Interoperability of Xarray

Xarray is designed to be extremely interoperable, in many orthogonal ways. Making xarray as flexible as possible is the common theme of most of the goals on our Development roadmap.

This interoperability comes via a set of flexible abstractions into which the user can plug in. The current full list is:

• Custom file backends via the BackendEntrypoint system,

• Numpy-like “duck” array wrapping, which supports the Python Array API Standard,

• Chunked distributed array computation via the ChunkManagerEntrypoint system,

• Custom Index objects for flexible label-based lookups,

• Extending xarray objects with domain-specific methods via custom accessors.

Warning

One obvious way in which xarray could be more flexible is that whilst subclassing xarray objects is possible, we currently don’t support it in most transformations, instead recommending composition over inheritance. See the internal design page for the rationale and look at the corresponding GH issue if you’re interested in improving support for subclassing!

Note

If you think there is another way in which xarray could become more generically flexible then please tell us your ideas by raising an issue to request the feature!

Whilst xarray was originally designed specifically to open netCDF4 files as numpy.ndarray objects labelled by pandas.Index objects, it is entirely possible today to:

• lazily open an xarray object directly from a custom binary file format (e.g. using xarray.open_dataset(path, engine='my_custom_format'),

• handle the data as any API-compliant numpy-like array type (e.g. sparse or GPU-backed),

• distribute out-of-core computation across that array type in parallel (e.g. via Parallel computing with Dask),

• track the physical units of the data through computations (e.g via pint-xarray),

• query the data via custom index logic optimized for specific applications (e.g. an Index object backed by a KDTree structure),

• attach domain-specific logic via accessor methods (e.g. to understand geographic Coordinate Reference System metadata),

• organize hierarchical groups of xarray data in a DataTree (e.g. to treat heterogeneous simulation and observational data together during analysis).

All of these features can be provided simultaneously, using libraries compatible with the rest of the scientific python ecosystem. In this situation xarray would be essentially a thin wrapper acting as pure-python framework, providing a common interface and separation of concerns via various domain-agnostic abstractions.

Most of the remaining pages in the documentation of xarray’s internals describe these various types of interoperability in more detail.