xarray
swifter
Our great sponsors
| xarray | swifter | |
|---|---|---|
| 7 | 3 | |
| 3,399 | 2,456 | |
| 1.4% | - | |
| 9.7 | 5.5 | |
| 3 days ago | 29 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT 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.
-
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.
swifter
-
Tidyverse equivalent in Python?
With concat, merge, melt, and pivot_table, that may cover everything I have ever needed. There may be more efficient ways at times, but swifter promises to do that for you, maybe it is true.
-
[D] A hacky work-around for slow linear algebra operations on pyspark.
Since you already have a working python script, you can try swifter with minimal effort to see if it brings about a significant speedup before digging further.
-
What Is The Best Performance Fix You Ever
With few lines of code? Swifter for quicker pandas apply and then there's numba. With concurrent.futures, it'll be a bit more lines of code.
What are some alternatives?
iris - A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
modin - Modin: Scale your Pandas workflows by changing a single line of code
tensorflow - An Open Source Machine Learning Framework for Everyone
Dask - Parallel computing with task scheduling
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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
dask-awkward - Native Dask collection for awkward arrays, and the library to use it.
pandera - A light-weight, flexible, and expressive statistical data testing library
siuba - Python library for using dplyr like syntax with pandas and SQL
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
xgboost_ray - Distributed XGBoost on Ray