lineapy
fugue
| lineapy | fugue | |
|---|---|---|
| 7 | 11 | |
| 656 | 1,880 | |
| 0.5% | 1.4% | |
| 2.0 | 6.4 | |
| 9 months ago | 7 days ago | |
| Jupyter Notebook | 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.
lineapy
-
Rant: Jupyter notebooks are trash.
There are a few projects that can help close this gap between notebook prototype -> production. One of them is ipyflow (https://github.com/ipyflow/ipyflow), another is lineapy (https://github.com/linealabs/lineapy).
-
The hand-picked selection of the best Python libraries and tools of 2022
LineaPy — notebooks in production
-
Model artifacts mess and how to deal with it?
If you are mainly using python, there is a library called lineapy that is pretty much trying to solve all the challenges you just listed.
- lineapy: Data engineering, simplified. LineaPy creates a frictionless path for taking your data science artifact from development to production.
-
Overwhelmed about consolidating code
Hi, I'm a contributor of LineaPy. We're building a tool that solves this problem. Our goal is to reduce the friction between developing Jupyter notebooks(or python scripts) and production codes.
-
When to use Jupyter Notebooks vs. “Organized” Python Code?
I think you might want to give LineaPy a try! It is a tool trying to bridge the gap between Jupyter notebooks and production pipelines. One of the feature it provides is extracting codes only related to objects(you've selected) from your notebook into a python script and I think it is helpful for anyone who is using both Jupyter notebooks and python scripts.
-
Introducing LineaPy!
GitHub
fugue
- FLaNK Stack Weekly 22 January 2024
-
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?
-
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.
-
The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
-
[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
-
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.
-
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?
ruff - An extremely fast Python linter and code formatter, written in Rust.
modin - Modin: Scale your Pandas workflows by changing a single line of code
lingua-py - The most accurate natural language detection library for Python, suitable for short text and mixed-language text
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.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
python-benedict - :blue_book: dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
ipyflow - A reactive Python kernel for Jupyter notebooks.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
xarray - N-D labeled arrays and datasets in Python