schema
typer
| schema | typer | |
|---|---|---|
| 3 | 87 | |
| 2,834 | 14,398 | |
| - | - | |
| 6.8 | 8.7 | |
| 5 days ago | 2 days ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
schema
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Python: Data validation
Validation using Schema
- tqdm (Python)
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How can I Unit Test on a text file?
I generally use the https://github.com/keleshev/schema to validate data. You can have a script running that monitors for updated files, then sounds up a new process to actually validate the file.
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?
Cerberus - Lightweight, extensible data validation library for Python
click - Python composable command line interface toolkit
jsonschema - An implementation of the JSON Schema specification for Python
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
voluptuous - CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
Schematics - Python Data Structures for Humans™.
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
colander - A serialization/deserialization/validation library for strings, mappings and lists.
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
valideer - Lightweight data validation and adaptation Python library.
cement - Application Framework for Python