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Is there a way to include a brew instillation in a python build?

I'm trying to build a speech recognition application that works in a browser. Currently using pyodide with a web worker. I have my own package, built alongside pyaudio, that I use for the web worker.
Is there a way that I can include a brew instillation, specifically portaudio, inside of my python package so that when I build the package, portaudio is included in the wheel file? I need portaudio included for this to work in the browser.
Thank you!

I'm understanding two different questions here, so I'll try to answer them both.
Can I have a python build fetch a Homebrew project during buildtime
To my knowledge, the answer is no. The Python distribution system is separate from Homebrew, and they can't interact in this fashion.
Even if they could, this wouldn't necessarily be desirable:
What happens if the user isn't on macOS (or Linux)? Then the build would fail.
The prefix that Homebrew will install the package in isn't very deterministic. The user might be using a custom prefix, or they might be on Apple Silicon (which has a different default prefix to Intel).
Your python package might run into some difficulty locating the package.
What about if they don't have Homebrew installed? They might have another package manager like MacPorts or Fink, or maybe none at all.
Can I bundle portaudio into the build distribution?
Maybe? Even if you could, I almost certainly wouldn't recommend it.
Bundling dependencies increases the size of the distribution unnecessarily.
It would take a reasonable amount of effort to setup, assuming you can do it.
All these reasons are why for the majority of projects that have a similar setup, you will find that they recommend installing certain packages with their system package manager first, before building the Python source code.
This allows them to choose whatever package manager they have installed, and it should also be a quick and painless process.
Therefore, just change your installation instructions to the following:
# On macOS
brew install portaudio
pip install ...

setup.py + virtualenv = chicken and egg issue?

I'm a Java/Scala dev transitioning to Python for a work project. To dust off the cobwebs on the Python side of my brain, I wrote a webapp that acts as a front-end for Docker when doing local Docker work. I'm now working on packaging it up and, as such, am learning about setup.py and virtualenv. Coming from the JVM world, where dependencies aren't "installed" so much as downloaded to a repository and referenced when needed, the way pip handles things is a bit foreign. It seems like best practice for production Python work is to first create a virtual environment for your project, do your coding work, then package it up with setup.py.
My question is, what happens on the other end when someone needs to install what I've written? They too will have to create a virtual environment for the package but won't know how to set it up without inspecting the setup.py file to figure out what version of Python to use, etc. Is there a way for me to create a setup.py file that also creates the appropriate virtual environment as part of the install process? If not — or if that's considered a "no" as this respondent stated to this SO post — what is considered "best practice" in this situation?

You can think of virtualenv as an isolation for every package you install using pip. It is a simple way to handle different versions of python and packages. For instance you have two projects which use same packages but different versions of them. So, by using virtualenv you can isolate those two projects and install different version of packages separately, not on your working system.
Now, let's say, you want work on a project with your friend. In order to have the same packages installed you have to share somehow what versions and which packages your project depends on. If you are delivering a reusable package (a library) then you need to distribute it and here where setup.py helps. You can learn more in Quick Start
However, if you work on a web site, all you need is to put libraries versions into a separate file. Best practice is to create separate requirements for tests, development and production. In order to see the format of the file - write pip freeze. You will be presented with a list of packages installed on the system (or in the virtualenv) right now. Put it into the file and you can install it later on another pc, with completely clear virtualenv using pip install -r development.txt
And one more thing, please do not put strict versions of packages like pip freeze shows, most of time you want >= at least X.X version. And good news here is that pip handles dependencies by its own. It means you do not have to put dependent packages there, pip will sort it out.
Talking about deploy, you may want to check tox, a tool for managing virtualenvs. It helps a lot with deploy.

Python default package path always point to system environment, that need Administrator access to install. Virtualenv able to localised the installation to an isolated environment.
For deployment/distribution of package, you can choose to
Distribute by source code. User need to run python setup.py --install, or
Pack your python package and upload to Pypi or custom Devpi. So the user can simply use pip install <yourpackage>
However, as you notice the issue on top : without virtualenv, they user need administrator access to install any python package.
In addition, the Pypi package worlds contains a certain amount of badly tested package that doesn't work out of the box.
Note : virtualenv itself is actually a hack to achieve isolation.

