New-style classes were introduced to Python with the release of Python 2.2. And with these new-style classes came descriptors and properties. This article will introduce the descriptor protocol, descriptors, and properties.
New-style classes were introduced to Python with the release of Python 2.2. A new-style class is any class that is derived from the object base class. New-style classes give Python programmers many new (and initially confusing) features. One such feature is the descriptor protocol, and more specifically descriptors themselves.
Descriptors give Python programmers the ability to easily and efficiently create “managed attributes”. Managed attributes can be thought of as attributes that are not accessed directly. Instead their access is “managed” by something else, generally a class or a function.
If you haven’t come across this before you are probably wondering why one would want to manage attribute access? One reason might be that you don’t want people to be able to delete the attribute. Another reason may be that you need to ensure that your attribute data is always valid. Or perhaps attribute x is based on attribute y, so every time the value of y changes you want to update the value of x. From these few examples you can see the many possible cases where you might want to control access to certain attributes.
For those of you familiar with other programming languages, this type of access is often referred to as “getters and setters”. In many language, implementing “getters and setters” means using private variables and public functions that get and set the variable’s value. Since Python doesn’t (really) have private variables, the descriptor protocol is basically a built-in and Python-ic way to way to achieve something similar.
This article will introduce you to the descriptor protocol, descriptors, and properties. It will focus on demonstrating how to use them to create managed attributes. Since the descriptor protocol requires new-style classes, all of the examples in this article require Python 2.2 or newer.
I just wanted to re-point out the fact that there are some forums associated with this blog. There’s not much happening there, and recently they have become a haven for spammers, but I’m trying to clean them up and if other Python programmers read this blog maybe the forums could actually become useful!
GUI programming, like many other types of programming, can sometimes prove exhausting because you must repeat yourself over and over again. AVC is one tool available to Python GUI programmers that attempts to simplify things by synchronizing application data and GUI widgets.
Every once in a while I find myself browsing the Internet trying to find out what’s new and exciting in the Python world. Sometimes I browse to find topics for this article; other times mere curiosity draws me across the web. While I was browsing the other day, I stumbled across AVC: the Application View Controller. I was immediately intrigued by it because itsÃ¢Â€Â™ name is so similar to the Model View Controller (MVC) pattern. Being familiar with the Model View Controller pattern, and admittedly having struggles with it in the past, I decided to check out AVC to determine if it might be a viable alternative.
After reading about AVC I was intrigued for several reasons. The main reason was the promise of “a multiplatform, fully automatic, live connection among graphical interface widgets and application variables.”  This means that graphical widgets can be connected to variables and automatically synchronized. One of the (many?) problems with Graphical User Interface (GUI) programming is that you often find yourself doing the same thing over and over again. One of the things that you end of doing over and over again is setting the contents of a widget based on the value of a variable, and then subsequently, setting that variable’s value based on the current state of the widget. Whenever someone promises me an automatic connection between GUI widgets and my variables, I’m interested.
Edit: Due to popular demand (well a couple of comments) I’ve decided to allow multiple answers to the poll. This should make everyone that uses two versions happy.
With two major versions of Python available to us Python programmers (2.X and 3.X) I thought it would be interesting to see which version the readers of this blog are using and targeting.
Personally I’m still using 2.5 on my Debian box because it’s the default, and 2.6 on my Windows PC. While I have used 3.X and have it installed, I’ve remained on the 2.X branch largely because many of the modules that I play around with are still focused on the 2.X branch so that’s where my focus has remained.
I voted 2.5 since I do that majority of my programing on my Debian box. So now what about you:
Which version of Python do you use?
2.6 (59%, 700 Votes)
3.1 (16%, 187 Votes)
2.5 (15%, 182 Votes)
2.7 (4%, 48 Votes)
2.4 (3%, 32 Votes)
3.0 (2%, 20 Votes)
2.3 (0%, 5 Votes)
2.1 (0%, 3 Votes)
1.5 (0%, 2 Votes)
2.2 (0%, 1 Votes)
2.0 (0%, 1 Votes)
1.6 (0%, 1 Votes)
Total Voters: 1,084
If you want to explain your choice leave a comment below.
Over the past few years Google has expanded it’s services beyond those of a normal search engine. One of those new services is the Google Calendar. This article will provide an introduction to working with the Google Calendar using Python.
As many of you know, Google has branched out and started offering more services besides their ubiquitous search engine. You have email, calendars, documents, spreadsheets, photos, maps, videos, source code hosting, and the list goes on. Fortunately for us Python programmers, Google released the Google data Python Client Library on March 26th, 2007, giving Python programmers easy access to some of these services.
Of all the tasks assigned to programmers, commenting code and writing documentation are among the most disliked. This article introduces you to Python’s documentation strings. While they won’t make commenting your code any more enjoyable, they will provide a systematic approach to doing it, as well as access to additional tools for documentation generation and testing.
Iterators, iterables, and generators are features handled so wall by Python that people programming in other languages cannot help but drool over. Fortunately for us, creating iterators, iterables and generators is a relatively simple task. This article introduces the concepts of iterators, iterables, and generators and illustrates how easy it is to add them to your code.
So just to prove that I’m a masochist and that the Dodger Editor is not dead (even though I don’t think that anyone has been using it) I thought I’d post a quick update. No the editor is not dead and no neither am I.
I’m still writing for Python Magazine and working on the editor in some of the free time that I get. I just added a selection cursor mode to the editor which makes it easier for me to use. You can see it in the second row of the pallet in the following screen shot.
If anyone is at all interested in this project, whether it’s using or helping to develop, or just some suggestions please let me know. I’m always open to opinions. If I get the time I’d like to work on a short post showing you how you can use the Dodger Editor to create the level files for a game, when that will be I don’t know but hopefully soon.
It’s been a long time since I worked on TextWidget at all, but since someone posted a question about it I decided to fix the issue and re-release the source. But since I didn’t want to simply update the blog post I decided to give the project a proper home on google code: http://code.google.com/p/textwidget/
The project is really simple and meant as an easy way for you to create “text buttons” for your PyGame projects. It’s not meant to be the definitive way to do this, just a simple solution for people that just want to drop a class in and have working “text buttons”. It’s LGPL so you can use it in whatever way you want. If you do decide to use it please drop me an email and let me know.
For more information on how to use the project please take a look at the initial blog post.
Note: This article was first published the December 2007 issue of Python Magazine
While the command line will never cease to be useful, nothing will impress your friends more than your latest python masterpiece wrapped up in a slick cross-platform Graphical User Interface (GUI). This tutorial will show you how to create a simple GUI in Python using PyQt4.