xarray
orange
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xarray | orange | |
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7 | 27 | |
3,404 | 4,604 | |
1.5% | 1.7% | |
9.7 | 9.6 | |
2 days ago | 5 days ago | |
Python | Python | |
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.
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.
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
iris - A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
glue - Linked Data Visualizations Across Multiple Files
tensorflow - An Open Source Machine Learning Framework for Everyone
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
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
RDKit - The official sources for the RDKit library
dask-awkward - Native Dask collection for awkward arrays, and the library to use it.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Dask - Parallel computing with task scheduling
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
NumPy - The fundamental package for scientific computing with Python.