ghostpii_client
modin
| ghostpii_client | modin | |
|---|---|---|
| 3 | 11 | |
| 23 | 9,486 | |
| - | 0.5% | |
| 1.1 | 9.6 | |
| about 1 year ago | about 7 hours ago | |
| Python | Python | |
| MIT License | 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.
ghostpii_client
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Help me spread the word, or at least play with a free toy
I am an entrepreneur trying to get a movement going to really start using this tech at big corporations to keep them out of trouble. I am guessing the conversation in here is a little more abstract than my usual day-to-day (although I am a reformed mathematician) but I wanted to introduce myself nonetheless.
If anybody is interested we maintain a software library, implemented in Python, that is designed to let relatively everyday people (software engineers, data scientists, etc.) use these privacy-enhancing techniques in a familiar interface without a rocket science course. If you go to the GitHub page I link below there is a Binder server where you can play with it right now via a Jupyter notebook over the web with basically no work or commitment.
https://github.com/capnion/ghostpii_client
I also put a ton of content out on LinkedIn, mostly oriented towards why businesses should adopt these things, what to do with them, and how they relate to other trends.
https://www.linkedin.com/in/alexander-c-mueller-phd-0272a6108/
I would greatly appreciate engagement of any kind: test-drivers, early-adopters, complainers, design feedback, likes, reshares, stars, emails. I am a true believer trying to this tech out where it can do some good and I need to spread the word.
- help me spread the word, or at least play with a free toy
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?
python-fpe - FPE - Format Preserving Encryption with FF3 in Python
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
sayn - Data processing and modelling framework for automating tasks (incl. Python & SQL transformations).
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
linkedin-visualizer - The missing feature in LinkedIn
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
dagster - An orchestration platform for the development, production, and observation of data assets.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
PandasGUI - A GUI for Pandas DataFrames
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
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.