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by Jesse Noller
It is vitally important that we take a break from the rush of our day-to-day lives and look back at what Python was, is, and is meant to become. Whether you have used Python from the beginning or just picking it up, this talk is for you as we will hit all the big major topics of the community and the language. This is more than a survey of Python, this is a call to action to focus on the greatness of the language. A call to stop the complaining and bitter infighting, to band together as a community in order to fulfill the vision of the language. It is up to us to turn the tide of FUD. It is up to us to be inspired and to be inspirational. The future is bright.
The much maligned Global Interpreter Lock (GIL) never gets any respect---in fact most Python programmers don't even think it has a right to exist. In this talk, I'll defend the GIL by making the case that improving it might be a more attractive (and achievable) option than trying to get rid of it entirely. I'll discuss some of my past efforts to understand the GIL, describe weaknesses in the current Python 3 implementation, and give some practical programming advice for working around it.
In a world of many programming languages, the popularity of Python continues to grow without bound. We examine what makes it special and how it influences the way we program:
by Leah Culver
by Alex Gaynor
PyPy's recently started to get quite a bit of press because of our speed, however many people don't know PyPy has been influencing the direction of the language for a while. This talk will travel through history, seeing PyPy's influence, showing why you should use it right now, and exploring the cool plans we have for the future.
Every day, Mozilla collects three million Firefox crash reports from around the world. The data in these reports drives the bug-fixing priorities of Firefox engineers, and is critical to understanding the stability of our platform. In this case study, I'll describe the challenges we've faced and the architecture and infrastructure we use to process, store, and analyze approximately 110TB of crash reports using Python, with Hadoop, PostgreSQL, and a few other things thrown in for good measure.
Invaluable tools like Github, readthedocs.org, Packaginator, and PyPI have been of incredible value to the Python community. Each iteration of these tools has seen a step forward in a direction of shared code and putting the spotlight on new projects. Yet critical pieces are missing. Can the Python community successfully address these issues and perhaps share them with the rest of the technical world?
Python now has a mature and easy-to-use dependency management and isolation tools in the form of Virtualenv and Pip/Pyg. All applications should be using these. In this talk, we'll see why using Virtualenv + Pip or Pyg brings a plethora of benefits, from ramping up new developers to automation in the cloud. Key points and take aways will include:
In summary, you're crazy if you're not completely and explicitly declaring your dependencies with Pip or Pyg and isolating them with Virtualenv.
Unfortunately, solving simple problems with Python isn't always 'import antigravity'. This talk will analyze the high barriers of entry that clutter the Python landscape. We'll discuss ways to make Python more accessable for newcomers and less of a headache for seasoned veterans.
People keep trying to invent ways to make python run faster - JITs, Java and .Net runtimes, threads, eliminating the Global Interpreter Lock. So why can bup process more than 80 megabytes per second per core? Why can the sshuttle VPN route packets at wire speeds? How does plain python thoroughly trounce JITted languages in certain kinds of benchmarks? And what's really so great about deterministic destructors, anyway? Come hear Avery explain it all in this action-packed whirlwind of fact, fiction, and "other."
by Audrey Roy
As lead for PyLadies and co-lead dev of Django Packages / the Packaginator framework, I've witnessed and helped make amazing things happen in the open source community. I'd like to share thoughts on:
Every intermediate to experienced Python developer has code that can be turned into a reusable Python package. There are sneaky tricks to giving people a gentle push to open-source their code, which have the side consequence of making them better developers.
What makes a Python package useful? How can we improve the quality of our own individual packages?
Watching the Django Packages ecosystem and the PyLadies community thrive and grow has given me ideas about how to jumpstart or grow similar ecosystems and communities. How can you steal ideas from these projects and use them as templates for your own Python niches?
I've made a huge mistake: I've judged programming languages and communities without cause and been the worse for it. With luck, my story of failure will save others the misfortune and embarrassment.
Django, Pyramid, Flask, WSGI itself, so many choices. This talk goes into detail how to make different frameworks play together and which parts of WSGI are good and which ones you should avoid. The talk also shows how to combine applications with code written in other languages and answers why having the choice of multiple solutions is good and not a bad thing.
Breakdancer is a simple python testing tool that allows you to test all possibilities of complex interactions in your application by expressing simple constraints and effects. More details available here: http://dustin.github.com/2010/10...
by Tracy Osborn
Anyone can launch their first web startup, but there are a lot of important factors to keep in mind to ensure success. This talk will cover tips and tricks for learning new skills, building and launching quickly as a new programmer, stepping outside your comfort zone, pitfalls to avoid, and fast-tracking yourself to succeed.
In this talk I will describe what NumPy is and why it matters. I will then talk about NumPy and SciPy's future as it should evolve to allow high-level descriptions of optimized low-level calculations as well as it's connection to large-scale data manipulation and processing. I will then spend a few minutes talking about early efforts in evolving NumPy and SciPy and on where people can help.