xarray
Overview-of-the-Julia-Python-R-Universe
| xarray | Overview-of-the-Julia-Python-R-Universe | |
|---|---|---|
| 7 | 2 | |
| 3,420 | 2 | |
| 1.0% | - | |
| 9.7 | 3.2 | |
| about 8 hours ago | almost 2 years ago | |
| Python | ||
| Apache License 2.0 | Creative Commons Zero v1.0 Universal |
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.
xarray
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Request for Startups: Climate Tech
PyTorch and JAX are used heavily in climate science on the ML side. For more general analytics, not so much. Many of our users like to use Xarray as a high-level API. There has been some work to integrate Xarray with PyTorch (https://github.com/pydata/xarray/issues/3232) but we're not there yet.
The Python Array API standard should help align these different back-ends: https://data-apis.org/array-api/latest/
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Python for Data Analysis, 3rd Edition – The Open Access Version Online
Does polars have N-D labelled arrays, and if so can it perform computations on them quickly? I've been thinking of moving from pandas to xarray [0], but might consider poplars too if it has some of that functionality.
[0] https://xarray.dev/
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What is lacking in Julia ecosystem?
https://xarray.dev
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How we found and helped fix 24
bugs in 24 hours (in Tensorflow, Sentry, V8, PyTorch, Hue, and more)
Pydata's xarray
- Xarray awarded a support grant from NASA
- xarray: N-Dimensional labeled arrays and datasets in Python
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Could somebody who has experience with reading .asc files / using xarray please give me some direction?
It does seem like it isn't installed. If you take a look at the source, it catches import errors, meaning it won't error out immediately if the package isn't installed.
Overview-of-the-Julia-Python-R-Universe
- Contribute on github to the side by side overview of the three leading open source data science ecosystems of today
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What is lacking in Julia ecosystem?
Somebody left really good feedback / additions on the page. Thank you. Thought that maybe a github repo would make contributions (and credit) easier!
What are some alternatives?
iris - A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
YAXArrays.jl - Yet Another XArray-like Julia package
tensorflow - An Open Source Machine Learning Framework for Everyone
Gtk.jl - Julia interface to Gtk windowing toolkit.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
QML.jl - Build Qt6 QML interfaces for Julia programs.
dask-awkward - Native Dask collection for awkward arrays, and the library to use it.
CImGui.jl - Julia wrapper for cimgui
Dask - Parallel computing with task scheduling
zoose-gitpod - Run Zoose Quantum in the browser with no installation
wxee - A Python interface between Earth Engine and xarray for processing time series data
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.