One of the most unfortunate things about learning Python is that there are so many different ways to get it installed, and you need to choose one before you even begin. The differences can also be subtle and require technical depth to truly understand, which you don’t have yet.¹ Even experts can be missing information about which one to use and why.

There are perhaps more of these on macOS than on any other platform, and that’s the platform I primarily use these days. If you’re using macOS, I’d like to make it simple for you.

The One You Probably Want: Python.org

My recommendation is to use an official build from python.org.

I recommed the official installer for most uses, and if you were just looking for a choice about which one to use, you can stop reading now. Thanks for your time, and have fun with Python.

If you want to get into the nerdy nuances, read on.

For starters, the official builds are compiled in such a way that they will run on a wide range of macs, both new and old. They are universal2 binaries, unlike some other builds, which means you can distribute them as part of a mac application.

The main advantage that the Python.org build has, though, is very subtle, and not any concrete technical detail. It’s a social, structural issue: the Python.org builds are produced by the people who make CPython, who are more likely to know about the nuances of what options it can be built with, and who are more likely to adopt their own improvements as they are released. Third party builders who are focused on a more niche use-case may not realize that there are build options or environment requirements that could make their Pythons better.

I’m being a bit vague deliberately here, because at any particular moment in time, this may not be an advantage at all. Third party integrators generally catch up to changes, and eventually achieve parity. But for a specific upcoming example, PEP 703 will have extensive build-time implications, and I would trust the python.org team to be keeping pace with all those subtle details immediately as releases happen.

(And Auto-Update It)

The one downside of the official build is that you have to return to the website to check for security updates. Unlike other options described below, there’s no built-in auto-updater for security patches. If you follow the normal process, you still have to click around in a GUI installer to update it once you’ve clicked around on the website to get the file.

I have written a micro-tool to address this and you can pip install mopup and then periodically run mopup and it will install any security updates for your current version of Python, with no interaction besides entering your admin password.

(And Always Use Virtual Environments)

Once you have installed Python from python.org, never pip install anything globally into that Python, even using the --user flag. Always, always use a virtual environment of some kind. In fact, I recommend configuring it so that it is not even possible to do so, by putting this in your ~/.pip/pip.conf:

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[global]
require-virtualenv = true

This will avoid damaging your Python installation by polluting it with libraries that you install and then forget about. Any time you need to do something new, you should make a fresh virtual environment, and then you don’t have to worry about library conflicts between different projects that you may work on.

If you need to install tools written in Python, don’t manage those environments directly, install the tools with pipx. By using pipx, you allow each tool to maintain its own set dependencies, which means you don’t need to worry about whether two tools you use have conflicting version requirements, or whether the tools conflict with your own code.²

The Others

There are, of course, several other ways to install Python, which you probably don’t want to use.

The One For Running Other People’s Code, Not Yours: Homebrew

In general, Homebrew Python is not for you.

The purpose of Homebrew’s python is to support applications packaged within Homebrew, which have all been tested against the versions of python libraries also packaged within Homebrew. It may upgrade without warning on just about any brew operation, and you can’t downgrade it without breaking other parts of your install.

Specifically for creating redistributable binaries, Homebrew python is typically compiled only for your specific architecture, and thus will not create binaries that can be used on Intel macs if you have an Apple Silicon machine, or will run slower on Apple Silicon machines if you have an Intel mac. Also, if there are prebuilt wheels which don’t yet exist for Apple Silicon, you cannot easily arch -x86_64 python ... and just install them; you have to install a whole second copy of Homebrew in a different location, which is a headache.

In other words, homebrew is an alternative to pipx, not to Python. For that purpose, it’s fine.

The One For When You Need 20 Different Pythons For Debugging: pyenv

Like Homebrew, pyenv will default to building a single-architecture binary. Even worse, it will not build a Framework build of Python, which means several things related to being a mac app just won’t work properly. Remember those build-time esoterica that the core team is on top of but third parties may not be? “Should I use a Framework build” is an enduring piece of said esoterica.

The purpose of pyenv is to provide a large matrix of different, precise legacy versions of python for library authors to test compatibility against those older Pythons. If you need to do that, particularly if you work on different projects where you may need to install some random super-old version of Python that you would not normally use to test something on, then pyenv is great. But if you only need one version of Python, it’s not a great way to get it.

The Other One That’s Exactly Like pyenv: asdf-python

The issues are exactly the same as with pyenv, as the tool is a straightforward alternative for the exact same purpose. It’s a bit less focused on Python than pyenv, which has pros and cons; it has broader community support, but it’s less specifically tuned for Python. But a comparative exploration of their differences is beyond the scope of this post.

The Built-In One That Isn’t Really Built-In: /usr/bin/python3

There is a binary in /usr/bin/python3 which might seem like an appealing option — it comes from Apple, after all! — but it is provided as a developer tool, for running things like build scripts. It isn’t for building applications with.

That binary is not a “system python”; the thing in the operating system itself is only a shim, which will determine if you have development tools, and shell out to a tool that will download the development tools for you if you don’t. There is unfortunately a lot of folk wisdom among older Python programmers who remember a time when apple did actually package an antedeluvian version of the interpreter that seemed to be supported forever, and might suggest it for things intended to be self-contained or have minimal bundled dependencies, but this is exactly the reason that Apple stopped shipping that.

If you use this option, it means that your Python might come from the Xcode Command Line Tools, or the Xcode application, depending on the state of xcode-select in your current environment and the order in which you installed them.

Upgrading Xcode via the app store or a developer.apple.com manual download — or its command-line tools, which are installed separately, and updated via the “settings” application in a completely different workflow — therefore also upgrades your version of Python without an easy way to downgrade, unless you manage multiple Xcode installs. Which, at 12G per install, is probably not an appealing option.³

The One With The Data And The Science: Conda

As someone with a limited understanding of data science and scientific computing, I’m not really qualified to go into the detailed pros and cons here, but luckily, Itamar Turner-Trauring is, and he did.

My one coda to his detailed exploration here is that while there are good reasons to want to use Anaconda — particularly if you are managing a data-science workload across multiple platforms and you want a consistent, holistic development experience across a large team supporting heterogenous platforms — some people will tell you that you need Conda to get you your libraries if you want to do data science or numerical work with Python at all, because Conda is how you install those libraries, and otherwise things just won’t work.

This is a historical artifact that is no longer true. Over the last decade, Python Wheels have been comprehensively adopted across the Python community, and almost every popular library with an extension module ships pre-built binaries to multiple platforms. There may be some libraries that only have prebuilt binaries for conda, but they are sufficiently specialized that I don’t know what they are.

The One for Being Consistent With Your Cloud Hosting

Another way to run Python on macOS is to not run it on macOS, but to get another computer inside your computer that isn’t running macOS, and instead run Python inside that, usually using Docker.⁴

There are good reasons to want to use a containerized configuration for development, but they start to drift away from the point of this post and into more complicated stuff about how to get your Python into the cloud.

