django-tailwind VS pip-tools

Most of our installation is done but we haven't configured Tailwind yet. django-tailwind is already installed with the required Python packages but we'll need to compile this to fully setup Tailwind and make it ready to work in our local development environment.

I'm currently using Django + TailwindCSS to build small apps, sometimes relying on JS or even with some other Django app to handle dynamic behavior on the front-end (e.g. django-formset for improving forms). I've been achieving very interesting results with this stack so far.

I like the way that django-tailwind handles it. Still requires npm locally, but puts some nice management command wrappers around it.

There are many ways to set up Tailwind CSS in Django. One way to do so is by using a package called django-tailwind. This package provides an easy way to use Tailwind CSS in a Django project.

Hi, Thanks for taking the time to reply. I've had several issues with trying to integrate it; the key problem I'm having at the moment is running manage.py tailwind commands in my dockerfile. The example dockerfile requires these commands to be run, but for some reason I am constantly getting a manage.py cannot be found error. I have checked my directories, and it seems to be in the right place, but I cannot seem to resolve this error at the moment.

django-tailwind improved my experience with this stack markedly.

Technically, you can use any CSS framework, including Tailwind, for Django. Most of the time, dropping that CDN into your template will be enough (i.e. bootstrap 5 or bulma) to get started.

Are you using the django-tailwind package?

> Why is the "requirements.txt" file a stupid flat listing of all transitive dependencies with pinned versions? It makes it harder to change library versions even if there are no true conflicts.

My friend, here is what you seek: https://github.com/jazzband/pip-tools

requirements.txt is flat because it's really the output of `pip freeze`. It's supposed to completely and exactly rebuild the environment. Unfortunately it's far too flexible and people abuse it by putting in only direct dependencies etc.

If you're writing packages, you don't need a requirements.txt at all, by the way. Package dependencies (only direct dependencies) live in pyproject.toml with the rest of the package config. requirements.txt (and pip tools) are only for when you want to freeze the whole environment, like for a server deployment.

For all my projects I found myself regenerating manual lock files using complex shell commands with pip-compile to get a reproducible environments across devices using a custom pre-install-command. I finally decided that instead of hacking together the same solution on all my projects I would build a plugin that handles this complexity for me.

Instead of venv, we are using pip-tools in this starter kit. pip-tools take things further in dependency management. Check out what pip-tools does in their official GitHub repo. In short, it helps your project find the best match for the dependent packages. For example, you might need two packages A and B in your project that requires same package C under the hood. But A requires any version of C from 1.0.1 to 1.0.10 and B requires any version of C from 1.0.7 to 1.0.15. Pip tools will automatically compile the version of 'C' that suits for both of your packages.

I've created a small project called just-pip-tools that combines pip-tools and just to manage Python dependencies in a layered approach. This isn't a magic bullet; it's a set of files you can adapt to your needs.

For small projects I recommend pip-tools. Just write packet list in requirements.in and pip-compile compile a requirements.txt with comments.

Take a look at pip-tools, great package. https://github.com/jazzband/pip-tools

pip-tools

I recommend generating a lockfile to document this information, as you might do with pip-tools.

Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.

[1]: https://github.com/jazzband/pip-tools