xarray
Dask
Our great sponsors
| xarray | Dask | |
|---|---|---|
| 7 | 32 | |
| 3,404 | 11,982 | |
| 1.5% | 1.5% | |
| 9.7 | 9.7 | |
| 3 days 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.
xarray
-
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/
-
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/
-
What is lacking in Julia ecosystem?
https://xarray.dev
-
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
-
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.
Dask
- The Distributed Tensor Algebra Compiler (2022)
-
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
-
What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
-
Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
-
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.
-
What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
-
Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
-
Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
-
Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
iris - A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
tensorflow - An Open Source Machine Learning Framework for Everyone
Numba - NumPy aware dynamic Python compiler using LLVM
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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.
dask-awkward - Native Dask collection for awkward arrays, and the library to use it.
NetworkX - Network Analysis 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.
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
wxee - A Python interface between Earth Engine and xarray for processing time series data
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python