So rather than saying “use Python.org native Python instead of Docker”, I am specifically not covering Docker as a replacement for a native mac Python here because in a lot of cases, it can’t be one. Many tools require native mac facilities like displaying GUIs or scripting applications, or want to be able to take a path name to a file without elaborate pre-work to allow the program to access it.

Summary

If you didn’t want to read all of that, here’s the summary.

If you use a mac:

1. Get your Python interpreter from python.org.
2. Update it with mopup so you don’t fall behind on security updates.
3. Always use venvs for specific projects, never pip install anything directly.
4. Use pipx to manage your Python applications so you don’t have to worry about dependency conflicts.
5. Don’t worry if Homebrew also installs a python executable, but don’t use it for your own stuff.
6. You might need a different Python interpreter if you have any specialized requirements, but you’ll probably know if you do.

Acknowledgements

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support me on Patreon as well! I am also available for consulting work if you think your organization could benefit from expertise on topics like “which Python is the really good one”.

One of the perennial talking points in the Python packaging discourse is that it’s unnecessarily difficult to create a simple, single-file binary that you can hand to users.

This complaint is understandable. In most other programming languages, the first thing you sit down to do is to invoke the compiler, get an executable, and run it. Other, more recently created programming languages — particularly Go and Rust — have really excellent toolchains for doing this which eliminate a lot of classes of error during that build-and-run process. A single, dependency-free, binary executable file that you can run is an eminently comprehensible artifact, an obvious endpoint for “packaging” as an endeavor.

While Rust and Go are producing these artifacts as effectively their only output, Python has such a dizzying array of tooling complexity that even attempting to describe “the problem” takes many thousands of words and one may not ever even get around to fully describing the complexity of the issues involved in the course of those words. All the more understandable, then, that we should urgently add this functionality to Python.

A programmer might see Python produce wheels and virtualenvs which can break when anything in their environment changes, and see the complexity of that situation. Then, they see Rust produce a statically-linked executable which “just works”, and they see its toolchain simplicity. I agree that this shouldn’t be so hard, and some of the architectural decisions that make this difficult in Python are indeed unfortunate.

But then, I think, our hypothetical programmer gets confused. They think that Rust is simple because it produces an executable, and they think Python’s complexity comes from all the packaging standards and tools. But this is not entirely true.

Python’s packaging complexity, and indeed some of those packaging standards, arises from the fact that it is often used as a glue language. Packaging pure Python is really not all that hard. And although the tools aren’t included with the language and the DX isn’t as smooth, even packaging pure Python into a single-file executable is pretty trivial.

But almost nobody wants to package a single pure-python script. The whole reason you’re writing in Python is because you want to rely on an enormous ecosystem of libraries, and some low but critical percentage of those libraries include things like their own statically-linked copies of OpenSSL or a few megabytes of FORTRAN code with its own extremely finicky build system you don’t want to have to interact with.

When you look aside to other ecosystems, while Python still definitely has some unique challenges, shipping Rust with a ton of FFI, or Go with a bunch of Cgo is substantially more complex than the default out-of-the-box single-file “it just works” executable you get at the start.¹

Still, all things being equal, I think single-file executable builds would be nice for Python to have as a building block. It’s certainly easier to produce a package for a platform if your starting point is that you have a known-good, functioning single-file executable and you all you need to do is embed it in some kind of environment-specific envelope. Certainly if what you want is to copy a simple microservice executable into a container image, you might really want to have this rather than setting up what is functionally a full Python development environment in your Dockerfile. After team-wide philosophical debates over what virtual environment manager to use, those Golang Dockerfiles that seem to be nothing but the following 4 lines are really appealing:

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FROM golang
COPY *.go ./
RUN go build -o /app
ENTRYPOINT ["/app"]

All things are rarely equal, however.

The issue that we, as a community, ought to be trying to address with build tools is to get the software into users’ hands, not to produce a specific file format. In my opinion, single-file binary builds are not a great tool for this. They’re fundamentally not how people, even quite tech-savvy programming people, find and manage their tools.

A brief summary of the problems with single-file distributions:

1. They’re not discoverable. A single file linked on your website will not be found via something like brew search, apt search, choco search or searching in a platform’s GUI app store’s search bar.
2. They’re not updatable. People expect their system package manager to update stuff for them. Standalone binaries might add their own updaters, but now you’re shipping a whole software-update system inside your binary. More likely, it’ll go stale forever while better-packaged software will be updated and managed properly.
3. They have trouble packaging resources. Once you’ve got your code stuffed into a binary, how do you distribute images, configuration files, or other data resources along with it? This isn’t impossible to solve, but in other programming languages which do have a great single-file binary story, this problem is typically solved by third party tooling which, while it might work fine, will still generally exist in multiple alternative forms which adds its own complexity.

So while it might be a useful building-block that simplifies those annoying container builds a lot, it hardly solves the problem comprehensively.

If we were to build a big new tool, the tool we need is something that standardizes the input format to produce a variety of different complex, multi-file output formats, including things like:

• deb packages (including uploading to PPA archives so people can add an apt line; a manual dpkg -i has many of the same issues as a single file)
• container images (including the upload to a registry so that people can "$(shuf -n 1 -e nerdctl docker podman)" pull or FROM it)
• Flatpak apps
• Snaps
• macOS apps
• Microsoft store apps
• MSI installers
• Chocolatey / NuGet packages
• Homebrew formulae

In other words, ask yourself, as a user of an application, how do you want to consume it? It depends what kind of tool it is, and there is no one-size-fits-all answer.

In any software ecosystem, if a feature is a building block which doesn’t fully solve the problem, that is an issue with the feature, but in many cases, that’s fine. We need lots of building blocks to get to full solutions. This is the story of open source.

However, if I had to take a crack at summing up the infinite-headed hydra of the Problem With Python Packaging, I’d put it like this:

Python has a wide array of tools which can be used to package your Python code for almost any platform, in almost any form, if you are sufficiently determined. The problem is that the end-to-end experience of shipping an application to end users who are not Python programmers² for any particular platform has a terrible user experience. What we need are more holistic solutions, not more building blocks.³

This makes me want to push back against this tendency whenever I see it, and to try to point towards more efficient ways to achieving a good user experience, with the relatively scarce community resources at our collective disposal⁴. Efficiency isn’t exclusively about ideal outcomes, though; it’s the optimization a cost/benefit ratio. In terms of benefits, it’s relatively low, as I hope I’ve shown above.

