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[PR #1217] [CLOSED] Add xarray #1086

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opened 2025-11-06 13:08:58 -06:00 by GiteaMirror · 0 comments
GiteaMirror commented 2025-11-06 13:08:58 -06:00
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📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/1217
Author: @TomNicholas
Created: 1/21/2019
Status: ❌ Closed

Base: master ← Head: add_xarray

📝 Commits (2)

  • 06e1eee Added link to Python-for-Scientists
  • d0ee863 Added link to xarray

📊 Changes

1 file changed (+2 additions, -0 deletions)

View changed files

📝 README.md (+2 -0)

📄 Description

What is this Python project?

xarray (formerly xray) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”) are an essential part of computational science. They are encountered in a wide range of fields, including physics, astronomy, geoscience, bioinformatics, engineering, finance, and deep learning. In Python, NumPy provides the fundamental data structure and API for working with raw ND arrays. However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc.

By introducing dimensions, coordinates, and attributes on top of raw NumPy-like arrays, xarray is able to understand these labels and use them to provide a more intuitive, more concise, and less error-prone experience. Xarray also provides a large and growing library of functions for advanced analytics and visualization with these data structures. Xarray was inspired by and borrows heavily from pandas, the popular data analysis package focused on labelled tabular data. Xarray can read and write data from most common labeled ND-array storage formats and is particularly tailored to working with netCDF files, which were the source of xarray’s data model.

Can also auto-parallelize operations using dask.

What's the difference between this Python project and similar ones?

Similar to Pandas, but for truly N-dimensional data. Part of the pydata project.

--

Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/1217 **Author:** [@TomNicholas](https://github.com/TomNicholas) **Created:** 1/21/2019 **Status:** ❌ Closed **Base:** `master` ← **Head:** `add_xarray` --- ### 📝 Commits (2) - [`06e1eee`](https://github.com/vinta/awesome-python/commit/06e1eee8da5b06cefb5cae42bef3012c757b9d88) Added link to Python-for-Scientists - [`d0ee863`](https://github.com/vinta/awesome-python/commit/d0ee86329cbbb4805da942c09317ed8be8d9a39e) Added link to xarray ### 📊 Changes **1 file changed** (+2 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+2 -0) </details> ### 📄 Description ## What is this Python project? xarray (formerly xray) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun! Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”) are an essential part of computational science. They are encountered in a wide range of fields, including physics, astronomy, geoscience, bioinformatics, engineering, finance, and deep learning. In Python, NumPy provides the fundamental data structure and API for working with raw ND arrays. However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc. By introducing dimensions, coordinates, and attributes on top of raw NumPy-like arrays, xarray is able to understand these labels and use them to provide a more intuitive, more concise, and less error-prone experience. Xarray also provides a large and growing library of functions for advanced analytics and visualization with these data structures. Xarray was inspired by and borrows heavily from pandas, the popular data analysis package focused on labelled tabular data. Xarray can read and write data from most common labeled ND-array storage formats and is particularly tailored to working with netCDF files, which were the source of xarray’s data model. Can also auto-parallelize operations using dask. ## What's the difference between this Python project and similar ones? Similar to Pandas, but for truly N-dimensional data. Part of the pydata project. -- Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
GiteaMirror added the pull-request label 2025-11-06 13:08:58 -06:00
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