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This tutorial teaches the fundamental concepts of symbolic debuggers. We will be using Winpdb to debug Python scripts. The concepts that you learn here, however, are fundamental to working with debuggers in general. At the end of the exercise, you should feel comfortable with using symbolic debuggers and have an inherent understanding of why these tools prove to reduce time and effort needed in debugging without altering the code itself to do so.

Neat script I wrote for a friend to annotate a Python script with the types observed during a run of the program

Your example works as you describe on recent matplotlib versions. I suspect you are using an old one. The preferred way of handling missing points in numpy, and therefore in matplotlib and pylab, however, is via masked arrays.import pylabimport numpy as npfrom numpy import maa = [1,2,3,4,5]b = np.array([6,2,np.nan,1,9])bm = ma.masked_where(np.isnan(b), b)pylab.plot(a,bm)pylab.show()There are many other examples of masked array use in the examples directory of the matplotlib distribution.EricFernando Abilleira wrote:> Dear sourceforge community,> > I come from a Matlab environment so I am used to plotting matrices that > contain NaN elements. This is very useful because in some cases one > doesn't have data for the entire matrix. If one tries plotting the data, > the NaN elements won't be plotted.

I hope this article encourages you to think about your command line programs in a different light, and to treat them as first class objects. Using inheritance to share code is so common in other areas of development that it is hardly given a second thought in most cases. As has been shown with the SQLiteAppBase programs, the same technique can be just as powerful when applied to building command line programs, saving development time and testing effort as a result. CommandLineApp has been used as the foundation for dozens of types of programs, and could be just what you need the next time you have to write a new command line program.

Stackless Python is an alternative implementation of Python created by independent developer Christian Tismer. He started with the conventional Python language processor managed by the language's inventor, Guido van Rossum, and patched his own Stackless invention in place of a small but central part of Python's internals. Stackless Python is the result. This article introduces Tismer's technology and its significance. In future articles, you'll be able to read about how to make your own start at programming Stackless Python, as well as the prospects for a merger between Stackless and the main Python distribution.

Last week we looked at the itertools module and how its iterator based functions can be faster than doing things from scratch. One of the examples showed a function using itertools.takewhile to be much faster than one using itertools.groupby. Some feedback we got, asked, what then is the point of groupby if it's just a slower version of other functionality?

Pyro is short for PYthon Remote Objects. It is an advanced and powerful Distributed Object Technology system written entirely in Python, that is designed to be very easy to use. Never worry about writing network communication code again, when using Pyro you just write your Python objects like you would normally. With only a few lines of extra code, Pyro takes care of the network communication between your objects once you split them over different machines on the network. All the gory socket programming details are taken care of, you just call a method on a remote object as if it were a local object!

tinypy is a minimalist implementation of python in 64k of code