Overview: Why xarray?


Adding dimensions names and coordinate indexes to numpy’s ndarray makes many powerful array operations possible:

  • Apply operations over dimensions by name: x.sum('time').
  • Select values by label instead of integer location: x.loc['2014-01-01'] or x.sel(time='2014-01-01').
  • Mathematical operations (e.g., x - y) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.
  • Flexible split-apply-combine operations with groupby: x.groupby('time.dayofyear').mean().
  • Database like alignment based on coordinate labels that smoothly handles missing values: x, y = xr.align(x, y, join='outer').
  • Keep track of arbitrary metadata in the form of a Python dictionary: x.attrs.

pandas provides many of these features, but it does not make use of dimension names, and its core data structures are fixed dimensional arrays.

The N-dimensional nature of xarray’s data structures makes it suitable for dealing with multi-dimensional scientific data, and its use of dimension names instead of axis labels (dim='time' instead of axis=0) makes such arrays much more manageable than the raw numpy ndarray: with xarray, you don’t need to keep track of the order of arrays dimensions or insert dummy dimensions (e.g., np.newaxis) to align arrays.

Core data structures

xarray has two core data structures. Both are fundamentally N-dimensional:

  • DataArray is our implementation of a labeled, N-dimensional array. It is an N-D generalization of a pandas.Series. The name DataArray itself is borrowed from Fernando Perez’s datarray project, which prototyped a similar data structure.
  • Dataset is a multi-dimensional, in-memory array database. It is a dict-like container of DataArray objects aligned along any number of shared dimensions, and serves a similar purpose in xarray to the pandas.DataFrame.

The value of attaching labels to numpy’s numpy.ndarray may be fairly obvious, but the dataset may need more motivation.

The power of the dataset over a plain dictionary is that, in addition to pulling out arrays by name, it is possible to select or combine data along a dimension across all arrays simultaneously. Like a DataFrame, datasets facilitate array operations with heterogeneous data – the difference is that the arrays in a dataset can not only have different data types, but can also have different numbers of dimensions.

This data model is borrowed from the netCDF file format, which also provides xarray with a natural and portable serialization format. NetCDF is very popular in the geosciences, and there are existing libraries for reading and writing netCDF in many programming languages, including Python.

xarray distinguishes itself from many tools for working with netCDF data in-so-far as it provides data structures for in-memory analytics that both utilize and preserve labels. You only need to do the tedious work of adding metadata once, not every time you save a file.

Goals and aspirations

pandas excels at working with tabular data. That suffices for many statistical analyses, but physical scientists rely on N-dimensional arrays – which is where xarray comes in.

xarray aims to provide a data analysis toolkit as powerful as pandas but designed for working with homogeneous N-dimensional arrays instead of tabular data. When possible, we copy the pandas API and rely on pandas’s highly optimized internals (in particular, for fast indexing).

Importantly, xarray has robust support for converting its objects to and from a numpy ndarray or a pandas DataFrame or Series, providing compatibility with the full PyData ecosystem.

Our target audience is anyone who needs N-dimensional labeled arrays, but we are particularly focused on the data analysis needs of physical scientists – especially geoscientists who already know and love netCDF.