fugue
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| normconf2022 | fugue | |
|---|---|---|
| 2 | 11 | |
| 0 | 1,869 | |
| - | 1.9% | |
| 10.0 | 6.7 | |
| over 1 year ago | 6 days ago | |
| HTML | Python | |
| MIT License | Apache License 2.0 |
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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.
normconf2022
- Replacing Pandas with Polars. A Practical Guide
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PRQL a simple, powerful, pipelined SQL replacement
The last example in this notebook [0] shows how similar PRQL is to dplyr. The rest of the notebook shows how you can use PRQL from R, Python and the command line.
[0]: https://github.com/snth/normconf2022/blob/main/notebooks/nor...
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.
- 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.
What are some alternatives?
FunSQL.jl - Julia library for compositional construction of SQL queries
modin - Modin: Scale your Pandas workflows by changing a single line of code
ddl-diff - Generates SQL migrations by parsing and diffing DDL
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.
trustfall - A query engine for any combination of data sources. Query your files and APIs as if they were databases!
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
cargo-semver-checks - Scan your Rust crate for semver violations.
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
prql-query - Query and transform data with PRQL
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
xarray - N-D labeled arrays and datasets in Python