Differences between distribute, distutils, setuptools and distutils2?

Question

The Situation

I’m trying to port an open-source library to Python 3. (SymPy, if anyone is wondering.)

So, I need to run 2to3 automatically when building for Python 3. To do that, I need to use distribute. Therefore, I need to port the current system, which (according to the doctest) is distutils.

The Problem

Unfortunately, I’m not sure what’s the difference between these modules—distutils, distribute, setuptools. The documentation is sketchy as best, as they all seem to be a fork of one another, intended to be compatible in most circumstances (but actually, not all)…and so on, and so forth.

The Question

Could someone explain the differences? What am I supposed to use? What is the most modern solution? (As an aside, I’d also appreciate some guide on porting to Distribute, but that’s a tad beyond the scope of the question…)

Answer 1

I’m a distutils maintainer and distutils2/packaging contributor. I did a talk about Python packaging at ConFoo 2011 and these days I’m writing an extended version of it. It’s not published yet, so here are excerpts that should help define things.

• Distutils is the standard tool used for packaging. It works rather well for simple needs, but is limited and not trivial to extend.

• Setuptools is a project born from the desire to fill missing distutils functionality and explore new directions. In some subcommunities, it’s a de facto standard. It uses monkey-patching and magic that is frowned upon by Python core developers.

• Distribute is a fork of Setuptools that was started by developers feeling that its development pace was too slow and that it was not possible to evolve it. Its development was considerably slowed when distutils2 was started by the same group. 2013-August update: distribute is merged back into setuptools and discontinued.

• Distutils2 is a new distutils library, started as a fork of the distutils codebase, with good ideas taken from setup tools (of which some were thoroughly discussed in PEPs), and a basic installer inspired by pip. The actual name you use to import Distutils2 is packaging in the Python 3.3+ standard library, or distutils2 in 2.4+ and 3.1–3.2. (A backport will be available soon.) Distutils2 did not make the Python 3.3 release, and it was put on hold.

More info:

• The fate of Distutils – Pycon Summit + Packaging Sprint detailed report
• A Quick Diff between Distutils and Distutils2

I hope to finish my guide soon, it will contain more info about each library’s strong and weak points and a transition guide.

Answer 2

NOTE: Answer deprecated, Distribute now obsolete. This answer is no longer valid since the Python Packaging Authority was formed and has done a lot of work cleaning this up.

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Yep, you got it. :-o I think at this time the preferred package is Distribute, which is a fork of setuptools, which are an extension of distutils (the original packaging system). Setuptools was not being maintained so is was forked and renamed, however when installed it uses the package name of setuptools! I think most Python developers now use Distribute, and I can say for sure that I do.

Answer 3

I realize that I have replied to your secondary question without addressing unquestioned assumptions in your original problem:

I'm trying to port an open-source library (SymPy, if anyone is wondering) to Python 3. To do this, I need to run 2to3 automatically when building for Python 3.

You may, not need. Other strategies are described at http://docs.python.org/dev/howto/pyporting

To do that, I need to use distribute,

You may :) distutils supports build-time 2to3 conversion for code (not docstrings), in a different manner that distribute’s: http://docs.python.org/dev/howto/pyporting#during-installation

Answer 4

Updating this question in late 2014 where fortunately the Python packaging chaos has been greatly cleaned up by Continuum's "conda" package manager.

In particular, conda quickly enables the creation of conda "environments". You can configure your environments with different versions of Python. For example:

conda create -n py34 python=3.4 anaconda

conda create -n py26 python=2.6 anaconda

will create two ("py34" or "py26") Python environments with different versions of Python.

Afterwards you can invoke the environment with the specific version of Python with:

source activate <env name>

This feature seems especially useful in your case where you are having to deal with different version of Python.

Moreover, conda has the following features:

• Python agnostic
• Cross platform
• No admin privileges required
• Smart dependency management (by way of a SAT solver)
• Nicely deals with C, Fortran and system level libraries that you may have to link against

That last point is especially important if you are in the scientific computing arena.