Best practices for Python deployment -- multiple versions, standard install locations, packaging tools etc

Many posts on different aspects of this question but I haven't seen a post that brings it all together.
First a subjective statement: it seems like the simplicity we experience when working with the Python language is shot to pieces when we move outside the interpreter and start grappling with deployment issues. How best to have multiple versions of Python on the same machine? Where should packages be installed? Disutils vs. setuptools vs. pip etc. It seems like the Zen of Python is being abused pretty badly when it comes to deployment. I'm feeling eerie echoes of the "DLL hell" experience on Windows.
Do the experts agree on some degree of best practice on these questions?
Do you run multiple versions of Python on the same machine? How do you remain confident that they can co-exist -- and the newer version doesn't break assumptions of other processes that rely on the earlier version (scripts provided by OS vendor, for example)? Is this safe? Does virtualenv suffice?
What are the best choices for locations for different components of the Python environment (including 3rd party packages) on the local file system? Is there a strict or rough correspondence between locations for many different versions of Unixy and Windows OS's that can be relied upon?
And the murkiest corner of the swamp -- what install tools do you use (setuptools, distutils, pip etc.) and do they play well with your choices re: file locations, Python virtual environments, Python path etc.
These sound like hard questions. I'm hopeful the experienced Pythonistas may have defined a canonical approach (or two) to these challenges. Any approach that "hangs together" as a system that can be used with confidence (feeling less like separate, unrelated tools) would be very helpful.

I've found that virtualenv is the only reliable way to configure and maintain multiple environments on the same machine. It even has as a way of packaging up environment and installing it on another machine.
For package management I always use pip since it works so nicely with virtualenv. It also makes it easy to install and upgrade packages from a variety of sources such a git repositories.

I agree this is quite a broad question, but I’ll try to address its many parts anyway.
About your subjective statement: I don’t see why the simplicity and elegance of Python would imply that packaging and deployment matters suddenly should become simple things. Some things related to packaging are simple, other are not, other could be. It would be best for users if we had one complete, robust and easy packaging system, but it hasn’t turned that way. distutils was created and then its development paused, setuptools was created and added new solutions and new problems, distribute was forked from setuptools because of social problems, and finally distutils2 was created to make one official complete library. (More on Differences between distribute, distutils, setuptools and distutils2?) The situation is far from ideal for developers and users, but we are working on making it better.
How best to have multiple versions of Python on the same machine? Use your package manager if you’re on a modern OS, or use “make altinstall” if you compile from source on UNIX, or use the similar non-conflicting installation scheme if you compile from source on Windows. As a Debian user, I know that I can call the individual versions by using “pythonX.Y”, and that what the default versions (“python” and “python3”) are is decided by the Debian developers. A few OSes have started to break the assumption that python == python2, so there is a PEP in progress to bless or condemn that: http://www.python.org/dev/peps/pep-0394/ Windows seems to lack a way to use one Python version as default, so there’s another PEP: http://www.python.org/dev/peps/pep-0397/
Where should packages be installed? Using distutils, I can install projects into my user site-packages directory (see PEP 370 or docs.python.org). What exactly is the question?
Parallel installation of different versions of the same project is not supported. It would need a PEP to discuss the changes to the import system and the packaging tools. Before someone starts that discussion, using virtualenv or buildout works well enough.
I don’t understand the question about the location of “components of the Python environment”.
I mostly use system packages (i.e. using the Aptitude package manager on Debian). To try out projects, I clone their repository. If I need something that’s not available with Aptitude, I install (or put a .pth file to the repo) in my user site-packages directory. I don’t need a custom PYTHONPATH, but I have changed the location of my user site-packages with PYTHONUSERBASE. I don’t like the magic and the eggs concept in setuptools/distribute, so I don’t use them. I’ve started to use virtualenv and pip for one project though (they use setuptools under the cover, but I made a private install so my global Python does not have setuptools).

One resource for this area is the book Expert Python Programming by Tarek Ziade. I'm ambivalent about the quality of the book, but the topics covered are just what you're focusing on.

