Scott Henderson

Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets. Many real-world datasets often have hierarchical or heterogeneous structure, and are best organized through groups of related data arrays.

We illustrate the power and flexibility of a new extension point in Xarray's data model: "custom indexes" that allow Xarray users to neatly handle complex grids, and enables at least one new data model (vector data cubes). We present a whirlwind tour of specific examples to illustrate the power of this feature, and aim to stimulate experimentation during the sprints.

Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets with many related variables. Xarray combines the convenience of labeled data structures inspired by Pandas with NumPy-like multi-dimensional arrays to provide an intuitive and scalable interface for scientific analysis.

The open-source project, Xarray, combines labeled data structures inspired by Pandas with NumPy-like multi-dimensional arrays to provide an intuitive and scalable interface for scientific analysis. Xarray has strong user bases in the physical sciences and geospatial community.

Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets with many related variables. Xarray combines the convenience of labeled data structures inspired by Pandas with NumPy-like multi-dimensional arrays to provide an intuitive and scalable interface for scientific analysis.

Ever-increasing demands from the business, the growth of new AI workloads, and the increasing pull of public clouds call for faster and more flexible ways to get data where it’s needed in computations. Fortunately, the vendor community is competing and collaborating to solve these problems.