flake8-comments
koda-validate
| flake8-comments | koda-validate | |
|---|---|---|
| 8 | 10 | |
| 9 | 109 | |
| - | - | |
| 2.6 | 5.2 | |
| almost 3 years ago | about 1 month 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.
flake8-comments
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beartype: It has documentation now. It only took two years, my last hair follicle, precious sanity points (SPs), and working with Sphinx. Don't be like @leycec. Go hard on documentation early.
Useless comments are my old enemy. I try to come up with objective metrics but I have only a few so far. If anyone curious, I have flake8-comments to find comments like that.
- flake8-comments: Report redundant comments in python code
koda-validate
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Pydantic V2 leverages Rust's Superpowers [video]
As someone who built a pure python validation library[0] that's much faster than pydantic (~1.5x - 12x depending on the benchmark), I have to say that this whole focus on Rust seems premature. There's clearly a lot of room for pydantic to optimize its Python implementation.
Beyond that, rust seems like a great fit for tooling (i.e. ruff), but as a library used at runtime, it seems a little odd to make a validation library (which can expect to receive any kind of data valid python data) to also be constrained by a separate set of data types which are valid in rust.
[0]: https://github.com/keithasaurus/koda-validate
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beartype: It has documentation now. It only took two years, my last hair follicle, precious sanity points (SPs), and working with Sphinx. Don't be like @leycec. Go hard on documentation early.
For the sake of comparison I built a validate_signature function as part of koda-validate, which has a lot of overlap with beartype. I haven't really compared it to beartype, so I'd be interested to see people's thoughts on how the two approaches compare.
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Pydantic vs Protobuf vs Namedtuples vs Dataclasses
FYI I wrote koda-validate, which is significantly faster than pydantic, has a similar API, and is pure python.
- Koda Validate: Alternative to Pydantic that is faster, more flexible, and async-compatible.
- I built a composable validation library. It plays nice with type hints and asyncio. Would love some feedback!
- Koda Validate: Flexible, Explicit, Async-compatible Validation in Python
- Show HN: Koda Validate 2.0 – Async Validation in Python
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This Week In Python
koda-validate – Typesafe Validation
- Show HN: Koda Validate – Typesafe, combinable validation for Python
What are some alternatives?
django-comments-xtd - A pluggable Django comments application with thread support, follow-up notifications, mail confirmation, like/dislike flags, moderation, a ReactJS plugin and Bootstrap 5.3.
pydantic-core - Core validation logic for pydantic written in rust
flakehell - Flake8 wrapper to make it nice, legacy-friendly, configurable.
katara - Synthesize CRDTs from classic data types with verified lifting!
flake8-todos - Lint TODO comments in a Python code
serialite - Serialization and deserialization library for Python
mccabe - McCabe complexity checker for Python
typedload - Python library to load dynamically typed data into statically typed data structures
django-comments-ink - Django pluggable commenting app with comment threads, follow-up notifications, mail confirmation, comment reactions and votes, and comment moderation. It supersedes django-comments-xtd.
django-pgtransaction - A context manager/decorator which extends Django's atomic function with the ability to set isolation level and retries for a given transaction.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
puff - ☁ Puff ☁ - The deep stack framework.