Migrating to pip+virtualenv from setuptools

So pip and virtualenv sound wonderful compared to setuptools. Being able to uninstall would be great. But my project is already using setuptools, so how do I migrate? The web sites I've been able to find so far are very vague and general. So here's an anthology of questions after reading the main web sites and trying stuff out:
First of all, are virtualenv and pip supposed to be in a usable state by now? If not, please disregard the rest as the ravings of a madman.
How should virtualenv be installed? I'm not quite ready to believe it's as convoluted as explained elsewhere.
Is there a set of tested instructions for how to install matplotlib in a virtual environment? For some reason it always wants to compile it here instead of just installing a package, and it always ends in failure (even after build-dep which took up 250 MB of disk space). After a whole bunch of warnings it prints src/mplutils.cpp:17: error: ‘vsprintf’ was not declared in this scope.
How does either tool interact with setup.py? pip is supposed to replace easy_install, but it's not clear whether it's a drop-in or more complicated relationship.
Is virtualenv only for development mode, or should the users also install it?
Will the resulting package be installed with the minimum requirements (like the current egg), or will it be installed with sources & binaries for all dependencies plus all the build tools, creating a gigabyte monster in the virtual environment?
Will the users have to modify their $PATH and $PYTHONPATH to run the resulting package if it's installed in a virtual environment?
Do I need to create a script from a text string for virtualenv like in the bad old days?
What is with the #egg=Package URL syntax? That's not part of the standard URL, so why isn't it a separate parameter?
Where is #rev included in the URL? At the end I suppose, but the documentation is not clear about this ("You can also include #rev in the URL").
What is supposed to be understood by using an existing requirements file as "as a sort of template for the new file"? This could mean any number of things.

