numpyeigen
Dask
| numpyeigen | Dask | |
|---|---|---|
| 1 | 32 | |
| 127 | 12,022 | |
| - | 0.8% | |
| 6.8 | 9.6 | |
| 8 months ago | 1 day ago | |
| Python | Python | |
| MIT License | 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.
numpyeigen
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Show HN: Point Cloud Utils – A Python library for 3D point clouds and meshes
This is a utility library I slowly built up during my PhD and has become my swiss army knife for processing 3D data. It's super easy to install (only depends on NumPy and SciPy).
The goal of the library to have an extremely simple API for geometry processing which uses NumPy arrays as a core data structure (so it can be dropped into whatever numerical codebase you're working with).
Most of the library is written in C++ using a custom binding framework (https://github.com/fwilliams/numpyeigen) that I wrote which avoids copies when converting NumPy arrays to Eigen Matrix types.
Happy to answer any questions you might have about it and I hope Point Cloud Utils is useful to you!
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?
skinner - Skin export / import tools for Autodesk Maya
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Numba - NumPy aware dynamic Python compiler using LLVM
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.
NetworkX - Network Analysis in Python
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
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
blaze - NumPy and Pandas interface to Big Data
PyMC - Bayesian Modeling and Probabilistic Programming in Python
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.
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.