Building a tool that makes arbitrary Python code into a fully self-contained executable is also very high-cost, in terms of community effort, for a bunch of reasons. For starters, in any moderately-large collection of popular dependencies from PyPI, at least a few of them are going to want to find their own resources via __file__, and you need to hack in a way to find those, which is itself error prone. Python also expects dynamic linking in a lot of places, and messing around with C linkers to change that around is a complex process with its own huge pile of failure modes. You need to do this on pre-existing binaries built with options you can’t necessarily control, because making everyone rebuild all the binary wheels they find on PyPI is a huge step backwards in terms of exposing app developers to confusing infrastructure complexity.

Now, none of this is impossible. There are even existing tools to do some of the scarier low-level parts of these problems. But one of the reasons that all the existing tools for doing similar things have folk-wisdom reputations and even official documentation expecting constant pain is that part of the project here is conducting a full audit of every usage of __file__ on PyPI and replacing it with some resource-locating API which we haven’t even got a mature version of yet⁵.

Whereas copying all the files into the right spots in an archive file that can be directly deployed to an existing platform is tedious, but only moderately challenging. It usually doesn’t involve fundamentally changing how the code being packaged works, only where it is placed.

To the extent that we have a choice between “make it easy to produce a single-file binary without needing to understand the complexities of binaries” or “make it easy to produce a Homebrew formula / Flatpak build / etc without the user needing to understand Homebrew / Flatpak / etc”, we should always choose the latter.

If this is giving you déjà vu, I’ve gestured at this general concept more vaguely in a few places, including tweeting⁶ about it in 2019, saying vaguely similar stuff:

If you're making a packaging tool for Python, stop trying to make single-file executables. They are a pointless technical flourish. You need to build:
• .app bundles
• .deb/.rpm/flatpack packages
• container images
• .msi installers
• Homebrew formulæ
• a codesigning pipeline

— glyph ⎷⃣ (@glyph) August 5, 2019

Everything I’ve written here so far is debatable.

You can find that debate both in replies to that original tweet and in various other comments and posts elsewhere that I’ve grumbled about this. I still don’t agree with that criticism, but there are very clever people working on complex tools which are slowly gaining popularity and might be making the overall packaging situation better.

So while I think we should in general direct efforts more towards integrating with full-featured packaging standards, I don’t want to yuck anybody’s yum when it comes to producing clean single-file executables in general. If you want to build that tool and it turns out to be a more useful building block than I’m giving it credit for, knock yourself out.

However, in addition to having a comprehensive write-up of my previously-stated opinions here, I want to impart a more concrete, less debatable issue. To wit: single-file executables as a distribution mechanism, specifically on macOS is not only sub-optimal, but a complete waste of time.

Late last year, Hynek wrote a great post about his desire for, and experience of, packaging a single-file binary for multiple platforms. This should serve as an illustrative example of my point here. I don’t want to pick on Hynek. Prominent Python programmers wish for this all the time.. In fact, Hynek also did the thing I said is a good idea here, and did, in fact, create a Homebrew tap, and that’s the one the README recommends.

So since he kindly supplied a perfect case-study of the contrasting options, let’s contrast them!

The first thing I notice is that the Homebrew version is Apple Silicon native, whereas the single-file binary is still x86_64, as the brew build and test infrastructure apparently deals with architectural differences (probably pretty easy given it can use Homebrew’s existing Python build) but the more hand-rolled PyOxidizer setup builds only for the host platform, which in this case is still an Intel mac thanks to GitHub dragging their feet.

The second is that the Homebrew version runs as I expect it to. I run doc2dash in my terminal and I see Usage: doc2dash [OPTIONS] SOURCE, as I should.

So, A+ on the Homebrew tap. No notes. I did not have to know anything about Python being in the loop at all here, it “just works” like every Ruby, Clojure, Rust, or Go tool I’ve installed with the same toolchain.

Over to the single-file brew-less version.

Beyond the architecture being emulated and having to download Rosetta2⁸, I have to note that this “single file” binary already comes in a zip file, since it needs to include the license in a separate file.⁷ Now that it’s unarchived, I have some choices to make about where to put it on my $PATH. But let’s ignore that for now and focus on the experience of running it. I fire up a terminal, and run cd Downloads/doc2dash.x86_64-apple-darwin/ and then ./doc2dash.

Now we hit the more intractable problem:

The executable does not launch because it is neither code-signed nor notarized. I’m not going to go through the actual demonstration here, because you already know how annoying this is, and also, you can’t actually do it.

Code-signing is more or less fine. The codesign tool will do its thing, and that will change the wording in the angry dialog box from something about an “unidentified developer” to being “unable to check for malware”, which is not much of a help. You still need to notarize it, and notarization can’t work.

macOS really wants your executable code to be in a bundle (i.e., an App) so that it can know various things about its provenance and structure. CLI tools are expected to be in the operating system, or managed by a tool like brew that acts like a sort of bootleg secondary operating-system-ish thing and knows how to manage binaries.

If it isn’t in a bundle, then it needs to be in a platform-specific .pkg file, which is installed with the built-in Installer app. This is because apple cannot notarize a stand-alone binary executable file.

Part of the notarization process involves stapling an external “notarization ticket” to your code, and if you’ve only got a single file, it has nowhere to put that ticket. You can’t even submit a stand-alone binary; you have to package it in a format that is legible to Apple’s notarization service, which for a pure-CLI tool, means a .pkg.

What about corporate distributions of proprietary command-line tools, like the 1Password CLI? Oh look, their official instructions also tell you to use their Homebrew formula too. Homebrew really is the standard developer-CLI platform at this point for macOS. When 1Password distributes stuff outside of Homebrew, as with their beta builds, it’s stuff that lives in a .pkg as well.

It is possible to work around all of this.

I could open the unzipped file, right-click on the CLI tool, go to “Open”, get a subtly differently worded error dialog, like this…

…watch it open Terminal for me and then exit, then wait multiple seconds for it to run each time I want to re-run it at the command line. Did I mention that? The single-file option takes 2-3 seconds doing who-knows what (maybe some kind of security check, maybe pyoxidizer overhead, I don’t know) but the Homebrew version starts imperceptibly instantly.

Also, I then need to manually re-do this process in the GUI every time I want to update it.

If you know about the magic of how this all actually works, you can also do xattr -d com.apple.quarantine doc2dash by hand, but I feel like xattr -d is a step lower down in the user-friendliness hierarchy than python3 -m pip install⁹, and not only because making a habit of clearing quarantine attributes manually is a little like cutting the brake lines on Gatekeeper’s ability to keep out malware.

But the point of distributing a single-file binary is to make it “easy” for end users, and is explaining gatekeeper’s integrity verification accomplishing that goal?

Apple’s effectively mandatory code-signing verification on macOS is far out ahead of other desktop platforms right now, both in terms of its security and in terms of its obnoxiousness. But every mobile platform is like this, and I think that as everyone gets more and more panicked about malicious interference with software delivery, we’re going to see more and more official requirements that software must come packaged in one of these containers.

