanted to know about Pickling, but were afraid to ask » Pickling is a serialization module in Python’s standard library. The title says it all.
The Data Structures of Python » This talk starts with the basics (lists, tuples, sets, etc.), but moves on to more advanced topics. For instance, I didn’t know about the abstract base classes that live in the collections module. Money Quote: “We read Knuth so you don’t have to.”
Talks About How People Are Using Python
The Secret Sauce in the Open Cloud » Jason Huggins from Sauce Labs (Selenium) talks about how to put together an automated virtual machine environment together using VirtualBox, Vagrant, and OpenStack.
How to kill a patent with Python » Using natural language processing to find prior art in order to challenge software patents.
Reverse engineering Ian Bicking’s brain: inside pip and virtualenv » I use pip and virtualenv all the time, and it was nice get a peek under the hood.
Ten Years of Twisted » Twisted is an asynchronous networking framework in Python. This talk gives some history and a gentle introduction. Twisted has been around a long time, is very stable, and really useful. The project ships with protocols you’d actually use like HTTP, SMTP, POP3, SSH, and more.
Writing Great Documentation » Jacob Kaplan-Moss, from the Django project, talks about the different types of documentation that help other people use and contribute to your project. Money quote: “Documentation is fractal.”
API Design Anti-Patterns » Are you designing API’s other people are going to use? Alex Martelli has some high-level advice.
Handling ridiculous amounts of data with probablistic data structures » C. Titus Brown from MSU talks about analyzing genetic data using bloom filters. One of the interesting points he makes is that genetic researchers are generating data faster than Moore’s Law.
The Linguistics of Twitter » Applying natural language processing techniques on Twitter data.
Statistical machine learning for text classification with scikit-learn » An introduction to the machine learning library, scikit-learn. This is a hard topic to present on, having the slides will help.
Introduction to Parallel Computing on an NVIDIA GPU using PyCUDA » Get to know the basics of NVIDIA’s CUDA API and how to use it from Python.
The fine folks at inSCIght were kind enough to invite me to participate in their PyCon 2011 wrap-up podcast. I think we covered a lot of the highlights we experienced at the conference, but I’m sure we didn’t see it all. If you think I’ve left anything out, let me know!