tributary
Dask
| tributary | Dask | |
|---|---|---|
| 3 | 32 | |
| 23 | 11,999 | |
| - | 0.6% | |
| 6.7 | 9.6 | |
| 3 months ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
tributary
- Show HN: Mr. Graph. A graph deifnition and execution library for Python
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Show HN: Hamilton, a Microframework for Creating Dataframes
Having worked on "Dagger", you may be interested in https://github.com/timkpaine/tributary
- A simple lazy Python Calculation Engine (with spreadsheet demo)
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
lazy-table - A python-tabulate wrapper for producing tables from generators
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
koila - Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.
Numba - NumPy aware dynamic Python compiler using LLVM
webssh - :seedling: Web based ssh client
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
asyncauth - A powerful, simple, and async security library for Sanic. [Moved to: https://github.com/sunset-developer/sanic-security]
NetworkX - Network Analysis in Python
datajob - Build and deploy a serverless data pipeline on AWS with no effort.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
plugin.video.sendtokodi - :tv: plays various stream sites on kodi using youtube-dl
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python