pydantic-core
modin
Our great sponsors
| pydantic-core | modin | |
|---|---|---|
| 18 | 11 | |
| 1,270 | 9,476 | |
| 3.1% | 1.3% | |
| 9.6 | 9.6 | |
| 3 days ago | 1 day ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
pydantic-core
-
Is there a pydantic.BaseSettings equivalent in rust?
Funny that you ask... https://github.com/pydantic/pydantic-core Unfortunately it seems that the functionality you ask for is not (yet) part of this ...
-
Investigating Pydantic v2's Bold Performance Claims
I encourage you to checkout the official benchmarks for more realistic and detailed examples, and, as always, YMMV.
-
Pydantic V2 leverages Rust's Superpowers [video]
> to also be constrained by a separate set of data types which are legal in rust.
This isn't really how writing rust/python iterop works. You tend to have opaque handles you call python methods on. Here's a decent example I found skimming the code.
https://github.com/pydantic/pydantic-core/blob/main/src/inpu...
-
Pydantic vs Protobuf vs Namedtuples vs Dataclasses
Thanks for pointing out to that, I did not know about it. Also attaching repo in case someone would be interested as well - https://github.com/pydantic/pydantic-core
-
Introducing CodSpeed: Continuous Performance Measurement
pydantic-core: The core validation logic for pydantic, a Python data parsing and validation library.
-
Show HN: Python framework is faster than Golang Fiber
pydandic-core [0] will hopefully solve this issue (written in Rust)
[0] -- https://github.com/pydantic/pydantic-core
- Scala or Rust? which one will rule in future?
-
Rust for Data Engineering—what's the hype about? 🦀
LinkedIn influencers are weird lol. Rust v Python is apples and oranges. Rust would be glued together by python just like it does with C/C++ and Java/Spark today. We’re already seeing some packages go this direction, like pydantic v2 is rewriting its core validation in rust.
- Python file structure with Rust extensions
- Pydantic 2 rewritten in Rust was merged
modin
- The Distributed Tensor Algebra Compiler (2022)
-
A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
-
Modern Polars: an extensive side-by-side comparison of Polars and Pandas
Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.
Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.
-
Polars: The Next Big Python Data Science Library... written in RUST?
If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.
-
Working with more than 10gb csv
Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin
- Modern Python Performance Considerations
-
I made a video about efficient memory use in pandas dataframes!
If you really want speed you should try modin.pandas which makes pandas multi-threaded.
-
Almost no one knows how easily you can optimize your AI models
I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin
-
TIL about modin.pandas which significantly speeds up pandas if you import modin.pandas instead of pandas.
Source
- How to Speed Up Pandas with 1 Line of Code
What are some alternatives?
aiohttp-apispec - Build and document REST APIs with aiohttp and apispec
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
pymartini - A Cython port of Martini for fast RTIN terrain mesh generation
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
koda-validate - Typesafe, Composable Validation
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
typedload - Python library to load dynamically typed data into statically typed data structures
PandasGUI - A GUI for Pandas DataFrames
uvloop - Ultra fast asyncio event loop.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.