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
Documentation: https://docs.xarray.dev
Issue tracker: https://github.com/pydata/xarray/issues
Source code: https://github.com/pydata/xarray
SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk
4 maintainers
Metadata
4 maintainers
Release files for xarray 2022.11.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| xarray-2022.11.0.tar.gz | 3.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| xarray-2022.11.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.0 MB
Release files / xarray-2022.11.0.tar.gz
| Download URL | xarray-2022.11.0.tar.gz |
|---|---|
| Size | 3.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
3008a302877f87a0f9043b1f01a4ad4e85b668bbdd38d488764098624632f527
|
|
BLAKE2b-256 checksum How to use checksums |
472653b4d9ce691f73ab17abaeb66c53dcda5a799ba1b1f32f21fb84440e7acf
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.15
|
Release files / xarray-2022.11.0-py3-none-any.whl
| Download URL | xarray-2022.11.0-py3-none-any.whl |
|---|---|
| Size | 963.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
228ed50ab3f1f7e2ba66670ded05f0bee46c69c4f902f346947b5723479af97f
|
|
BLAKE2b-256 checksum How to use checksums |
ff640e77f335d33bc30f8b35ea291b3408df262f46646c65d04215a2f3e427b6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.15
|