Wow, that's quite a set of questions. Many of them would really deserve their own SO question with more details. I'll do my best:
First of all, are virtualenv and pip
supposed to be in a usable state by
now?
Yes, although they don't serve everyone's needs. Pip and virtualenv (along with everything else in Python package management) are far from perfect, but they are widely used and depended upon nonetheless.
How should virtualenv be installed?
I'm not quite ready to believe it's as
convoluted as explained elsewhere.
The answer you link is complex because it is trying to avoid making any changes at all to your global Python installation and install everything in ~/.local instead. This has some advantages, but is more complex to setup. It's also installing virtualenvwrapper, which is a set of convenience bash scripts for working with virtualenv, but is not necessary for using virtualenv.
If you are on Ubuntu, aptitude install python-setuptools followed by easy_install virtualenv should get you a working virtualenv installation without doing any damage to your global python environment (unless you also had the Ubuntu virtualenv package installed, which I don't recommend as it will likely be an old version).
Is there a set of tested instructions
for how to install matplotlib in a
virtual environment? For some reason
it always wants to compile it here
instead of just installing a package,
and it always ends in failure (even
after build-dep which took up 250 MB
of disk space). After a whole bunch of
warnings it prints
src/mplutils.cpp:17: error: ‘vsprintf’
was not declared in this scope.
It "always wants to compile" because pip, by design, installs only from source, it doesn't install pre-compiled binaries. This is a controversial choice, and is probably the primary reason why pip has seen widest adoption among Python web developers, who use more pure-Python packages and commonly develop and deploy in POSIX environments where a working compilation chain is standard.
The reason for the design choice is that providing precompiled binaries has a combinatorial explosion problem with different platforms and build architectures (including python version, UCS-2 vs UCS-4 python builds, 32 vs 64-bit...). The way easy_install finds the right binary package on PyPI sort of works, most of the time, but doesn't account for all these factors and can break. So pip just avoids that issue altogether (replacing it with a requirement that you have a working compilation environment).
In many cases, packages that require C compilation also have a slower-moving release schedule and it's acceptable to simply install OS packages for them instead. This doesn't allow working with different versions of them in different virtualenvs, though.
I don't know what's causing your compilation error, it works for me (on Ubuntu 10.10) with this series of commands:
virtualenv --no-site-packages tmp
. tmp/bin/activate
pip install numpy
pip install -f http://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.0.1/matplotlib-1.0.1.tar.gz matplotlib
The "-f" link is necessary to get the most recent version, due to matplotlib's unusual download URLs on PyPI.
How does either tool interact with
setup.py? pip is supposed to replace
easy_install, but it's not clear
whether it's a drop-in or more
complicated relationship.
The setup.py file is a convention of distutils, the Python standard library's package management "solution." distutils alone is missing some key features, and setuptools is a widely-used third-party package that "embraces and extends" distutils to provide some additional features. setuptools also uses setup.py. easy_install is the installer bundled with setuptools. Setuptools development stalled for several years, and distribute was a fork of setuptools to fix some longstanding bugs. Eventually the fork was resolved with a merge of distribute back into setuptools, and setuptools development is now active again (with a new maintainer).
distutils2 was a mostly-rewritten new version of distutils that attempted to incorporate the best ideas from setuptools/distribute, and was supposed to become part of the Python standard library. Unfortunately this effort failed, so for the time being setuptools remains the de facto standard for Python packaging.
Pip replaces easy_install, but it does not replace setuptools; it requires setuptools and builds on top of it. Thus it also uses setup.py.
Is virtualenv only for development
mode, or should the users also install
it?
There's no single right answer to that; it can be used either way. In the end it's really your user's choice, and your software ideally should be able to be installed inside or out of a virtualenv; though you might choose to document and emphasize one approach or the other. It depends very much on who your users are and what environments they are likely to need to install your software into.
Will the resulting package be
installed with the minimum
requirements (like the current egg),
or will it be installed with sources &
binaries for all dependencies plus all
the build tools, creating a gigabyte
monster in the virtual environment?
If a package that requires compilation is installed via pip, it will need to be compiled from source. That also applies to any dependencies that require compilation.
This is unrelated to the question of whether you use a virtualenv. easy_install is available by default in a virtualenv and works just fine there. It can install pre-compiled binary eggs, just like it does outside of a virtualenv.
Will the users have to modify their
$PATH and $PYTHONPATH to run the
resulting package if it's installed in
a virtual environment?
In order to use anything installed in a virtualenv, you need to use the python binary in the virtualenv's bin/ directory (or another script installed into the virtualenv that references this binary). The most common way to do this is to use the virtualenv's activate or activate.bat script to temporarily modify the shell PATH so the virtualenv's bin/ directory is first. Modifying PYTHONPATH is not generally useful or necessary with virtualenv.
Do I need to create a script from a
text string for virtualenv like in the
bad old days?
No.
What is with the #egg=Package URL
syntax? That's not part of the
standard URL, so why isn't it a
separate parameter?
The "#egg=projectname-version" URL fragment hack was first introduced by setuptools and easy_install. Since easy_install scrapes links from the web to find candidate distributions to install for a given package name and version, this hack allowed package authors to add links on PyPI that easy_install could understand, even if they didn't use easy_install's standard naming conventions for their files.
Where is #rev included in the URL? At
the end I suppose, but the
documentation is not clear about this
("You can also include #rev in the
URL").
A couple sentences after that quoted fragment there is a link to "read the requirements file format to learn about other features." The #rev feature is fully documented and demonstrated there.
What is supposed to be understood by
using an existing requirements file as
"as a sort of template for the new
file"? This could mean any number of
things.
The very next sentence says "it will keep the packages listed in devel-req.txt in order and preserve comments." I'm not sure what would be a better concise description.

I can't answer all your questions, but hopefully the following helps.
Both virtualenv and pip are very usable. Many Python devs use these everyday.
Since you have a working easy_install, the easiest way to install both is the following:
easy_install pip
easy_install virtualenv
Once you have virtualenv, just type virtualenv yourEnvName and you'll get your new python virtual environment in a directory named yourEnvName.
From there, it's as easy as source yourEnvName/bin/activate and the virtual python interpreter will be your active. I know nothing about matplotlib, but following the installation interactions should work out ok unless there are weird hard-coded path issues.
If you can install something via easy_install you can usually install it via pip. I haven't found anything that easy_install could do that pip couldn't.
I wouldn't count on users being able to install virtualenv (it depends on who your users are). Technically, a virtual python interpreter can be treated as a real one for most cases. It's main use is not cluttering up the real interpreter's site-packages and if you have two libraries/apps that require different and incompatible versions of the same library.
If you or a user install something in a virtualenv, it won't be available in other virtualenvs or the system Python interpreter. You'll need to use source /path/to/yourvirtualenv/bin/activate command to switch to a virtual environment you installed the library on.
What they mean by "as a sort of template for the new file" is that the pip freeze -r devel-req.txt > stable-req.txt command will create a new file stable-req.txt based on the existing file devel-req.txt. The only difference will be anything installed not already specified in the existing file will be in the new file.

