django_for_startups
typer
| django_for_startups | typer | |
|---|---|---|
| 2 | 87 | |
| 137 | 14,398 | |
| - | - | |
| 0.0 | 8.7 | |
| about 1 year ago | 3 days ago | |
| Python | Python | |
| MIT License | MIT License |
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django_for_startups
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Django 4.0 Released
> function based views
Class-based views, in their most basic form, are a lot easier to read. E.g. look at the DRF CBVs I have here:
https://github.com/Alex3917/django_for_startups/blob/main/dj...
If you can avoid Generic CBVs (things like ListView) and inheritance, then the only difference between FBVs and CBVs is that CBVs make it easier to see what's a GET / PUT / POST / DELETE.
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Django for Startup Founders: A better software architecture for SaaS startups
No, but all the code for the book is on GitHub: https://github.com/Alex3917/django_for_startups
typer
- Typer: Python library for building CLI applications
- Copilot for your GitHub stars
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Things I've learned about building CLI tools in Python
I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.
https://typer.tiangolo.com/
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Things to do with standalone script
Adding CLI capabilities. My preferred library here is typer.
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Where to start for managing a Python code base for public distribution
I just heard about this but it seems to be pretty much the type of thing you want and want fast.
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Help on Docstrings
Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.
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Which best practices do you follow to build robust & extensible ETL jobs?
Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).
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Devs on teams that deploy anytime you want, what does your SDLC workflow look like?
So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.
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The different uses of Python type hints
Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.
[1] https://typer.tiangolo.com/
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Command line parser library, which one do you like the most, regardless of language?
interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?
What are some alternatives?
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
click - Python composable command line interface toolkit
fquery - A graph query engine
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
django-zen-queries - Explicit control over database query execution in Django applications
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
re-frame - A ClojureScript framework for building user interfaces, leveraging React
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
django-extensions - This is a repository for collecting global custom management extensions for the Django Framework.
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
Poetry - Python packaging and dependency management made easy
cement - Application Framework for Python