Differences between distribute, distutils, setuptools and distutils2?

As of May 2022, most of the other answers to this question are several years out-of-date. When you come across advice on Python packaging issues, remember to look at the date of publication, and don't trust out-of-date information.

The Python Packaging User Guide is worth a read. Every page has a "last updated" date displayed, so you can check the recency of the manual, and it's quite comprehensive. The fact that it's hosted on a subdomain of python.org of the Python Software Foundation just adds credence to it. The Project Summaries page is especially relevant here.

Summary of tools:

Here's a summary of the Python packaging landscape:

Supported tools:

setuptools was developed to overcome Distutils' limitations, and is not included in the standard library. It introduced a command-line utility called easy_install. It also introduced the setuptools Python package that can be imported in your setup.py script, and the pkg_resources Python package that can be imported in your code to locate data files installed with a distribution. One of its gotchas is that it monkey-patches the distutils Python package. It should work well with pip. It sees regular releases. Official docs | Pypi page | GitHub repo | setuptools section of Python Package User Guide

Official docs | Pypi page | GitHub repo | setuptools section of Python Package User Guide

scikit-build is an improved build system generator that internally uses CMake to build compiled Python extensions. Because scikit-build isn't based on distutils, it doesn't really have any of its limitations. When ninja-build is present, scikit-build can compile large projects over three times faster than the alternatives. It should work well with pip. Official docs | Pypi page | GitHub repo | scikit-build section of Python Package User Guide

Official docs | Pypi page | GitHub repo | scikit-build section of Python Package User Guide

distlib is a library that provides functionality that is used by higher level tools like pip. Official Docs | Pypi page | Bitbucket repo | distlib section of Python Package User Guide

Official Docs | Pypi page | Bitbucket repo | distlib section of Python Package User Guide

packaging is also a library that provides functionality used by higher level tools like pip and setuptools Official Docs | Pypi page | GitHub repo | packaging section of Python Package User Guide

Official Docs | Pypi page | GitHub repo | packaging section of Python Package User Guide

Deprecated/abandoned tools:

distutils is still included in the standard library of Python, but is considered deprecated as of Python 3.10. It is useful for simple Python distributions, but lacks features. It introduces the distutils Python package that can be imported in your setup.py script. Official docs | distutils section of Python Package User Guide

Official docs | distutils section of Python Package User Guide

distribute was a fork of setuptools. It shared the same namespace, so if you had Distribute installed, import setuptools would actually import the package distributed with Distribute. Distribute was merged back into Setuptools 0.7, so you don't need to use Distribute any more. In fact, the version on Pypi is just a compatibility layer that installs Setuptools.

distutils2 was an attempt to take the best of distutils, setuptools and distribute and become the standard tool included in Python's standard library. The idea was that distutils2 would be distributed for old Python versions, and that distutils2 would be renamed to packaging for Python 3.3, which would include it in its standard library. These plans did not go as intended, however, and currently, distutils2 is an abandoned project. The latest release was in March 2012, and its Pypi home page has finally been updated to reflect its death.

Others:

There are other tools, if you are interested, read Project Summaries in the Python Packaging User Guide. I won't list them all, to not repeat that page, and to keep the answer matching the question, which was only about distribute, distutils, setuptools and distutils2.

Recommendation:

If all of this is new to you, and you don't know where to start, I would recommend learning setuptools, along with pip and virtualenv, which all work very well together.

If you're looking into virtualenv, you might be interested in this question: What is the difference between venv, pyvenv, pyenv, virtualenv, virtualenvwrapper, etc?. (Yes, I know, I groan with you.)

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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.

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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.

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.

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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

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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.

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