Python is easier to read

That’s it. For all the joys of programming in dynamically-typed languages, I think there is one major problem: in many ways these languages favour writers over readers. I covered one example of this problem back in my post on Duck Typing vs Readability. This is why it is so important that a dynamic language is designed with readability in mind.

In my opinion, Python really shines readability-wise. And this is no accident — if you follow design discussions about Python you will see that Guido is always concerned with code clarity. A feature is not worth adding just because it makes code more compact; it must make the code more concise. Features have even been removed from Python because they were seen to encourage compact but difficult to comprehend code (reduce being a classic example). Even controversial design choices like significant whitespace and the explicit use of “self” are actually good for readability.

The philosophy behind Python also extends beyond the language design. Clever tricks and one-liners are not encouraged by the community. Rather, the community aims for Pythonic code:

To be Pythonic is to use the Python constructs and datastructures with clean, readable idioms.

This is surely refreshing compared to the misguided boasting about how much can be crammed in to a few lines of [insert other scripting language]. Certainly, if I ever have to pick up and maintain another person’s dynamically-typed code, I hope it is Python. And since I know I will have to maintain my own code, when I go dynamic I always pick Python.

Enter Python. Or, more specifically, the new(ish) subprocess module (introduced in Python 2.4). With this module Python finally has a concise but powerful way to launch and control subprocesses. It also includes convenient ways to handle subprocess I/O, including setting up pipelines. What’s more, I have so far found it to be quite portable between Linux and Windows.

Starting with something simple, let’s say you just want to launch a command synchronously (much as you would in a shell script):

Simple. Want to set up a pipeline like `dmesg | grep hda` (capturing the final output)? Here’s an example straight from the doco:

For my specific case, I used the ability to launch a Pulse server processes in the background and capture the output extensively. When done, I shut down the server using the standard shutdown script (testing of said script being a nice side-effect). An asynchronous child is started simply by creating a Popen object:

I have simplified this example a bit, but the real work is all there, and it’s pleasantly straightforward. This combined well with Python’s simple XML-RPC API, which I used to manipulate the running server.

So far the switch to from bash/Ant to Python has been a great success. If you need a cross-platform way to automate your acceptance testing, give it a go.