Microsoft will probably fix their absolute trash-fire of a codesigning system one day too. I predict that something vaguely like this will eventually even come to most Linux distributions. Not necessarily a prohibition on individual binaries like this, or like a GUI launch-prevention tool, but some sort of requirement imposed by the OS that every binary file be traceable to some sort of package, maybe enforced with some sort of annoying AppArmor profile if you don’t do it.

The practical, immediate message today is: “don’t bother producing a single-file binary for macOS users, we don’t want it and we will have a hard time using it”. But the longer term message is that focusing on creating single-file binaries is, in general, skating to where the puck used to be.

If we want Python to have a good platform-specific distribution mechanism for every platform, so it’s easy for developers to get their tools to users without having to teach all their users a bunch of nonsense about setuptools and virtualenvs first, we need to build that, and not get hung up on making a single-file executable packer a core part of the developer experience.

Thanks very much to my patrons for their support of writing like this, and software like these.

Oh, right. This is where I put the marketing “call to action”. Still getting the hang of these.

Did you enjoy this post and want me to write more like it, and/or did you hate it and want the psychological leverage and moral authority to tell me to stop and do something else? You can sign up here!

What have I been up to?

Late last year, I launched a Patreon. Although not quite a “soft” launch — I did toot about it, after all — I didn’t promote it very much.

I started this way because I realized that if I didn’t just put something up I’d be dithering forever. I’d previously been writing a sprawling monster of an announcement post that went into way too much detail, and kept expanding to encompass more and more ideas until I came to understand that salvaging it was going to be an editing process just as brutal and interminable as the writing itself.

However, that post also included a section where I just wrote about what I was actually doing.

So, for lots of reasons¹, there are a diverse array of loosely related (or unrelated) projects below which may not get finished any time soon. Or, indeed, may go unfinished entirely. Some are “done enough” now, and just won’t receive much in the way of future polish.

That is an intentional choice.

The rationale, as briefly as I can manage, is: I want to lean into the my strength² of creative, divergent thinking, and see how these ideas pan out without committing to them particularly intensely. My habitual impulse, for many years, has been to lean extremely hard on strategies that compensate for my weaknesses in organization, planning, and continued focus, and attempt to commit to finishing every project to prove that I’ll never flake on anything.

While the reward tiers for the Patreon remain deliberately ambiguous³, I think it would be fair to say that patrons will have some level of influence in directing my focus by providing feedback on these projects, and requesting that I work more on some and less on others.

So, with no further ado: what have I been working on, and what work would you be supporting if you signed up? For each project, I’ll be answering 3 questions:

1. What is it?
2. What have I been doing with it recently?
3. What are my plans for it?

This. i.e. blog.glyph.im

What is it?

For starters, I write stuff here. I guess you’re reading this post for some reason, so you might like the stuff I write? I feel like this doesn’t require much explanation.

What have I done with it recently?

You might appreciate the explicitly patron-requested Potato Programming post, a screed about dataclass, or a deep dive on the difficulties of codesigning and notarization on macOS along with an announcement of a tool to remediate them.

What are my plans for it?

You can probably expect more of the same; just all the latest thoughts & ideas from Glyph.

Twisted

What is it?

If you know of me you probably know of me as “the Twisted guy” and yeah, I am still that. If, somehow, you’ve ended up here and you don’t know what it is, wow, that’s cool, thanks for coming, super interested to know what you do know me for.

Twisted is an event-driven networking engine written in Python, the precursor and inspiration for the asyncio module, and a suite of event-driven programming abstractions, network protocol implementations, and general utility code.

What have I done with it recently?

I’ve gotten a few things merged, including type annotations for getPrimes and making the bundled CLI OpenSSH server replacement work at all with public key authentication again, as well as some test cleanups that reduce the overall surface area of old-style Deferred-returning tests that can be flaky and slow.

I’ve also landed a posix_spawnp-based spawnProcess implementation which speed up process spawning significantly; this can be as much as 3x faster if you do a lot of spawning of short-running processes.

I have a bunch of PRs in flight, too, including better annotations for FilePath Deferred, and IReactorProcess, as well as a fix for the aforementioned posix_spawnp implementation.

What are my plans for it?

A lot of the projects below use Twisted in some way, and I continue to maintain it for my own uses. My particular focus is in quality-of-life improvements; issues that someone starting out with a Twisted project will bump into and find confusing or difficult. I want it to be really easy to write applications with Twisted and I want to use my own experiences with it.

I also do code reviews of other folks’ contributions; we do still have over 100 open PRs right now.

DateType

What is it?

DateType is a workaround for a very specific bug in the way that the datetime standard library module deals with type composition: to wit, that datetime is a subclass of date but is not Liskov-substitutable for it. There are even #type:ignore comments in the standard library type stubs to work around this problem, because if you did this in your own code, it simply wouldn’t type-check.

What have I done with it recently?

I updated it a few months ago to expose DateTime and Time directly (as opposed to AwareDateTime and NaiveDateTime), so that users could specialize their own functions that took either naive or aware times without ugly and slightly-incorrect unions.

What are my plans for it?

This library is mostly done for the time being, but if I had to polish it a bit I’d probably do two things:

1. a readthedocs page for nice documentation
2. write a PEP to get this integrated into the standard library

Although the compatibility problems are obviously very tricky and a PEP would probably be controversial, this is ultimately a bug in the stdlib, and should be fixed upstream there.

Automat

What is it?

It’s a library to make deterministic finite-state automata easier to create and work with.

What have I done with it recently?

Back in the middle of last year, I opened a PR to create a new, completely different front-end API for state machine definition. Instead of something like this:

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class MachineExample:
    machine = MethodicalMachine()

    @machine.state()
    def a_state(self): ...

    @machine.state()
    def other_state(self): ...

    @machine.input()
    def flip(self): ...

    @machine.output()
    def _do_flip(self): return ...

    on.upon(flip, enter=off, outputs=[_do_flip], collector=list)
    off.upon(flip, enter=on, outputs=[_do_flip], collector=list)

this branch lets you instead do something like this:

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class MachineProtocol(Protocol):
    def flip(self) -> None: ...

class MachineCore: ...

def buildCore() -> MachineCore: ...
machine = TypicalBuilder(MachineProtocol, buildCore)

@machine.state()
class _OffState:
    @machine.handle(MachineProtocol.flip, enter=lambda: _OnState)
    def flip(self) -> None: ...

@machine.state()
class _OnState:
    @machine.handle(MachineProtocol.flip, enter=lambda: _OffState)
    def flip(self) -> None: ...

MachineImplementation = machine.buildClass()

In other words, it creates a state for every type, and type safety that much more cleanly expresses what methods can be called and by whom; no need to make everything private with tons of underscore-prefixed methods and attributes, since all the caller can see is “an implementation of MachineProtocol”; your state classes can otherwise just be normal classes, which do not require special logic to be instantiated if you want to use them directly.

