eland
modin
| eland | modin | |
|---|---|---|
| 4 | 11 | |
| 609 | 9,486 | |
| 0.5% | 0.5% | |
| 8.5 | 9.6 | |
| 25 days ago | about 10 hours 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.
eland
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I'm getting elasticsearch.BadRequestError: BadRequestError(400, 'illegal_argument_exception', "specified fields can't be null or empty") using Eland library
We have a fix for this issue reported here merged and pending a release. Hopefully that release will happen in the next few days, then you can upgrade and the default experience for everyone won't be as confusing :)
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is it possible to use log data from elastic search and visualise it to a custom made dashboard in python?
Another option depending on what sort of data you want, and if you want to use python, is to use Eland: https://github.com/elastic/eland, together with for example Jupyter notebooks you can create super quick visualizations :)
- hey, any idea about how to automatically extract data from kibana to a panda data frame in order to be analyzed?
- Explore and analyze Elasticsearch data with Pandas-Compatible API
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
What are some alternatives?
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
pandastable - Table analysis in Tkinter using pandas DataFrames.
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
kangas - 🦘 Explore multimedia datasets at scale
nixtla - Python SDK for TimeGPT, a foundational time series model
PandasGUI - A GUI for Pandas DataFrames
Solomon - Data Exploration tool.
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.