Cubes
Dask
Our great sponsors
| Cubes | Dask | |
|---|---|---|
| 1 | 32 | |
| 1,490 | 11,999 | |
| 0.0% | 1.6% | |
| 0.0 | 9.6 | |
| about 2 years ago | 3 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | 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.
Cubes
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Building data analysis apps
I'm looking for materials and tools to learn. I'm reading up on OLAP and cubes. I found cubes python package but it hasn't been updated in years. Could you give me some tips on what to learn in 2021?
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?
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
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
NumPy - The fundamental package for scientific computing with Python.
Numba - NumPy aware dynamic Python compiler using LLVM
Bubbles - [NOT MAINTAINED] Bubbles – Python ETL framework
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.
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
NetworkX - Network Analysis in Python
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
blaze - NumPy and Pandas interface to Big Data