Also, by making a state for every type, it’s a lot cleaner to express that certain methods require certain attributes, by simply making them available as attributes on that state and then requiring an argument of that state type; you don’t need to plot your way through the outputs generated in your state graph.

What are my plans for it?

I want to finish up dealing with some issues with that branch - particularly the ugly patterns for communicating portions of the state core to the caller and also the documentation; there are a lot of magic signatures which make sense in heavy usage but are a bit mysterious to understand while you’re getting started.

I’d also like the visualizer to work on it, which it doesn’t yet, because the visualizer cribs a bunch of state from MethodicalMachine when it should be working purely on core objects.

Secretly

What is it?

This is an attempt at a holistic, end-to-end secret management wrapper around Keyring. Whereas Keyring handles password storage, this handles the whole lifecycle of looking up the secret to see if it’s there, displaying UI to prompt the user (leveraging a pinentry program from GPG if available)

What have I done with it recently?

It’s been a long time since I touched it.

What are my plans for it?

• Documentation. It’s totally undocumented.
• It could be written to be a bit more abstract. It dates from a time before asyncio, so its current Twisted requirement for Deferred could be made into a generic Awaitable one.
• Better platform support for Linux & Windows when GPG’s pinentry is not available.
• Support for multiple accounts so that when the user is prompted for the relevant credential, they can store it.
• Integration with 1Password via some of their many potentially relevant APIs.

Fritter

What is it?

Fritter is a frame-rate independent timer tree.

In the course of developing Twisted, I learned a lot about time and timers. LoopingCall encodes some of this knowledge, but it’s very tightly coupled to the somewhat limited IReactorTime API.

Also, LoopingCall was originally designed with the needs of media playback (particularly network streaming audio playback) in mind, but I have used it more for background maintenance tasks and for animations. Both of these things have requirements that LoopingCall makes awkward but FRITTer is designed to meet:

1. At higher loads, surprising interactions can occur with the underlying priority queue implementation, and different algorithms may make a significant difference to performance. Fritter has a pluggable implementation of a priority queue and is carefully minimally coupled to it.

2. Driver selection is a first-class part of the API, with an included, public “Memory” driver for testing, rather than LoopingCall’s “testing is at least possible” .reactor attribute. This means that out of the box it supports both Twisted and asyncio, and can easily have other things added.

3. The API is actually generic on what constitutes time itself, which means that you can use it for both short-term (i.e.: monotonic clock values as float-seconds) and long-term (civil times as timezone-aware datetime objects) recurring tasks. Recurrence rules can also be arbitrary functions.

4. There is a recursive driver (this is the “tree” part) which both allows for:

   a. groups of timers which can be suspended and resumed together, and

   b. scaling of time, so that you can e.g. speed up or slow down the ticks for AIs, groups of animations, and so on, also in groups.

5. The API is also generic on what constitutes work. This means that, for example, in a certain timer you can say “all work units scheduled on this scheduler, in addition to being callable, must also have an asJSON method”. And in fact that’s exactly what the longterm module in Fritter does.

I can neither confirm nor deny that this project was factored out of a game engine for a secret game project which does not appear on this list.

What have I done with it recently?

Besides realizing, in the course of writing this blog post, that its CI was failing its code quality static checks (oops), the last big change was the preliminary support for recursive timers and serialization.

What are my plans for it?

• These haven’t been tested in anger yet and I want to actually use them in a larger project to make sure that they don’t have any necessary missing pieces.

• Documentation.

Encrust

What is it?

I have written about Encrust quite recently so if you want to know about it, you should probably read that post. In brief, it is a code-shipping tool for py2app. It takes care of architecture-independence, code-signing, and notarization.

What have I done with it recently?

Wrote it. It’s brand new as of this month.

What are my plans for it?

I really want this project to go away as a tool with an independent existence. Either I want its lessons to be fully absorbed into Briefcase or perhaps py2app itself, or for it to become a library that those things call into to do its thing.

Various Small Mac Utilities

What is it?

• QuickMacApp is a very small library for creating status-item “menu bar apps” in Python which don’t have much of a UI but want to run some Python code in the background and occasionally pop up a notification or ask the user a question or something. The idea is that if you have a utility that needs a minimal UI to just ask the user one or two things, you should be able to give it a GUI immediately, without thinking about it too much.
• QuickMacHotkey this is a very minimal API to register hotkeys on macOS. this example is what comes up if you search the web for such a thing, but it hasn’t worked on a current Python for about 11 years. This isn’t the “right” way to do such a thing, since it provides no UI to set the shortcut, you’d have to hard-code it. But MASShortcut is now archived and I haven’t had the opportunity to investigate HotKey, so for the time being, it’s a handy thing, and totally adequate for the sort of quick-and-dirty applications you might make with QuickMacApp.
• VEnvDotApp is a way of giving a virtualenv its own Info.plist and bundle ID, so that command-line python tools that just need to pop up a little mac GUI, like an alert or a notification, can do so with cross-platform tools without looking like it’s an app called “Python”, or in some cases breaking entirely.
• MOPUp is a command-line updater for upstream Python.org macOS Python. For distributing third-party apps, Python.org’s version is really the one you want to use (it’s universal2, and it’s generally built with compiler options that make it a distributable thing itself) but updating it by downloading a .pkg file from a web browser is kind of annoying.

What have I done with it recently?

I’ve been releasing all these tools as they emerge and are factored out of other work, and they’re all fairly recent.

What are my plans for it?

I will continue to factor out any general-purpose tools from my platform-specific Python explorations — hopefully more Linux and Windows too, once I’ve got writing code for my own computer down, but most of the tools above are kind of “done” on their own, at the moment.

The two things that come to mind though are that QuickMacApp should have a way of owning the menubar sometimes (if you don’t have something like Bartender, menu-bar-status-item-only apps can look like they don’t do anything when you launch them), and that MOPUp should probably be upstreamed to python.org.

Pomodouroboros

What is it?

Pomodouroboros is a pomodoro timer with a highly opinionated take. It’s based on my own experience of ADHD time blindness, and is more like a therapeutic intervention for that specific condition than a typical “productivity” timer app.

In short, it has two important features that I have found lacking in other tools:

1. A gigantic, absolutely impossible to ignore visual timer that presents a HUD overlay over your entire desktop. It remains low-opacity and static most of the time but pulses every 30 seconds to remind you that time is passing.
2. Rather than requiring you to remember to set a timer before anything happens, it has an idea of “work hours” when you want to be time-sensitive and presents constant prompting to get started.

What have I done with it recently?

I’ve been working on it fairly consistently lately. The big things I’ve been doing have been:

1. factoring things out of the Pomodouroboros-specific code and into QuickMacApp and Encrust.
2. Porting the UI to the redesigned core of the application, which has been implemented and tested in platform-agnostic Python but does not have any UI yet.
3. fully productionizing the build process and ensuring that Encrust is producing binary app bundles that people can use.

