# pydata/xarray

N-D labeled arrays and datasets in Python

Repository: https://github.com/pydata/xarray
Canonical: https://ross.abutalabs.com/products/xarray
Homepage: https://xarray.dev
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, netcdf, numpy, pandas, xarray, dask
Last push: 2026-08-24T12:53:34+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 92, longevity 100
- inputs: {"age_days": 4720, "days_push": 9, "days_rel": 55, "gap_med": 26, "n_releases_24m": 24}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4190, forks 1334 (observed 2026-08-28T04:08:38.812364+00:00)

## What it is
Xarray is a Python library that adds labeled dimensions, coordinates, and attributes on top of NumPy-like N-dimensional arrays and datasets. It provides domain-agnostic analytics, GroupBy operations, and I/O backends for NetCDF, HDF, Zarr, OpenDAP, and GRIB, integrating with Dask, Pandas, and Matplotlib.

## Use cases
- work with multidimensional labeled arrays in python
- read and write netcdf files
- analyze climate or weather gridded datasets
- process large arrays with dask out-of-core computation
- group and align data by coordinate labels
- convert between netcdf, zarr, and grib formats

## When to choose
- you work with gridded, multi-dimensional scientific data like climate or geospatial arrays
- you want pandas-like label-based indexing for N-dimensional data
- you need scalable array processing with dask integration

## When to avoid
- you only need simple 1D/2D tabular data, where pandas suffices
- you need unlabeled raw array math with minimal overhead, where numpy is enough

## Facets
- artifact type: library
- maturity: stable
- function: data-science, data-visualization, etl
- domain: data-science, big-data, analytics
- platform: python, cross-platform
- tags: labeled-arrays, netcdf, numpy, pandas, dask, zarr, multidimensional-data, climate-data, scientific-computing

## Member repositories
- pydata/xarray (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.812364+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:36.295804+00:00, confidence not recorded.
  - readme: https://github.com/pydata/xarray (fetched 2026-08-28T04:08:38.812364+00:00, sha 57d32f628329)
  - homepage: https://xarray.dev (fetched 2026-08-29T09:15:41.735748+00:00, sha b88fcd1591ef)
  - registry_pypi: https://pypi.org/pypi/xarray/json (fetched 2026-08-29T09:15:41.745514+00:00, sha a843f366194c)
- Data as of 2026-08-30T08:39:29.467469+00:00.