Skip to main content

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

xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic 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. It is particularly tailored to working with netCDF files, which were the source of xarray’s data model, and integrates tightly with dask for parallel computing.

Why xarray?

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.

xarray doesn’t just keep track of labels on arrays – it uses them to provide a powerful and concise interface. For example:

  • Apply operations over dimensions by name: x.sum('time').

  • Select values by label instead of integer location: x.loc['2014-01-01'] or x.sel(time='2014-01-01').

  • Mathematical operations (e.g., x - y) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.

  • Flexible split-apply-combine operations with groupby: x.groupby('time.dayofyear').mean().

  • Database like alignment based on coordinate labels that smoothly handles missing values: x, y = xr.align(x, y, join='outer').

  • Keep track of arbitrary metadata in the form of a Python dictionary: x.attrs.

Learn more

Metadata

Release files for xarray 0.18.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for xarray 0.18.2
File Size Uploaded
xarray-0.18.2.tar.gz 2.9 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for xarray 0.18.2
File Interpreter ABI Platform
xarray-0.18.2-py3-none-any.whl Python 3 none any Details

Total release size: 3.7 MB

Release files / xarray-0.18.2.tar.gz

Download URL xarray-0.18.2.tar.gz
Size 2.9 MB
Tags Source
SHA-256 checksum
How to use checksums
5d2e72a228286fcf60f66e16876bd27629a1a70bf64822c565f16515c4d10284
BLAKE2b-256 checksum
How to use checksums
6c3ffee12087871642bce75e1e1a88363e709e6feb3a01050dd381b1977fc981
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

Release files / xarray-0.18.2-py3-none-any.whl

Download URL xarray-0.18.2-py3-none-any.whl
Size 807.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a0b14b888b90a3ad8ecec5db9862f3fd02f8c0cc15066a3429d37da62fc37219
BLAKE2b-256 checksum
How to use checksums
984ff5ef867f0e8a91eb528edfcfb6d0e82d758ae507fa0456eb1fe40e289362
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.5

Release history Release notifications | RSS feed

0.21.0

2 release files

0.20.2

2 release files

0.19.0

2 release files

This release

0.18.2 This release

2 release files

0.18.1

2 release files

0.17.0

2 release files

0.16.2

2 release files

0.16.1

2 release files

0.16.0

2 release files

0.15.1

2 release files

0.15.0

2 release files

0.14.1

2 release files

0.14.0

2 release files

0.13.0

2 release files

0.12.3

2 release files

0.12.2

2 release files

0.12.0

2 release files

0.11.3

2 release files

0.11.1

2 release files

0.10.9

2 release files

0.10.8

2 release files

0.10.5

1 release file

0.10.4

2 release files

0.10.3

2 release files

0.10.2

2 release files

0.10.1

2 release files

0.10.0

2 release files

0.9.6

2 release files

0.9.5

2 release files

0.9.4

2 release files

0.9.3

2 release files

0.9.2

2 release files

0.9.1

2 release files

0.9.0

2 release files

0.8.2

2 release files

0.8.1

2 release files

0.8.0

2 release files

0.7.2

2 release files

0.7.1

2 release files

0.7.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page