django-zen-queries
typer
| django-zen-queries | typer | |
|---|---|---|
| 3 | 87 | |
| 354 | 14,347 | |
| 0.3% | - | |
| 1.5 | 8.7 | |
| about 1 month ago | 7 days ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | MIT License |
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django-zen-queries
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Ban 1+N in Django
See also django-zen-queries https://github.com/dabapps/django-zen-queries , which can make it impossible for changes to a template to trigger queries.
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Django wants you to build poor quality apps. Here's how to fix this.
I have no idea but the best way to make QuerySets explicit is by using a package called django-zen-queries.
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Django 4.0 Released
> Making it more clear when you’re going to hit the database (explicit is better than implicit).
You might like https://github.com/dabapps/django-zen-queries
Gives you fine grain control about database queries
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-seal - Django application providing queryset sealing capability.
click - Python composable command line interface toolkit
django-auto-prefetching - Automatic prefetching for Django
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
django_for_startups - Code for the book Django for Startups
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
django-orm-plus
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
pellet - Pellet helps improve your Django app performance by discovering N+1 queries.
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
cement - Application Framework for Python