What are my plans for it?

In brief, “finish the app”. I want this to have its own website and find a life beyond the Python community, with people who just want a timer app and don’t care how it’s written. The top priority is to replace the current data model, which is to say the parts of the UI that set and evaluate timers and edit the list of upcoming timers (the timer countdown HUD UI itself is fine).

I also want to port it to other platforms, particularly desktop Linux, where I know there are many users interested in such a thing. I also want to do a CLI version for folks who live on the command line.

Finally: Pomodouroboros serves as a test-bed for a larger goal, which is that I want to make it easier for Python programmers, particularly beginners who are just getting into coding at all, to write code that not only interacts with their own computer, but that they can share with other users in a real way. As you can see with Encrust and other projects above, as much as I can I want my bumpy ride to production code to serve as trailblazing so that future travelers of this path find it as easy as possible.

And Here Is Where The CTA Goes

If this stuff sounds compelling, you can obviously sign up, that would be great. But also, if you’re just curious, go ahead and give some of these projects some stars on GitHub or just share this post. I’d also love to hear from you about any of this!

If a lot of people find this compelling, then pursuing these ideas will become a full-time job, but I’m pretty far from that threshold right now. In the meanwhile, I will also be doing a bit of consulting work.

I believe much of my upcoming month will be spoken for with contracting, although quite a bit of that work will also be open source maintenance, for which I am very grateful to my generous clients. Please do get in touch if you have something more specific you’d like me to work on, and you’d like to become one of those clients as well.

Why Bother With All This?

In other words: if you want to run on an Apple platform, why not just write everything in an Apple programming language, like Swift? If you need to ship to multiple platforms, you might have to rewrite it all anyway, so why not give up?

Despite the significant investment that platform vendors make in their tools, I fundamentally believe that the core logic in any software application ought to be where its most important value lies. For small, independent developers, having portable logic that can be faithfully replicated on every platform without massive rework might be tricky to get started with, but if you can’t do it, it may not be cost effective to support multiple platforms at all.

So, it makes sense for me to write my applications in Python to achieve this sort of portability, even though on each platform it’s going to be a little bit more of a hassle to get it all built and shipped since the default tools don’t account for the use of Python.

But how much more is “a little bit” more of a hassle? I’ve been slowly learning about the pipeline to ship independently-distributed¹ macOS applications for the last few years, and I’ve encountered a ton of annoying roadblocks.

Didn’t You Do This Already? What’s New?

So nice of you to remember. Thanks for asking. While I’ve gotten this to mostly work in the past, some things have changed since then:

• the notarization toolchain has been updated (altool is now notarytool),
• I’ve had to ship libraries other than just PyGame,
• Apple Silicon launched, necessitating another dimension of build complexity to account for multiple architectures,
• Perhaps most significantly, I have written a tool that attempts to encode as much of this knowledge as possible, Encrust, which I have put on PyPI and GitHub. If this is of interest to you, I would encourage you to file bugs on it, and hopefully add in more corner cases which I have missed.

I’ve also recently shipped my first build of an end-user application that successfully launches on both Apple Silicon and Intel macs, so here is a brief summary of the hoops I needed to jump through, from the beginning, in order to make everything work.

Wait did you say you wrote a tool? Is this fixed, then?

Encrust is, I hope, a temporary stopgap on the way to a much better comprehensive solution.

Specifically, I believe that Briefcase is a much more holistic solution to the general problem being described here, but it doesn’t suit my very specific needs right now⁴, and it doesn’t address a couple of minor points that I was running into here.

It is mostly glue that is shelling out to other tools that already solve portions of the problem, even when better APIs exist. It addresses three very specific layers of complexity:

1. It enforces architecture independence, so that your app built on an M1 machine will still actually run on about half of the macs remaining out there².
2. It remembers tricky nuances of the notarization submission process, such as the highly specific way I need to generate my zip files to avoid mysterious notarization rejections³.
3. Providing a common and central way to store the configuration for these things across repositories so I don’t need to repeat this process and copy/paste a shell script every time I make a tiny new application.

It only works on Apple Silicon macs, because I didn’t bother to figure out how pip actually determines which architecture to download wheels for.

As such, unfortunately, Encrust is mostly a place for other people who have already solved this problem to collaborate to centralize this sort of knowledge and share ideas about where this code should ultimately go, rather than a tool for users trying to get started with shipping an app.

Open Offer

That said:

1. I want to help Python developers ship their Python apps to users who are not also Python developers.
2. macOS is an even trickier platform to do that on than most.
3. It’s now easy for me to sign, notarize, and release new applications reliably

Therefore:

If you have an open source Python application that runs on macOS⁵ but can’t ship to macOS — either because:

1. you’ve gotten stuck on one of the roadblocks that this post describes,
2. you don’t have $100 to give to Apple, or because
3. the app is using a cross-platform toolkit that should work just fine and you don’t have access to a mac at all, then

Send me an email and I’ll sign and post your releases.

What’s this post about, then?

People still frequently complain that “Python packaging” is really bad. And I’m on record that packaging Python (in the sense of “code”) for Python (in the sense of “deployment platform”) is actually kind of fine right now; if what you’re trying to get to is a package that can be pip installed, you can have a reasonably good experience modulo a few small onboarding hiccups that are well-understood in the community and fairly easy to overcome.

However, it’s still unfortunately hard to get Python code into the hands of users who are not also Python programmers with their own development environments.

My goal here is to document the difficulties themselves to try to provide a snapshot of what happens if you try to get started from scratch today. I think it is useful to record all the snags and inscrutable error messages that you will hit in a row, so we can see what the experience really feels like.

I hope that everyone will find it entertaining.

• Other Mac python programmers might find pieces of trivia useful, and
• Linux users will have fun making fun of the hoops we have to jump through on Apple platforms,

but the main audience is the maintainers of tools like Briefcase and py2app to evaluate the new-user experience holistically, and to see how much the use of their tools feels like this. This necessarily includes the parts of the process that are not actually packaging.

This is why I’m starting from the beginning again, and going through all the stuff that I’ve discussed in previous posts again, to present the whole experience.

