modin
Kedro
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modin | Kedro | |
---|---|---|
11 | 29 | |
9,476 | 9,362 | |
1.3% | 1.6% | |
9.6 | 9.7 | |
4 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | 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.
modin
- The Distributed Tensor Algebra Compiler (2022)
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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.
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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.
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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.
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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
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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.
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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
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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
Kedro
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Nextflow: Data-Driven Computational Pipelines
Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).
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A Polars exploration into Kedro
# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]
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What are some open-source ML pipeline managers that are easy to use?
So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.
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How do data scientists combine Kedro and Databricks?
We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.
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How do you organize yourself during projects?
you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data
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Futuristic documentation systems in Python, part 1: aiming for more
Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.
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Python projects with best practices on Github?
You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate für den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
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Dabbling with Dagster vs. Airflow
An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
Dask - Parallel computing with task scheduling
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
PandasGUI - A GUI for Pandas DataFrames
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
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.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!