django-environ
pip-tools
django-environ | pip-tools | |
---|---|---|
12 | 58 | |
2,937 | 7,495 | |
- | 1.0% | |
6.0 | 8.9 | |
3 months ago | 8 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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django-environ
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Setting up Django in a Better Way in 5 Minutes and Understanding How It Works
This Django Starter kit takes care of automated creation of virtual environment and installing of Python packages and setting up the database with bash scripts. In addition to PostgreSQL and TailwindCSS, all the sensitive values are taken care of in a .env file using django-environ package. The virtual environment is maintained using pip-tools.
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Tricks for starting a new project
I used to do this but recently switched to using environment variables and now prefer this approach. Essentially you keep the single settings.py file that is generated with startproject, and use os.environ or os.getenv to set certain settings. Check out the FeedHQ settings.py for an example. I use direnv to automatically set my environment variables on my local machine, but django-environ is a popular alternative.
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Django Deployment - Postgres DBaaS
Here i decided to use django-environ's env.db() for the DATABASE_URL.
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Switch between development and production
You might want to use django-environ package for this issue. Create a .env file in the project folder and follow these steps.
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Django deployment with App Platform & S3 Space
For this i use django-environ. Here are a few basic settings:
- Django Production (Env variable)
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Six things I do every time I start a Django project
You could also use just django-environ package to both import config from .env and set a database url instead of using 2 dependencies. I also think of a couple things I could add to the list, maybe I should a write a blog post as well?
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How to share my portfolio projects to Github?
You can use django-environ
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A complete guide to organizing settings in Django
Docker does not do any quote parsing. For this same env file, it will set the value of the variable to `"foo"` (retaining the doublequotes in the value).
Bash, of course, requires quotes if the variable contains any special bash characters (for example, literal JSON with curly brackets), but its quote handling is much more complex. django-environ doesn't interpret bash code; it just does simple quote chomping.
There's no reliable .env syntax you can use that works in all 3 of django-environ, Docker, and bash; and any variable that should start and end with quotes that are not stripped off can't be expressed in a way that both Docker and django-environ will read in the same way.
This may seem like a nit-picking edge case, but it's indicative of the design philosophy in django-environ of trying to be "helpful", but in ways which lead to subtle confusion. The way it guesses the path to your `.env` file is another example.
[1] https://github.com/joke2k/django-environ/blob/main/environ/e...
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The best way to handle private keys
For Django use useful environ-wrapper: django-environ
pip-tools
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Pyenv – lets you easily switch between multiple versions of Python
> 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.
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lockfiles for hatch projects
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.
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Setting up Django in a Better Way in 5 Minutes and Understanding How It Works
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.
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just-pip-tools: An example of managing python dependencies as layered lock files with just and pip-tools
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.
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Maintaining virtual environments
For small projects I recommend pip-tools. Just write packet list in requirements.in and pip-compile compile a requirements.txt with comments.
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how to upgrade psycopg2 to psycopg3 as per django latest documentation
Take a look at pip-tools, great package. https://github.com/jazzband/pip-tools
- Single-file scripts that download their dependencies
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What are people using to organize virtual environments these days?
pip-tools
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How to know what a package depend on when pip is installing it?
I recommend generating a lockfile to document this information, as you might do with pip-tools.
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A not so unfortunate sharp edge in Pipenv
Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.
[1]: https://github.com/jazzband/pip-tools
What are some alternatives?
python-dotenv - Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.
Poetry - Python packaging and dependency management made easy
python-decouple - Strict separation of config from code.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
environs - simplified environment variable parsing
Pipenv - Python Development Workflow for Humans.
django-dotenv - Loads environment variables from .env
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
hydra - Hydra is a framework for elegantly configuring complex applications
pip - The Python package installer
dynaconf - Configuration Management for Python ⚙
miniforge - A conda-forge distribution.