python_backend_template
beartype
python_backend_template | beartype | |
---|---|---|
1 | 18 | |
9 | 2,430 | |
- | 2.8% | |
4.6 | 9.4 | |
almost 3 years ago | 2 days ago | |
TypeScript | Python | |
- | MIT License |
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python_backend_template
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DoorDash: Migrating From Python to Kotlin for Our Backend Services
In general I think well written Python avoids the problems DoorDash faced. I've created a GitHub template so all my products start in a clean way: https://github.com/hbrooks/python_backend_template
beartype
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Writing Python Like Rust
https://github.com/beartype/beartype
I wish more people started using Beartype, it makes Python bearable
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ChatGPT Git Hook Writes Your Commit Messages
I saw this on /r/Python the other day...
- When the client's management is happy but their dev team is a pain
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Returning to snake's nest after a long journey, any major advances in python for science ?
As other folks have commented, type hints are now a big deal. For static typing the best checker is pyright. For runtime checking there is typeguard and beartype. These can be integrated with array libraries through jaxtyping. (Which also works for PyTorch/numpy/etc., despite the name.)
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What are some features you wish Python had?
Maybe you're looking for https://github.com/beartype/beartype for runtime type enforcement; it's only at function calls, though, but probably a decent solution for codebases that are not completely typed for MyPy or pyright.
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svg.py: Type-safe and powerful Python library to generate SVG files
It is though, if you add a type checker to your pipeline and use it without any escape hatches such as `Any` or `type: ignore`, you are essentially making the promise that your code is statically typed. But I say it is a matter of perspective because in my opinion runtime type checking should be avoided if we can get away with statically typed code, but there are type checkers that perform runtime type checking via annotations such as [Beartype](https://github.com/beartype/beartype) (with some trickery like assuming homogenous data structures as to not have to check every element of every structure). Anyway the definition of "type safe" is not 100% even in compiled languages.
- Python’s “Type Hints” are a bit of a disappointment to me
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What's the best practice to validate parameter types at runtime in Python, with and without a third-party module?
There is the beartype project.
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Statically typed Python
Personally I find working around mypy's quirks to be more effort than it's worth, so to offer another option: typeguard or beartype can be used to perform run-time type checking.
- Beartype: Unbearably fast runtime type checking in Python
What are some alternatives?
fastapi-starter - A FastAPI based low code starter/boilerplate: SQLAlchemy 2.0 (async), Postgres, React-Admin, pytest and cypress
typeguard - Run-time type checker for Python
react-wasm-github-api-demo - A demo application to serve as a template for your Rust & React needs. With a sample GraphQL backend.
pydantic - Data validation using Python type hints
cdk-eventbridge-socket - CDK construct that creates a WebSocket endpoint for you for any EventBridge rule you are interested in. (Built for debugging + testing )
mypy - Optional static typing for Python
Quasar - Fibers, Channels and Actors for the JVM
mypyc - Compile type annotated Python to fast C extensions
vector - A high-performance observability data pipeline.
toit - Program your microcontrollers in a fast and robust high-level language.
Rope - a python refactoring library
benchmarks - Some benchmarks of different languages