Does Python have a package/module management system?

Does Python have a package/module management system, similar to how Ruby has rubygems where you can do gem install packagename?
On Installing Python Modules, I only see references to python setup.py install, but that requires you to find the package first.

Recent progress
March 2014: Good news! Python 3.4 ships with Pip. Pip has long been Python's de-facto standard package manager. You can install a package like this:
pip install httpie
Wahey! This is the best feature of any Python release. It makes the community's wealth of libraries accessible to everyone. Newbies are no longer excluded from using community libraries by the prohibitive difficulty of setup.
However, there remains a number of outstanding frustrations with the Python packaging experience. Cumulatively, they make Python very unwelcoming for newbies. Also, the long history of neglect (ie. not shipping with a package manager for 14 years from Python 2.0 to Python 3.3) did damage to the community. I describe both below.
Outstanding frustrations
It's important to understand that while experienced users are able to work around these frustrations, they are significant barriers to people new to Python. In fact, the difficulty and general user-unfriendliness is likely to deter many of them.
PyPI website is counter-helpful
Every language with a package manager has an official (or quasi-official) repository for the community to download and publish packages. Python has the Python Package Index, PyPI. https://pypi.python.org/pypi
Let's compare its pages with those of RubyGems and Npm (the Node package manager).
https://rubygems.org/gems/rails RubyGems page for the package rails
https://www.npmjs.org/package/express Npm page for the package express
https://pypi.python.org/pypi/simplejson/ PyPI page for the package simplejson
You'll see the RubyGems and Npm pages both begin with a one-line description of the package, then large friendly instructions how to install it.
Meanwhile, woe to any hapless Python user who naively browses to PyPI. On https://pypi.python.org/pypi/simplejson/ , they'll find no such helpful instructions. There is however, a large green 'Download' link. It's not unreasonable to follow it. Aha, they click! Their browser downloads a .tar.gz file. Many Windows users can't even open it, but if they persevere they may eventually extract it, then run setup.py and eventually with the help of Google setup.py install. Some will give up and reinvent the wheel..
Of course, all of this is wrong. The easiest way to install a package is with a Pip command. But PyPI didn't even mention Pip. Instead, it led them down an archaic and tedious path.
Error: Unable to find vcvarsall.bat
Numpy is one of Python's most popular libraries. Try to install it with Pip, you get this cryptic error message:
Error: Unable to find vcvarsall.bat
Trying to fix that is one of the most popular questions on Stack Overflow: "error: Unable to find vcvarsall.bat"
Few people succeed.
For comparison, in the same situation, Ruby prints this message, which explains what's going on and how to fix it:
Please update your PATH to include build tools or download the DevKit from http://rubyinstaller.org/downloads and follow the instructions at http://github.com/oneclick/rubyinstaller/wiki/Development-Kit
Publishing packages is hard
Ruby and Nodejs ship with full-featured package managers, Gem (since 2007) and Npm (since 2011), and have nurtured sharing communities centred around GitHub. Npm makes publishing packages as easy as installing them, it already has 64k packages. RubyGems lists 72k packages. The venerable Python package index lists only 41k.
History
Flying in the face of its "batteries included" motto, Python shipped without a package manager until 2014.
Until Pip, the de facto standard was a command easy_install. It was woefully inadequate. The was no command to uninstall packages.
Pip was a massive improvement. It had most the features of Ruby's Gem. Unfortunately, Pip was--until recently--ironically difficult to install. In fact, the problem remains a top Python question on Stack Overflow: "How do I install pip on Windows?"

And just to provide a contrast, there's also pip.

