fugue
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| fugue | mlToolKits | |
|---|---|---|
| 11 | 1 | |
| 1,876 | 74 | |
| 2.3% | - | |
| 6.7 | 0.0 | |
| 3 days ago | 11 months ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
fugue
- FLaNK Stack Weekly 22 January 2024
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Please integrate it with Fugue.
https://github.com/fugue-project/fugue
- Fugue: A unified interface for distributed computing
- [Discussion] Open Source beats Google's AutoML for Time series
- Ask HN: How do you test SQL?
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Replacing Pandas with Polars. A Practical Guide
Fugue is an interesting library in this space , though I haven’t tried it
https://github.com/fugue-project/fugue
A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.
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The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
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[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
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What the Duck?!
I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.
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Pyspark now provides a native Pandas API
There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.
mlToolKits
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Someone with a good experience in python can rate my code?
https://github.com/learningOrchestra/learningOrchestra/blob/552994056f2415ee54c24f2e1101fcca7fbd694b/microservices/code_executor_image/utils.py#L93-L94
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
docker-etcd-cluster - Very simple etcd cluster powered by docker compose
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
swarm-kit - 🔥 Self-Hosted Docker Swarm Toolkit
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Contra - Contra is a lightweight, production ready Tensorflow alternative for solving time series prediction challenges with AI
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
listenbrainz-server - Server for the ListenBrainz project, including the front-end (javascript/react) code that it serves and all of the data processing components that LB uses.
xarray - N-D labeled arrays and datasets in Python
docker-stack-deploy - Utility to improve docker stack deploy
chispa - PySpark test helper methods with beautiful error messages
dcos - DC/OS - The Datacenter Operating System