Using the inspect and re modules, write a piece of code that update itself every time it is run.

The idea is come from the PEAK ez_setup.py script.

Video showing how to remotely participate in the PyCon 2010 conference in Atlanta by watching the live note taking of some thoughtful and friendly participants using EtherPad. Go to https://twitter.com/pyconpads to find pads. (Author: John Graves from Auckland, New Zealand has other videos at http://bit.ly/openallure and an Open Source project http://openallureds.org )

In this tutorial screencast you will learn how to install pygame on Linux, Mac, and Windows. I explain what you can find on http://pygame.org/download.shtml and how to find out if pygame wasn’t installed correctly.

In this final segment, we highlight concepts important for new scientific Python programmers, especially those coming from a Matlab environment. We discuss effective use of Python dictionaries and the timeit profiling module. We also discuss the defaultdict object for quick and easy database creation useful for keeping track of data in multiple parameter runs.

How to instantiate the previously created class ( Foo ) is shown, using IDLE (the Integrated Development Environment , that comes with the standard CPython distribution). 2 referrers are created to understand that up until all referrers are deleted, the Python garbage collector may claim the associated instance (destruct it, deallocate it).

variables in Python are simple names associated to an instance (it could be a value). Examples are shown to enforce this concept.

In this segment, we continue with the case study of robust regression methods, but here we show how to use generators and the parallel computing features of IPython to accelerate the computation.

In this segment, we discuss how the least-median estimate can be computed from the sample dataset using generators.

In this case study, we pull together everything we have learned so far in order to compute the least MEDIAN squared estimate of a line given data with outliers present. This segment describes the fundamental problem.

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the computations that run on them.

In this segment, we discuss the map feature to distribute computations across the available nodes, how to use targets to specify which nodes to use, and how to use blocking to avoid waiting for long computations to finish.

In this segment, we discuss cover importing modules for the remote nodes and how to distribute data (i.e. scatter, gather) to and from these nodes.

IPython provides excellent interactive parallel computing tools. In this segment, we discuss how to set up remote computational engines using ipcluster and how to use special magic commands in IPython to use these remote engines.

Here we show how to run the IPython interpreter on the Windows PC (as usual), but connect to and interact with remote computational engines.

We view an animated illustration of how the garbage collector collects objects under Python 3.0 . That is, two referrers are created by being associated to the Foo class instance, and the Foo class instance itself its created when at least one referrer is left, destroyed when no referrers to it are left.
the weakref being associated to the Foo instance, at the end of the animation, is not accounted for the reference count (as seen in the refcount counter at the top-right side of the animation).
I type in the class Foo that we will be using in further videos, to explain garbage collection.

Note : the class created here is not intended for any practical purpose. It is just of use for the exercise on learning the concepts behind garbage collection in Python.

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the computations that run on them.

In this segment, we discuss cover importing modules for the remote nodes and how to distribute data (i.e. scatter, gather) to and from these nodes.

IPython provides excellent interactive parallel computing tools. In this segment, we discuss how to set up remote computational engines using ipcluster and how to use special magic commands in IPython to use these remote engines.

Here we show how to run the IPython interpreter on the Windows PC (as usual), but connect to and interact with remote computational engines.

We view an animated illustration of how the garbage collector collects objects under Python 3.0 . That is, two referrers are created by being associated to the Foo class instance, and the Foo class instance itself its created when at least one referrer is left, destroyed when no referrers to it are left.
the weakref being associated to the Foo instance, at the end of the animation, is not accounted for the reference count (as seen in the refcount counter at the top-right side of the animation).
I type in the class Foo that we will be using in further videos, to explain garbage collection.

Note : the class created here is not intended for any practical purpose. It is just of use for the exercise on learning the concepts behind garbage collection in Python.

Matplotlib is one of the best, if not the best, plotting package for scientific Python. In this segment, we discuss how to best use the Matplotlib website to get started with plotting with Matplotlib.

Introducing Sympy for symbolic mathematics within IPython. Basic operations and advice for Windows users.

Basic profiling methods available in IPython.

This segment discusses the MEC and MECXLocal nodes in the IPython library to quickly prototype a parallel computation without having a prior connection to a backend cluster.