The Python Package Index (PyPI) seems to be standard:
To install a package:
pip install MyProject
To update a package
pip install --upgrade MyProject
To fix a version of a package pip install MyProject==1.0
You can install the package manager as follows:
curl -O http://python-distribute.org/distribute_setup.py
python distribute_setup.py
easy_install pip
References:
http://guide.python-distribute.org/
http://pypi.python.org/pypi/distribute

As a Ruby and Perl developer and learning-Python guy, I haven't found easy_install or pip to be the equivalent to RubyGems or CPAN.
I tend to keep my development systems running the latest versions of modules as the developers update them, and freeze my production systems at set versions. Both RubyGems and CPAN make it easy to find modules by listing what's available, then install and later update them individually or in bulk if desired.
easy_install and pip make it easy to install a module ONCE I located it via a browser search or learned about it by some other means, but they won't tell me what is available. I can explicitly name the module to be updated, but the apps won't tell me what has been updated nor will they update everything in bulk if I want.
So, the basic functionality is there in pip and easy_install but there are features missing that I'd like to see that would make them friendlier and easier to use and on par with CPAN and RubyGems.

There are at least two, easy_install and its successor pip.

As of at least late 2014, Continuum Analytics' Anaconda Python distribution with the conda package manager should be considered. It solves most of the serious issues people run into with Python in general (managing different Python versions, updating Python versions, package management, virtual environments, Windows/Mac compatibility) in one cohesive download.
It enables you to do pretty much everything you could want to with Python without having to change the system at all. My next preferred solution is pip + virtualenv, but you either have to install virtualenv into your system Python (and your system Python may not be the version you want), or build from source. Anaconda makes this whole process the click of a button, as well as adding a bunch of other features.

That'd be easy_install.

It's called setuptools. You run it with the "easy_install" command.
You can find the directory at http://pypi.python.org/

I don't see either MacPorts or Homebrew mentioned in other answers here, but since I do see them mentioned elsewhere on Stack Overflow for related questions, I'll add my own US$0.02 that many folks seem to consider MacPorts as not only a package manager for packages in general (as of today they list 16311 packages/ports, 2931 matching "python", albeit only for Macs), but also as a decent (maybe better) package manager for Python packages/modules:
Question
"...what is the method that Mac python developers use to manage their modules?"
Answers
"MacPorts is perfect for Python on the Mac."
"The best way is to use MacPorts."
"I prefer MacPorts..."
"With my MacPorts setup..."
"I use MacPorts to install ... third-party modules tracked by MacPorts"
SciPy
"Macs (unlike Linux) don’t come with a package manager, but there are a couple of popular package managers you can install.
Macports..."
I'm still debating on whether or not to use MacPorts myself, but at the moment I'm leaning in that direction.

On Windows install http://chocolatey.org/ then
choco install python
Open a new cmd-window with the updated PATH. Next, do
choco install pip
After that you can
pip install pyside
pip install ipython
...

Since no one has mentioned pipenv here, I would like to describe my views why everyone should use it for managing python packages.
As #ColonelPanic mentioned there are several issues with the Python Package Index and with pip and virtualenv also.
Pipenv solves most of the issues with pip and provides additional features also.
Pipenv features
Pipenv is intended to replace pip and virtualenv, which means pipenv will automatically create a separate virtual environment for every project thus avoiding conflicts between different python versions/package versions for different projects.
Enables truly deterministic builds, while easily specifying only what you want.
Generates and checks file hashes for locked dependencies.
Automatically install required Pythons, if pyenv is available.
Automatically finds your project home, recursively, by looking for a Pipfile.
Automatically generates a Pipfile, if one doesn’t exist.
Automatically creates a virtualenv in a standard location.
Automatically adds/removes packages to a Pipfile when they are un/installed.
Automatically loads .env files, if they exist.
If you have worked on python projects before, you would realize these features make managing packages way easier.
Other Commands
check checks for security vulnerabilities and asserts that PEP 508 requirements are being met by the current environment. (which I think is a great feature especially after this - Malicious packages on PyPi)
graph will show you a dependency graph, of your installed dependencies.
You can read more about it here - Pipenv.
Installation
You can find the installation documentation here
P.S.: If you liked working with the Python Package requests , you would be pleased to know that pipenv is by the same developer Kenneth Reitz

In 2019 poetry is the package and dependency manager you are looking for.
https://github.com/sdispater/poetry#why
It's modern, simple and reliable.

Poetry is what you're looking for. It takes care of dependency management, virtual environments, running.