Here Goes

So, with no further ado, here is a non-exhaustive list of frustrations that I have encountered in this process:

• Okay. Time to get started. How do I display a GUI at all? Nothing happens when I call some nominally GUI API. Oops: I need my app to exist in an app bundle, which means I need to have a framework build. Time to throw those partially-broken pyenv pythons in the trash, and carefully sidestep around Homebrew; best to use the official Python.org from here on out.
• Bonus Frustration since I’m using AppKit directly: why is my app segfaulting all the time? Oh, target is a weak reference in objective C, so if I make a window and put a button in it that points at a Python object, the Python interpreter deallocates it immediately because only the window (which is “nothing” as it’s a weakref) is referring to it. I need to start stuffing every Python object that talks to a UI element like a window or a button into a global list, or manually calling .retain() on all of them and hoping I don’t leak memory.
• Everything seems to be using the default Python Launcher icon, and the app menu says “Python”. That wouldn’t look too good to end users. I should probably have my own app.
• I’ll skip the part here where the author of a new application might have to investigate py2app, briefcase, pyoxidizer, and pyinstaller separately and try to figure out which one works the best right now. As I said above, I started with py2app and I’m stubborn to a fault, so that is the one I’m going to make work.
• Now I need to set up py2app. Oops, I can’t use pyproject.toml any more, time to go back to setup.py.
• Now I built it and the the app is crashing on startup when I click on it. I can’t see a traceback anywhere, so I guess I need to do something in the console.
  • Wow; the console is an unusable flood of useless garbage. Forget that.
  • I guess I need to run it in the terminal somehow. After some googling I figure out it’s ./dist/MyApp.app/Contents/Resources/MacOS/MyApp. Aha, okay, I can see the traceback now, and it’s … an import error?
  • Ugh, py2app isn’t actually including all of my code, it’s using some magic to figure out which modules are actually used, but it’s doing it by traversing import statements, which means I need to put a bunch of fake static import statements for everything that is used indirectly at the top of my app’s main script so that it gets found by the build. I experimentally discover a half a dozen things that are dynamically imported inside libraries that I use and jam them all in there.
• Okay. Now at least it starts up. The blank app icon is uninspiring, though, time to actually get my own icon in there. Cool, I’ll make an icon in my favorite image editor, and save it as... icons must be PNGs, right? Uhh... no, looks like they have to be .icns files. But luckily I can convert the PNG I saved with a simple 12-line shell script that invokes sips and iconutil⁶.

At this point I have an app bundle which kinda works. But in order to run on anyone else’s computer, I have to code-sign it.

• In order to code-sign anything, I have to have an account with Apple that costs $99 per year, on developer.apple.com.
• The easiest way to get these certificates is to log in to Xcode itself. There’s a web portal too but using it appears to involve a lot more manual management of key material, so, no thanks. This requires the full-fat Xcode.app though, not just the command-line tools that come down when I run xcode-select --install, so, time to wait for an 11GB download.
• Oops, I made the wrong certificate type. Apparently the only right answer here is a “Developer ID Application” certificate.
• Now that I’ve logged in to Xcode to get the certificate, I need to figure out how to tell my command-line tools about it (for starters, “codesign”). Looks like I need to run security find-identity -v -p codesigning.
• Time to sign the application’s code.
  • The codesign tool has a --deep option which can sign the whole bundle. Great!
  • Except, that doesn’t work, because Python ships shared libraries in locations that macOS doesn’t automatically expect, so I have to manually locate those files and sign them, invoking codesign once for each.
  • Also, --deep is deprecated. There’s no replacement.
  • Logically, it seems like I still need --deep, because it does some poorly-explained stuff with non-code resource files that maybe doesn’t happen properly if I don’t? Oh well. Let's drop the option and hope for the best.⁸
  • With a few heuristics I think we can find all the relevant files with a little script⁷.

Now my app bundle is signed! Hooray. 12 years ago, I’d be all set. But today I need some additional steps.

• After I sign my app, Apple needs to sign my app (to indicate they’ve checked it for malware), which is called “notarization”.
  • In order to be eligible for notarization, I can’t just code-sign my app. I have to code-sign it with entitlements.
  • Also I can’t just code sign it with entitlements, I have to sign it with the hardened runtime, or it fails notarization.
  • Oops, out of the box, the hardened runtime is incompatible with a bunch of stuff in Python, including cffi and ctypes, because nobody has implemented support for MAP_JIT yet, so it crashes at startup. After some thrashing around I discover that I need a legacy “allow unsigned executable memory” entitlement. I can’t avoid importing this because a bunch of things in py2app’s bootstrapping code import things that use ctypes, and probably a bunch of packages which I’m definitely going to need, like cryptography require cffi directly anyway.
  • In order to set up notarization external to Xcode, I need to create an App Password which is set up at appleid.apple.com, not the developer portal.
  • Bonus Frustration since I’ve been doing this for a few years: Originally this used to be even more annoying as I needed to wait for an email (with altool), and so I couldn’t script it directly. Now, at least, the new notarytool (which will shortly be mandatory) has a --wait flag.
  • Although the tool is documented under man notarytool, I actually have to run it as xcrun notarytool, even though codesign can be run either directly or via xcrun codesign.
  • Great, we’re ready to zip up our app and submit to Apple. Wait, they’re rejecting it? Why???
  • Aah, I need to manually copy and paste the UUID in the console output of xcrun notarytool submit into xcrun notarytool log to get some JSON that has some error messages embedded in it.
  • Oh. The bundle contains internal symlinks, so when I zipped it without the -y option, I got a corrupt archive.
  • Great, resubmitted with zip -y.
  • Oops, just kidding, that only works sometimes. Later, a different submission with a different hash will fail, and I’ll learn that the correct command line is actually ditto -c -k --sequesterRsrc --keepParent MyApp.app MyApp.app.zip.
    • Note that, for extra entertainment value, the position of the archive itself and directory are reversed on the command line from zip (and tar, and every other archive tool).
  • notarytool doesn’t record anything in my app though; it puts the “notarization ticket” on Apple's servers. Apparently, I still need to run stapler for users to be able to launch it while those servers are inaccessible, like, for example, if they’re offline.
  • Oops, not stapler. xcrun stapler. Whatever.
  • Except notarytool operates on a zip archive, but stapler operates on an app bundle. So we have to save the original app bundle, run stapler on it, then re-archive the whole thing into a new archive.

Hooray! Time to release my great app!

• Whoops, just got a bug report that it crashes immediately on every Intel mac. What’s going on?
• Turns out I’m using a library whose authors distribute both aarch64 and x86_64 wheels; pip will prefer single-architecture wheels even if universal2 wheels are also available, so I’ve got to somehow get fat binaries put together. Am I going to have to build a huge pile of C code by myself? I thought all these Python hassles would at least let me avoid the C hassles!

• Whew, okay, no need for that: there’s an amazing Swiss-army knife for macOS binary wheels, called delocate that includes a delocate-fuse tool that can fuse two wheels together. So I just need to figure out which binaries are the wrong architecture and somehow install my fixed/fused wheels before building my app with py2app.

  • except, oops, this tool just rewrites the file in-place without even changing its name, so I have to write some janky shell scripts to do the reinstallation. Ugh.

• OK now that all that is in place, I just need to re-do all the steps:

  • universal2-ize my virtualenv!
  • build!
  • sign!
  • archive!
  • notarize!
  • wait!!!
  • staple!
  • re-archive!
  • upload!

