Multidimensional Arrays: Reference

refer to this page for access to the tutorial data

unlabelled, N-dimensional arrays of numbers (e.g. NumPy’s ndarray) are the most widely used data structure in scientific computing

these arrays lack meaningful metadata, so users must track indices in an arbitrary fashion

in-memory operations, needed to process and visualize large arrays, are reaching limits as datasets grow in size

xarray is build on the netCDF data model

xarray has two main data structures: DataArray and Dataset

DataArrays store the multi-dimensional arrays

Datasets are the multi-dimensional equivalent of a Pandas dataframe

xarray’s labeled dimensions free the user from having to track positional ordering of dimensions when accessing data, creating a more simplified workflow

xarray has plotting functinality that is a thin wrapper around the Matplotlib library

xarray uses syntax and function names from Matplotlib whenever possible

xarray’s labeled dimensions enable simplified arithmetic and data aggregation, enabling many powerful shortcuts

xarray provides Pandas-like methods for performing data aggregation over defined groupings in the data

dask integration with xarray allows you to work with large datasets that “fit on disk” rather than having to “fit in memory”.

It is important to chunk the data correctly for this to work.

xarray provides tools for creating and analyzing masked data.

xarray provides tools for creating and analyzing masked data.

A summary of everything so far