Iris implements a data model based on the CF conventions giving you a powerful, format-agnostic interface for working with your data. It excels when working with multi-dimensional Earth Science data, where tabular representations become unwieldy and inefficient.

CF Standard names, units, and coordinate metadata are built into Iris, giving you a rich and expressive interface for maintaining an accurate representation of your data. Its treatment of data and associated metadata as first-class objects includes:

  • a visualisation interface based on matplotlib and cartopy,
  • unit conversion,
  • subsetting and extraction,
  • merge and concatenate,
  • aggregations and reductions (including min, max, mean and weighted averages),
  • interpolation and regridding (including nearest-neighbor, linear and area-weighted), and
  • operator overloads (+, -, *, /, etc.).

A number of file formats are recognised by Iris, including CF-compliant NetCDF, GRIB, and PP, and it has a plugin architecture to allow other formats to be added seamlessly.

Building upon NumPy and dask, Iris scales from efficient single-machine workflows right through to multi-core clusters and HPC. Interoperability with packages from the wider scientific Python ecosystem comes from Iris' use of standard NumPy/dask arrays as its underlying data storage.