And we have an application bundle we can ship to users.

It’s just that easy.

As long as I don’t need sandboxing or Mac App Store distribution, of course. That’s a challenge for another day.

So, that was terrible. But what should be happening here?

Some of this is impossible to simplify beyond a certain point - many of the things above are not really about Python, but are about distribution requirements for macOS specifically, and we in the Python community can’t affect operating system vendors’ tooling.

What we can do is build tools that produce clear guidance on what step is required next, handle edge cases on their own, and generally guide users through these complex processes without requiring them to hit weird binary-format or cryptographic-signing errors on their own with no explanation of what to do next.

I do not think that documentation is the answer here. The necessary steps should be discoverable. If you need to go to a website, the tool should use the webbrowser module to open a website. If you need to launch an app, the tool should launch that app.

With Encrust, I am hoping to generalize the solutions that I found while working on this for this one specific slice of the app distribution pipeline — i.e. a macOS desktop application desktop, as distributed independently and not through the mac app store — but other platforms will need the same treatment.

However, even without really changing py2app or any of the existing tooling, we could imagine a tool that would interactively prompt the user for each manual step, automate as much of it as possible, verify that it was performed correctly, and give comprehensible error messages if it was not.

For a lot of users, this full code-signing journey may not be necessary; if you just want to run your code on one or two friends’ computers, telling them to right click, go to ‘open’ and enter their password is not too bad. But it may not even be clear to them what the trade-off is, exactly; it looks like the app is just broken when you download it. The app build pipeline should tell you what the limitations are.

Other parts of this just need bug-fixes to address. py2app specifically, for example, could have a better self-test for its module-collecting behavior, launching an app to make sure it didn’t leave anything out.

Interactive prompts to set up a Homebrew tap, or a Flatpak build, or a Microsoft Store Metro app, might be similarly useful. These all have outside-of-Python required manual steps, and all of them are also amenable to at least partial automation.

Thanks to my patrons on Patreon for supporting this sort of work, including development of Encrust, of Pomodouroboros, of posts like this one and of that offer to sign other people’s apps. If you think this sort of stuff is worthwhile, you might want to consider supporting me over there as well.

Is there a place for non-@dataclass classes in Python any more?

I have previously — and somewhat famously — written favorably about @dataclass’s venerable progenitor, attrs, and how you should use it for pretty much everything.

At the time, attrs was an additional dependency, a piece of technology that you could bolt on to your Python stack to make your particular code better. While I advocated for it strongly, there are all the usual implicit reasons against using a new thing. It was an additional dependency, it might not interoperate with other convenience mechanisms for type declarations that you were already using (i.e. NamedTuple), it might look weird to other Python programmers familiar with existing tools, and so on. I don’t think that any of these were good counterpoints, but there was nevertheless a robust discussion to be had in addressing them all.

But for many years now, dataclasses have been — and currently are — built in to the language. They are increasingly integrated to the toolchain at a deep level that is difficult for application code — or even other specialized tools — to replicate. Everybody knows what they are. Few or none of those reasons apply any longer.

For example, classes defined with @dataclass are now optimized as a C structure might be when you compile them with mypyc, a trick that is extremely useful in some circumstances, which even attrs itself now has trouble keeping up with.

This all raises the question for me: beyond backwards compatibility, is there any point to having non-@dataclass classes any more? Is there any remaining justification for writing them in new code?

Consider my original example, translated from attrs to dataclasses. First, the non-dataclass version:

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2
3
4
5

class Point3D:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

And now the dataclass one:

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from dataclasses import dataclass

@dataclass
class Point3D:
    x: int
    y: int
    z: int

Many of my original points still stand. It’s still less repetitive. In fewer characters, we’ve expressed considerably more information, and we get more functionality (repr, sorting, hashing, etc). There doesn’t seem to be much of a downside besides the strictness of the types, and if typing.Any were a builtin, x: any would be fine for those who don’t want to unduly constrain their code.

The one real downside of the latter over the former right now is the need for an import. Which, at this point, just seems… confusing? Wouldn’t it be nicer to be able to just write this:

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class Point3D:
    x: int
    y: int
    z: int

and not need to faff around with decorator semantics and fudging the difference between Mypy (or Pyright or Pyre) type-check-time and Mypyc or Cython compile time? Or even better, to not need to explain the complexity of all these weird little distinctions to new learners of Python, and to have to cover import before class?

These tools all already treat the @dataclass decorator as a totally special language construct, not really like a decorator at all, so to really explore it you have to explain a special case and then a special case of a special case. The extension hook for this special case of the special case notwithstanding.

If we didn’t want any new syntax, we would need a from __future__ import dataclassification or some such for a while, but this doesn’t seem like an impossible bar to clear.

There are still some folks who don’t like type annotations at all, and there’s still the possibility of awkward implicit changes in meaning when transplanting code from a place with dataclassification enabled to one without, so perhaps an entirely new unambiguous syntax could be provided. One that more closely mirrors the meaning of parentheses in def, moving inheritance (a feature which, whether you like it or not, is clearly far less central to class definitions than ‘what fields do I have’) off to its own part of the syntax:

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data Point3D(x: int, y: int, z: int) from Vector:
    def method(self):
        ...

which, for the “I don’t like types” contingent, could reduce to this in the minimal case:

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data Point3D(x, y, z):
    pass

Just thinking pedagogically, I find it super compelling to imagine moving from teaching def foo(x, y, z):... to data Foo(x, y, z):... as opposed to @dataclass class Foo: x: int....

I don’t have any desire for semantic changes to accompany this, just to make it possible for newcomers to ignore the circuitous historical route of the @dataclass syntax and get straight into defining their own types with legible reprs from the very beginning of their Python journey.

(And make it possible for me to skip a couple of lines of boilerplate in short examples, as a bonus.)

I’m curious to know what y’all think, though. Shoot me an email or a toot and let me know.

In particular:

1. Do you think there’s some reason I’m missing why Python’s current method for defining classes via a bunch of dunder methods is still better than dataclasses, or should stick around into the future for reasons beyond “compatibility”?
2. Do you think “compatibility” is sufficient reason to keep the syntax the way it is forever, and I’m underestimating the cost of adding a keyword like this?
3. If you do think that a change should be made, would you prefer:
   1. changing the meaning of class itself via a __future__ import,
   2. a new data keyword like the one I’ve proposed,
   3. a new keyword that functions exactly like the one I have proposed but really want to bikeshed the word data a bunch,
   4. something more incremental like just putting dataclass and field in builtins,
   5. or an option I haven’t even contemplated here?

If I find I’m not alone in this perhaps I will wander over to the Python discussion boards to have a more substantive conversation...

Thank you to my patrons who are helping me while I try to turn… whatever this is… along with open source maintenance and application development, into a real job. Do you want to see me pursue ideas like this one further? If so, you can support me on Patreon as well!