template-python-hello-world
geospatial-data-lake
| template-python-hello-world | geospatial-data-lake | |
|---|---|---|
| 2 | 5 | |
| 18 | 32 | |
| - | - | |
| 8.8 | 0.0 | |
| 3 days ago | about 1 year 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.
template-python-hello-world
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Python Best Practices for a New Project in 2021
or VMs, and the like.
[1] https://github.com/linz/template-python-hello-world/pull/106...
geospatial-data-lake
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A curated list of questionable installation instructions
One option is to trust on first use, checksum the installation script and at least casually verify the diff each time the checksum changes[1].
Pros:
- Protects against simple hijacking.
- Reproducible as long as the installer doesn't also call out to a moving target, such as example.com/releases/latest.
Cons:
- Build breaks as soon as the installer is bumped. If it's bumped often (or just before an important release) this can cause pain.
- TOFU may not be acceptable, but of course you could review the code thoroughly before even the first use.
[1] https://github.com/linz/geostore/blob/b3cd162605109da8a3a688...
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Ask HN: Good Python projects to read for modern Python?
I'd recommend a project from work, Geostore[1]. Highlights:
- 100% test coverage (with some typical exceptions like `if __name__ == "__main__":` blocks)
- Randomises test sequence and inputs reproducibly
- Passes Pylint with max McCabe complexity of 6
- Passes `mypy --strict`
- Formatted using Black and isort
[1] https://github.com/linz/geostore
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Python Best Practices for a New Project in 2021
The current work project[1] has all of these: Pyenv, Poetry, Pytest, pytest-cov with 100% branch coverage, pre-commit, Pylint rather than Flake8, Black, mypy (with a stricter configuration than recommended here), and finally isort. These are all super helpful.
There's also a simpler template repo[2] with almost all of these.
[1] https://github.com/linz/geostore/
[2] https://github.com/linz/template-python-hello-world
- Codecov bash uploader was compromised
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AWS CloudFormation Best Practices
As someone who's used CDK for a few months and never handcoded CF, that sounds completely correct. If you're comfortable with Python, here's a simple but non-trivial architecture you can check out: https://github.com/linz/geospatial-data-lake/blob/master/app....
What are some alternatives?
miniforge - A conda-forge distribution.
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
helm - The Kubernetes Package Manager
asgi-correlation-id - Request ID propagation for ASGI apps
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
PyInstaller - Freeze (package) Python programs into stand-alone executables
dev-tasks - Automated development tasks for my own projects
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
pip - The Python package installer
pydantic - Data validation using Python type hints
devpi - Python PyPi staging server and packaging, testing, release tool