Authors: Stephan Hoyer, Joe Hamman and xarray developers
Date: July 24, 2018
Xarray is an open source Python library for labeled multidimensional arrays and datasets.
Why has xarray been successful? In our opinion:
Xarray does a great job of solving specific use-cases for multidimensional data analysis:
The dominant use-case for xarray is for analysis of gridded dataset in the geosciences, e.g., as part of the Pangeo project.
Finally, xarray is used in a variety of other domains, including finance, probabilistic programming and genomics.
Xarray is also a domain agnostic solution:
We focus on providing a flexible set of functionality related labeled multidimensional arrays, rather than solving particular problems.
This facilitates collaboration between users with different needs, and helps us attract a broad community of contributers.
Importantly, this retains flexibility, for use cases that don’t fit particularly well into existing frameworks.
Xarray integrates well with other libraries in the scientific Python stack.
We leverage first-class external libraries for core features of xarray (e.g., NumPy for ndarrays, pandas for indexing, dask for parallel computing)
We expose our internal abstractions to users (e.g.,
apply_ufunc()), which facilitates extending xarray in various ways.
Together, these features have made xarray a first-class choice for labeled multidimensional arrays in Python.
We want to double-down on xarray’s strengths by making it an even more flexible and powerful tool for multidimensional data analysis. We want to continue to engage xarray’s core geoscience users, and to also reach out to new domains to learn from other successful data models like those of yt or the OLAP cube.
The user community has voiced a number specific needs related to how xarray interfaces with domain specific problems. Xarray may not solve all of these issues directly, but these areas provide opportunities for xarray to provide better, more extensible, interfaces. Some examples of these common needs are:
Non-regular grids (e.g., staggered and unstructured meshes).
Lazily computed arrays (e.g., for coordinate systems).
We think the right approach to extending xarray’s user community and the usefulness of the project is to focus on improving key interfaces that can be used externally to meet domain-specific needs.
We can generalize the community’s needs into three main catagories:
More flexible grids/indexing.
More flexible arrays/computing.
More flexible storage backends.
Each of these are detailed further in the subsections below.
Xarray currently keeps track of indexes associated with coordinates by
storing them in the form of a
pandas.Index in special
The limitations of this model became clear with the addition of
pandas.MultiIndex support in xarray 0.9, where a single index
corresponds to multiple xarray variables. MultiIndex support is highly
useful, but xarray now has numerous special cases to check for
A cleaner model would be to elevate
indexes to an explicit part of
xarray’s data model, e.g., as attributes on the
DataArray classes. Indexes would need to be propagated along with
coordinates in xarray operations, but will no longer would need to have
a one-to-one correspondance with coordinate variables. Instead, an index
should be able to refer to multiple (possibly multidimensional)
coordinates that define it. See GH
1603 for full details
xarray.Dataset, as dictionaries that map from coordinate names to xarray index objects.
Use the new index interface to write wrappers for
Expose the interface externally to allow third-party libraries to implement custom indexing routines, e.g., for geospatial look-ups on the surface of the Earth.
In addition to the new features it directly enables, this clean up will allow xarray to more easily implement some long-awaited features that build upon indexing, such as groupby operations with multiple variables.
Xarray currently supports wrapping multidimensional arrays defined by NumPy, dask and to a limited-extent pandas. It would be nice to have interfaces that allow xarray to wrap alternative N-D array implementations, e.g.:
Arrays holding physical units.
Lazily computed arrays.
Other ndarray objects, e.g., sparse, xnd, xtensor.
Our strategy has been to pursue upstream improvements in NumPy (see NEP-22) for supporting a complete duck-typing interface using with NumPy’s higher level array API. Improvements in NumPy’s support for custom data types would also be highly useful for xarray users.
By pursuing these improvements in NumPy we hope to extend the benefits to the full scientific Python community, and avoid tight coupling between xarray and specific third-party libraries (e.g., for implementing untis). This will allow xarray to maintain its domain agnostic strengths.
We expect that we may eventually add some minimal interfaces in xarray for features that we delegate to external array libraries (e.g., for getting units and changing units). If we do add these features, we expect them to be thin wrappers, with core functionality implemented by third-party libraries.
The xarray backends module has grown in size and complexity. Much of this growth has been “organic” and mostly to support incremental additions to the supported backends. This has left us with a fragile internal API that is difficult for even experienced xarray developers to use. Moreover, the lack of a public facing API for building xarray backends means that users can not easily build backend interface for xarray in third-party libraries.
The idea of refactoring the backends API and exposing it to users was originally proposed in GH 1970. The idea would be to develop a well tested and generic backend base class and associated utilities for external use. Specific tasks for this development would include:
Exposing an abstract backend for writing new storage systems.
Exposing utilities for features like automatic closing of files, LRU-caching and explicit/lazy indexing.
Possibly moving some infrequently used backends to third-party packages.
Engaging more users¶
Like many open-source projects, the documentation of xarray has grown together with the library’s features. While we think that the xarray documentation is comprehensive already, we acknowledge that the adoption of xarray might be slowed down because of the substantial time investment required to learn its working principles. In particular, non-computer scientists or users less familiar with the pydata ecosystem might find it difficult to learn xarray and realize how xarray can help them in their daily work.
In order to lower this adoption barrier, we propose to:
Develop entry-level tutorials for users with different backgrounds. For example, we would like to develop tutorials for users with or without previous knowledge of pandas, numpy, netCDF, etc. These tutorials may be built as part of xarray’s documentation or included in a separate repository to enable interactive use (e.g. mybinder.org).
Document typical user workflows in a dedicated website, following the example of dask-stories.
Write a basic glossary that defines terms that might not be familiar to all (e.g. “lazy”, “labeled”, “serialization”, “indexing”, “backend”).
Current core developers¶
On July 16, 2018, Joe and Stephan submitted xarray’s fiscal sponsorship application to NumFOCUS.