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WatchPyConAU 2010: State Of Python
Published 2 weeks ago byState Of Python
Presented by Richard Jones
This talk will present a whirlwind update of the state of Python as of June 2010. It will cover the evolution of the language itself, its variants, versions and veracity. It will cover the state of packaging, distributing and pypi.
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PyConAU 2010: Getting started with Apache/mod_wsgi
Published 3 weeks ago byGetting started with Apache/mod_wsgi
Presented by Graham Dumpleton
An introduction to using Apache/mod_wsgi to host Python web applications supporting the Python Web Server Gateway Interface (WSGI).
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PyCon 2010: Powerful Pythonic Patterns (#186)
Published 4 months ago byPowerful Pythonic Patterns video
Presented by Alex Martelli
An extremely misguided meme is going around: that Python doesn't have, or need, any Design Patterns. Maybe the meme springs from not realizing what the Gang Of Four state so plainly in their historical "Design Patterns" book: which design patterns are useful DOES depend on the programming language one targets -- design is NOT independent of implementation, as the epic-fail "Waterfall" Methodology Pattern would suggest. If you examine a "classic DP" that's basically a workaround for some other language's lack of garbage collection, or for a clumsy static-typing system, it may indeed be worthless for Python. But many other DPs are still perfectly useful and applicable, and indeed Python's strengths as a language afford riffing on them to develop highly Pythonic, powerful, productive variants.
In this talk, I analyze some of my favorite pattern families -- e.g., Template Method and its variants, Dependency Injection and its ilk, Callback and friends -- in a highly Pythonic context. Non-pattern Idioms, and Patterns that aren't really Design Patterns but rather Architecture or Methodology ones, also make cameo appearances.
PyConAU 2010: State Of Python
State Of Python Presented by Richard Jones This talk will present a whirlwind update of the state of Python as of June 2010. It will cover the evolution of the language itself, its variants, versions and veracity. It will cover the state of packaging, distributing and pypi.Published 2 weeks ago
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PyPy and Unladen-Swallow: Making your Python Fast
PyPy and Unladen-Swallow: Making your Python fast Presented by Alex Gaynor Python has a reputation for being a bit slow, but it doesn't have to be that way. This talk will cover why Python is slow, and what two of the most exciting virtual machines are doing about it.Published 2 weeks ago
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Programming with Twisted
Dan Griffen presents Programming with Twisted What twisted is and its purpose in life (ie the reactor pattern). Non-blocking network programming. The confusing topic of deferreds and callbacks everywhere. Methods for dealing with blocking APIs. [VIDEO HAS ISSUES: I didn't have a proper mic, so audience questions are what they are]Published 2 weeks ago
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PyConAU 2010: Introducing Python to a Java shop
Introducing Python to a Java shop Presented by Juergen Brendel (MuleSoft) There is a time and place for everything: For some applications Python is perfect, for others a language like Java and its entire ecosystem is more ideal. In this talk we see how Python was introduced to a Java shop as a vehicle for rapid and dynamic prototyping of a new project. The talk then explains how Jython/Java integration was used to translate this project step by step from Python to Java. Going beyond the advocacy focus, the talk then shows the interfaces to call Java from Python and vice versa and illustrates those with examples. Furthermore, unit testing of Java classes via test cases written in Python is also touched upon.Published 3 weeks ago
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PyConAU 2010: An Introduction to Processing
An Introduction to Processing Presented by Clinton J Roy (University of Queensland) Processing is an Open Source language designed to make interactive graphics and animation programming easier than traditional GUI toolkits. Processing is used to prototype systems and create interactive data visualisations. [NOTES: Missing first 2 minutes of audio, then camera microphone for first section.]Published 3 weeks ago
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PyCon 2010: Mastering Team Play: Four powerful examples of composing Python tools (#184)
Mastering Team Play: Four powerful examples of composing Python tools Presented by Raymond Hettinger Starts with a quick review of the performance characteristics of major individual tools in Python: bisect, heapq, lists, deques, sets, frozensets, class structures, sorts, and weakreferences. Show how these tools can be powerfully combined to create elegant solutions to four hard problems. Random sampling: when one data structure isn't enough. Discuss how the nature of the problem dictates when to use one of two alternate data structures. Ordered dictionaries: with the right compostion of dictionaries, linked lists, and weak references, a dictionary can remember its insertion order without any impact on its big-Oh running times. NFA to DFA conversion. The classic, but difficult, algorithm for lexical analysis becomes simple when composing Python's dicts and frozensets. Running median: the obvious approaches are horribly slow. The problem centers around how to efficiently maintain sorted data while advancing a large sliding window one value at a time. A list of deques provides a dramatic and scalable improvement in running time.Published 5 months ago
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PyCon 2010: Python Metaprogramming (#64)
Python Metaprogramming Presented by Nicolas Lara Python makes a great metaprogramming languages. Metaprogramming is great for solving problems faster, in a more generic way and providing rich APIs. This talk explores the different options provided in python to do metaprogramming and the right way to use them.Published 5 months ago
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PyCon 2010: Decorators From Basics to Class Decorators to Decorator Libraries (#138)
Decorators from Basics to Class Decorators to Decorator Libraries Presented by Charles Merriam Decorators simplify design and improve readability and reliability. This tutorial starts from the beginning with basic decorators, showing how simple tasks like restricting administration functions are clearer and more readable with decorators, and exactly how the decorators work. A simple library of decorators for tracing, logging, and caching results is presented. This leads into writing your own decorators and parameterized decorators either from scratch or by using libraries like Simionato's Decorator Decorator and "@call_before(fn)". Finally, using Python 3.x class decorators in place of metaclasses for most common dictionary modifications is covered. [VIDEO HAS ISSUES: Missing about 1 minute at start, because of technical problems.]Published 5 months ago
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PyCon 2009: Python 102 (Part 1 of 3)
Python 102 (Part 1 of 3) This half-day tutorial presents intermediate and some advanced features of Python to beginning and intermediate programmers. It pairs well with Python 101 following a similar outline but at a deeper level. Rather than starting with "Hello, world!", we use the interactive Python interpreter to get hands-on familiarity with much of core Python before covering blocks and scripts. The tutorial is divided into short sections, each with presentation and exercises, to quickly cover lots of ground. Advanced topics are introduced throughout and covered in "extra credit" exercises such that more advanced participants can learn at their own pace.Published 1 year ago
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PyCon 2010: The Python and the Elephant: Large Scale Natural Language Processing with NLTK and Dumbo (#120)
The Python and the Elephant: Large Scale Natural Language Processing with NLTK and Dumbo Presented by Nitin Madnani (University of Maryland, College Park); Dr. Jimmy J Lin (University of Maryland) A practical look at NLTK and Dumbo, python-powered and open-source toolkits and APIs for processing natural language on a large scale. For people like us who make a living trying to make a computer "understand" human language, Python is a very powerful language, given its rapid prototyping abilities, native unicode support and a stellar standard library. This relationship has been strengthened further by an open-source, python-based Natural Language ToolKit (www.nltk.org) which is being widely used in the community for both teaching and research purposes and gaining traction in the general Python community as well (www.nltk.org/book). Recently, the Python community has seen the release of Dumbo (http://wiki.github.com/klbostee/dumbo), an open-source, python-based cloud-computing API (based on Hadoop) via the hands of Klaas Bosteels. In this talk, we show how the amalgamation of Python, NLTK and Dumbo can allow for very large-scale natural language processing efficiently and elegantly.Published 5 months ago
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